Use of Convective Initiation Information Derived From GOES Satellite Data In The High Resolution Rapid Refresh (HRRR) Forecast System

Abstract

For this project, GOES cloud-top cooling rate data provided by the University of Alabama Huntsville (UAH) are being assimilated into an experimental High Resolution Rapid Refresh (HRRR) version at the Global Systems Division of the Earth Systems Research Laboratory. Within this modeling framework, the cloud-top cooling rate data are mapped to latent heating profiles and are applied during the HRRR one-hour pre-forecast period. During this period, GOES-satellite-based cloud-top cooling rate information is blended with radar reflectivity data at 15-min intervals to create unified convective heating rate fields. These four 15-minute latent heating fields are then applied during a pre-forecast hour of integration, followed by a final application of GSI at 3-km to fit the latest conventional observation data. Previous work on this project has demonstrated that these cloud-top cooling rates can help with the location and intensity of storms in the RAP and HRRR systems. A new retrospective period of May 26, 2015 has been chosen to continue investigation of the use of cloud top cooling rates in partnership with other satellite derived convective initiation indicators in the HRRR forecasts. This day was quite active with severe storms with numerous tornadoes, wind and large hail reports over the period. Other parameters to be evaluated are the CI probability information provided by UAH and the impact in variation in the vertical structure of the assumed heating profile using information on the cumulus clouds as derived from GOES. At the conference, we will report on these results and work to include these data in a real-time parallel HHHR. Convective Initiation (CI) information derived from GOES satellite data has been assimilated into the Rapid Refresh (RAP) and High Resolution Rapid Refresh (HRRR) models in previous retrospective tests. The RAP is an hourly updated mesoscale assimilation and prediction system developed at NOAA/ESRL, producing 0 to 18-h weather forecasts each hour. The RAP was initially implemented at NCEP as a NOAA operational model in May 2012, with an operational upgrade (RAPv2) in February 2014. The 3-km HRRR runs hourly out to 15 hours, using RAP initial and lateral boundary fields. A one-hour pre-forecast data assimilation cycle was added to the HRRR in 2013. The HRRR has been running operationally at NCEP since September 2014. The RAP and HRRR both use the WRF ARW model core and the Gridpoint Statistical Interpolation (GSI) assimilation system.