Evaluation of Gridded Precipitation Datasets in Malaysia

Abstract

This study compares five readily available gridded precipitation satellite products namely: Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) at 0.05° and 0.25° resolution, Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA 3B42v7) and Princeton Global Forcings (PGFv3), both at 0.25°, and Global Satellite Mapping of Precipitation Reanalysis (GSMaP_RNL) at 0.1°, and evaluates their quality and reliability against 41 rain gauge stations in Malaysia. The evaluation was based on three numerical statistical scores (r, Root Mean Squared Error (RMSE) and Bias) and three categorical scores (Probability of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI)) at temporal resolutions of daily, monthly and seasonal. The results showed that TMPA 3B42v7, PGFv3, CHIRPS25 and CHIRPS05 slightly overestimated the rain gauge data, while the GSMaP_RNL underestimated the value with the largest bias for monthly data. The CHIRPS25 showed the best POD score, while TMPA 3B42v7 scored highest for FAR and CSI. Overall, TMPA 3B42v7 was found to be the best-performing dataset, while PGFv3 registered the worst performance for both for numerical (monthly) and categorical (daily) scores. All products captured the intensity of heavy rainfall (20–50 mm/day) rather well, but tended to underestimate the intensity for categories of no or little rain (rain <1 mm/day) and extremely heavy rain (rain >50 mm/day). In addition, overestimation occurred for low moderate (2–5 mm/day) to low heavy rain and (10–20 mm/day). In the case study of the extreme flooding event of 2006/2007 in the southern area of Peninsular Malaysia, TMPA 3B42v7 and GSMaP_RNL performed well in capturing most heavy rainfall events but tended to overestimate light rainfalls, consistent with their performance for the occurrence intensity of rainfall at different intensity level.

Climatological Features of Squall Line at the Borneo Coastline during Southwest Monsoon

“…Furthermore, the validation of TRMM-3B42V7 over Malaysia and the neighboring regions also showed high consistency with ground observation [22][23][24][25][26][27]. A recent study by [28] showed that TRMM-3B42V7 is a reliable gridded…”

Evaluation of satellite-based precipitation products from GPM IMERG and GSMaP over the three-river headwaters region, China

“…Therefore, it is necessary to further improve the observation ability of IMERG under heavy rainfall conditions. Ayoub et al (2020) and Prakash et al (2016), respectively, analyzed the precipitation accuracy of GSMaP satellite products and TMPA products in Malaysia and India, and the results showed that GSMaP could better detect the spatial distribution pattern of precipitation than TMPA, and the accuracy was the highest in summer. In addition, Aslami et al (2019), Zhou et al (2020) and Lu & Yong (2018) also compared the difference in precipitation accuracy between GSMaP and IMERG in Iran, mainland China and the Qinghai-Tibet Plateau, and found that the precipitation value estimated by IMERG was relatively close to the meteorological station record, and could replace the observation station in the study area where there was a lack of meteorological stations, thus having high hydrological simulation effect.…”

Quantitative Classification of Desertification Severity for Degraded Aquifer Based on Remotely Sensed Drought Assessment

“…CHIRPS is a fusion of satellite images and data from rain-gauge stations. More detailed information on CHIRPS can be found in recent publications [32][33][34][35]. CHIRPS data perform well at the watershed scale.…”

“…SPI is based on the normalized probability distribution (Figure 2), which can compare both the dryness and wetness in different regions. The numerical SPI value is extracted from a given probability distribution function [32,35,37]. Assume that x is the cumulated monthly precipitation in the selected time scale (1, 3, 6, 12 months), which fits a gamma probability density function g(x) as follows:…”