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The influence of observation errors on analysis error and forecast skill investigated with an observing system simulation experiment
Author(s) -
Privé N. C.,
Errico R. M.,
Tai K.S.
Publication year - 2013
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/jgrd.50452
Subject(s) - data assimilation , forecast skill , forecast error , error analysis , mean squared error , meteorology , statistics , errors in variables models , forecast verification , environmental science , climatology , computer science , mathematics , econometrics , geology , geography
The National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) observing system simulation experiment (OSSE) framework is used to explore the response of analysis error and forecast skill to observation quality. In an OSSE, synthetic observations may be created that have much smaller error than real observations, and precisely quantified error may be applied to these synthetic observations. Three experiments are performed in which synthetic observations with magnitudes of applied observation error that vary from zero to twice the estimated realistic error are ingested into the Goddard Earth Observing System Model (GEOS‐5) with Gridpoint Statistical Interpolation (GSI) data assimilation for a 1 month period representing July. The analysis increment and observation innovation are strongly impacted by observation error, with much larger variances for increased observation error. The analysis quality is degraded by increased observation error, but the change in root‐mean‐square error of the analysis state is small relative to the total analysis error. Surprisingly, in the 120 h forecast, increased observation error only yields a slight decline in forecast skill in the extratropics and no discernible degradation of forecast skill in the tropics.