Merged and Gridded GPM and Atmospheric River Data Product.

The Global Precipitation Measurement (GPM) Mission Core Observatory satellite launched in 2014 as a joint mission between National Aeronautics and Space Administration (NASA) and JAXA. Global Precipitation Measurement (GPM) has, since that time, provided continuous, valuable dual‐frequency radar and passive microwave radiometer observations. Here, we introduce a gridded data set of collocated GPM Core Observatory observational products merged with a reanalysis‐derived Atmospheric river (AR) data set in the North Atlantic and North Pacific sectors. The three data sets that are merged and gridded are: (a) the NASA Goddard Profiling (GPROF) precipitation product, which uses GPM passive microwave radiometer observations to derive surface precipitation rates, (b) a water vapor data product derived from the GPM Core Observatory radiometer, provided by Remote Sensing Systems (RSS), and (c) the Mattingly et al. (2018, https://doi.org/10.1029/2018jd028714) AR data set that is specifically tuned to the high‐latitude regions. This novel merged data set spans from May 2014 to December 2022 with plans to update annually through 2026 at minimum. This gridded product combines RSS passive water vapor and precipitation estimates with coincident AR detection. This data product benefits the scientific community by providing (a) user‐friendly gridded satellite data compared to standard satellite data sets, while maintaining high temporal resolution, and (b) coincident satellite observations to assess the link between ARs and precipitation. Plain Language Summary: This paper summarizes the creation of a new, gridded data set containing precipitation, atmospheric water vapor content, and atmospheric river (AR) information from May 2014 to December 2022. The precipitation and water vapor data are both remotely‐sensed data products using the Global Precipitation Measurement Core Observatory Microwave Imager (GMI). The AR algorithm detection is more sensitive to moisture transport than global algorithms and is thus better suited to colder and drier environments. We temporally match and interpolate all data to a common grid and provide examples of the merged data to illustrate the seasonality and impacts of AR presence on precipitation. Lastly, we demonstrate the utility of this data set by investigating the assumptions used to estimate precipitation from GMI measurements. The data set confirms that ARs are associated with warmer temperatures, higher water vapor content, and more intense precipitation rates. Key Points: A new data set is presented that collocates satellite‐derived precipitation rates and water vapor with reanalysis‐derived Atmospheric rivers (ARs)Higher precipitation rates and water vapor content are coincident with AR events in the high‐latitude regionsAn example application illustrates warmer, more moist conditions during precipitating AR events that are present in the GPROF database [ABSTRACT FROM AUTHOR]