Volume 3 DEW-DROP March 2017 73 Validation of Satellite-Derived Rainfall Estimations and Observed Precipitation of Cyclone Roanu over the Bay of Bengal S. M. Quamrul Hassan*, Md. Shadekul Alam, Md. Bazlur Rashid, Kh. Hafizur Rahman, M. A. K. Mallik and Md. Sanaul Hoque Mondal Bangladesh Meteorological Department, Agargaon, Dhaka-1207, Bangladesh *Corresponding Author Email: smquamrul77@yahoo.com Abstract The precipitation along with storm surge due tropical cyclone is one of the most significant causes of economy and life fatalities over Bangladesh. An accurate forecast of rainfall by landfalling tropical cyclones is vital for the guidance to the decision makers and agencies that are engaged in taking safety measures or rehabilitations works in the coastal areas. Continuous high temporal resolution satellite data covering a large area are increasingly used for estimation of rainfall brought by tropical cyclone. In the present study, for inter-comparison of four satellite derived rainfall products namely the Precipitation Estimation algorithm from Remotely-Sensed Information using an Artificial Neural Network (PERSIANN), Climate Prediction Center MORPHING (CMORPH), the Global Satellite Mapping of Precipitation (GSMaP) and 3B42RT of Tropical Rainfall Measuring Mission (TRMM) daily data were spatially analyzed and also compared with observed rainfall. It is found that the spatial pattern of satellite derived rainfall reasonably well matched with that of observed but the amount varied. Key words: PERSIANN, CMORPH, GSMaP and TRMM. 1. Introduction Tropical cyclone that causes maximum disasters over low lying coastal areas are due to storm surge and strong winds, but torrential heavy rain from landfalling tropical cyclone producing freshwater floods is also equally important. Whenever heavy rainfall occurs accompanied by storm surges, it becomes more dangerous over the affected low lying coastal areas. Heavy rainfall also creates many difficulties in rehabilitation mechanisms. So, forecast for the rainfall potential of landfalling tropical cyclones is vital for guidance to the planers and agencies who are engaged in taking safety measures or rehabilitations works in the littoral areas. Satellite-based precipitation products have a significant importance for regional and global hydro- meteorological studies, especially for remote and data spares regions because they have large-scale coverage, high spatial and temporal resolution and are publicly available. Furthermore, these high-resolution products have been increasingly used in a wide range of applications, such as natural hazards (e.g., heavy rainfall, flood and landslide) monitoring, climate research, and hydro-meteorology-related fields. Continuous high temporal resolution satellite data covering a large area are available only from instruments onboard geostationary satellites. The lack of visible data at night has generally restricted geostationary rainfall monitoring technique to the use of IR data alone. Though the satellite IR algorithms benefit from high temporal sampling, the IR radiances emitting from cloud top have only an indirect relationship with surface rainfall which, in turn, results in weak statistical relationships between rainfall and cloudiness. The most common technique for IR rain estimate counts cloudy pixels within a given area that are colder than a given threshold temperature (e.g. 235°K in cloud indexing method by Arkin et al.) [1] .The pixels that are colder than threshold temperature are probably associated with precipitating convective clouds possessing cold high tops. Currently there are several quasi-global high-resolution satellite precipitation products including near-real-time (3B42RT, daily derived from 3B42RT) of TRMM, CMORPH algorithm) [2,3] , PERSIANN [4,5] and GSMaP. Because such products have global (or quasi-global) orientation, the performances of satellite precipitation products are expected to vary from place to place. It is thus necessary to evaluate the performances of satellite precipitation products with local rain gauge data before these products can be used with high confidence in a specific study area. Such evaluation and inter-comparison can also help to identify the most accurate and appropriate satellite precipitation product among various alternatives. Zifeng Yu et. el., evaluate the abilities of 3B42, CMORPH, and GMS5-TBB data in reflecting the gauge rainfall for Typhoon ‘Bilis’ (2006) and found that three retrieved rainfall datasets could reflect the rainfall patterns well and show considerable skill in the light and moderate rainfall categories, but not for heavy rainfall [6] . Estimation of maximum rainfall potential using TRMM derived multi satellite rain rate data appears to be useful to provide good guidance to the forecaster for forecasting of 24 hours rainfall amount expected from a landfalling cyclone like ‘Aila’ [7] . Chang et al. analyzed TRMM 3B42 rainfall estimates for ten typhoon that made landfall over Taiwan during 2007-2010 and it is found by comparing with radar reflectivity maps that the overall rain band structures within the Tropical Cyclones are revealed by the TRMM data quite well when the Tropical Cyclones are both over ocean and land [8] .