Application of Artificial Intelligence in Extreme Weather Monitoring and Forecasting

Abstract submission deadline

31 May 2027

Manuscript submission deadline

31 August 2027

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864

Topic Information

Dear Colleagues,

Severe weather events such as typhoons, rainstorms, tornadoes, and hailstorms pose severe threats to human lives, ecological environments, and socioeconomic development, making accurate monitoring and timely forecasting an urgent research priority. Remote sensing technology, with its advantages of large-scale coverage, real-time dynamic observation, and multi-source data acquisition, has become a core data source for severe weather research, while artificial intelligence (AI) provides an efficient analytical framework for mining the complex nonlinear characteristics of remote sensing data. This Topic focuses on the cutting-edge AI applications of remote sensing data in the whole chain of severe weather monitoring and forecasting, including data preprocessing, feature extraction, early warning model construction, and forecast accuracy improvement. We welcome original research papers, review articles, and technical notes that explore novel AI algorithms, multi-source remote sensing data fusion strategies, and practical engineering applications. Studies combining satellite, airborne, and ground-based remote sensing with AI to address key challenges in severe weather prediction are particularly encouraged, aiming to promote the integration of remote sensing and AI technologies and provide scientific support for severe weather disaster prevention and mitigation.

Prof. Dr. Leiming Ma
Prof. Dr. Changjiang Zhang
Dr. Zhaoliang Zeng
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Climate climate	4.0	6.5	2013	20.5 Days	CHF 1800	Submit
Forecasting forecasting	4.2	7.1	2019	23.8 Days	CHF 1800	Submit
Meteorology meteorology	-	2.5	2022	23.3 Days	CHF 1000	Submit
Remote Sensing remotesensing	4.3	9.4	2009	22 Days	CHF 2700	Submit

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Published Papers (1 paper)

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All Climate Forecasting Meteorology Remote Sensing

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Open AccessArticle

Assessing the Value of FY-4A/B Cloud-Top Height for Deep Learning-Based Tropical Cyclone Intensity Estimation over the Western North Pacific

by Xishu Huang, Xinyi Chen, Yuan Sun, Chaoxiong Xu, Wei Zhong and Hongrang He

Remote Sens. 2026, 18(17), 3030; https://doi.org/10.3390/rs18173030 - 4 Sep 2026

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Abstract

Tropical cyclone (TC) intensity estimation over the western North Pacific remains affected by uncertainties in satellite observations, best-track records, and rapidly evolving inner-core structures. To further exploit information on cloud-system vertical structure and its temporal evolution, this study introduces FY-4A/B cloud-top height (CTH) [...] Read more.

Tropical cyclone (TC) intensity estimation over the western North Pacific remains affected by uncertainties in satellite observations, best-track records, and rapidly evolving inner-core structures. To further exploit information on cloud-system vertical structure and its temporal evolution, this study introduces FY-4A/B cloud-top height (CTH) products and develops CTH-TCNet, a three-branch gated-fusion model that integrates infrared brightness temperature, CMORPH precipitation, and multidimensional CTH information for TC intensity estimation. The model consists of a CNN-based spatial branch, an LSTM-based temporal branch representing CTH evolution over the preceding 12 h, and a shortcut branch preserving current-time CTH statistics. Systematic ablation experiments show that the contribution of CTH is closely related to its representation and fusion strategy. Directly adding a single-time-step two-dimensional CTH field as an additional spatial channel provides no further performance gain, whereas historical CTH evolution and current-time CTH statistics provide complementary information. Jointly representing these two types of information through the temporal and shortcut branches yields the best performance. The final model achieves an MAE of 6.26 kt and an RMSE of 7.41 kt on the test sets. Intensity-stratified results further show that CTH generally provides larger improvements for TY, STY, and Super TY than for TS. Interpretability analyses indicate that, as TC intensity increases, the model exhibits greater reliance on CTH temporal evolution and structural information from the inner-core and eyewall-adjacent regions, with these dependence patterns being broadly consistent with known characteristics of TC inner-core convective organization and eyewall-related structures. These results indicate that FY-4A/B CTH provides valuable complementary structural and temporal information for satellite-based TC intensity estimation. Full article

(This article belongs to the Topic Application of Artificial Intelligence in Extreme Weather Monitoring and Forecasting)

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