Abstract
Abstract. Precipitation is the most important weather parameter in the Philippines. Made up of more than 7100 islands, the Philippine archipelago is an agricultural country that depends on rain-fed crops. Located in the western rim of the North West Pacific Ocean, this tropical island country is very vulnerable to tropical cyclones that lead to severe flooding events. Recently, satellite-based precipitation estimates have improved significantly and can serve as alternatives to ground-based observations. These data can be used to fill data gaps not only for climatic studies, but can also be utilized for disaster risk reduction and management activities. This study characterized the statistical errors of daily precipitation from four satellite-based rainfall products from (1) the Tropical Rainfall Measuring Mission (TRMM), (2) the CPC Morphing technique (CMORPH) of NOAA and (3) the Global Satellite Mapping of Precipitation (GSMAP) and (4) Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks (PERSIANN). Precipitation data were compared to 52 synoptic weather stations located all over the Philippines. Results show GSMAP to have over all lower bias and CMORPH with lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). In addition, a dichotomous rainfall test reveals GSMAP and CMORPH have low Proportion Correct (PC) for convective and stratiform rainclouds, respectively. TRMM consistently showed high PC for almost all raincloud types. Moreover, all four satellite precipitation showed high Correct Negatives (CN) values for the north-western part of the country during the North-East monsoon and spring monsoonal transition periods.
Highlights
1.1 General InstructionsIn the Philippines where agricultural sector employs a third of its workforce (World Bank, 2012), rain is the most important daily weather phenomenon
One of the most commonly used datasets is from the operational Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) that has 17 years of highresolution global Quantitative Precipitation Estimation (QPE)
The 3B42 version 7 gridded data are gauge adjusted and provides coverage from 50N–50S. It is based on the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), Advanced Microwave Sounding Unit-B (AMSU-B), TRMM Microwave Imager (TMI) and Spectral Sensor Microwave Imager/Sounder (SSMI/S)
Summary
1.1 General InstructionsIn the Philippines where agricultural sector employs a third of its workforce (World Bank, 2012), rain is the most important daily weather phenomenon. The meridional migration of the Inter-Tropical Convergence Zone across the archipelago that gives rise to the summer and winter monsoon periods determines the seasonality of rainfall in different regions of the Philippines. The Philippines is categorized as a tropical rainforest / monsoon climate (Koppen, 1936) It is divided into four distinct climate types by Corona (1923), and later modified by Flores (1969) where Type I climate covers most of the western region having two pronounced seasons, dry from November to April and wet the rest of the year. Type II climate describes the eastern regions where there is no dry season for the whole year but with more rain during the winter monsoon period. The aim of this study is to assess four satellite precipitation datasets, namely Tropical Rainfall Measurement Monitoring
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
