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Improve GSI Forward Model For Surface Observation In Complex Terrain Area


The Gridpoint Statistical Interpolation (GSI) Data Assimilation System has been developed as analysis component for both global (GFS) and regional (NAM, RAP/HRRR) numerical weather forecast systems in NOAA/NCEP. GSI can analyze many types of the observations with well-developed forward model, such as soundings, aircraft observations, satellite radiance, and GPS refractivity/bending angle. But in most of the GSI applications, including RAP/HRRR, the same forward model that interpolates background fields to observation location only based on the distance among observation and the grid point is used for upper air and surface observations. This forward model for surface observations can induce large representativeness error when surface conditions are inhomogeneous, such as on coastlines and in complex terrain regions. In this study, the data analysis problem in complex terrain area will be analyzed through the HRRR system. An enhanced observation operator that can pick only nearby grid points having similar terrain elevation is developed for effective surface assimilation in complex terrain. Retrospective cases will be conducted to test and verify the improved forward model. Results from those tests will be discussed during the conference.

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Published On
January 01, 2018

This publication was presented at the following:

AMS 98th Annual meeting - 2018
American Meteorological Society
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