Wednesday, 9 January 2013: 5:00 PM
Room 9B (Austin Convention Center)
The Rapid Refresh (RAP) is an hourly-updated mesoscale atmospheric 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 sophisticated set of analysis procedures have been developed for initializing cloudy and precipitating areas, as well as near surface fields. Clouds are initialized via a non-variational cloud analysis procedure, which uses METAR and satellite-derived cloud information to modify hourly cycled explicit cloud hydrometeor fields. Recent work has focused on 1) reducing a positive moisture bias (relative to radiosonde observations) introduced from satellite observation-based mid- and high-level cloud building and 2) recasting the current non-variational cloud analysis within a variational framework. Radar assimilation is done by mapping observed radar reflectivity into a latent heating based temperature tendency field, then prescribing this temperature tendency during the forward integration portion of the RAP diabatic digital filter initialization procedure. A variety of special procedures are used for the assimilation of surface observations, including creation of moisture pseudo-innovations to better capture the vertical moisture structure for well mixed situations and accounting for terrain height differences between the model and observations in the observation forward model.
In addition to use of the cloud, radar and surface observation assimilation modules in the standard 3D 13-km RAP analysis, work is underway on two new 3-km analysis applications. The first is a collaborative effort with NCEP EMC and involves application of a special 2D version GSI to 3-km High Resolution Rapid Refresh (HRRR) background grids to create a detailed surface analysis suitable for Real-Time Mesoscale Analysis (RTMA) applications. This 2D version of GSI has been previously developed at EMC for application to down-scaled (to 2.5-km) RUC / RAP fields and includes use of a recursive filter package to provide anisotropic error covariance fields. The second effort involves the application of the fully 3D GSI (including the cloud, radar, and surface analysis modules) to 3-km HRRR background fields. Initial results have been encouraging and the fully 3D 3-km analysis has many possible uses, including improved HRRR forecasts and initializing the Rapidly Updated Analysis (RUA). At the conference, we will report recent progress on the two cloud analysis tasks (reducing moisture bias and converting to variational formulation) and describe the recent work on the two 3-km GSI applications (anisotropic surface 2DVAR for RTMA and fully 3D application for RUA and improved HRRR forecasts).
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