Climate 2015, 3(2), 329-348; doi:10.3390/cli3020329
Probabilistic Precipitation Estimation with a Satellite Product
1
Department of Civil Engineering and NOAA-CREST, The City College of New York, New York, NY 10031, USA
2
Department of Geosciences, University of Rhode Island, Kingston, RI 02888, USA
3
The Small Earth Nepal, Kathmandu 44600, Nepal
4
Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, CO 80523, USA
*
Author to whom correspondence should be addressed.
Received: 19 March 2015 / Revised: 20 April 2015 / Accepted: 24 April 2015 / Published: 28 April 2015
(This article belongs to the Special Issue Climate Change and Development in South Asia)
Abstract
Satellite-based precipitation products have been shown to represent precipitation well over Nepal at monthly resolution, compared to ground-based stations. Here, we extend our analysis to the daily and subdaily timescales, which are relevant for mapping the hazards caused by storms as well as drought. We compared the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42RT product with individual stations and with the gridded APHRODITE product to evaluate its ability to retrieve different precipitation intensities. We find that 3B42RT, which is freely available in near real time, has reasonable correspondence with ground-based precipitation products on a daily timescale; rank correlation coefficients approach 0.6, almost as high as the retrospectively calibrated TMPA 3B42 product. We also find that higher-quality ground and satellite precipitation observations improve the correspondence between the two on the daily timescale, suggesting opportunities for improvement in satellite-based monitoring technology. Correlation of 3B42RT and 3B42 with station observations is lower on subdaily timescales, although the mean diurnal cycle of precipitation is roughly correct. We develop a probabilistic precipitation monitoring methodology that uses previous observations (climatology) as well as 3B42RT as input to generate daily precipitation accumulation probability distributions at each 0.25° x 0.25° grid cell in Nepal and surrounding areas. We quantify the information gain associated with using 3B42RT in the probabilistic model instead of relying only on climatology and show that the quantitative precipitation estimates produced by this model are well calibrated compared to APHRODITE. View Full-TextKeywords:
quantitative precipitation estimation; generalized linear model; Nepal; himalayas; monsoon Asia
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style
Krakauer, N.Y.; Pradhanang, S.M.; Panthi, J.; Lakhankar, T.; Jha, A.K. Probabilistic Precipitation Estimation with a Satellite Product. Climate 2015, 3, 329-348.