Modeling Level 2 Passive Microwave Precipitation Retrieval Error Over Complex Terrain Using a Nonparametric Statistical Technique

Yagmur Derin; Md Abul Ehsan Bhuiyan; Emmanouil Anagnostou; John Kalogiros; Marios N. Anagnostou

doi:10.1109/TGRS.2020.3038343

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Modeling Level 2 Passive Microwave Precipitation Retrieval Error Over Complex Terrain Using a Nonparametric Statistical Technique

Journal article

Peer reviewed

Modeling Level 2 Passive Microwave Precipitation Retrieval Error Over Complex Terrain Using a Nonparametric Statistical Technique

Yagmur Derin, Md Abul Ehsan Bhuiyan, Emmanouil Anagnostou, John Kalogiros and Marios N. Anagnostou

IEEE transactions on geoscience and remote sensing, Vol.59(11), pp.9021-9032

11/01/2021

DOI: 10.1109/TGRS.2020.3038343

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Abstract

The representation of precipitation variability over mountainous regions by ground-based sensors is an open problem in hydrometeorological applications that necessitates the use of satellite-based precipitation products (SPPs). An extended network of ground-based X-band radar (GR) deployments over complex terrain areas, including the northeastern Italian Alps, North Carolina, Olympic Mountain, and the southern tip of Vancouver Island, is used in this study as a benchmark rainfall data set for error characterization and modeling of Level 2 PMW retrievals (Goddard profiling (GPROF) V05 algorithm) for the different sensors: the Microwave Humidity Sounder (MHS), the Special Sensor Microwave Imager/Sounder (SSMIS), the Global Precipitation Measurement Microwave Imager (GMI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2). Matchups of Level 2 PMW/GR rainfall are extracted based on a matching methodology that identifies GR volume scans with PMW overpasses, and scales GR parameters to the satellite products' nominal spatial resolution. The error model is the nonparametric machine learning tree-based quantile regression forest (QRF), which we developed using matchups of PMW/GR rainfall data from the different study areas. Validation of the error model is conducted using three cross-validation techniques: the k-fold, leave-one region out, and enforced. All validations showed that the error model-based corrections can significantly reduce both the mean relative error and the random component of PMW products. Moreover, the error reduction demonstrated with the leave-one region out cross-validation technique indicated that the error model is transferable among complex terrain regions. Algorithm developers may find this error model useful to integrate in the Level 3 products.

Data models

Error analysis

machine learning

Microwave imaging

Microwave measurement

Microwave radiometry

Microwave theory and techniques

passive microwave

precipitation

quantile regression forest (QRF)

Radar

Rain

X-band dual polarization radar

Details

Title: Subtitle: Modeling Level 2 Passive Microwave Precipitation Retrieval Error Over Complex Terrain Using a Nonparametric Statistical Technique
Creators: Yagmur Derin - University of Oklahoma
Md Abul Ehsan Bhuiyan - University of Connecticut
Emmanouil Anagnostou - University of Connecticut
John Kalogiros - National Observatory of Athens
Marios N. Anagnostou - National Observatory of Athens
Resource Type: Journal article
Publication Details: IEEE transactions on geoscience and remote sensing, Vol.59(11), pp.9021-9032
DOI: 10.1109/TGRS.2020.3038343
ISSN: 0196-2892
eISSN: 1558-0644
Publisher: IEEE
Number of pages: 12
Grant note: Ever-source Energy Center at the University of Connecticut, Storrs, CT USA (10.13039/100007710)
Language: English
Date published: 11/01/2021
Academic Unit: Civil and Environmental Engineering
Record Identifier: 9984944741802771

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