14.5
Assimilation of radar reflectivity data within the RUC and Rapid Refresh models and initialization of High Resolution Rapid Refresh forecasts from RUC/RR grids
Curtis R. Alexander, NOAA/ESRL/GSD and CIRES-Univ. of Colorado, Boulder, CO; and M. Hu, S. G. Benjamin, S. S. Weygandt, T. G. Smirnova, S. E. Peckham, K. J. Brundage, J. M. Brown, and G. S. Manikin
A diabatic digital filter initialization (DFI)-based procedure for assimilating radar reflectivity data within the hourly updated Rapid Update Cycle (RUC) was developed in 2007 and implemented at NCEP in Dec. 2008, marking the first operational use of WSR-88D mosaic reflectivity data in an NCEP operational model. The radar assimilation procedure utilizes latent heating-based temperature tendency information obtained from reflectivity mosaic data to induce storm-scale circulation within the DFI. This procedure has significantly improved RUC short-range precipitation forecasts, especially for convective systems.
Recent research has extended the radar assimilation work in two directions. First, we are using hourly updated initial forecast fields from the radar assimilating RUC to initialize a 3-km explicit convection resolving nest, known as the High Resolution Rapid Refresh (HRRR). Run each hour out to 12-h over domain that has recently been expanded to cover most of the eastern 2/3 of the U.S., the HRRR has also benefitted significantly from the use of RUC grids that include the reflectivity assimilation. For the 2009 convective season, a more extensive demonstration / evaluation of the utility of the HRRR in providing convective storm guidance for aviation (in conjunction with CoSPA) and other applications (severe weather) is ongoing. In addition, we have recently begun creating a probabilistic thunderstorm guidance product from time-lagged ensemble output from the HRRR.
A second focus area has been porting of the radar assimilation procedure to the Rapid Refresh (RR) system. This has required changes to both the Gridpoint Statistical Interpolation (GSI) package (used for the data assimilation portion of the RR) and the WRF ARW system (used for the model forecast portion of the RR). For the data assimilation part, a generalized cloud analysis has been incorporated into GSI, in which NSSL radar reflectivity mosaic data are used to compute a latent heating-based temperature tendency. Within the WRF model, the latent heating-based temperature tendency replaces the temperature tendency from the cumulus scheme and the explicit microphysics in the forward integration portion of the DFI.
At the conference, we will describe the work in all of these areas and provide quantitative verification and case studies examples to illustrate the forecast improvement from the radar assimilation for the RUC, RR, and HRRR model forecasts.
Session 14, Data Assimilation II
Friday, 9 October 2009, 10:30 AM-12:00 PM, Auditorium
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