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

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.

[1]  T. Kailath,et al.  An innovations approach to least-squares estimation--Part II: Linear smoothing in additive white noise , 1968 .

[2]  B. Friedland.  Treatment of bias in recursive filtering , 1969 .

[3]  A. Jazwinski.  Stochastic Processes and Filtering Theory , 1970 .

[4]  William H. Press,et al.  Numerical Recipes in FORTRAN - The Art of Scientific Computing, 2nd Edition , 1987 .

[5]  H. Jean Thiébaux,et al.  Short‐term systematic errors in global forecasts: their estimation and removal , 1990 .

[6]  D. Dee.  Simplification of the Kalman filter for meteorological data assimilation , 1991 .

[7]  R. Daley.  The Effect of Serially Correlated Observation and Model Error on Atmospheric Data Assimilation , 1992 .

[8]  Suranjana Saha,et al.  Response of the NMC MRF Model to Systematic-Error Correction within Integration , 1992 .

[9]  R. Daley.  The Lagged Innovation Covariance: A Performance Diagnostic for Atmospheric Data Assimilation , 1992 .

[10]  R. Koster,et al.  Modeling the land surface boundary in climate models as a composite of independent vegetation stands , 1992 .

[11]  D. Dee.  On-line Estimation of Error Covariance Parameters for Atmospheric Data Assimilation , 1995 .

[12]  Impact of a Revised Analysis Algorithm on an Operational Data Assimilation System , 1996 .

[13]  Lawrence L. Takacs,et al.  Data Assimilation Using Incremental Analysis Updates , 1996 .

[14]  Assimiliation.  A Simple Bias Correction Algorithm for use in Data , 1996 .

[15]  Nancy Nichols,et al.  Accounting for Model Error in Data Assimilation using Adjoint Methods , 1996 .

[16]  Dick Dee,et al.  Ooce Note Series on Global Modeling and Data Assimilation Maximum-likelihood Estimation of Forecast and Observation Error Covariance Parameters , 2022 .

[17]  Lippincott Williams Wilkins,et al.  Part I , 1997, Neurology.

[18]  The Vertical Extent of the Influence of the Land Surface Boundary on the Atmosphere Above , 1998 .

[19]  W. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[20]  R. Rood,et al.  Upper tropospheric water vapor from GEOS reanalysis and UARS , 1998 .

[21]  S. Cohn,et al.  Assessing the Effects of Data Selection with the DAO Physical-Space Statistical Analysis System* , 1998 .

[22]  Arlindo da Silva,et al.  Data assimilation in the presence of forecast bias , 1998 .

[23]  Frank J. Wentz,et al.  A Well Calibrated Ocean Algorithm for SSM/I , 1999 .

[24]  Dick Dee,et al.  Maximum-Likelihood Estimation of Forecast and Observation Error Covariance Parameters. Part II: Applications , 1999 .

[25]  Arthur Y. Hou,et al.  Empirical Correction of a Dynamical Model. Part I: Fundamental Issues , 1999 .

[26]  Dick Dee,et al.  Maximum-Likelihood Estimation of Forecast and Observation Error Covariance Parameters. Part I: Methodology , 1999 .

[27]  L. Rokke,et al.  Variational cloud‐clearing with TOVS data , 2000 .

[28]  Robert Atlas,et al.  Assimilation of SSM/I-Derived Surface Rainfall and Total Precipitable Water for Improving the GEOS Analysis for Climate Studies , 2000 .