Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically-Based Retrieval Scheme
The estimation of precipitation across the globe from satellite sensors provides a key resource in the observation and understanding of our climate system. Estimates from all pertinent satellite observations are critical in providing the necessary temporal sampling. However, consistency in these est...
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ftnasantrs:oai:casi.ntrs.nasa.gov:20170003704 2023-05-15T15:10:31+02:00 Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically-Based Retrieval Scheme Kummerow, Christian Matsui, Toshi Kidd, Chris Chern, Jiundar Randel, Dave Mohr, Karen Unclassified, Unlimited, Publicly available December 29, 2015 application/pdf http://hdl.handle.net/2060/20170003704 unknown Document ID: 20170003704 http://hdl.handle.net/2060/20170003704 Copyright, Distribution as joint owner in the copyright CASI Meteorology and Climatology GSFC-E-DAA-TN41841 Journal of Hydrometeorology (ISSN 1525-755X) (e-ISSN 1525-7541); 17; 1; 383–400 2015 ftnasantrs 2019-07-20T23:35:00Z The estimation of precipitation across the globe from satellite sensors provides a key resource in the observation and understanding of our climate system. Estimates from all pertinent satellite observations are critical in providing the necessary temporal sampling. However, consistency in these estimates from instruments with different frequencies and resolutions is critical. This paper details the physically based retrieval scheme to estimate precipitation from cross-track (XT) passive microwave (PM) sensors on board the constellation satellites of the Global Precipitation Measurement (GPM) mission. Here the Goddard profiling algorithm (GPROF), a physically based Bayesian scheme developed for conically scanning (CS) sensors, is adapted for use with XT PM sensors. The present XT GPROF scheme utilizes a model-generated database to overcome issues encountered with an observational database as used by the CS scheme. The model database ensures greater consistency across meteorological regimes and surface types by providing a more comprehensive set of precipitation profiles. The database is corrected for bias against the CS database to ensure consistency in the final product. Statistical comparisons over western Europe and the United States show that the XT GPROF estimates are comparable with those from the CS scheme. Indeed, the XT estimates have higher correlations against surface radar data, while maintaining similar root-mean-square errors. Latitudinal profiles of precipitation show the XT estimates are generally comparable with the CS estimates, although in the southern midlatitudes the peak precipitation is shifted equatorward while over the Arctic large differences are seen between the XT and the CS retrievals. Other/Unknown Material Arctic NASA Technical Reports Server (NTRS) Arctic |
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NASA Technical Reports Server (NTRS) |
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Meteorology and Climatology |
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Meteorology and Climatology Kummerow, Christian Matsui, Toshi Kidd, Chris Chern, Jiundar Randel, Dave Mohr, Karen Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically-Based Retrieval Scheme |
topic_facet |
Meteorology and Climatology |
description |
The estimation of precipitation across the globe from satellite sensors provides a key resource in the observation and understanding of our climate system. Estimates from all pertinent satellite observations are critical in providing the necessary temporal sampling. However, consistency in these estimates from instruments with different frequencies and resolutions is critical. This paper details the physically based retrieval scheme to estimate precipitation from cross-track (XT) passive microwave (PM) sensors on board the constellation satellites of the Global Precipitation Measurement (GPM) mission. Here the Goddard profiling algorithm (GPROF), a physically based Bayesian scheme developed for conically scanning (CS) sensors, is adapted for use with XT PM sensors. The present XT GPROF scheme utilizes a model-generated database to overcome issues encountered with an observational database as used by the CS scheme. The model database ensures greater consistency across meteorological regimes and surface types by providing a more comprehensive set of precipitation profiles. The database is corrected for bias against the CS database to ensure consistency in the final product. Statistical comparisons over western Europe and the United States show that the XT GPROF estimates are comparable with those from the CS scheme. Indeed, the XT estimates have higher correlations against surface radar data, while maintaining similar root-mean-square errors. Latitudinal profiles of precipitation show the XT estimates are generally comparable with the CS estimates, although in the southern midlatitudes the peak precipitation is shifted equatorward while over the Arctic large differences are seen between the XT and the CS retrievals. |
format |
Other/Unknown Material |
author |
Kummerow, Christian Matsui, Toshi Kidd, Chris Chern, Jiundar Randel, Dave Mohr, Karen |
author_facet |
Kummerow, Christian Matsui, Toshi Kidd, Chris Chern, Jiundar Randel, Dave Mohr, Karen |
author_sort |
Kummerow, Christian |
title |
Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically-Based Retrieval Scheme |
title_short |
Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically-Based Retrieval Scheme |
title_full |
Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically-Based Retrieval Scheme |
title_fullStr |
Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically-Based Retrieval Scheme |
title_full_unstemmed |
Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically-Based Retrieval Scheme |
title_sort |
global precipitation estimates from cross-track passive microwave observations using a physically-based retrieval scheme |
publishDate |
2015 |
url |
http://hdl.handle.net/2060/20170003704 |
op_coverage |
Unclassified, Unlimited, Publicly available |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
CASI |
op_relation |
Document ID: 20170003704 http://hdl.handle.net/2060/20170003704 |
op_rights |
Copyright, Distribution as joint owner in the copyright |
_version_ |
1766341532312403968 |