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Remote Sensing
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Improving Meteorological Forecasting Models Using Remote Sensing Data
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Improving Meteorological Forecasting Models Using Remote Sensing Data

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1218

Special Issue Editors

Prof. Dr. Kaijun Ren

E-Mail Website
Guest Editor
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
Interests: remote sensing; computer science; high performance computing; applications in ocean science and meteorology areas
Dr. Wenjing Zhao

E-Mail Website
Guest Editor
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
Interests: numerical modeling of land-surface processes; land data assimilation; land–atmosphere coupling; remote sensing
Dr. Jia Liu

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan 430074, China
Interests: high performance computing; real time intelligent processing and analysis of remote sensing data

Special Issue Information

Dear Colleagues,

The ability to generate accurate, timely, and reliable weather forecasts remains a critical challenge, particularly in the context of increasing extreme weather events. Remote sensing, especially satellite-based observations, can provide large-scale, high-resolution, and near-real-time data on atmospheric parameters and surface conditions. It offers an opportunity to promote the development of meteorological forecasting through multiple aspects such as initial field construction, physical process optimization, forecast evaluation, etc.

We are pleased to invite you to contribute to this Special Issue, which focuses on improving meteorological forecasting models using remote sensing data. This Special Issue aims to introduce the advanced research achievements to enhance forecasting capabilities and expand application fields. The topics of interest include, but are not limited to, data assimilation methods that incorporate remote sensing observations into meteorological forecasting models, complex process modeling using remote sensing inputs, machine learning and artificial intelligence methods for interpreting remote sensing data in meteorology, monitoring and prediction of extreme events, and operational application pipelines that support disaster prevention and mitigation, etc. This Special Issue welcomes original research articles and reviews.

Prof. Dr. Kaijun Ren
Dr. Wenjing Zhao
Dr. Jia Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • meteorological satellite remote sensing
  • atmospheric parameters
  • data assimilation
  • AI/ML in meteorology
  • model optimization
  • meteorological forecasting
  • extreme weather events
 

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Published Papers (2 papers)

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Research

24 pages, 4292 KB  
Article
An Interpretable Nonlinear Intelligent Bias Correction Method for FY-4A/GIIRS Hyperspectral Infrared Brightness Temperatures
by Gen Wang, Bing Xu, Song Ye, Xiefei Zhi, Tiening Zhang, Youpeng Yang, Yang Liu, Feng Xie, Qiao Liu and Haili Zhang
Remote Sens. 2026, 18(5), 748; https://doi.org/10.3390/rs18050748 - 1 Mar 2026
Viewed by 292
Abstract
The hyperspectral infrared observations of the Geostationary Interferometric Infrared Sounder (GIIRS) on the Fengyun-4A (FY-4A) satellite are an important data source for numerical weather prediction (NWP) assimilation. However, there are systematic differences between observed and simulated brightness temperatures (i.e., the observation increments contain [...] Read more.
The hyperspectral infrared observations of the Geostationary Interferometric Infrared Sounder (GIIRS) on the Fengyun-4A (FY-4A) satellite are an important data source for numerical weather prediction (NWP) assimilation. However, there are systematic differences between observed and simulated brightness temperatures (i.e., the observation increments contain predictable systematic bias components). To address the issue that traditional linear methods struggle to capture the nonlinear relationships between biases and forecast predictors, this study proposes an intelligent bias correction method that integrates ensemble learning and explainable artificial intelligence. First, the entropy reduction method is used to select 69 mid-wave channels. Then, Random Forest, XGBoost, LightGBM, Decision Tree, and Extra Tree are used as base learners to construct a weighted average ensemble model. Training and validation are conducted using high-frequency clear-sky observation data from FY-4A/GIIRS during Typhoon Lekima. The results show that: (1) the ensemble learning correction method outperforms single models and traditional offline methods, with root mean square errors of brightness temperature bias of less than 0.9209 K for the training set and 1.4447 K for the test set; (2) Shapley Additive Explanations (SHAP)-based interpretability analysis reveals the contribution and nonlinear influence mechanisms of factors such as longitude, atmospheric thickness, surface temperature, and total precipitable water on bias correction. This study provides an intelligent bias correction framework with both high precision and explainability, offering a reference for the bias correction and assimilation applications of hyperspectral satellite observations like GIIRS. Full article
(This article belongs to the Special Issue Improving Meteorological Forecasting Models Using Remote Sensing Data)
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26 pages, 10692 KB  
Article
TPDTC-Net-DRA: Enhancing Nowcasting of Heavy Precipitation via Dynamic Region Attention
by Xinhua Qi, Yingzhuo Du, Chongjiu Deng, Jiang Liu, Jia Liu, Kefeng Deng and Xiang Wang
Remote Sens. 2026, 18(3), 490; https://doi.org/10.3390/rs18030490 - 3 Feb 2026
Viewed by 429
Abstract
Heavy precipitation events are characterized by sudden onset, limited spatiotemporal scales, rapid evolution, and high disaster potential, posing long-standing challenges in weather forecasting. With the development of deep learning, an increasing number of researchers have leveraged its powerful feature representation and non-linear modeling [...] Read more.
Heavy precipitation events are characterized by sudden onset, limited spatiotemporal scales, rapid evolution, and high disaster potential, posing long-standing challenges in weather forecasting. With the development of deep learning, an increasing number of researchers have leveraged its powerful feature representation and non-linear modeling capabilities to address the challenge of precipitation nowcasting. Despite recent advances in deep learning for precipitation nowcasting, most existing methods do not explicitly separate precipitation from non-precipitation regions. This often leads to the extraction of redundant or irrelevant features, thereby causing models to learn misleading patterns and ultimately reducing their predictive capability for heavy precipitation events. To address this issue, we propose a novel dynamic region attention (DRA) mechanism, and an improved model TPDTC-Net-DRA, based on our previously introduced TPDTC-Net. The proposed TPDTC-Net-DRA applies the DRA mechanism and incorporates its two key components: a dynamic region module and a weight control module. The dynamic region module generates a mask matrix that is applied to the feature maps, guiding the attention mechanism to focus only on precipitation areas. Meanwhile, the weight control module produces a location-sensitive weight matrix to direct the model’s attention toward regions with intense precipitation. Extensive experiments demonstrate that TPDTC-Net-DRA achieves superior performance for heavy precipitation, outperforming current state-of-the-art methods, and indicate that the proposed DRA mechanism exhibits strong generalization ability across diverse model architectures. Full article
(This article belongs to the Special Issue Improving Meteorological Forecasting Models Using Remote Sensing Data)
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