Comparing Reanalyses Using Analysis Increment Statistics

Jean Fitzmaurice Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts

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Rafael L. Bras Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts

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Abstract

Reanalysis data are an important source of information for hydrometeorology applications, which use data assimilation to combine an imperfect atmospheric model with uncertain observations. However, uncertainty estimates are not normally provided with reanalyses. The model “first guess” (6-h forecast) is sometimes saved along with reanalysis estimates, which allows the calculation of the analysis increment (AI), defined as the analysis minus the model first guess. Analysis increment statistics could provide a quantitative index for comparing models in regions with sufficient observations. Monthly analysis increment statistics for the NCEP–NCAR Global Reanalysis 1 (NCEP-R1) and the NCEP/Department of Energy Global Reanalysis 2 (NCEP-R2) are computed for a North American and South American location for zonal and meridional wind and specific humidity at three atmospheric levels for 1998–2001. NCEP-R2 specific humidity was found to have a smaller mean monthly standard deviation of the analysis increment than NCEP-R1 at the North American location at the 300-mb level. The NCEP-R2 specific humidity monthly standard deviation at the South American location is much larger than NCEP-R1 for September–November 1998, which may be related to the transition to La Niña. For both zonal and meridional wind, the monthly AI standard deviations are similar for NCEP-R1 and NCEP-R2 at all atmospheric levels for the North American location. The South American location exhibits similar behavior for the wind AI statistics as for specific humidity: NCEP-R2 has a much larger monthly standard deviation of the AI for September–November 1998. The analysis increment statistics could be one method for quantitatively comparing reanalyses. First guess information should be available to the user in reanalysis archives.

Corresponding author address: Jean Fitzmaurice, c/o Rafael L. Bras, Rm. 48–211, MIT, Cambridge, MA 02139. Email: jfitz@mit.edu

Abstract

Reanalysis data are an important source of information for hydrometeorology applications, which use data assimilation to combine an imperfect atmospheric model with uncertain observations. However, uncertainty estimates are not normally provided with reanalyses. The model “first guess” (6-h forecast) is sometimes saved along with reanalysis estimates, which allows the calculation of the analysis increment (AI), defined as the analysis minus the model first guess. Analysis increment statistics could provide a quantitative index for comparing models in regions with sufficient observations. Monthly analysis increment statistics for the NCEP–NCAR Global Reanalysis 1 (NCEP-R1) and the NCEP/Department of Energy Global Reanalysis 2 (NCEP-R2) are computed for a North American and South American location for zonal and meridional wind and specific humidity at three atmospheric levels for 1998–2001. NCEP-R2 specific humidity was found to have a smaller mean monthly standard deviation of the analysis increment than NCEP-R1 at the North American location at the 300-mb level. The NCEP-R2 specific humidity monthly standard deviation at the South American location is much larger than NCEP-R1 for September–November 1998, which may be related to the transition to La Niña. For both zonal and meridional wind, the monthly AI standard deviations are similar for NCEP-R1 and NCEP-R2 at all atmospheric levels for the North American location. The South American location exhibits similar behavior for the wind AI statistics as for specific humidity: NCEP-R2 has a much larger monthly standard deviation of the AI for September–November 1998. The analysis increment statistics could be one method for quantitatively comparing reanalyses. First guess information should be available to the user in reanalysis archives.

Corresponding author address: Jean Fitzmaurice, c/o Rafael L. Bras, Rm. 48–211, MIT, Cambridge, MA 02139. Email: jfitz@mit.edu

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