Big Picture Overview

Before we dive into the models, I always like to look at the big picture of the upper atmosphere across the United States. That way, we know where steering currents are. Additionally, we’ll also be able to know whether there’s anything in the upper atmosphere that could strengthen or weaken Ida as it churns towards the Gulf Coast.

The main thing that stands out to me on this map is the presence of what is largely zonal flow. In other words, the jet stream is fairly straight, running right along the US-Canada border. It’s a long way from the Gulf Coast, so it likely will not affect Ida the way it did Henri last weekend. Additionally, there is an area of high pressure centered over the Carolinas that is acting to steer Ida from Cuba straight up into Louisiana.

Furthermore, it means there is little to no shear over the southern United States and northern Mexico. Coupled with the very warm waters over the Gulf of Mexico, we expect Ida will rapidly intensify as it clears Cuba and the Yucatán.

Current Watches and Warnings for Ida on the Gulf Coast

As of the 1 PM CDT advisory on Friday, Ida is officially a hurricane. Earlier in the day, the National Hurricane Center issued watches and warnings for the Gulf Coast.

Hurricane Warnings are currently in effect from:

• Vermillion Bay, Louisiana to the Mississippi/Alabama state line, including all of Lake Pontchartrain

Topical Storm Warnings are currently in effect fom

• Vermillion Bay, Louisiana to Cameron, Louisiana
• The Louisiana/Mississippi state line to the Mississippi/Alabama state line

Tropical Storm Watches are currently in effect from

• The Mississippi/Alabama state line to Orange Beach, Alabama, including all of Mobile Bay

In addition, Storm Surge Warnings are in effect from the Texas/Louisiana state line all the way to the Florida/Alabama state line. Storm surge is always a problem with Gulf of Mexico hurricanes because the water has nowhere to go but inland. On its current track, storm surge is expected to peak between 10 and 15 feet between Morgan City, LA and Lake Pontchartrain. That’s plenty high enough to cause serious flooding problems. If you live in southeastern Louisiana and are outside of the levee system, I would be getting out of there. Right now.

Model Overview

Unlike Henri, there hasn’t been much deviation in the models over the past 48 hours. They are all in lockstep with each other. As a result, we can make forecasts with a high degree of confidence. Also, please remember not to focus on one particular outcome or solution when you look at model output. Instead, look for patterns. Where do they agree? Where do they disagree? If they disagree, why do they disagree? Are there any anomalous runs that should be immediately discounted? Models that have been consistently accurate that are in agreement are the ones you want to focus on.

The GFS (American) Model

The GFS absolutely nailed its prediction of Hurricane Henri last weekend, unlike the other models that drifted west prior to landfall. Because it did such a good job, we’ll again use it as our basis for forecasting Ida’s approach to the Gulf coast. This morning’s GFS runs remain consistent with both Friday’s and Thursday’s runs.

The ECMWF (European) Model

The ECMWF remains largely in agreement with the GFS for both strength and track. However, it does slow Ida down prior to landfall, bringing it ashore Sunday evening instead of midday Sunday. Thankfully, if that does verify, a lot of Ida will be over land when the slow down happens. You can’t rule out any significant additional strengthening, but it’s unlikely.

The UKMET (United Kingdom) Model

On Friday, the UKMET brings Ida noticeably further west than either the GFS or the ECMWF. Interestingly, the UKMET strongly favored a more westward track with Henri as well before coming into agreement with the other models within 24 hours of landfall. It appears to be doing the same thing with Ida. Based on its behavior with Henri, we’ll definitely need to take that into account when we make our official forecast.

The UKMET has also slightly reduced Ida’s strength at landfall, from 105 knots down to 96 knots. Keep in mind, 96 knot wind speeds still constitute a Category 3 Major Hurricane. However, I believe that the reduction in strength is a result of the shift in track back to the east. It is not due to any atmospheric conditions that would hinder strengthening. The more easterly track means the less time Ida spends over the warm, open waters of the Gulf of Mexico. Therefore, it will not be as strong when it makes landfall.

The GDPS (Canadian) Model

The GDPS really stands out as an outlier for Ida. It has Ida making landfall further east and as a much weaker storm than any of the other three models. It also predicts that Ida will be moving faster than the other models, making landfall on Sunday morning.

Interestingly, the GDPS has been very consistent with its previous runs on both Friday and Thursday. However, the track is starting to shift back to the west this morning, which closer aligns it with the other three models. This mornings GDPS runs also show stronger winds, but they’re still far less than the other three models. Coupled with how well it performed with Henri, we certainly cannot rule out its forecast, but my initial gut feeling is that we’ll have to give it less weight than the other three models.

Model Summary for Ida on the Gulf Coast

This morning’s model are all in close agreement with each other. Unlike Henri, model runs have also been very consistent over the past few days. As a result, we’ll be able to give each model close to equal weight, with the possible exception of the GDPS. We can also make our forecast with a high degree of confidence.

Our Official Forecast for the Impact of Ida on the Gulf Coast

With the models largely in close agreement, we’ll weigh the GFS, ECMWF, and the UKMET essentially the same. However, I believe we can completely disregard the strength forecast of the GDPS model. I lived in Tampa, Florida for six years and watched plenty of Gulf of Mexico hurricanes over those years.

Over the past 20 years, do you know how many hurricanes have emerged in the central Gulf just north of Cuba and the Yucatán in late August and early September and did not rapidly intensify? Aside from a small handful of poorly organized tropical depressions and weak tropical storms, essentially none. That’s why I believe there is basically zero chance of Ida being a Category 1 or weak Category 2 hurricane when it hits the Gulf Coast as the GDPS says.

For the track forecast, I’m inclined to give a bit less weight to the UKMET’s westerly track, especially now that it’s shifting back to the east. Last weekend, it heavily favored a westerly track for Henri before shifting back east in the 24 hours leading up to landfall. It is doing that again with Ida.

With the cutoff for a major hurricane being 96 knots or 111 mph, we can confidently say that Ida will be a major hurricane when it slams into the Gulf Coast in Louisiana.

Post Landfall

After landfall, Ida will continue to post a significant risk of both coastal and inland flooding. Parts of southeastern Louisiana could see 10 to 20 inches of rain. Further north, the highest risk of flooding remains across all of southern and central Mississippi and northern Alabama. Stream and creek flooding is also possible across much of Tennessee.

The National Hurricane Center Forecast

Because the models are so tightly in agreement, our forecast is nearly identical to the Hurricane Center’s official cone. I believe Ida will follow the center of the Hurricane Center’s cone, unlike at certain times last weekend.

Conclusion

Ida is a serious and dangerous storm. If you’re on the Gulf Coast, you need to take it seriously. Please heed all mandatory evacuations and local orders. There’s usually a reason they issue them. If you need to evacuate, you need to be getting out right now.

Ida will likely be a major hurricane when it makes landfall in Louisiana. However, one bit of solace is that Ida did pass over Cuba instead of staying over the open waters between Cuba and the Yucatán. Unfortunately, that is unlikely to weaken Ida, but instead will just delay the strengthening and intensification. Stay calm, and remember: Don’t be scared. Be prepared.

The post Hurricane Ida on the Gulf Coast: Saturday Morning Outlook appeared first on Matthew Gove Blog.

As expected, the upper-level steering currents have pushed Henri back to the east. It looks like the GFS (American) model was right all along. Its consistency and history of being right with Henri is why have been saying over the past three days that Henri will track east of the center of the National Hurricane Center‘s cones.

I still expect Henri to make a left hand turn while it comes ashore, as an upper-level low tries to sling shot the hurricane into Ontario. However, as we discussed yesterday, it will quickly run into a road block from a strong high pressure over Québec, briefly stalling out before being ejected across Northern New England and up into the Canadian Maritimes.

Current Watches and Warnings

The watches and warnings for Henri remain unchanged since yesterday. Hurricane Warnings remain in effect from

• The Massachusetts/Rhode Island State Line to New Haven, CT
• Block Island
• Port Jefferson, NY to the west end of Fire Island, NY

Likewise, Tropical Storm Warnings remain in effect from

• Chatham, MA to the Massachusetts/Rhode Island State Line
• Martha’s Vineyard and Nantucket
• New Haven, CT to Port Jefferson, NY, including all of New York City
• The west end of Fire Island, NY to Point Pleasant, NJ

Storm Threats and the Timing of Each

With Henri’s imminent landfall in southern New England, we are going to shift a bit from discussing exactly where the eye will come ashore to the timing of the threats Henri will bring to the region.

Wind

Because Henri is expected to weaken so rapidly as it starts interacting with the land in southern New England, most places along the south coast will likely see the strongest winds before the storm makes landfall. Even if you remove the effect of the land, Henri is over cool waters, and will be weakening as it approaches the coast.

This timing of the strongest winds is especially true for the eastern half of the storm, primarily from Narragansett Bay to Cape Cod and the Islands. Because Henri has been pushed back tot he east, those locations will likely see stronger winds than were predicted just yesterday. Wind gusts in excess of 50 knots are all but certain, except possibly for the outer portions of Cape Cod. The potential is there to see gusts to 70 knots, but it’s far from a guarantee.

The one exception is in the area immediately around the location that the eye of Henri makes landfall. The eye is expected to pass near Block Island and the far eastern tip of Long Island before coming ashore near the Connecticut/Rhode Island state line.

Storm Surge

Because winds are out of the south (onshore) in the eastern half of the storm, Rhode Island and southeast Massachusetts will bear the brunt of the storm surge. West of Henri’s eye, winds will be out of the north (offshore), with the exception of the north coast of Long Island. However, Long Island Sound is so small compared to the open Atlantic Ocean, storm surge effects on the north shore of Long Island will be less than points further east.

To determine which locations will see the greatest impact from the storm surge, you simply need to look at where the window for the strongest winds overlaps with the timing of high tide. You’ll find the lowest impacts where the strongest winds overlap with low tide, which occurs about 6 hours before and after each high tide. Also, don’t forget that locations closer to the eye tend to see greater impacts than locations on the edge of Henri.

What Does This Table Mean?

Thankfully, there is not one location that really jumps out at me as being much higher risk for storm surge. High tide in most spots in both Narragansett Bay and Buzzards Bay will fall at the beginning of that window of maximum winds, which will help limit the impact of the storm surge. Hyannis is far enough out in the outer part of Henri’s circulation that lower wind speeds will help offset the fact that high tide falls at the end of window of maximum winds. Finally, the Connecticut locations will see most likely see offshore winds. New London may see a brief period of onshore winds this morning, but they will quickly shift as Henri approaches. I would expect to see 3 to 5 foot storm surge in all of the above locations.

Inland Flooding

Based on model guidance and early radar returns, the majority of the rain is in the western Half of Henri. As a result, the highest risk for inland flooding will be in Connecticut, western Massachusetts, southeastern New York, and northern New Jersey. Depending on where Henri stalls out to make its turn to the east, southern Vermont could be at a higher risk for flooding as well. The rest of New England is by no means out of the woods, but is at much lower risk given the sharp gradient in forecast rainfall totals. Remember, if you see flooding, Turn Around, Don’t Drown!

Power Outages

Widespread power outages should be expected across most of Connecticut, Rhode Island, Massachusetts, and Long Island. Downed trees are the #1 cause of power outages in storms like this. New England has had a lot of rain in recent weeks. As a result, the soil is quite saturated, making it easier for Henri to uproot trees. If you see downed power lines, don’t go anywhere near them! Back away and call the electric company.

Be prepared to be without power for a while if you do lose it. We’re talking days here, not hours. But thankfully, barring any unforeseen catastrophes, it shouldn’t be weeks, either, despite some of the reports you may have heard on the news. Further north, it would not surprise me at all to see isolated power outages across parts of Vermont, New Hampshire, and southern Maine.

Conclusion

Hurricane Henri has arrived in southern New England. By now, you should be hunkered down and sheltering in place until the storm passes. Henri will likely be the most significant tropical cyclone to impact southern New England since Hurricane Bob in 1991.

Once Henri makes landfall, it will rapidly weaken. It is expected to be a tropical depression by 2 AM EDT Monday, and will be just a remnant low by midday. However, that should not be taken lightly. Just because the wind diminishes, it does not mean the threat for flooding has dissipated, too. In fact, it’s quite the opposite. Be smart, stay safe, and enjoy the ride.

The post Hurricane Henri in New England: Sunday Morning Outlook appeared first on Matthew Gove Blog.

Big Picture Overview

The primary steering currents that are driving Henri have strengthened since yesterday’s discussion. The low has set up over West Virginia and continues to dig to the south and east. Similarly, the high pressure has firmly established itself over Québec.

Because air moves from areas of high pressure to areas of low, the low pressure system over West Virginia will try to suck Henri into it. You can see the effect in the upper-level steering currents, which the green arrows mark in the plot above. As a result, that low has been responsible for the drastic westward shift in Henri’s forecast track over the past 48 hours or so.

Unfortunately, it’s not as simple as Henri getting sucked into the upper-level steering currents and getting flung up into Ontario. Remember that big ridge of high pressure over Québec? That acts like a roadblock, preventing Henri from moving north and northwest once it makes landfall in southern New England. Furthermore, as the low pressure expands to the south and east as it strengthens, it will actually push Henri back to the east as it tries to pull the hurricane into it.

So what’s the end result? As Henri gets pulled into the low, it will screech to a halt once it hits the blocking effect from the high. As the low pressure system over West Virginia move east, it will eventually grab hold of Henri and eject it out over eastern Massachusetts and up into the Gulf of Maine. If you’ve looked at the 5 AM EDT outlook from the Hurricane Center this morning, that’s why there’s such a big kink in the track. We’ll come back to this shortly.

Current Watches and Warnings for Henri in New England

As of 11 AM EDT, Hurricane Warnings are now in effect from

• The Massachusetts/Rhode Island State Line to New Haven, CT
• Block Island
• Port Jefferson, NY to the west end of Fire Island, NY

Likewise, Tropical Storm Warnings are now in effect from

• Chatham, MA to the Massachusetts/Rhode Island State Line
• Martha’s Vineyard and Nantucket
• New Haven, CT to Port Jefferson, NY, including all of New York City
• The west end of Fire Island, NY to Point Pleasant, NJ

Storm Surge Warnings are also in effect for the entire south coast of New England and Long Island as Henri approaches. While the surge will not be catastrophic, it will likely flood low-lying coastal areas with 3 to 5 foot storm surge. If you live in an area that often floods, I would expect it to flood as Henri comes ashore. Please be aware there will likely be very dangerous rip currents in the warning area as well.

Model Overview

Models are finally converging on a solution after a lot of uncertainty over the past 48 hours. Remember, don’t focus on one particular outcome. Instead, you want to look for patterns. Where do they agree? Where do they disagree? If they disagree, why do they disagree? Are there any anomalous runs that should be immediately discounted? Models that have been consistently accurate that are in agreement are the ones you want to focus on.

The GFS (American) Model

The GFS has been the most stable, consistent, and accurate model over Henri’s lifecycle, so we will once again use it for the basis of our forecast. This morning’s runs remain consistent with both Friday’s and Thursday’s runs. Neither Henri’s track nor strength have changed much in the GFS runs over the past two to three days.

The ECMWF (European) Model

Over the past two or three days, the ECMWF’s strength forecast has been very much an outlier. This morning, it is finally coming into agreement with the other models. It still shows weaker wind speeds at landfall than any of the other models. However, because it is now in close agreement with the other models, we can give it much more weight when we make our official forecast.

The UKMET (United Kingdom) Model

In yesterday’s model run, the track of the UKMET was quite anomalous, tracking much further west than any of the other models. As expected, that too, has largely come back into agreement with the rest of the models this morning.

It’s also worth noting that the UKMET has Henri making landfall sooner and with slightly stronger winds than any of the other models. That’s a result of the model having Henri moving faster. In the UKMET scenario, Henri spends less time over the cooler waters off the coast of New Jersey, and thus has less time to weaken. We will need to take that into account when we make our official forecast.

The GDPS (Canadian) Model

The GDPS has been in lockstep with the GFS for days. This morning, it remains that way, bringing Henri ashore at the Connecticut/Rhode Island state line. Much like the GFS, the GDPS has been very consistent, stable, and accurate over the past few days. As a result, we can use it to make forecasts with a high degree of confidence.

Despite our confidence in the model, this individual snapshot is actually a bit misleading with regards to wind speed. You may look at this map and think, oh boy, that’s in line with the weaker winds of the ECMWF. And that’s true…to a degree. All models have Henri weakening rapidly once it makes landfall on the south coast of New England. If you look at the GDPS snapshot for six hours earlier (Sunday at 8 AM EDT), winds are much stronger.

As a result, much of southern New England and Long Island will likely experience stronger winds than the 2 PM EDT snapshot indicates as the eye of Henri approaches landfall. Additionally, a small uptick in Henri’s forward speed means that it could come ashore with much stronger winds.

Model Summary for Henri in Southern New England

This morning’s model runs are in close agreement with each other. After all of the uncertainty of the past few days, it’s about as good of an agreement as we can ask for. In our official forecast, we’ll be able to give each model close to equal weight and make our forecast with a high degree of confidence.

Our Official Forecast for the Impact of Henri in New England

With the models now aligning with each other, our forecast should be pretty easy to make. However, there is one parameter that I think we can discount. Based on over a decade of experience with working with mathematical models, my meteorological intuition is telling me that there is basically no way that Henri will have 90 mph winds at landfall like the UKMET says. I don’t want to discount its prediction fully, but we’ll give much less weight to it.

Additionally, because Henri is expected to weaken so rapidly once it makes landfall, the greatest coastal wind impacts will likely be felt prior to landfall. This is particularly true for areas in the eastern half of the storm…Rhode Island and southeastern Massachusetts, I’m looking at you here. Flooding will remain a threat until either Henri weakens sufficiently or it exits the area.

Post-Landfall

Henri will continue to be pulled into the upper-level low that currently sits over West Virginia as it approaches landfall. Interestingly, the moment that the sling-shot effect (when a mid-latitude low grabs hold of a hurricane and flings it north) occurs will be at the same time as Henri runs into the road block from the strong high over Québec.

As a result, you’ll see Henri make a sharp left (westward) turn right after landfall, which is from the low trying to fling Henri up into Ontario. It will almost immediately run into the roadblock from the high over Québec and temporarily stall out over northern Connecticut and/or western Massachusetts. As the upper-level low moves east, it will push Henri east with it, eventually sling-shotting it across southern New Hampshire and eastern Massachusetts and up into the Canadian Maritimes.

The National Hurricane Center Forecast

Our forecast remains largely in agreement with the National Hurricane Center. However, from model analysis and my meteorological intuition, I believe that Henri will track slightly east of the center of the official Hurricane Center cone. While I don’t think landfall in western Rhode Island is the most likely outcome at this time, it’s certainly possible. It’s not a major difference, but it certainly could make for an interesting ride on Block Island much of the Rhode Island coast if it verifies.

Conclusion

The picture is certainly coming into focus as Henri approaches Southern New England. It remains a flip of a coin whether Henri will be a tropical storm or a hurricane when it makes landfall, but you will likely see hurricane-force wind gusts across much of southern New England. If you’re in the path of Henri, please stay safe and enjoy the ride. Please let me know if you have any questions, and stay tuned for tomorrow morning’s update.

The post Hurricane Henri in New England: Saturday Morning Outlook appeared first on Matthew Gove Blog.

Big Picture Overview

Before we jump into models, let’s look at the big picture. Here’s what the upper-level winds across the continental US look like. Henri is currently sitting in the bight between the Bahamas and Cape Hatteras.

Looking at the map, there are a couple things that will impact Henri as it churns towards New England.

1. A large ridge of high pressure sits over Ontario and Québec
2. A mini-trough (it’s a little big to call it a shortwave) currently centered over Pittsburgh is passing through the Ohio Valley

So how will they impact Henri? Quite a bit. The high pressure system will slow Henri down as it approaches New England. The mini-trough, which is a weak area of low pressure, will pull Henri towards it. That trough is the culprit behind why the track has so drastically shifted west over the past 24 hours.

Additionally, Henri has been in a sheared environment for the past 24-36 hours. That shear has dissipated, creating very favorable conditions for Henri to strengthen as it moves north.

Current Watches and Warnings for Henri in New England

As expected, the National Hurricane Center issued Hurricane and Tropical Storm watches for southern New England and parts of New York this morning with its 5 AM EDT advisory for Henri.

Hurricane watches extend from Plymouth, MA to New Haven, CT, and include all of Cape Cod and the Islands. If you’re on Long Island, you’re under a hurricane watch if you’re east of a line from Port Jefferson to the west end of Fire Island. Tropical Storm Watches cover the rest of Long Island Sound and the south coast of Long Island. As of right now, New York City is not under any watches, but that will likely change over the next 12 to 24 hours. The same goes for Boston.

What Do The Models Predict for Henri’s Impact in New England

A good forecaster knows how to look at the models and determine which ones to give the most weight to when they make their forecast. I have my favorite collection of models I turn to when forecasting hurricanes. I want to emphasize when you look at the models, don’t focus on one particular outcome. Instead, you want to look for patterns. Where do they agree? Where do they disagree? If they disagree, why do they disagree? Are there any anomalous runs that should be immediately discounted? Models that have been consistently accurate that are in agreement are the ones you want to focus on.

The GFS (American) Model

The GFS has been the most consistently accurate model for both Hurricane Henri as well as Hurricane Grace, which is currently heading towards a second landfall on Mexico’s Gulf Coast after passing over the Yucatán. As a result, we’ll use it as the basis of our forecast to which we can compare the other models. This morning’s runs remain consistent with yesterday’s. The GFS has Henri following a very similar path to Hurricane Bob into southern New England. It makes landfall in Narragansett Bay with wind speeds of 74 knots (85 mph).

The ECMWF (European) Model

The ECMWF has been a bit of an outlier with Henri’s strength, but it has been in lockstep with the GFS for where Henri will track. While you can’t completely discount its wind speed forecast, you will notice that it is noticeably weaker than the other models. However, wind speeds in the ECMWF forecast for Henri have trended up over the past 24 hours, meaning that it will likely align with the GFS as Henri approaches New England.

The UKMET (United Kingdom) Model

While the ECMWF was an outlier for Henri’s strength, the UKMET has done the exact opposite. Its strength forecasts are in strong agreement with the GFS, but its forecast track for Henri has trended much further west than the other models. Interestingly, the UKMET is also showing signs of coming into agreement with the GFS. In its runs just yesterday, the UKMET showed landfall possibly as far west as New Jersey. This morning’s runs have pulled it much further back to the east and closer to both the GFS and the ECMWF tracks.

The GDPS (Canadian) Model

The GDPS has largely been in agreement with the GFS on both strength and track over the past 48 hours. From a forecasting standpoint, that’s a very good thing. It means we can give less weight to the ECMWF’s outlying strength and the UKMET’s outlying track. It also means that we should expect those outliers to align with the rest of the models that are in agreement within the next 36 hours or so.

Model Summary

Before we put our official forecast together, let’s stop and think about which models should get the most weight. Because the GFS (American) and GDPS (Canadian) are in close agreement, we’ll give the most weight to those. Additionally, the ECMWF’s track and the UKMET’s strength are also in close agreement with the other two models, so we can give them plenty of weight as well. As for the outliers, I don’t want to completely discount them because they are trending back towards the models that are all in agreement. If anything, the fact that they’re trending back only reinforces our confidence in the other models.

Our Official Forecast for the Impact of Henri in New England

Based on the models we looked at, along with a little intuition and gut feelings, we can make our own official forecast. Hurricane Henri making landfall is southern New England is all but guaranteed. The big questions are where will it hit, and how strong will it be. Here are our best guesses.

How Does It Compare to the National Hurricane Center’s Advisory?

If we did our forecasting correctly, our forecast should be pretty similar to the National Hurricane Center’s official forecast. Indeed, the center of the National Hurricane Center’s cone of uncertainty brings Henri ashore near the Connecticut/Rhode Island State line as a strong tropical storm.

Conclusion

Henri is a potent tropical cyclone that folks in New England should take seriously. If it makes landfall as a hurricane, it will be the first hurricane to hit New England since Hurricane Bob in 1991. Whether it can actually do that remains a flip of a coin. I’ll be posting daily updates about Henri at least until it makes landfall. Please don’t hesitate to reach out if you have any questions. Be smart and stay safe.

The post Hurricane Henri in New England: Friday Morning Outlook appeared first on Matthew Gove Blog.

In the past, we’ve looked at many different uses for Python, including how to make basic GIS maps. Unfortunately, the Basemap module is quite limiting on its own. My biggest complaint about it is actually how the maps look. It’s far too easy to make low-quality maps that look like they’re stuck in the 1980’s.

Enter Python’s GeoPandas Project

The Python Pandas library is an incredibly powerful data processing tool. Built on the popular numpy and matplotlib libraries, it’s a sleek combination of power, speed, and efficiency. You can easily perform complex data analysis in just a fraction of the time you could with Microsoft Excel or even raw Python.

GeoPandas is an add-on for Pandas that lets you plot geospatial data on a map. In addition to all of the benefits of Pandas, you can create high-quality maps that look incredible in any publication and on any medium. It supports nearly all GIS file formats, including ESRI Shapefiles. And do you know what the best part is? You don’t even need a GIS program to do it.

Today, we’re going to learn how to use GeoPandas to easily make simple, but stunning GIS maps. We’ll generate each map with less than 40 lines of code. The Python code is easy to read and understand, even for a beginner.

Getting Started with GeoPandas

Before getting started, you’ll need to install GeoPandas using either pip or anaconda. You can find the installation instructions from their website below. Please note their warning that pip may not install all of the required dependencies. In that case, you’ll have to install them manually.

All right, let’s dive in to the fun stuff. As always, you can download the full scripts from the Bitbucket repository.

Exercise #1: Display an ESRI Shapefile on a Map

Before we do any kind of number crunching and data analysis, we need to make sure we can load, read, and plot a shapefile using GeoPandas. In this example, we’ll use a shapefile of Mexican State borders, but you can use any shapefile you desire.

First, let’s import the libraries and modules we’ll be using. In addition to GeoPandas, we’ll be using matplotlib, as well as contextily. The contextily library allows us to set a modern, detailed basemap.

import geopandas
import matplotlib.pyplot as plt
import contextily as ctx

Diving into the code, we’ll first read the shapefile into pandas.

shp_path = "mexico-states/mexstates.shp"
mexico = geopandas.read_file(shp_path)

A Word on Projections

If we were to plot the map right now, we’d run into a major issue. Do you have any guesses as to what that issue might be? The boundaries in the shapefile will not align with the boundaries on the basemap because they use different coordinate reference systems (CRS’s), or projections. In that event, you’ll wind up with a map like this.

While the basemap is in the Pseudo-Mercator Projection, the shapefile is in the North American Datum, or NAD-83, projection. The European Petroleum Survey Group (EPSG) maintains a standardized database of all coordinate reference systems. Thankfully, you only need one line of code to convert the shapefile to the Pseudo-Mercator Projection. The EPSG code for the Pseudo-Mercator Projection is 3857.

mexico = mexico.to_crs(epsg=3857)

Plot the State Borders and Basemap

With both the basemap and the shapefile in the same coordinate reference system, we can plot them on the map. First, we’ll do the shapefile. We’ll pass the plot() method three parameters.

• Figure Size is 1200×800 pixels
• 50% Transparency, or alpha=0.5
• State borders should be black (edgecolor="k"). Geopandas uses matplotlib syntax to define colors.

ax = mexico.plot(figsize=(12,8), alpha=0.5, edgecolor="k")

Now, let’s use the contextily library to add the basemap. You can set the zoom level of the basemap, but I prefer to let Geopandas figure it out automatically. If you zoom in too much, you can easily crash your Python script.

ctx.add_basemap(ax)

Finally, save the figure to your hard drive.

plt.savefig("mexico-state-borders.png")

There’s still plenty of room for improvement, but our map is off to a great start!

Exercise #2: Use Layers to Map a Hurricane’s Cone of Uncertainty

Layers make GIS, graphic design, and much more incredibly powerful. In this example, let’s stack three layers. We’ll generate a map of the cone of uncertainty of a hurricane. If we can generate the plot quickly, warnings and evacuation orders can be issued, and we can save lives. We’ll look at Hurricane Dorian as it bears down on Florida and the Bahamas in 2019.

The National Hurricane Center GIS Portal

The National Hurricane Center maintains a portal of both live GIS data as well as archives dating back to 2008. While I included the Hurricane Dorian shapefiles in the Bitbucket repository, I encourage you to browse the NHC archives and run our script for other hurricanes. Their file naming system can be a bit cryptic, so you can look up advisory numbers in their graphics archive.

Using the Hurricane Dorian shapefile as an example, here’s how the filename breaks down. The filename is al052019-033_5day_pgn.shp

• al: Atlantic Hurricane
• 05: The fifth storm of the season
• 2019: The 2019 calendar year
• 033: NHC Advisory #33
• 5day: 5-Day Forecast
• pgn: The polygon of the cone of uncertainty.

The NHC also provides shapefiles for the center line and points of where the center of the hurricane is forecast to be at each subsequent advisory. We’ll use all three in this example.

Python Code

Like the Mexico map, let’s start by importing the modules and libraries we’ll be using.

import geopandas
import matplotlib.pyplot as plt
import contextily as ctx

There are three components of the cone of uncertainty: the polygon, the center line, and the points where the center of the storm is forecast at each subsequent advisory. Each has its own shapefile. We’ll use a string substitution shortcut here so we don’t have to retype the filename three times. The .format() method substitutes the parameter passed to it into the curly brackets in the filepath.

SHP_PATH = "shp/hurricane-dorian/al052019-033_5day_{}.shp"
polygon_path = SHP_PATH.format("pgn")
line_path = SHP_PATH.format("lin")
point_path = SHP_PATH.format("pts")

Now, read the three shapefiles into GeoPandas.

polygons = geopandas.read_file(polygon_path)
lines = geopandas.read_file(line_path)
points = geopandas.read_file(point_path)

For the projections, we’re going change things up slightly from the Mexico map. Look at the x and y-axis labels of the Mexico map. They’re in the units of the projection instead of latitude and longitude. Instead of using the pseudo-mercator projection let’s use the WGS-84 projection, which uses EPSG code 4326. WGS-84 uses latitude and longitude as its coordinate system, so the axis labels will be latitude and longitude.

polygons = polygons.to_crs(epsg=4326)
lines = lines.to_crs(epsg=4326)
points = points.to_crs(epsg=4326)

Layering the Shapefiles on a Single Map

Before plotting the shapefiles, think about how you may want to color them. Because we’re dealing with a Category 5 hurricane that’s an imminent threat to population centers, let’s shade the cone red. While we’re at it, let’s make the center line and points black so they stand out.

The facecolor parameter defines the color a polygon is shaded. We’ll also make the cone more transparent so you can see the basemap underneath it better. That way, there’s no doubt as to where the storm is heading.

To stack the layers on a single map, define a figure (fig) variable with the initial layer. Then reference that variable to tell GeoPandas to plot each subsequent layer on the same map (ax=fig).

fig = polygons.plot(figsize=(10,12), alpha=0.3, facecolor="r", edgecolor="k")
lines.plot(ax=fig, edgecolor="k")
points.plot(ax=fig, facecolor="k")

Since this map would be published to the public in the real world, let’s spruce it up with a title and axis labels so there are no doubts about our warnings and messaging.

plot_title = "Hurricane Dorian Advisory #33\n11 AM EDT      1 September, 2019"
fig.set_title(plot_title)
fig.set_xlabel("Longitude")
fig.set_ylabel("Latitude")

Correctly Project the Basemap into WGS-84

The last map layer is the basemap. Because the basemap is not in the WGS-84 projection by default, we’ll need to pass that as well. To avoid type-o’s, we’ll reference it from one of the shapefiles that’s already in the WGS-84 projection. We’ll also manually set the zoom level to optimize the size and placement of state and city names on the basemap.

ctx.add_basemap(fig, crs=polygons.crs.to_string(), zoom=7)

Finally, save the figure to your hard drive.

plt.savefig("hurricane-dorian-cone-33.png")

Plenty of Room for Improvement

That’s a perfectly fine map, but I believe we can do better. While the cone itself looks great, the basemap leaves a bit to be desired. City and state labels are hard to read, especially when they’re inside the cone. The basemap looks blurry, no matter the zoom level you set it too.

Additionally, the green terrain draws the eye away from the cone. In emergency situations like a major hurricane making landfall, you want to convey a bit of urgency. The red just doesn’t “pop” off the page.

So how do you fix the map to convey more urgency? Change the basemap. The terrain is too distracting. Ideally, when you look at the map, you should instantly be able to identify the map’s location using the basemap. After that, though, the basemap should “fade” into the background, allowing your reader to focus on the data. Using muted or greyscale colors on the basemap is the best way to accomplish that.

Thankfully, GeoPandas provides a good selection of basemaps. Have a look at the basemaps in that link (at the bottom of the page). Can you identify any basemaps that use muted or greyscale colors? The one that catches my eye is called “Stamen Toner Lite”.

Updating Our Python Code

Updating our script is easy. When you call the add_basemap() method, you can specify which basemap you use by passing it the source parameter.

ctx.add_basemap(fig, crs=polygons.crs.to_string(), source=ctx.providers.Stamen.TonerLite)

After running the script, the difference is striking. It’s amazing the difference just changing a few colors makes.

So how did we do? Instantly identify the location on the map? Check. Eye instantly drawn to the red? Check. The red pops off the page? You bet. For a final comparison, here’s the actual graphic from the National Hurricane Center. I’ll let you decide which map you like best.

The Hurricane Center actually provides all of the GIS data and layers to recreate their official advisory graphics. Unfortunately, that’s outside the scope of this tutorial, but we’ll create those maps in a future lesson.

Pro Tip: Use this Python script in real time this hurricane season. You just need to change the shapefile path to the live URL on the National Hurricane Center Website.

Conclusion

It wasn’t too long ago that you needed expensive GIS software to make high-quality, publication-ready figures. Thankfully, those days are forever behind us. With web-based and open-source GIS platforms coming online, geospatial data processing is not only becoming much more affordable. It’s also gotten exponentially more powerful.

This tutorial doesn’t even begin to scratch the surface of what you can do with Python GeoPandas. As a result, we’ll be exploring the GeoPandas tool much more as we go though this summer and into the fall.

In our next tutorial, we’ll be analyzing data from one of the busiest and deadly tornado season in US history. Make sure you come back later this month when that drops. In the meantime, please sign up for our email newsletter to stay on top of industry news. And if you have any questions, please get in touch or leave a comment below. See you next time.

Top Photo: A Secluded Stretch of Beach on the Intracoastal Waterway
St. Petersburg, Florida – March, 2011

The post Python GeoPandas: Easily Create Stunning Maps without a GIS Application appeared first on Matthew Gove Blog.

You can ask anyone I know, and they’ll all tell you the exact same thing: I know very little about anything that has to do with the medical field and absolutely nothing about epidemiology and disease. So how did I build a COVID-19 model that averaged getting 70-90% of its predictions correct on its May and June runs? Amazingly, I used my knowledge of meteorology to do it.

Now, I know your next question. Doctors and disease experts are constantly being criticized that their models are not accurate. How does an average Joe such as yourself get as much as 90% of your predictions correct on a single run? My response is quite simple: the models that have been under scrutiny project too far out into the future to have any kind of reliable accuracy. Those models make predictions 4 to 6 months into the future, while my model only forecasts 2 weeks and 1 month out. I’ll elaborate on this in a bit.

Before we dive in, it is long been my philosophy that modelers should stand by their model’s projections and take responsibility for them. That said, please hold me accountable for my model’s performance, which you can find on my COVID-19 Dashboard. If it stinks, please don’t hesitate to call me out on it.

1. I may not know anything about epidemiology, but I know a lot about mathematical modeling, especially in the context of meteorology.

While completing my Bachelor’s degree in math and physics, I developed a passion for numerical modeling. In classes and projects, I modeled everything from Hopf Bifurcations to Lorenz Systems to Fractals. For one of my senior seminars, I presented the efficacy of various weather models’ predictions of Hurricane Katrina.

By the time I arrived at the University of Oklahoma to continue my education in meteorology, I knew I wanted to study meteorological models extensively. During my time at OU, I worked with some of the most widely-used weather models, including the Global Forecast System (GFS), North American Mesoscale (NAM), Rapid Refresh (RAP), European Centre for Medium-Range Weather Forecasts (ECMWF), and the High Resolution Rapid Refresh (HRRR).

That knowledge forms the foundation of my COVID model. We usually don’t associate meteorology with epidemiology and disease, but it turns out that in the context of a pandemic, there is one very common thread that runs through both fields.

2. All outbreaks behave the same way, regardless if it’s a tornado outbreak or an outbreak of disease.

Back in March, we looked at modeling different types of outbreaks using Gaussian Functions, or bell curves. Remember that, in general terms, outbreaks tend to go through three phases:

1. Some kind of trigger kicks off the initial case of the outbreak, causing initial growth to accelerate rapidly, often exponentially.
2. The outbreak will at some point hit a ceiling or peak, causing new cases to level off and begin to decline.
3. Cases will decline until they run out of fuel, at which point they will cease.

Example #1: A Tornado Outbreak

For example, the phases for a tornado outbreak would be:

1. Peak heating in the afternoon maximizes extreme instability in the atmosphere. Storms begin to fire along the dryline, and rapidly grow in count and intensity.
2. As the sun goes down, instability decreases, weakening existing storms and making it more difficult for new storms to form.
3. As the evening cools, instability drops rapidly. While it may not drop to zero, it drops low enough to prevent new storms from forming. With their fuel supply cut off, any remaining storms eventually fizzle out, ending the outbreak.

Example #2: An Outbreak of Disease

Likewise, the phases for an outbreak of disease are:

1. A new highly contagious disease starts rapidly spreading through the vulnerable population, often undetected at first.
2. People begin taking precautions to protect against the spread of the disease, which slows the exponential spread. In extreme cases, the government may implement restrictions such as bans on large gatherings, business closures, mask mandates, and lockdowns.
3. To end the outbreak, one of two things happens
   • The virus can’t spread fast enough to maintain itself and fizzles out on its own.
   • The population gains immunity, either through a vaccine or through herd immunity.

You’ve probably heard people on TV talking about multiple waves of COVID-19. It turns out that all types of outbreaks can have multiple waves. One of the worst multi-wave tornado outbreaks I witnessed occurred from 18 to 21 May, 2013. You can think of each day as an individual wave of the outbreak. In total, 67 tornadoes were confirmed across four days in the southern Great Plains, including the horrific EF-5 that struck Moore, Oklahoma on 20 May.

3. Keep how far in the future you’re forecasting to a minimum

The Global Forecast System, or GFS, is an American weather model that is the base of almost every forecast in North America. It makes worldwide projections in 6-hour time blocks up to 16 days in the future. While the model is well-known for its very accurate forecasts less than 5 days into the future, it’s shocking how quickly that accuracy drops once you get beyond 5 days. Do you know how many of its forecasts are correct 10 days away? Less than 10% of them. And if you go out to the full 16 days? Forget about it.

This phenomenon is observed in all models, including our COVID-19 model. Any guesses as to why it happens? When you break any model down to its nuts and bolts, every calculation a model makes is essentially an approximation, no matter how fine the resolution is. Small errors are introduced in every calculation, which are then passed to the next calculation. Repeat the process enough times, and the errors can get big enough to send model predictions off course.

Using Weather Models as a Basis for our COVID-19 Model

I used the GFS’s 5-day accuracy threshold as a basis for our COVID-19 model. The question seems simple: How far out is the threshold where COVID-19 predictions start to rapidly decline in accuracy? After some trial and error, I discovered that our COVID-19 model could hold its accuracy up to about 2 weeks out. I decided that the model would make the following predictions.

• 2-Week Projection
  • Goal: 65% correct rate, with 80% of incorrect projections missing by 5,000 cases or less
• 1-Month Projection
  • Goal: 50% correct rate, with 50% of incorrect projections missing by 5,000 cases or less

So how has the model performed? While it performed well above expectations in May and June, here is how it’s done overall.

So why have other COVID-19 models been criticized for being wrong so often? They simply have been trying to make projections too far into the future. Many of the models cited by the media, as well as federal, state, and local governments are trying to make forecasts up to 4 to 6 months into the future. Look at the drop in accuracy in my model just going from 2 weeks to 1 month. How well do you think that’s going to work going out 4 months?

4. Past performance is the best indicator of future behavior.

You hear this said all the time on both fictional and true crime shows on TV. Investigators use this technique to profile and catch up to criminals when they’re a step behind.

It’s no different in terms of COVID-19 modeling. In fact, you can actually use it on two fronts. First, let’s look at human behavior. Remember the protests to the stay-at-home orders across the United States back in April? That was the writing on the wall for the current explosion of cases throughout the US. We just didn’t see it that way at the time.

As expected, the minute the stay-at-home orders were lifted, too many people went back to their “pre-pandemic” routine, and the rest is history. I have yet to meet anyone who doesn’t want to get back to normal, but there’s a right way and a wrong way to do it. The European Union, Australia, and New Zealand all did it right. The US, well, not so much.

Fitting the Curve of Our COVID-19 Model

On the mathematical front, implementing “past performance is the best indicator of future behavior” is as simple as best-fitting the actual data to the model data. To do that, I wrote a simple algorithm:

1. Using a piece-wise numerical integration, best-fit each day’s worth of actual data to the model’s two inputs: the R Naught value and the percent of population expected to be infected at the end of the pandemic.
2. Using the most recent 10 days best fit parameters, calculate the weighted average of those best-fit parameters to get a single value for each parameter that is input into the model. The most recent days are given the strongest weight in the calculation.

Is the best fit method perfect? No, but it works pretty darn well, especially in the short term. Best of all, it eliminates the need to make assumptions about extra parameters (a model is only as good as the assumptions it makes), which leads right to my next point.

5. K.I.S.S. – Keep It Simple, Stupid

I live by this motto. In the context of mathematical modeling, complexity is very much a double-edged sword. On one hand, if you can add complexity and at the same time get all of your assumptions and calculations right, you can greatly improve the accuracy of the model’s projections.

On the other hand, every additional piece of complexity you add to the model increases, often greatly, the risk of the model’s forecasts careening off course. Remember what I said earlier that every calculation the model makes adds to the potential for small errors to be introduced, and many small errors added together equal one big error. Every piece of complexity adds at least one additional calculation, and thus adds additional risk of sending the model’s predictions awry.

So just how much complexity is in our COVID-19 model?

6. Break the outbreak down into its nuts and bolts to better understand the mathematics.

Our COVID-19 model consists of a system of three differential equations. Come on, you can’t be serious? Yup, three pretty simple equations are all that make up the model. And you know what? Those three equations drive a lot of other COVID-19 models, too. Those three base equations make up the Susceptible – Infected – Removed, or SIR, model, and are defined as:

In laymen’s terms, these equations mean:

dS/dt: The Change in Susceptible People over Time

dS/dt models interactions between susceptible and infected people, and calculates the number of transmissions of the virus based on its transmission rate. It states that for every unit time, the susceptible population decreases by the number of susceptible people who become infected.

dI/dt: The Change in the Number of Infected Individuals over Time

dI/dt calculates the infection rate per unit time by subtracting the number of people who either recover or die from the number of susceptible people who became infected during that time period.

If the outbreak is accelerating, the number of new infections will be greater than the number of people who recover or die, so dI/dt will be positive, and the rate of infection will grow.

If the outbreak is waning, the number of new infections will be less than the number of people who recover or die, so dI/dt will be negative and the rate of infection will decline.

dR/dt: The Change in the Number of People Who Have Recovered or Died over Time

dR/dt calculates the change in the number of people who are removed from the pool of susceptible and infected population. In other words, it means they have either recovered or died. The calculation simply multiplies the recovery rate by the number of infected people.

To calculate how many people have died from the disease, simply multiply the number of “removed” population by the death rate.

The model uses Python’s Scipy package to solve the system of ordinary differential equations.

7. As uncertainty in a prediction increases, the model output must respond accordingly.

While it may not be obvious, the output of all forecasts is the expected range in which the model thinks the actual value will fall. In terms of weather forecasting, you’ve probably heard meteorologists say things like:

• Today’s high will be in the upper 70’s to low 80’s
• Winds will be out of the south at 10 to 15 mph
• There’s a 30% chance of rain this afternoon

So why do models output a range of values? There’s actually two reasons.

Reason #1: It accounts for small deviations or errors in the actual value.

Let’s consider two hypothetical wind forecasts. One says winds will be out of the southwest at 12 mph. The other says winds will be out of the southwest at 10 to 15 mph. These forecasts wouldn’t be a second thought for most people. Now, what happens when it blows 13 mph instead of 12? The first forecast is wrong, but the second one is still right. While this may not seem like a big deal in day-to-day weather forecasting, it can be the difference between life and death when you’re modeling, planning for, and responding to extreme events like natural disasters and pandemics.

Reason #2: The model will have a high likelihood of being correct even when uncertainty is high.

As I mentioned earlier, uncertainty gets larger the further out in time a model makes predictions. How do we counteract that increase in uncertainty? By simply making the range larger. There is no better real-world example of this than the “Cone of Uncertainty” that is used to forecast the track of a hurricane.

In the example hurricane forecast below, take note of how the Cone of Uncertainty starts off narrow and then gets wider as you get further out in time because uncertainty increases.

Defining a Cone of Uncertainty for Our COVID-19 Model

Now, let’s look at how this translates into COVID-19 modeling. We’ll have a look at two states: one where cases are spiking and one where cases are stable. Take a second and think about what uncertainties might be introduced to each scenario and which plot you would expect to show higher uncertainty.

Here are a few uncertainties I though up that might impact the scenario where cases are spiking:

• Will the government implement any restrictions or mandates, such as bans on large gatherings, business closures, or lockdowns to help slow the spread?
• When will these restrictions be implemented?
• Even in the absence of government action, does the general public take extra precautions, such as wearing masks and not going out as much, to protect themselves against the rapid spread of the virus? When does this start happening? How much of it is happening already?

A Graphical Look at Our COVID-19 Model’s Cone of Uncertainty

You can probably see that once you start thinking about these, it snowballs really quickly. As a result, you see huge uncertainties in COVID-19 model forecasts for states where cases are spiking, such as Florida, Texas, and Arizona, while you see much less uncertainty in states where COVID-19 has stabilized.

Let’s compare COVID-19 uncertainties for Florida, which is averaging close to 10,000 new cases per day right now, to New York, which was hit viciously hard back in March and April, but has since stabilized. The plots are from yesterday’s model run. Take note of how closer together the model output lines are on the New York plot than the Florida plot because of the lower uncertainties in the projections for New York.

Here are the same projections from yesterday’s model run in table form. Note the difference in the size of each range between the two states.

Conclusion

A mathematical modeler has a vast array of tools and tricks at their disposal. Like a carpenter, he or she must know when, where, and how to use each tool. When properly implemented, model outputs can account for high levels of uncertainty and still be correct in their projections. Just don’t take it too far. You don’t want to lose credibility, either.

Top Photo: The sparkling azure waters of the Sea of Cortez provide a stunning backdrop to the Malecón
Puerto Peñasco, Sonora, Mexico – August, 2019

The post I Built a COVID-19 Model that got 70-90% of its May and June predictions correct. I know nothing about epidemiology. Here’s How I Did It. appeared first on Matthew Gove Blog.

While the model runs this morning somewhat resemble being in agreement, especially when compared to the runs yesterday morning, the run-to-run differences are still very erratic, so we’re not quite at the stage where we can say for certain it’s going to go to this spot at that strength, but we should know a lot more in the coming days. The most recent model runs have been pushing the storm further offshore, but it’s really important, especially at this stage of the game, not to get involved in chasing the models around.

NHC Forecast: 8 AM EDT Sunday, August 24th

As for the specific models, the European and Canadian models have been much more consistent on both intensity and track from run to run than the GFS has. These models both keep the storm slightly closer to shore in their latest runs than the spaghetti plots above show. The GFS is showing the same thing it did yesterday: the evening run has the storm as a Category 2 hurricane, while the morning run has the storm as a pretty weak pile of mush (shown below). Deferring back to the much more consistent European model, my best guess would put the storm somewhere between the two.

European Model Forecast: 2 AM EDT Friday, August 29 

GFS Forecast (Morning Run): 2 AM EDT Friday, August 29

My guess is that the models will begin to converge on a better solution over the next 24-36 hours or so as they gather more data from the now-formed storm. Right now though, the storm does not appear to be a significant threat to the east coast of the US.

The post Tropical Storm Cristobal Forms Near The Bahamas appeared first on Matthew Gove Blog.

The latest model runs are still in pretty complete disagreement on both intensity and track for this storm. The general trend of the models is to take the storm northwest towards the east coast of the US and bend it back out to sea. I have seen models take it right up the US east coast, and I have seen models take it east of Bermuda, so it’s impossible to say right now exactly where it will go.

Looking at a couple model runs for the GFS should hammer home exactly how much the storm can vary from run to run. Take a look at the following plots from the GFS run from last night (Friday, August 22 at 8 PM EDT) and early this morning (Saturday, August 23 at 2 AM EDT). You are looking at the 925 mb wind plot (the winds about 1 km up) for next Saturday, August 31st.

GFS Friday Evening Run: Strong Category 1 Hurricane 

GFS Saturday Morning Run: Struggling to be a Tropical Depression

With this type of variance in just one model, it’s impossible to say right now what will happen, but I will say this. Looking at the other model runs, the potential is certainly there to get a hurricane off the east coast of the US, but because it’s an El Nino year (which helps hinder Atlantic tropical cyclone activity) and it has been a cool summer across the eastern US, leaning towards a weaker system would probably be the best bet at this time. However, there’s not much we can say for certain right now until both the tropical cyclone forms and the models begin to resemble some form of agreeing with each other.

The post Possible Tropical Storm Brewing Near Puerto Rico appeared first on Matthew Gove Blog.

So let’s have a quick look at what might be in store for Arthur. This early in the season, sea surface temperatures play a critical role in the health and lifespan of tropical cyclones since water temperatures are so cold from about Virginia northward.

Sea Surface Temperatures – Tuesday, July 2nd

Because of the cold sea surface temperatures (the blue color on the map), ranging from about 60 to 75°F, Arthur will not hold together as a hurricane if it hugs the coast on its journey northward, and will rapidly weaken north of the Outer Banks. Remember that tropical cyclones generally require sea surface temperatures above 85°F to be able to maintain themselves or intensify.

However, if Arthur remains further offshore, it may be able to tap into the Gulf Stream (the wedge of warm sea surface temperatures extending northeast from the Outer Banks). If this happens, it has a much better chance of maintaining hurricane intensity as it progresses northward. This is why the Hurricane Center maintains Arthur as a hurricane so far north. However, a slight deviation left or right would put the storm over much colder waters. Either way this is good news for the east coast.

The other important factor to consider with any tropical cyclone is wind shear. The plots below show shear and shear tendency. There does not appear to be much by the way of significant wind shear in Arthur’s forecast path right now and the tendencies are for the shear to decrease, which would favor the storm to strengthen before it gets to the cold water.

Wind Shear: Tuesday Morning, July 2nd

 

Shear Tendency: Tuesday Morning, July 2nd

I do want to note that the jet stream is forecast to be near the northeast coast of the US by the time Arthur gets up that way. There are several things that could happen as the two begin to interact. The most likely thing to happen is that Arthur will transition to extratropical and essentially become a nor’easter as it is absorbed into the jet stream. This would bring gusty winds and heavy rains to the east coast, and especially to New England. Forecast soundings from Chatham, MA are currently showing sustained winds out of the northwest at anywhere from 15 to 45 mph at Arthur’s closest pass.

GFS Surface Wind/Pressure: Saturday at 2 AM EDT 

GFS 500 mb Wind/Height: Saturday at 2 AM EDT

Right now, the only area that’s really under the gun from this storm is the Outer Banks of North Carolina, which will likely see either strong tropical storm or minimum Category 1 hurricane conditions. After that, the storm will likely move offshore, but if it deviates closer to the coast, it will be over much colder waters and will have a very hard time staying together.

The post A Quick Look at Tropical Storm Arthur appeared first on Matthew Gove Blog.

Back in May, we watched one of the most powerful tornadoes on record buzzsaw across the plains near El Reno, Oklahoma. This week, Super Typhoon Haiyan, one of the most powerful tropical cyclones on record (it may be the most powerful) slammed into the Philippines. At landfall yesterday, its estimated sustained winds were 195 mph with gusts to 235 mph. To help put that in perspective, the EF-5 tornado that struck Moore, OK back on May 20th had estimated winds of 210 mph.

Haiyan is an absolutely perfect example of what a well-organized tropical cyclone looks like. The symmetry is perfect. The eye is razor sharp and is perfectly circular. Even the storm’s anticyclonic outflow aloft is perfect. It is literally a saw blade cutting across the southwest Pacific and the South China Sea.

Haiyan Satellite on Thurs, Nov. 7, 2013 

Models are indicating that Haiyan will continue to push northwest across the South China Sea and slam into the east coast of Vietnam. Due to the orientation of the Vietnamese coastline, the storm may move close to parallel to the coast, which could lead to a larger damage swath. Flooding could become a major problem if the storm moves far enough north, as there is nowhere for the water to go if it gets up in the area near the China-Vietnam border. The good news is that the storm is expected to move over an area of both lower sea surface temperatures and higher shear, which will continue to weaken the storm. That being said, however, the storm is so strong it will not weaken very quickly and is expected to make landfall in Vietnam as a Category 3 storm, with sustained winds around 120 mph.

Now let’s change gears completely and talk about more extreme weather at the opposite end of the spectrum. From Haiyan, let’s shift about 2300 miles northwest to eastern Nepal and talk about some extreme mountain weather. During the fall, the polar jet stream shifts south as the transition from summer to winter occurs. The world’s tallest mountain range sits in a prime location to interact with the jet stream as it shifts south, which brings incredibly extreme conditions to the higher elevations.

Currently, a powerful upper-level storm system has been centered over the far western part of China, with the main branch of the 500 mb jet streaming across China, Nepal, and Tibet. That storm will reach its peak intensity sometime this weekend, with the southwesterly flow of the jet stream reachings peak strength from the northern plains of India across Nepal and into Tibet and China. One small wrench that gets thrown into forecasting for this region is that the 500 mb jet is usually found around 17,000 feet, and many of the Himalayan peaks are much higher than that, so we need to look higher in the atmosphere.

GFS 500 mb forecast for Saturday at 0600 UTC 

Conveniently, many of these powerful upper-level storm systems feature a much more powerful jet at 300 mb (it blows harder due to lower air pressure). This 300 mb jet is typically found around 30,000 feet, which matches up nearly perfectly with the highest peaks of the Himalayas. One peak that aligns nearly perfectly with the strongest area of the jet stream is Mt. Kangchenjunga, which at 28,171 feet is the third highest mountain in the world. The peak is located in the far northeastern corner of Nepal and sits right on the border with India.

If we look at the GFS forecast at 300 mb (the red x marks the location of Mt. Kangchenjunga), we can see a clear jet streak centered over Mt. Kangchenjunga. The ferocious winds of this jet streak, combined with the cold air being brought down by the upper-level low, will lead to bone crushing wind chills and incredibly extreme conditions this weekend. At the summit of Mt. Kangchenjunga, sustained winds will be out of the southwest at 140 mph, with actual temperature in the -20’s Farenheit. This combination will result in wind chills between -70 and -75°F. Thankfully, nobody lives at these extreme elevations, but you can sure see why climbers need to pick their days carefully when they attempt to summit the Himalayan Peaks.

GFS 300 mb wind forecast for Saturday at 0000 UTC 

These conditions are somewhat common on the peaks of the Himalayas as upper-level storm systems pass through. They just don’t get a lot of coverage because they are not impacting any forms of life. The only other place in the world that sees conditions similar to this are the highest mountain peaks in Alaska and the Yukon Territories. While the Rocky Mountains see plenty of their own extreme weather, they are not tall enough to pierce the main branch of the jet stream. Mountain meteorology is pretty fascinating this time of year, and I will definitely be posting more on this topic as we get into winter.

The post Extreme Weather Slams Parts of the Far East appeared first on Matthew Gove Blog.