NSSL Research: Winter Weather
Just like any other storm at other times of the year, the right combination of ingredients is necessary for a winter storm to develop. Small variations in temperature determine whether precipitation will fall as sleet, snow, or freezing rain, making forecasting these events very difficult.
Winter Weather Research Areas
Dual Polarization Radar
NSSL was a pioneer in dual-polarization radar technology, now installed on NWS radars across the U.S. Forecasters use dual-polarization technology to clearly identify rain, hail, snow or ice pellets. This gives forecasters more confidence to accurately assess weather events because they will have more information to forecast what kind of precipitation there will be and how much to expect.
Improving Precipitation Estimation (QPE) in Winter Weather
NSSL's MRMS system is a suite of automated algorithms that uses multiple sensing devices, including polarimetric radars, to detect rain and snow and to provide an estimation of snowfall amounts (in liquid water equivalent) every 1km x 1km across the continental United States and Alaska. Enhanced reflectivity from the melting of frozen precipitation, called a “brightband” can cause severe overestimation of surface rainfall from radar data. MRMS has a brightband identification algorithm to search for the brightband and to correct for its effect in the surface rainfall estimation. The identified brightband height also reflects melting and therefore the height of the rain-snow line.
FACETs for Winter Weather
NSSL's FACETs program is being extended into the winter season. Forecasting snow, ice, and any type of mixed winter precipitation is very difficult. In the past, weather models have produced a single forecast for a winter storm (“deterministic” forecasting). But with new advances in computer technology, weather models are now able to produce multiple forecasts for a winter storm (“probabilistic” forecasting). Probabilistic information gives forecasters the ability to see different winter weather scenarios and provides an idea of how much confidence a forecaster can place in the forecast. Researchers at NSSL are using these new forecasting techniques to develop probabilistic tools for winter weather threats. These tools will help forecasters see where and when winter hazards will occur so that cities and towns can be better prepared for snow removal and de-icing efforts.
Simulating Radar Signatures of Snow
The advancement of dual-polarization radar has provided scientists with a lot of potential new information about the size, shape, and density of snowflakes as they form, grow, and melt into rain. Scientists at NSSL are developing models that simulate these processes and what they would look like to a dual-polarization radar. By comparing this with real data from radars, scientists can learn more about things like how snowflakes stick together as they fall, how long it will take snowflakes to reach the surface, and how the air temperature and relative humidity will be affected by snowflakes melting. This type of knowledge will also help forecast models better predict precipitation types and snow amounts and meteorologists interpret what they are seeing in the radar data.
Advanced Diagnoses of Precipitation Type
Determining what type of precipitation is falling during winter storms – be it rain, snow, freezing rain, sleet, or a combination of those – is an ongoing challenge. NSSL is developing an algorithm known as the Spectral Bin Classifier (SBC) to better enable forecasters to get an accurate representation of what precipitation types are falling throughout a winter storm. The SBC algorithm is capable of representing the melting and freezing of particles (snowflakes, raindrops) as they fall from cloud top to the ground.This algorithm not only benefits forecasts of winter weather precipitation type, but it allows for the diagnosis of liquid water below freezing, also known as supercooled water. Supercooled water freezes quickly on contact with solid surfaces, making it a major transportation hazard by quickly icing roads, cars, and aircraft.
These new tools are validated using NSSL's mPING application, which collects weather information from the public through their smart phone or mobile device. Researchers compare the reports of precipitation with what is detected by the dual-polarized radar data to refine the HCA (Hydrometeor Classifcation Algorithm), an algorithm which sorts through radar echoes to identify what kind of precipitation is falling.
Past Winter Weather Research
NSSL researchers have studied winter thunderstorms. They found that there is some evidence that snowfall is heavier during reports of thunder and lighting at the same place and time. They also learned that these winter thunderstorms, although rare, occur most often in the central United States, Great Lakes, the east coast of the U.S. and Canada, and northern Canada during the winter and spring.
Freezing Rain Climatology
NSSL developed freezing rain climatology for local and national forecasting centers to help forecasters better understand regional and temporal susceptibility to freezing rain. They found that over New York and Pennsylvania, cold-air outbreaks interact with coastal cyclones, making the area more susceptible to freezing rain. Another area experiencing higher reports of freezing rain is in the Pacific Northwest, where storm systems generate precipitation and interact with cold air trapped in the Basin. A third area is located in the lee of the Appalachian Mountains. In this region, cold air damming is a common occurrence during the winter months, contributing to significant ice storms. Precipitation systems, often originating in the Gulf of Mexico or forming along the coastal front, interact with the subfreezing air in the damming region to produce freezing rain.
Radar observations of Lake-Effect snowstorms
NSSL studied the NWS radar monitoring of shallow lake-effect snowstorms over and around Lake Ontario, and made simulations of how detection could improve if the radar was operated using lower elevation angles. Currently, WSR-88D radars do not operate below +0.5 degrees. Shallow lake-effect snowstorms over and around lake Ontario pose a detection and warning challenge for the Buffalo, NY NWS Forecast Office. Limited measurements in the lower portions of the storms limit reliable quantitative precipitation estimation in much of the coverage area. Simulations showed when the elevation angle of the radar beam is lowered, shallow lake-effect storms would be detected over the entire lake and surrounding coastal regions and reliable QPE information would be available for the entire region.
Southwest Colorado Radar Project
NSSL deployed the NOAA X-Pol (NOXP) mobile radar in southwestern Colorado as part of the Southwest Colorado Radar Project to collect data on snowfall in the area. NOXP is equipped with dual-polarization technology, which provides detailed information about the water content of snow, providing better estimates of precipitation amounts. Data from NOXP was processed through NSSL's MRMS multi-sensor precipitation estimation system. Forecasters used the information to enrich their winter weather forecasts. Local data users included county search and rescue and airport operations.
The mostly student-run NSSL/CIMMS Severe Hazards Analysis and Verification Experiment (SHAVE) collected winter weather precipitation reports through phone surveys. SHAVE reports, when combined with the voluntary reports collected by the NWS, created a unique and comprehensive database of winter weather weather events used to evaluate algorithm performance.
The Intermountain Precipitation Experiment (IPEX) studied winter weather across northern Utah to develop a better understanding of the structure and evolution of winter storms. During January and February 2000, scientists made detailed observations of several large storms including one that produced three feet of snow. They also made unprecedented measurements of electrification and lightning in winter storms and the first dual-Doppler radar analysis of a cold front interacting with the Great Salt Lake and surrounding mountains. Researchers used data gathered to validate precipitation estimates from Doppler weather radars located at high elevations, to improve computer-based forecast models used in mountainous regions, and to study terrain-induced precipitation events and interactions that produce lake-effect snow bands.
Shannon, Ireland, was the base of field operations for the Fronts and Atlantic Storm Tracks Experiment (FASTEX), a multinational program that intensely documented and studied the life cycles of cyclones originating over the data-sparse North Atlantic during January and February 1997. During FASTEX, observations were made using up to seven research aircraft and four research ships. NSSL scientists played a lead role in the design and execution of FASTEX as principal investigators, aircraft chief scientists and members of the P-3 aircraft crew. FASTEX provided the first data sets to document the evolution of rapidly developing cyclones over the ocean. FASTEX was also the first project to target observations where numerical models indicated there would be a benefit to forecasts of cyclone development. Using data collected during FASTEX, scientists are making numerical simulations of cyclone structure and dynamics. Researchers expect results to apply to storm tracks over both the Pacific and Atlantic oceans.