Successful analysis and prediction of hazardous weather requires accurate data assimilation and numerical weather prediction at high temporal frequency and high spatial resolutions.  The Assimilation and Verification Innovation Division (AVID) at GSL leads research to design, develop, and improve rapidly updating models at convection-permitting and convection-resolving scales.  GSL specializes in transferring these scientific advances to operations at the National Weather Service.  The innovations are of paramount importance for many applications including severe and hazardous weather, aviation/transportation, hydrology, and energy.

Development of the Rapid Refresh Forecast System (RRFS), the regional, convection-permitting, and rapidly updating model within NOAA’s Unified Forecast System (UFS) is underway at GSL/AVID. There are research opportunities for postdoctoral study in data assimilation, modeling, and verification that can contribute to RRFS and future operational modeling systems.  Examples include: Exploration of new observation inputs (e.g. UAS, additional satellite information).  Advances for existing observation inputs (e.g. radar, aircraft).  Improving the application of ensemble and ensemble-variational data assimilation techniques and exploration of new techniques (e.g. particle filters, machine learning). 

key words

Data Assimilation; Numerical Weather Prediction; Convection-allowing models; Convection; Rapid Refresh Forecast System (RRFS); UFS; R2O; Cloud assimilation; Radar assimilation;

Citizenship:  Open to U.S. citizens, permanent residents and non-U.S. citizens

Level:  Open to Postdoctoral and Senior applicants