Editorial Type:
Article
Article Type:
Research Article

Data Assimilation in the Presence of Forecast Bias: The GEOS Moisture Analysis

Dick P. Dee Data Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Ricardo Todling Data Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Print Publication:
01 Sep 2000
DOI:
https://doi.org/10.1175/1520-0493(2000)128<3268:DAITPO>2.0.CO;2
Page(s):
3268–3282
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Abstract

The authors describe the application of the unbiased sequential analysis algorithm developed by Dee and da Silva to the Goddard Earth Observing System moisture analysis. The algorithm estimates the slowly varying, systematic component of model error from rawinsonde observations and adjusts the first-guess moisture field accordingly. Results of two seasonal data assimilation cycles show that moisture analysis bias is almost completely eliminated in all observed regions. The improved analyses cause a sizable reduction in the 6-h forecast bias and a marginal improvement in the error standard deviations.

Corresponding author address: Dr. Dick P. Dee, NASA/GSFC, Mail Code 910.3, Greenbelt, MD 20771.

Email: dee@dao.gsfc.nasa.gov

Abstract

The authors describe the application of the unbiased sequential analysis algorithm developed by Dee and da Silva to the Goddard Earth Observing System moisture analysis. The algorithm estimates the slowly varying, systematic component of model error from rawinsonde observations and adjusts the first-guess moisture field accordingly. Results of two seasonal data assimilation cycles show that moisture analysis bias is almost completely eliminated in all observed regions. The improved analyses cause a sizable reduction in the 6-h forecast bias and a marginal improvement in the error standard deviations.

Corresponding author address: Dr. Dick P. Dee, NASA/GSFC, Mail Code 910.3, Greenbelt, MD 20771.

Email: dee@dao.gsfc.nasa.gov

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