Evaluating the impact of climate change in threshold values of thermodynamic indices during pre-monsoon thunderstorm season over Eastern India

Sahu, Rajesh Kumar; Dadich, Jiteshwar; Tyagi, Bhishma; Vissa, Naresh Krishna; Singh, Jyotsna

doi:10.1007/s11069-020-03978-x

Cited by 30 publications

(31 citation statements)

References 92 publications

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“…The high sea surface temperature triggered a rise in CAPE in most parts of Bangladesh (Wahiduzzaman et al, 2020;Sahu et al, 2020). These findings support the outcomes of Glazer et al (2020), who assessed the variations in TSD in Bangladesh due to global climate change and revealed an increase in TSD in many regions of the country.…”

Section: Discussionsupporting

confidence: 76%

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

et al. 2021

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Accurate thunderstorm frequency (TSF) prediction is of great significance under climate extremes for reducing potential damages. However, TSF prediction has received little attention because a thunderstorm event is a combination of intricate and unique weather scenarios with high instability, making it difficult to predict. To close this gap, we proposed a novel hybrid machine learning model through hybridization of data pre-processing Ensemble Empirical Mode Decomposition (EEMD) with two state-of-arts models namely artificial neural network (EEMD-ANN), support vector machine (EEMD-SVM) for TSF prediction at three categories of yearly frequencies over Bangladesh. We were demarcated the yearly TSF datasets into three categories for the period 1981-2016 recorded at 28 sites; high (March-June), moderate (July-October), and low (November-February) TSF months. The performance of the proposed EEMD-ANN and EEMD-SVM hybrid models was compared 2 with classical ANN, SVM, Autoregressive Integrated Moving Average (ARIMA). EEMD-ANN and EEMD-SVM hybrid models showed 8.02%-22.48% higher performance precision in terms of root mean square error (RMSE) compared to other models at high, moderate and low-frequency categories. Eleven out of 21 input parameters were selected based on the Random Forest (RF) variable importance analysis. The sensitivity analysis results showed that each input parameter was positively contributed to building the best model of each category and thunderstorm days are the most contributing parameters influencing TSF prediction. The proposed hybrid models outperformed the conventional models where EEMD-ANN is the most skillful for high TSF prediction, and EEMD-SVM is for moderate and low TSF prediction. The findings indicate the potential of hybridization of EEMD with the conventional models for improving prediction precision. The hybrid model developed in this work can be adopted for TSF prediction in Bangladesh as well as different parts of the world.

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Section: Discussionsupporting

confidence: 76%

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

et al. 2021

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“…An interaction of moist air from the Bay of Bengal with the hills in the north causes a rapid uplifting of air and triggering of thunderstorms [25]. The topography and wind regimes made Bangladesh and Northeast India bordering Bangladesh highly favorable for the occurrence of thunderstorms [26]. Therefore, the region experiences the maximum thunderstorm activities compared to other parts of South Asia.…”

Section: Discussionmentioning

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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“…2b, d). These reduced values from third week of March are related to frequent thunderstorms occurring over the region [22], associated with a short, intense spell of rainfall washout/rainout the pollutants. (Table 1).…”

Section: Variation In Particulate Matter (Pm 25 and Pm 10 ) Before Amentioning

confidence: 99%

Significant change in air quality parameters during the year 2020 over 1st smart city of India: Bhubaneswar

et al. 2020

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Evaluating the impact of climate change in threshold values of thermodynamic indices during pre-monsoon thunderstorm season over Eastern India

Cited by 30 publications

References 92 publications

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

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

Significant change in air quality parameters during the year 2020 over 1st smart city of India: Bhubaneswar

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