Development and Testing of Hybrid Ensemble-variational Data Assimilation For Cloud Hydrometeors In GSI

Abstract

Successful data assimilation is of paramount importance for initializing numerical models, and Gridpoint Statistical Interpolation (GSI) is the community data assimilation system used by NOAA to assimilate observations in operational models. NOAA/ESRL Global Systems Division (GSD) developed an option within GSI for a non-variational cloud and precipitation hydrometeor (HM) analysis (hereafter referred to as the HM-analysis) to incorporate cloud/precipitation hydrometeor observations. In order to further improve prediction of clouds and precipitation, a transition of the HM-analysis to a variational and then hybrid ensemble-variational framework within GSI is now underway. This transition will also enable the HM-analysis to be used in any modeling system that interfaces with GSI because no individual model drivers or libraries are required. The new variational HM-analysis is applied initially to the hourly updating 13-km Rapid Refresh (RAP) and 3-km convective-allowing High-Resolution Rapid Refresh (HRRR). This presentation documents the creation of new hydrometeor observation operators, the development of cloud control variables, and the establishment of static background covariances for cloud hydrometeors. Retrospective case studies with both the RAP and HRRR are discussed. Results are compared to the real-time RAP and HRRR, which use the earlier, non-variational HM-analysis. The assessment focuses on low-level cloud increments via ceiling analysis verification, moisture bias compared to aircraft and sounding observations, and cloud retention in the forecasts.