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

Abstract

AbstractAncillary information that exists within rain gauge and radar-based data sets 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 data sets used for reference; the National Centers for Environmental Prediction (NCEP) stage IV and the NOAA NEXRAD Reanalysis (NNR) gridded data sets. The NCEP stage IV data set is available at 4km hourly and includes radar-gauge bias adjusted precipitation estimates. The NNR data set is available at 1km at 5-minute 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 Z-R 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-minute 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-minute 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.