Cloud and precipitating hydrometeor analysis improvements within the 13-km RAP and 3-km HRRR hourly updated forecast systems

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Thursday, 6 February 2014: 4:00 PM
Room C203 (The Georgia World Congress Center )
Patrick Hofmann, NOAA/ESRL, Boulder, CO; and M. Hu, C. Alexander, S. G. Benjamin, S. S. Weygandt, and D. C. Dowell

The Rapid Refresh (RAP) is an hourly-updated mesoscale prediction system run operationally at the National Centers for Environmental Prediction. Consistent with the RAP focus on providing short-range “situational awareness” guidance for severe weather, aviation, renewable energy and other forecast applications; a detailed non-variational cloud analysis procedure has been developed for initializing cloudy and precipitating areas. This procedure uses METAR and satellite-derived cloud information to modify hourly cycled explicit cloud hydrometeor fields (Qc, Qi, Cn). Similarly, radar reflectivity data are used to make more limited modifications to cycled precipitation hydrometeor fields (Qr, Qs, Qg). Given the incomplete nature of these observation datasets, a complex conditioned hierarchy of building and clearing criteria has been developed and is applied in both the RAP and the HRRR. Key model fields, such as cloud diagnostics, precipitation, and soil moisture content depend critically on the details of the scheme, thus optimizing the analysis design is extremely important.

Recent work has focused on 1) reducing a positive moisture bias (relative to radiosonde observations) introduced from satellite observation-based cloud hydrometeor building, and 2) determining the analyzed precipitating hydrometeor state where observations are inconclusive.

Work on reducing the moisture bias is also motivated by renewable energy applications and associated efforts to assess and improve HRRR solar radiation forecasting. Recent efforts have included evaluation of new cloud hydrometeor building algorithms using CLAVR-x (CLouds from AVHRR Extended) satellite cloud data. Latest results will be shown on our progress reducing the high moisture bias associated with the full column cloud building by using cloud emissivity as a proxy for cloud fraction.

The precipitating hydrometeor analysis work has focused on addressing specific issues related to incomplete radar data coverage. Regions below the lowest radar scan and above the highest scan pose challenges for precipitation hydrometeor clearing, with potential for partial clearing in the vertical of background precipitation hydrometeors. Results from approaches using additional model background information such as equilibrium level and lapse rate in combination with the existing observations will be presented to highlight improvements in the precipitating hydrometeor analysis for these structures.

In addition to use of the satellite, radar and surface observation assimilation in the 13-km RAP and 3-km HRRR to establish initial conditions for model forecasts, work is ongoing with the Rapidly Updated Analysis (RUA) to provide a high resolution (3-km) estimate of the current 3-D atmospheric state including full column cloud and precipitating hydrometeors in a physically and dynamically consistent background that is particularly important in observation sparse regions. This effort involves the application of 3-D GSI hydrometeor-only analysis using satellite, radar and surface observations with a 3-km HRRR 1-hr forecast as the background. We will report on recent progress with the 3-D GSI application for RUA.