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Rain detection and measurement from Megha-Tropiques microwave sounderSAPHIR

Varma, Atul Kumar and Piyush, DN and Gohil, BS and Pal, PK and Srinivasan, J (2016) Rain detection and measurement from Megha-Tropiques microwave sounderSAPHIR. In: JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 121 (15). pp. 9191-9207.

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Official URL: http://dx.doi.org/10.1002/2016JD024907


The Megha-Tropiques, an Indo-French satellite, carries on board a microwave sounder, Sondeur Atmospherique du Profil d'Humidite Intertropical par Radiometrie (SAPHIR), and a microwave radiometer, Microwave Analysis and Detection of Rain and Atmospheric Structures (MADRAS), along with two other instruments. Being a Global Precipitation Measurement constellation satellite MT-MADRAS was an important sensor to study the convective clouds and rainfall. Due to the nonfunctioning of MADRAS, the possibility of detection and estimation of rain from SAPHIR is explored. Using near-concurrent SAPHIR and precipitation radar (PR) onboard Tropical Rainfall Measuring Mission (TRMM) observations, the rain effect on SAPHIR channels is examined. All the six channels of the SAPHIR are used to calculate the average rain probability (P-R) for each SAPHIR pixel. Further, an exponential rain retrieval algorithm is developed. This algorithm explains a correlation of 0.72, RMS error of 0.75mm/h, and bias of 0.04mm/h. When rain identification and retrieval algorithms are applied together, it explains a correlation of 0.69 with an RMS error of 0.47mm/h and bias of 0.01mm/h. On applying the algorithm to the independent SAPHIR data set and compared with TRMM-3B42 rain on monthly scale, it explains a correlation of 0.85 and RMS error of 0.09mm/h. Further distribution of rain difference of SAPHIR with other rain products is presented on global scale as well as for the climatic zones. For examining the capability of SAPHIR to measure intense rain, instantaneous rain over Phailin cyclone from SAPHIR is compared with other standard satellite-based rain products such as 3B42, Global Satellite Mapping of Precipitation, and Precipitation Estimation from Remote Sensing Information using Artificial Neural Network.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the AMER GEOPHYSICAL UNION, 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA
Department/Centre: Division of Mechanical Sciences > Divecha Centre for Climate Change
Date Deposited: 28 Oct 2016 07:21
Last Modified: 28 Oct 2016 07:21
URI: http://eprints.iisc.ac.in/id/eprint/55160

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