Merged and Gridded GPM and Atmospheric River Data Product

Abstract 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...

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Published in:PLOS ONE
Main Authors: Marian E. Mateling, Claire Pettersen, Kyle Mattingly, Sarah Ringerud
Format: Article in Journal/Newspaper
Language:English
Published: American Geophysical Union (AGU) 2024
Subjects:
Online Access:https://doi.org/10.1029/2023EA003333
https://doaj.org/article/d8641588c94445879ce3eac8850825ec
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spelling ftdoajarticles:oai:doaj.org/article:d8641588c94445879ce3eac8850825ec 2024-09-15T18:23:42+00:00 Merged and Gridded GPM and Atmospheric River Data Product Marian E. Mateling Claire Pettersen Kyle Mattingly Sarah Ringerud 2024-05-01T00:00:00Z https://doi.org/10.1029/2023EA003333 https://doaj.org/article/d8641588c94445879ce3eac8850825ec EN eng American Geophysical Union (AGU) https://doi.org/10.1029/2023EA003333 https://doaj.org/toc/2333-5084 2333-5084 doi:10.1029/2023EA003333 https://doaj.org/article/d8641588c94445879ce3eac8850825ec Earth and Space Science, Vol 11, Iss 5, Pp n/a-n/a (2024) precipitation remote sensing atmospheric rivers Astronomy QB1-991 Geology QE1-996.5 article 2024 ftdoajarticles https://doi.org/10.1029/2023EA003333 2024-08-05T17:49:13Z Abstract 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. Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles PLOS ONE 19 6 e0304744
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic precipitation
remote sensing
atmospheric rivers
Astronomy
QB1-991
Geology
QE1-996.5
spellingShingle precipitation
remote sensing
atmospheric rivers
Astronomy
QB1-991
Geology
QE1-996.5
Marian E. Mateling
Claire Pettersen
Kyle Mattingly
Sarah Ringerud
Merged and Gridded GPM and Atmospheric River Data Product
topic_facet precipitation
remote sensing
atmospheric rivers
Astronomy
QB1-991
Geology
QE1-996.5
description Abstract 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.
format Article in Journal/Newspaper
author Marian E. Mateling
Claire Pettersen
Kyle Mattingly
Sarah Ringerud
author_facet Marian E. Mateling
Claire Pettersen
Kyle Mattingly
Sarah Ringerud
author_sort Marian E. Mateling
title Merged and Gridded GPM and Atmospheric River Data Product
title_short Merged and Gridded GPM and Atmospheric River Data Product
title_full Merged and Gridded GPM and Atmospheric River Data Product
title_fullStr Merged and Gridded GPM and Atmospheric River Data Product
title_full_unstemmed Merged and Gridded GPM and Atmospheric River Data Product
title_sort merged and gridded gpm and atmospheric river data product
publisher American Geophysical Union (AGU)
publishDate 2024
url https://doi.org/10.1029/2023EA003333
https://doaj.org/article/d8641588c94445879ce3eac8850825ec
genre North Atlantic
genre_facet North Atlantic
op_source Earth and Space Science, Vol 11, Iss 5, Pp n/a-n/a (2024)
op_relation https://doi.org/10.1029/2023EA003333
https://doaj.org/toc/2333-5084
2333-5084
doi:10.1029/2023EA003333
https://doaj.org/article/d8641588c94445879ce3eac8850825ec
op_doi https://doi.org/10.1029/2023EA003333
container_title PLOS ONE
container_volume 19
container_issue 6
container_start_page e0304744
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