Next Article in Journal
Crop Growth Monitoring with Drone-Borne DInSAR
Previous Article in Journal
Land Cover Changes in Open-Cast Mining Complexes Based on High-Resolution Remote Sensing Data
Previous Article in Special Issue
A Deep Learning Trained Clear-Sky Mask Algorithm for VIIRS Radiometric Bias Assessment
Open AccessArticle

Evaluation of Gridded Precipitation Datasets in Malaysia

1
Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
2
Geography Section, School of Humanities, Universiti Sains Malaysia, 11800, Penang, Malaysia
3
Institut Oseanografi dan Sekitaran (INOS), Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(4), 613; https://doi.org/10.3390/rs12040613 (registering DOI)
Received: 17 December 2019 / Revised: 7 February 2020 / Accepted: 8 February 2020 / Published: 12 February 2020
(This article belongs to the Special Issue Satellite Remote Sensing for Tropical Meteorology and Climatology)
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.
Keywords: precipitation gridded products; TRMM; CHIRPS; PGFv3; GSMaP; Malaysia precipitation gridded products; TRMM; CHIRPS; PGFv3; GSMaP; Malaysia
Show Figures

Graphical abstract

MDPI and ACS Style

Ayoub, A.B.; Tangang, F.; Juneng, L.; Tan, M.L.; Chung, J.X. Evaluation of Gridded Precipitation Datasets in Malaysia. Remote Sens. 2020, 12, 613.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop