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.

I only ran the models out until early May because we need to focus on what’s going to happen in the next two to three weeks, not what’s going to be happening several months in the future. Additionally, model forecasts get less accurate the further into the future you go. This run assumes current social distancing restrictions remain in place through mid-May and does not account for any additional surges or waves of the virus that may occur later this spring, this summer, or this fall.

Don’t forget, you can always get more information about the model and view detailed case data on my COVID-19 Dashboard. Additionally, I will write up a separate post about the mathematics, equations, and methodologies used in my coronavirus model, which I’m hoping to get posted in the next day or two. Stay tuned for additional discussions about each hot zone as more data comes in.

Alberta

Arizona

British Columbia

California

Connecticut

Florida

Georgia

Illinois

Louisiana

Maryland

Massachusetts

Michigan

New Jersey

New York

Ohio

Oklahoma

Ontario

Oregon

Pennsylvania

Québec

Rhode Island

South Dakota

Tennessee

Texas

U.S. Virgin Islands

Washington (State)

After several requests, I updated this post on 16 April, 2020 to include additional states and provinces, bringing the total to 26 plots instead of 24.

Top Photo: Palo Duro Canyon State Park – Amarillo, Texas – August, 2019

The post A Look at the Best-Fit COVID-19 Model Curves for 24 Key States and Provinces appeared first on Matthew Gove Blog.

Overview of SIR Model Output

Each city has four plots. The top row is the “working” model output, with the model curve best fit to the actual data. The bottom row is an experimental model output showing the effect of social distancing. In the “working” model runs on the top row, there are 5 lines on each plot. The middle line is the R Naught value that was reverse-engineered by fitting the model output to the actual data, and there are two lines on each side of the best-fit line showing different R Naught values in steps of 0.2.

Note: The y-axis on some of the experimental social distancing plots showing the total case count (bottom right plot for each city) is mislabeled. It should read “Total Cases”, not “Number of Infected”.

Finally, don’t forget that the plots below assume the R Naught values and the amount of social distancing remains constant throughout the entire time series. In reality, additional social distancing restrictions will dampen the curve and shift it to the right, while removing social distancing restrictions will cause the curve to accelerate and shift to the left.

Confidence in SIR Model Predictions

My confidence level in the “working”/top row model outputs is as follows:

• Predicting the apex of the outbreak: medium-high to high. The curves should at least be “in the ballpark.”
• Predicting the total number of cases: low to very low. With how fast things are changing right now and how fast new data is coming in, we just don’t know at this point. My gut feeling is that the case count projections in these model runs are likely high overall, but from a public health perspective, I would much rather have the model overestimate case counts than underestimate them.

Plots are in alphabetical order by city, with a table of additional cities at the bottom. Click on any plot to view it full size.

Boston, Massachusetts

Chicago, Illinois

Detroit, Michigan

Los Angeles, California

Montréal, Québec

New Orleans, Louisiana

New York, New York

Oklahoma City, Oklahoma

Ottawa, Ontario

Portland, Oregon

Phoenix, Arizona

San Francisco, California

Tampa, Florida

Toronto, Ontario

SIR Model Outputs for Additional Cities

Please note that this table contains outputs of just this single model run and does not necessarily reflect what my actual predictions are. I will be putting this table on my COVID-19 Pandemic Tracker later this week and regularly updating it there.

Data points I’m skeptical of in this output (with some comments):

• Chicago, IL: Case count is likely overestimated. I’m not sure why, but the most likely reason is good social distancing.
• Los Angeles, CA: Case count is likely overestimated due to California being better at social distancing than what was input into the model
• Seattle, WA: Peak date is incorrect due to the State of Washington’s 100th case occurring before John’s Hopkins began breaking down data by state.
• Washington, DC: Not enough data to accurately fit the curve
• Winnipeg, MB: Not enough data to accurately fit the curve

The post Complete Revised SIR Model Forecasts (8 April): USA and Canada appeared first on Matthew Gove Blog.

Disclaimer: As I’ve mentioned many times before, I claim zero knowledge about anything in the medical field, including infectious disease. These analyses are based solely on my expertise in mathematical modeling.

An Important Note on Making Predictions From Mathematical Models

One of the most common uses of mathematical models to make predictions in day-to-day life is weather forecasting. Luckily, meteorology happens to be a field in which I do have extensive knowledge. When forecasting any type of weather, it’s important to look at multiple models. Do not make any predictions based solely on any one model. In an ideal world, the models are all in agreement. In reality, you need to use your knowledge and expertise to gauge how much weight to give each model when you make your prediction. The same principle applies to the current pandemic.

Additionally, from a purely mathematical standpoint, all outbreaks follow roughly the same bell-shaped curve when plotted over time. Different types of outbreaks can use vastly different equations in their models. While I do not have much experience with outbreaks of infectious disease, I have worked with and extensively studied outbreaks of tornadoes and severe weather during my time as a meteorology student at the University of Oklahoma. We can apply that knowledge when making predictions about the current COVID-19 outbreak.

Adding a Social Distancing Factor to the SIR Model

I have been running a lot of possible scenarios in Python using the Susceptible – Infected – Removed, or SIR model. I recenly covered the mathematics of the model, so I’m not going to state all of that again. You just need to know that the model is based on a system of three ordinary differential equations.

With the help of my uncle, who also has a lot of experience with mathematical models, we set out to add a parameter, which we’ll call f, to the SIR model. f represents the percentage of the population who are strictly complying with social distancing guidelines.

Accounting for Social Distancing in the Model

To incorporate f into the SIR model, we need to look at the terms that are modeling the contacts between the infected and susceptible people. The rate of contact would be reduced by (1 – f)² because the fraction of both the susceptible and infected population interacting would be reduced by a factor of (1 – f). The removed (recovered/dead) population would not be affected by the social distancing parameter. Therefore, the differential equations for the susceptible and infected population would then become:

We are still working on fine-tuning these equations, but this is what we’re going with for now.

Estimating a Baseline Infection Rate

The final piece of the puzzle is to set the baseline for beta. That baseline is the infection rate assuming 0% social distancing compliance. In other words, beta is the number of infecting contacts per day between an infected and susceptible person. Beta is also part of the equation that calculates R Naught.

Since I only plan to model the effect of social distancing at the city level, I simply scaled beta based on the city’s population density to set the baseline infection rate. Cities with higher population densities would have a higher rate of infection than those with lower population densities. COVID-19 is spreading much faster in New York or Chicago than it is in Topeka, Kansas or Bismarck, North Dakota.

The scaled baseline for the infection rate at 0% social distancing compliance is simply:

where d is the population density in units of people per square kilometer. The “plus one” is because some small cities in Alaska have population densities of less than 10 people per square kilometer. Low population densities result in very small and unrealistic beta and R Naught values. It’s not an exact science, so the equation will be tweaked as we go along.

Limitations of the SIR Model

Not surprisingly, mathematical models are only as good as the assumptions that they make. The COVID-19 models make plenty of them. Some of the assumptions and limitations the SIR model (plus our revisions) make include, but are not limited to:

• Uniform population density across the entity being modeled. This means the SIR model predicts outbreaks in individual cities much more accurately than it does in states and countries.
• Population remains constant – i.e. people are not being born or dying of causes other than the disease. The SIR model has terms to account for birth and death rates, but its effect on the model is negligible. I don’t have birth and death rates in my database, so we will simply set those terms to zero.
• R Naught and the percentage of people social distancing are constant throughout the outbreak of the disease. This can cause both the peak of the outbreak and the date of that peak to be either over- or under-estimated.

Outputs From Our Revised SIR Model

We now have enough data from the United States, Canada, and Australia. Let’s fit the model output to each state’s existing curve. We can then use that output to model major cities in each state.

I’ve included model outputs from a few cities in the United States, and will include more in a separate post. When looking at the model outputs, you want to focus on general trends. Do not look at specific dates or case counts. Instead, focus on something happening in periods such as “mid-April” or “early May” instead of “April 15th” or “May 1st”.

Our Revised SIR Model’s Assumptions

Don’t forget that our revised SIR model assumes a constant level of social distancing. We are just starting to see the effects of stricter social distancing show up in the data set. As a result, the model will likely underestimate the amount of social distancing being done. Therefore, it will overestimate the number of cases. I’ve included curves for several R Naught values both less than and greater than the R Naught values that I reverse engineered by fitting the model curve to the existing data.

The revised SIR model seems to do a decent job predicting the apex of the outbreak. I have checked the apexes on the first plot you see below in each city against what health officials in Arizona, New York, Michigan, Massachusetts, and Louisiana have said. They’re at least “in the ballpark” with what health officials are saying. The social distancing plot is primarily to drive home the importance of social distancing and flattening the curve during the pandemic.

Speaking of social distancing, don’t forget about assumptions. The plots below assume the amount of social distancing remains constant throughout the entire time series. In reality, additional social distancing restrictions will dampen the curve and shift it to the right. Removing social distancing restrictions will cause the curve to accelerate and shift to the left.

Confidence Levels in the Model

My confidence level in these model outputs is as follows:

• Predicting the apex of the outbreak: medium-high to high. The curves should at least be “in the ballpark.”
• Predicting the total number of cases: low to very low. With how fast things are changing right now and how fast new data is coming in, we just don’t know at this point. My gut feeling is that the case count projections in these model runs may be high. From a public health perspective, I would much rather have the model overestimate case counts than underestimate them.

With regards to the projected case counts, look at the how the University of Washington model has been all over the place over the past week. That is how the model tells us it doesn’t know. We can’t make any meaningful forecasts from it at least until it starts to stabilize from day to day. This would be like trying to make a forecast and issue warnings for a hurricane using a model that keeps flip-flopping between the hurricane making landfall in Houston and making landfall in Miami.

View the full output from our revised SIR model for most major cities in the United States and Canada.

New York, NY

New Orleans, LA

Detroit, MI

Plots for additional cities will be posted in a separate post.

Looking at the University of Washington Model

A model developed by the University of Washington has made headlines over the past several weeks. It is one of the most trusted models out there for predicting the pandemic (at least in the United States). However, I worry that too many people are focusing only on this one model and are not considering what other models are forecasting.

Like every model, the University of Washington model makes its fair share of assumptions:

• The model accounts for social distancing restrictions, such as closing businesses and schools and issuing stay-at-home orders. However, it does not address how social distancing restrictions are lifted. It assumes that Americans continue to practice full social distancing practices through at least sometime between June 1 and August 1.
• The model does not account for any resurgences or additional waves that may occur later this summer or fall.
• The model does not account for hospital staff shortages due to being out sick, running out of supplies, or any other reason.
• It is also important to note that the University of Washington model forecasts deaths and hospital usage. It does not predict the total number of cases.

I believe that this is probably the most accurate model out there right now. My gut instinct is that because of the assumptions, it may slightly underestimate both the hospital needs and death counts.

Also note that the plots below are for hospital needs. It looks like 20 to 25 percent of all COVID-19 cases require hospitalization. Multiply the y-axis values by 4 to 5 to get the approximate number of cases the model is predicting.

My Predictions

Because of the fluidity of the situation and how fast everything seems to be changing, focusing my predictions on exact numbers will only contribute to the spread of misinformation. Instead, I will be focused on general trends, as I discussed earlier. These predictions are based on the two models I discussed in this post, as well as several others.

Expectations for the United States and Canada

• Nearly every city and state in the United States will reach its apex within the next 4 to 5 weeks. I’m still working on projections for Canada.
• At the end of the pandemic, total case counts in every major city in both the US and Canada will be in the tens of thousands, if not more. New York City is currently just shy of 80,000 cases.
• Total or cumulative case counts for the both the United States and Canada will be in the millions. I think Canada can still keep their case count below 1 million with proper social distancing. That ship has long since sailed for the US.
  • For perspective, let’s use the Centers for Disease Control’s recent prediction of 80,000 deaths in the United States. Using the current death rate based on data from the past week, it works out to about 2.5 to 3.5 million total cases in the US.
  • In Canada, the Government of Ontario recently released a study projecting at least 15,000 deaths in the province. Using the same calculation as above, that works out to 500,000 to 750,000 cases in Ontario alone.
• New cases will be reported in both the US and Canada every day through at least late June/early July. Expect government mandated social distancing protocols to remain in effect through at least the end of May in most states/provinces. These restrictions include stay-at-home orders, closures of restaurants, schools, and other places of gathering, etc.
• The outbreak in Canada will have a flatter curve and grow slower than the outbreak in the United States. As a result, Canada will peak later than the outbreak in the US.
• Don’t forget that like weather forecasting, the models get less accurate the further into the future you get. I will be posting routine updates as new case data comes in.

One Final Thought

When a hurricane makes landfall, it ravages the coast with the eye wall – the most powerful part of the storm – before the calm of the eye passes over. Inside the eye, winds drop down close to zero, the sun comes out, and it looks like a beautiful day. In the first half of the 20th century, before weather radar became mainstream, coastal residents commonly mistook being inside the eye to mean the storm was over. They then went back to their normal lives. As a result, they were then blindsided when the back side of the eye wall came ripping ashore like a buzzsaw. They didn’t realize that they still had the whole second half of the hurricane to go. Such a mistake undoubtedly caused numerous injuries and deaths that should have been preventable.

Don’t Repeat Past Mistakes

I am a bit concerned that a lot of people will be making the same mistake by going back to their normal lives once their city and state hits the apex of the pandemic. Like the eye of a hurricane, the apex is only the half-way point. You still need to come down the back side of the curve. Look at any of the plots above. In fact, the apex is likely not even the half-way point. It almost always takes longer to come down the back of the curve than it does to go up the front of it. Expect the Stay at Home orders to remain in place for at least 4 to 6 weeks after your state passes its apex. We will likely be into June before we can even begin to think about going back to our normal lives.

Look, I understand the cabin fever is real. You want to go see your friends and loved ones. Be patient and be smart. We don’t need any more people dying from this virus than already are.

The post Latest SIR Model Outlooks: COVID-19 Pandemic appeared first on Matthew Gove Blog.

Northern Gulf Coast

One of the trickiest aspect of a winter storm forecast this far south is to figure out just exactly how the warm sea surface temperatures will affect the coastal temperatures and precipitation types. Water temperatures along the northern Gulf Coast are in the 50s, which is plenty warm enough to impact the precipitation type at the coast. Models are currently showing that the snow/freezing rain line will be well north of the Gulf Coast on Tuesday evening, situated roughly along a line from Lake Charles, LA to Hattiesburg, MS to Columbus, GA. With the timing of the precipitation, I would expect coastal areas to see primarily sleet and freezing rain, since the precipitation should end at the coast before the mid levels of the atmosphere get cold enough to change the precipitation over to snow.

The coastal locales that are most likely to see snow are the areas between New Orleans, LA and Pensacola, FL. Atmospheric profiles may get cold enough for a change over to snow to occur shortly before the precipitation ends, so any accumulations will be minimal, if they occur at all. Coastal snow could fall as far east as Panama City, FL. The window for snow at the coast appears to be between 9 PM CST Tuesday and 3 AM CST Wednesday.

Further inland, areas along and north of Interstate 10 are much more likely to see accumulating snow. Atmospheric profiles there will be cold enough for it to snow, and with a driving north wind, those areas will not be affected by thermal radiation coming off the warm waters of the Gulf. Models are currently show a 1 to 3 inch swath of snow accumulations falling between Slidell, LA and Pensacola, FL. That swath is surrounded by an area of up to 1 inch accumulations between Baton Rouge, LA and Fort Walton Beach, FL, extending up into southeast Alabama and southwest Georgia. Snow could fall as far north as a line from Houston, TX to Jackson, MS to Birmingham, AL. Temperatures will quickly warm later in the week, so any snow and ice accumulations will not last very long.

Georgia and The Carolinas

The North and South Carolina coasts appear to be on tap to absorb the brunt of this storm. Atmospheric profiles along and north of a line from Savannah, GA to Panama City, FL should be cold enough for precipitation to fall as snow. The winter storm will have plenty of moisture available after tapping into the Gulf of Mexico moisture, but it will also have an ample supply of moisture available off the southeast coast, especially with the Gulf Stream so close by.

Soundings along the Carolina coasts are textbook winter weather soundings. There will likely be a warm layer around 4,000 feet over the South Carolina coast when the precipitation first starts falling, so it may start as sleet and freezing rain before changing over to snow. Over North Carolina, however, all layers will be below freezing from the outset, so it may start as a wintry mix before changing over to snow. Any sleet and freezing rain that falls will reduce snow totals, which could greatly affect snowfall totals in the Carolinas.

Models are currently showing that the coastal snow will fall between Savannah, GA and the southern tip of the Delmarva. Unlike the northern Gulf Coast, the duration of the precipitation will be much longer on the Carolina coast. Precipitation should really start cranking up around 7 or 8 AM EST on Tuesday and should last for about 24 hours. Models are showing impressive snowfall totals for the coastal areas, but I think they may be a little agressive. Some areas between Wilmington, NC and the Pamlico River could see up to a foot of snow, but I would expect to see most totals in the 4 to 8 inch range. Snowfall totals between 2 and 8 inches are possible between Charleston, SC and the North Carolina/Virginia border.

Further, inland, areas east of Interstate 95 between Richmond, VA and the South Carolina/Georgia border could see 2 to 6 inches, and 1 to 2 inch totals are possible across much of the remaining areas of North Carolina, South Carolina, and northeast Georgia. Just remember that there are a lot of variables in play for these complex forecasts, so the duration of any freezing rain and sleet that falls will have a significant impact on snowfall totals. This is one of those storms that could be a big snowstorm or a big ice storm, so stay tuned to your local news or weather bureau for the latest information.

The post Rare Winter Storm to Impact the Gulf Coast and Deep South appeared first on Matthew Gove Blog.