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  • Author or Editor: Olivier P. Prat x
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Brian R. Nelson, Olivier P. Prat, and Ronald D. Leeper


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.

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Olivier P. Prat, Brian R. Nelson, Elsa Nickl, and Ronald D. Leeper


Three satellite gridded daily precipitation datasets—PERSIANN-CDR, GPCP, and CMORPH—that are part of the NOAA/Climate Data Record (CDR) program are evaluated in this work. The three satellite precipitation products (SPPs) are analyzed over their entire period of record, ranging from over 20 years to over 35 years. The products intercomparisons are performed at various temporal (daily to annual) resolutions and for different spatial domains in order to provide a detailed assessment of each SPP strengths and weaknesses. This evaluation includes comparison with in situ datasets from the Global Historical Climatology Network (GHCN-Daily) and the U.S. Climate Reference Network (USCRN). While the three SPPs exhibited comparable annual average precipitation, significant differences were found with respect to the occurrence and the distribution of daily rainfall events, particularly in the low and high rainfall rate ranges. Using USCRN stations over CONUS, results indicated that CMORPH performed consistently better than GPCP and PERSIANN-CDR for the usual metrics used for SPP evaluation (bias, correlation, accuracy, probability of detection, and false alarm ratio, among others). All SPPs were found to underestimate extreme rainfall (i.e., above the 90th percentile) from about −20% for CMORPH to −50% for PERSIANN-CDR. Those differences in performance indicate that the use of each SPP has to be considered with respect to the application envisioned, from the long-term qualitative analysis of hydroclimatological properties to the quantification of daily extreme events, for example. In that regard, the three satellite precipitation CDRs constitute a unique portfolio that can be used for various long-term climatological and hydrological applications.

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