Bulletin of Atmospheric Science and Technology

Ipshita Priyadarsini Pradhan

Kirti Kumar Mahanta

Yuei-An Liou

Akshansha Chauhan

Dericks Praise Shukla

Machine learning based high-resolution air temperature modelling from landsat-8, MODIS, and In-Situ measurements with ERA-5 inter-comparison in the data sparse regions of Himachal Pradesh

基于机器学习的高分辨率气温建模：利用Landsat-8、MODIS和实地测量数据与ERA-5在喜马偕尔邦数据稀疏区域的对比研究

Ninu Krishnan Modon Valappil

Fahad Alshehri

Vijith Hamza

Statistical characteristics of remotely sensed daily ultraviolet index (UVI) over Riyadh Province, Saudi Arabia: an OMI/AURA data-based assessment

沙特阿拉伯利雅得省遥感日紫外线指数（UVI）的统计特征：基于OMI/AURA数据的评估

Fabio Massimo Grasso

Umberto Rizza

Mauro Morichetti

Elenio Avolio

Alessandro Tiesi

Ferdinando De Tomasi

The interplay of mineral dust with radiation: analysis of the giant dust intrusion in the Atlantic Ocean on June 2020

矿物尘埃与辐射的相互作用：2020年6月大西洋巨型尘埃入侵的分析

Abstract In Nigeria, particularly in urban areas like Lagos, flooding is a frequent natural hazard. In 2011, Lagos experienced one of its worst floods resulting in significant economic losses and displacement of people. In recent years, Lagos has continued to grapple with flooding challenges, with an equally significant flood episode occurring in 2021. This study focuses on predicting floods and forecasting extremely heavy rainfall in West Africa's equatorial zone using the Weather Research and Forecasting (WRF) model, particularly in humid tropical environments like Lagos. The study discusses the need to review existing flood models and adopt alternative flood models to address the limitations of flood prediction. As potential causes of these rainfall episodes, the interconnections between synoptic systems such as tropical easterly waves, southwesterly winds related to the West African Monsoon, and local topography and oceanic conditions are investigated. Three key metrics: root mean square error (RMSE), mean bias (MB), and mean absolute error (MAE) are used to assess the effectiveness of the computational model. Results indicate that the WRF model, specifically when using the Thompson parameterisation, can estimate the amount of rainfall accumulated over a 24-h period. This suggests that the model can predict the size of daily precipitation during intense rain events. The Thompson scheme shows better performance compared to the WSM6 scheme while evaluating the stations and episodes. During the rainfall episode on July 10, 2011, Thompson's spatial rainfall predictions were better than WSM6, resulting in a decrease in root mean square error (RMSE) of 15–31% depending on the area. Simulations of the July 2021 episode also show better performance, with a decrease in RMSE of 11–25% when comparing Thompson to WSM6 scheme. The Thompson scheme’s improved ability is directly linked to a more accurate depiction of the microphysical mechanisms that control the rainfall formation. By explicitly simulating the dynamics of ice crystals and graupel, it is possible to accurately replicate the processes of orographic lifting and moist convection that are responsible for driving intense monsoon precipitation. In addition, Thompson scheme shows a reduced degree of systemic bias in comparison to WSM6, with a 75% reduction in the average bias in rainfall accumulation over the research area. The combination of the advanced Thompson microphysics method and WRF's atmospheric dynamics shows a high level of accuracy in predicting intense rainfall and the risk of floods in this area with diverse tropical topography.

Oluwatobi Oyegbile

Andy Chan

Maggie Ooi

Parvez Anwar

Abdullahi Ali Mohamed

Li Li

Evaluation of WRF model performance with different microphysics schemes for extreme rainfall prediction in Lagos, Nigeria: Implications for urban flood risk management

评估WRF模型在尼日利亚拉各斯极端降雨预测中不同微物理方案的性能：对城市洪水风险管理的启示

Nicola Luciano

Alessandra Balzarini

Anna Maria Toppetti

Paolo Omodeo

Guido Pirovano

A comparative analysis of an aerosol dry deposition microscale model for overhead powerlines insulators

架空输电线绝缘子气溶胶干沉积微尺度模型的比较分析

Publication Information	Publisher: Springer Nature，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2020，Proportion of original research papers: ，Self Citation Rate:， Gold OA Rate:

Bulletin of Atmospheric Science and Technology

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Machine learning based high-resolution air temperature modelling from landsat-8, MODIS, and In-Situ measurements with ERA-5 inter-comparison in the data sparse regions of Himachal Pradesh

Statistical characteristics of remotely sensed daily ultraviolet index (UVI) over Riyadh Province, Saudi Arabia: an OMI/AURA data-based assessment

The interplay of mineral dust with radiation: analysis of the giant dust intrusion in the Atlantic Ocean on June 2020

Evaluation of WRF model performance with different microphysics schemes for extreme rainfall prediction in Lagos, Nigeria: Implications for urban flood risk management

A comparative analysis of an aerosol dry deposition microscale model for overhead powerlines insulators

Machine learning based high-resolution air temperature modelling from landsat-8, MODIS, and In-Situ measurements with ERA-5 inter-comparison in the data sparse regions of Himachal Pradesh

Statistical characteristics of remotely sensed daily ultraviolet index (UVI) over Riyadh Province, Saudi Arabia: an OMI/AURA data-based assessment

The interplay of mineral dust with radiation: analysis of the giant dust intrusion in the Atlantic Ocean on June 2020

Evaluation of WRF model performance with different microphysics schemes for extreme rainfall prediction in Lagos, Nigeria: Implications for urban flood risk management

A comparative analysis of an aerosol dry deposition microscale model for overhead powerlines insulators

ISSN	2662-1495, 2662-1509
h-index	8