Let's Talk Probabilities for the January 2015 Blizzard

If you're a typical individual, you probably had a very busy weekend with a rainy Saturday (on top of a couple inches of snow, gross!) and then a frantic Sunday packed with errands to get ready for the final week of January. If you hit the grocery store a bit early today you may have noticed that it was slightly more crowded than normal. If you hit the grocery store this evening, you're lucky to be back in the comfort of your own home after that stressful, chaotic affair! The news is definitely out that Long Island is in for a massive blizzard Monday through Tuesday.

I'm sure you have a few questions such as--
1) Where the heck did this storm come from?!
2) Are these forecast snowfall totals for real??
3) What cool data does Stony Brook University have to share for this historic event?

With this blizzard, it's all about probabilities. Probabilistic information is such an important part of weather forecasting. The atmosphere is so chaotic that predicting it perfectly is impossible (improbable, too!). That's where ranges of values (such as snowfall) and measures of confidence (such as lack of or overly so) really come in handy. Well, let's waste no time to answer the above questions!

Numerical Weather Prediction: Ensembles "Jazz Up" The Forecast

In previous posts, we've discussed numerical weather prediction. Weather models were showing a system moving from Canada through the Midwest US and to the East Coast bringing the possibility of some accumulating snow after the dud of a snow event we had on January 24th. However, few (if any), had this system strengthening close enough off of the coast to cause any major impacts. Well, that may have been true for what are called the "deterministic" models.

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.

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.

While the deterministic models may have been "out to sea" or weaker with this storm, the ensembles were showing the possibility (read: slight probability) of a stronger storm and closer to the coast. To provide an example, let's look at the Global Ensemble Forecast System (GEFS) run by NCEP. 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. Let's compare the mean (average) and spread (variations from the mean) for two forecast times-- 4 days and 2 days from 7 AM EST (12 UTC) on Tuesday, January 27th.

GEFS Sea Level Pressure Ensemble Mean and Spread 4 days out.

GEFS Sea Level Pressure Ensemble Mean and Spread 2 days out.

Using data from Friday morning, this particular ensemble was showing the average low of 994 mb located far off the coast with a clustering of ensemble members farther west towards the coast. While there was still uncertainty 4 days away, there's still the probability of a coastal low having more impacts over land.

Comparing that with Sunday morning's ensemble run, there is much less spread which shows that the ensemble members are converging on a solution (the truth?) as one would hope as an event gets closer in time. The ensemble mean is now showing a deeper (984 mb) low closer to the coast than a couple of days ago. By this time, all of the deterministic models have converged on a similar solution which increases confidence in that solution being what will happen. Keep in mind this wasn't the case just a few days ago!

This brings us to the first question, "Where the heck did this storm come from?!" Using the ensemble sensitivity analysis tool developed at SBU, the key feature from that would impact the location and intensity of the coastal low development 4 days later was the intensity (or amplitude) of a shortwave, or weak low pressure system, developing lee of the Canadian Rockies and amplifying the existing trough behind the departing Saturday storm that had yet to even happen. Now that this system has formed, its strength using such tools as satellite imagery can be assessed and compared with what models were showing to see whether it is weaker/stronger or in a different location.

GOES-East Water Vapor Image from 7:15 EST on January 25th  (0015 UTC 26 Jan)

Ready the Plows... or the Front Loaders?

Now that we are closer to the event, snowfall forecasts are everywhere! So that brings us to the second question-- "Are these forecast snowfall totals for real?" As of tonight (Sunday night), the intensity and placement of the low pressure center is almost certain to be in the "sweet spot" for significant snow accumulations on Long Island.

NCEP Forecast low tracks for various times denoted by the colors and marker shapes. 

In a previous post, we've discussed why snowbands love Long Island. The feeling is mutual, of course! Once a coastal low forms, it usually strengthens and moves northeastward along the coast up towards New England. The broad area of precipitation that is usually light in nature can organize itself into narrow areas of intense snowfall, known as a snowband. A snowband can be a few miles wide and a few tens to about a hundred miles long-- and like most things they come in a lot of different shapes and sizes. When a coastal low forms, there is typically warm air to the southeast of it and cold air to the northwest of its center. Air moves in a cyclonic or counterclockwise direction which acts to create fronts, or boundaries between the distinct air masses. A snowband that is set to impact Long Island typically forms north of the warm front where warm, moist air is ascending over a wall of colder, more dense air. As the low strengthens, this warm air is vigorously churned counterclockwise towards the west and the snowband pivots to the northwest of the surface cyclone center. As the low matures the snowband can sometimes be found to the west of the surface cyclone center. You can see the positions of the snowband relative to the surface low in the following schematic:

The recipe for a very strong snowband includes strong winds clashing together to the northwest of a low pressure center that extends a few miles up into the atmosphere, sufficient moisture so that there is plenty of water vapor to condense into clouds and provide a lot of precipitation, and just like with summertime thunderstorms there should be a little bit of instability or a region of air in which a pocket of air will be less dense and able to rise rapidly. A lot of research has been completed investigating these snowbands and a Stony Brook University alumnus, David Novak, published many peer-reviewed papers about those that occur in the Northeast. He looked at many storms and determined the general ingredients needed to cook up a great snowband as summarized in the following figure.

The ingredients for a mesoscale snowband modified from Novak et al. 2004.

The two locations highlighted by the stars indicate the preferred locations for snowbands. In the red shading are regions of frontogenesis, or the creation (i.e. genesis) of fronts or the clashing of two air masses. The region enveloped in a scalloped line shows a region of deformation or where the winds have a component that are converging which enhances frontogenesis. While there may be snow all around the surface low for miles and miles it shouldn't be as heavy as within the regions of snowbands indicated by those stars-- all thanks to frontogenesis. Let's talk more about frontogenesis and not just because it's a really fun word to say.

Frontogenesis, or the formation of fronts, means that the atmosphere is unbalanced. The fact that there is a temperature gradient (that's what a front is) means that the atmosphere is a bit unstable and wants to even out that temperature gradient. How can it create a uniform temperature distribution if it has to fight against the really strong horizontal winds that keep making the temperature gradient stronger? To combat the imbalance, the atmosphere initiates a vertical wind circulation because when you can't go out you go up, right? The atmosphere induces a frontogenetical circulation that causes warm air to rise and cool on the warm side of the front (cooling down the warmer air) and cold air to descend and warm on the cold side of the front (warming up the colder air). This warm, rising air is usually incredibly moist so there's plenty of water vapor to create deep clouds that produce heavy snow and with that the snowband is formed. This circulation is usually very small because the temperature gradient at the front itself is very narrow in width so the snowband is very narrow as well. A summarizing schematic is provided below that shows a horizontal view of the snowband (yellow star) to the northwest of the surface low with the counterclockwise winds in the upper-right corner above a cross section showing the vertical circulation that causes a lot more snow to fall on your buddy's house (yellow star) versus your house (cozy cottage to the west or mansion to the east depending on your preference).

So why doesn't the weather forecaster know exactly where the snowband is going to set up and instead calls for a large amount of snow for the whole region? As you are now aware of, snowbands are very sensitive to particular ingredients with the most tricky being the location of the surface low pressure center. If the low travels a bit farther off of the coast then so will the snowband and the East End of Long Island may get hit a lot worse than NYC. If the low tracks closer to NYC then perhaps NJ will get hit harder. See the pattern?

This is where probabilities can come in very handy! Using our good friends, ensembles forecasting systems, we can determine the probability of exceeding a certain snowfall threshold. The NOAA/NWS Weather Prediction Center (WPC) has online tools to plot such data. Using 57 total members from several different models (more info here), the WPC creates its probability plots. Here is the probability of accumulating greater than or equal to 18 inches of snow in a 24-hour period between 7 PM EST Monday and 7 PM EST Tuesday:

WPC 24-hour probability of snowfall accumulating greater than or equal to 18 in by 7 PM EST Tuesday.

Most of Long Island is shown as seeing >50% probability of at last 18 inches of snow (the highest amount that is available to be plotted via WPC) with a maximum of >80% over central CT. Those are high probability values given the number of different ensemble members that go into that product! 

Looking at a more local scale, the National Weather Service Upton, NY Forecast Office has issued their storm total snowfall ranges, but we'd also like to highlight some experimental graphics that use probabilities. The storm total forecast as of Sunday night is showing between 24-36 inches of snow for all of Long Island. Some experimental graphics show the minimum expected storm total snowfall (6 - 9 inches) or the 10th percentile amount and the maximum expected storm total snowfall (29 - 35 inches) or the 90th percentile amount. These graphics can be accessed here. 

Now it's one thing to have snow, it's another thing to have a blizzard. The NOAA/NWS Gray, ME Forecast Office put together a great graphic to summarize blizzard conditions. It's the combination of winds > 35 mph, fast snowfall rates, and blowing snow creating white-out conditions. A Blizzard Warning has been issued by our local NWS Forecast Office from 1 PM EST Monday to Midnight on Tuesday forecasting for snowfall rates of 2 - 4 inches per hour and wind gusts to 65 mph. The blizzard conditions are resulting from the rapid development, or bombing out, of the low that is forecast to deepen (strengthen) > 24 mb in 24 h. The models vary on its forecast intensification, but there will be enough of a pressure gradient between it and the high to our north to create the potential for wind gusts almost approaching hurricane-force! For example, the SBU GFS-WRF is showing sustained winds of >35 kts (> 40 mph) at Montauk Point on Monday night!

Montauk Point Time Series of Simulated Wind Speeds and Directions from the SBU GFS-WRF initialized 12 UTC 25 Jan.

Given this probabilistic information, it makes sense that Stony Brook University cancelled its first two days of classes (Monday and Tuesday) while the system is still hanging out near West Virginia! However, we will need to pay attention to any snowbands that develop north and west of the surface cyclone center that may determine who gets the maximum amounts and sees the most treacherous conditions. 

Play with the snow, from the comfort of your own home! 

Now that we've determined that this storm developed within the available weather models within the past couple of days, there is potential for possibly (probably!) historic snow accumulations especially under any snowbands that develop, we can get to our third question, "What cool data does Stony Brook University have to share for this historic event?"

We still have our vertically pointing radar (MRR) on the roof of Endeavour Hall on campus that can be used to compliment conventional radar data from Upton (KOKX) as well as show fall speeds of the hydrometeors (just snow for this event). Joining it up there this year is a brand-new instrument that actually takes pictures of snowflakes in free-fall! The Multi Angle Snowflake Camera (MASC) was developed at the University of Utah by Dr. Tim Garrett and is supported by both Dr. Brian Colle of Stony Brook University and Dr. Sandra Yuter of North Carolina State University. Mark Lang of SBU and Dr. Matthew Miller of NCState implemented a website that will show real-time images of the snowflakes falling and being captured by the camera! Here's an example of some from Saturday's (brief) snow event:

MASC image from 6:05 PM EST January 24th

Pretty cool, right? (Well, cold, in this case!) To conclude, this storm will likely be remembered as the "Blizzard of 2015" and is an example of why forecasting using ensemble forecasting systems is incredibly important as well as paying attention to the potential development of any smaller-scale (mesoscale) details such as snowbands. The MASC is now available to provide a real-time eye into the storm. The main point with a storm like this is to exercise extreme caution to stay safe and then we can all appreciate this storm for its intensity in safety. Best of luck in the blizzard! You probably won't forget it anytime soon! 

Main links used in this post:

- Stony Brook University Ensemble Sensitivity Analysis: http://dendrite.somas.stonybrook.edu/CSTAR/Ensemble_Sensitivity/EnSense_Main.html

- Stony Brook University WRF Ensemble: http://itpa.somas.stonybrook.edu/LI_WRF/

- NOAA/NWS New York, NY Forecast Office Winter Weather Page: http://www.weather.gov/okx/winter 

- MASC Real-time Images at SBU: http://itpa.somas.stonybrook.edu/MASC/snowflakes_test.html

It's Almost Snowtime: Did the Atmosphere Get Cold Feet?

I know what you are thinking. "I thought someone said we'd get a foot of snow. Now we may only get six inches? And why are we still talking about snow now that it is the month of March?" Well, there's still plenty of cold air up in the Arctic and that keeps seeping down into our neck of the woods as we witnessed this past week with the passage of some arctic cold fronts and snow showers. A cold front is forecast to pass through and move slowly offshore south of Long Island on Sunday and waves of low pressure allowing for heavy snowfall will likely form along it. The fact that this is atypical and not a classic nor'easter for our region makes this an interesting case for a discussion about predictability, or the measure of how predictable/well-forecast a storm is. Of course, the storm hasn't happened yet so we'll see how the forecast has evolved as we get closer in time to it.

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.

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.

From 0-12 Inches of Snow: A Discussion of Predictability

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.

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.

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).

Snapshots for 7 PM EST Sunday March 2nd of simulated reflectivity from the NAM-WRF (left) and GFS-WRF (right).

From this snapshot for 7 PM EST on Sunday, March 2nd you can see that there are some timing differences between both models of where the heavy precipitation is forecast to be at that time. Snow showers should be starting across NYC according to the NAM-WRF on the left panel but the area should be dry according to the GFS-WRF on the right panel. Looking at another snapshot for 7 AM EST on Monday, March 3rd shows much different scenarios.

Snapshots for 7 AM EST Monday March 3rd of simulated reflectivity from the NAM-WRF (left) and GFS-WRF (right).

The system will already have moved south of Long Island according to the GFS-WRF (right panel) but the area could be on the fringe of heavy snow showers according to the NAM-WRF (left panel). Focusing on Stony Brook, below are model time series of accumulated precipitation (the liquid equivalent, so if you multiply the value by 10 it can give a rough estimate of what that would be in terms of snow in inches).

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).

First, please note that the y-axis ranges are different for each model which highlights that the GFS-WRF is a much wetter model (is forecasting much more precipitation) with about 3 inches but the NAM-WRF is forecasting a mere 1.3 inches. These values are from the 12Z run of the model, or the model that was run from data taken at 7 AM EST on Sunday so this is a 48-hour forecast. As the event gets closer, there should be some convergence on a solution but because it's weather forecasts that everyone wants not weather observations, it's important to keep a range of values in mind. According to these two model forecasts from one model run, it's safe to go with a forecast for Stony Brook of 1-3 inches. Is that on the low-end? Absolutely. Will it likely snow more than that and agree with official forecasts which are using many, many more weather models to arrive at their decision? Most likely.

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/

A Tale of Two Days of Snow Showers

You are late to leave your house and are hustling around to grab your bag and car keys and once you walk outside of your front door you see... it's snowing?! Why is it snowing? They didn't say it would snow. I don't have any bread and milk! As similar thoughts run through your mind as you start your car and back out of your driveway, the sun comes out. Oh, that's all? Wow, I give up on the weather. Don't give up! There's a great explanation for why for the past two days (Wednesday, February 26th and Thursday, February 27th) there have been lines or cells of snow showers flowing across our area.

These snow showers have been the result of the clashing of air masses in a smaller way than producing a large, coastal cyclone as has been discussed in previous posts on major snowbands. That's why they are so transient, or move through quickly without too much accumulation or disruption of travel. However, they are capable of quickly dumping over an inch of snow and causing dangerously decreased visibility while driving. These snow showers can form as a result of surface forcing from cold air pushing warm air up and over it which condenses any water vapor into clouds which can precipitate. They can also form as a result of upper-level forcing which means that the air's motion high up in the atmosphere can actually cause air at the surface to rise and any water vapor condenses into clouds which can precipitate. Most times, it's a bit of both mechanisms.

So what was up with the snow showers on both February 26th and 27th? They were similar but also a little bit different, as I'll explain.

A Tale of Two Days of Snow Showers

A very cold air mass has been creeping down into the Northeast from Canada and with it have been several fronts, or boundaries, of cold air making its advance towards our region. The image below shows a surface map for Wednesday, February 26th with a deep low pressure center north of the Great Lakes indicating that there's cold air in place and a weaker low pressure center in Quebec north of New York with a cold front draped off the coast. The cold air behind this front and the fact that there was some help at upper-levels of the atmosphere acted to organize some lines of snow showers on this day.

NOAA/NWS Weather Prediction Center Surface Analysis and Radar for 2100Z 26 Feb (4 PM).

Things were slightly different for Thursday, February 27th, however. That strong low pressure center and much colder air has made its way east and its the cold front associated with this that at 1 PM EST was analyzed to be stretching southward through Central New York and Pennsylvania that, combined with some favorable conditions at low-levels and upper-level forcing acted to organize some cells of snow showers across our area in the mid-afternoon.

NOAA/NWS Weather Prediction Center Surface Analysis and Radar for 1800Z 27 Feb (1 PM).

The Wednesday, Feb. 26th snow showers were post-frontal when there was cold air advection (the wind blows colder air into the area) within the lower portion of the atmosphere. The Thursday, Feb. 27th snow showers were pre-frontal when there was warm air advection (the wind blows warmer air into the area) within the lower portion of the atmosphere. To look into this in more depth, the Stony Brook University-Weather Research and Forecasting (SBU-WRF) model was used to plot some variables which are operationally available to the public at the following website. The use of a weather model in a forecasting sense is also proof that these snow showers were predictable even though they were on such a small scale. The plots below show a slice of the atmosphere near the surface with temperatures shaded with the cooler colors indicating cold temperatures and the warmer colors indicating warm temperatures (can we go to Florida now, please?!) with Wednesday on the left and Thursday on the right. Notice first how much colder the air mass over the upper Great Plains has gotten over the course of a day. Notice second how within our box there is a transition, or gradient, between colder air and warmer air with colder air being to our northwest and warmer air to our southeast. It's hard to tell but the winds within this box show the advection at this level, or the transport of air of a certain temperature into the region. On Wednesday, winds are mostly blowing from the northwest ushering in colder air. While on Thursday, winds are mostly blowing from the west-southwest which is actually ushering in a little bit of "warmer" air ahead of the air mass boundary.

Left: Wednesday, February 26th at 3 PM and Right: Thursday, February 27th at 12 PM showing 925 mb temperatures shaded and a box highlighting our region.

This pattern of cold air and warm air advection can be seen be plotting a time series of temperature and wind direction at a location at the surface. Here's the model forecast time series plots for Stony Brook University. When the winds are directed more from the north (indicated by the barbs that are pointing into the wind pointed more towards the northwest on the bottom plot) then there is colder air being brought to the station and the temperatures decrease. The reverse is true for when the winds blow more from the south or southwest which corresponds to warm air advection and the temperature increases.

SBU-WRF 12Z GFS 26 Feb Model Time Series of temperature/dew point temperature on the top and wind speed and direction (indicated by the barbs pointing into the wind). The blue boxes and ovals show times of cold air advection and the red ovals/pink box show times of warm air advection.

The fact that the air mass can change at different levels of the atmosphere (low, middle and high) can help air rise or sink. For example, when there's warm air advection in the lower part of the atmosphere, this may cause that air the be unstable and it will rise until it cools off to the average temperature of the rest of the air mass. Conversely, if there is cold air advection at mid-levels, air will want to sink until it will warm to the average temperature of the rest of the air mass. The result of the cold advection on Wednesday versus the warm advection on Thursday was that the snow showers were organized a bit differently across Long Island.

The following images show observed (left plot) and modeled (right plot) of radar reflectivity of the snow showers from the NWS/New York Radar.

Observed (left) and modeled (right) base radar reflectivity at 1700Z (12 PM EST).

The above image is for the Wednesday snow showers which shows heavier precipitation to the southwest associated with the cold front but smaller banded snow showers with the same orientation as the cold front but much smaller and weaker. These are likely resulting from some forcing at upper levels of the atmosphere related to cold frontal structure.

Observed (left) and modeled (right) base radar reflectivity for 3 PM EST.

The above image is for Thursday's snow showers. Notice how there are scattered, cellular snow showers found just east of Long Island whereas there are more linear features found across Central New York (upper-left portion of box in the observed and upper-left of model area). The linear feature is associated with the surface-based cold front, however the warm air advection out ahead of it as previously discussed allowed there to be favorable conditions for sporadic rising air which combined with the forcing at upper-levels of the atmosphere led to some snow showers. The Health Sciences Center (HSC) at Stony Brook University as weather instrumentation complete with a camera which is useful in capturing events like this. Check out the image below for a snapshot from 3 PM EST Thursday.

Snapshot from HSC camera showing the back-end of some of the snow-producing clouds at 3 PM EST.

So the moral of the story, or the tale of two days of snow showers, is that not every snowflake that is seen falling to the ground spells doom for Long Island. We are capable of getting weak snowfalls just as we are capable of getting crippling nor'easters, given the right air masses. While the mechanics behind these snow showers can be different and a little complicated, it's nice to experience them because it's similar to the type of precipitation structure we'd experience during the warmer seasons but loads cooler because there's snow falling! Maybe it's just that I'm super into snow, but I'd much rather get snowed on while walking unprepared to my car than get drenched by rain. Enjoy the snow while it lasts this winter season!

Snowbands & Why They Love Long Island

If you've ever experienced a coastal winter storm while living on Long Island or the surrounding Metro area then you've likely seen the following evolution: A forecast comes out calling for a little bit of snow in the next few days which incites little panic among the public. The day before the snowstorm the forecast changes because there had been a lot of model uncertainty and suddenly the forecast snow totals are a lot higher and panic ensues! (Cue bread and milk chaos!) The storm causes numerous traffic problems including airport and even highway closures throughout the Tri-state area. The snowstorm ends and it turns out that your buddy a few towns over got a ton more snow than you did. What gives? The forecast snow totals were correct, but for your buddy and not for you. Some examples include the February Blizzard of 2013, "Snowmageddon" of December 26-27 2010, among many others. This scenario rings some bells, right? Let's get to the bottom of it.

Long Island is jutting right out into the storm track that is very active during the cool season months, i.e. October - March where coastal lows tend to develop after upper-atmospheric energy is transferred from the west over the Appalachian Mountains and interacts with the existing temperature gradient (baroclinic zone) that is in place at the coast sandwiched between the cold, continental air over the Eastern U.S. and the warm, moist air over the Western Atlantic Ocean fed by the Gulf Stream.

North American Storm Tracks (Source: Understanding Weather and Climate, 6th ed. by Aguado & Burt)

Once a coastal low forms, it usually strengthens and moves northeastward along the coast up towards New England. The broad area of precipiation that is usually light in nature can organize itself into narrow areas of intense snowfall, known as a snowband. A snowband can be a few miles wide and a few tens to about a hundred miles long-- and like most things they come in a lot of different shapes and sizes. When a coastal low forms, there is typically warm air to the southeast of it and cold air to the northwest of its center. Air moves in a cyclonic or counterclockwise direction which acts to create fronts, or boundaries between the distinct air masses. A snowband that is set to impact Long Island typically forms north of the warm front where warm, moist air is ascending over a wall of colder, more dense air. As the low strengthens, this warm air is vigorously churned counterclockwise towards the west and the snowband pivots to the northwest of the surface cyclone center. As the low matures the snowband can sometimes be found to the west of the surface cyclone center. You can see the positions of the snowband relative to the surface low in the following schematic:

The recipe for a very strong snowband includes strong winds clashing together to the northwest of a low pressure center that extends a few miles up into the atmosphere, sufficient moisture so that there is plenty of water vapor to condense into clouds and provide a lot of precipitation, and just like with summertime thunderstorms there should be a little bit of instability or a region of air in which a pocket of air will be less dense and able to rise rapidly. A lot of research has been completed investigating these snowbands and a Stony Brook University alumnus, David Novak, published many peer-reviewed papers about those that occur in the Northeast. He looked at many storms and determined the general ingredients needed to cook up a great snowband as summarized in the following figure.

The ingredients for a mesoscale snowband modified from Novak et al. 2004.

The two locations highlighted by the stars indicate the preferred locations for snowbands. In the red shading are regions of frontogenesis, or the creation (i.e. genesis) of fronts or the clashing of two air masses. The region enveloped in a scalloped line shows a region of deformation or where the winds have a component that are converging which enhances frontogenesis. While there may be snow all around the surface low for miles and miles it shouldn't be as heavy as within the regions of snowbands indicated by those stars-- all thanks to frontogenesis. Let's talk more about frontogenesis and not just because it's a really fun word to say.

Frontogenesis, or the formation of fronts, means that the atmosphere is unbalanced. The fact that there is a temperature gradient (that's what a front is) means that the atmosphere is a bit unstable and wants to even out that temperature gradient. How can it create a uniform temperature distribution if it has to fight against the really strong horizontal winds that keep making the temperature gradient stronger? To combat the imbalance, the atmosphere initiates a vertical wind circulation because when you can't go out you go up, right? The atmosphere induces a frontogenetical circulation that causes warm air to rise and cool on the warm side of the front (cooling down the warmer air) and cold air to descend and warm on the cold side of the front (warming up the colder air). This warm, rising air is usually incredibly moist so there's plenty of water vapor to create deep clouds that produce heavy snow and with that the snowband is formed. This circulation is usually very small because the temperature gradient at the front itself is very narrow in width so the snowband is very narrow as well. A summarizing schematic is provided below that shows a horizontal view of the snowband (yellow star) to the northwest of the surface low with the counterclockwise winds in the upper-right corner above a cross section showing the vertical circulation that causes a lot more snow to fall on your buddy's house (yellow star) versus your house (cozy cottage to the west or mansion to the east depending on your preference).

So why doesn't the weather forecaster know exactly where the snowband is going to set up and instead calls for a large amount of snow for the whole region? As you are now aware of, snowbands are very sensitive to particular ingredients with the most tricky being the location of the surface low pressure center. If the low travels a bit farther off of the coast then so will the snowband and the East End of Long Island may get hit a lot worse than NYC. If the low tracks closer to NYC then perhaps NJ will get hit harder. See the pattern? The following image shows the different weather model simulated locations of the surface low with the orange markers corresponding to the position of the low at 7 PM on Tuesday, January 20th from the Weather Prediction Center. There's quite a spread in locations, huh?

Low tracks from the Weather Prediction Center. Source

 Another issue is the fact that we've discussed that snowbands are a relatively small-scale phenomena. If weather models have really coarse resolution, or they can only predict larger systems better because of the size of their grid systems, then small-scale snowbands are really a challenge for them. Thankfully there is a whole suite of weather models made just for the purpose of looking at small-scale weather phenomena known as mesoscale models. Stony Brook University actually runs a two-member model ensemble in real-time and the model output can be accessed from our webpage. So what do our models say about the storm? They each tell a different story which is interesting but evident that this storm is presenting a real forecasting challenge. Here is a snapshot of simulated reflectivity from the 7 AM model-run valid for 1 AM on Wednesday, January 22nd.

SBU-WRF comparison simulated reflectivity for 1 AM EST January 22, 2014.

The model on the right has a snowband over central Long Island (indicated by the star but please don't expect stars to appear on actual radar data) whereas the model on the left doesn't have that feature. The model on the right also has the low pressure center farther northwestward towards the coast which is helpful with getting snowbands closer to the Island. In looking at the model more in-depth, the ingredients are there for snowband formation-- a well-defined low-to-mid-level circulation, deformation north and west of that circulation center and sufficient moisture and instability at times (especially ~11 PM EST). The fact that temperatures will remain quite cold throughout the most important layers of the atmosphere means that the snow will be dry and fluffy and this fact could lead to large accumulations versus if it was wet (see the NWS infographic on snow ratios).

As of 11 PM EST on Monday, January 20th the NWS has issued a Winter Storm Warning for most of the Tri-state area from noon on Tuesday to 6 AM on Wednesday. The heaviest snowfall should occur after 5 PM EST and the amounts can total as much as 14 inches with the snowband expected to affect the eastern portions of Long Island. You can pay attention to the regional doppler radar for narrow swaths of larger values of reflectivity that indicate a snowband and see where one sets up tomorrow night. Remember to stay safe and stay warm but most of all, enjoy the snow!

- For more information about mesoscale snowbands see this webpage: TheWeatherPrediction.com

- For forecasts and warnings please see the local New York Forecast Office of the National Weather Service (and check out their Facebook Page here!): http://www.erh.noaa.gov/okx/

- For information about the formation of snowflakes and how they'll be observed at the ground please see this previous post: http://longislandweatherandclimate.blogspot.com/2013/12/a-snowflakes-journey-is-deciphered-on.html

A Snowflake's Journey is Deciphered on the Ground

It is now officially the winter season and we've already had a few snow events on Long Island in the late fall of 2013. Although we likely won't be able to look outside and see more snow before the Holiday Season is over, there are plenty of snowflake decorations adorning buildings, shops and lawns. There is a rich history of science associated with snowflakes, all of which started with observations using the human eye like you've likely done when snow falls on your coat or gloves. You've heard the saying that no two snowflakes are alike but sometimes the ones that fall at one time are very similar. Therefore, snowflakes tell a story about the atmospheric environment above our heads that they fell through in order to reach us on the ground that's definitely worth investigating.

Photomicrograph of a snowflake by Wilson A. Bentley. (Source: http://snowflakebentley.com/WBpopmech.htm)

Birth of an Ice Crystal
As discussed in the previous post about general precipitation types, water comes in three forms- ice (solid), water (liquid), and water vapor (gas). Water changes between these three phases in the atmosphere depending mainly on the environmental temperature. An interesting feature about water is that it doesn't automatically freeze when the temperature drops below 0 degrees Celsius (C) or 32 degrees Fahrenheit (F) but actually can remain in liquid form until the temperature gets to -40 C (-40 F). The reverse is not true, however, and ice always melts when the temperature gets above 0 C (32 F). The water droplets that exist between -40 to 0 C (-40 to 32 F) are called "supercooled liquid water droplets" because they are just so hip and with-it, right? Just kidding, but they do allow for a lot of interesting details allowing for snowflake growth.

So let's say the temperature within a cloud is about -10 C (14 F) and there are a whole bunch of supercooled liquid water droplets hanging around. If the temperature doesn't decrease by much, how do you get ice growth since all of the droplets won't spontaneously freeze at that warm temperature (which is known as homogeneous nucleation)? The answer is heterogeneous nucleation which can take three forms: growth by deposition, growth by aggregation and growth by accretion. We'll get to those methods in the next section. To understand each of these methods of growth we need to discuss one more thing-- water vapor pressure.

Water and ice behave differently when interacting with water vapor. We know that with liquid water there are the processes of evaporation (changing phase from liquid to gas) and condensation (going from gas to liquid) and those processes occur simultaneously and constantly when the air is saturated with respect to liquid water. If the air is sub-saturated then more evaporation occurs than condensation versus when the air is super-saturated in which case more condensation occurs (and that's linked to how raindrops form!). Similar processes occur for ice, or solid phase water, and are called sublimation and deposition. Sublimation is like evaporation because water goes from the ice phase to vapor phase (skipping the middleman or the liquid phase). Deposition is like condensation or when water vapor changes phase straight to ice (again skipping the liquid phase). So water vapor interacts with both liquid water and ice, but how does it choose which one to interact with when they are mixed together? That is where vapor pressure comes into play. There is an equilibrium state for both liquid water and ice for it to exist in a saturated environment which would mean the rate of evaporation = condensation and the rate of sublimation = deposition. The pressure exerted by the vapor molecules in each of those separate phase scenarios is known as the saturation vapor pressure and they are different values for the liquid phase and the solid or ice phase. The saturation vapor pressure with respect to ice is less than that of liquid water which means that if given the choice, water vapor will preferentially react with the ice and help the ice to grow instead of reacting with the liquid water. Deposition wins over condensation in those cases which is good news for snowflake growth! Below is an idealized schematic for how the different phases of water may exist in the atmosphere. Notice how the temperature decreases with height so there are more ice crystals at higher altitudes.

Source: http://www.meted.ucar.edu/norlat/snow/preciptype/

A snowflake is born when supercooled water droplets freeze, most likely onto an ice nuclei which is a small piece of ice that can be found within a cloud around temperatures of -10 C (14 F) that may have been a piece of salt or even dirt that water vapor can freeze upon. That ice nuclei can smash into supercooled water droplets and cause them to freeze on contact. The best freezing occurs when an ice nucleus becomes completely submerged within a supercooled water droplet and cause it to completely freeze over, symmetrically. Due to the shape of the water molecule itself, an ice crystal will grow to usually mimic that shape and form into a hexagon, or six-sided polygon. Once there exists an ice crystal, a snowflake is born!

Growing Up (or Radially Outward)
The snowflakes you've seen likely aren't little tiny hexagons but rather the beautiful six-cornered geometric masterpieces that make for wonderful wintertime decorations. But how do snowflakes grow from tiny ice nuclei to what you've seen on your coat? The answer is just like how we grow up- with time and travel, of course! 

The three methods of snowflake growth that I introduced above include the following: growth by deposition, growth by aggregation and growth by accretion. Let's start with growth by deposition.

As stated above, deposition means water vapor freezing directly into ice, or in this case onto an ice crystal. When an ice crystal is present in a cloud with a bunch of supercooled water droplets, water will actually be transferred from the droplet to the ice crystal because of the difference in saturation vapor pressure between liquid and ice. The image below depicts that process and was borrowed from the UCAR COMET module "Topics in Precipitation Type Forecasting." 

Source: http://www.meted.ucar.edu/norlat/snow/preciptype/

It takes less energy for water vapor to freeze onto rough and jagged edges than over smooth, even surfaces of the ice crystal so that is why each of the six snowflake "arms" get to grow to such lengths radially outward from the center of the ice nucleus at a rapid rate. Each arm grows the same way at the same time which is why snowflakes are so symmetric. However, how they grow is dependent on the temperature through which they are traveling. That's how a snowflake can have six arms that all tell the same complex story of how they formed. Check out this video of crystal growth to see what I'm talking about! Since it's highly unlikely that two snowflakes took the same exact journey through the same exact path in the atmosphere-- that's why Wilson A. Bentley coined the conjecture that "no two snowflakes are alike!" Snowflakes have been classified into types, or habits, that are very dependent on temperature. The following chart shows the favored snow habits according to the atmospheric temperature in which they grow. The most "ideal" six-pronged snowflakes are called dendrites and typically form when the atmospheric temperature is between -12 and -16 C (3.2 to 10.4 F) in a layer appropriately known as the dendtritic growth zone.

Source: http://www.meted.ucar.edu/norlat/snow/preciptype/

Snowflakes only lose their symmetry if they crash into other snowflakes and break like what can happen during growth by aggregation. Growth by aggregation is a process by which snowflakes collide and stick together. They can become physically intertwined (like how your paperclips always do) or pieces can actually melt at the edges to fuse snowflakes together (usually around 0 C (32 F)). The diagram shown above of the layers of the atmosphere illustrates how ice crystals may grow aloft and then fall down through layers containing other types of snowflakes, so they are likely to interact.

Growth by accretion is also known as growth by riming. This occurs when a snowflake falls through supercooled liquid water and the droplets actually freeze upon its surface. This process can be seen in the diagram below.

Source: http://www.meted.ucar.edu/norlat/snow/preciptype/

As the snowflake travels towards the ground (as indicated by the black arrow) it falls faster than the water droplets around it so it encounters many and they freeze onto its surface. This process of riming makes for less "idealistic" snowflakes but allows for great snowman-making snow on the ground!

The snowflakes that reach the ground sometimes grow by all three processes described above. In fierce winter storms, known as blizzards, intense updrafts of vertically-moving air and gusts of wind can take snowflakes on such an incredible journey that they don't even know who they are anymore when they reach the ground. They have melted, refrozen, gathered more snowflakes and sometimes even remained boring liquid rain. The previous post described all of the precipitation types we are so familiar with on Long Island (including sleet, yuck)! While the most gentle snowfall of flakes growing slowly within a cloud and falling straight downwards towards the ground may yield the "prettiest" dendritic snowflakes, it's important to appreciate the story that even the "ugly" snowflakes tell us upon reaching the ground. And that is just what some scientists are doing.

Reaching the Ground and Catching the Curiosity of Scientists
The history of snowflake observations goes back not quite to Ancient Greece (given their warm, Mediterranean climate so they missed out on some great times with nature) but to about the middle of the 13th century. For example, curious minds such as Albertus Magnus pondered their shape and later Johannes Kepler wrote a paper trying to understand why snowflakes have six corners in 1610. More and more scientists and travelers wanted to understand why snowflakes were so artistic and tried to sketch their melting, fleeting forms by hand without the best success. It was actually a non-scientist, bachelor farmer in Vermont who begun the pioneering work of photographing them through a microscope.

Wilson A. Bentley lived from 1865-1931 in Jericho, Vermont and spent his lifetime taking photomicrographs, or photos through a microscope of snowflakes that fell during most storms that hit his farm. He had no weather charts (initially) nor training in meteorology but did have some standard atmospheric measurement equipment (with a thermometer being the most important!) and a microscope, blackboard, and a camera. He was humble, curious and diligent with his photographs and likely struggled a little bit financially because he sold copies of his images for only 5 cents! An incredible book written by an incredible scientist who worked in cloud microphysics and cloud seeding, Duncan C. Blanchard, is called "The Snowflake Man: A Biography of Wilson A. Bentley" and is a highly recommended read. If you ever travel up north to Vermont, they have a museum in Jericho with some of his equipment and snowflake images. Check out this youtube video for a short documentary if you just can't wait to see more but can't travel up there anytime soon! The Snowflake Man is truly a gem to meteorology and shows how anyone, as long as they have the curiosity, can do great things for science.

There has been a lot of research on snowflakes and snow crystal growth since, both through observations and using computer models. There are still a ton of unanswered questions in this relatively new science and this PBS article highlights a few of them. On a local note, Dr. Brian Colle and members of the Coastal Meteorology and Atmospheric Prediction Group are continuing to look into the stories that snowflakes tell us when they reach the ground and if a weather model can even try to get that story right. He is currently working with Ruyi Yu to compare weather model results of snowflake growth with observations taken from aircraft data from a flight right through a snowband. That research is still being completed and is supercool! (Pun intended.)

A Special Look into Snowflake Research on Long Island
Dr. Brian Colle has been working with a few of his graduate students for a number of years to understand how computer models calculate snow crystal growth. He has been using observations on the ground from a vertically pointing radar located on the roof of a building at Stony Brook University's School of Marine and Atmospheric Sciences, the local NOAA/National Weather Service NYC Radar located in Upton, NY, a particle distrometer that can differentiate between rain and snow and drizzle that is also up on the roof at SBU, and his own photomicrographs. Yes, Long Island has it's own Snowflake Man!

He most recently published a paper with a recent graduate student (David Stark now at the NOAA/NWS NYC Weather Forecast Office) and a collaborator (Dr. Sandra Yuter of NCState) on the observed snowflake habits during two East Coast winter storms. During a number of storms while you were likely relaxing with a cup of cocoa after frantically buying some bread and milk (or at least that's what I was doing!) Dr. Colle and David would stay up all night if necessary to take snowfall measurements and photomicrographs of what was falling to the ground. They would then go back and analyze recorded atmospheric conditions such as temperature and vertical air motion to really understand what was going on above our heads that would lead to the distinct snowflake habits. Below is a snapshot of some photomicrographs during a snowstorm on December 19th, 2009. The images are not as picturesque as those of Wilson A. Bentley because they wanted to capture the whole complicated story of what was falling to the ground whereas Bentley would take a feather to his slide and push away all of the "unwanted" flakes. The diversity of the snow habits on their slides really provides insight into the complex storm dynamics and temperature structure that were occurring.

Stark et al. 2013: Fig. 7.

By taking such observations through many winter storms, they were able to match certain snow habits to certain phases of a cyclone or certain distances between Long Island and the surface cyclone center. Be on the lookout for more of their published results! Although David has graduated with his Masters degree, Dr. Colle soldiers on and continues to be Long Island's own Snowflake Man to increase the understanding and prediction of what will fall on the ground of Long Island by understanding each flake's incredible journey.

Would you like more information? Check out the following resources:

• For more information about ice nucleation, refer to this College of Dupage webpage: http://weather.cod.edu/sirvatka/bergeron.html
• Sign up with a free account from the University Corporation for Atmospheric Research (UCAR) Meteorological Education (MetEd) COMET Program and learn all about precipitation type at this site: http://www.meted.ucar.edu/norlat/snow/preciptype/
• Scientific American Article by Charles A. Knight: http://www.scientificamerican.com/article.cfm?id=why-do-snowflakes-crystal
• Stark et al. 2013 article about the microphysics within two East Coast winter storms: http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-12-00276.1?prevSearch=[Contrib%3A+david+stark]&searchHistoryKey=
• All other sources of information are found as embedded links within the above article.