Enhancing FAA tactical forecasts of convective initiation and growth

This project is towards optimizing a GOES convective initiation (CI) 0-1 hr nowcasting algorithm for performance across various “convective regime” types, and transitioning the algorithm into a fielded FAA decision support system (DSS). The goal is to enhance predictability of the timing, location and growth rate of CI by more succinctly defining the characteristics of convective cloud development, using satellite data. The focus will be on nowcasting the first occurrence of ≥35 dBZ echoes within convective clouds—i.e. CI, across various thermodynamic environments that generate convection (i.e. “regimes”). The hypothesis is that this enhanced predictability of CI will lead to more accurate forecasts of the onset and intensity of hazardous convective-scale weather events that impact aviation across large sections of the U.S., as demonstrated through DSS forecasts. Examples of convective regime types include dry mountainous environments, and humid tropical conditions.

NASA assets are helping to optimize the GOES-based CI method via the in-line convective-regime “training” information they provide via a multi-parameter database—statistical look-up table approach. In particular, NASA's CloudSat Cloud Profiling Radar (CPR), the Atmospheric Infrared Sounder (AIRS), and the MODerate resolution Infrared Spectrometer (MODIS; aboard the Aqua satellite) observations (along with numerical weather prediction model thermodynamic fields) improve the GOES CI algorithm's accuracy as atmospheric conditions unique to various convective regimes are accounted for. Specifically, CloudSat, AIRS and MODIS data increase knowledge of three critical aspects of growing cumulus clouds well beyond what GOES alone provides: cloud-top height, cloud-top temperature, and an estimate of cloud-top glaciation.

The CI algorithm has been incorporated into the Corridor Integrated Weather System (CIWS) DSS, which improves nowcasting accuracy to automated convective weather forecasts in real-time for FAA decision makers. Through this effort, detections of early storm development and growth rates will be enhanced within CIWS, to yield an overall increase in convective forecast lead-times and accuracy. More precise knowledge of the timing, location, and mode of CI are necessary to the forecasting problem at any time scale, but are particularly important for short-term (0-2 hr) weather-based FAA decision support. Satellites are the primary source of CI (pre-radar echo) information, representing a powerful capability to improve fine-scale convective-scale forecasts, and therefore the utility of the DSS. SS.