Evaluation of five satellite products for estimation of rainfall over Uganda / Evaluation de cinq produits satellitaires pour l'estimation des précipitations en Ouganda

Abstract Five satellite-based rainfall estimation algorithms (TRMM 3B42, CMORPH, TAMSAT, RFE 2.0 and PERSIANN) are assessed against historical monthly rainfall statistics from raingauges within four regions of Uganda. Results are discussed in terms of the accuracy of the products, the effect of climate variability, and differences between products. Products are able to reasonably reflect seasonal patterns in rainfall, and also the spatial patterns in rainfall between regions. Patterns in the occurrence of rainfall are better reflected than patterns in rainfall amounts. There is significant scope for improving the estimation of amounts by calibration to the raingauge data. TRMM 3B42, CMORPH and TAMSAT show most promise in this application followed by RFE 2.0 and the PERSIANN system. However, the relative performance of the products depends on what aspects of the rainfall regime are being considered. Differences between the products are large and the use of more than one product for any application is recommended.

[1]  M. Ward,et al.  Towards the prediction of the East Africa short rains based on sea‐surface temperature–atmosphere coupling , 1998 .

[2]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[3]  Beverly D. McIntyre,et al.  ENSO and interannual rainfall variability in Uganda: implications for agricultural management , 2000 .

[4]  F. Semazzi,et al.  ENSO signals in East African rainfall seasons , 2000 .

[5]  Lotta Andersson,et al.  Estimating rainfall and water balance over the Okavango River Basin for hydrological applications , 2006 .

[6]  David I. F. Grimes,et al.  Comparison of TAMSAT and CPC rainfall estimates with raingauges, for southern Africa , 2001 .

[7]  C. Ropelewski,et al.  Validation of satellite rainfall products over East Africa's complex topography , 2007 .

[8]  S. Sorooshian,et al.  Evaluation of PERSIANN system satellite-based estimates of tropical rainfall , 2000 .

[9]  S. Sorooshian,et al.  Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks , 1997 .

[10]  J. Janowiak,et al.  CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution , 2004 .

[11]  John E. M. Brown An analysis of the performance of hybrid infrared and microwave satellite precipitation algorithms over India and adjacent regions , 2006 .

[12]  J. Janowiak,et al.  COMPARISON OF NEAR-REAL-TIME PRECIPITATION ESTIMATES FROM SATELLITE OBSERVATIONS AND NUMERICAL MODELS , 2007 .

[13]  C. Basalirwa Delineation of Uganda into climatological rainfall zones using the method of principal component analysis , 1995 .

[14]  Denis A. Hughes,et al.  Comparison of satellite rainfall data with observations from gauging station networks , 2006 .

[15]  D. Grimes,et al.  Satellite-based rainfall estimation for river flow forecasting in Africa. I: Rainfall estimates and hydrological forecasts , 2003 .

[16]  Marisa Goulden,et al.  Rainfall variability in East Africa: implications for natural resources management and livelihoods , 2005, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.