Journal of Atmospheric Science Research

Deep Learning-based Flood Risk Prediction for Climate Resilience Planning in Malawi

2025-06-13T17:26:26+08:00

Climate change resilience in Malawi faces an institutional gap because most institutions often fail to prioritize risk data when dealing with climate extremes such as floods. This unfortunate gap forces many Malawians to fend for themselves during times of climate extremes This situation is also heightened by a few studies that utilize Time Series Analysis (TSA) and Deep Learning Models (DLM) to predict climate extremes for decision-making processes. Therefore, this study focused on flood risk prediction and assessment in six selected districts of Malawi: Chikwawa, Blantyre, Phalombe, Zomba, Rumphi, and Karonga. Traditional Time Series Models (ARIMA) and Semantic Convolution Deep Learning Analysis were used for this purpose. Data were retrieved from the database of the US National Aeronautics and Space Administration (NASA). The results revealed frequent and significant precipitation peaks in Blantyre and Chikwawa, particularly during the rainy season, suggesting that the areas are at a higher risk of flooding, with a high probability of infrastructural damage and economic losses. Karonga and Phalombe revealed cyclical trends with prominent spikes in rainfall. In contrast, Rumphi and Zomba exhibit less pronounced trends, though there are still significant fluctuations in rainfall patterns, suggesting an increasing likelihood of flood risk in future climate extremes. This study situates its policy implications by emphasizing that residents, institutions, government, partners, and NGOs need to take a problem-focused approach towards climate resilience planning, including updating flood risk maps, designing flood protection infrastructure, and preparing emergency response plans tailored to the specific needs of each district in Malawi.

Machine Learning Based Drought Prediction Using the Standardized Precipitation Evapotranspiration Index (SPEI) in Kebbi State, Nigeria

2025-04-14T09:13:21+08:00

Drought represents a major threat to livelihoods and economic stability in regions prone to its occurrence. This paper aims to address the gap in applying machine learning techniques for enhanced meteorological drought prediction to support resilience and preparedness. The study focuses on Kebbi State, located in northwest Nigeria, which experiences droughts with devastating agricultural, ecological and humanitarian impacts. The Standardized Precipitation Evapotranspiration Index (SPEI) was used to calculate different drought severity based on rainfall deficit, over varying accumulation periods (3-month, 6-month) over four decades (1980–2022). Different time series meteorological parameters such as mean temperature, maximum temperature, minimum temperature, radiation, wind speed, precipitation was used in training machine learning models to predict and forecast future drought risk across Kebbi’s regions. Four candidate models were evaluated Random Forest (RF), Extreme Gradient Boosting (XGB), 1D Convolutional Neural Networks (CNN), and Long Short-Term Memory Networks (LSTM). Results indicate RF models consistently achieved highest prediction accuracy (R2: 47–67%) for both short and long-term SPEI forecasts across different regions over the other models, while LSTM was not able to make good prediction for drought in Kebbi state. Optimized XGB models also performed reasonably well for specific locations. One-year lead SPEI projections exhibit XGB potential for advancing early warning given forecast reliabilities. This pioneering study provides robust evidence for integrating machine learning for drought prediction in Kebbi state, Nigeria which is located in the sub-Sahara region.

The Influence of Atmospheric Microplastics on Global Climate Dynamics: An Interdisciplinary Review

2025-05-19T10:53:37+08:00

This article examines the growing concern over microplastics in the atmosphere and their potential effects on climate systems and atmospheric circulation. It explores the role of natural aerosols in atmospheric processes, highlighting how these particles influence cloud formation, radiative forcing, and global circulation patterns. It contrasts these natural aerosols with microplastics, which, because of their unique physical and chemical properties, behave differently in the atmosphere. Microplastics, unlike natural aerosols, are resistant to degradation, leading to their cumulative accumulation in the atmosphere. Their persistence and transport in the atmospheric column are influenced by diffusion dynamics, allowing them to travel over long distances, potentially impacting weather patterns and climate systems far from their original sources. Microparticles may also alter cloud properties, influencing precipitation, radiation balance, and atmospheric chemistry. The diffusion behavior of microplastics, their interaction with other airborne pollutants, and their potential to influence advanced climate models are discussed. The cumulative effect of these persistent pollutants, coupled with their resistance to biological degradation, may have serious long-term implications for atmospheric composition and global climate patterns. There is a growing need for further interdisciplinary research into the interaction between microplastics and natural aerosols in order to fully understand their diverse impacts on climate systems and atmospheric dynamics.