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Using Ancillary Information from Radar-Based Observations and Rain Gauges to Identify Error and Bias

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  • 1 a NOAA/NESDIS/National Centers for Environmental Information, Asheville, North Carolina
  • | 2 b North Carolina Institute for Climate Studies, North Carolina State University, Asheville, North Carolina
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Abstract

Ancillary information that exists within rain gauge and radar-based datasets provides opportunities to better identify error and bias between the two observing platforms as compared to error and bias statistics without ancillary information. These variables include precipitation type identification, air temperature, and radar quality. There are two NEXRAD-based datasets used for reference: the National Centers for Environmental Prediction (NCEP) Stage IV and the NOAA NEXRAD Reanalysis (NNR) gridded datasets. The NCEP Stage IV dataset is available at 4 km hourly and includes radar–gauge bias adjusted precipitation estimates. The NNR dataset is available at 1 km at 5-min and hourly time intervals and includes several different variables such as reflectivity, radar-only estimates, precipitation flag, radar quality indicator, and radar–gauge bias adjusted precipitation estimates. The NNR data product provides additional information to apply quality control such as identification of precipitation type, identification of storm type and ZR relation. Other measures of quality control are a part of the NNR data product development. In addition, some of the variables are available at 5-min scale. We compare the radar-based estimates with the rain gauge observations from the U.S. Climate Reference Network (USCRN). The USCRN network is available at the 5-min scale and includes observations of air temperature, wind, and soil moisture, among others. We present statistical comparisons of rain gauge observations with radar-based estimates by segmenting information based on precipitation type, air temperature, and radar quality indicator.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Brian R. Nelson, brian.nelson@noaa.gov

Abstract

Ancillary information that exists within rain gauge and radar-based datasets provides opportunities to better identify error and bias between the two observing platforms as compared to error and bias statistics without ancillary information. These variables include precipitation type identification, air temperature, and radar quality. There are two NEXRAD-based datasets used for reference: the National Centers for Environmental Prediction (NCEP) Stage IV and the NOAA NEXRAD Reanalysis (NNR) gridded datasets. The NCEP Stage IV dataset is available at 4 km hourly and includes radar–gauge bias adjusted precipitation estimates. The NNR dataset is available at 1 km at 5-min and hourly time intervals and includes several different variables such as reflectivity, radar-only estimates, precipitation flag, radar quality indicator, and radar–gauge bias adjusted precipitation estimates. The NNR data product provides additional information to apply quality control such as identification of precipitation type, identification of storm type and ZR relation. Other measures of quality control are a part of the NNR data product development. In addition, some of the variables are available at 5-min scale. We compare the radar-based estimates with the rain gauge observations from the U.S. Climate Reference Network (USCRN). The USCRN network is available at the 5-min scale and includes observations of air temperature, wind, and soil moisture, among others. We present statistical comparisons of rain gauge observations with radar-based estimates by segmenting information based on precipitation type, air temperature, and radar quality indicator.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Brian R. Nelson, brian.nelson@noaa.gov
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