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Correction of the High-Latitude Rain Day Anomaly in the NCEP–NCAR Reanalysis for Land Surface Hydrological Modeling

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  • 1 Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey
  • | 2 Institute of Water Resources and Environmental Engineering, Sun Yat-Sen University, Guangzhou, China
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Abstract

A spurious wavelike pattern in the monthly rain day statistics exists within the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis precipitation product. The anomaly, which is an artifact of the parameterization of moisture diffusion, occurs during the winter months in the Northern and Southern Hemisphere high latitudes. The anomaly is corrected by using monthly statistics from three different global precipitation products from 1) the University of Washington (UW), 2) the Global Precipitation Climate Project (GPCP), and 3) the Climatic Research Unit (CRU), resulting in three slightly different corrected precipitation products. The correction methodology, however, compromises spatial consistency (e.g., storm tracking) on a daily time scale. The effect that the precipitation correction has on the reanalysis-derived global land surface water budgets is investigated by forcing the Variable Infiltration Capacity (VIC) land surface model with all four datasets (i.e., the original reanalysis product and the three corrected datasets). The main components of the land surface water budget cycle are not affected substantially; however, the increased spatial variability in precipitation is reflected in the evaporation and runoff components but reduced in the case of soil moisture. Furthermore, the partitioning of precipitation into canopy evaporation and throughfall is sensitive to the rain day statistics of the correcting dataset, especially in the Tropics, and this has implications for the required accuracy of the correcting dataset. The output fields from these long-term land surface simulations provide a global, consistent dataset of water and energy states and fluxes that can be used for model intercomparisons, studies of annual and seasonal climate variability, and comparisons with current versions of numerical weather prediction models.

Corresponding author address: Justin Sheffield, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544. Email: justin@princeton.edu

Abstract

A spurious wavelike pattern in the monthly rain day statistics exists within the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis precipitation product. The anomaly, which is an artifact of the parameterization of moisture diffusion, occurs during the winter months in the Northern and Southern Hemisphere high latitudes. The anomaly is corrected by using monthly statistics from three different global precipitation products from 1) the University of Washington (UW), 2) the Global Precipitation Climate Project (GPCP), and 3) the Climatic Research Unit (CRU), resulting in three slightly different corrected precipitation products. The correction methodology, however, compromises spatial consistency (e.g., storm tracking) on a daily time scale. The effect that the precipitation correction has on the reanalysis-derived global land surface water budgets is investigated by forcing the Variable Infiltration Capacity (VIC) land surface model with all four datasets (i.e., the original reanalysis product and the three corrected datasets). The main components of the land surface water budget cycle are not affected substantially; however, the increased spatial variability in precipitation is reflected in the evaporation and runoff components but reduced in the case of soil moisture. Furthermore, the partitioning of precipitation into canopy evaporation and throughfall is sensitive to the rain day statistics of the correcting dataset, especially in the Tropics, and this has implications for the required accuracy of the correcting dataset. The output fields from these long-term land surface simulations provide a global, consistent dataset of water and energy states and fluxes that can be used for model intercomparisons, studies of annual and seasonal climate variability, and comparisons with current versions of numerical weather prediction models.

Corresponding author address: Justin Sheffield, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544. Email: justin@princeton.edu

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