Detecting and Forecasting Winter Weather
What does a winter storm look like?
From the ground a winter storm just looks like a cloudy gray day! Luckily, winter storms are usually easily detected by satellite, and areas of precipitation are visible on radar. Different from thunderstorms, we usually can tell well in advance that a winter storm will be coming.
Satellite evidence
Meteorologists look for jet streaks and dry slots, areas of greatest spin, shear zones, areas of sinking air, and cold cloud tops, among others. All of these features are recognizable with enough practice! Comma clouds are a little easier seen on satellite by the untrained eye, and can be associated with winter storms.
Radar evidence
Radar can give us clues as to what type of precipitations is falling. Sleet shows up well on radar because it is a solid ball of ice. Sometimes it can be mistaken for heavy snow on radar, so forecasters must use both surface observations and radar to make accurate forecasts. One problem is that extremely light snow can go undetected because snowflakes have lower moisture and higher air content than other types of precipitation (remember snow flakes have holes). Forecasters must combine both surface observations and information from Doppler radar to determine where and how fast the snow is falling and accumulating.
Bright bands in radar data are caused by melting snow or ice as it falls to
the ground. The areas of melting snow and ice cause stronger echoes and thus
overestimation of precipitation. A bright band also indicates the
lowest elevation of any wet snow. The bright band is created because snow falls
slowly, and reflects less than water. When the snowflakes start melting, snowflakes
get covered by water – and look like large raindrops – reflecting a lot more
of the radar beam. When the melting finishes, the soon-to-be raindrop shrinks
and speeds up, and the reflectivity decreases. When snow melts into rain, the
region where this melting occurs reflects the radar beam more than the snow
above or the rain below. With more of the beam reflected, it creates an area
on radar that is brighter than the surrounding precipitation.
Doppler radar can show the wind direction too, which is helpful when forecasting near mountains and large bodies of water. If the radar shows wind blowing up the mountain (upslope), forecasters know that automatically, one of the ingredients is in place of the development of precipitation: lift. Doppler radar can also tell you the depth of the cold air. If you know the slope of the terrain and the moisture content of the air, you could have a better guess towards snow accumulation rates. If the radar shows wind blowing over a large section of a body of water (fetch), then they know that another ingredient is present for the formation of precipitation – moisture.
Radar velocities can help identify the location of cold fronts because there is usually a sharp change in wind direction and will show up as a discontinuity on Doppler radar.
HOW DOES NSSL CONTRIBUTE?
Advances in the prediction and detection of snow and ice are being translated into decision-making tools for forecasters. By integrating new applications of science and analysis into innovative products, forecasters will be able to produce more fundamentally sound forecasts. NSSL works to develop forecasting techniques that help forecasters anticipate these events and alert the public and commercial interests with climatological information about hazardous winter weather.
QPESUMS is a real-time automated algorithm using multiple sensors that was developed by NSSL. The algorithm was designed to detect mixed phase precipitation and be able to handle rain at low elevations and snow at high elevations by using surface temperature data, mesoscale model data, satellite information, data from multiple radars, and bright band information. Enhanced reflectivity from the melting of hydrometeors (bright band) can cause severe overestimation of surface rainfall. QPESUMS has a brightband identification algorithm to search for the brightband and report its height. The information is then used in QPESUMS to ensure that contaminated reflectivity from this layer is removed. The brightband height also reflects melting and therefore the height of the rain-snow line.
NSSL studied the WSR-88D 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.
Winter precipitation type classification proves to be promising with a polarimetric WSR-88D radar. Polarimetric radars can provide information that was previously unavailable on cloud and precipitation particle size, shape, and ice density. This can help identify precipitation type in winter storms and locate areas of aircraft icing conditions.
NSSL is developing and testing an algorithm to integrate data from surface temperature sensors, numerical weather prediction model thermodynamic output, and dual-polarimetric radar hydrometeor classification algorithm output to produce a surface precipitation type product. The technique shows promise in accurately depicting regions of freezing rain, snow, and rain at the surface, aiding aviation ground operation during winter storms.
IPEX – The Intermountain Precipitation Experiment 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.
FASTEX – Shannon, Ireland, was the base of field operations for the Fronts and Atlantic Storm Tracks Experiment, 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.