JACEN  Vol.9 No.4 , November 2020
An Assessment of Spatial Distribution of Four Different Satellite-Derived Rainfall Estimations and Observed Precipitation over Bangladesh
Abstract: Given that precipitation is a major component of the earth’s water and energy cycles, reliable information on the monthly spatial distribution of precipitation is also crucial for climate science, climatological water-resource research studies, and for the evaluation of regional model simulations. In this paper, four satellite derived precipitation datasets: Climate Prediction Center MORPHING (CMORPH), Tropical Rainfall Measuring Mission (TRMM), the Precipitation Estimation Algorithm from Remotely-Sensed Information using an Artificial Neural Network (PERSIANN), and the global Satellite Mapping of Precipitation (GSMaP) are spatially analyzed and compared with the observed precipitation data provided by Bangladesh Meteorological Department (BMD). For this study, the different precipitations data sets are spatially analyzed from 2nd May 2019 to 4th May 2019 at the time of Cyclone FANI. It is found that the satellite derived precipitation datasets are reasonably matched with the observed but slightly different.
Cite this paper: Roy, D. , Hassan, S. and Sultana, S. (2020) An Assessment of Spatial Distribution of Four Different Satellite-Derived Rainfall Estimations and Observed Precipitation over Bangladesh. Journal of Agricultural Chemistry and Environment, 9, 195-205. doi: 10.4236/jacen.2020.94016.

[1]   de Goncalves, L.G.G., Shuttleworth, W.J., Nijssen, B., Burke, E.J., Marengo, J.A., Chou, S.C., Houser, P. and Toll, D.L. (2006) Evaluation of Model-Derived and Remotely Sensed Precipitation Products for Continental South America. Journal of Geophysical Research, 111, D16113.

[2]   Dai, A., Lin, X. and Hsu, K.L. (2007) The Frequency, Intensity, and Diurnal Cycle of Precipitation in Surface and Satellite Observations over Low- and Mid-Latitudes. Climate Dynamics, 29, 727-744.

[3]   Asadullah, A., McIntyre, N. and Kigobe, M. (2008) Evaluation of Five Satellite Products for Estimation of Rainfall over Uganda. Hydrological Sciences, 53, 1137-1150.

[4]   Nesbitt, S.W., Gochis, D.J. and Lang, T.J. (2008) The Diurnal Cycle of Clouds and Precipitation along the Sierra Madre Occidental Observed during NAME-2004: Implications for Warm Season Precipitation Estimation in Complex Terrain. Journal of Hydrometeorology, 9, 728-743.

[5]   Zhou, T., Yu, R., Chen, H., Dai, A. and Pan, Y. (2008) Summer Precipitation Frequency, Intensity, and Diurnal Cycle over China: A Comparison of Satellite Data with Rain Gauge Observations. Journal of Climate, 21, 3997-4010.

[6]   Stisen, S. and Sandholt, I. (2009) Evaluation of Remote-Sensing-Based Rainfall Products through Predictive Capability in Hydrological Runoff Modelling, Hydrological Processes, 24, 879-891.

[7]   Zifeng, Y., et al. (2009) Verification of Tropical Cyclone-Related Satellite Precipitation Estimates in Mainland China. Journal of Applied Meteorology and Climatology, 48, 2227-2241.

[8]   Biswas, H.R. (2013) A Case Study for Cyclone “Aila” for Forecasting Rainfall Using Satellite Derived Rain Rate Data. Mausam, 64, 77-82.

[9]   Chang, L.T.C., Cheung, K.K.W. and McAneney, J. (2013) Case Study of TRMM Satellite Rainfall Estimation for Landfalling Tropical Cyclones: Issues and Challenges. Tropical Cyclone Research and Review, 2, 109-123.

[10]   Dinku, T., Chidzambwa, S., Ceccato, P., Connor, S.J. and Ropelewski, C.F. (2008) Validation of High-Resolution Satellite Rainfall Products over Complex Terrain. International Journal of Remote Sensing, 29, 4097-4110.

[11]   Meng, J., Li, L., Hao, Z.C., Wang, J.H. and Shao, Q.X. (2014) Suitability of TRMM Satellite Rainfall in Driving a Distributed Hydrological Model in the Source Region of Yellow River. Journal of Hydrology, 509, 320-332.

[12]   Xue, X., Hong, Y., Limaye, A.S., Gourley, J.J., Huffman, G.J., Khan, S.I. and Chen, S. (2013) Statistical and Hydrological Evaluation of TRMM-Based Multi-Satellite Precipitation Analysis over the Wangchu Basin of Bhutan: Are the Latest Satellite Precipitation Products 3B43V7 Ready for Use in Ungauged Basin? Journal of Hydrology, 499, 91-99.

[13]   Kummerow, C., Barnes, W., Koju, T., Shiue, J. and Simpson, J. (1998) The Tropical Rainfall Measuring Mission (TRMM) Sensor Package, Journal of Atmospheric and Oceanic Technology, 15, 809-817.;2

[14]   Gao, H., Wood, E.F., Jackson, T.J., Drusch, M. and Bindlish, R. (2006) Using TRMM/TMI to Retrieve Surface Soil Moisture over the Southern United States from 1998 to 2002. Journal of Hydrometeorology, 7, 23-38.

[15]   Guo, R. and Liu, Y. (2014) Evaluation of Satellite Precipitation Products with Rain Gauge Data at Different Scales: Implications for Hydrological Applications. Water, 8, 281.

[16]   Huffman, G., Bolvin, D., Nelkin, E., Wolff, D., Adler, R., Gu, G. and Stocker, E. (2007) The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales. Journal of Hydrometeorology, 8, 38-55.

[17]   Hsu, K.-L., Gao, X., Sorooshian, S. and Gupta, H.V. (1997) Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks. Journal of Applied Meteorology, 36, 1176-1190.;2

[18]   Novella, N. and Thiaw, W. (2010) Validation of Satellite-Derived Rainfall Products over the Sahel. 1-9, Camp Springs, Wyle Information Systems/CPC/NOAA, MD.

[19]   Novella, N.S. and Thiaw, W.M. (2013) African Rainfall Climatology Version 2 for Famine Early Warning Systems. Journal of Applied Meteorology and Climatology, 52, 588-606.

[20]   Ashouri, H., Hsu, K.-L., Sorooshian, S., Braithwaite, D.K., Knapp, K.R., Cecil, L.D., Nelson, B.R. and Prat, O.P. (2015) PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies. Bulletin of the American Meteorological Society, 96, 69-83.

[21]   Ushio, T., Sasashige, K., Kubota, T., Shige, S., Okamoto, K., Aonashi, K., Inoue, T., Takahashi, N., Iguchi, T., Kachi, M., Oki, R., Morimoto, T. and Kawasaki, Z.-I. (2009) A Kalman Filter Approach to the Global Satellite Mapping of Precipitation (GSMaP) from Combined Passive Microwave and Infrared Radiometric Data. Journal of the Meteorological Society of Japan, 87A, 137-151.