Assimilation of GOES-16 Radiances and Retrievals into the Warn-on-Forecast System

Details

• Journal Title:
  Monthly Weather Review
• Personal Author:
  Jones, Thomas A. ; Skinner, Patrick ; Yussouf, Nusrat ; Knopfmeier, Kent ; Reinhart, Anthony ; Wang, Xuguang ; Bedka, Kristopher ; Smith, William ; Palikonda, Rabindra
• NOAA Program & Office:
  CIMMS (Cooperative Institute for Mesoscale Meteorological Studies) ; OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory)
• Description:
  The increasing maturity of the Warn-on-Forecast System (WoFS) coupled with the now operational GOES-16 satellite allows for the first time a comprehensive analysis of the relative impacts of assimilating GOES-16 all-sky 6.2-, 6.9-, and 7.3-μm channel radiances compared to other radar and satellite observations. The WoFS relies on cloud property retrievals such as cloud water path, which have been proven to increase forecast skill compared to only assimilating radar data and other conventional observations. The impacts of assimilating clear-sky radiances have also been explored and shown to provide useful information on midtropospheric moisture content in the near-storm environment. Assimilation of all-sky radiances adds a layer of complexity and is tested to determine its effectiveness across four events occurring in the spring and summer of 2019. Qualitative and object-based verification of severe weather and the near-storm environment are used to assess the impact of assimilating all-sky radiances compared to the current model configuration. We focus our study through the entire WoFS analysis and forecasting cycle (1900–0600 UTC, daily) so that the impacts throughout the evolution of convection from initiation to large upscale growth can be assessed. Overall, assimilating satellite data improves forecasts relative to radar-only assimilation experiments. The retrieval method with clear-sky radiances performs best overall, but assimilating all-sky radiances does have very positive impacts in certain conditions. In particular, all-sky radiance assimilation improved convective initiation forecast of severe storms in several instances. This work represents an initial attempt at assimilating all-sky radiances into the WoFS and additional research is ongoing to further improve forecast skill.
• Keywords:
  Numerical Weather Forecasting Remote Sensing Numerical Weather Forecasting Remote Sensing
• Source:
  Mon. Wea. Rev. (2020) 148 (5): 1829–1859.
• DOI:
  https://doi.org/10.1175/MWR-D-19-0379.1
• Document Type:
  Journal Article
• Funding:
  Grant no. NA16OAR4590242 ; Grant no. NA16OAR4320115
• Rights Information:
  Other
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:f40269d7e98a0696835740dedbca08233d56be273492753d7062090bbc219db4b9bdf1ccef40c6797670b63e3f17fdfc89683b3d940416c2bed2d0f173414685
• Download URL:
  https://repository.library.noaa.gov/view/noaa/26131/noaa_26131_DS1.pdf
• File Type:
  [PDF - 9.21 MB ]