4.4 JCSDA New Sensor and All-Sky Data Assimilation Activities

Wednesday, 25 January 2017: 4:45 PM
Conference Center: Yakima 2 (Washington State Convention Center )
Benjamin Johnson, AER, College Park, MD; and B. Orescanin, L. Liu, T. Auligne, and J. G. Yoe

A primary goal of the JCSDA is to accelerate the use of satellite-based observations to improve numerical weather prediction/analysis in all conditions, over all surface types.   This encompasses a wide range of technical and scientific challenges that have spanned decades of research and development, and are truly global in scope.      Traditionally, numerical weather prediction in the U.S. has focused primarily on clear-sky data assimilation.    However, with recent and planned updates to the global/regional weather prediction/analysis systems across various departments and agencies, it is evident that all-sky data assimilation is quickly becoming the focus for many research groups.  One aim of the JCSDA is to align research activities across the various partners to:

(a) leverage existing scientific and technical knowledge;

(b) avoid duplication of efforts;

(c) streamline existing techniques and develop more accurate methods for all-sky data assimilation; and

(d) to develop methods for accelerating the assimilation of new satellite sensors and reviewing and improving assimilation of existing sensors.  

This presentation will provide an overview for the work of the JCSDA Project on New and Improved Observations, which encompasses the assimilation of new sensors and the optimization of existing ones. Major scientific frontiers focus on improving data assimilation of radiances over-land and in all-sky conditions through improved scientific algorithms, data ingest, quality control, and the modifications to CRTM and GSI to support these efforts.   Additionally, this talk will give a brief overview of JPSS/GOES-R data assimilation challenges and plan of action for making the optimal use of these critically important satellite resources for analysis and weather prediction requirements.

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