Analysis of rainfall events over Peninsular Malaysia

Chin, Ren Jie; Hin, Lai Sai; Chang, Kian Boon; Othman, Faridah; Jaafar, Wan Zurina Wan

doi:10.1002/wea.2723

Cited by 8 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The minimum rainfall during the JJA period and lower rainfall during DJF are associated with the blocking effects of the Barisan mountain over Sumatra and the Titiwangsa mountain over Peninsular Malaysia, respectively. On the east coast of Peninsular Malaysia, rainfall peaks during DJF to coincide with the cold surges and Borneo vortex [35]. Due to multiple interactions of various factors (cold surges, Borneo vortex and Madden-Julian Oscillations), extreme precipitation events that can lead to major floods can occur during DJF on the east coast of Peninsular Malaysia [33].…”

Section: Study Area and Its Climatologymentioning

confidence: 99%

Evaluation of Gridded Precipitation Datasets in Malaysia

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Study Area and Its Climatologymentioning

confidence: 99%

Evaluation of Gridded Precipitation Datasets in Malaysia

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Malaysia, [14] [15]; Australian drylands, [16]). In this study, we have used eight properties (used by the many researchers) to characterize rainfall.…”

Section: Introductionmentioning

confidence: 99%

Precipitation Regionalization Using Self-Organizing Maps for Mumbai City, India

Parchure¹,

Gedam²

2018

JWARP

View full text Add to dashboard Cite

The detailed analysis of individual rain events characteristics is an essential step for improving our understanding of variation in precipitation over different topographies. In this study, the homogeneity among rain gauges was investigated using the concept of "rain event properties," linking them to the main atmospheric system that affects the rainfall in the region. For this, eight properties of more than 23,000 rain events recorded at 47 meteorological stations in Mumbai, India, were analyzed utilizing seasonal (June-September) rainfall records over 2006-2016. The high similarities among the properties indicated the similarities among the rain gauges. Furthermore, similar rain gauges were distinguished, investigated and characterized by cluster analysis using self-organizing maps (SOM). The cluster analysis results show six clusters of similarly behaving rain gauges, where each cluster addresses one isolated class of variables for the rain gauge. Additionally, the clusters confirm the spatial variation of rainfall caused by the complex topography of Mumbai, comprising the flatland near the Arabian Sea, high-rise buildings (urban area) and mountain and hills areas (Sanjay Gandhi National Park located in the northern part of Mumbai).

show abstract

“…Based on 5 years of data from a rain gauge located in Western Australia, Dunkerley found significant variation in the properties of rainfall events (mean values of duration, depth, intensity, and gaps between events) depending on the adopted MIT values (from 15 min to 24 h). This strong impact of the MIT on the estimated values of rainfall event properties was further investigated in other regions worldwide (e.g., Alang Othman et al, 2020;Barbosa et al, 2018;Chin et al, 2016;Peng et al, 2019;Wang et al, 2016). Nevertheless, MIT-based studies remain limited in some parts of the world, as such investigations require distributed sub-daily and sub-hourly data of good quality, that is, they require a well-monitored rain gauge network.…”

Section: Introductionmentioning

confidence: 83%

Spatiotemporal distribution of precipitation and its characteristics under tropical atmospheric systems of Brazil: Insights from a large sub‐hourly database

Lemos,

Coelho,

Freitas

et al. 2023

Hydrological Processes

View full text Add to dashboard Cite

The study of rainfall properties at various spatiotemporal scales is deeply important for understanding a large range of socio‐environmental processes and variables (e.g., water resources, agriculture, socio‐ecosystemic services, natural risk assessment). However, such studies on rainfall characteristics, especially at sub‐hourly resolutions, are scarce in large areas of South America due to the lack of a high‐resolution temporal database. The Brazilian National Centre for Monitoring and Early Warning of Natural Disasters (CEMADEN) has gradually implemented, starting in 2013, a sub‐hourly monitoring network composed of approximately 3500 automated rain gauges distributed across Brazil, enabling access to new hydrological studies in this vast tropical country featuring a range of biomes. This study analysed the characteristics of rainfall events for the whole of Brazil on sub‐daily and sub‐hourly timescales, using 7 years of data (from 2014 to 2021) provided by CEMADEN. Rainfall events were defined by the minimum inter‐event time (MIT) and the minimum depth (1 mm). Seven MITs (30, 60, 120, 180, 360, 720, and 1440 min) were considered to evaluate the behaviour of rainfall event characteristics and their relationships. The Gaussian mixture model method was applied to identify regions with similar rainfall patterns according to the MIT. Six groups with homogeneous characteristics were identified, evidencing the climatic diversity of Brazil. The results show that the MIT strongly influences precipitation properties (especially the dry time and the number of events). The highest number of events occurred in the North‐east Coast region, which exceeded 200 events per year (MIT < 60 min), while the lowest number of events was observed in the Semiarid region, which reached only 38 events per year with an MIT of 1440 min. Moreover, the events with the highest rainfall intensities were found in the Central region. The results found in this study provide a better understanding of precipitation and its characteristics in Brazil, highlighting the climatological diversity of this country.

show abstract

Analysis of rainfall events over Peninsular Malaysia

Cited by 8 publications

References 19 publications

Evaluation of Gridded Precipitation Datasets in Malaysia

Evaluation of Gridded Precipitation Datasets in Malaysia

Precipitation Regionalization Using Self-Organizing Maps for Mumbai City, India

Spatiotemporal distribution of precipitation and its characteristics under tropical atmospheric systems of Brazil: Insights from a large sub‐hourly database

Contact Info

Product

Resources

About