Review of Environment and Earth Sciences

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Online ISSN: 2313-8440
Print ISSN: 2409-2150
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No. 1

Comparative Analysis of Nested Domain Sensitivity on Thunderstorm Simulation using WRF-ARW Model: A Case Study Over Bangladesh

Pages: 8-24
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Comparative Analysis of Nested Domain Sensitivity on Thunderstorm Simulation using WRF-ARW Model: A Case Study Over Bangladesh

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DOI: 10.18488/journal.80.2021.81.8.24

Md. Mijanur Rahman , Muhammad Abul Kalam Mallik , Md. Abdus Samad

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Md. Mijanur Rahman , Muhammad Abul Kalam Mallik , Md. Abdus Samad (2021). Comparative Analysis of Nested Domain Sensitivity on Thunderstorm Simulation using WRF-ARW Model: A Case Study Over Bangladesh. Review of Environment and Earth Sciences, 8(1): 8-24. DOI: 10.18488/journal.80.2021.81.8.24
Several thunderstorm indicators (TI) and thermodynamic features were evaluated and compared by simulating a thunderstorm (TS) event over Sylhet (24.89° N, 91.86° E), Bangladesh that occurred from 1429 UTC to 1441 UTC on 29 March 2018 using the Advanced Research dynamics solver of Weather Research and Forecasting model (WRF-ARW). The model was run to conduct a simulation for 36 hours utilizing six-hourly Global Final Analysis (FNL) datasets from 0600 UTC of 29 March 2018 to 1200 UTC of 30 March 2018 as initial and lateral boundary conditions. The domain was nested in two different ways: (a) two domains of 15 and 3 km horizontal resolution, and (b) three domains of 12, 6 and 3 km horizontal resolution. These domains were nested with varying outer-domain horizontal grid spacing but a constant 3km inner-domain resolution in order to reasonably verify the effect of nesting on the approximation of the thermodynamic indicators by WRF-ARW. The model outputs were generated with a 10-minute interval for the innermost domain. These outputs were analyzed numerically and graphically using Grid Analysis and Display System (GrADS). Model evaluations of mean sea level pressure (MSLP), maximum and minimum temperature, relative humidity (RH) and 24-hour rainfall were compared with available observational data obtained from Bangladesh Meteorological Department (BMD) to validate the model performance in each case. Based on the analyses and comparisons, it is found that the estimated values in the case of three-way nesting were better indicators of the likelihood of a TS event over that area.
Contribution/ Originality
This study is one of the very few studies that estimate several important thunderstorm indicators of a thunderstorm event over Bangladesh using the WRF-ARW model. This paper’s major contribution is the comparative analysis of those indicators based on different nested domain configurations.

Drought Prediction with Raw Satellite Imagery and Ensemble Supervised Machine Learning

Pages: 1-7
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Drought Prediction with Raw Satellite Imagery and Ensemble Supervised Machine Learning

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DOI: 10.18488/journal.80.2021.81.1.7

Owais Raza , Mohsin Memon , Sania Bhatti , Nazia Pathan

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Owais Raza , Mohsin Memon , Sania Bhatti , Nazia Pathan (2021). Drought Prediction with Raw Satellite Imagery and Ensemble Supervised Machine Learning. Review of Environment and Earth Sciences, 8(1): 1-7. DOI: 10.18488/journal.80.2021.81.1.7
Drought is one of the biggest challenges that environmentalists face today because of rapidly evolving climate. The negative impacts of drought on the economy, humans and other living organisms endure long after the ending of drought, and with time its intensity also increases. One way to fight the adverse effects of drought is to perform drought prediction and so that appropriate decisions can be made accordingly. Drought prediction can be made considering vegetation and water level in any region therefore, in this research we are using satellite images to predict drought conditions and its various stages, like if it is about to come or it has passed. All these predictions will be helpful for authorities to make informed decisions. We are employing supervised machine learning nevertheless to obtain the best results. We are using boosting and bagging which is ensemble supervised machine learning techniques. The experiments performed proved that bagging is better than boosting classifiers and it is less computationally expensive; and boosting on the other hand is less accurate and computationally expensive.
Contribution/ Originality
This research performs drought prediction on tharparkar district using raw satellite imagery and ensemble machine learning techniques.