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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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 |
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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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1810463955148079104 |