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.

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.