705A New GOES-R Risk Reduction Activities at CIRA

Tuesday, 9 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Matthew A. Rogers, CIRA/Colorado State Univ., Fort Collins, CO; and S. Miller, L. Grasso, J. M. Haynes, Y. J. Noh, J. Forsythe, M. Zupanski, D. T. Lindsey, and J. E. Solbrig

A team of atmospheric scientists at the Cooperative Institute for Research in the Atmosphere (CIRA) at the Colorado State University has been selected by the National Oceanic and Atmospheric Administration’s (NOAA) GOES-R Risk Reduction (GOES-R3) science program to develop applications to enhance the utilization of the GOES-R sensors, including the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The selected project topics follow NOAA’s Research and Development Objectives listed in its 5-year Strategic Plan. The projects will be carried out over a three-year period which started on 1 July 2017 and will end on 30 June 2019.

CIRA is working on five GOES-R3 application developments:

  • Developing an Environmental Awareness Repertoire of ABI Imagery (‘DEAR-ABII’) to Advise the Operational Weather Forecaster. DEAR-ABII maximizes the vast potential of the new GOES-R/GOES-16 sensor technology.
  • GOES-R ABI channel differencing used to reveal cloud-free zones of ‘precursors of convective initiation’. This product identifies where convective initiation may occur in cloud free skies.
  • Improving the ABI Cloud Layers Product for Multiple Layer Cloud Systems and Aviation Forecast Applications. This project aims to improve the GOES-16 cloud layer product by providing information on the boundaries of cloud layers even when one layer overlies another.
  • Using the New Capabilities of GOES-R to Improve Blended, Multisensor Water Vapor Products for Forecasters. GOES-R TPW retrievals will be merged with TPW derived from polar orbiter and surface data to improve the operational NOAA blended TPW product.
  • Data assimilation of GLM observations in HWRF/GSI system. Assimilation of GOES-R GLM observations for the NOAA operational hurricane model with the goal to improve operational hurricane forecasting.

Examples for each of these applications will be presented.

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