Forecast Models
NSSL scientists are collaborating on an advanced numerical weather prediction model, testing new parameterization schemes that add physical processes to existing models to improve model performance and accuracy, and developing ensemble forecasting techniques.
- Probabalistic forecasts from ensembles will provide
more complete information and extend warning lead times on high-impact
events like severe weather outbreaks.
NSSL scientists are at the forefront of exploring the use of different models and different model parameterizations within short-range ensembles and data assimilation methods. Results indicate that ensemble forecasts are more skillful when model error is included explicitly as part of the ensemble, either through the use of different models in the ensemble or through the use of different parameterization schemes within the ensemble.
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. Researchers believe 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. - 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.
NSSL scientists are studying how assimilating lightning observations into mesoscale forecast models could improve the forecasts of initial conditions. Lightning data could be important because it can pinpoint the location of current convection and may be a measure of its intensity. Using lightning data could also improve the effects of prior convection on the initial conditions of the forecast period.
Researchers studying thunderstorm electrification in cloud and mesoscale models explore the charge distribution and lightning produced in storms by various electrification mechanisms. - A new high-resolution numerical weather prediction
system that can be configured for both research and operations will benefit
a community of users.
The Weather Research and Forecasting (WRF) model is a next-generation mesocale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. NSSL scientists have been major contributors to WRF development efforts and continue to provide leadership in the operational implementation and testing of WRF.
