8A.1
Advancing the GSD Cloud and Precipitation Hydrometeor Analysis Towards Hybrid Ensemble-Variational Data Assimilation

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Wednesday, 1 July 2015: 8:00 AM
Salon A-2 (Hilton Chicago)
Therese Ladwig, NOAA/Earth System Research Laboratory/Global Sciences Division and CIRES/Univ. of Colorado, Boulder, CO; and M. Hu, S. Weygandt, S. G. Benjamin, C. Alexander, and D. C. Dowell

Successful data assimilation is of paramount importance for initializing numerical models. The Gridpoint Statistical Interpolation (GSI) is the data assimilation system used at NCEP to assimilate many observation types in operational models. Using the GSI framework, the NOAA/ESRL Global Systems Division (GSD) cloud and precipitation hydrometeor analysis system (hereafter referred to as the HM-analysis) was developed to incorporate cloud and precipitation observations into the analysis. Currently, the HM-analysis is an analysis component that can be run after the GSI variational/hybrid minimization steps. The HM-analysis is non-variational and explicitly builds or removes cloud and precipitating hydrometeors and correspondingly adjusts moisture and temperature fields to support the cloud hydrometeor changes. In order to improve the analysis and prediction of clouds and precipitation, the HM-analysis is being transitioned to a variational and then hybrid ensemble-variational framework within GSI. This transition will also enable the HM-analysis to be used in any modeling system that interfaces with GSI.

The current HM-analysis is applied to the real-time hourly updating 13-km Rapid Refresh (RAP) and 3-km convective-allowing High-Resolution Rapid Refresh (HRRR) run operationally at NCEP and experimentally at GSD. This presentation will demonstrate initial work on the formation of new hydrometeor observation operators, the development of cloud control variables and establishing static background covariances for cloud hydrometeors. Retrospective case studies with both the RAP and HRRR will be discussed. In addition, improvements through the generalization of background and observation input-output will enable the HM-analysis to be applied to the Global Forecast System (GFS) for the first time. The extension of the HM-analysis for the GFS will also be discussed.