More on the Storm: The Key Atmospheric Players
Player #1: Surface Cold Front
As I mentioned above there is a cold frontal passage expected Sunday across Long Island. After the front passes, colder air will come into the area. This can be seen on the left panel of the image below of the forecast of basic weather from the Weather Prediction Center with the blue arrow pointing to the cold front that is just situated over Long Island around 7 AM EST and indicating the direction of the cold air moving in. Along this surface frontal boundary, weak low pressure systems are forecast to form and move along the front to the northeast. This can be seen on the right panel of the image below. The green oval is outlining the expected precipitation with the system and the pink arrow is pointing to the pink dashed line that is showing the separation between rain and snow. The cold air mass behind the cold front will likely keep our precipitation falling as all snow. For more information about precipitation type, check out the previous blog post, "Rain, Sleet or Snow? Wouldn't You Just Love to Know?". Player #1 is a surface cold front and one cool thing to note about the atmosphere is that when you have a surface cold front like that, there is a jet of high winds in the upper atmosphere, which brings us to Player #2.
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| NOAA's Weather Prediction Center Surface Forecasts for 7 AM EST Sunday (left) and 7 AM EST Monday (right) showing the forecast positions of the cold front and surface low pressure systems as well as the swath of precipitation. |
Player #2: Two Pieces of Upper Atmospheric Energy
When atmospheric scientists think about the atmosphere, they love to think about it in layers just like those of a cake, onion or ogre. Or parfaits. The lower levels of the atmosphere are those found near the surface such as below the base of clouds where precipitation falls into. The middle layers of the atmosphere are generally within the depth of cloud and there's a lot of importance with understanding vertical motions moving air upward or downward within this section. The upper levels are the air at the top of the troposphere near the tropopause which separates the troposphere (the layer within which weather happens) from the stratosphere (the layer within which it's so dry so there's no weather and red bull sponsors jumps from). Since air is a fluid, what happens at the bottom affects the top and vice versa. If you push water away from you while you're swimming in the ocean off of Jones Beach, water will fill in that gap, right? The winds in the upper levels of the atmosphere can act to drive vertical atmospheric motions of air which cause it to either sink or rise from below. That's why it's so important for weather balloons to reach the upper portions of the atmosphere before they burst.
The upper atmospheric energy that is important for the March 2-3rd snow event are found in two parts, the northern stream (jet stream found closer to the North Pole and its meanderings are responsible for our spells of the polar vortex cold of doom) and the southern stream (jet stream found closer to the equator). These massive circulations of cores of fast winds sometimes meander away from each other and sometimes collide. During certain times of the year they are even morphed into the same, singular jet stream. The northern stream is stronger and it is its collision with the southern stream jet stream that will allow a stronger jet core of winds of 180 mph at an elevation of 8 km/~5 miles. The position of this core will tell where the areas of most intense rising motion will occur. The schematic below highlights that in the lower-right and upper-left quadrant of a jet core is where you'll have the forcing for rising motion from lower levels of the atmosphere and likely precipitation development or enhancement if there's enough moisture present in the air (as indicated by stars on the schematic). In our case, the snowfall will likely be heaviest when a jet core passes overhead and aligns one of its favorable quadrants near us with the moisture brought into the region from the southern stream.
A Background of Numerical Weather Prediction
I'm going to skip the detailed history lesson because I still have to run out and get my bread and milk but the atmosphere is a fluid and as such, great minds have been able to describe its motions and behavior with mathematical equations. Every beautiful cloud or gust of wind can be written down as part of an equation. There are several main variables that consist of well-known quantities such as wind, pressure, temperature and humidity. What's something great about math? It can be used as a tool to take those equations and by using some calculus, calculating what certain variables would be at some point later in time. That's a forecast! Although that sounds foolproof because math is clearly infallible and no one would ever mess with it, these equations had to be simplified otherwise it would take a computer a horrifically long time to get at a perfect solution to the equations. A funny note is that one scientist envisioned a recital hall full of people solving the equations and passing them to the person next to them and were known as "computers" way back before computers as we know existed. Since computers have been around they have been used in the form of a supercomputer which just means a hugely powerful computer that could run circles around your laptop and more importantly handle massive amounts of input and output of data. So how does numerical weather prediction, or the prediction of weather from math, work with supercomputers?
Step 1: Know The Atmosphere
The equations have to be initialized with the important variables (wind, temperature, pressure, etc.) that are retrieved from the atmosphere by a variety of methods. A few examples follow. Surface weather stations are set up across the U.S., North America and even the entire world which can measure quantities near the surface. Weather balloons are launched at least twice daily from over 100 stations within the United States which measure temperature, pressure and humidity and can be used to calculate wind speed and direction.
Step 2: Know The Model
If all weather models were designed the same then they'd all agree a lot more on how much snow we should expect from this storm. However, there are many differences between the weather models which stem from their design, how they calculate certain physical mechanisms (e.g. small-scale gusty air moving near the surface, cloud formation and the production of rain and snow) and over what area of the globe they are calculating the variables for.
The two main differences are between global models and regional models. As their names suggest, global models calculate quantities over the entire globe (like in the picture below) whereas regional models focus the calculations only over a specific region. An example of U.S. models are the GFS which is the Global Forecast System and the NAM which is the North American Model. Because the regional models (e.g. NAM) focus on a smaller portion of the globe, they can have a higher resolution or calculate the variables at more points in the atmosphere than global models. The schematic below shows an example of a grid setup and a physical mechanism that has to be calculated within a numerical weather model.
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| Numerical Weather Prediction Schematic. Source. |
There are strengths and weaknesses for all types of weather models. Higher resolution models are thought to be more robust in being able to predict certain weather phenomena better than those with lower resolution, but sometimes they don't perform like that.
Step 3: Calculate the Variables Later (Forecast) in Time and Perform Analysis
Once you have your initial conditions from observations and a weather model, you can run the model and look at the model output/your forecast. You may be happy with the model output from the GFS versus that of the NAM or you may not be. Most weather models are run four times daily at 00, 06, 12, 18 UTC or 7 PM, 1 AM, 7 AM, 1 PM EST. Each time they are run they take in new initial conditions to make their forecast. That's why there's a lot of run-to-run variability or changes in the forecast from the same weather model each time it is run. The single weather models are called deterministic models because they take observations just as they are and create a forecast. There is another method of modeling the weather that instead of providing one solution provides a whole ensemble of solutions and as such is known as ensemble system forecasting.
Step 4: Try an Ensemble System
The basis behind ensemble forecasting is that there are a lot of errors with weather models, especially those that arise from the initial conditions. What if a weather station in Oklahoma is broken but no one notices and you input that bad data into your weather model? What if the weather balloon from Montana got hijacked by a goose and your data reflects that wild goose chase and you put it into your weather model? To account for this, ensemble forecast systems sometimes take the initial conditions and perturb them, or change them slightly, and then run the same weather model with this new input. Another method behind ensemble systems is to have the same input but change some of the ways that the model calculates key processes such as the formation of snow.
The output, or forecast, provides an envelope of likely solutions. It's like if you were to go to the doctor when you stub your toe. The best case scenario is that the pain will go away in ten minutes. The worst case scenario is that she'll have to amputate. Given that ensemble of solutions, you can understand what's likely and make a judgement which is usually somewhere in the middle which is known as the ensemble mean. Mean in the statistical sense, not the emotional sense, so it just means the average.
Step 5: Given All Model Data, Make a Forecast!
So if a bunch of different countries each have their own weather models that are slightly different and some even have extensive ensemble forecast systems, it makes it a challenging job for a weather forecaster to sift through all of that data and scrutinize the results to come up with a forecast. Sometimes certain model systems behave one way (global models) versus others (regional models) as was the case with this snow event.
Predictability of This Event
This event was calculated by the long-range forecast models many days in advance. However, the snowfall amounts were very high compared to what the official forecasts are calling for less than a day from the event.
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| Storm Total Forecast as of 9:31 PM EST March 1st for the Long Island/NYC Metro Area. |
The global models trended the axis of heaviest snowfall farther south towards the D.C. Metro area earlier (i.e. a few forecast model cycles before) the regional models. There are tools to see what atmospheric players are most important according to some ensemble systems that can impact the intensity or track of storms. One such tool is developed at the School of Marine and Atmospheric Sciences of Stony Brook University in collaboration with the National Weather Service to look into these key players so that forecasters can keep an eye on them and how they are shown in the weather models as they get closer to the storm event. For the March 2-3 event, the tool showed that it was the upper atmospheric energy in northern Canada associated with the northern stream jet that will determine whether the cold front and low pressure system along it will be more towards the north or the south. As the upper-level energy gets closer to the observational network of weather balloons, the forecasts should start to agree more on a solution that should (hopefully) actually occur.
From the discussion of weather models and how they work, it's easy to understand why it's hard to take a single model's forecast all too seriously when you are still many days out from an event. If the key players haven't even formed yet in the atmosphere, who is to say the model is forecasting them right? It's good to know the heads-up that there's the possibility of a storm but it's unwise to fuss about specific snowfall totals. It might be better to let the atmosphere do it's thing and then observe it and and feel more confident in saying there will likely be 4-8 inches instead of saying there will be a foot of snow.
A Summary: There Will Be Snow (According to Our Models At Least!)
The point of this post was to discuss the meteorology and uncertainty behind the upcoming snow event that is sparking a lot of people's interest. The key points were to discuss how weather models function and how while interpreting model data is important, it's really important not to emphasize one model forecast from one run time ever and even better to discuss the ensemble of likely outcomes. With that said, I'd like to highlight one run time from two models that are run within the COMAP Group at SBU.
A little bit of background on the models, first. The model is run twice, once using the GFS (global model) forecast as its initial conditions and once using the NAM (regional model) as its initial conditions. It takes those as its input and is run at a high resolution over a smaller area with special attention given over the Northeast. This high resolution can be useful for small, complex systems or for getting the smaller-scale details within larger-scale systems like the cold front and waves of low pressure developing along it of this event. Each model is closely tied to the data that it was initialized with, however, but can add detail to it and provide a slightly different solution. Here's what it is showing for the snow event.
Below are two snapshots of simulated reflectivity near the surface, similar to what you'd see if you looked at radar during the event (we hope).
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| Snapshots for 7 PM EST Sunday March 2nd of simulated reflectivity from the NAM-WRF (left) and GFS-WRF (right). |
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| Snapshots for 7 AM EST Monday March 3rd of simulated reflectivity from the NAM-WRF (left) and GFS-WRF (right). |
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| Liquid equivalent precipitation (water, not snow so to get snow multiply each value by ~10) for Stony Brook University (SBU) from the 12Z Run of the NAM-WRF (top panel) and GFS-WRF (bottom panel). |
So grab a cup of cocoa and relax and enjoy the storm-- weather forecasting is an incredibly challenging science especially given the complexity of weather models that I hope you have a better appreciation for. The great thing about weather is that it never stops, so each forecasting challenge can be a learning tool as one looks ahead in the future to the next storm.
- For more information about numerical weather prediction, check out the entry in Wikipedia: http://en.wikipedia.org/wiki/Numerical_weather_prediction
- For winter weather forecasts from the Weather Prediction Center, check out their website: http://www.wpc.ncep.noaa.gov/wwd/winter_wx.shtml
- For data from SBU's Weather Research and Forecasting Model, check out their website: http://dendrite.somas.stonybrook.edu/LI_WRF/
