Forecasting Thunderstorms
Can thunderstorms be forecast or predicted?
MODELS
Forecasters often rely on massive computer programs called numerical weather prediction models to help them decide if conditions will be right for the development of thunderstorms. The models start with current weather observations and attempt to predict future weather using physics and dynamics to mathematically describe the atmosphere's behavior.
Numerical weather prediction models have long been used to guide forecasters as they produce forecast products. These models are computer programs that ingest observations from around the world and use complicated mathematical equations to predict the weather – something that can only be done by huge computers. The predictions are usually output in text and graphics (mostly maps).
Numerical weather prediction models are designed to calculate what the atmosphere will do at certain points over a large area, from the Earth's surface to the top of the atmosphere. Accurate observations about what the weather is doing now is key to help predict what it will do in the future. Data is gathered from weather balloons launched around the globe twice each day, in addition to measurements from satellites, aircraft, and temperature profilers and surface weather stations. The more "grid points," the better the model will predict.
Ensemble forecasting
Computer models work great if the weather follows the rules we have set. When the weather breaks the rules, the predictions have trouble too. Another technique being developed is the concept of "ensemble forecasting." Instead of using just one model, a supercomputer runs several models at one time – an ensemble. If each run looks similar, then we can assume the weather will likely follow the rules. If the runs look different in different places, then we understand that something in the atmosphere is causing the weather to misbehave.
Interpreting the model output is key, and takes a lot of practice. Forecasters use their experience, knowledge, persistence (what makes us think the weather is going to change from what it is now?) and eyes (looking out the window!) to fine-tune their forecasts. An important advancement has been made in model displays – the output used to be on black and white maps. Now forecasters can look at the output on their computer workstations and use different colors to understand more clearly what is happening.
SATELLITE
Satellites are critical in short-term forecasting. Satellite images can give an early indication of a developing thunderstorm by showing where cumulus clouds are forming. Cumulus clouds grow rapidly into cumulonimbus clouds if conditions are right, and you can track their growth using satellite images.
Since the satellites are positioned over the equator, they are viewing the northern hemisphere at an angle so you can get a sense of the vertical development of the clouds. Also taller clouds will cast shadows onto lower ones so visible imagery is an excellent tool for locating developing thunderstorms.
There are three types of satellite images:
- Visible imagery
Visible imagery is just like the name suggests; an image of the earth in visible light. This is a similar manner to that of a person taking a picture with a camera. The satellite senses sunlight reflected from objects within the viewfinder. In the case of the satellite, the objects are the upper surfaces of clouds. Thick clouds do a much better job of reflecting light and therefore appear brighter in visible photos. When the satellites are positioned over the equator, they view the northern hemisphere at an angle so you can get a sense of the vertical development of the clouds. Also taller clouds will cast shadows onto lower ones so visible imagery is an excellent tool for locating developing thunderstorms.
Example of visible satellite image - Infrared imagery
The obvious problem with visible imagery is that it is only available during the day. To combat this problem, the infrared (IR) sensor was devised. It senses radiant (heat) energy given off by the clouds. Warmer (lower in the atmosphere) clouds give off more energy than cold (higher) clouds. The IR sensor measures the heat and produces several images based upon different wavelengths in the IR range of the electromagnetic spectrum. Often these images are color enhanced to help better distinguish the taller (coldest, usually from thunderstorms) cloud tops. - Water vapor imagery
Water vapor imagery is unique in that it can detect water vapor (water in a gas state) in addition to clouds. However, due to absorption of energy by the atmosphere this view only "sees" of the top third of the troposphere. While the low level moisture is hidden from the satellite sensor, the upper level moist/dry areas are plainly observable. Moist areas show up as white, dry areas as black.
The GOES weather satellites also have equipment that will acquire profiles of temperature and moisture for clear or partly clear fields of view. These profiles are further processed to produce several derived meteorological parameters. In addition, cloud tracking allows for the measurement of wind in the atmosphere. This information is used for input to the weather models which result in improved weather analysis and forecasting.
HOW DOES NSSL CONTRIBUTE?
A move from "warn on detection" to "warn on forecast" paradigm will extend warning lead times. NSSL is beginning to study ensembles for very short-range (0 to 1 h) forecasts of severe weather events. These ensembles assimilate Doppler radar data into cloud-scale numerical models to provide improved predictions of thunderstorms and their associated severe weather. While still in a research mode, initial results suggest that it may be possible to use these forecasts in warning operations, leading to a shift from the present "warn on detection" strategy to a "warn on forecast" strategy that would provide longer lead times for severe weather events.
NSSL scientists are collaborating on an advanced numerical weather prediction model (the The Weather Research and Forecasting Model, or WRF), testing new parameterization schemes that add physical processes to existing models to improve model performance and accuracy, and developing ensemble forecasting techniques.
NSSL is actively involved in refining and building new conceptual models of severe storms, supercell structures and mesoscale convective complexes and systems. These conceptual models have led to improved forecasting and warnings, and have improved our understanding of environments that are favorable for the formation of thunderstorms.
NSSL scientists are studying the technique of "ensemble forecasting." Ensemble forecasting involves running a large number of forecasts with different initial conditions or a large number of different models together. This appears to provide more accurate forecasts than a single model by itself. NSSL scientists have also experimented with using direct forecaster input to identify regions and conditions that seem to be at a higher risk of severe weather. This way the forecaster could use his judgment and experience and input it into the model before the computer begins its computations.
Developing and testing data assimilation methods for Doppler radar and lightning data will improve numerical analyses and predictions of severe storms. In an initial effort to assimilate phased array radar data into numerical models, scientists are taking a comprehensive approach to attack problems in: (i) velocity dealiasing in radar data quality control, (ii) error covariance estimation, and (iii) radar data assimilation using the estimated error statistics.
NSSL scientists are also finding innovative ways to incorporate high-resolution radar observations into computer models to improve the short-term prediction of mesoscale convective systems (MCSs), which often produce widespread severe winds and heavy rainfall.
Spring Experiments – every year NSSL and the Storm Prediction Center work closely together in the Hazardous Weather Testbed for 6-8 weeks during the spring to assess new forecasting models and discuss their effect on forecasting operations.
Field projects – one way NSSL learns more about thunderstorms is by conducting field experiments. You can learn more about projects that focused on thunderstorms – such as TELEx, IHOP2002, BAMEx, and STEPS – in the field observations section.
Radar applications – NSSL and the National Weather Service collaborate to streamline research into operations. NSSL has developed severe weather warning applications and decision support systems that will make the forecasters job easier. The result will be improved NWS warning services for the public, increased detection accuracy, and longer lead times.