2.2 Assimilating simulated radar and satellite data using an OSSE experiment from 24 December 2009

Tuesday, 8 January 2013: 11:15 AM
Ballroom G (Austin Convention Center)
Thomas A. Jones, CIMMS/Univ. of Oklahoma, NOAA/OAR/NSSL, Norman, OK; and J. A. Otkin, D. J. Stensrud, and K. H. Knopfmeier

An Observing System Simulation Experiment (OSSE) was used to examine the impact of assimilating GOES-R 6.95 μm radiances and WSR-88D Doppler radar reflectivity and radial velocity observations for a case study occurring on 24 December 2009. This event produced blizzard conditions over much of the Southern Plains with severe weather occurring further east over Arkansas and Louisiana. An ensemble Kalman filter (EnKF) assimilation system was used to perform several assimilation experiments for different combinations of satellite and radar observations. Data were assimilated for a 48-member ensemble covering the CONUS with 15 km horizontal resolution and 51 vertical levels. Simulated radar observations were assimilated for 13 radar sites located over the Central and Southern Plains where most of the precipitation occurred, whereas the satellite observations were assimilated over the entire domain. All observations were assimilated at 5-minute intervals during a 1-hr period with 3-hr forecasts generated thereafter. Results from four assimilation experiments: conventional data only (CONV), conventional + satellite data (SAT), conventional + radar (RAD), and conventional + satellite + radar (RADSAT) are compared to atmospheric analyses from the “Truth” simulation. Results indicate that both satellite and radar observations reduce the RMSE for total cloud water (QALL) compared to the CONV experiment. Satellite data provided the most impact in the mid- to upper troposphere where the 6.95 μm water vapor band is most sensitive. Radar data improved QALL both near the surface and aloft due to the greater vertical resolution of these observations. Assimilating radar radial velocity also significantly reduces the wind errors where satellite data has limited impact. The RADSAT experiment consistent produces a model analysis with the lowest RMSE, which indicates that both radar and satellite observations observations provide independent information. Assimilating these data also produced a much more accurate depiction of the horizontal and vertical characteristics of the cloud water and ice fields for this case study. The positive results found by assimilating both satellite and radar data illustrates the potential for these data sets to improve mesoscale model analyses and ensuing forecasts.
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