For this project, GOES cloud-top cooling rate data provided by the University of Alabama Huntsville (UAH) are being assimilated into an experimental Rapid Refresh version at the Global Systems Division of the Earth Systems Research Laboratory. Within this RAP modeling framework, the cloud-top cooling rate data are mapped to latent heating profiles and are applied as prescribed heating during the diabatic forward model integration part of the RAP digital filter initialization (DFI). A similar forward integration only procedure is used to prescribe heating in the HRRR one-hour pre-forecast cycle. For both the RAP and the HRRR, the GOES-satellite-based cloud-top cooling rate information is blended with data from radar reflectivity and lightning flash density to create a unified convective heating rate field. In the current HRRR configuration, four 15-min cycles of latent heating are applied during a pre-forecast hour of integration. This is 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 system. A new retrospective period of June 15-22, 2014 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 RAP forecasts. This period was quite active with severe storms, especially in the northern Plains states, with numerous tornadoes and large hail reports over the period. In addition to the RAP model, we are also investigating the impact of the satellite derived cloud-top cooling rates in the HRRR model that uses the RAP for boundary conditions. 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. We will report on these results at the conference.