Paper No. 7
Presentation Time: 9:45 AM


ALSAARAN, Nasser, Department of Geography, King Saud University, P. O. Box 2456, Riyadh, 11451, Saudi Arabia,

Reliable spatially-distributed daily rainfall fields are critical for a wide range of meteorological, hydrological, engineering and agricultural applications. Satellite-based daily rainfall estimates from a variety of operational and semi-operational algorithms incorporating microwave, infra-red and visible observations are available; However, these products are based on indirect observations and thus it is important to know their accuracy and expected error characteristics to use them appropriately. The need for satellite-based daily rainfall estimates over Arabia is dictated by the very sparse rainfall gauge network and its arid subtropical climate which hinders the reliability of numerical weather prediction (NMP) models-based rainfall estimates. This paper validates daily precipitation estimates by the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) over Arabia for the period 2001-2010 using NOAA Climate Prediction Center (CPC) Unified Gauge-based Analysis of Global Daily Precipitation as the reference standard. Overall and seasonal characteristics of estimation errors were examined. TMPA’s skill for rainfall occurrence was assessed using categorical statistics including frequency bias (FB), probability of detection (POD), false alarm ratio (FAR), equitable threat score (ETS), and Heidke Skill Score (HSS), with results reported for a range of rainfall thresholds. Algorithm’s errors in rainfall amount are quantified by mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (CC).