7B.8 Storm-Scale Satellite Data Assimilation into the WoF GSI-EnKF System: Recent Results and Future Plans

Tuesday, 5 June 2018: 3:15 PM
Colorado B (Grand Hyatt Denver)
Swapan Mallick, CIMMS, NOAA/ NSSL, Norman, OK; and T. A. Jones, K. H. Knopfmeier, D. C. Dowell, X. Wang, P. Skinner, and P. Minnis

Better representation in the model initial fields (or analysis) of the near-storm environments is very important for further improvement of convective-scale forecast skill. The NOAA’s ensemble based Warn-on-Forecast (WoF) project, known as the NSSL Experimental Warn-on-Forecast System (NEWS-e) has been developed to improve high impact weather forecasts. A convective-scale (~3 km) ensemble data assimilation with frequent update cycle and forecast system is developed using the WRF-ARW model and the Grid-point Statistical Interpolation (GSI) based Ensemble Kalman Filter (EnKF; GSI-EnKF) data assimilation system.

The main focus will be on satellite assimilation and the impact study of three different satellite data sets. These satellite data sets are (1) the clear-sky hyperspectral radiances from Atmospheric Infrared Sounder (AIRS) and the Cross-track Infrared Sounder (CrIS); (2) the cloud water path (CWP) retrievals from Geostationary Operational Environmental Satellite (GOES)-13 and (3) the atmospheric motion vectors (AMVs) from geostationary satellite GOES. In addition to the satellite data, we assimilate the available conventional data from Oklahoma mesonet observations; WSR-88D reflectivity from the NSSL- Multi Radar Multi Sensor (MRMS) product and Level 2 Doppler radial velocity from all radars in the storm-scale domain. All the data sets were assimilated at 15 minute assimilation cycles into the WoF GSI-EnKF system. The initial and the boundary conditions are provided by an experimental High-Resolution Rapid Refresh ensemble (HRRR-e).

The hyperspectral infrared sounder temperature and humidity from the weather satellites are important sources of data for the numerical weather prediction (NWP). Application of hyperspectral polar-orbiting satellite data in the high-resolution storm-scale model is limited due to the low temporal resolution, non-uniformity, and the lower model heights. This part of the work aims to improve short-term (0-3 hour) forecasts of high impact weather by assimilating the clear-sky satellite hyperspectral radiances. The clear-sky hyperspectral data are considered from AIRS instruments on board the Earth Observing System (EOS) Aqua satellite and the CrIS on board the Suomi-NPP spacecraft. The Community Radiative Transfer Model (CRTM) is used to calculate the simulated brightness temperature from the model state variables.

Cloud water path observations continue to be assimilated into the NEWS-e after successful evaluation during the 2016 and 2017 real-time experiments. Overall, assimilating CWP observations improved probabilistic forecasts of reflectivity and rotation objects compared to only assimilating radar and conventional observations. For 2018 and beyond, CWP retrievals from GOES-16 are being used, which offer improved spectral, spatial, and temporal resolution than the previous GOES-13 product. Tests of assimilating GOES-16 clear-sky water vapor radiances is also underway to complement the CWP assimilation with the goal of improving the moisture analysis in the near storm environment.

Beginning in 2018, tests of assimilating satellite-derived wind from cloud and moisture features of geostationary satellites (or Atmospheric Motion Vectors, AMVs) will be performed with a planned 2019 operational implementation into the WoF system. The AMVs are very important and valuable with high temporal frequency data to create more accurate analysis fields. These data can supplement radial velocity observations in radar coverage gaps and add upper-level wind information where sounding and aircraft data are not assimilated.

Key words: GSI-EnKF, Satellite data assimilation, Hyperspectral infrared radiance, Cloud water path, Atmospheric Motion Vectors, NWP, Warn-on-Forecast

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