1200 Assimilation of GOES-13 Imager Clear-Sky Water Vapor (6.5 mm) Radiances into a Warn-on-Forecast System

Wednesday, 10 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Thomas A. Jones, Cooperative Institute for Mesoscale Meteorological Studies/Univ. of Oklahoma, and NOAA/OAR/NSSL, Norman, OK; and X. Wang and P. S. Skinner

The goal of the Warn-on-Forecast (WoF) project is to provide probabilistic short-term (0-3 h) forecast guidance for high impact weather events such as tornadoes, hail, high winds, and flash flooding. A prototype convection allowing system using the Advanced Weather and Research Forecasting (WRF-ARW) model employing an ensemble Kalman filter (EnKF) data assimilation technique has been developed and used during the spring 2016 and 2017 Hazardous Weather Testbeds. This system assimilates WSR-88D reflectivity and radial velocity and geostationary satellite cloud water path (CWP) retrievals as well as available surface observations over a regional domain with a 3 km horizontal resolution at 15 minute intervals. Initial conditions are provided by an experimental High Resolution Rapid Refresh ensemble (HRRR-e). This system has been shown to provide skillful forecasts of high impact weather events with radar and satellite data providing an accurate analysis of convection within the model. However, no information on the atmospheric conditions above the surface is currently assimilated as few timely observations exist. This lack of observations can lead to a poor analysis of the near-storm environment resulting in poor forecasts even if the analysis of the convection is excellent.

One potential solution to this problem is to also assimilate clear-sky satellite radiances, which provide information on mid- and upper-tropospheric temperature and moisture conditions. This research assimilates GOES-13 imager water vapor band radiances using the GSI-EnKF system to take advantage of the Community Radiative Transfer Model (CRTM) integration. The GOES-13 water vapor band is sensitive to the mid- and upper-tropospheric water vapor content and when assimilated, can adjust the model environment to better correspond to observed conditions. We assimilated water vapor radiances for 4 case study events that occurred during May 2016. Results showed that assimilating these radiances generally had the correct impact on the model environment, reducing humidity errors at the appropriate model levels where verification observations were present. The impact on high impact weather forecasts, as verified against forecast reflectivity and updraft helicity, were mixed. Two cases (9 and 22 May) showed some improvement in skill while the other two (24 and 25 May) were inconclusive, despite the latter’s significantly improved environment. This research represents the first step in designing a high-resolution ensemble data assimilation system to use GOES-16 Advanced Baseline Imager data, which provides three water vapor bands and will have greater potential to improve the model environment and high impact weather forecasts.

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