Before you begin, however, you need to figure out the point-of-view you from which you will tell your story. Will it be first person from the one of your characters’ points of view? Or perhaps you prefer to tell it through the third person as an ousider. There are plenty of viewpoints you can use to tell your story. Regardless of what you pick, your decision will influence all elements your story.

Use Freytag’s Pyramid to Write Better Story Elements

Freytag’s Pyramid is a five-part dramatic structure showing the elements and flow of a story. It’s the brain child of 19th century playwright Gustav Freytag. He theorized that there are five key stages of a story that are used to conceptualize or write an engaging story from start to finish.

Below, we’ll go over each stage of Freytag’s Pyramid in detail. I’ll also share some real-world examples of Freytag’s Pyramid in action. Finally, we’ll use the example of climbing a mountain as the perfect metaphor to demonstrate Freytag’s Pyramid.

1. The Protagonist

One of the most important elements in your story, the protagonist is the main character, or good guy in your story. They’re the one who will be going on the journey that your story tells. While many stories have just a single protagonist, yours certainly doesn’t have to. You can have multiple protagonists, or a group of people that function as a single protagonist. For example, if you were telling the story of a trip you took with a tour group, the group would essentially function as a single protagonist.

In the context of travel stories, you (the traveler) are nearly always the protagonist. And since traveling is literally going on a journey, you should be able to pretty easily identify the parts of your journey and how they relate to the parts of stories we’ll cover below.

Interestingly, there is one notable exception, which you’ll find if you’re traveling for a cause, such as to do volunteer work. In that case, the protagonist is often the people or group that you’re helping with your volunteer work. However, you shouldn’t overlook the fact that volunteer work can have a profound impact on the volunteers as well. In that case, I encourage you to include yourself as a protagonist and share your side of the story. On the other hand, you can also re-tell the same story from your point of view. That can be a very powerful way to raise awareness to causes you’re passionate about back home.

2. The Antagonist

Sometimes referred to as the ”anti-protagonist”, the antagonist is the bad guy in your story. The antagonist actively opposes and provides resistance to your protagonist on their journey. One of the most famous protagonist/antagonist pairs is Luke Skywalker and Darth Vader in the Star Wars series.

In many stories, the antagonist is a person or a group of people. However, the antagonist can be anything that stands in the way of the protagonist accomplishing their goal. This is particularly true for telling stories about travel.

But Wait…Travel Stories Don’t Usually Have Clear Cut Good Guys and Bad Guys the Way Star Wars Does. How Do You Identify the Antagonist in Your Travel Stories?

Good question! For travel stories, antagonists don’t always take the traditional forms that they do in many Hollywood movies, or even in the world of news or sports. In fact, in some contexts, the antagonist can be one of the more difficult elements of your story to identify. Take solo travel as an example. You’re just out documenting your adventures, and having a blast doing it. How can there possibly be bad guys when you’re having so much fun?

It turns out that you’re right, in that there are physically no bad people getting in the way of you having fun. Instead, the antagonist takes on different forms. Often times, the travel itself – the act of going from Point A to Point B – is the antagonist. Here are just a few examples of possible sources of resistance, or antagonists, you may encounter while traveling.

• Traffic Jams
• Border Crossings
• Language Barriers
• Natural Barriers such as Mountains or Rivers
• Bad Weather
• Vehicle Breakdowns
• Missed Buses, Trains, or Flights

Interestingly, if you’re traveling for a cause and/or doing volunteer work, the antagonist is the hardship of the group you’re helping out. Some examples include poverty, hunger, war, disease, natural disasters, and much more.

3. A Conflict

We’ve all heard it a million times. Every story needs a conflict. You can’t have a story without one. Yes, it needs to be said. And no, I won’t beat that dead horse any more than I already have.

The conflict is where the protagonist and antagonist cross paths in your story. Remember, each side has opposite goals, so there is a natural conflict there. Your story can have a single conflict, or it can be comprised of a series of conflicts. That’s completely up to you.

How to Identify Conflicts in Your Travel Stories

Like antagonists, conflicts in travel stories don’t always come to life in traditional ways. Indeed, it’s no surprise that so many travel bloggers, photographers, and video storytellers struggle to find conflict in their story. It turns out that you may need to dig down to find the conflict, but it’s there.

If you’re having trouble identifying the conflict in your travel story, go back and re-read the previous section about the antagonist. Look at the bulleted list of examples of antagonists in travel stories and see if you can identify them in your story. Once you know who or what your antagonists are, finding your conflict is easy. Any time you run into or have direct interaction with those antagonists is where your conflict occurs.

Don’t Forget to Provide a Hook for Your Audience

Have you ever binge-watched a show on Netflix? I know I have. And do you know what makes a show bingeworthy? The hook. It’s one of the most critically important elements of your story. Bingeworthy shows are incredibly good at ”hooking” the audience in, leaving them not just wanting to see what happens in the next episode, but craving it. And introducing conflict is one of the best ways to ”hook” your audience.

The best audience hooks usually take the form of teasers, plot twists, secrets, and cliffhangers. Go back and watch one of the shows you’ve binge-watched in the past. Pay particular attention to how they end each episode. They often introduce a new conflict without providing any idea of how it will be resolved. And do you know when they’ll resolve it? In the next episode. That’s how they keep bringing their audience back. And you should try to do the same thing when you tell your stories. At the very least, it will make the story more engaging, dramatic, and gripping.

Finally, you should introduce the first three elements we discussed here (the first or primary protagonist, antagonist, and conflict) before your main character even sets off on their journey. Giving your audience the proper background to the context of your story will better engage them, and keep them coming back for more.

4. Rising Action

As soon as your protagonist sets off on their journey, the rising action begins. Throughout the rising action, each step of the protagonist’s journey should put them into more and more direct contact with the antagonist, as well as the conflict. As a result, there is an increasing escalation each step of the journey. By the end of the rising action, there should be a dramatic build-up to the climax. But don’t reveal too much. You don’t want to give anything away before the climax. Use powerful words, vivid imagery, and/or dramatic music in your final build-up to the climax.

Climbing a mountain is the perfect metaphor for the rising action. Your journey up the mountain starts out pretty easily. The weather’s nice, the terrain isn’t steep, and you feel good and fresh. As you make your way up the mountain, though, conditions begin to change. Not only does the terrain get steeper, but the weather starts to turn. Winds pick up and temperatures start to drop. Furthermore, there is less air to breathe at higher elevations, tiring you out that much quicker.

The most difficult part of the ascent is the final push to the summit. The terrain is treacherous, making for slow progress. Icy winds and heavy snow only compound the problem. Finally, the rarified air leaves you gasping for breath every step. But you keep pushing forward because you know there is an incredible reward at the summit. That’s exactly how the final build-up to the climax of your story should work.

Short Example of a Rising Action in Action

In the short video below, you’ll find an announcement video I made for the next chapter of my life and business. In the first 30 seconds of the clip, I go over my history and accomplishments as a still photographer. At the end of that rising action, you’ll see a build-up to the big reveal that I’m adding video to my arsenal of visual storytelling. Take special note of the music, imagery, and words being used. Notice how the tension and drama builds up a lot quicker right before the reveal than it does at the beginning.

5. Climax

This is the moment everyone’s been waiting for, and the most exciting of your story elements. Tension is high coming out of your rising action as the audience sits on the edge of their seats, eagerly waiting to see what happens. Then you make the big reveal.

The climax is the most pivotal and exciting part of the story. It’s the moment you reveal the resolution to your protagonist’s conflict. As the action of the story transitions towards the conclusion, you transition from the rising action to the falling action. You don’t want the climax right at the beginning of your story, but it can go anywhere in the middle or towards the end.

You should maximize tension, drama and emotion at the end of the rising action right before you reveal the resolution to the conflict. As a result, use your most vivid imagery and most powerful language to show your story’s climax. But your story isn’t quite over, yet. You’ll likely still have some loose ends to tie up, which you’ll take care of in the next section.

Going back to the metaphor of climbing a mountain, the climax occurs when you reach the summit. You can finally exhale, take a break, and soak in the spectacular views. It’s a well-earned reward after a grueling hike up the mountain. However, you shouldn’t relax too much. Your journey is only half-way done because you still have to get back down the mountain.

6. Dénouement or Falling Action

Now that you’ve unveiled the resolution of your story, it’s time to tie up those loose ends left over from the climax. Ironically, the word dénouement literally translates from French as ”the untying of the knot.” However, the context of dénouement refers to the conflict as a knot that you’re untying. Don’t confuse it with the fact that you’re also tying up loose ends in your story.

In addition to tying up loose ends, the dénouement should also begin to establish the main characters’ new normal. Reveal any last secrets or fates that were left over from the climax. The dénouement should not just end the story. It should validate it.

Unlike the rising action, tension, drama, and emotion should drop throughout the course of the dénouement, opposite to how it rose throughout the rising action. Using the mountain climbing metaphor, the dénouement is the descent back down the mountain after you summit. Just like the final push to the summit is the hardest part of the ascent, the first part of the descent is the hardest part of the way down. The descent gets easier as the further down you get. The terrain gets less difficult, the weather gets warmer, and there’s more air to breathe as you drop in elevation.

Finally, if you’re planning anything further, such as another chapter, episode, or sequel, plant the hook to that next segment at the end of the dénouement. Leave your audience quenching to come back and see what happens next.

7. The Characters’ Lives After This Journey

While many storytellers end their stories after the dénouement, I prefer to have one extra element, especially if there will not be any further chapters, episodes, or sequels. In this section, we dive in and fully immerse ourselves in the protagonist’s new day-to-day life. Bring the story full-circle and show your protagonist’s new normal and how the journey changed them.

• Did they learn a lesson from the experience?
• Do they have a new outlook on life? Why or why not?
• How else have they changed? What else are they doing differently?
• What, if anything, of relevance lies ahead for them?

Conclusion

Following Freytag’s Pyramid is one of the easiest and most effective ways to tell a story that engages, hooks, and even grips your audience. Without those elements, you really don’t have a story to tell. Storytelling is an art form, and it’s not uncommon to struggle with it, especially when you’re first starting out. But just remember, while telling is literally in the word storytelling, you want to show your audience, not tell them. And once you master the art of storytelling, it’s amazing the doors it will open for you.

Top Image: A Snow-Packed Road en Route to Grand Canyon
Flagstaff, Arizona – January, 2017

The post How to Use Freytag’s Pyramid Elements to Tell an Engaging, Gripping Story appeared first on Matthew Gove Blog.

EXIF stands for Exchangeable Image Format. It’s a standard that defines the data or information related to any media you capture with a digital camera. It can include data such as:

• Image Size
• File Name and Location on Your Computer
• Camera and Lens Make/Model
• Exposure Settings (Aperture, Shutter, ISO, etc)
• Date and Time the Photo was Taken
• Location and Elevation Where the Photo was Taken
• Photographer’s Name and Contact Info
• Copyright Info
• Software Used to Post-Process the Image
• Much More

Why Do You Need to Edit EXIF Data?

Regardless of whether you need to add or remove EXIF data, there are plenty of reasons to edit it. In my nearly two decades doing photography, here are some of the more common reasons I’ve had to edit EXIF data.

• Strip out sensitive information when you post photos publicly on the web
• Add location data or geotag images for cameras that don’t have GPS
• Add or update titles, descriptions, and captions
• Rate, label, and tag images
• Add or update your contact info and/or copyright information

If you’re planning to sell your photography in any way, shape, or form, you better have fully populated EXIF data. For example, let’s consider stock photography websites. They use the EXIF data embedded in your images to return the most relevant images in search results. Without fully-populated EXIF data, your images won’t be returned in their searches, and you won’t make any sales as a result.

Available Tools to Bulk Edit EXIF Data

Thankfully, there are numerous tools available so you can edit your photos’ EXIF data. They all support bulk editing, so it doesn’t matter whether you’re updating one photo or a million.

• Photo editors and organizers such as Adobe Lightroom
• EXIF Editors are available for all operating systems, including iOS and Android. Many are free.
• Python

Do be aware that while many EXIF editors advertise themselves as free, they often come with heavy restrictions if you don’t want to pay for the full software. Because of these restrictions, Python is one of the few tools that can edit EXIF data both for free and without restrictions. In this tutorial, we’re going to use Python to add EXIF data to a series of images.

Python Image Libraries

In previous tutorials, we’ve used Python’s Pillow Library to do everything from editing and post-processing to removing noise from and adding location data to photos. And we’ll show in this tutorial that you can use Pillow to add, edit, and remove EXIF data. However, there’s a better Python tool to manage and edit your EXIF data: the exif library.

So what makes the exif library better than Pillow? For that, we have to look at how the computer stores and reads EXIF data. In the computer, EXIF parameters are stored as numeric codes instead of English words. For example, instead of “Camera Make”, the computer just sees 271. Likewise, the computer sees 36867 instead of “Date/Time Image Taken”.

To edit the EXIF data using Pillow, you need to know the numeric key for each field you want to edit. Considering that there are thousands of these numeric keys in use, you’ll spend an incredible amount of time just searching for they numeric keys you want. On the other hand, the exif library uses human-readable keys to edit the EXIF data. We’ll go over how to use both libraries, so you can decide which one you prefer.

Install the Pillow and Exif Libraries (If You Haven’t Already)

Before diving into EXIF data, you’ll need to install the Python Pillow and Exif libraries if you haven’t already. With pip, it’s a couple quick commands in a Terminal or Command Prompt.

pip3 install pillow
pip3 install exif

Import the Image Property from both the Pillow and Exif Libraries into Your Python Script

In order to run code from the Pillow and Exif libraries, you’ll need to import the Image property from each library into your Python script. To avoid name conflicts, we’ll call them ExifImage and PillowImage. From the Pillow library, we’ll also import the ExifTags property, which converts the numeric EXIF tags into human-readable tags.

from exif import Image as ExifImage
from PIL import Image as PillowImage
from PIL import ExifTags

Images for this Demo

I’ve included three images with this tutorial, but you can add as many of your own as you please. One set has the metadata intact, which we’ll use for reading the metadata. I stripped the EXIF data out of the other set, so we can add it with Python. I also took the images with three different cameras.

Back Up Your Images Before You Begin

Before you do anything with the Python code, make a copy of the folder containing your original images. You never know when something will go wrong. With a backup, you can always restore your images to their original state.

Reading EXIF Data with Python

First, we’ll loop through the images and extract the camera make and model, the date and time the image was taken, as well as the location data.

The steps to read the EXIF data from each image and output it to the terminal window are as follows.

1. Open the image
2. Extract the value of each metadata tag displayed in the table above
3. Print the human-readable tag and the value in the terminal window.

Define the Universal Parameters You’ll Use to Extract EXIF Data in the Python Script

In our Python script, the first thing we need to do is define the universal parameters we’ll use throughout the script. First, we’ll create a list of the image filenames.

images = ["baseball.jpg", "cape-cod.jpg", "stonehenge.jpg"]

Next, define the EXIF Tags for the Pillow library that will extract the data we want. Remember, that Pillow uses the EXIF numeric tags.

PILLOW_TAGS = [
    271,    # Camera Make
    272,    # Camera Model
    36867,  # Date/Time Photo Taken
    34853,  # GPS Info
]

Finally, create a variable to store the EXIF tags for the Exif library. The Exif library uses human-readable tags, so please consult their documentation for the full list of tags.

EXIF_TAGS = [
    "make",
    "model",
    "datetime_original",
    "gps_latitude",
    "gps_latitude_ref",
    "gps_longitude",
    "gps_longitude_ref",
    "gps_altitude",
]

Read EXIF Data with the Pillow Library

To extract the EXIF data from all of the images at once, we’ll loop through the images variable we defined above. We’ll print the image filename and then set the image path inside the with-metadata folder.

for img in images:
    print(img)
    image_path = "with-metadata/{}".format(img)

Next, open the image with Pillow and extract the EXIF data using the getexif() method.

pillow_img = PillowImage.open(image_path)
img_exif = pillow_img.getexif()

Now, we’ll loop through the tags. Pillow has a property called ExifTags that we’ll use to get the human-readable definition of each numeric tag. Do note that you’ll need to wrap it in a try/except block to skip properties that are not set. Without it, you’ll get an error and the script will crash if a property is not set. For example, the Cape Cod and Stonehenge images do not have GPS/location data. Finally, print the human-readable tag and the value to the Terminal window.

for tag in PILLOW_TAGS:
    try:
        english_tag = ExifTags.TAGS[tag]
        value = img_exif[tag]
    except:
        continue
    print("{}: {}".format(english_tag, value))

Final Pillow Code

Put it all together into nice, compact block of code.

for img in images:
    print(img)
    image_path = "with-metadata/{}".format(img)
    pillow_img = PillowImage.open(image_path)
    img_exif = pillow_img.getexif()
    
    for tag in PILLOW_TAGS:
        try:
            english_tag = ExifTags.TAGS[tag]
            value = img_exif[tag]
        except:
            continue
        print("{}: {}".format(english_tag, value))

When you run the script, it will output the info about each photo.

baseball.jpg
Make: samsung
Model: SM-G965F
DateTimeOriginal: 2019:03:25 18:06:05
GPSInfo: æ0: b'Øx02Øx02Øx00Øx00', 1: 'N', 2: (33.0, 25.0, 50.0), 3: 'W', 4: (111.0, 52.0, 53.0), 5: b'Øx00', 6: 347.0, 7: (1.0, 6.0, 0.0), 27: b'ASCIIØx00Øx00Øx00GPS', 29: '2019:03:26'å

cape-cod.jpg
Make: Canon
Model: Canon EOS R5
DateTimeOriginal: 2022:03:22 20:58:52

stonehenge.jpg
Make: NIKON CORPORATION
Model: NIKON D3000
DateTimeOriginal: 2022:02:16 21:36:08

A Quick Word on Interpreting the GPS Output

When you look at the GPS output, you’re probably wondering what the hell you’re looking at. To decipher it, let’s break it down and look at the important components. FYI, Pillow returns latitude and longitude coordinates as tuples of (degrees, minutes, seconds).

1: 'N', 
2: (33.0, 25.0, 50.0), 
3: 'W', 
4: (111.0, 52.0, 53.0),
6: 347.0,

Here’s what it all means.

1. 'N' indicates that the latitude coordinate is in the northern hemisphere. It returns 'S' for southern hemisphere latitudes.
2. (33.0, 25.0, 50.0) contains the degrees, minutes, and seconds of the latitude coordinates. In this case, it’s 33°25’50″N.
3. 'W' indicates that the longitude coordinate is in the western hemisphere. It returns 'E' for eastern hemisphere longitudes.
4. The (111.0, 52.0, 53.0) tuple contains the degrees, minutes, and seconds of the longitude coordinates. Here, it’s 111°52’53″W.
5. 347.0 is the altitude at which the photo was taken, in meters.

Remember, it’s the baseball picture that’s geotagged. If we plot those coordinates on a map, it should return the Chicago Cubs’ Spring Training ballpark in Mesa, Arizona. Indeed, it even correctly shows us sitting down the first base line.

Read EXIF Data with the Exif Library

Extracting EXIF data from your photos using the Exif library is very similar to the Pillow library. Again, we’ll start by printing the image filename and set the image path inside the with-metadata folder.

for img in images:
    print(img)
    image_path = "with-metadata/{}".format(img)

Next, we’ll read the image into the Exif library. However, unlike Pillow, the Exif library automatically extracts all the EXIF data when you instantiate the Image object. As a result, we do not need to call any additional methods or functions.

with open(image_path, "rb") as input_file:
    img = ExifImage(input_file)

Because the Exif library automatically extracts the EXIF data, all you need to do is just loop through the tags and extract each one with the get() method. And unlike the Pillow library, the Exif library also automatically handles instances where data points are missing. It won’t throw an error, so you don’t need to wrap it in a try/except block.

for tag in EXIF_TAGS:
    value = img.get(tag)
    print(“{}: {}”.format(tag, value))

Final Exif Library Code

When you put everything together, it’s even cleaner than using the Pillow library.

for img in images:
    print(img)
    image_path = “with-metadata/{}”.format(img)
    with open(image_pdaath, ”rb”) as input_file:
        img = ExifImage(img_file)

    for tag in EXIF_TAGS:
        value = img.get(tag)
        print(“{}: {}”.format(tag, value))

When you run the script, the output from the Exif library should be identical to the output from the Pillow library, with one exception. The Exif Library breaks down the GPS data into its components. You’ll still get the same tuples you do with the Pillow library, but it labels what each component of the GPS data is using English words instead of numeric codes.

baseball.jpg
make: samsung
model: SM-G965F
datetime_original: 2019:03:25 18:06:05
gps_latitude: (33.0, 25.0, 50.0)
gps_latitude_ref: N
gps_longitude: (111.0, 52.0, 53.0)
gps_longitude_ref: W
gps_altitude: 347.0

cape-cod.jpg
make: Canon
model: Canon EOS R5
datetime_original: 2022:03:22 20:58:52
gps_latitude: None
gps_latitude_ref: None
gps_longitude: None
gps_longitude_ref: None
gps_altitude: None

stonehenge.jpg
make: NIKON CORPORATION
model: NIKON D3000
datetime_original: 2022:02:16 21:36:08
gps_latitude: None
gps_latitude_ref: None
gps_longitude: None
gps_longitude_ref: None
gps_altitude: None

Writing, Editing, and Updating EXIF Data Using Python

To demonstrate how to write and edit EXIF data, we’re going to add a simple copyright message to the all three images. That message will simply say ”Copyright 2022. All Rights Reserved.” We’ll also add your name to the EXIF data as the artist/photographer.

Universal Tags We’ll Use Throughout the Python Script

Just like we did when we read the EXIF data from the image, we’ll define the artist and copyright tags we’ll use to edit the EXIF data in each library. We’ll also store the values we’ll set the tags to in the VALUES variable.

PILLOW_TAGS = [
    315,     # Artist Name
    33432,   # Copyright Message
[

EXIF_TAGS = [
    “artist”,
    ”copyright”,
]

VALUES = [
    “Matthew Gove”,    # Artist Name
    ”Copyright 2022 Matthew Gove. All Rights Reserved.”  # Copyright Message
]

How to Edit EXIF Data with the Pillow Library

In order to edit the EXIF data, you need to open the image with the Pillow Library and load the EXIF data using the getexif() method. This code is identical to when we read the metadata. The only difference is that we’re loaded the image from the without-metadata folder.

for img in images:
    image_path = “without-metadata/{}”.format(img)
    pillow_image = PillowImage.open(image_path)
    img_exif = pillow_img.getexif()

Now, all we have to do is loop through the tags we want to set (which are in the PILLOW_TAGS variable) and set them to the corresponding values in VALUES.

for tag, value in zip(PILLOW_TAGS, VALUES):
    img_exif[tag] = value

Finally, just save the changes to your image. For the purposes of this tutorial, we are saving the final images separate from the originals. When you update your EXIF data, feel free to overwrite the original image. You can always restore from the backup we made if needed.

output_file = img
pillow_img.save(output_file, exif=img_exif)

That’s all there is to it. When you put it all together, you have a nice, efficient, and compact block of code.

for img in images:
    image_path = “without-metadata/{}”.format(img)
    pillow_image = PillowImage.open(image_path)
    img_exif = pillow_img.getexif()

    for tag, value in zip(PILLOW_TAGS, VALUES):
        img_exif[tag] = value

    output_file = img
    pillow_img.save(output_file, exif=img_exif)

How to Edit EXIF Data with the Exif Library

Editing EXIF data with the Exif library is even easier than it is using Pillow. We’ll start by loading the image without the metadata into the Exif library. You can cut and paste this code from the script that reads the EXIF data. Just don’t forget to change the with-metadata folder to without-metadata.

for img in images:
    image_path = “without-metadata/{}”.format(img)
    with open(image_path, ”rb”) as input_file:
        exif_img = ExifImage(input_file)

Here’s where it gets really easy to edit the EXIF data and set new values. If you have a lot of EXIF data to edit, by all means put everything into a loop. However, for simplicity, you also do this.

exif_img.artist = “Matthew Gove
exif_img.copyright = “Copyright 2022 Matthew Gove. All Rights Reserved.”

Then save the file. Like we did with the Pillow library, we’ll save everything to a new file for purposes of the tutorial. However, feel free to overwrite the images when you use it in the real world.

output_filepath = img
with open(output_filepath, ”wb”) as ofile:
    ofile.write(exif_img.get_file())

Put it all together and you can update and edit your EXIF data with just 10 lines of Python code.

for img in images:
    image_path = "without-metadata/{}".format(img)
    with open(image_path, "rb") as input_file:
        exif_img = ExifImage(input_file)
    
    exif_img.artist = "Matthew Gove"
    exif_img.copyright = "Copyright 2022 Matthew Gove. All Rights Reserved."

    with open(img, "wb") as ofile:
        ofile.write(exif_img.get_file())

Confirming Your EXIF Edits Worked

The final step in editing your EXIF data is to confirm that the Python code actually worked. In the script, I copied logic from when we read the EXIF data to confirm that our edits were added and saved correctly. Indeed, when you run the script, you’ll see the following confirmation in the Terminal window. Alternatively, you can open the photo in any photo editor, such as Adobe Lightroom, to confirm that the new EXIF data has been added to it.

PILLOW
=======
baseball.jpg 
Artist: Matthew Gove
Copyright: Copyright 2022 Matthew Gove. All Rights Reserved.

cape-cod.jpg
Artist: Matthew Gove
Copyright: Copyright 2022 Matthew Gove. All Rights Reserved.

stonehenge.jpg
Artist: Matthew Gove
Copyright: Copyright 2022 Matthew Gove. All Rights Reserved.

################################

EXIF
======
baseball.jpg
artist: Matthew Gove
copyright: Copyright 2022 Matthew Gove. All Rights Reserved.

cape-cod.jpg
artist: Matthew Gove
copyright: Copyright 2022 Matthew Gove. All Rights Reserved.

stonehenge.jpg
artist: Matthew Gove
copyright: Copyright 2022 Matthew Gove. All Rights Reserved.

Download the Code in This Tutorial

You can download the code we wrote in this tutorial from our Bitbucket repository. Please feel free to play around with it and update it to suit your needs. If you have any questions, leave them in the comments below.

Conclusion

Python is an incredibly powerful tool to update and edit your EXIF data. And best of all, it’s one of the few EXIF editing tools that is completely free, without any restrictions on what you can do with it. It’s fast, easy-to-use, and infintely scalable. EXIF metadata is not the sexiest aspect of photography by any means. But it is one of the most critical. When you don’t manage it correctly, you are literally costing yourself both time and money.

If you want help getting started with your EXIF data, please get in touch with us today. As experts in both photography and data science, there are not many people who know the ins and outs of EXIF data better than we do. Alternatively, if you would just like to see more tutorials, I invite you to please join our email list and subscribe to our YouTube channel. See you in the next tutorial.

The post How to Bulk Edit Your Photos’ EXIF Data with 10 Lines of Python appeared first on Matthew Gove Blog.

An Introduction to the Lightroom Map Area

Adobe Lightroom comes with a really nice map interface to browse, explore, and view your photos. Unfortunately, if your photos aren’t geotagged, the feature is worthless.

Lightroom’s map feature lets you browse, explore, and view your photos and videos on an interactive map. You can also easily edit and geotag your photos from the map, as well as create collections, tag keywords, and much more.

Are You One of the Many Travel Photographers Struggling to Geotag Your Photos in Lightroom Without a Built-in GPS?

If your photos and videos are already geotagged when you import them into Lightroom, the map gets set up automatically. However, many cameras still don’t have built-in GPS. You can buy a GPS adaptor to mount to the top of your camera, but they come with plenty of drawbacks. First, with a GPS mounted to the top of your camera, you won’t be able to mount a flash or a mic there. And what if you have multiple cameras? At $200-300 a pop, the cost of outfitting each camera with a GPS unit can snowball out of control incredibly fast.

Alternatively, you can use a smartphone app that your camera manufacturer makes. When you snap a picture, the app uses the GPS in your phone to record the location and sends it to your camera over bluetooth. While I have not used these apps much, be aware that smartphones usually disable the GPS when it’s not actively in use to conserve battery. Unfortunately, if you phone’s GPS goes into standby or sleep mode during a photo shoot, it will often send the last location the GPS registered, which may not necessarily be your current location. As a result, many people have complained that these apps are not accurate or reliable for geotagging photos and videos.

So where do you go from here? This is where my method comes in. This method is by no means anything bleeding edge or earth-shattering. In fact, a quick Google search will reveal that it’s widely used throughout the photography and filmmaking industries. And best of all, it only requires one GPS regardless of how many cameras you have. However, there are still a few places it can trip you up. As a result, I want to use my background in GIS and data science to make sure that you fully understand both what the data is and what you’re doing with it. You unfortunately won’t find that in many other tutorials.

How to Use Handheld GPS (or Smartphone) to Geotag Your Photos and Videos in Adobe Lightroom

The strategy to geotag photos in Adobe Lightroom without a built-in GPS is staggeringly simple. While you’re out on your photo or video shoot, use a handheld GPS to record your movement. Then, in post-production match the timestamp on the GPS track to the timestamp on your camera to get the latitude and longitude coordinates for the geotag. Once you add the latitude and longitude to the metadata, Lightroom will automatically look up and add the city, state/province, and country that correspond to those coordinates.

Software You’ll Need

The only software you need to geotag your photos without a built-in GPS is Adobe Lightroom itself. Additionally, depending on what GPS and computer models you’re using, you may need additional software to pull the track off the GPS. If you have a Garmin GPS, don’t worry. All of their software is available free of charge. For example, my handheld GPS uses Garmin’s Basecamp software to offload the track data. On the other hand, the GPS I use in the car mounts like an external hard drive, so I can copy the track to my local disk using the Finder or File Explorer.

The track files you pull off your GPS are stored in GPS Exchange, or GPX format. GPX is an open source, non-proprietary file that stores GPS data for software applications. Data is stored in XML format, which is light-weight and usable in both desktop and web-based applications. In addition to latitude/longitude coordinates and timestamps, GPX files can also store speed, elevation, waypoints, routes, points of interest, and much more.

You can view GPX files in numerous desktop and web-based applications, ranging from Google Maps to QGIS and ArcMap to even the Matt Gove Photo maps. However, for the purpose of geotagging photos, the easiest application to view your GPX files is actually Lightroom. You can preview your track right in the Lightroom map before you geotag your photos.

Sync the Clocks on Your GPS and Camera Before You Head Out in the Field

Before you head out for your shoot, your single most important task is to sync the clocks between your handheld GPS and each camera you’ll be using. If the clocks are not synced, your photos and videos will not be geotagged in the correct location.

If your travels keep you confined to a single time zone, feel free to sync everything to your local time zone. However, if your travels take you across multiple time zones, you should sync all of your devices to UTC. Also called Zulu Time, UTC is the modern standard that the world uses to regulate time. It differs from Greenwich Mean Time (GMT), as GMT is based on the Earth’s rotation, while the more accurate UTC is based on the atomic measurements.

If your camera or GPS does not offer UTC as a time zone, set it to London (make sure daylight savings is off) or Iceland. In addition, Dakar, the capital of Senegal, is on UTC year-round.

Once you get out in the field where you’ll be shooting, simply turn on your handheld GPS and throw it in your bag, or put it somewhere that will be out of the way, but with you all day. Make sure the batteries are all charged at the beginning of the day. Finally, don’t forget to carry a spare set of batteries if you’re expecting a lengthy shoot.

Add Location Data in Post-Production in Adobe Lightroom

Now that your shoot is finished, it’s time to geotag your photos and videos in Lightroom. Before we get started, though, there’s another important note about time zones that if not done correctly, will result in your photos not being geotagged correctly.

A Word of Caution About Time Zones in GPX Data vs EXIF Camera Metadata

If you’ve synced your GPS and cameras to UTC, be aware of the difference in how GPX files handle time zones vs the camera’s EXIF metadata. GPX files have the time zone embedded in them. EXIF metadata records the time in the time zone that your camera is set to, but does not actually record the time zone itself in the metadata. In other words, the GPX file sees the time stamp as “2022-04-11 15:21 UTC”, while the EXIF metadata in the camera only sees “2022-04-11 15:21”.

Why is this important, you ask? Because when you offload the GPX file to your computer, your computer automatically converts its timestamp back to local time. For example, let’s say I’m on the east coast of the US, which is 4 hours behind UTC. The computer all of a sudden now sees the GPX timestamp as “2022-04-11 11:21 EDT”, while it still sees the EXIF metadata as “2022-04-11 15:21” (which it incorrectly assumes is also EDT because the EXIF data does not include the time zone). If you try to sync your photos to the GPX track, they’ll all be four hours off!

Thankfully, re-aligning the time zones is incredibly easy in Adobe Lightroom. When you load the GPX file into Lightroom, it will ask you if you want to correct the time zone. If you’ve synced your cameras to UTC, simply take the UTC offset of your local timezone and reverse the sign. For example, if you’re on the US east coast, which is UTC-4, set the time zone correction to +4 hours. This tells Lightroom to add 4 hours to the “2022-04-11 11:21 EDT” GPX timestamp, syncing it back up with the “2022-04-11 15:21” timestamp on the camera.

Still confused? If you don’t want to deal with having to adjust timezones in Lightroom, there’s an easy alternative. Simply set your computer’s time zone to UTC before you import anything into Lightroom. Once you’re finished geotagging your photos, set the time zone on your computer back to local time.

Import the GPX File and the Photos and Videos From Your Shoot into Lightroom

To import the GPX File into Lightroom, first open the map viewer. At the bottom of the window, you’ll see a bar to select the map style, a zoom slider, a lock, and a track button, which is circled in green below.

Click the track button and select “Load Tracklog” at the very top of the menu. Navigate to the tracklogs you just pulled off your GPS. Click OK to load them into Lightroom.

Now, you’ll set the time zone offset as we discussed in the previous section. If you’re working in your local time zone (or have set your computer’s time zone to UTC to match the camera and GPS), you don’t need to add any offset to the track. Remember, if you synced the camera and GPS to UTC, simply take the UTC offset of your local time zone and flip the sign. In other words, set the offset to +4 hours for Eastern Time (UTC-4) or +7 hours for Pacific Time (UTC-7). Click OK to confirm the time zone offset.

You should see the trackline from your adventure appear on the map. Do note that if Lightroom detects that your tracklog time zone does not appear to match the time zone of your photos, it will highlight the trackline log time in red. In that case, click the track button at the bottom and select “Set Time Zone Offset” to set the correct time zone.

Once you’ve confirmed that the track loaded correctly, then import your photos and videos just like you always have.

Geotag Your Photos in Lightroom with the Click of a Button

Now that both the track and your media are all in place, it’s time to geotag them in Lightroom. In the filmstrip at the bottom of the window, select the photos and videos you want to geotag. Then, click on the track button once again, and select “Auto-Tag XX Selected Photos”, where XX is the number of photos you selected.

You should see a bunch of points appear on your trackline. Hover over them and you’ll see your photos. If they’re not quite in the right position, you can drag them around to put them in the right place.

Don’t Have a Handheld GPS? Use a Smartphone Instead.

If you don’t have a handheld GPS, there are plenty of smartphone apps out there to generate a GPX file of your adventure. In the past I’ve always used the AllTrails app, which is available for free for both Apple and Android devices. AllTrails is designed for hiking and biking, but you can use it to track any activity. Here are directions to export your track from the AllTrails app. Make sure you export it as a GPX track, and not a GPX route.

Alternate Methods to Geotag Your Photos in Lightroom without a Built-in GPS

While geotagging photos and videos in Adobe Lightroom using a GPX track is by far the easiest and most accurate way to add location data to your images and videos, it’s not the only way.

First off, Lightroom offers several ways to add location data manually. You can directly edit the metadata of your images directly in Lightroom, or use the map interface to geotag your images. You can find plenty of tutorials for manual geotagging with a quick Google search. For more information, here is Adobe’s official documentation.

Unfortunately, there are quite a few drawbacks to geotagging your images manually. While it works fine for a few photos, it’s impossible to scale up to large photo albums, collections, and libraries, unless you have a serious amount of both time and will to put into it.

You can also geotag your photos using Python. We’ll cover this in a future tutorial, but you can use Python’s Pillow library to add location to your images’ metadata. You’ll need to loop through your image files, add the appropriate location metadata (lat/long coordinates or city/state/country), and then save the files. Be aware that this can get very complicated if you have a lot of photos taken in a lot of different locations. However, unlike manually adding location data, using Python is infinitely scalable, both up and down.

Conclusion

Geotagging photos is a critical part of both the workflow and staying organized as a landscape and travel photographer. With so many cameras still lacking built-in GPS functionalities, it becomes even more critical to know how to geotag photos in Adobe Lightroom without built-in GPS. Using GPX track files is by far the best alternative to built-in GPS that’s out there. Except for a few spots time zones can trip you up, the method is foolproof, accurate, reliable, and only requires one GPS, regardless of how many cameras you have. In my book, though, the benefits of having a geotagged library far outweigh the occasional hiccup from a mislabeled time zone.

Interested in more of these tutorials? I’n addition to the blog entries, I’ll be posting them to YouTube and sending them out via our email newsletter as well. Please subscribe to our email newsletter and our YouTube channel for the latest tutorials, and get exclusive deals to our online store that are not available anywhere else.

The post How to Geotag Your Photos in Adobe Lightroom Without a Built-in GPS appeared first on Matthew Gove Blog.

Today, we’ll be using these essential story elements to tell the story of the EF-5 tornado that struck Moore, Oklahoma on 20 May, 2013. Even though we’ll be using maps and photography to tell the story, you could easily use a video, blog post, podcast, and much more, too.

Plan As Many Story Elements as You Can

It’s hard to tell a story if you don’t know the basic elements. As a result, you should plan out as many elements of your story as possible. If you’re planning a photo and video shoot, these elements of your story don’t need to be set in stone – there’s a lot if improvising in making travel videos, for example, especially if you’re going to be shooting in a location you’ve never been to before.

But you should have at least a general idea of how you’ll portray your story to your readers. Without that planning, you’ll likely miss shots while you’re out filming, and negatively impact the quality of your final presentation. Simply do your research and plan out your story before you go out on a photo or video shoot. This technique works, even for difficult-to-plan genres, such as travel videos.

1. Set the Stage in the Setting

At the beginning of your story, you have a very limited time to set the stage for your story. With video, you you need to both set the stage and hook your viewer in the first 10-15 seconds.

For the Moore tornado, the stage will be set on the morning of 20 May. It’s the third, and most dangerous day of a three-day tornado outbreak across the southern plains. The previous day had seen violent tornadoes in Oklahoma, including an EF-4 inside the Oklahoma City metro that carved a path from Norman to Shawnee. We’ll use the Day 1 SPC outlooks and discussions to set the stage (note the usage of the strong, long-track tornadoes wording), as well as storm reports from the 19th. Furthermore, when you stepped outside that morning, it just had “that felling” that something significant was about to happen.

2. Determine the Point of View From Which Your Story is Told

From whose point of view will you be telling your story? Consider a murder mystery. The story will have a very different feel being told from the murderer’s point of view vs the detectives’ point of view.

For an event like the Moore tornado, you could choose to tell it through the point of view of the news media. While there is nothing wrong with this approach, there is a much better way to tell the story. And that’s through the eyes of someone who was there when it happened. As a result, I’ll be sharing my firsthand account of my experience that day.

3. Introduce Your Characters

Before you begin telling your story, you should at the very least know who the main characters are. For the Moore tornado (and some of my travel videos), I am the main character. On the other hand, if you’re telling the story of a place or event with historical significance, you’ll need to transport your audience back in time. The people who lived through those historical events will be your main characters. But to fully immerse your audience in your story, try making them the main character. Tell your story in second person, and let your audience experience it.

If you’re planning the story of something that can’t be scripted – like travel videos or blogs – it’s perfectly okay to not know every single character. When you’re traveling, you never know the interesting people with colorful personalities you’ll meet along the way. This could be a random stranger you’re sitting next to at lunch or on the train. Maybe it’s the proprietor of an incredible hole-in-the-wall coffee shop you stopped at along the way. Or perhaps, it’s the local guide that you hired for that bucket-list experience. Once you get done filming your adventure, just make sure you know how every character works into your story. And if they don’t play a meaningful role in moving your story along, leave them out.

4. Every Story Needs a Hook

The hook is one of the most important elements of your story, if not the most important. As you set the stage for your story, you also need to dangle a “hook” to your reader or viewer. That hook is designed to draw them into the story. It shouldn’t leave them just yearning to see what happens next. It should leave them craving it. Have you ever binge-watched a show all at once? The writers of bingeable shows are incredibly gifted at creating effective hooks. Those hooks are what keeps you pushing forward to the next episode instead of turning off the TV and going to do something else.

A good hook gives a sneak peak of what’s coming, but doesn’t give the storyline away. It could be a review of the conflict, the resolution, or anything else in the story. For the Moore tornado, we can state what the tornado hit – two elementary schools and a hospital. Notice that we didn’t say how much damage was done or if there were any injuries or causalities. We could also show the radar and the tornado emergency that was issued for the City of Moore as the tornado barreled towards it.

5. The Plot

The plot is the most important of all story elements, by far. In fact, I could write an entire post (and I probably will) on how to structure your plot to keep your readers engaged and wanting to know what will happen next. Plots are broken into 5 elements, and we can use a Freytag’s Pyramid to illustrate those 5 stages.

Plot Elements for Your Story

1. Exposition. Set the stage for your story. Introduce your characters, give your audience the hook to draw them in, and begin to introduce the primary conflict.
2. Rising Action. In this stage, your protagonist addresses the primary conflict with a form of action. As you approach the climax, those actions should build and escalate tension, like approaching the top of a roller coaster.
3. Climax. This is the pivotal moment your audience has been waiting for. Your protagonist will encounter their greatest challenge of the entire journey. It’s the culmination of the buildup of tension during the rising action phase. Make it exciting for your audience!
4. Falling Action. Your protagonist will deal with the consequences and fallout – both good and bad – of everything that happened during the climax. Keep your audience engaged by setting the stage for the story’s conclusion. By the end of this phase, you’ll be well on your way to a (hopefully satisfying) conclusion. Additionally, you should start resolving any conflicts that arose as a result of the climax.
5. Resolution or Dénouement. You can go one of two ways here. If this is actually the end of your story, wrap everything up. Tie up loose ends. Give your audience a sense of closure so they know the fate of your protagonist. On the other hand, if you’re writing a series or sequel, you should introduce another hook to leave your audience craving the next episode. Cliffhangers work exceptionally well as that hook.

Now, let’s look at how we can apply Freytag’s Pyramid to the plot of the story of the Moore tornado.

The Plot Elements of the Moore Tornado

6. Without a Conflict, There is No Story

Your story’s conflict answers the question of why your character is embarking on this journey. Without a conflict, you don’t have a story. It’s as simple as that. In your story, the conflict is what causes your character to take action and move the story forward. Conflicts can be both physical and mental. For example, if you’re telling a story about climbing Mt. Everest, the physical or external conflict consists of all the dangers your character encounters on their way to the summit. From frigid temperatures to thin air to dangerous terrain to altitude sickness, one false move could kill your character as they ascend the mountain.

On the other hand, let’s look at a mental conflict. Mental conflicts are internal journeys, and often tend to focus on a single main character. The best example of a simple mental conflict is a character’s journey to overcome their fear of heights so they can go skydiving or bungee jumping. You could also tell the story of how your protagonist overcame their stutter to become a great public speaker.

Keep in mind that while there is usually only one primary conflict, most stories have multiple conflicts. Additional conflicts tend to come in two forms. First, they can be sub-conflicts, that when put together, make up the primary conflict. If you’ve ever seen Monty Python and the Holy Grail, King Arthur’s quest to find the grail is comprised of numerous smaller conflicts they encounter along the way. The conflicts escalate as they get closer to the grail, culminating with the Bridge of Death and the Killer Bunny.

Cascading Conflicts in the Aftermath of the Moore Tornado

Conflicts in the story of the Moore Tornado fall into the second category of multiple conflicts. In these stories, the primary conflict sets off a series of additional conflicts during the falling action. In Moore, the primary conflict is the tornado itself. But after the tornado levels the city, a whole new slate of smaller conflicts emerges.

• With the city’s critical infrastructure destroyed, how do search and rescue teams coordinate and communicate their efforts?
• Debris is everywhere, rendering roads impassable. How do search and rescue teams get into these areas and rescue survivors without using the roads?
• The damage path is 17 miles long and 1 mile wide. Where do search and rescue teams, as well as city and state resources, prioritize their efforts?
• What do survivors do and where do we send them once they’re rescued? How do we get relief to storm victims as soon as possible?

There are obviously many more conflicts than just this following a major tornado, but this should get you started.

Even the Simplest Stories Have Conflicts

When you tell your story, remember that you can find conflict in even the simplest, most monotonous things. Take going to the grocery store as an example. I can think of one major conflict we’ve encountered going to the grocery store recently: the COVID-19 pandemic.

But even without a global pandemic, you can still find conflict to tell the story of your trip to the grocery store. Maybe you’re looking for a very special ingredient and have to go to 3 or 4 stores before you find it. Perhaps your character has fallen on hard times and needs to stretch a tight budget as far as possible. Or what if there’s a major winter storm coming and you have to fight through treacherous conditions and low supply to stock up ahead of the storm.

Conflicts can be really anything you think of, but you need to know your audience. If you create a story that your audience isn’t interested in, they’re not going to listen to you tell it. If you’re stuck looking for a conflict, ask yourself why your character is going on this journey. The answer to that question is the conflict in your story.

7. The Resolution

At the end of your story, you should tie up all loose ends and give your audience a sense of closure for how your story ended. Regardless of whether your story has a happy or sad ending, your audience should know what the characters’ lives will look like now that their struggles are over and the conflict has been resolved.

However, if you’re planning on writing a sequel or another episode, you can easily leave the door open to another chapter of the story. While one conflict is resolved, your character may facing another one. In that case, dangle another hook or cliffhanger to leave your audience eagerly waiting to come back for the next chapter. Then you can go back to the beginning of this guide and start the journey all over again.

The Moore Tornado Story in Maps and Pictures

Conclusion

Regardless of what media you are using to tell your story, you’ll be using the same seven elements to tell it. Planning is critical to being able to tell an insightful and engaging story, especially if you have to go out and shoot photos or video of it. Without a plan, your story will wander and ramble, and your audience will lose interest. Set the stage, hook them in, and leave them craving to see what happens next. Because at the end of the day, you shouldn’t want to just tell your story. You should want your audience to experience it.

Top Photo: The Reward at the End of a Tough Hike
Sedona, Arizona – August, 2016

The post The 7 Essential Elements You Need to Tell Your Story appeared first on Matthew Gove Blog.

Thankfully, we can turn to our secret weapon, Python, to remove noise from our photos. While it’s not well documented, Python has some incredibly powerful image processing libraries. With a proper understanding of the algorithms, we can use Python to remove nearly all the noise from even the grainiest of photos. And to put our money where our mouth is, we’re going to put our Python script up against Adobe Lightroom to see which one can better remove noise from photos.

The Problem with Noise in Low Light Photos

No matter your skill level, whenever you head out to take photos in low light, you probably dream of coming home with a photo that looks like this.

Instead, you come home with a monstrosity like this.

Despite these two pictures being taken with the same camera less than 20 minutes from each other, why did the first one come out so much better than the first? Yes, post-processing does play a small role in it, but the main culprit is the camera settings and the photo composition. No amount of post-processing can bring back lost data in a photo that’s incorrectly composed. Because the second photo is not correctly composed or exposed, much of the data in the bottom half of the frame is lost.

Let’s compare the two photos.

Poor Composition Leads to Noise in Photos

From the photo metadata, we can easily conclude that the difference between the two shots is indeed the composition. More specifically, it’s the sun angle. When you take a picture looking into the sun, it will be higher contrast than a picture that’s taken with the sun behind you. When taken to extremes, you can actually have data loss at both the dark and light ends of the spectrum.

And that’s exactly the result when you take the photo a half hour vs 15 minutes after sunset. Because the second photo looks into the sunset, being further past sunset exacerbates the increase in contrast. As a result, you need to choose whether you want to properly expose the dark land or the colorful sky. You can’t have both. On the other hand, the first photo is able to capture the full spectrum of light that’s available, resulting in the spectacular dusk colors.

What Causes Noise: A Crash Course in ISO Levels

The ISO level sets how sensitive you camera is to light. Most cameras set the ISO levels automatically by default. The more sensitive your camera is to light, the brighter your photos will be. Lower ISO levels result in sharper images, while high ISO levels lead to grain in your photos.

Exactly how much grain appears in your photos depends on your camera’s sensor size. Professional cameras with large sensors often don’t have much of an issue with grain. On the other end of the spectrum, small or entry-level cameras are much more sensitive to grain because their sensors are so much smaller. Tiny sensors are why cell phone cameras struggle so much in low light. The technology has certainly gotten better over the past five years, but it’s still far from perfect.

On a bright, sunny day, use low ISO levels to keep photos sharp and avoid overexposing them. Alternatively, use high ISO levels for low light or night photography. Under normal conditions, your ISO levels should be between 200 and 1600. However, ISO levels on some very high end cameras can reach as high as 2 million.

Even Professional Image Processors Like Adobe Lightroom Can Only Do So Much to Remove Noise from Your Photos

As powerful as Adobe Lightroom is, it has its limits. You can’t just blindly take photos in low light and expect to turn them into masterpieces with some combination of Lightroom and Photoshop. As we mentioned earlier, no amount of post-processing can recover lost data in your photos. It’s up to you to properly compose your photos and use the correct camera settings.

However, even with proper composition, professional image processors like Adobe Lightroom can only get rid of so much noise. Adobe Lightroom does a spectacular job getting rid of much of the noise in your photos, but you’ll eventually find yourself in a situation where there’s just too much noise for it to handle.

However, where Adobe Lightroom leaves off, our secret weapon takes over.

Python Has Powerful Image Processing Capabilities

It’s not well advertised, but Python has incredibly powerful image processing libraries that as a photographer, you can use to boost both your productivity and income. However, I want to caution that you should use Python as a tool to compliment Adobe Lightroom, not replace it. Being able to write your own scripts, functions, and algorithms in Python to add to the functionalities of Lightroom is incredibly powerful and will set you apart from just about every other photographer.

Indeed, I do my post processing with Adobe Lightroom. Afterwards, I use Python to format, scale, and watermark pictures that I post both to this blog and to the Matt Gove Photo website. When I used to write blog posts that had lots of pictures (and before I had Adobe), it often took me upwards of an hour or more to manually scale and watermark each image. Then I had to make sure nothing sensitive was being put on the internet in the metadata. That all can now be accomplished in just a few seconds, regardless of how many pictures I have. Furthermore, my Python script will automatically remove sensitive metadata that I don’t want out on the internet as well.

You may recall that in some of our previous Python tutorials, we have used the Python Imaging Library, or Pillow, to process photos. Today, we’ll be using the OpenCV library to remove noise from our photos.

How Python Algorithms Remove Noise From Photos

Whenever you write Python code, you should try to understand what built-in functions are doing. This will not only give you a better understanding of what your script is doing, but you will also write code that is faster and more efficient. That’s especially critical when processing large images that require lots of computing power.

Example: Removing Noise from COVID-19 Data

Before diving into our photos, let’s look at a very simple example of removing noise from a dataset. Head over to our COVID-19 dashboard and look at the time series plots of either new daily cases or new daily deaths. Without any noise removal, the plots of the raw data are messy to say the least.

To smooth the data curves and remove noise, we’ll use a moving average. For every data point on the curve, we’ll calculate the average number of new daily cases over the previous seven days. You can actually average as many days as you want, but the industry standard for COVID-19 data is seven days. We’ll plot that 7-Day Moving Average instead of the raw data. The resulting curves are much cleaner and presentable.

People use moving averages more much more than just COVID-19 data. It’s often used to smooth time series in the Stock Market, scientific research, professional sports, and much more. And we’ll use that exact same concept to remove noise in our photos.

How to Average Values with Python to Remove Noise in Photos

There are several ways to go about doing this. The easiest way is if you take several versions of the same shot, lay them on top of each other, and average the corresponding pixels in each shot. The more shots you take, the more noise will be removed. To ensure that your scene does not shift in the frame as you take the shots, use a tripod.

Mathematically, noise is random, so averaging noise pixels will effectively remove the noise. The scene that is actually in your shot does not change, so the non-noise pixels should far outnumber the noise pixels when you calculate the average. As a result, calculating the average removes the noise.

Consider the following equations. For the sake of this argument, let’s say you’re looking at just a single pixel. The actual value of the pixel in the scene is 10. However, in four of your shots, noise is introduced, and the camera records values of 4, 15, 9, and 18. Remember that the average is the sum of the values divided by the number of values.

In your first attempt, you take 10 shots of the scene. How would you do in noise removal?

average = ((6*10) + 4 + 15 + 9 + 18) / 10 = 106 / 10 = 10.6

Not bad, seeing as the actual value of the pixel should be 10. But we can do better. Instead of taking 10 shots of the scene, let’s take 100 instead.

average = ((96*10) + 4 + 15 + 9 + 18) / 100 = 10.06

That’s much better. It may not seem like much, but even just a small change in value can make a big difference for removing noise.

What Does This Method of Removing Noise From Photos Look Like in Python

Believe it or not, we can write the “stacking average” algorithm to remove noise from photos in just 12 lines of Python. We’ll use numpy for the calculations because it can natively store, calculate, and manipulate grids or matrices of data with just a line or two of code. As a result, all of the photo data will remain in the grid or matrix of pixels we’re familiar with. We don’t need to break it down into rows, columns, or anything else.

First let’s make sure you have installed numpy and OpenCV. If you haven’t, you can easily install them with pip. Open a Terminal or Command Prompt and execute the following commands.

pip3 install numpy
pip3 install opencv-python

Next, it’s time to write our Python script. Let’s start by importing everything we need. The cv2 library we’re importing is part of OpenCV.

import os
import numpy as np
import cv2

Second, tell Python which folder the image you’ll be averaging are stored in. Then list their filenames, skipping any hidden files that are in the image directory.

folder = "noise-imgs"
image_files = [f for f in os.listdir(folder) if not f.startswith('.')]

Third, open and read the first image into the average variable using OpenCV. Store pixel data as a numpy floating point number. You’ll use this variable to store image data and calculate the average of the image pixels.

path = "{}/{}".format(folder, files[0])
average = cv2.imread(path).astype(np.float)

Fourth, add all of the remaining images in the directory to you average variable. Don’t forget to skip the first image because we’ve already added it in the previous step.

for f in files[1:]:
    path = "{}/{}".format(folder, f)
    image = cv2.imread(path)
    average += image

Fifth, divide your average variable by the number of images to calculate your average value.

average /= len(image_files)

Finally, normalize the averaged image and output it to a jpeg file. The cv2.normalize() function boosts the quality, sharpens the image and ensures the colors are not dark, faded, or washed out.

output = cv2.normalize(average, None, 0, 255, cv2.NORM_MINMAX)
cv2.imwrite("output.jpg", output)

That’s it. There are only 14 lines of code. It’s one of the easiest scripts you’ll ever write.

Example: Dusk in the Oregon Pines

We’ll throw our Python algorithm a tough one to start. Let’s use a photo of a stand of pine trees in Oregon taken at dusk on a cloudy winter night. Here’s the original.

The photo is definitely grainy and really lacks that really crisp sharpness and detail. I don’t know about you, but I’d be tempted to just throw it away at first glance. However, what happens if we let our Python algorithm have a crack at removing the noise from the photo?

That looks much better! I’ll admit, the first time I ran the algorithm, I was absolutely floored at how well it worked. The detail was amazingly sharp and crisp. I unfortunately had to shrink the final image above to optimize it for the web, so the detail doesn’t appear quite as well as it does in the original. For the ultimate test, we’ll put our Python algorithm up against Adobe Lightroom’s noise removal shortly.

A Travel Photography Problem: What Happens If You Only Have a Single Shot and It’s Impossible to Recreate the Scene to Get Multiple Shots?

Good question. This is a common problem with travel photography, and is why I always encourage you to take multiple shots of things while you’re traveling. You never know when you might need them. Unfortunately, the above method really doesn’t work very well in this case. However, there are other ways to remove the noise.

We’ll use the same strategy to remove the noise as we did for the COVID-19 data. But instead of averaging over the previous seven days, we’ll average each pixel or cluster of pixels with the pixels that surround it. However, there’s a catch, here. The more layers of pixels you include in your average, the less sharp your image will be. You’ll need to play around to see what the exact balance is for your specific photo, but the OpenCV documentation recommends 21 pixels.

Thankfully, the OpenCV library has this algorithm built into it, so we don’t need to write it.

cv2.fastNlMeansDenoisingColored(source, destination, templateWindowSize, searchWindowSize, h, hColor)

• source The original image
• destination The output image. Must be same dimensions as source image.
• templateWindowSize Size in pixels of the template patch that is used to compute weights. Should be odd. Defaults to and is recommended to be 7 pixels.
• searchWindowSize Size in pixels of the window that is used to compute weighted average for given pixel. It’s value should be odd. Defaults to is recommended to be 21 pixels
• h Luminance component filter component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
• hColor Color component filter component. 10 should be enough to remove colored noise and do not distort colors in most images.

When we run the image through the OpenCV algorithm, it outputs the following.

The noise is certainly removed, but the image is still very dark and you can see the fuzziness around the edges. To sharpen the image back up, go back into Lightroom and find the original image. Remove as much of the noise from the original image as possible in Lightroom, and then export it. Next, average the OpenCV image and the Lightroom image using the stacking method from the previous section. That will both sharpen the image and brighten the colors.

That looks much better than the original photo. Other than a little touch-up post processing in Lightroom, that’s about as much as we can help this photo.

How Does Our Python Script Hold Up Against Adobe Lightroom’s Noise Reduction?

All right, it’s time for the ultimate test. It’s time to see how our Python algorithm does against Lightroom’s noise reduction capabilities. If you read the title of this post, you can probably guess how this went. Turns out it wasn’t even close. Our Python script blows Lightroom out of the water.

Despite the results, I want to caution you that this comparison is a bit like comparing apples to oranges. Sticking with the pine tree reference, it would be like cutting a tree down with a chainsaw vs. a hand saw. Because Lightroom only has access to the single photo, it must use the algorithm that takes a cluster of pixels and averages the pixels surrounding it to remove the noise. Do you notice how much sharper the Python image is compared to the Lightroom image? It’s because our Python algorithm has far more resources available to it to remove the noise from the photo. 63 times the resources to be exact. That’s why it’s not exactly a fair comparison.

Lightroom vs. Python Comparison on a Level Playing Field

To level the playing field, forget about averaging over multiple photos to remove the noise. Let’s say we only took one photo of the pine forest in Oregon. As a result, we can use only the single original image. We’ll process it using the same method we did for the sunset at Arches National Park in the above section. When we put it up against Lightroom this time, it’s a much closer contest. However, I still give the edge to the Python algorithm because the final image is noticeably sharper.

Want to Try the Python Script Yourself?

If you want to try out any of the Python algorithms we covered in this post, please download the scripts for from out Bitbucket repository.

Conclusion

Removing noise from photos is an endless frustration for photographers at all skill levels. To add insult to injury, high-end image processors such as Adobe Lightroom can only do so much to remove noise. However, with mathematical knowledge of how noise works, we can write an algorithm that does even better than Lightroom. And best of all, it’s only 14 lines of Python code. You can actually apply these algorithms to videos as well, but that’s a discussion for another day.

However, even though we put our algorithm up against Lightroom, we mustn’t forget that as photographers and image processors, Python must be used as a tool to complement Lightroom, not replace it. Because when we pit the two against each other, it’s only us that suffer from reduced productivity and a less effective workflow. If you’d like to boost your productivity by adding Python to your photography workflow, but don’t know where to start, please reach out to me directly or schedule a free info session today. I can’t wait to see what the power of Python can do for you.

The post How to Remove Noise from Photos with 14 Lines of Python…and Blow Lightroom Out of the Water appeared first on Matthew Gove Blog.

Update: Beginning 31 March, 2022, Indiana Dunes National Park will charge $25 per vehicle to enter the park.

A Brief History of the Indiana Dunes

In 1899, Henry Chandler Cowles, a botanist at the University of Chicago, began the first movement to preserve what’s now the Indiana Dunes. Cowles’ movement cited the unique flora at the dunes as the reason to preserve the area. The movement rapidly gained momentum. By 1916, the National Parks Service held hearings in Chicago about preserving the area as Sand Dunes National Park.

Unfortunately, local manufacturing plants had discovered that the sand at the Indiana Dunes was ideal for making glass. As a result, the 1916 hearing went nowhere. Glass manufacturing had completely consumed one of the most famous dunes by 1920. Facing backlash from the local residents, the State of Indiana stepped in and designated the area as Indiana Dunes State Park in 1926. You can still see evidence today of the manufacturing that went on at the Indiana Dunes in the early 20th century.

The federal government didn’t show any interest in the Indiana Dunes until the 1950’s. Preservation efforts ramped up again when a Port of Indiana was proposed to maximize economic development in the area. Activists began a nationwide campaign to buy the land and preserve the dunes. Their efforts were successful. In 1966, the U.S. Congress passed a bill to preserve the area as the Indiana Dunes National Lakeshore. Between 1976 and 1992, Congress expanded the national lakeshore four times, bringing it to the size it is today.

In 2017, both senators and representatives from Indiana sponsored a bill to turn the Indiana Dunes National Lakeshore into a National Park. While it took a couple years to finally get a vote on the bill, it passed Congress and became law on 15 Feburary, 2019. Indiana had its first National Park.

Where is Indiana Dunes National Park?

Indiana Dunes National Park covers 15 miles (24 km) of shoreline on Lake Michigan between Gary and Michigan City, Indiana. It’s just a 45 minute drive from both downtown Chicago and South Bend. If you’re coming from further away, the dunes are an easy day’s drive from Detroit, Indianapolis, much of Ohio, and even St. Louis.

The Indiana Dunes Can Migrate Up to 18 Feet Per Year…and Swallow Everything in Their Path

Indiana Dunes National Park has some of the most fascinating geology east of the Mississippi. At times, the dunes can behave like a living, breathing creature. In fact, the U.S. Geological Survey makes quite a statement about them on their website.

Dunes in the park are still actively migrating downwind. They move as layer after layer of sand is blown from the front of the dune over to the slipface. The most active dune, Mount Baldy, can move up to 18 feet in a year, swallowing up entire trees as it advances.

U.S. Geological Survey

When I first read that, I thought, “Come on. I get that dunes are always moving, but entire trees? Really?” When I got to Indiana Dunes National Park, Mt. Baldy happened to be my very first stop. When I pulled into the parking lot, this is what I saw. Insert foot into mouth.

Interestingly, I continued to notice the dunes just swallowing everything as I made my way through the park. Trees, signs, benches, parking lots. You name it, the dunes were swallowing it. The Indiana Dunes actually remind me a lot of White Sands National Park in New Mexico. Both parks have a lot of fine, sugary sand. Dunes are constantly in motion, advancing and retreating as the wind shifts throughout the year. However, unlike White Sands, the sand at Indiana Dunes National Park does not dissolve in water.

Indiana Dunes National Park Offers Outdoor Activities Year Round

While Indiana Dunes is best known for its summer recreation and relaxation, the park offers an abundance of outdoor activities year round. Don’t discount the shoulder or offseason. There is plenty to do, and the lack of crowds in any National Park makes for a completely immersive and memorable experience.

• Hiking
• Biking
• Swimming and Sunbathing
• Boating
• Cross Country Skiing, Snowshoeing, and Sledding
• View the spectacular shelf ice on Lake Michigan in the winter
• See beautiful colors in the fall
• Scenic Drives
• Birdwatching and Animal Watching
• Horseback Riding
• Camping and Picnicking
• Learn About the History of the Area

Take in Breathtaking Views of Lake Michigan

For being in a state that has a reputation of being very flat, the Indiana Dunes offer an incredibly diverse choice of absolutely stunning viewpoints and lookouts over the turquoise waters of Lake Michigan. Hike up to the top of the tallest dunes and paths for stunning panoramic vistas nearly 200 feet above the lake. Alternatively, soak in the sun and scenery from lake level as you walk along the beach or wade in the water. I could go on and on here, but I’ll let the photos speak for themselves.

See Indiana’s Rarest Flora and Fauna

Indiana Dunes is one of the most diverse regions in the United States. The park is home to some of Indiana’s rarest flora, as well as many species of fauna. If you’re looking for a specific species, make sure you know the best season for viewing them before you plan your trip. If you don’t have a specific species in mind, the Indiana Dunes boasts both spectacular fall colors and an incredible variety of wildflowers in the spring. Even when I visited in early December, it’s easy to get caught up and absorbed in nature. There was no shortage of plants and animals to look at in the winter.

Indiana Dunes Photography and Videography Tips

To get the most from your photo or video shoot, you’ll want to spend at least one full day at the park. Take advantage of the Golden Hour at both ends of the day to get some spectacular sunrise and sunset pictures. Let the low light bathe the dunes in vibrant warm colors. During the day, shift your focus to the water. Let the powerful midday sun bring out the best teals, blues, and greens in Lake Michigan. Additionally, go for a hike or a scenic drive to photograph some of the lesser seen parts of the park in the woods, marshes, grasslands, and other surrounding areas.

Furthermore, Indiana Dunes National Park offers so many different stories you can tell through your photos and videos. Whether you’re into nature, history, recreation, or anything else, there’s a story for you to tell. I encourage you to partake in any of the activities we discussed earlier to help you tell your story.

Best Locations to Shoot Photos and Videos

You can take great pictures and videos pretty much anywhere inside Indiana Dunes National Park. But here are my favorite places for a shoot.

• Mt. Baldy
• Central Avenue Beach
• Dunbar Beach
• Porter Beach
• Forest scenery along US-12

Advantages of Visiting in the Winter Offseason

There is one major reason to visit Indiana Dunes National Park during the winter offseason: the lack of crowds. When I visited in early December, there were certainly a few other people out and about, but I largely had the place to myself. You don’t have to worry about traffic or parking, even in places where parking is typically very limited. And being able to shoot photos and videos on a nearly empty beach is simply magical.

Furthermore, the low sun angle in the wintertime makes for some really beautiful light on the dunes for landscape photography. The sun remains high enough in the winter that you can still get shots of the brilliant turquoise, blue, and green waters in the middle of the day. And with sunset being so early, you’ll be done with your sunset shoot long before dinner.

Conclusion

Indiana Dunes is one of America’s newest, most diverse, and most underrated National Parks. Regardless of your interests, hobbies, and passions, the park offers outdoor activities, places to explore, and stories to tell for everyone year round. Have you been or are you planning to go? What was your favorite part? What are you most looking forward to? Let us know in the comments below.

Top Photo: The turquoise waters of Lake Michigan shimmer under the brilliant sunlight
Indiana Dunes National Park, Indiana – December, 2021

The post Indiana Dunes: One of America’s Most Underrated National Parks appeared first on Matthew Gove Blog.

So what’s my secret? I apply my education and experience in meteorology and storm chasing to make weather a focal point of my landscape photography and travel videos. Being proactive instead of reactive allows me to stay in front of changing weather. As a result, I am already in position ready to shoot whenever my target weather arrives. It doesn’t matter if I’m waiting for a sunset, a blizzard, or a thunderstorm. The strategy is the same. And today, I want to teach you that strategy so you can use weather to improve your landscape photography and travel videos.

A Word About Safety While Filming Weather

Whenever you go out in the field when hazardous weather is expected, safety should always be your number one concern. You can easily get yourself hurt or killed if you bite off more than you can chew. For example, don’t try to shoot lightning in the middle of an open field. If you don’t feel comfortable doing something, then don’t do it. It’s not worth hurting or killing yourself just to get “the shot”.

The High-Resolution Rapid Refresh (HRRR) Model

The U.S. Federal Government’s National Oceanic and Atmospheric Administration (NOAA) developed the HRRR model (pronounced “her”). As a result, its spatial domain is limited to the United States. Because of its extremely fine resolution, it is highly accurate, having never let me down once during my tenure chasing storms. My own intuition ignored the model a few times, and let’s just say those always ended in busts.

The HRRR has several key features.

• Initialized from the Rapid Refresh (RAP) model, which gets its data from the global GFS (American) model.
• 3 km resolution is fine enough to resolve most individual thunderstorms, making it an invaluable tool for storm chasing
• Runs once per hour, on the top of the hour
• Forecasts 48 hours into the future, a significant increase over its 12-hour forecasts when I started storm chasing

While you can easily get HRRR predictions from most modeling sites, I prefer to get it straight from NOAA. When you load the NOAA site, you’ll see an interface that looks like this. To zoom in on a particular geographic area, select a region from the “Domain” dropdown. The timestamps contain the day of the week and the hour of the day, in UTC. Each row is a different model parameter. Click on a cell for the parameter and forecast hour you want to see, or click on the check in the “Loop” column to see a loop of all times.

Basic HRRR Parameters

Before I begin a model analysis, I like to look at the basic weather parameters, both on a national and regional scale. Here are the HRRR parameters that correspond to the basic weather data. We’ll define them shortly once we dive into some examples.

We’ll dive into additional parameters that are specific to certain types of weather phenomena, photography, and videography later in this tutorial, but these are more than enough to get you going.

HRRR Time Zones

All HRRR parameters and runs are initialized and output using Universal Coordinated Time (UTC), or Greenwich Mean Time. UTC always uses the 24-hour clock, so you don’t need to worry about AM or PM. The model often uses Zulu notation to indicate times. For example, if the model date says “14 Dec 2021 – 17Z”, that means that the model was run on 14 December, 2021 at 17:00 UTC. In the model output, “Wed 08” indicates the model’s prediction for Wednesday at 08:00 UTC.

Basic Storm Chasing Strategy for Weather Photography

You can ask three different storm chasers for their strategy, and you’ll probably get three very different answers. However, I prefer to keep my strategy as simple as possible. Not only because I’m a big believer in the moniker “Keep It Simple, Stupid”, but also because it makes it much easier to share my knowledge with you. Even though I designed this strategy for storm chasing, you can apply it to every type of landscape photography or travel video.

Step 1: 1 to 2 Days Before the Chase

Look at the Storm Prediction Center‘s (SPC) Day 2 and 3 Severe Weather Outlooks. Next, read the forecasts and discussions from your local National Weather Service Office. Finally, have a look at the weather models, looking for where the parameters best come together. At the very least, look at the GFS (American) and ECMWF (European) models. You may not quite be into the HRRR’s time range yet. However, if you are, please use the HRRR, too.

Your goal is to identify broad potential target areas. For example, you could identify Western Oklahoma, Central Kansas, and the Texas Panhandle as potential targets. While the outlook above doesn’t give the whole picture, targeting Northwestern Oklahoma and South-Central Kansas seems like a pretty safe bet. Don’t worry about specific locations within that target area yet. You’ll figure that out once the event gets a little closer and the models get a better idea of what’s going to happen.

Step 2: The Evening Before the Chase

Using the same resources you used in Step 1 to choose your preferred target area. If you can identify a backup target area in case your primary target doesn’t work out, great, but it’s certainly not necessary. At this point, you can start looking at specific areas inside your broader target area. You just want to identify them, since you won’t choose one until tomorrow.

Step 3: The Morning of the Chase

Have a final look at the models, SPC Outlooks, and local forecasts before you hit the road. Confirm or adjust your chosen target area as needed. After that, choose a specific area to start within that broader target area.

Additionally, you should identify a jumping off point before departing for the chase. The jumping off point should be close enough to where storms are expected to fire, but far enough in front of them so you’re not trying to outrun them just to get ahead. I often used small towns, truck stops, and scenic lookouts as jumping off points. Look for places where two major roads intersect. You want to quickly and easily be able to go north, south, east, or west once storms fire.

Step 4: Drive to Your Target Area

Once you’re on the road, you should be checking the HRRR every hour or two. That way, as you drive to your jumping off point, you can easily adjust it as necessary. Try to arrive at least 30 minutes before storms are expected to initiate so you can get your gear set up. If you pick your jumping off point correctly, you’ll be in perfect position when storms do fire.

Step 5: Wait for Storms to Fire

Once storms initiate, use doppler radar to identify the specific storm you want to chase. Your target storm should align with your goals for the chase. For example, you could pick very different storms depending on whether you were doing weather or landscape photography versus trying to deploy sensors into the storm. Then, the chase is on.

My Greatest Storm Chasing Success: The 19 May, 2012 Harper, Kansas Tornadoes

My greatest storm chasing success came when a hunch, model intuition, and a little luck all came together just perfectly. I could write an entire post telling this story, so I’ll give you the abridged version here.

For several days leading up to 19 May, 2012, it became clear that there was a very good chance for tornadoes near a triple point that was setting up in south-central Nebraska. If you’re unfamiliar with the concept of a triple point, it’s the point where a warm front, cold front, and dryline meet. Model runs the morning of the chase confirmed that Nebraska was the most likely spot for tornadoes.

I wasn’t all that keen on driving from Oklahoma all the way to Nebraska, so I instead decided to look for something closer to home. That’s when I turned to the HRRR. It showed a window of very favorable conditions for tornadoes opening along the Kansas-Oklahoma border just before sunset. It was a very brief window – only about 20 minutes or so – but it looked even better than Nebraska. Timing would be critical.

Not wanting to rely on just a single model, I looked at several other models. They all showed the same window for tornadoes opening up along the Kansas-Oklahoma border. I had to give it a shot. Before I knew it, I was on the road, heading north up Interstate 35.

Everything Comes Together Perfectly for Awe-Inspiring Weather and Landscape Photography

I got up to the Kansas-Oklahoma border about 2 hours before sunset. My first stop was right off I-35 in Blackwell, Oklahoma to set up my jumping off point. A quick look at the HRRR showed everything was still in place for tornadoes at sunset just north of the state line. I decided to head west and make Medford, Oklahoma my jumping off point, which gave me easy access to a northbound road (US-81) into Kansas.

Before long, clouds started to bubble up on the dryline out to the west. Satellite and radar confirmed the HRRR’s predictions that the storms were going to be north of the state line, so I decided to move my jumping off point up to Caldwell, Kansas. By the time I got to Caldwell, the storms had fired and were heading towards the town of Harper, Kansas. I continued north and the chase was on. By the time I got to US-160, the weather radio was already blaring with Tornado Warnings. All I had to do was head west.

Just east of Harper, I pulled off onto a side street and had the whole show to myself. There was not another vehicle around, let alone any chaser traffic. That cluster of supercells produced over a dozen tornadoes in about 20 minutes, capped off by a breathtaking EF-3 tornado packing winds over 160 mph. The setting sun behind it was just icing on the cake.

Then, just like that, our very brief window for tornadoes slammed shut. The tornado became rain-wrapped before lifting as the sun set and darkness set in.

The Sweetest Victory Lies in the Photography

As I made my way back to I-35 to head home, lots of storm chasers started passing me going the other direction. After being so void of vehicles the entire chase, I couldn’t believe how many storm chasers were now heading towards Harper. But I knew they were too late. The tornadoes were done. The window was closed.

Interestingly, I didn’t realize the sweetest part of my victory until the next morning when I turned on the local news. Remember that triple point up in Nebraska? It had completely busted. Have a look at the storm reports. The red dots are confirmed tornadoes.

As a result, all of the chaser traffic I encountered on my way home were everyone who had been up in Nebraska racing down trying (unsuccessfully) to catch the storms in Kansas. I was one of only a small handful of people who had gotten footage of tornadoes that day.

Severe Weather in Landscape Photography and Travel Videos

As you can probably guess, the most practical application of our storm chasing strategy is for severe weather photography. Here are some severe weather parameters you should consider for your photo or video shoot. I’ve defined them in layman’s terms to help you understand them. You need to look where all of these come together with the target values. Just one parameter being off can completely shut off all storm activity.

Sunrises and Sunsets in Landscape Photography and Travel Videos

The Golden Hour is one of the most sought after period for landscape photographers and travel videographers. The low, warm light seems to make the landscape glow and the shadows dance. It’s a truly magical time of day. In fact, weather is what transforms you sunset landscape photography from okay to jaw dropping. Fortunately, the HRRR makes it pretty easy to identify the best location to film a sunrise or sunset.

Before we dive into the HRRR parameters, let’s recall what makes a good sunset. Brilliant sunset colors come from light refraction through clouds, dust, and other particles, so we need to examine cloud cover and thickness closely. Too many or too few clouds will result in a lousy sunset.

Unfortunately, the HRRR does not output cloud thickness as a parameter. However, it does output all of the parameters we need to calculate it. To get cloud thickness, simply use one of the following equations. The terms of each equation are defined below.

cloud thickness = cloud top height - ceiling
cloud thickness = cloud top height - LCL

Do note that if you’re using the second equation, cloud top heights are output in feet, while the LCL is output in meters! For best sunset colors, you want 25 to 45% coverage of thin, mid-to-high-level cirrus or cumulus clouds.

A Word About the Cloud Ceiling

The cloud ceiling is primarily used in aviation to indicate the height of the bottom of obstructing clouds. That means that if there is a cloud ceiling present, clouds will likely be thick enough to obscure the sunset, regardless of whether you find them in the low, mid, or high levels.

Additionally, don’t forget that rain showers can also make for spectacular sunsets. However, you should only try to integrate rain showers into your landscape photography in the summer. Small, pop-up summer showers can refract the light in spectacular ways. Winter showers are most often too thick and widespread to refract any light, which will ruin your sunset. Use the 1 km agl reflectivity parameter to evaluate rain shower potential.

Finally, know what compass bearing the sun sets or rises at. That bearing varies by both location and by time of year.

Winter Weather in Landscape Photography and Travel Videos

I break winter weather photography into two categories: inside the storm and post-storm. Both have their pros and cons. On one hand, you can capture the drama and intensity of blowing snow and bitter cold temperatures from inside the storm. On the other hand, a post-winter storm period can often be a spectacular 24-hour long Golden Hour to add breathtaking weather scenes to your landscape photography or travel videos. A fresh blanket of snow on a dramatic landscape makes for absolutely stunning photos and videos. For a textbook example, just have a look the Grand Canyon under a fresh blanket of snow.

Thankfully, both types of winter weather photography use the exact same strategy and parameters with the HRRR. The only difference is the timing.

For in-storm filming, you may want to also consider both wind speed and visibility. Alternatively, if you’re heading out after the storm, you’ll generally want at least 5 miles (8 km) of visibility, with at least a little sunlight poking through the clouds.

Finally, a word of caution. Be very careful around winter weather. Roads can close and travel can become impossible with little to no warning. If you don’t feel comfortable doing something, don’t do it. Trust me, you do not want to be stranded in your car in the middle of a major winter storm. If you have four wheel drive and/or tire chains, use them.

Lightning in Landscape Photography and Travel Videos

Lightning photography is one of the most challenging types of weather photography, but also one of the most rewarding. If just 5% of your lightning photos come out, you’re doing extraordinarily well. Thankfully, lightning happens everywhere, so you shouldn’t have to travel great distances to film it. In fact, you don’t need severe weather to get good lightning.

Before setting off to photography lightning, you must ensure your own safety. Lightning is one of the top weather killers not just in the United States, but around the world. Always shoot lightning from inside a building or car, or at the vary least, a grounded overhang. Do not under any circumstance stand under trees to try to film lightning. Trees often explode when struck by lightning, which will shower you in splinters, jagged wood, and molten sap.

Lightning Strategy

The strategy for lightning photography is staggeringly simple: set up in a dark spot at night, open the shutter, and let the picture take itself. If you’re shooting video, you can film lightning in the daytime, but even then, I still find your best shots come at night. Set up a ways from the storm to shoot lightning. That way, you’ll stay out of the rain. You’ll need a bit of luck, but when you do succeed, the results are, quite literally, electric.

While it’s impossible to predict exactly when and where lightning will strike, the HRRR will give you enough information to have a really good shot at it. Try to set up in a location where you don’t put yourself directly in the storm’s path.

Rainbows in Landscape Photography and Travel Videos

Most rainbow photos occur when you happen to look up and see a rainbow. But believe it or not, rainbow chasing is actually a thing. And unlike tornadoes, lightning, and blizzards, rainbows are one phenomenon you don’t have to worry about killing you while you’re out doing weather or landscape photography.

In order to see a rainbow, you need to put yourself between the sun and the rain, with the sun behind you and the rain in front of you. In order to see a rainbow in the afternoon or evening, you want to be looking east at the rain. On the other hand, you want to look west to see rainbows in the morning.

Additionally, sun angles play a critical role in finding rainbows. Unless you’re standing on top of a mountain or skyscraper, it’s much easier to put yourself between the sun and the rain when sun angles are low. As a result, you are much more likely to encounter rainbows during the Golden Hour period near sunrise and sunset than you are at high noon.

You can easily track cloud cover and precipitation with the HRRR. However, keep in mind that rainbows are far from guaranteed under any circumstance. No model is accurate enough to predict exactly where a rainbow will occur.

Weather in Seascape Photography

Seascapes are a stunningly effective way to integrate weather into your landscape photography and travel videos. Similar to winter weather photography, you have two options when it comes to the seascape side of landscape photography. With a few very unique exceptions, they require being in vastly different locations. If you’re looking to double-dip and get both types in one shoot, you’re likely going to be disappointed.

Cold Weather Seascape Photography

Largely grey and void of color, when taken correctly, viewers can almost feel the cold from a maritime layer that’s often thick and penetrating when they look at the photo or video. Locations such as downeast Maine, northern Europe, the Pacific Northwest, and the Canadian Maritimes come to mind when you think of cold weather seascapes. You’ll need to look at a few HRRR parameters

Tropical Seascape Photography

White sand. Warm breezes. Salty air. Lit up with brilliant and vibrant greens, blues, and turquoises, tropical seascapes will whisk you off to paradise. They’re warm, inviting, and relaxing, putting you in that vacation mode whenever you look at them, seemingly an escape from your reality. That’s probably why you have them as your computer desktop and have them hanging throughout your office. You can almost taste the fruity cocktails before you snap back into reality.

Interestingly, tropical seascapes are one of the only types of outdoor photography or videography that are more striking in the middle of the day than during the Golden Hour. Applying color theory explains a lot. Warm low light doesn’t draw out greens and blues. In fact, it does the opposite.

Finally, don’t forget about the optics and the physics of your tropical seascape. Those brilliant colors come from the sunlight refracting in the water. In order to maximize the brilliance of those colors, the sun must be as high in the sky as possible. Thick cloud cover blocks much of the sunlight, significantly limiting the amount of light that can refract in the water. As a result, colors will appear dull, dim, and muted.

Temperatures for Seascape Photography

You may have noticed that temperature is missing from the HRRR parameters for seascape photography and videos. Why is that? It’s because you don’t actually need cold temperatures for grey seascapes or warm temperatures for tropical beach photos. Don’t believe me? Have a look at these pictures I took at Lake Tahoe during the month of February. Temperatures that day topped out at 41°F (5°C), with plenty of fresh snow in the mountains.

Mist or Fog in Forest Landscape Photography and Travel Videos

I love forests shrouded in mist. They instill a sense of mystery and adventure, often whisking you away to another world. There’s a reason they are the setting of so many adventure movies. And I just love the striking contrast of the sun shining through the mist like a spotlight.

Best of all, you can find misty forests year round. One of my favorite locations to capture misty scenes is at Great Smoky Mountains National Park, which sits on the border between North Carolina and Tennessee. The sequoia and redwood forests in California are another top destination for misty forest photography and videography. Make sure you pick a destination that still has plenty of green in the forest. Early mornings in the spring and fall work best for mist, but you can get some stunning winter pictures in a forest of evergreens.

Before we dive into HRRR parameters, let’s have a look at what conditions make for the best mist photography. First and foremost, you need to have 100% relative humidity. Mist will not condense out of the air if the humidity is below 100%. Second, there should not be any wind. Wind causes mist and fog to mix out and burn off.

Go Up in Elevation to Lengthen Your Window for Mist

Once you start photographing fog and mist, you’ll be amazed at how quickly it comes and goes. This is especially true early in the day, as heat from the morning sun drops the relative humidity, rapidly burning off any mist or fog. However, there is one more secret weapon in our back pocket to maximize the length of your window for shooting mist: the much overlooked z-axis, or, to put in layman’s terms, controlling your elevation.

As you go up in elevation, the temperature cools. Because cooler air can’t hold as much moisture as warmer air, more moisture will condense out at higher elevations. As a result, fog and mist will hang around longer because it requires more energy to burn them off. But, like everything, it comes with a catch. If too much water condenses out, the mist and fog will be too thick to let the sunlight shine through. Those photos and videos can still be stunning, but you won’t get those really striking pictures of the sun shining through the mist. If you ever find yourself in this situation, go back down to lower elevations to thin out the fog and mist.

Live in the Desert? You’re Not Completely Out of Luck

And if you live in an arid climate, don’t worry, you can still get in on the action. Sunlight filtering through dust, pollution, or wildfire smoke can give you the same effect. Even in a desert climate like Arizona, you can still get spectacular mist scenery in the winter, when cooler temperatures are much more conducive to condensing out what little water there is in the atmosphere. Dawn after an overnight rain will present you with your best photo and video opportunities for mist and fog.

Get Started Boosting Your Efficiency Adding Weather to Your Landscape Photography and Travel Videos

NOAA’s High Resolution Rapid Refresh model is an incredibly powerful tool whose applications stretch far beyond storm chasing. When used with storm chasing strategy, you can take the guess work out of adding weather to your landscape photography and travel videos. Give yourself more control over your photo and video shoots and work much more efficiently. And at the end of the day, you’ll ultimately be able to boost your revenues. What are you waiting for?

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Top Photo: A large dust storm swallows up a mountain range as it crosses from Mexico into the United States
Why, Arizona – July, 2018

The post The HRRR Weather Model: How To Add Dramatic Skies To Your Landscape Photography appeared first on Matthew Gove Blog.

Right on cue, a line of massive rotating supercell thunderstorms explodes on the dryline in the late afternoon. You don’t have to wait long before Tornado Warnings start blaring on the weather radio. Now, you have some decisions to make.

• What storm in the line do you target? The strategy for choosing a target storm for photography or video can be very different from deploying sensors in its path.
• How close to the storm can you get and still stay safe? Consider both storm intensity and speed at the very minimum.
• Are there any storms nearby that could cut off possible escape routes? On a high risk day, there likely will be.

Those are just a few of the decisions you’ll need to constantly be making while you’re actively chasing a storm. Because things happen so fast, you have to constantly evaluate and adjust as needed. But where do you get this info?

Enter Weather Apps

If you’re like me, you lack the budget for the state-of-the-art technology the professional photographers and videographers use for not just storm chasing, but any outdoor adventure. Unfortunately, most weather apps (especially the free ones) don’t give you the information you need to properly plan an outdoor photo or video shoot. But that doesn’t mean you’re out of luck.

While there is no one “silver bullet” app that will give you all the information you need, I will be giving you the storm chaser’s toolbox of weather apps to plan your next outdoor photo shoot. You’ll be amazed at how well these weather apps work for landscape photography and travel videos. And best of all, they’re affordable. There’s no need to shell out hundreds or thousands of dollars on high end software anymore.

I also want to point out that I am not affiliated with or paid by these applications in any way. This is just a collection of my favorite weather apps that I use on most of my landscape and outdoor photography and video shoots.

Weather Apps for Landscape Photography and Travel Videos

Weather is a key component of not just landscape photography, but also travel, adventure, and outdoor videography. It can make or break your shot. In fact, weather is often the difference between that awe-inspiring shot that will sell your photo or video and a visual media file that gets deleted before you even get a chance to post-process it.

No matter what type of weather you need for your shot, these apps will give you the information you need to ensure that you get the shot you want. They cover blue skies to blizzards, tornadoes to sunsets, and everything in between. Once you assemble this toolbox of weather apps for your landscape photography or travel video shoot, you will no longer need to waste time just “taking a chance” on a good sunset or an approaching storm. Instead, you’ll already be in position ready to start filming before your target weather phenomenon even arrives.

RadarScope: The Cadillac of Weather Apps

Platform: iOS, Android, macOS, Windows
$9.99 (mobile), $29.99 (desktop)

Originally developed in the weather mecca of Norman, Oklahoma, RadarScope was built with one goal in mind: to keep you safe during severe weather. It was my number one go-to app during the height of my storm chasing days nearly 10 years ago, and it remains the go-to app for storm chasers and weather enthusiasts today. Its user base now reaches much further than just the storm chasing community. And it includes both landscape photographers and travel and outdoor videographers.

RadarScope displays highly detailed doppler radar data on an easy-to-read map. Even better, they have managed to ver successfully pull off what I consider to be the Holy Grail of GIS. When you look at the screen, the map seemingly fades into the background, drawing your eye to the radar data. Yet at the same time, you can instantly tell where the severe weather is with just a quick glance. In the world of GIS, that’s an incredibly difficult thing to do, and they have pulled it off absolutely flawlessly.

In addition to viewable radar data, RadarScope comes with a plethora of features and functionalities.

Key Features of RadarScope

• GPS Support. Plot your location on the map with radar data
• Severe weather warnings displayed on the map. Tap on the warning to read the text of the warning.
• In addition to reflectivity data, it supports all types of doppler radar data, such as wind speeds, echo tops, estimated precipitation totals, and dual-pol technologies.
• Includes a distance measuring tool so you can easily see how far you are from severe weather threats or measure how high the radar beam is at any given location
• Drawing tool lets you mark up and share the radar image
• Quickly export maps and data as either animated GIFs or as still images
• Supports both metric and imperial units
• Currently supports radar in all US States and Territories, as well as Canada, Australia, several European Union nations, Japan, and South Korea.
• Pro version has even more features, such as lightning strikes, split screen comparisons, watches and mesoscale discussions, storm reports, and much more.

Nothing has proven more valuable for my storm chasing, photography, and adventures than RadarScope’s GPS feature. Being able to plot your location on the map is critical to ensure that you are in the best position to capture the shots you need for your project. Even for benign weather features such as sunsets, things happen incredibly fast once you get out in the field. You don’t want to miss your shot trying to figure out where on the map you are. RadarScope’s GPS ensures that you can reposition and make adjustments as quickly as possible.

Windy

Platform: iOS, Android, Web Browser
Free, Pro Features Available

Windy is my favorite app for viewing model data on my phone or tablet. Best suited for detailed short-term forecasting at all geographic scales, Windy has a stunning display showing atmospheric flow around the world. View real-time observed data or model predictions in four dimensions. Windy provides two-dimensional maps at numerous heights throughout the atmosphere, as well as vertical soundings and time-series point forecasts for your specific location.

Windy currently provides model predictions for four models. You can find support for the GFS (American), ECMWF (European), and NAM (North American Mesoscale) models, as well as a German model called ICON, which stands for Icosahedral Nonhydrostatic.

Key Features of Windy

• Stunningly beautiful display for analyzing data
• Huge choice of weather parameters to plot
• Data available in four dimensions and all geographic scales, including point forecasts
• View both observed data and model predictions on the same map
• Includes forecasts for airports, sports/recreation, wildfires, tides, and much more
• Bookmark your favorite locations for easy reference
• While it doesn’t support plotting your location on a map like RadarScope does, Windy does have GPS functionality that allows you to quickly and easily get data for your current location.
• Supports both metric and imperial units

Pivotal Weather

Platform: Web Browser
Free, Pro Features Available

If you’re looking for comprehensive model data, Pivotal Weather is where you need to be. Best used for both short and long-term modeling, you’ll find detailed model forecasts for over 20 global, regional, and mesoscale models. Like Windy, Pivotal Weather allows you to display data in four dimensions at all geographic scales. It works on a global scale, so you’re not restricted to specific countries or other geographic boundaries. We used Pivotal Weather extensively during our analysis of Hurricane Henri and Hurricane Ida last summer.

My favorite feature of Pivotal Weather is its high quality maps. So many weather modeling websites have such poor quality maps that it can be difficult in some situations to pin down exactly where a weather event will take place. While it’s not a big deal on a large scale, it can become a major issue once you drill down to the local level. Pivotal Weather lets you plot model data at those local levels, plus displays the predicted value as you mouse over the map.

Key Features of Pivotal Weather

• More than 20 global, regional, and mesoscale models
• Provides data worldwide
• Displays model predictions in four dimensions at all geographic scales
• Numerous choice of map scale levels
• Much higher quality maps than most weather modeling websites.
• You’ll get the best experience viewing on a computer, not a phone
• Additional features available with Pivotal Weather Plus

Federal Weather Bureaus

Platform: Web Browser
Free

How many times have you opened a free app or website and just got bombarded with ads, pop-ups, and other promotions? That’s why I often go straight to the source for weather data and information: the federal government. Because federal weather bureaus in every country are government agencies, you won’t get bombarded with all the ads, video clips, and other useless promotions you find on so many other apps and websites.

Federal weather bureaus are one-stop shopping for observations, forecasts, analysis, and past data. In addition to their own analysis, most federal weather bureaus provide the data so you can also do your own analysis. You’ll have all tools to look at all geographic scales, regardless of whether you’re looking at the entire world or your neighborhood. Use the models and forecasts to identify the best spot for your shoot. Once you get out in the field, use observations to fine-tune and adjust your strategy and location as needed.

Here are a few links to federal weather bureaus around the world. If your country is not listed below, a quick Google search will find it pretty quickly.

National Centers for Environmental Protection

Platform: Web Browser
Free

If you’re in the United States, the National Centers for Environmental Protection, or NCEP, contains all of the weather information you need to plan and execute a successful outdoor photo or video shoot. Run by NOAA and the National Weather Service, NCEP is comprised of 8 centers. While they are primarily aimed at the United States, many of them make predictions that go beyond America’s borders.

The Possibilities for Using NCEP Weather Apps for Landscape Photography and Travel Videos are Endless

The possibilities for using these weather apps for landscape photography and travel videos are endless. Use the Climate Prediction Center to look at historical weather patterns to ensure that the weather will cooperate for your shoot. For instance, you don’t want to head down to the Caribbean to film a hurricane only to find out that a strong El Niño has neutralized the Atlantic Hurricane Season.

Additionally, visit the Aviation Weather Center for all your drone photography and video needs. Perhaps you want to try your hand at storm chasing? In that case, the Storm Prediction Center has all of the information you need. Likewise, use the Space Weather Prediction Center to plan your Aurora Borealis or astrophotography shoot. The list goes on and on.

I could write an entire blog post on NCEP alone, but you get the idea.

NOAA High Resolution Rapid Refresh (HRRR) Model

If RadarScope is my favorite weather app to use in the field for landscape photography and travel videos, then NOAA’s HRRR model is its best compliment. Excelling in day-of-event modeling and forecasting, use the HRRR to anticipate any adjustments you’ll need to make in your shoot. Its 3 km resolution is fine enough to resolve most individual thunderstorms, making it an incredibly powerful tool for outdoor photography and videos. As a result, it has never let me down in every storm chase I’ve taken part in since 2011.

For example, consider a simple sunset shoot. Sounds easy enough, right? Conditions in the morning look perfect for a spectacular sunset. Unfortunately, you are completely unaware a storm system is moving in from the southwest. Thick clouds will cover the western sky, completely obscuring the sunset.

Thankfully, you have been monitoring the HRRR throughout the day. As a result, you see that your original plan for a spectacular sunset will go down in flames. Additionally, you see that the spectacular sunset will occur about 70 miles up the coast. You adjust your plan accordingly, leaving an hour earlier so you can get up the coast in time for sunset.

Most importantly, though, you capture one of the best sunsets you’ve ever seen. As soon as the prints hit your online store, they start selling like hot cakes. Imagine how different things would have turned out if you hadn’t been able to anticipate that storm system coming in.

Use the HRRR for All Types of Outdoor Photography and Videos

The HRRR includes highly detailed information for every type of outdoor photography or videography. That’s what makes it so powerful. You’ll be able to use it for everything from sunsets to winter weather, fire weather to space weather, and lightning to beach photography.

Next week, we’ll cover the HRRR model in detail. You’ll learn how to use the HRRR to apply storm chasing strategy to your outdoor photography and videography. After that, you’ll be armed with the tools you need to take your landscape photography and travel videos to the next level.

How Will You Use Weather Apps for Your Landscape Photography and Outdoor Shoots?

Weather is an often mundane part of our everyday lives. However, once you get out in the field to film it, weather seems to happen extremely fast. They key to success with any type of outdoor photography or videography is to stay ahead of the weather. These weather apps provide you with the toolset you need to take your landscape photography, travel videos, and other outdoor media to the next level. Use them responsibly, and always keep safety in mind first.

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The post 6 Powerful Weather Apps for Stunning Landscape Photography appeared first on Matthew Gove Blog.

Why Is Weather Forecasting Important for Landscape Photography?

Anyone can go out and take pictures of a beautiful landscape. We all have cameras on our smartphones these days. But what separates your “Average Joe” tourist from a world-renown National Geographic photographer? It’s a long list, but one of the primary reasons is that most tourists don’t take weather into consideration. They just shoot.

In the worst-case scenario, bad weather will ruin a photo op. At best, you’re missing out on an incredible opportunity. In most landscape photos, the sky takes up at least one third of the frame. That’s a lot of wasted real estate. On the other hand, use weather to your advantage and instantly set yourself apart from the bulk of the competition.

But just hoping you’ll get lucky with the weather is not enough. Getting the right weather for your shot is a crapshoot at the best of times. Without a strategy, you’re setting yourself up for a low success rate and an inefficient workflow. However, when armed with basic knowledge of weather models, you’ll be able to target your photo shoots with laser-like precision. The frustration will be gone, and you can enjoy much more efficiency and success.

Being Flexible and Adaptable is Key to Your Success in Weather Forecasting for Landscape Photography

Let’s say you get up in the morning hoping to get a good sunset picture later in the day. After a quick look at the models, you identify a precise location with ideal conditions for sunset photos. Even better, it overlaps with the evening Golden Hour. As you go through the day, model runs start showing a significant increase in thick, low-level clouds in the evening. Instead of giving up, toss your planned sunset shoot out the window. You pick a new location and shoot some breathtaking black-and-whites of dramatic sunlight shining through the thick low-level clouds on the rugged landscape like a spotlight.

Without the help of the weather models, you would have come away with nothing. When integrating weather into my landscape photography, I use the same strategy I did when I was storm chasing.

Applying Storm Chasing Strategy to Landscape Photography

On paper, storm chasing strategy is shockingly simple.

1. Look for where and when the ingredients for severe thunderstorms and tornadoes best come together.
2. Drive to that target area, arriving shortly before that window of peak potential opens.
3. Wait for storms to fire.
4. Once storms initiate, go chase them, keeping in mind the important balance of safety vs getting the shot.

Unfortunately, in practice, it’s never that easy. Your window of opportunity will constantly shift in both time and space. Better opportunities will appear elsewhere. Sometimes, those opportunities won’t even manifest, leaving you with the inevitable bust. Things happen incredibly fast when you’re storm chasing, so you need to be quick on your feet and always be able to react to whatever curveballs Mother Nature throws at you.

Thankfully, things don’t happen as fast in the world of landscape photography. Having more time to react means you have a higher likelihood of success. However, you’ll still need to be just as able to react and adjust, because Mother Nature will throw you curveballs. You can easily apply basic storm chasing strategy to landscape photography using different parameters. Instead of looking for where severe storms are most likely to occur, look for where you’ll get the best sunsets, golden hours, fog, etc. We’ll circle back to this in a bit.

Learn to Embrace Failure in Your Landscape Photography

When dealing with the weather, the only thing that’s for certain is uncertainty. Succeeding at storm chasing requires skill, quick thinking, and luck, as most tornadoes are only on the ground for less than 30 seconds. The same goes for lightning photography. If just 5% of your lightning photos come out, you’re doing extraordinarily well.

Rest assured, you will have a far greater success rate integrating weather into your landscape photography. They’ll be absolutely stunning when you get it right. But you must accept that things can and will go wrong. You will have days where you completely bust. Yes, it’s incredibly frustrating when it happens, but it’s part of the game. Always remember that even the best in the business have off days.

My Own Hero to Zero Experience

Over the course of 9 days in 2012, I pulled the ultimate hero to zero move. However, I still managed to get breathtaking photos despite the most epic storm chasing bust I ever experienced. On 19 May, a powerful storm system came off the Rocky Mountains and across the central Great Plains. Everything seemed to be in place for a massive outbreak of tornadoes across southern Nebraska.

I was living in Norman, Oklahoma at the time, and really didn’t want to drive all the way to Nebraska to have to fight the storm chaser crowds. Instead, I searched the models for a target closer to home. Models hinted at a very brief window opening up along the Kansas-Oklahoma border that was very favorable for tornadoes right before sunset. It wasn’t much of a window – only about 15 to 20 minutes, but it was low risk and high reward. I had to take the gamble.

Right on cue, storms were firing just as I crossed the state line from Oklahoma into Kansas. I got on the first storm I could find and hoped for the best. And boy, did that gamble pay off. Over the course of about 25 minutes, that supercell produced nearly a dozen tornadoes. A spectacular EF-3 tornado capped the evening off, creating a dramatic scene with the setting sun behind it.

The Sweetest Weather Forecasting Victory

Now, here’s where that victory gets even sweeter. The target up in Nebraska that looked really juicy at the start of the day completely fell apart. There was not a single tornado up there, while I got to enjoy the show in Kansas all to myself. As I drove back towards Interstate 35 to head home, I passed all kinds of chase vehicles going towards the storm. I knew the storm was already wrapped in rain and had finished producing tornadoes. They were too late.

An Epic Weather Forecasting Bust Leads to a Satisfying Day of Landscape Photography

Eight days later, I was back in the field for another round of storm chasing. This time, western Kansas was the target, and conditions looked very favorable for tornadoes. I ended up driving nearly 300 miles from Norman, and didn’t see much more than a couple fair weather puffy clouds. The capping inversion hadn’t broken. There would be no storms that day. Then I had to drive the same 300 miles home.

After abandoning the storm chase, I was determined to come home with something…anything. I knew the spring wheat harvest takes place in late May in western Oklahoma, so I decided to try to get some photos of the wheat fields in the late afternoon sun and then catch the sunset at Gloss Mountain State Park. If you’ve never seen wheat fields at harvest time, I highly recommend it. You’ll see right away why Katharine Lee Bates used the “amber waves of grain” lyrics in America the Beautiful.

The photos were certainly nothing I’d be rushing out to try to sell to an art gallery, but as an alternative to coming home empty-handed, it was oddly and uniquely very satisfying.

Weather Forecasting Models for Landscape Photography

For landscape photography weather forecasting, I use the same models that I use for my weather analyses and storm chasing. For the greatest success, you’ll want to use a combination of global and regional models over both the short and long term. My goal here is to introduce you to each model so that you know when to use each model, as well as what their strengths and weaknesses are. We’ll dive into model interpretation and analysis in a future post.

What Is Output When the Weather Models Run?

All weather models output their forecasts in four dimensions: latitude, longitude, height, and time. Logic may dictate that the output formats may vary from model to model, but in reality, they generally output the same three formats.

• 2D Geographic Maps
• Vertical Cross-Sections of the Atmosphere
• Time Series Graphs

For basic landscape photography weather forecasting, you can gather all you need from the 2D geographic maps, so these tutorials will focus our efforts on those maps. If you’re interested in learning more, we will cover the other two outputs in future tutorials and online courses.

Global Forecast System (GFS) Model

The GFS model is one of the go-to models for general global forecasting. It has received criticism in the past for poor performance, most notably when it predicted that Hurricane Sandy would go harmlessly out to sea. As a result, the model received major upgrades in 2017, 2019, and 2021. While it has performed much better as of late, especially with tropical weather, the GFS has still not fully closed the performance gap with the European model.

European Centre for Medium-Range Weather Forecasts (ECMWF) Model

The ECMWF model has been around since 1975, but really cemented itself amongst the world’s top weather models when it absolutely nailed its prediction for Hurricane Sandy. Even 10 days out, the ECMWF missed the exact location of Sandy’s landfall by less than 100 km. Today, the ECMWF is still considered to be the most accurate global model, but other models are closing the gap. However, in 2020, ECMWF scientists were awarded time on the world’s most supercomputer to run their model at a 1 km resolution on a global scale. If that can be successful in the long-term, it will be a game changer.

United Kingdom Meteorological Agency (UKMET) Model

The UKMET model is designed for making medium-range forecasts throughout the entire northern hemisphere. However, it is best known for being used in tropical weather prediction. It is routinely used in tandem with the GFS and the ECMWF when making forecasts.

Global Deterministic Prediction System (GDPS) Model

Also known as the GEM model, Environment Canada originally created the GDPS model as a comparison or check to the GFS model. While it is now the default weather model that the Government of Canada uses, it can be used interchangeably with or in place of the GFS model.

North American Mesoscale (NAM) Model

20 years ago, the NAM was the best model available for storm chasers. While other models have since overtaken it, the NAM is still a very accurate model for significant weather events across North America. It initializes itself with GFS data, so it’s backed by one of the most respected models in the world.

Rapid Refresh (RAP) Model

Designed as a fast-updating version of the NAM, the Rapid Refresh model is a favorite amongst storm chasers and hurricane fanatics alike. When using it for storm chasing, it’s one of the most accurate models available today. However, you must keep in mind not to rely too heavily on it. Its 13 km resolution is too coarse to resolve individual thunderstorms.

High-Resolution Rapid Refresh (HRRR) Model

The HRRR model is the most accurate short-term model available today. I used it all the time for storm chasing, and it never let me down once. Its 3 km resolution if fine enough to resolve nearly every type of weather phenomenon, allowing you to pinpoint precise targets with laser-focused accuracy. In the context of weather forecasting for landscape photography, use it to target sunrises, sunsets, storms, cold fronts, fog/mist, snow, and much more. You can even go beyond Earth’s atmosphere and use it to identify the best nights for astrophotography.

Where to Get Weather Model Output Online

Are you ready to dive into the models and take advantage of weather forecasting to improve your landscape photography? The outputs for all of the models we have covered are readily available online free of charge. While I am in no way affiliated with any of the following organizations, these are my favorite resources for weather models, in no particular order. You can find many more with a quick Google Search.

• Twister Data
• Pivotal Weather
• NOAA NCEP Model Analyses and Guidance
• NOAA: Rapid Refresh and HRRR
• Penn State University E-Wall

General Strategy for Model Analysis and Weather Forecasting

We will dive into model analysis in much greater detail in future tutorials and online courses, but I wanted to at least give you a brief intro. Without knowing how to analyze them, the models are completely worthless. This strategy can be applied to any type of modeling. It’s not limited to just weather forecasting or anything to do with landscape photography.

First and foremost, always use multiple models, regardless of the type of forecasting you’re doing. The more models you have in agreement, the higher the confidence in your forecast will be. Additionally, consider the Hurricane Sandy example. The GFS showed Sandy going harmlessly out to sea. All the other models showed it slamming into the east coast of the United States. Imagine what would have happened if emergency management had been using only the GFS. They would have been caught totally flat footed. Once you have the models selected you want to use, start with the following strategy.

1. Look at the current observations and the synoptic (large) scale picture. What’s going on at the regional and/or national level?
2. Then start to drill down to your target area. As you zoom in, use models with a finer resolution if you can. You can’t understand the small-scale meteorology without knowing what’s going on at the large scale.
3. Look for where the parameters for your desired photography best come together.

Know Which Models to Favor in Your Weather Forecasting

If the models you’re using do not agree, it’s critical to know which ones to favor. You can conduct a quick model evaluation by answering the following questions.

• How have the models performed in recent runs? Have they been accurate?
• How has the model historically performed for the type of weather you wish to include in your landscape photography? Look at its performance over the past 5 years or so.
• Has the model been consistent from run-to-run? Or is it all over the place?

Remember how consistent the GFS was during my analysis of Hurricane Henri? That’s why I favored it so heavily in my forecasts. And in the end, it ended up being correct. In the 48 hours prior to landfall, the other models brought Henri’s projected track as far west as New York City prior to swinging back east. Henri made landfall near Westerly, Rhode Island.

Model Parameters You’ll Commonly Use Weather Forecasting For Landscape Photography

Weather models calculate and output tons of parameters. For landscape photography, there are several that you will routinely use.

• Temperature
• Wind, Height, and Pressure
• Dewpoint and Relative Humidity
• Cloud Cover, given as a percentage
• Predicted Radar
• Precipitable Water (how much water is available in the atmosphere to make precipitation)
• Vorticity and Vertical Velocity (used to determine if cloud cover is increasing or decreasing)

We’ll cover parameters specific to severe, fire, and winter weather in a future tutorial.

Quick Overview of Weather Forecasting Parameters in Landscape Photography

In landscape photography, you’ll find that you have a core set of parameters that you routinely use. Here are some of the most common ones.

Next Steps

Now that you’ve been introduced to the models, the next step is to dive into how to use them. In the next tutorial, we’ll expand on the last section. You’ll learn what each weather forecasting parameter is as well as how to apply it to landscape photography. If you have any questions, please leave them in the comments below or email them to me directly. I look forward to seeing you in our next tutorial.

Top Photo: Beautiful Fall Cape Cod Sunset
Woods Hole, Massachusetts – October, 2021

The post 7 Weather Forecasting Models That Will Improve Your Landscape Photography appeared first on Matthew Gove Blog.

Weather can be an incredibly powerful way to set the mood and tell your story. Indeed, you can use weather not just in your landscape photos, but also in other types of photography, as well as videography. While photography is primarily a visual medium, when executed properly, use of weather in your photos will stimulate other senses as well, such as sound, smell, and feel.

A grey, snowy scene might conjure up a cozy feeling of sitting around a warm fire with the smell of hot coffee or hot cocoa wafting through the room. On the other hand, a picture of a bright and sunny tropical beach bursting with vibrant colors puts you in a relaxing mood. Close your eyes and you’ll be able to feel the warm, gentle breeze and smell the salty air coming off the ocean. You don’t just want your viewer to see the scene in your photos. You want them to experience it.

Make a Specific Weather Feature the Subject of Your Landscape Photos

There can be a fine line between weather and landscape photography. And I’ll be the first to admit that I’ve toed that line more times than I can remember. While it may seem like everyone has their own definition these days, I prefer to keep things simple. If the landscape is the subject of the photo, it’s a landscape photo. Likewise, if the weather is the subject of the photo, it’s a weather photo.

So what kind of weather features can you use as the subject of your landscape photos? Turns out, just about anything. The only weather feature that really doesn’t work is a cloudless, sunny day. I would argue that no matter how hard you try to make the cloudless, sunny day the subject of your photo, your viewers’ eye will always be drawn to the landscape. However, I find that some weather features tend to perform better than others.

• Lightning
• Tornadoes
• Severe Thunderstorm Clouds and Cloud Formations
• Snow
• Heavy Wind
• Fog and Mist

And that’s just to name a few. The good news is that no matter where in the world you are, you have both landscapes and weather. You’ll need to know what the best photo opportunities are based on the landscape, season, and typical weather patterns. I certainly wouldn’t be trying to get pictures of snowy, majestic mountains in Texas or pictures of fog and mist in Arizona.

Example #1: Go All-In and Transform Your Landscape Photos into Weather Photos

If you have a homogeneous landscape or a landscape that leaves a little something to be desired, you’ll want to go for the straight weather photo. Anyone who has gone storm chasing in Tornado Alley has used this strategy. Coupled with the fact that these photo ops are restricted to such a small geographic area for only a few months of the year, it’s why photos of supercells and tornadoes so often leave you in complete awe.

Let’s look at a few examples of true weather photos. The landscapes in these photos all have one thing in common. Can you figure out what it is?

Were you able to spot what the landscape in all three photos have in common? They are all homogeneous landscapes that are, on their own, actually quite boring. It’s the weather in each photo that gives it its pop and pizazz. If we take the weather out of those photos and just look at the landscape, you won’t feel much of a reaction. As a result, your viewer won’t be able to get that fully immersive experience that truly great photographs can offer.

Example #2: Use Weather to Subtly, yet Powerfully Draw Out Reactions and Emotions in Your Landscape Photos

On the other hand, what if you’re somewhere that has particularly beautiful or dramatic landscapes? You’re probably a little hesitant to completely abandon the landscape in your composition like we did with straight weather photography. I don’t blame you!

Instead, you want to leave the landscape as the subject of the photo and use weather to set the mood, trigger an emotion, or tell a story. To illustrate how to properly do this, let’s take a trip down to the white sandy beaches of Florida. You should feel two very different emotions when you look at these photos, which were taken within just a few miles of each other.

How did you feel when you looked at those photos? The first photo should leave you feeling relaxed, secluded, and tucked away. You may even feel refreshed at the thought of cooling off in the inviting waters. On the other hand, the second photo should get the adrenaline pumping a little. You probably feel a bit threatened or exposed, too, like you need to get to shelter.

We’ve only just scratched the surface of showing how powerful weather can be in landscape photos. You’ve got two photos with very similar landscapes that would not appear boring or homogenous on their own. Yet at the same time, two very different weather phenomena leave your viewer feeling two very different reactions to each respective photo.

Before we move onto the next section, have a look at some more examples of using weather to draw out reactions and emotions in landscape photos. Pay special attention to how you’re reacting to seeing each photo. Then use those feelings to inspire your own landscape photography.

Use Color Theory to Your Advantage

The premise of color theory is simple. You want to use complimentary colors that not only look stunning together, but can also provoke reactions and emotions that align with your brand’s mission and values. You can keep color theory as simple as just looking at a color wheel, or your can dive into the fascinating mathematics behind it. We covered it all in full detail back in July, so I’ll refer you there for the details about color theory.

That being said, do recall that there are four bases that make up the basis of color theory.

Keep Color Theory in Mind When Composing Your Photos

Have you ever seen photos from Havasupai Falls in Arizona? The brilliant red rocks provide a stunning backdrop to the brilliant turquoise waters in the falls. Yet when you look at photos of the emerald waters of the Colorado River inside of Grand Canyon, it just doesn’t generate the same reaction and emotions that Havasupai does. Why is that? They’re equally beautiful places.

The reason lies in color theory. The red rocks and turquoise waters at Havasupai falls are nearly perfect complimentary colors. You can actually mathematically prove that just the turquoise waters alone at Havasupai falls will look more stunning against the red rocks than the emerald waters of the Colorado River.

While I have never actually been to Havasupai Falls, I have been to the confluence of the Little Colorado River, which has the same brilliant turquoise waters as Havasupai. Have a look at the following pictures yourself. They were taken within about an hour of each other. Which one do you like better?

The Psychology of Color

I am no psychologist, but research has proven that different colors invoke different emotions. Indeed, businesses use the psychology of color in their branding and marketing. It’s very subtle, but when used correctly can be very powerful. It’s all done in the name of portraying your brand exactly how you want to.

For full details, here’s a really good article about the psychology of color.

Use Overcast Skies and Winter Landscapes for a Dramatic Pseudo-Black and White Effect

Embrace the lack of color on grey and cloudy days. While color theory can make those brilliantly colorful scenes dazzle, the lack of color can be equally as beautiful. As good as the lack of color looks on thick overcast days, it really shines in winter scenery. A little bit of color poking through a fresh blanket of snow can be spectacular.

While you can certainly go full black and white if you want, I prefer to use the little bit of color in those images to my advantage. Use it to highlight the subject of your image or to accent the scene around the edges, like I did in the above photos.

You can also use light to highlight parts of your image in the total absence of color. Look for a situation where you have sunlight shining through broken clouds that shines on your subject like a spotlight. It should really stand out against a dark and colorless backdrop. Add a little color to your subject, and it’s pure magic.

I do want to point out one important detail. Even though the background and foreground are dark and colorless, you can still both see it and tell what it is. This important detail gives your photo both depth and context. Without it, you won’t generate the reactions or evoke the emotions you had hoped.

Experiment with Different Times of Day When Taking Landscape Photos. But Even With the Addition of Weather, the Golden Hour is Still King.

Armed with basic knowledge of weather and color theory, there is no bad time for landscape photography. I don’t ever want to hear an excuse that you can’t take landscape photos in the middle of the day because the shadows are too harsh. Go out and take them anyway. You’ll be surprised at what you get. Even more, in the worst case scenario, just delete them if they’re no good. Nothing lost, nothing gained.

I also encourage you to try taking landscape photos at night. You can add some really cool effects to your photos using long exposures at night. If you’re in a really dark area, try looking beyond the weather and into outer space. Try to capture the Milky Way Galaxy in the sky or use a really long exposure to show the Earth’s rotation in the stars. On the other hand, if you’re just beginning, start out with a classic cityscape at night. You can’t go wrong with the glistening skyline of your favorite city lit up at night.

The Golden Hour Remains Second-to-None

While you can certainly make the argument that you can take beautiful landscape photos at any time of day, the Golden Hour remains the best time of day for landscape photography. No amount of weather can change that.

If you’re unfamiliar with the Golden Hour, it refers to the last hour before sunset and the first hour after sunrise. During that hour, the low sun casts a warm glow across the landscape. Long shadows add relief, texture, depth, and drama to your photo.

Not surprisingly, some of the best landscape and weather photos I’ve ever taken came during the Golden Hour. While I consider myself very fortunate to have such beautiful landscapes in Arizona, the Golden Hour photos I’ve taken elsewhere rival the Arizona photos more often than not. I could go on, but I’ll let the photos speak for themselves.

A Dramatic Sky and a Dramatic Landscape Are Spectacular Together

This one is simple math. The best of your weather composition plus the best of your landscape composition equals spectacular unrivaled beauty.

Use Long Exposures to Add Motion to Wind, Rain, and Snow in Your Landscape Photos

Because lighting for landscape photography cannot be controlled in a studio, using long exposures can risk permanently losing data to overexposure. To combat overexposure, shoot during the Golden Hour using a small aperture and/or filters. Once you lose data to overexposure, no amount of post-processing can recover it.

If you’re using certain types of weather to improve your landscape photos, showing motion is critical to your viewer being able to get the maximum experience. Unfortunately, showing motion in weather often subjects you to shooting in heavy wind. As a result, you really need to be using a tripod if you want any kind of viable results. Trying to hold the camera steady just won’t cut it. If you don’t have a tripod, try to use an elevated surface, post, or tree limb to hold your camera steady. It’s not perfect, but it’s a lot better than nothing.

To properly show motion in your landscape photos, only the objects that are actually in motion should appear to be moving. Everything else should be steady. If you try to show everything in motion, you’ll just wind up with a blurry photo. It’s the same as when you try to please everybody, you end up pleasing no one.

It’s generally okay to show small amounts of motion in objects that sway back and forth in the wind. I’m talking about the ends of tree limbs, flags, waves, moving vehicles, and the like here. If your photos depict entire trees and buildings in motion, you’re not doing it right.

Here are a few examples of photos that show motion correctly. I also want to point out that some of these certainly fall into the category of weather photos as opposed to landscape photography.

After the Storm Can be a 24-Hour Long Golden Hour

When I lived in Oklahoma, one thing that really struck me was just how spectacular the weather was after a major severe weather event. Never was this more true than following the 20 May, 2013 EF-5 tornado that tore through Moore, Oklahoma. A few days after witnessing that disaster unfold firsthand, I was sitting in my back yard reflecting on what had happened. The sun was out, temperatures were cool, and the weather was absolutely perfect.

As I reflected on the tornado, it dawned on me how some of the most breathtaking landscape photo ops occur during the first 24 hours immediately following a storm. This is exceptionally true following blizzards and winter storms. You can’t beat a fresh blanket of snow on any landscape. I’ll let the photos speak for themselves.

You can also find some extended Golden Hours leading up to storms. In the case of severe thunderstorms, you’ll find amazing photo ops out in front of the storm as it approaches, too. This is what the entire concept of storm chasing is based off of, so I could write an entire post on it. However, that’s a discussion for another day.

Conclusion

Weather is one of the most powerful and effective ways to inject new life, mood, and story into your landscape photos. From dramatic scenes to vibrant colors to the beautiful Golden Hour, there’s something for every landscape photographer’s style. No matter where you are in the world, you have both weather and landscapes at your fingertips to perfect your craft.

The strategy to using weather to get good landscape photos is similar to storm chasing. A little knowledge of weather and forecasting will put you ahead of the competition. You’ll know where the best spots to target for the best landscape photos are instead of just having to guess. You won’t always get it right, and it won’t happen overnight, but your landscape photos will go from good to amazing.

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Top Photo: An EF-3 Tornado Tears Across an Open Prairie During the Golden Hour
Harper, Kansas – May, 2012

The post How to Use Weather to Take Amazing Landscape Photos appeared first on Matthew Gove Blog.