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.

The Basics of the Google Algorithm

I don’t even begin to know the intricacies of Google’s algorithm, but the bottom line is that Google looks for sources that have two things: reputation and value. It promotes what it determines to be the highest value content from the most reputable sources. As a result, Google will consider a blog with high volumes of traffic and engagement rates that has been around for years a much more reputable source than a brand new blog that has barely any traffic.

To be successful with organic search traffic, every piece of content you publish should work to build your reputation and provide incredible value. That value will slowly grow your reputation over time, as blogging success is very much a long-term game. Don’t expect to see any kind of success overnight.

While this blog has existed in various forms for the better part of 15 years, we’ve only been doing it seriously for the past two and a half years. In 2021, we finally started to see our efforts pay off, but there’s still so much potential and growth we have yet to unlock. And that’s exactly what our next chapter aims to accomplish.

Traffic Volume Analytics

There are three key metrics you should be tracking to monitor your blog or website’s traffic volume. Traffic volume is simply the amount of users and visitors that visit your site.

So what is the relationship between these metrics? A single user can initiate multiple sessions. In fact, they start a session whenever they visit any page on your website. Likewise, a single session can consist of multiple page views.

User Acquisition Analytics

Knowing where your users are coming from is critical for your success. It’ll tell you whether your marketing and SEO efforts are working. Furthermore, it’ll help you optimize when to post new content so you can maximize the number of people that consume your content. Finally, identify flaws in your website’s design, holes in your workflow, and leaks in your funnel. Then you can easily fix them so you can accomplish the goals, sales, and conversions that matter most to you.

Where Are Your Users Coming From?

Google Analytics breaks down traffic sources to your website or blog into 10 channels, which are just categories. In Google Analytics 4, these sources are listed under “Default Channel Grouping.” On the other hand, they’re listed under “Top Channels” if you’re using Universal Analytics.

If you’ve implemented your SEO correctly, the vast majority of your traffic should come from Organic Search. Indeed, the analytics show that about 90% of the traffic to this blog this past week came from organic search. Even though we don’t run paid ads and are not terribly active on social media outside of YouTube, we plan to further diversify those sources over the next six to nine months as our marketing efforts ramp up.

Drill Down Even Further into Your Analytics

You shouldn’t stop there when it comes to evaluating the sources of your traffic. To go further, look at the Source/Medium metric in your analytics. They will tell you exactly where your traffic is coming from. And you may be surprised at the results. Even though most of our traffic this week came from Google, that’s not always the case.

For example, you may find that the majority of your search engine traffic is coming from Bing or DuckDuckGo instead of Google. Or maybe you’re getting more conversions from Twitter and LinkedIn instead of Facebook and Instagram. Will that affect your marketing or SEO strategy?

How Are Visitors Finding Your Blog?

Google Analytics provides a wealth of information about how your users are finding your blog. But to unlock its full potential, you’ll need to tap into Google Search Console. The search console analytics will tell you exactly how your website or blog is performing in Google’s search engine. Most powerfully, it will tell you the exact search terms or queries people are using to reach your site.

As you consistently add more content, you should see these metrics slowly creep up over time. Remember that SEO is a long game. You won’t see immediate success overnight. So what do these analytics mean?

Keep in mind that Google Search Console only covers Google searches. Because the majority of our traffic comes from Google, the search console is a key part of our SEO efforts. However, you shouldn’t stop there, though. Take advantage of analytics from Facebook, Twitter, Instagram, YouTube, and wherever else you get traffic from. Without that data, you’re essentially running blind. Only then will you get the full story of how your visitors are finding you.

What Geographic Location Are Your Users Coming From?

Knowing the geographic location your users are coming from can be very beneficial to your marketing and content schedule. It can also help you identify new potential markets to push into.

Like most location-based data, this information is incredibly valuable. And I’m not just saying that as a GIS expert. Drill down in your analytics to determine exactly where in each country your content has the highest consumption rates. With Google Analytics, you can see as specific as which city or town your traffic is coming from.

So what exactly can you do with all of this data? The list is endless, but there are a few things that come to mind at first.

• Identify geographic regions for which to tailor your content
• Target new geographic regions into which you can expand your business
• Plan your next trip, photo shoot, blog post, or other content creation
• Target certain types of advertising for different geographic regions
• Add support for languages in countries from which you’re getting lots of new traffic

When are Users Visiting Your Website or Blog?

Google Analytics provides two easy ways to track which days of the week and what time of day your visitors are coming to your blog. First, you can use the grid you see to the right to identify exactly which days and times your blog gets the most traffic.

Why is this information important? It allows you to optimize what days and times you release new content. Take a look at the chart to the right. It shows that we’ve been getting the most traffic between about 8 and 11 AM on Tuesdays and Wednesdays.

Try scheduling your content releases to coincide with the peak traffic to your website or blog. To get the best idea of when you should release your content, change the timeframe of your traffic grid. Look at data from the past 1, 3, 6, and 12 months. If traffic patterns are consistent throughout those time periods, schedule your content to be published right before your peak traffic.

In the example above, it looks like we should probably change our publishing schedule. I would consider publishing either Monday afternoon or Tuesday at 5 or 6 AM. It may take some trial and error to get right, but it’s well-worth the effort.

Additionally, if the grid confuses you, you can always publish the same data on a good old fashioned bar chart. On a dashboard, create a bar chart showing Users that is grouped by Day of Week Name. It won’t give you times of day, but it’s clear as day which day of the week generates the most traffic.

What Devices Are Your Visitors Using?

By implementing a proper responsive web design, your website or blog should be optimized for screens of all sizes. However, that doesn’t necessarily mean that your traffic will be evenly split between desktop, mobile, and tablets. Most websites and blogs will see the majority of their traffic coming from desktop devices, a metric which includes laptops.

Once you have a baseline established, keep monitoring your analytics. If those metrics start to change, it’s time to figure out why. User behavior will shift and evolve over time. However, if you notice a big shift all of a sudden, there’s likely something wrong with your website or web design that is driving users of a particular device off of your website.

Drilling down, you can monitor plenty of details in your blog analytics. At the very minimum, keep track of what screen size, browser, and operating system your visitors are using. That way, you can make sure that your blog is best optimized for the devices that are giving you the most traffic.

Visitor Behavior

Now that you know the volume and source of traffic to your blog, let’s have a look at how people are behaving when they’re on your site. Using Google Analytics, you can pinpoint the exact elements that visitors are clicking on and interacting with. Use this knowledge to optimize your goals and funnels, and in turn, maximize your conversions.

Are Your Visitors Engaged?

You can have all the traffic in the world, but until you can engage your visitors, it’s meaningless. However, as we begin to shift from Universal Analytics to Google Analytics 4, you need to know the difference in how each version measures engagement rate.

If you’ve used Google’s Universal Analytics, you’re probably familiar with the bounce rate. The bounce rate is simply the percentage of sessions where users only viewed a single page and triggered a single request to the Analytics server. However, if you have a single page website or landing page, the bounce rate can be very misleading. Because there are no other pages to visit, the bounce rate will always be 100%. Unfortunately for you, Google interprets that as a completely disengaged page that is not providing any value.

Google Analytics 4 Makes Tracking Engagement Much Easier

Thankfully, the engagement rate in Google Analytics 4 is a much more accurate way to measure visitor engagement. Google has done away with the Bounce Rate and replaced it with the Engaged Sessions Rate. To be considered engaged, your visitor must do one of the following during their session.

• Actively engage with your website or blog for at least 10 seconds in the active window or tab.
• Successfully complete a conversion
• Visit two or more pages

As a result, single pages no need to worry about being shunned for having a high bounce rate. By actively engaging the user or completing a conversion, they can be considered engaged without requiring the user to visit a second page.

What Are Visitors Clicking On and Interacting With?

Google Analytics provides a very valuable tool that can measure how users are interacting with your website or blog. Use Google Tag Manager to set custom events to track what your visitors are engaging with. I use custom events to track nearly every clickable item throughout all of my websites, but especially on the home pages. Because the home page is one of the primary landing pages, we need to ensure that users can find the content they’re looking for.

If something on your page is getting a lot of interaction, double down on it. On the other hand, maybe something else is not getting any clicks or engagement. The analytics will tell you why, so you can fix it or replace it with something that does.

What Pages are Your Visitors Navigating To on Your Blog? How are They Flowing Through Your Website?

Google Analytics shows you exactly how visitors are flowing through your website. It’s a great way to plot multiple conversion funnels on one chart. Unfortunately, it can be intimidating if you’re not familiar with flow charts and data analytics.

In addition to visitors’ progression through your funnel, you’ll see the number of sessions and drop-offs at each step. Depending on what kind of website you’re running, your baseline Key Performance Indicators (KPI’s) will be slightly different. For example, a blog will have a pretty high drop-off rate after the first landing page or interaction. Think about how you read blogs. You enter your search term into Google, and click on a result. You read the article, and then navigate back to the search results to visit the next article. Turns out that’s what most other people do too. That’s why blogs tend to have high drop-off rates.

On the other hand, e-commerce websites have a much more specific and highly-tuned conversion funnel because the conversions are sales. They should have much lower drop-off rates than a blog. Furthermore, you should look at the percentage of drop-offs, not just the raw number of drop-offs because the numbers can be misleading. More popular pages will have more drop-offs simply because they get more traffic. That doesn’t necessarily mean there is a problem converting visitors.

Regardless of what kind of website you have, look for pages that have a high percentage of drop-offs. You’ve just identified the leaks in your conversion funnel. Once you know where the leaks are, you need to figure out why they’re leaking. Most likely, you’re missing a clear next step or call to action.

How Long are Visitors Spending on Each Page of Your Website or Blog?

If you’re running a blog, you should have a pretty good idea of how long it takes to read or skim a particular article. Your analytics should reflect those times. If most visitors are only spending a few seconds on your page, you are either not making a good impression or your content is not providing enough value. Longer times that still come up well short of your expected read times often indicate readers are losing interest in your content mid-way through. I actually set up scroll events to track how far users scroll down through each post before leaving.

On this blog, I know that most people can skim or read our posts in anywhere from two to five minutes. Indeed, the analytics from March, 2022 reflects that. You won’t hit your targets every single day. However, as long as most of your data falls within that target range and the misses are close, you’re doing fine.

Unfortunately, the Average Session Time in Universal Analytics can get skewed if someone reads your article, and then leaves it open in their browser. If you see session durations spike really high for just one day, that’s likely from people leaving your site open in their browser. However, you should always check your session times against bounce rates. If high session times coincide with a drop in bounce rate, congratulations! Your site is actively and highly engaging visitors. It means that visitors are spending high amounts of active time on your blog and navigating to multiple pages on the site.

Because Google completely redefined engagement in Google Analytics 4, you’ll find more accurate metrics there. Combined into a single “Average Engagement Time” metric, you no longer need to compare two different metrics to obtain an accurate measure of engagement on your blog. It’s much easier to interpret and understand than the combination of session duration and bounce rate you need to use in Universal Analytics.

What Landing Pages are Visitors Landing On? What Page are They Exiting from?

Knowing the entry and exit points of each funnel on your website or blog is critical to your success. And best of all, you don’t need the complicated flow charts from earlier to identify them. I actually created a custom dashboard in Google Analytics to track the information on both this blog and all of my websites. It’s certainly not the sexiest thing in the world, but it contains a ton of information.

Let’s go over what those four tables are telling you. The top two are just overall summaries of page views and user engagement. Likewise, the bottom tables have the most valuable information. The bottom left table tells me where visitors are entering this blog during a given time frame. It lists the most popular landing pages, and the percentage of new sessions from users who have never been to my site for each landing page.

On the other hand, the bottom right table shows where visitors are leaving the blog. Like we discussed earlier, don’t forget that popular pages will get more exits than your less-visited pages. And that does not mean that they are a failure. Instead, you’ll want to consider exit percentages as well. To calculate those percentages, simply divide the number of exits by the number of page views.

exit_percentage = exits / pageviews

Unfortunately Google Analytics does not provide the exit percentage. If you want to add a calculated field, you’ll need to put everything into Google Data Studio. We’ll cover that in a future tutorial. But for now, I find that a quick back-of-the-envelope calculation works fine for me.

An Important Lesson from My Blog

You may recall that up until quite recently – less than a year ago – this blog was nothing more than a feed of posts that supplemented the Matt Gove Photo and Matthew Gove Web Development websites. It lacked the home page, the Start Here page, the portfolio, and all of the bio sections. As a result, we suffered from high bounce rates, low engagement, and poor search performance. At the end of July, 2021, one simple update changed everything.

We decided to transform the blog from just a feed that supplemented our other websites until a full-fledged website and valuable resource that could stand on its own. We added the Home Page and the Start Here pages. After optimizing all of our old posts to align with our new SEO strategy, we pumped every page, menu, and sidebar full of links to supplemental valuable content, as well as calls to action for the next steps in our conversion funnel.

After this one, albeit major, upgrade to the blog, we saw results in the analytics less than a month later. Remember with the Bounce Rate, lower is better.

Additionally, we saw traffic increase nearly an order of magnitude, or about 800%, between August and December, 2021. As a result, our Google search performance shot way up as well.

The lesson here is to use your blog or website’s analytics to identify, where holes in your conversion funnel, why users are leaving your site, and how you can patch those holes to both retain and grow you follower base. Get your content in front of as many people however you can. Remember you need to publish content that has value. You can’t just spam people asking them to visit your blog.

While 2021 was a significant step forward for our blog, we hope there are many milestones to come as we grow both this year and beyond. This is still just the beginning for us. We can’t wait to share more of this incredible journey with you.

Conclusion

Data analytics are one of the most important components of a successful website or blog. When implemented correctly, your blog analytics will tell you what’s working and what’s not. In fact, they’ll even guide you down the path to success. Because without blog analytics, you’re literally flying blind. In today’s hyper-competitive environment, you simply can’t afford to.

We’ve only scratched the surface of the power of blog analytics. In addition to Google Analytics, we’ll cover how to use Google Search Console and Data Studio in the context of blogging in future posts. While we’re at it, we’ll go over the critical analytics to track for e-commerce sites as well. If you would like more of these tutorials, please subscribe to our newsletter, and leave any questions or thoughts you have in the comments below.

The post Bloggers, Are You Taking Advantage of These Powerful Analytics? appeared first on Matthew Gove Blog.

Anthony Bourdain once famously said, “Travel changes you. As you move through this life and this world you change things slightly, you leave marks behind, however small. And in return, life — and travel — leaves marks on you.” Travel has left plenty of marks on me over the years. However, this quote really hit home after a recent road trip I took through California. Today, I want to share that story with you.

What Causes Severe Drought in Western States Such as California?

Most people attribute drought to the lack of rain. However, it’s actually not the primary cause of severe drought in the western United States. To find the culprit, you need to look in an unexpected place. In fact, you’ll find it in the exact opposite of the summer season that makes headlines for searing temperatures and massive wildfires.

The winter season actually determines how severe the drought will be in California and throughout the west. More specifically, the amount of snow that falls in the mountains is what drives drought levels. In the spring, the snow melt feeds rivers, streams, and creeks, providing water to the valleys below. Unlike a rainstorm, snow melt is a slow process that provides a steady supply of water for months. And for many rivers, snow melt is their only source of water.

Most of California’s mountains snows fall in the Sierra Nevada. Rising over 14,000 feet above sea level, the Sierra runs pretty much the entire length of California’s eastern border with Nevada. During normal winters, pretty much every snow event in the Sierras is measured in feet, not inches. Snow melt from the Sierra Nevada alone provides water over 25 million Californians – about 65% of the state’s population.

The California Drought: Data and Statistics

Before diving into the drought data for the entire state of California, let’s have a look at annual snowfall in the Sierra Nevada over the past ten years. We’ll at two hotspots for heavy snow. First up is Donner Pass, which sits along Interstate 80 just west of Truckee and northwest of Lake Tahoe. Then, we’ll look Mammoth Mountain, one of California’s most popular ski resorts. Mammoth Mountain sits about 45 miles (72 km) southeast of Yosemite National Park.

Snowfalls are Way Down in Recent Years Throughout the West

From the bar chart, it’s pretty clear that snow levels in the Sierra Nevada are way down every year since 2018. This pattern is echoed across mountain ranges throughout the west, from Washington to New Mexico. As a result, water levels have plummeted across the entire region due to the lack of snow melt. The southwestern states, plus California have born the brunt of the recent drought.

To further compound the matter, smaller snowpacks mean that the snow melts faster. In 2021, snowpack across the entire state of California had completely melted by May, about two to three months earlier than average. The faster rate of snow melt results in most of the water running off instead of being absorbed into soils that so desperately need it.

As you can probably deduce, dry soils lock a layer of extremely dry air at the surface throughout the summer months. Unfortunately, that only makes it harder for much needed rain to reach the ground. Much of California is caught in this vicious positive feedback loop that only makes the drought worse.

An Anthony Bourdain Quote and a Trip Up Interstate 5 Makes the Severity of the California Drought Really Hit Home

In January 2022, I took a road trip from Las Vegas to Salem, Oregon. Coupled with a stop to visit one of my best friends in San Francisco, the trip took me across nearly the entire state of California. It marked my first visit to California since February, 2020.

During that time, both the drought and wildfires have gotten exponentially worse, thanks to a lack of snowpack in the mountains. And seeing areas that were so lush and vibrant in 2017 now completely ravaged by drought and wildfire really left a big mark on me. In ways that really didn’t expect.

“Travel changes you. As you move through this life and this world you change things slightly, you leave marks behind, however small. And in return, life – and travel – leaves marks on you.”

Anthony Bourdain

A Stunning Drive Through California’s Central Valley

If you’ve never driven through California’s Central Valley, it’s one of the more uniquely spectacular drives in the United States. Responsible for 8% of the United States’ agricultural output, the Central Valley features lush and fertile fields surrounded by majestic mountains. It’s hard to not feel a special connection to nature. And if you drive through at the right time of year, the smell of fresh fruits and vegetables is heavenly.

Furthermore, when I drove through in mid-January, farmers were in the process of prepping and seeding their fields for the spring and summer growing seasons. Passing through the Central Valley was like a cleansing and rebirth for you mind and soul. At times, you felt like you were in a fairy tale, having been teleported to some faraway land. The pandemic and all the other problems in the world felt so far away.

A Stunning Snow-Capped Mt. Shasta Completes the Fairy Tale

Towering 14,179 feet above sea level and over 10,000 feet above the surrounding terrain, Mt. Shasta dominates the northern California landscape. It’s the second tallest mountain in the Cascades, behind only Mt. Rainier. And Rainier is only 250 feet taller. On a clear day, you can see Mt. Shasta from as far as Central Oregon, over 140 miles away. You can actually find some of the best views of Mt. Shasta right from both US-97 and Interstate 5.

Surrounding Mt. Shasta, you’ll find simply breathtaking scenery. Golden meadows, stunning rolling hills, and refreshing pine forests transport you even further into that fairy tale. All those worries you had to start the day seem even further away than they did down in the Central Valley.

As a photographer, I try to keep an eye out for good photo ops on road trips. However, a truly spectacular composition catches your eye right away. Shortly after beginning the climb out of the Central Valley up into the Cascades, I came around a corner on Interstate 5 and into a clearing. And what lay before me was one of the most perfectly composed landscapes I had ever seen.

Off in the distance sat Mt. Shasta, perfectly framed between two smaller mountains. It provided a beautiful backdrop for seemingly endless meadows and rolling hills that disappeared into a light haze that shrouded the base of the mountain. Small ponds dotted the foreground on the side of the freeway, adding even more diversity to the shot. It was a combination of beautiful, mysterious, and exotic, balanced to perfection. The perfect composition to capture the fairy tale I had drifted off into.

You Can’t Stop on the Freeway to Take Pictures

Unfortunately, I also had a few things working against me. Most critically, I was cruising at 70 miles per hour up Interstate 5. There were no exits or pullouts nearby, and I wasn’t about to stop in the middle of a busy freeway just to take a picture. Furthermore, there was no safe way for me to take a picture with my DSLR camera while I was driving. But I wasn’t going to let this perfect photo op go to waste.

While using my big camera was out of the question, I still had one more tool available to me. And at this point, it was really my only option. I grabbed my phone, rolled down the passenger side window, leaned over far enough to get the window frame out of the picture, and blindly started snapping as I kept looking forward to keep my eyes on the road.

I expected to get a decent shot. What I didn’t expect was to get one of the best photos I’ve ever taken, and one that will be put up for sale in our store very soon.

Then reality hit, hard. Like being hit by a Mack truck hard.

A California Landscape Ravaged by Wildfire and Drought

Over the past five years, parts of the Shasta-Trinity National Forest have been devastated by several major wildfires. The extreme drought that has suffocated California for much of that time has only made these fires larger and more intense. Most notable, the Carr Fire torched nearly 230,000 acres in the summer of 2018, forcing 38,000 people to evacuate their homes. It killed 11 people and caused $1.7 billion in damage.

Fast-forward three years. In late June, 2021, another fire broke out along Interstate 5 just north of Lake Shasta. That blaze, dubbed the Salt Fire, torched 13,000 acres on both sides of the freeway. While few people live in the area, the intensity of the fire devastated the landscape, changing it into a barren wasteland for years to come. The drought is one of the prime culprits behind the increase in both intensity and magnitude of these fires in California. But it’s not the only culprit. And there were far more blazes than just the two we’ve discussed here.

From Lush Forest to Charred Hellscape

Like many natural disasters, you simply cannot grasp the scale of these California wildfires from newscasts on television. The only way to properly do so is by traveling to the burn area and seeing the devastation firsthand. And when you’re driving through lush green forests, it hits e0ven harder when it catches you by surprise. Which is exactly what the burn scar from the Salt Fire did. You come around a corner, and bam, the landscape instantly changes.

The Salt Fire completely incinerated everything in its path. It wiped entire parts of the National Forest completely off the map. It was just blackened hills and a few remaining trunks of the trees that had burned. Everything else was gone. As far as the eye could see. Even six months after the fire, the smoky smell of burnt wood still faintly hung in the air.

Combined with the Wildfire Devastation, the California Drought at Lake Shasta Really Left Its Mark on Me. It Should Leave its Mark on You, Too.

The second I drove into that burn scar north of Redding, it instantly ended that fairy tale from the Central Valley. But interestingly, it didn’t leave as big a mark as you would have thought. For that, it took driving across the bridge at Lake Shasta to see the true effect the drought and the fires have had on California. It hits hard and it hits heavy, but it’s a series of pictures everyone should see.

View of Lake Shasta from Interstate 5 in August, 2017 (left) vs January, 2022 (right). Click on the image to enlarge.

And while the water level in the lake should be what instantly catches your eye, did you notice the mountains behind the lake in the 2022 picture? They were all burned in the recent fires. It’s hard to see in the 2017 photo because the lake was shrouded in smoke, but the forest was lush green back then.

Conclusion

As Anthony Bourdain so famously said, travel changes you and leaves its mark on you. But at the same time, it gives us the opportunity to leave our mark on it. We can use these experiences and lessons to influence our own decisions. The vast majority of wildfires in the United States are the result of human error. We can make better decisions to help prevent states like California from burning, as well as reduce the effect of severe drought. What steps will you take?

To wrap this all up, let’s go full circle and end with another Anthony Bourdain quote: “Travel isn’t always pretty. It isn’t always comfortable. Sometimes it hurts, it even breaks your heart. But that’s okay. The journey changes you; it should change you. It leaves marks on your memory, on your consciousness, on your heart, and on your body. You take something with you. Hopefully, you leave something good behind.” What good will you leave behind? Let us know in the comments.

If you want to stay up-to-date with our latest adventures or want more travel guides, please consider signing up for our email newsletter. We’ll send these travel guides, plus exclusive deals, tutorials, and much more, straight to your inbox, twice per month, all for free.

Top Photo: Low Water Levels Due to Severe Drought
Lake Shasta, California – January, 2022

The post A Powerful, Heartbreaking Exhibit of the Severe Drought in California 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.

We’re going to look at the same four models as we did for Hurricanes Henri and Ida last summer. However, I just launched a complete redesign of this blog less than a week ago. That redesign will allow us to analyze the models in a way that’s much easier to compare, and hopefully much easier to understand. Let’s get started.

Big Picture Overview

When I look at the “big picture” meteorological setup, I immediately see just how similar this setup is to the October, 2021 nor’easter. That storm slammed into southern New England on 26 October, packing wind gusts over 100 mph (160 km/h) and heavy rains. Trees and power lines were down all over the place, knocking out power for several days. At one point all of Massachusetts south and east of the I-95 corridor was 100% without power.

Likewise, the current storm is a rapidly strengthening, or “bombing” cyclone. To be classified as a bomb cyclone, a storm must undergo a 24 millibar pressure drop in 24 hours. Will that happen with this storm? It remains to be seen, but it’s quite likely.

On the upper air map, you’ll see a large, powerful trough digging south over the Carolinas. That trough will rapidly strengthen, undergoing bombogenesis as it pull north. Look at all the energy, shown in the orange and red colors, off the coast of Georgia and Florida.

What Causes Bombogenesis?

There’s one major feature on the above map that jumps out at me. See the corridor of strong winds that stretches from northern Mexico to the southern tip of Florida? That’s the subtropical jet, which serves two purposes here.

1. Funnels a nearly endless stream of rich tropical moisture from the Gulf of Mexico into the developing bomb cyclone.
2. Exerts a west-to-east force on the southern edge of the nor’easter, which accelerates the spin of the upper-level low.

Both influences will have significant impacts on rain and snowfall totals, as well as wind speeds. We’ll dive into those details shortly. Furthermore, even without the subtropical jet, the storm will track pretty much right over the Gulf Stream. The Gulf Stream alone provides more than enough fuel for the storm to rapidly strengthen and maintain itself.

Similarities to the October, 2021 Bomb Cyclone

So just how similar are the meteorological setups between this storm and the October nor’easter?

• Both storms are bomb cyclones
• They both formed off the coast of Georgia and the Carolinas
• There is an immense amount of tropical moisture to tap into from the Gulf Stream
• Steering currents are nearly identical

This Bomb Cyclone will not Impact New England like the October Storm Did

The greatest impact of the October storm was the widespread power outages. As a New England native, I’ve been through some monster storms over the years. I’ve never seen power outages and downed trees anywhere close to the magnitude we saw following the October nor’easter.

Thankfully, it’s extremely unlikely you’ll see anything remotely close to the magnitude of power outages in October. The biggest difference is that the leaves are no longer on the trees. As a result, the surface area of the trees is far less, meaning that it takes much greater winds to do the same amount of damage. Additionally, the most vulnerable limbs, branches, and trees came down in the October storm. This time around, trees and limbs won’t come down nearly as easily. Don’t get complacent, though. The risk of power outages is definitely there with this storm.

However, where you may dodge one bullet, there are others you’ll have to content with. The shift from fall into winter brings in much colder air. The precipitation in the October storm all fell as rain. This time around, you’ll be dealing with snow. And lots of it.

A Better Storm For Comparison

In fact, for a much similar storm, forget the October nor’easter. Instead, go back to exactly 7 years ago today – 27 January, 2015. That day, the first Blizzard of 2015 dumped over 3 feet of snow across southern New England. It kicked off an infamous snowmageddon winter, that plunged the region into a months-long deep freeze.

All right, enough history. Let’s dive into the models.

Model Comparison: Bomb Cyclone Track and Timing

Let’s look at the same models we did with our analysis of Hurricanes Henri and Ida last summer. If you’ve forgotten those models, here they are.

For tracking and timing, you want to focus on the position of the center of the surface low, denoted by the red “L” on the map. In addition, note the timestamp on the upper left corner of the map. Those timestamps are in Zulu time, or UTC. Eastern time is 5 hours behind UTC. Don’t worry about the wind barbs for now. We’ll look at those in much more detail shortly. Click on any image to view it in full size.

As you can see, the American, European, and Canadian models are in very close agreement with each other. They show the the low passing just offshore of Cape Cod and the Islands around 00Z on Sunday (7 PM EST Saturday). The UKMET shouldn’t be discounted, either. It’s timing agrees with the other three models. Steering currents over the Appalachians can easily push the storm further offshore. However, it’s unlikely that it will pass any closer to the coast than what the GFS, ECMWF, or GDPS indicate.

Model Comparison: Bomb Cyclone Intensity (Pressure)

All right, it’s time to answer the million dollar question: will this storm bomb? To do this, we’ll need to figure out when each model expects the storm to reach its peak intensity, or minimum pressure. Then, we’ll compare the pressure at its peak intensity to the pressure 24 hours earlier. Remember, in order for a storm to be considered a bomb cyclone, it must undergo a 24 millibar pressure drop in 24 hours. Here is when each model expects the storm to reach its peak intensity.

Now, all we need to do is compare it to the same plots 24 hours earlier.

So do the models expect the storm to bomb? Here are their official predictions.

Models are usually not this assertive, but that’s a pretty definitive yes. The storm will bomb. Cue Toots and the Maytals.

Wind Forecast

Whenever a nor’easter undergoes bombogenesis, one thing is assured: there will be wind. Lots of it. So just how much wind will there be? You probably remember the October bomb cyclone, which brought 100-plus mph (160 km/h) wind gusts to southeastern Massachusetts. Thankfully, I’ve got some good news for you: you won’t see winds like that with this storm.

Look North to Canada for the Best Indicators of Potential Wind Speeds

The fiercest nor’easters get their winds from the pressure gradient between the bombing low and a strong high pressure system over southern Québec. But have a look at this. The high over Québec is much further north and east than it traditionally is for the really bad storms. In fact, it’s not over Québec at all. It’s actually over Newfoundland and Labrador.

Because the high is further away, the pressure gradient won’t be as tight. As a result, wind speeds won’t be as high as they would have been had the high been closer. Don’t get me wrong, it’s still a tight pressure gradient, and you’ll still get plenty of wind. It just won’t be as bad as it could have been. Combined with the expectation that the center of the low will pass offshore instead of right over Cape Cod and the Islands, I expect winds to be less than the bomb cyclone that hit New England last October. Let’s look at the models.

When I look at the models’ wind predictions, I prefer to look at the sustained winds about 400 to 500 meters above the ground, at 925 mb. In coastal areas, models can sometimes underestimate wind speeds when they try to calculate how friction and terrain impact the wind as it comes off the ocean. The 925 mb (400-500 meter) predictions remove those possible anomalies, and also give you the maximum potential wind speeds.

How Much Wind to Expect in New England

In the wind forecasts above, I don’t see any plausible scenario where the ECMWF (European) model forecast verifies. You just simply aren’t going to get winds that strong that far inland. Using the other three models, it’s clear that the strongest winds will be contained to the immediate coastal areas.

Areas that are exposed to the north along the South Shore and the Cape and Islands will see the greatest impacts from the wind. You’ll find the strongest winds on the Cape and Islands. Right now, my best guess is that sustained winds will peak in the 40-50 knot range in exposed areas across the Cape and Islands. Hurricane-force gusts are certainly possible, but I don’t expect anything close to the 100 mph gusts that ripped through during the October storm.

Temperature and Wind Chill: How Cold will the Bomb Cyclone Get?

Despite the availability of rich, tropical moisture, the bomb cyclone will have a very well-established cold core by the time it reaches New England. Furthermore, all of New England and the Canadian Maritimes will be on the cold side of the storm as it passes by. As a result, you should expect bitterly cold wind chills during the bomb cyclone. The models are all in agreement.

Expected Temperatures

Expected Wind Chills

When looking at temperatures and wind chills, you really need to look at the coast vs inland. Even in extreme conditions, the ocean still helps regulate temperatures near the coast. That being said, with the exception of Martha’s Vineyard, Nantucket, and possibly parts of the outer Cape, wind chills will struggle to get out of the single digits. If you’re inland, you’ll see sub-zero wind chills for pretty much the duration of the event.

Precipitation Type

With strong northerly winds, sub-zero wind chills, and everywhere on the cold side of the system, it should not be a surprise that this will be a snow event. Parts of the outer Cape and the Islands may briefly see a little ice mix in during the warmest part of the storm early Saturday afternoon. Other than that, it will be all snow.

On the models, we’ll look at the maximum temperature in the vertical column of air during the warmest part of the storm. Blue and purple indicates that the entire column is below freezing. You will see snow in those areas. Areas in green may briefly see some ice or sleet mix in early Saturday afternoon before changing back to all snow. That’s a result of air on the warm side of the storm wrapping around the top of the low as it approaches.

The European and British models do not calculate the maximum vertical temperature, so we’ll only consider the American and Canadian models. As you can see, their two predictions are nearly identical.

Snowfall Totals Will Be Measured in Feet, Not Inches

Whenever you have a storm that has both bitterly cold temperatures and an ample fuel supply of rich, tropical moisture, you’re going to get massive snowfall totals. However, there is a bit of a silver lining. All four models are showing noticeably less snow totals than they were yesterday. Unfortunately, they are still showing around two to three feet maximum snowfall totals for this bomb cyclone.

Because the GFS and GDPS models use the Kuchera Ratio, which is the most accurate, to predict snowfall, we’ll give the heaviest weight to those models. However, for a number of reasons, nailing down exact snowfall totals for a precise location is extremely difficult in this scenario.

1. The low still hasn’t really formed yet, so we don’t have any actual data from it to feed into the models.
2. The snowfall gradients are tight. A small wobble in the storm track can make a big difference in the snow totals. For example, take any of the above predictions and shift the snowfall totals 40-50 miles east or west.
3. A westward shift in the storm’s track may mean some areas – particularly parts of the Cape and Islands – may see more ice, sleet, or mixed precipitation on Saturday afternoon, which would reduce snowfall totals. Again here, a small shift in the storm’s track can make a big difference. In this scenario, inland areas would also see greater snowfall totals.
4. An eastern shift in the storm’s track would shift the precipitation shield east, meaning the heaviest snow could fall offshore.

How Much Snow Should You Expect?

We can give equal weight to all four models to determine where the heaviest snow will fall. Given my experience both with the models and with these types of bomb cyclones, I think the heaviest snow will set up in southeastern New England, along and east of the Interstate 95 corridor. Rhode Island will take a pretty good wallop, but will ultimately be spared the worst of the snowfall. Total accumulations will drop rapidly once you get west of I-95.

For the largest snow totals, I think 20 to 24 inches across Bristol and Plymouth Counties in Massachusetts is your safest bet at this point. That swath will likely spread across western Barnstable County (Cape Cod) and up into the far southern suburbs of Boston as well. 28 to 32 inches in a few isolated spots is certainly not out of the question, either, but I am not expecting widespread totals above 2 feet.

Summary

I know there’s a lot of information in this post, so let’s put it into a nice, clean table to summarize everything.

Conclusion

Like many other bomb cyclones, this is certainly a storm that you’re certainly going to want to take seriously. However, New England has certainly gone through far worse in the past. Make sure you stock up on what you’ll need for a few days, and then hunker down at home and enjoy it. The storm is fast moving, so it’ll be in and out in only about 24 hours. Then it’s just a matter of digging out, cleaning up, and getting back to your normal routine.

If you have any questions about anything related to this storm, please let me know in the comments below or reach out to me directly.

The post Wicked Bomb Cyclone Set to Pound New England with Fierce Winds and Heavy Snow appeared first on Matthew Gove Blog.

1. A country announces they have discovered a new variant
2. Experts speculate that this is the variant that will evade vaccines
3. Everyone freaks out and panics for a few weeks
4. Data proves that vaccines still provide adequate protection against the variant
5. You see a wave of the variant sweep through many countries
6. Life goes on

With the omicron variant of COVID, most countries are currently in the early stages of step 5. However, there are still two big unknowns: how big will the wave be, and how quickly will it surge?

The Omicron Surge Will Resemble the COVID Spike in India From the Delta Variant

Remember how highly contagious the Delta Variant is? As you can probably guess, the Omicron Variant must be significantly more contagious in order to out-compete Delta. And initial data shows that it is. As a result, Omicron will spread much faster, making the spike taller. But there’s a silver lining: it will come and go quickly. Think of it coming through like a tornado instead of a hurricane. Recall the COVID spike in India from the Delta Variant.

Because Omicron is more contagious than Delta, that spike will be taller, but last shorter. And don’t forget to account for population. With nearly 1.4 billion people, India is the second most populous country in the world. Smaller populations in just about every other country will result in both a shorter and less severe spike. In an Omicron wave, the United States is the only country that has the potential to come anywhere remotely close to the 400,000 daily cases that India saw in their Delta spike. And I think even that is highly unlikely.

COVID Cases from the Omicron Variant Have Already Peaked in Some Countries

Yes, you read that right. The Omicron wave has already peaked in some countries. After South Africa first identified Omicron in late November, health officials quickly contact traced cases back to both Germany and the Netherlands. And guess what? New Omicron cases are now falling in all three countries.

Omicron has obviously spread far beyond those three countries. However, I expect any Omicron surges in other countries will resemble the time series above.

A Word of Caution About South Africa’s Demographics

South Africa has one of the most advanced and sophisticated health science programs in the world. There is nothing wrong with data coming out of South Africa. In fact, I trust their data 100%. The issue lies primarily in South Africa’s age demographics, which heavily skew towards younger people. Just 5.5% of South Africans are over 65. That’s a stark contrast to 17% in the United States, 16% in Canada, and 21% in the European Union. That’s why health officials originally cautioned about reports of omicron in South Africa being primarily mild. Thankfully, data from the European Union seems to confirm that omicron is less severe than Delta.

Additionally, don’t forget that South Africa’s location in the Southern Hemisphere means that they are heading into summer right now. Omicron is so contagious that summer vs winter may not make any significant difference anymore. However, data since the start of the pandemic has repeatedly shown that surges are worse in the winter season, regardless of which hemisphere you live in.

I Believe the Omicron Variant Originated in Europe, Not Africa

Just because South Africa discovered the Omicron variant doesn’t necessarily mean that it originated there. And after looking at the data, I believe that Omicron actually originated in Europe and was then brought to South Africa, not the other way around.

First, let’s recall the new daily COVID cases from Germany, the Netherlands, and South Africa we just looked at in the previous section.

What jumps out at me right away? The slope of the upward side of the omicron spike in late 2021 is identical in all three countries. While it’s not definitive proof, it’s likely that the same variant caused all three surges. And we know for certain that Omicron caused the surge in South Africa. In addition, Notice how the spike starts earlier in both Germany (black) and the Netherlands (red) earlier than it does in South Africa (green). We’ll circle back to this in a sec.

Second, look at how Omicron spread in South Africa. The first clusters emerged in Gauteng Province, which is mostly comprised of the City of Johannesburg. And do you know what’s in Johannesburg? South Africa’s largest international airport. Nearly all international flights in and out of the country go through Johannesburg. As Omicron spread throughout the country, the worst of the outbreak remained in Gauteng. Interestingly, Gauteng was also the first province in South Africa to reach the peak of the Omicron outbreak.

Botswana Contact Traces Omicron Back to Europe…Before Any Evidence of it Appeared in Africa

After popping up in South Africa, Omicron quickly jumped the border into neighboring Botswana. With the help of the South African Health Ministry, the Federal Government of Botswana contact traced omicron cases back to the Netherlands as early as 8 November. And it may have been in Germany earlier than that.

Furthermore, after extensive contact tracing, neither Botswana nor South Africa could find any evidence of the omicron variant in Africa prior to 15 November. If it was in Europe as early as 8 November, but didn’t appear in Africa until the 15th, how could it have originated in Africa? For reference, South Africa announced the discovery of Omicron on 26 November.

Timing of the Omicron Wave Lines Up Perfectly with it Originating in Europe

To prevent confusion, let’s have a look at new daily Covid cases in just Germany. The start of Germany’s Omicron spike lines up perfectly with Botswana’s contact tracing of Omicron back to the Netherlands on 8 November. Germany’s uneven uptick in cases in late October is likely from the Delta Variant.

Repeat the process for the Netherlands and you get the same perfectly-aligned timing.

Interestingly, the data out of both South Africa and Botswana seem to confirm the contact tracing that Omicron was not present in Africa prior to 15 November. Unlike Germany and the Netherlands, South Africa’s Omicron spike did not start until after they announced they had discovered Omicron on 26 November.

So is this definitive proof that Omicron originated in Europe? Most certainly not. However, it does illustrate how ineffective travel bans are in stopping COVID-19. If my theory is true, banning travel from southern Africa would have done absolutely nothing to stop the Omicron variant if it originated and had already taken hold in Europe.

What Will the Omicron Spike Look Like in the United States, Canada, and Great Britain?

All three countries will see something similar to what India saw with Delta, or what Germany, the Netherlands, and South Africa saw with Omicron. The million dollar question is how big will the spike get, and how long will it last?

To answer those questions, let’s look where each country stands right now. All three countries have started spiking from Omicron. The United States currently has the highest new daily case loads, and as a result, will likely get hit the hardest. The UK is experiencing the biggest spike, while Canada is in the best shape of the three.

For what to expect, let’s turn to the University of Washington’s Institute of Health Metrics and and Evaluation (IHME) model.

As expected, I tend to agree with the IHME’s most likely projections. I think both the Universal Masks case as well as the worst-case scenario are both highly unlikely. Holiday gatherings may slightly increase the peak daily cases as well as push the peak date shortly into the future.

We Are Much Better Prepared for Omicron than Any Previous Variant of COVID

Despite all of the doom and gloom predictions, the world is much better prepared for Omicron than any previous variant. First, and foremost, we still have highly effective vaccines. Yes, their effectiveness took a hit, but go back to the Fall of 2020. As companies raced to develop vaccines, most infectious disease experts said that vaccine effectiveness of 50-60% would be a major victory. After a booster shot, both Pfizer and Moderna are reporting 70-75% effectiveness against Omicron.

Furthermore, many more people have immunity after the Delta wave. Through both vaccinations and natural immunity, the pool of susceptible people is much smaller than previous variants had, and that pool keeps shrinking every day. Combined with the highly infectious nature of Omicron, the wave will be over before you know it.

In addition, antiviral treatments are becoming more effective and more widely available. In fact, some of the antivirals are not expected to lose any effectiveness because they don’t target the virus’ spike protein, which is where all of Omicron’s mutations are. And it seems like more treatments are being approved just about every day.

Finally, don’t forget about what I said about Delta. The more infectious it becomes, the harder it will be for other variants to compete with it. As a result, that may end up being a blessing in disguise and help us end the pandemic sooner. The same logic applies to Omicron.

Other Areas of Interest to Watch

Besides all of the countries and regions we have covered so far, there are a few additional areas to watch over the next few months as Omicron spreads around the world.

Conclusion

The Omicron Variant is a harsh reminder that the COVID-19 pandemic is still far from over. However, the panic and hysteria surrounding Omicron is largely unwarranted. Yes, some restrictions will likely be re-introduced, but we will not be going back to the dark lockdown days of March and April, 2020. Back then, there were no vaccines and no treatments.

Today, we have a much bigger toolbox. Go get your booster, and be smart about your holiday gatherings. The Omicron wave will be in and out quickly, regardless of what country you’re in. Then, we can get back to living our lives, and be one step closer to putting this awful pandemic behind us once and for all. Happy New Year, everyone!

Top Image: Matt’s COVID-19 Risk Index for the United States as of 23 December, 2021

The post Why You Shouldn’t Panic Over the Omicron Variant of COVID-19 appeared first on Matthew Gove Blog.

I also believe that the Delta Variant is so dominant that it will ultimately help us end the COVID-19 pandemic sooner. How is that, you ask? It’s actually quite simple

• It blocks other potentially vaccine-resistant variants from taking hold. The Mu Variant that everyone thought was vaccine-resistant has been eradicated. No other variants have been able to establish themselves since Delta became dominant.
• It spreads so fast that most places will reach herd immunity faster.
• Treatments continue to improve, which will help drive down the death rate

Furthermore, the models have spoken loud and clear about the outlook for this winter and the end game for the COVID-19 pandemic. And they’re largely in agreement, too. Barring some freak mutant variant emerging, the COVID-19 pandemic will finally start winding down in 2022.

End Game in the United States: COVID-19 Has Started Shifting from Pandemic to Endemic

As soon as COVID-19 began to spread around the world, it became clear the the only end game is for the virus to become endemic. In its final endemic phase, the virus continues to circulate through the population, but at a much slower rate than during the pandemic phase. As treatments become more effective and widely available, the virus becomes much less dangerous. Many infectious disease experts predict that once COVID-19 reaches its final endemic stage, it will be a similar threat to the flu or the common cold.

Current Status of the COVID-19 Pandemic in the United States

First, let’s look back at what the COVID-19 map looked like back in the summer. Highly vaccinated areas had largely suppressed COVID-19 spread. On the other hand, the Delta variant had set the southeast on fire, where you could find the lowest vaccination rates in the country.

By September, the epicenter had spread north and west, hitting the Inner Mountain West and the Ohio Valley particularly hard. Both areas still have large pockets of unvaccinated residents.

Today, the summer wave has largely subsided. The last dregs of it are rolling through the upper midwest and the northeast, as well as parts of the southwest. All in all, the country is in much better shape than it was back in September. Additionally, notice how the current map looks almost like the inverse of the August map. If reinfections are not occurring in the hard-hit southeast, that’s a major step forward to reaching herd immunity.

Despite the threat of another wave, I do not see any scenario where the United States implements more COVID-19 restrictions. Pandemic fatigue is real, and it’s unlikely further restrictions will be effective. So what exactly lies ahead? Let’s dive into the models.

University of Washington IHME Model

The University of Washington’s Institute for Health Metrics and Evaluation Model remains the gold standard go-to model in the United States. Everyone from the White House to the media to everyday citizens like you and me use it to plan their lives amidst the COVID-19 pandemic.

Before we dive into the cases and deaths, let’s first look at mobility and vaccination projections. Both parameters will help us understand the big picture. Lots of people will be traveling for the holidays this year, even though large pockets of the population remain unvaccinated. First, let’s look at the vaccination projections.

One thing really jumps out at me here. The model projects that vaccine coverage will basically plateau starting in January 22, with 63% of the population fully vaccinated. With a population of 330 million, that means that 125 million people in the United States will remain unvaccinated. Thankfully, that should be high enough to keep hospitals from being completely overwhelmed.

As for mobility, the IHME agrees with my prediction that the United States will not implement any further restrictions. Mobility should approach its pre-pandemic levels in early 2022.

Do Case Loads Lead to the End of the COVID-19 Pandemic?

Before we look towards the end of the COVID-19 pandemic in the United States, there is still one final hurdle to clear: the holidays. Remember that in 2020, holiday travel and gatherings sent COVID-19 cases spiking to nearly 300,000 per day in the U.S. Things are much different this year, but the threat of another wave is still very real.

A fifth wave in the United States would take on one of two forms. You could have a tsunami of cases like we did last winter or when the Delta Variant hit this summer. However, this scenario is quite unlikely due to the vaccines and the high number of infections in the United States. Instead, my gut feeling is that you’ll see much more of a minor uptick, similar to Scenario 2 in the plot below.

The IHME model agrees. Its official projection calls for a minor uptick of COVID-19 cases over the holidays. Only in the worst case scenario do you see anything like what the U.S. experienced this summer.

COVID-19 Simulator Model

The COVID-19 Simulator is run by Massachusetts General Hospital, Harvard Medical School, Georgia Tech, and the Boston Medical Center. We’ve used it in many of our past analyses and forecasts. It has been reliable and accurate throughout the duration of the COVID-19 pandemic.

Because I’m not expecting that the United States will make very many changes to the current COVID-19 protocols, let’s initialize the model to run on the current interventions for the full 16 week projection. To account for waning immunity, let’s also bump the vaccine efficacy down to 75% from its default 90%.

The COVID-19 Simulator falls largely in agreement with the IHME. In a worst-case scenario, new daily case loads would remain below the September, 2021 surge, peaking around 148,000 new cases daily. However, the far more likely scenario is that you’ll see a slight bump in cases as people gather for the holidays. Hopefully, once that’s done, we can have a much clearer view of the COVID-19 pandemic’s end game.

Massachusetts Institute of Technology Model

The MIT Model is based off of the SEIR (Susceptible, Exposed, Infected, Removed) Model. Like our model, it accounts for features specific to the COVID-19 pandemic, such as government intervention, vaccinations, and human behavior. And it’s probably the most optimistic of the three models. It doesn’t expect much, if any, surge due to the holidays. In fact, it predicts that the United States will only gain about 1 million more cases in the eight-week period between now and late January, 2022.

^ Cumulative case projections from the MIT Model ( https://www.covidanalytics.io/projections)

Places Delta Has Hit Hard Appear to Be Reaching Herd Immunity

One very encouraging pattern that has emerged is that places that the Delta Variant has hit very hard to not appear to be getting much by way of reinfections. Indeed, cases loads and risk levels across the southeastern United States are at the lowest levels they’ve been since the beginning of the pandemic.

This pattern is becoming prevalent around the world. India and Indonesia both experienced major spikes of the Delta Variant between April and July. Both countries have brought new case loads to near record lows and have kept them there. You can say the same thing for Japan. Following the Olympics in July, Japan experienced a major Delta Variant Spike. It’s now reporting less than 200 new cases per day. Even Brazil, which has had a moderate burn throughout the pandemic instead of a big spike, has gotten new case loads down to their lowest levels since May, 2020.

While these are only a few examples, this scenario is playing out in countries on all 7 continents.

Watch the Southern Hemisphere for a Preview of the COVID-19 End Game

Throughout the pandemic, Southern Hemisphere winters (June to September) have offered a glimpse into what the Northern Hemisphere should expect for its winters. While they haven’t been a perfect crystal ball, they have at least gotten us in the ball park for what to expect. And even though not all Southern Hemisphere countries have experienced a spike of the Delta Variant, the ones that did have all gotten case loads down to near record lows and kept them there.

Southern Hemisphere countries on the African continent have had particular success at keeping case loads down following a Delta Variant spike. And keep in mind, vaccines are still few and far between in many of those countries. In fact, the Southern Hemisphere as a whole seems to confirm the models’ predictions that the end game for the COVID-19 pandemic will come in 2022. However, for the best previews of what the United States has in store, I would watch Australia and South Africa.

How Close is the United States to Herd Immunity?

It’s hard to say for sure how close the United States is to herd immunity, but we can run some back-of-the-envelope calculations to get a ballpark number. First and foremost, there are likely far more actual infections than the data show. And that’s true in every single country across the board. The data only contains diagnosed cases from tests. With so many cases either asymptomatic or mild, many people did not get tested even though they contracted COVID-19.

Even though the U.S. has about 48 million positive tests, experts believe that the actual number of cases could be as high as 200 million. However, I think that it’s unlikely that high. Instead, let’s use the COVID-19 Simulator’s best estimate of total U.S. cases: 157 million.

US Population = 330 million
Estimated Cases = 157 million
157 million / 330 million = 47% of population has contracted COVID-19

Now, we’ll add in the vaccinations. About 59% of the U.S. population is fully vaccinated. However, we must keep in mind that a portion of the vaccinated population has also contracted COVID-19, either before vaccines were available, or as a breakthrough case. For purposes of this argument, let’s assume that half of the vaccinated population has also contracted COVID-19. When calculating the total population that has immunity, we’ll need to subtract those from the vaccinated pool so they’re not counted twice.

330 million * 0.59 = 195 million fully vaccinated
195 / 2 = 98 million vaccinated, but have not contracted COVID-19

157 million infections + 98 million vaccinated = 255 million immunized
255 million immunized / 330 million population = 77% of population immunized

It’s believed that 90 to 95 percent of the population must be immunized to reach full herd immunity against the Delta Variant. The United States isn’t quite there yet, but it’s getting close. These calculations also point to the COVID-19 end game coming in 2022.

A Tale of Caution: Zero-COVID Strategy Does Not Work Against the Delta Variant

Unfortunately, not every country is succeeding in the war against COVID-19. Europe has once again become the epicenter of the pandemic. Cases are surging in New Zealand. Much of Southeast Asia and Oceania are coming down off of record Delta spikes. What do these countries have in common? They all adapted a zero-COVID strategy at the onset of the pandemic, and the Delta Variant is forcing them to abandon that strategy because of its extraordinarily high transmissibility.

Can Zero-COVID Countries End the Pandemic with Vaccines?

It’s unlikely vaccines alone will end the pandemic. The Delta Variant is so contagious and transmissible that you’d need to vaccinate more than 95% of the population to reach herd immunity through vaccination alone. However, that doesn’t mean vaccines can’t suppress clusters, waves, and hotspots. Just have a look at Europe.

This graph tells the whole story.

So what does this all mean for Europe? Europe is likely going through the same Delta spike that the United States and so many other countries saw back in July and August. Did you notice on the figure above that neither Germany (black line) nor Czechia (blue line) had a major COVID-19 surge over the summer of 2021, while Spain did?

Rapid Vaccine Rollout Kills One-and-Done Delta Spikes in Southeast Asia

Southeast Asia had done a stunningly good job controlling COVID-19 until the Delta Variant arrived in May, 2021. After abandoning their zero-COVID strategy, many countries saw horrific Delta spikes as the variant ripped through the population. But the pattern has mirrored what the rest of the world has seen. You get one major spike in cases, and once it peaks, you can quickly suppress it through both vaccines and natural immunity.

Three countries in Southeast Asia really stand out for having some of the highest vaccination rates in the world. They are Cambodia, Malaysia, and Singapore. All three countries saw a major Delta spike earlier this year, and rapidly rolled out vaccines to kill the outbreak in its tracks. Even neighboring Thailand is seeing remarkable success despite having a much lower vaccination rate. Unlike the United States, all four countries continue to rapidly vaccinate their populations.

The lesson for the United States here is that it, too, can keep the Delta Variant at bay. Even if it can’t ramp up its vaccination rate, highly-effective new treatments coming on the market should help blunt the death rate, even in the unvaccinated. Herd immunity is in sight, but you can’t rule out another surge this winter. We just need do everything we can to get there as quickly and safely as possible.

Conclusion

It’s been a long two years, but the end game for the COVID-19 pandemic seems to be finally starting to come into focus. As weird as it sounds, the Delta Variant is actually helping us end the pandemic. It has kept other variants at bay, while establishing a clear pattern around the world. You’ll need to endure one major spike from the Delta variant. And once that’s finished, when coupled with a high vaccination rate, herd immunity should be in sight.

The United States has already endured the worst of the Delta spike. Whatever surge we get this winter should be minor in comparison. The combination of highly effective vaccines and treatments is the silver bullet we’ve been waiting 2 years for. Let’s finally put an end to all of this once and for all.

The post Is the United States Nearing the End of the COVID-19 Pandemic? Model Predictions May Surprise You. appeared first on Matthew Gove Blog.

At the storm’s peak, over 500,000 households in southeastern Massachusetts – about 20% of the state’s population – were without power. And the power outages in those households lasted days, not hours. I’ve been through plenty monster storms on the Cape. The only storm that comes anywhere close to the 2021 bomb cyclone in Massachusetts was Hurricane Bob in 1991. After the worst of the nor’easter passed on Tuesday night, the damage was so bad crews were unable to restore power to most customers until Saturday evening.

Furthermore, I’ve witnessed firsthand both EF-5 tornadoes (Moore in 2013) and Category 5 hurricanes (Wilma in 2005) while living in Oklahoma and Florida, respectively. While the magnitude of the damage to personal property from the 2021 Massachusetts Nor’easter pales in comparisons to those storms, it did just as much, if not more damage to the power infrastructure. This fact really piqued my interest, and I really wanted to know why. Today, we’re going to dive into exactly that.

Upper-Level Meteorological Analysis of the Massachusetts Bomb Cyclone

When we look at observations, we use the same strategy as we use for model analysis. We look at the big picture first, so we can better understand what’s going on at the local level.

Two things jump out at me immediately when I look at the upper-level trough over New England. First, the trough is negatively tilted, which means that its axis tilts from northwest to southeast. Negatively tilted troughs like to “dig” to the south, which increases their magnitude. As the trough’s magnitude increases, it pulls in more energy, causing it to strengthen.

Second, the presence of strong winds on the northern side of the storm indicate there is plenty of energy for the storm to tap into. You can see this in the blue hatched area indicated greater than 40 knot winds over New Hampshire and southern Maine. Under normal circumstances, the upper-level low gets its power from the jet stream driving its southern edge. Any additional forcing on the northern edge acts to supercharge the low. If you think of the strengthening low as a car rolling downhill, the additional forcing on the northern edge is the equivalent of stepping on the gas as you go downhill.

The 2021 Massachusetts Bomb Cyclone is Born

Indeed, as the upper-level low “digs” to the south, it rapidly strengthens. Notice how much bigger the blue hatched area (>40 kt winds) has gotten over New England. As the wind on the northern side of the low increases, the low rapidly gains strength. And as the low gains strength, it increases the wind on the northern side of the storm. This positive feedback loop is what meteorologists refer to as bombogenesis. As winds circling the low increase, the pressure at the center of the low decreases. When the pressure drops 24 mb in 24 hours, you have a bomb cyclone. Indeed, observed surface pressure at Nantucket fell 28 mb in 24 hours as the storm strengthened.

Going back to the car rolling downhill analogy, the 2021 Massachusetts bomb cyclone didn’t just hit the gas. It hit the supercharger and the nitrous, too.

Surface Analysis of the Massachusetts Bomb Cyclone

So what gave this bomb cyclone not just its fuel, but its nitrous, too? A very strong warm front passed over the northeast and mid-Atlantic on Monday, 25 October. In its wake, it left unseasonably warm temperatures and high dewpoints from Virginia to Cape Cod. Widespread temperatures in the 60’s and 70’s and dewpoints in the mid 60’s gave this storm more than enough fuel, er nitrous, it needed to undergo bombogenesis.

However, there’s much more to the story at the surface than just a warm front. We’ll need to look at the surface low itself.

A Very Tilted Low Pressure System off the Coast of Massachusetts

A vertical profile of the atmosphere can tell us a lot about whether a low pressure system is expected to strengthen. If the lows are in roughly the same location as you go up, that’s called a “stacked” low, which is the system’s preferred stable equilibrium. In a stacked low, both the upper-level and low-level lows compete for the same fuel supply. At that point, the storm has reached maturity and is unlikely to significantly strengthen.

On the other hand, if the low pressure centers differ in location as you go up, that’s called a tilted low. When a tilted low occurs, it will naturally try to align itself and become stacked. However, until it does, the lows at each different height do not have to compete for the same fuel supply. While the lows try to align themselves vertically, they pull in energy. As a result, the storm strengthens. In the case of the 2021 Massachusetts bomb cyclone, it created ideal conditions for rapid intensification because of the enormous amount of fuel at the surface and the low being so heavily tilted.

Use the sliders below to see the difference in position between the surface low and the upper-level low. You’ll see the surface low centered just off of Cape Cod, while the upper level low is over Delaware. In the second frame, which was about an hour after the storm had finished bombing and had reached its peak strength, notice how much closer the surface and the upper-level lows are to each other.

Cape Cod Bears the Brunt of Bombogenesis

The positioning of the lows played a major role in the bomb cyclone hitting Cape Cod and the South Shore so hard.. Because the upper-level low is much more powerful than the surface low, it pulls the surface low into it as it tries to stack itself. Unfortunately, the surface low happened to be sitting pretty much right over Cape Cod as that happened. As a result, the surface low did not move for more than 12 hours as it rapidly strengthened. It battered the Cape with 70-80 mph wind gusts throughout the daylight hours on Wednesday.

Interestingly, that’s not the full story of why the winds were so strong. For more, we actually need to look between the surface and upper-level lows.

Mid-Level Meteorological Analysis of the Massachusetts Bomb Cyclone

Whenever you’re dealing with a strong low pressure system like the 2021 Massachusetts bomb cyclone, there’s one final ingredient to the strong winds: the low-level jet. The low-level jet is a fast-moving current of air circulating around a low pressure system, usually at 850 mb, or about 1.5 km above the surface. It’s common in low pressure systems for the low-level jet to mix down, which often brings strong wind gusts to the surface.

Indeed, if you look at the low-level jet at the peak of the storm, the strongest winds are over southeastern Massachusetts and Rhode Island. With sustained winds in the low-level jet reaching 80 knots (92 mph), it’s certainly not a surprise that the low-level jet mixing down contributed to wind gusts between 95 and 100 mph across Cape Cod and the Islands.

To determine just how likely winds in the low-level jet will mix down to the surface, let’s look at the wind shear in the lowest levels of the atmosphere. The larger the gap in wind speed between the low-level jet and the surface winds, the more likely the low-level jet is to mix down and create very gusty winds. Indeed, that’s exactly what you see, circled in the pink box on the sounding. There is a gradient of about 40 knots over the lowest 500 meters or so of the atmosphere.

As a result, the low-level jet easily mixed down to the surface. Reports of wind gusts in excess of 80 mph poured in from southeastern Massachusetts throughout the day on Wednesday, 27 October.

Power Outages in Southeastern Massachusetts

Not surprisingly, a bomb cyclone of packing gusts close to 100 mph caused power outages. What surprised a lot of people, however, was just how widespread all of those power outages were. At the peak of the storm, over 500,000 households were without power. Plymouth and Barnstable Counties were close to 100% out of power. These outages include schools, hospitals, fire and police stations, and more. The storm spared nobody.

It took utility crews 5 days to restore power to most customers. At our house in Falmouth, we lost power shortly after 2 AM on Wednesday. It didn’t come back on until 10:15 PM on Friday.

So what cause such extreme widespread power outages? Let’s start with the peak wind gusts. If you line up a map of peak winds with the map of the power outages, they overlap nearly perfectly. And keep in mind that the actual peak wind gusts were likely higher than this map shows. The power was already out at most of these stations before the worst of the nor’easter hit. I can tell you from having been in the storm, the peak gusts in Falmouth were way higher than what this map shows.

When the wind gusts line up so perfectly with the power outages it means one thing. Trees falling on power lines were the primary culprit that causes the power outages.

Why Did So Many Trees Come Down?

It’s only logical to wonder why an October nor’easter knocked down more trees than most hurricanes. It comes down to a combination of three factors that came together absolutely perfectly.

First, a huge amount of rain fell ahead of the storm. The ground was fully saturated when the peak winds hit, making it much easier to uproot trees. When it was all said and done, the Upper Cape and South Shore (Plymouth County) took the brunt of the storm, and were without power for the longest. It’s not a coincidence that the highest rainfall totals were also in that same area.

Second, the leaves were still all on the trees when the bomb cyclone hit. If you think of the trees as a lever, the leaves give the wind much more surface area to blow on. As a result, any gust of wind has much more purchase and leverage than the same gust of wind in the middle of winter. It takes much less wind to knock down trees with leaves on them than without.

Finally, power infrastructure in New England dates back to the 1800’s. Much of that infrastructure is still in place. Unlike many other places, most power in New England relies on power lines instead of running underground. Couple that with shoddy tree trimming and power lines literally running through the middle of trees, and it’s a recipe for disaster.

What a Difference Underground Wires Makes

I was living in Norman, Oklahoma when the 2013 EF-5 tornado tore through the City of Moore packing winds of 210 mph. Many older neighborhoods throughout the Oklahoma City Metro are full of large trees, much like New England. At its closest point, the Moore Tornado passed less than 4 miles from my house.

In its aftermath, the power was out for less than 2 hours, not just in Norman, but also within Moore itself. So what’s the big difference compared to New England? Most power lines in central Oklahoma are underground, and those that aren’t are built to withstand up to an EF-3 tornado.

How Can We Prevent This Level of Damage in the Future?

As the climate warms, these bomb cyclones will become more common. The best solution is to bury the power lines. It’s impossible for falling trees to take out power lines that are underground. Unfortunately, the cost of burying power lines is extraordinary. As a result, the power companies, the town, and even the state routinely balk at it.

The next best solution is to pass laws that require trees to be trimmed a certain amount back from power lines. The goal here is not to prevent trees from falling on power lines, but to minimize the risk of it. California is starting to do this after power lines have started so many fires in the Golden State. The New England states should start giving it some serious thought, too, because this will happen again.

Conclusion

The 2021 bomb cyclone was basically a 100-mile wide EF-1 tornado that lasted for 12 hours in southeastern Massachusetts. As I drove around town in the aftermath of the storm, I found it very interesting that the damage much more closely resembled tornado damage than hurricane damage. Either way, it was one hell of a storm that won’t be forgotten any time soon.

I’ve also heard plenty of comparisons of the 2021 bomb cyclone to the Perfect Storm in 1991. Both storms occurred within a few days of Halloween, packing winds of 75-80 mph and pressures as low as 980 mb. However, that’s about where the similarities end in my opinion.

The Perfect Storm started as a hurricane and got absorbed into the jet stream, much like Hurricane Sandy did in 2012. The 2021 bomb cyclone never had any tropical characteristics. Instead, it was a cold-core mid-latitude upper-level low like every other nor’easter that has ever hit New England. Only this time, everything came together just perfectly for it to morph into a monster.

Top Photo: Downed Trees Hang From Power Lines Following the Bomb Cyclone
Falmouth, Massachusetts – October, 2021

The post A Meteorological Analysis of Massachusetts’ Stunning Bomb Cyclone Damage 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.

So has the Delta wave peaked? Because the United States is so big and diverse, the best answer I can give you right now, unfortunately is “it’s complicated.” The good news is that things are looking better than they were just a month ago…depending on where you are.

Current Overview

The COVID-19 Delta Variant continues to absolutely rip through states with low vaccination rates. The summer surge that originally started in Missouri and Arkansas quickly spread across Louisiana, Texas, Mississippi, Alabama, Georgia, and Florida. That epicenter has now starting to drift north into the Tennessee and Ohio Valleys.

Thankfully, case loads are starting to come down in some areas. However, case loads continue to rise across far too much of the country. We’ve still got a long ways to go before the pandemic is over.

The Summer Delta Wave has Peaked in the Southeast, but the Wave is Far From Over

After the relentless surge this summer, the delta wave has peaked in the southeast. New daily case counts across the majority of counties in Arkansas, Mississippi, Georgia, and Florida are now falling.

Unfortunately, this map gives me cause for concern. Even though I am cautiously optimistic the surge has peaked, plenty of red flags remain. An alarming number of counties are still showing an increase in cases over the past two weeks. This spread is still raging in Texas, Oklahoma, Louisiana, Alabama, Tennessee, and the Carolinas. These areas still have the lowest vaccination rates in the country. Therefore, there remains plenty of vulnerable population still left for the COVID-19 Delta Variant to infect.

It’s a cruel reminder of just how vicious the Delta Variant is, and that a decline in cases can turn around and start surging again anytime. Both the Delta wave and the COVID-19 pandemic are far from over.

The Delta Wave is Rapidly Spreading North and West

The southeast’s epicenter has spread north. Kentucky, Tennessee, and West Virginia are all on fire right now. Same with the southern parts of Ohio, Indiana and Illinois. Further west, Wyoming, Idaho, and the eastern parts of Washington and Oregon are experiencing rapid COVID-19 spread and extreme risk levels on Matt’s Risk Index.

Interestingly, the western epicenter is getting slammed on both sides. A cluster of Delta cases that started in northern California and southwest Oregon has been swiftly sweeping in from the southwest. On the other side, the Sturgis Motorcycle Rally seeded another superspreader cluster of the Delta Variant. That outbreak has been pushing into the same region from the east. That cluster has also rapidly spread south. It merged with the remains of the Missouri and Arkansas cluster from earlier in the summer. It has since spread across Kansas, Oklahoma, and Texas.

Finally, don’t forget about our friends in Alaska and Hawaii. The COVID-19 Delta Variant has been ravaging both states, with both cases and hospitalizations at all-time pandemic highs. While new daily cases appear to have peaked in Hawaii on 2 September, they are still rapidly rising in Alaska. Again, remember to take these statistics with caution. New case counts can quickly turn around and start rising again without warning.

If You’re In the Northeast, Don’t Rest on Your Laurels. The Delta Wave is Still Coming. It Just Won’t Be as Bad.

It’s easy to look at the lower COVID-19 case rates in the northeast and attribute it to the region’s high vaccination rate. Indeed, you should. A 60 to 70 percent vaccination rate is responsible for the northeast largely avoiding the ongoing Delta Wave.

However, there is still one big wild card in the northeast. It’s still far too early to establish the effect reopening schools has had on the ongoing Delta wave. Most schools in the northeast do not start until after Labor Day. As a result, their first day of school would have fallen during the week of 6 September this year.

Keep in mind that many K-12 students are not yet eligible for COVID-19 vaccines. Since it takes 10 to 14 days for COVID-19 transmission to show up in test results, we will not know the full effect of getting back in the classroom in the northeast for at least a couple more weeks. Additionally, cases are expected to increase as colder weather drives people back indoors as we go throughout the fall.

Thankfully, the northeast has the highest vaccination rate in the country. Any additional surge from the current Delta wave should be mild and peak quickly compared to the rest of the country. Yes, COVID-19 is absolutely ripping through schools in certain parts of the United States. However, unvaccinated adults remain the primary spreaders of the current Delta surge.

Once Again, Look to Australia for an Ominous Warning for the Winter

Australia’s location in the Southern Hemisphere has now twice given Northern Hemisphere countries a sneak preview of what could be in store for their winter. Indeed, Australia’s 2020 winter surge began in late June in the State of Victoria. That surge sent Melbourne into lockdown until the wave finally subsided in late September. The United States ignored that stark warning. The result was predictable: a relentless seven-month surge that peaked at nearly 300,000 cases per day.

Those same warning signs are now flashing once again. The Delta Variant has shattered COVID-19 records across Australia as winter now mercifully turns into spring down under. This year, Australia’s winter wave began around 10 July. It has since surged to more than four times the 2020 peak, thanks to the Delta Variant. As a result, Prime Minister Scott Morrison recently announced that Australia will shift from its zero-COVID strategy. Instead, it will learn to live with the virus as vaccinations ramp up over the next few months. If its current vaccination rate holds, Australia will vaccinate 70% of its eligible population by mid-October.

Winter Outlook for the United States

So what does this mean for the United States? At the very least, it all but guarantees a fifth wave as people gather indoors this fall and travel for the holidays. Will it be worse than last winter? We don’t know yet. It all boils down to the tug-of-war between the vaccines and the highly contagious Delta Variant. Whichever one wins will drive the course of the fifth wave. About 55% of the US population is fully vaccinated. At this point, it’s really a flip of a coin as to which way it goes.

One important thing to note is that the US is currently more vaccinated (55%) than Australia was (36%) heading into its winter back in April and May. However, Americans have a much higher resistance to COVID-19 restrictions and vaccines than Australia does, which will likely nullify that advantage.

Conclusion

I’m cautiously optimistic that, with a few exceptions, the Delta wave has reached its peak in the United States. However, it opens up a bigger question. How quickly will the case counts drop before the inevitable fifth wave sets in? And what happens once that fifth wave sets in? Only time will tell. But I’ll leave you with this. Have a look at the near perfect inverse relationship between vaccination rates and Matt’s Risk Index.

Many infectious disease experts said that the Delta Variant would set the end date of the pandemic back one year. Hopefully through vaccinations and infections, we can start approaching herd immunity be the Spring of 2022. In the meantime, there are a few promising signs. First, there are a growing number of countries that have had a major Delta Wave. Many of those countries have kept case loads down following those waves.

Second, for as contagious and nasty as the Delta Variant is, that may actually be working for us. Further variants have not been able to rapidly spread because Delta is so dominant. That doesn’t necessarily mean they can’t but if Delta remains dominant, it should be easier for us to corral it. The United States can still avoid the misery and disaster of last winter. But that window to avoid it is rapidly closing on us. Let’s all do our part to put this nightmare behind us once and for all.

Top Photo: Red Rock Backcountry
Kaibab National Forest, Arizona – July, 2016

The post Is the United States Mercifully Past the Delta Wave Peak? appeared first on Matthew Gove Blog.