Characterization of southwest monsoon onset over Myanmar

Sein, Zin Mie Mie; Islam, Abu Reza Md. Towfiqul; Maw, K. W.; Moya, T. B.

doi:10.1007/s00703-015-0386-0

Cited by 15 publications

(7 citation statements)

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“…Due to the monsoon process, the summer monsoon season in Myanmar Coast can be divided into three phases: onset phase (May), peak phase (June–August), and withdrawal phase (September–October) (Wang et al, 2014; Vergopolan et al, 2020). Thus, the results are agree with the findings of previous studies that reported higher precipitation in the months of June, July and August over Myanmar (Mie Sein et al ., 2015, 2021b; Shahzaman et al ., 2021b). On the other hand, the months of May (onset phase), September and October (withdrawal phase) reveal less precipitation in the range of 90 mm to 755 mm.…”

Section: Resultsmentioning

confidence: 99%

“…The region can be subdivided into three sub‐regions: namely, the Western fold belt (Western Ranges) in the West, the Central lowland in the middle, and the Shan‐Tanintharyi Block (the Eastern Highland) in the East (Kreft et al ., 2017; Oo et al ., 2020; Mie Sein et al ., 2021a). The climate is characteristically unstable due to steep slopes, unstable geology, and intense monsoon rains, especially mountainous areas, which are the most disaster‐prone regions (Mie Sein et al ., 2015). The country receives a large amount of precipitation in summer monsoon season, accounting for 90% of the country's total precipitation (Sein and Zhi, 2016; Ren et al ., 2017).…”

Section: Study Areamentioning

confidence: 99%

“…The country receives a large amount of precipitation in summer monsoon season, accounting for 90% of the country's total precipitation (Sein and Zhi, 2016;Ren et al, 2017). The summer precipitation is severely influenced by the monsoon precipitation system, which mainly occurs in the country's West Coast and Southern regions (Kreft and Eckstein, 2013;Singh et al, 2014;Mie Sein et al, 2015). of Meteorology and Hydrology (DMH) for 46 synoptic meteorological stations during 1981-2020 (Figure 1a).…”

Section: Study Areamentioning

confidence: 99%

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Recent variability of sub‐seasonal monsoon precipitation and its potential drivers in Myanmar using in‐situ observation during 1981–2020

Sein¹,

Zhi

Ullah

et al. 2021

Intl Journal of Climatology

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The present study assessed the spatiotemporal variation of summer monsoon precipitation and its potential drivers in Myanmar, utilizing monthly precipitation data from forty‐six (46) synoptic meteorological stations spanning 1981–2020. The nonparametric statistical Mann–Kendall (MK), Sequential Mann–Kendall (SQMK) test, Empirical Orthogonal Function (EOF), and Probability Distribution Function (PDF) were used to determine the spatiotemporal monsoon precipitation trends and variability over the study period. The results show that higher precipitation occurs during June, July and August (peak monsoon period), while low precipitation was detected in May (onset month), September and October (withdrawal monsoon period), respectively. Moreover, abrupt change in precipitation is observed after 1990 with a significant (95% confidence level) increasing trend from 2000 to 2020. Decadal precipitation experienced the highest fluctuation during 2011–2020, a positive shift and increased frequency in recent decades. The spatial trends for monthly and seasonal precipitation vary from station to station and region to region due to a fluctuated shift of climatic dynamics. During dry conditions, less cloud liquid water suppressed relative humidity and high air temperature were exhibited, thus implying less precipitation in the region. However, the wet years revealed strong moisture/water vapour into the inland regions from the ocean, increased relative humidity, and suppressed air temperature. In addition, no significant relationship was found between El‐Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and monsoon onset timing with precipitation variability over Myanmar. This study provides essential information on manageable climate adaptation, mitigation and weather forecasting strategies in Myanmar.

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Section: Resultsmentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

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Recent variability of sub‐seasonal monsoon precipitation and its potential drivers in Myanmar using in‐situ observation during 1981–2020

Sein¹,

Zhi

Ullah

et al. 2021

Intl Journal of Climatology

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“…This has become a common method for investigating the spatial characteristics of climatological elds. The EOFs are the eigenvectors of the covariance matrix deduced from the data eld's time series (Mie Sein et al, 2015). The time series associated with the rst two EOFs (PCs) are investigated concerning the temporal variability.…”

Section: Methodsmentioning

confidence: 99%

Inter-Annual Variability of Southwest Monsoon Rainfall in Myanmar: Insights from Ocean-Atmosphere Interactions

2023

Preprint

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The monsoon trough regions in Myanmar (90°E-105°E and 10°N-28°N) show a substantial influence on rainfall from June to September during the southwest monsoon season (JJAS), where the contributes ~90% of the annual accumulated rainfall. This finding aims to explain the variability of southwest monsoon rain and its connection to Myanmar's ocean-atmosphere pattern between 1981 and 2020, which is of great importance in the economy of Myanmar because JJAS is the summer monsoon cropping season which is the main economic sector. The dominant EOF mode captures the lag-correlated between the average trend of JJAS rainfall and SST for 40 years (1981– 2020) produced from the previous June-September SST correlation with rainfall variation over Myanmar. Furthermore, the correlation suggests the instance of El Niño–Southern Oscillation (ENSO) and IOD (DMI) influence the variability of inter-annually JJAS rainfall. Positive correlation between SST anomalies of Nino3.4 regions and western IO (DMI) with JJAS rainfall variation during this 40 years. Both are well-defined indices for the forecast of the study year’s JJAS rainfall over Myanmar. Furthermore, cooling (warming) across the Indo-Pacific influences the Hadley and Walker circulations, resulting in below (above) average rainfall over Myanmar. The impact of other indices on JJAS Myanmar rainfall requires additional investigation. The thorough examination of JJAS rainfall aids in understanding the occurrences of previous extreme events as support for predicting and monitoring drought and floods over Myanmar.

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“…It may be one of the primary and essential factors affecting South and East Asia's climate . According to Azad et al (2022); Dubache et al (2019); Mie Sein et al (2015) crop productivity, hydropower output, foliage, water management, and biodiversity of these sections are impacted by the intensity and length of the monsoonal rains. Additionally, "India's inter-annual variability of the monsoonal rainfall causes widespread droughts and floods, having a significant impact on the nation's food grain and economy" (Mohapatra et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Simulation of Rainfall Using Two Statistical Data Driven Models: A Study on Santhal Pargana Division of Jharkhand State, India

Raha

Gayen

2022

Geos. Ind.

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Although the variability and prediction of rainfall is an essential issue of the Santhal Pargana Division of the Jharkhand State but the issue is still far from its’ conclusive statement till date. Therefore, this study aimed to simulate the monthly rainfall from 1901 to 2020 using an eight-step procedure. After downloading the monthly rainfall for the Santhal Pargana Division from 1901 to 2020, the TBATS and Naive models were used to simulate the rainfall. The accuracy assessment of each model was done by using the MASE, MAE, RMSE, ME, and R. For the Naïve model, the Godda station was noticed with a comparatively high combined error. The lowest combined error was found for the Pakur station in case of Naïve models. Similar result was also obtained for the TBATS model. The TBATS was found with comparatively higher accuracy, as the combined error was less for the TBATS. The spatial assessment for the standardized rainfall varied from 84.419 mm. to 149.225 mm. For the Naïve predicted model, the rainfall was marked in between 8.133 mm. to 67.059 mm. For the TBATS fitted model, the rainfall fluctuated from the 37.127 mm. to 62.993 mm. Dumka station was noticed with comparatively low rainfall (i.e.,37.127 mm.). Deoghar and Jamtara stations were marked with a moderate rainfall. Remaining stations were marked with higher amount of rainfall for the TBATS fitted model. The Wilcoxon test proved that each model was significant at 95% confidence interval. The result produced in this research is fruitful enough to be utilized for agricultural planning in the Santhal Pargana Division of the Jharkhand state, India. Keywords : TBATS model; Naive model; simulation; accuracy Copyright (c) 2022 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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Characterization of southwest monsoon onset over Myanmar

Abstract: The aim of this paper was to characterize the southwest monsoon onset over Myanmar based on the model. The Regional Climate Model (RegCM3) was run for a period of 10 years (

Cited by 15 publications

References 47 publications

Recent variability of sub‐seasonal monsoon precipitation and its potential drivers in Myanmar using in‐situ observation during 1981–2020

Recent variability of sub‐seasonal monsoon precipitation and its potential drivers in Myanmar using in‐situ observation during 1981–2020

Inter-Annual Variability of Southwest Monsoon Rainfall in Myanmar: Insights from Ocean-Atmosphere Interactions

Simulation of Rainfall Using Two Statistical Data Driven Models: A Study on Santhal Pargana Division of Jharkhand State, India

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