Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically Based Retrieval Scheme

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...

Full description

Saved in:
Bibliographic Details
Published in:Journal of hydrometeorology 2016-01, Vol.17 (1), p.383-400
Main Authors: Kidd, Chris, Matsui, Toshihisa, Chern, Jiundar, Mohr, Karen, Kummerow, Chris, Randel, Dave
Format: Article
Language:eng
Subjects:
Bayesian analysis
Climate system
Clouds
Consistency
Estimates
Global precipitation
Instruments
Interdisciplinary aspects
Mathematical models
Meteorological regimes
Meteorological satellites
Polar environments
Precipitation
Precipitation estimation
Probability theory
Profiles
Radar
Radar data
Rain
Rainfall
Remote sensing
Retrieval
Satellite observation
Satellite tracking
Satellites
Sensors
Spaceborne remote sensing
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1525-755X
1525-7541