Understanding Overland Multisensor Satellite Precipitation Error in TMPA-RT Products
Autor: | YaoYao Zheng, Abebe S. Gebregiorgis, Nicholas Carr, Pierre-Emmanuel Kirstetter, Yang Hong, Walter A. Petersen, Jonathan J. Gourley |
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Rok vydání: | 2017 |
Předmět: |
Atmospheric Science
Quantitative precipitation estimation 010504 meteorology & atmospheric sciences Severe weather Meteorology 0208 environmental biotechnology Reference data (financial markets) 02 engineering and technology Satellite precipitation 01 natural sciences 020801 environmental engineering Multiple sensors law.invention law Environmental science Precipitation analysis Precipitation Radar 0105 earth and related environmental sciences Remote sensing |
Zdroj: | Journal of Hydrometeorology. 18:285-306 |
ISSN: | 1525-7541 1525-755X |
Popis: | The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) has provided the global community a widely used multisatellite (and multisensor type) estimate of quasi-global precipitation. One of the TMPA level-3 products, 3B42RT/TMPA-RT (where RT indicates real time), is a merged product of microwave (MW) and infrared (IR) precipitation estimates, which attempts to exploit the most desirable aspects of both types of sensors, namely, quality rainfall estimation and spatiotemporal resolution. This study extensively and systematically evaluates multisatellite precipitation errors by tracking the sensor-specific error sources and quantifying the biases originating from multiple sensors. High-resolution, ground-based radar precipitation estimates from the Multi-Radar Multi-Sensor (MRMS) system, developed by the National Severe Storms Laboratory (NSSL), are utilized as reference data. The analysis procedure involves segregating the grid precipitation estimate as a function of sensor source, decomposing the bias, and then quantifying the error contribution per grid. The results of this study reveal that while all three aspects of detection (i.e., hit, missed-rain, and false-rain biases) contribute to the total bias associated with IR precipitation estimates, overestimation bias (positive hit bias) and missed precipitation are the dominant error sources for MW precipitation estimates. Considering only MW sensors, the TRMM Microwave Imager (TMI) shows the largest missed-rain and overestimation biases (nearly double that of the other MW estimates) per grid box during the summer and winter seasons. The Special Sensor Microwave Imagers/Sounders (SSMIS on board F17 and F16) also show major error during winter and spring, respectively. |
Databáze: | OpenAIRE |
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