Journal of Atmospheric Science Research

The Warm-Sector Thunderstorms Triggered by Mesoscale Boundary-Layer Convergence over the DPRK

Kum-Ryong Jo — 2026-04-14

Warm-sector thunderstorms (WSTs), characterized by weak synoptic forcing and extreme precipitation rates, pose a major global forecasting challenge. This study investigates the mesoscale processes initiating WSTs over the complex terrain of the Democratic People's Republic of Korea (DPRK), a region where triggering mechanisms remain poorly understood. We analyze three extreme rainfall events (Hoichang 2016, Unpa 2017, Pyongyang 2018), each producing rainfall rates exceeding 60 mm h⁻1 under the weak forcing typical of the northwestern periphery of the West Pacific Subtropical High. While operational global models failed to predict these events, the convection-permitting WRF model skillfully replicated the initiating mechanisms and subsequent convection. Model performance was quantitatively assessed using multiple verification metrics, including Probability of Detection (POD), False Alarm Ratio (FAR), Bias, and Critical Success Index (CSI). Integrated analysis of observations and high-resolution (3 km) Weather Research and Forecasting (WRF) model simulations reveals a consistent trigger: mesoscale boundary-layer convergence lines. These zones formed through the interaction of synoptic southwesterlies with localized, terrain-modulated flows and were collocated with horizontal moisture gradients. Crucially, the three-dimensional structure of Convective Available Potential Energy (CAPE) manifested as narrow, vertical towers of high instability, delineating regions of deep convection initiation 2–4 h in advance. A pre-convective, deep moist layer (relative humidity >80% in the 850–700 hPa layer) was identified as a necessary precondition. This study establishes terrain-forced boundary-layer convergence as a primary trigger for WSTs over the DPRK, providing a valuable framework for improving prediction in other monsoonal regions with complex topography.

Hybrid Deep Learning and Bayesian Framework for Long-Term Fog Forecasting: A Case Study at Lucknow Airport in the Indo-Gangetic Plains

Deep Chaulya — 2026-04-28

Fog significantly affects aviation, transportation, agriculture, and energy in North India during winter. Accurate prediction is difficult due to nonlinear meteorological interactions and uncertainty. Traditional models provide point estimates without uncertainty, while Bayesian methods offer probabilistic outputs but struggle to capture complex temporal dependencies in atmospheric time-series data effectively. To address these limitations, this study proposes a hybrid long-term forecasting framework that integrates a three-layer Bidirectional Long Short-Term Memory (BiLSTM) network with a Modified Bayesian Beta Regression model. The model is trained and evaluated using 24 years (2000–2023) of meteorological observations from Lucknow Airport, representing a fog-prone region within the Indo-Gangetic Plains. Fog behaviour was represented through a normalized Fog Index (0–1) that incorporates both intensity and persistence, offering a more stable and informative forecast target than raw visibility. The BiLSTM component captures temporal dependencies and produces accurate point forecasts, achieving a root mean square error (RMSE) of 0.125 for a three-day prediction horizon. The Bayesian layer enhances reliability by generating calibrated uncertainty intervals. The resulting model achieved an average interval width of approximately 0.59 and a prediction interval coverage close to 80%, effectively representing both aleatoric and epistemic uncertainty. Additional techniques, including Min-Max normalization, sequence windowing, Fast Fourier Transform (FFT) feature augmentation, learning-rate scheduling, and Monte Carlo dropout, improved generalization and model stability. The proposed hybrid framework outperforms standalone models and shows strong potential for operational fog forecasting, offering both accurate predictions and uncertainty-aware confidence estimates for aviation, transportation, and early-warning systems.

Changed Relationships between El Niño-Southern Oscillation Events and Climate over the Democratic People's Republic of Korea since 1950

Kyong-Bok Sonu — 2025-04-30

The El Niño-Southern Oscillation (ENSO) is a dominant interannual climate mode influencing global weather patterns. This study investigates the evolving relationship between ENSO and seasonal climate over the Democratic People's Republic of Korea (DPRK) from 1950 to 2024. Using the Niño3.4 index alongside homogenized monthly temperature and precipitation records from 37 stations, we applied continuous wavelet analysis, Mann-Kendall abrupt change detection, and 21-year sliding correlation techniques. To isolate interannual ENSO signals, linear trends associated with global warming were removed from temperature data. Results indicate that 25 El Niño and 20 La Niña events occurred, with the Niño3.4 index exhibiting a dominant 1–5.7-year periodicity and a statistically significant regime shift in the late 1970s. Following detrending, the ENSO–winter temperature teleconnection weakened markedly after the late 1980s. Specifically, El Niño-induced DJF warming intensified by +0.45 °C (p < 0.01) in the 1991–2024 period compared to 1950–1990, whereas La Niña winters transitioned from anomalously warm to cold. Summer climate responses also shifted significantly: El Niño-related JJA precipitation decreased by 73.1 mm (from +20.7 to −52.4 mm, p < 0.05) in the DPRK, while La Niña summers exhibited opposite trends. These nonstationary relationships, driven by decadal reorganizations of Pacific-Asian atmospheric circulations, provide critical insights for improving seasonal climate prediction and informing regional climate adaptation strategies in the DPRK.

Diagnosing Seasonal Structures and Short-Term Forecasting of Tropospheric Ozone in a Tropical City Using Singular Spectrum Analysis and Linear Recurrent Formula

Amaury de Souza — 2026-04-28

Tropospheric ozone (O₃) is a secondary pollutant whose variability in tropical urban environments is strongly controlled by seasonal meteorology, photochemistry, and episodic emissions such as biomass burning. This study applies Singular Spectrum Analysis (SSA) combined with the Linear Recurrent Formula (LRF) to analyze and forecast daily tropospheric ozone in Campo Grande, Brazil, using SISAM/INPE satellite data from 2000 to 2018. In contrast to previous SSA-based applications, this work introduces a systematic evaluation of embedding window length (L = 30, 60, and 90) to assess the robustness of the decomposition and component separability. In addition, the spectral consistency of reconstructed components is examined to support the identification of dominant temporal modes. For forecasting, a strict out-of-sample framework is adopted, using 2000–2017 for training and 2018 for independent validation, ensuring no information leakage. The LRF model achieved RMSE = 0.79 ppb, MAE = 0.64 ppb, and MAPE = 3.8%, outperforming persistence and seasonal naïve benchmarks. Results indicate that ozone variability is predominantly seasonal, with weak long-term trends and relevant intra-seasonal fluctuations. The proposed framework provides a transparent, computationally efficient, and reproducible approach for diagnosing and forecasting ozone variability in tropical environments.

Space-Time Oscillations in Rainfall Onsets and Their Implications on Sustainable Staple Crop Production in Imo State, Nigeria

Ambrose Oluchukwu Abaneme — 2026-04-30

This study investigates the influence of space-time oscillations in rainfall onsets on sustainable staple crop production in agro-ecological zones from 1981 to 2023 in Imo State. The study area was stratified into three agro-ecological zones, comprising Imo East, Imo West and Imo North using stratified sampling techniques to ease data extraction. Data on rainfall onsets were obtained from NASA native resolution daily data PRECTOTCORR MERRA-2 using a gridded method 43 climatic years and analyzed using SPSS. The descriptive assessments of the monthly mean distributive patterns reveal that Imo East recorded the highest mean value of 369.2 mm in September, while Imo North recorded the lowest value of 15.19 mm in December. Also, the highest annual mean value of 68.419 mm is associated with Imo North, while the most dominant standard deviation and variance scores of 13.159 mm and 173.169 mm occur in Imo West agro-ecological zone. The time series with regression models of oscillations in rainfall onsets offered the generalized negative trends, but at varying rates. Comparatively, Imo West gives the highest predictive power of −0.244x + 70.59 and a corresponding highest R2 value of 0.054 in the sequence, while the ANOVA model gives a low value of 0.466. The results led to a conclusion that variations in agro-ecological zones have no statistically significant effects on rainfall onsets. This study recommends an urgent need to boost farmers’ adaptation capacity through proactive climate change education and training by agro-extension officers to increase crop production and sustainable food security in Imo State.

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

Isaac Kadono Mwalwimba — 2026-04-13

Data Availability Statement Correction

In the originally published version of this article, the Data Availability Statement did not provide sufficient detail regarding the specific data sources and access information.

To improve transparency and reproducibility, the Data Availability Statement has been updated as follows:

The data supporting the findings of this study are derived from a combination of publicly available datasets and primary research data.

Precipitation (rainfall) data were obtained from the NASA POWER Data Access Viewer, available at: https://power.larc.nasa.gov/data-access-viewer/.

Sea Surface Temperature (SST) data were retrieved via Google Earth Engine from the NOAA Optimum Interpolation Sea Surface Temperature Climate Data Record (NOAA/CDR/OISST/V2.1), covering the period 1990–2024.

Flood vulnerability data were derived from primary survey data collected by the corresponding author as part of doctoral research on flood vulnerability assessment in Malawi, supplemented by regional environmental and hydrological observational data.

The primary survey dataset is not fully publicly available due to ethical and data-sharing considerations; however, it can be made available from the authors upon reasonable request, subject to applicable conditions.

All publicly available datasets can be accessed through the links provided above and are sufficient to support the reproducibility of the study.

This correction does not affect the results or conclusions of the article. The original publication has also been updated.

DOI of original article: https://doi.org/10.30564/jasr.v8i2.10377

Correction Date: 13 April 2026

Refer to: Mwalwimba, I. K., Kalonjeka, B., Msadala, V., Katonda, V., Chisenga, C., Ngongondo, C., & Manda, M. (2025). Deep Learning-based Flood Risk Prediction for Climate Resilience Planning in Malawi. Journal of Atmospheric Science Research, 8(2), 37–50.