Evaluation of Long-Term SSM/I-Based Precipitation Records over Land
Name
Alemohammad-2014-Evaluation of Long-T.pdf
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2.47 MB
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Author(s)
Entekhabi, Dara
Alemohammad, Hamed
McLaughlin, Dennis
Date Issued
October 2014
Journal
Journal of Hydrometeorology
Publisher
American Meteorological Society
Citation
Alemohammad, Seyed Hamed, Dara Entekhabi, and Dennis B. McLaughlin. “Evaluation of Long-Term SSM/I-Based Precipitation Records over Land.” Journal of Hydrometeorology 15, no. 5 (October 2014): 2012–2029. © 2014 American Meteorological Society
Version
Final published version
Abstract
The record of global precipitation mapping using Special Sensor Microwave Imager (SSM/I) measurements now extends over two decades. Similar measurements, albeit with different retrieval algorithms, are to be used in the Global Precipitation Measurement (GPM) mission as part of a constellation to map global precipitation with a more frequent data refresh rate. Remotely sensed precipitation retrievals are prone to both magnitude (precipitation intensity) and phase (position) errors. In this study, the ground-based radar precipitation product from the Next Generation Weather Radar stage-IV (NEXRAD-IV) product is used to evaluate a new metric of error in the long-term SSM/I-based precipitation records. The new metric quantifies the proximity of two multidimensional datasets. Evaluation of the metric across the years shows marked seasonality and precipitation intensity dependence. Drifts and changes in the instrument suite are also evident. Additionally, the precipitation retrieval errors conditional on an estimate of background surface soil moisture are estimated. The dynamic soil moisture can produce temporal variability in surface emissivity, which is a source of error in retrievals. Proper filtering has been applied in the analysis to differentiate between the detection error and the retrieval error. The identification of the different types of errors and their dependence on season, intensity, instrument, and surface conditions provide guidance to the development of improved retrieval algorithms for use in GPM constellation-based precipitation data products.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1175/jhm-d-13-0171.1
