Seasonal forecasting of tropical cyclones in the North Indian Ocean region: the role of El Niño-Southern Oscillation

Wahiduzzaman, Md; Oliver, Eric C. J.; Wotherspoon, Simon; Luo, Jing–Jia

doi:10.1007/s00382-019-05075-7

Cited by 20 publications

(11 citation statements)

References 48 publications

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“…El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) are associated with monthly or seasonal climate anomalies at many places around the globe [3,4]. They might have an effect on TS activity over Bangladesh due to its tropical climate.…”

Section: Introductionmentioning

confidence: 99%

Trends and Variabilities of Thunderstorm Days over Bangladesh on the ENSO and IOD Timescales

et al. 2020

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Thunderstorms (TS) are one of the most devastating atmospheric phenomena, which causes massive damage and adverse losses in various sectors, including agriculture and infrastructure. This study investigates the spatiotemporal variabilities of TS days over Bangladesh and their connection with El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). The TS, ENSO and IOD years’ data for 42 years (1975–2016) are used. The trend in TS days at the spatiotemporal scale is calculated using Mann Kendall and Spearman’s rho test. Results suggest that the trend in TS days is positive for all months except December and January. The significant trends are found for May and June, particularly in the northern and northeastern regions of Bangladesh. In the decadal scale, most of the regions show a significant upward trend in TS days. Results from the Weibull probability distribution model show the highest TS days in the northeastern region. The connection between TS days and ENSO/IOD indicates a decrease in TS activities in Bangladesh during the El Niño and positive IOD years.

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Section: Introductionmentioning

confidence: 99%

Trends and Variabilities of Thunderstorm Days over Bangladesh on the ENSO and IOD Timescales

et al. 2020

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“…Geographically weighted regression (GWR) is a technique that analyzes the variables in spatially and models their local relationships [29,30]. GWR identifies spatial heterogeneities in regression models [31][32][33][34] of geo-referenced data. The development of GWR includes a number of techniques like local regression and smoothing, spatial autocorrelation, kernel bandwidths, generalized linear modelling and Bayesian GWR [35].…”

Section: Spatial Modelling and Geographical Weighted Regression For Bmentioning

confidence: 99%

Spatial Modelling of Bacterial Diversity over the Selected Regions in Bangladesh by Next-Generation Sequencing: Role of Water Temperature

et al. 2020

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In this study, a spatial model has been developed to investigate the role of water temperature to the distribution of bacteria over the selected regions in the Bay of Bengal, located in the southern region of Bangladesh using next-generation sequencing. Bacterial concentration, quantitative polymerase chain reactions, and sequencing were performed on water samples and identified Acidobacteria, Actinobacteria, Bacteroidetes, Chlorobi, Chloroflexi, Cyanobacteria, Firmicutes, Nitrospirae, Planctomycetes, Proteobacteria, and Verrucomicrobia. The spatial model tessellated the parts of the Bay of Bengal with hexagons and analyzed the relationship between the distribution of bacteria and water temperature. A geographically weighted regression was used to observe whether water temperature contributed strongly or weakly to the distribution of bacteria. The residuals were examined to assess the model's fitness. The spatial model has the potential to predict the bacterial diversity in the selected regions of Bangladesh.

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“…The post-monsoon season is a highly active period for the formation of TCs over the NIO. The TCs that are formed in the post-monsoon season are approximately twice the TCs that are formed in the pre-monsoon season [3]. As per the annual and primary reports of the Regional Specialized Meteorological Centre, Indian Meteorological Department (RSMC-IMD) for the TCs over the NIO, a total of 20 cyclones were formed in the last five years (i.e., from 2014 to 2018).…”

Section: Introductionmentioning

confidence: 97%

“…TCs are one of the most dangerous natural weather calamities, which have a significant impact on the socio-economic aspects of the countries along the rim of the NIO [2]. TCs will cause substantial loss to humans, physical property, ecology and the environment at different levels when they make landfall [3]. The damage caused by TCs along the rim of the NIO may be attributed to its shallow bathymetry, low-lying flood prone areas, dense population along the coastlines, and poor socio-economic conditions [4].…”

Section: Introductionmentioning

confidence: 99%

“…In this study, numerical experiments were conducted to revalidate the suggested microphysical schemes for the simulation of TCs. This study mainly concentrated on the prediction of post-monsoon TCs, because 71% of the TCs that are formed in the post-monsoon season make landfall over the Indian coast, whereas, in the pre-monsoon season, the majority of the TCs over the BoB make landfall near Myanmar, and the TCs that are formed over the Arabian Sea (AS) make landfall near the Gulf countries [3,12].…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity of Microphysical Schemes on the Simulation of Post-Monsoon Tropical Cyclones over the North Indian Ocean

2020

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Tropical Cyclones (TCs) are the most disastrous natural weather phenomenon, that have a significant impact on the socioeconomic development of the country. In the past two decades, Numerical Weather Prediction (NWP) models (e.g., Advanced Research WRF (ARW)) have been used for the prediction of TCs. Extensive studies were carried out on the prediction of TCs using the ARW model. However, these studies are limited to a single cyclone with varying physics schemes, or single physics schemes to more than one cyclone. Hence, there is a need to compare different physics schemes on multiple TCs to understand their effectiveness. In the present study, a total of 56 sensitivity experiments are conducted to investigate the impact of seven microphysical parameterization schemes on eight post-monsoon TCs formed over the North Indian Ocean (NIO) using the ARW model. The performance of the Ferrier, Lin, Morrison, Thompson, WSM3, WSM5, and WSM6 are evaluated using error metrics, namely Mean Absolute Error (MAE), Mean Square Error (MSE), Skill Score (SS), and average track error. The results are compared with Indian Meteorological Department (IMD) observations. From the sensitivity experiments, it is observed that the WSM3 scheme simulated the cyclones Nilofar, Kyant, Daye, and Phethai well, whereas the cyclones Hudhud, Titli, and Ockhi are best simulated by WSM6. The present study suggests that the WSM3 scheme can be used as the first best scheme for the prediction of post-monsoon tropical cyclones over the NIO.

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Seasonal forecasting of tropical cyclones in the North Indian Ocean region: the role of El Niño-Southern Oscillation

Cited by 20 publications

References 48 publications

Trends and Variabilities of Thunderstorm Days over Bangladesh on the ENSO and IOD Timescales

Trends and Variabilities of Thunderstorm Days over Bangladesh on the ENSO and IOD Timescales

Spatial Modelling of Bacterial Diversity over the Selected Regions in Bangladesh by Next-Generation Sequencing: Role of Water Temperature

Sensitivity of Microphysical Schemes on the Simulation of Post-Monsoon Tropical Cyclones over the North Indian Ocean

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