Climatology-Calibrated Precipitation Analysis at Fine Scales: Statistical Adjustment of Stage IV toward CPC Gauge-Based Analysis

Dingchen Hou * Environmental Modeling Center/NCEP/NWS/NOAA, College Park, Maryland

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Mike charles * Environmental Modeling Center/NCEP/NWS/NOAA, College Park, Maryland
Science Application International Corporation, Camp Springs, Maryland

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Yan Luo * Environmental Modeling Center/NCEP/NWS/NOAA, College Park, Maryland
I. M. Systems Group, Inc., Camp Springs, Maryland

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Zoltan Toth Global System Division/ESRL/OAR/NOAA, Boulder, Colorado

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Yuejian Zhu * Environmental Modeling Center/NCEP/NWS/NOAA, College Park, Maryland

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Roman Krzysztofowicz University of Virginia, Charlottesville, Virginia

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Ying Lin * Environmental Modeling Center/NCEP/NWS/NOAA, College Park, Maryland

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Pingping Xie ** Climate Prediction Center/NCEP/NWS/NOAA, College Park, Maryland

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Dong-Jun Seo Office of Hydrologic Development/NWS/NOAA, Silver Spring, Maryland

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Malaquias Pena * Environmental Modeling Center/NCEP/NWS/NOAA, College Park, Maryland
I. M. Systems Group, Inc., Camp Springs, Maryland

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Bo Cui * Environmental Modeling Center/NCEP/NWS/NOAA, College Park, Maryland
I. M. Systems Group, Inc., Camp Springs, Maryland

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Abstract

Two widely used precipitation analyses are the Climate Prediction Center (CPC) unified global daily gauge analysis and Stage IV analysis based on quantitative precipitation estimate with multisensor observations. The former is based on gauge records with a uniform quality control across the entire domain and thus bears more confidence, but provides only 24-h accumulation at ⅛° resolution. The Stage IV dataset, on the other hand, has higher spatial and temporal resolution, but is subject to different methods of quality control and adjustments by different River Forecasting Centers. This article describes a methodology used to generate a new dataset by adjusting the Stage IV 6-h accumulations based on available joint samples of the two analyses to take advantage of both datasets. A simple linear regression model is applied to the archived historical Stage IV and the CPC datasets after the former is aggregated to the CPC grid and daily accumulation. The aggregated Stage IV analysis is then adjusted based on this linear model and then downscaled back to its original resolution. The new dataset, named Climatology-Calibrated Precipitation Analysis (CCPA), retains the spatial and temporal patterns of the Stage IV analysis while having its long-term average and climate probability distribution closer to that of the CPC analysis. The limitation of the methodology at some locations is mainly associated with heavy to extreme precipitation events, which the Stage IV dataset tends to underestimate. CCPA cannot effectively correct this because of the linear regression model and the relative scarcity of heavy precipitation in the training data sample.

Current affiliation: Climate Prediction Center/NCEP/NWS/NOAA, College Park, Maryland.

Current affiliation: Department of Civil Engineering, University of Texas at Arlington, Arlington, Texas.

Corresponding author address: Dr. Dingchen Hou, Environmental Modeling Center/NCEP/NWS/NOAA, 5830 University Research Court, College Park, MD 20740. E-mail: dingchen.hou@noaa.gov

Abstract

Two widely used precipitation analyses are the Climate Prediction Center (CPC) unified global daily gauge analysis and Stage IV analysis based on quantitative precipitation estimate with multisensor observations. The former is based on gauge records with a uniform quality control across the entire domain and thus bears more confidence, but provides only 24-h accumulation at ⅛° resolution. The Stage IV dataset, on the other hand, has higher spatial and temporal resolution, but is subject to different methods of quality control and adjustments by different River Forecasting Centers. This article describes a methodology used to generate a new dataset by adjusting the Stage IV 6-h accumulations based on available joint samples of the two analyses to take advantage of both datasets. A simple linear regression model is applied to the archived historical Stage IV and the CPC datasets after the former is aggregated to the CPC grid and daily accumulation. The aggregated Stage IV analysis is then adjusted based on this linear model and then downscaled back to its original resolution. The new dataset, named Climatology-Calibrated Precipitation Analysis (CCPA), retains the spatial and temporal patterns of the Stage IV analysis while having its long-term average and climate probability distribution closer to that of the CPC analysis. The limitation of the methodology at some locations is mainly associated with heavy to extreme precipitation events, which the Stage IV dataset tends to underestimate. CCPA cannot effectively correct this because of the linear regression model and the relative scarcity of heavy precipitation in the training data sample.

Current affiliation: Climate Prediction Center/NCEP/NWS/NOAA, College Park, Maryland.

Current affiliation: Department of Civil Engineering, University of Texas at Arlington, Arlington, Texas.

Corresponding author address: Dr. Dingchen Hou, Environmental Modeling Center/NCEP/NWS/NOAA, 5830 University Research Court, College Park, MD 20740. E-mail: dingchen.hou@noaa.gov
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