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Multi-Source Atmospheric Remote Sensing: Enabling High-Precision Meteorological Monitoring and Forecasting

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 4234

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Special Issue Editors

Prof. Dr. Xiefei Zhi

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Guest Editor

School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: numerical weather prediction; climate change; short-term climate prediction; artificial intelligence
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Prof. Dr. Donglian Sun

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Guest Editor

College of Science, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA
Interests: land surface temperature retrieval; satellite-based observations of flood and standing water; seasonal prediction of tropical climate
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Prof. Dr. Yonghong Zhang

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Guest Editor

School of Atmosphere and Remote Sensing, School of Automation, Wuxi University, Wuxi 214105, China
Interests: remote sensing; deep learning; short term precipitation forecast; disaster assessment; environmental monitoring; artificial intelligence
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Dr. Yan-An Liu

E-Mail Website
Guest Editor

School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Interests: data assimilation; hyperspectral infrared remote sensing; retrieval of atmospheric parameters; application of meteorological satellite data; extreme weather simulation and prediction
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Dr. Wen Huo

E-Mail Website
Guest Editor

Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
Interests: disastrous weather and climate; desert boundary layer; observation of sandstorm
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Special Issue Information

Dear Colleagues,

The integration of multi-source atmospheric remote sensing has revolutionized meteorological monitoring and forecasting by providing high-resolution, multi-dimensional observations of the atmosphere. With advancements in satellite, ground-based, and airborne remote sensing technologies, as well as the development of sophisticated data assimilation techniques, it is now possible to achieve a more precise and dynamic characterization of atmospheric processes. These innovations are critical for improving the accuracy of weather prediction and mitigating the impacts of extreme meteorological events.

However, challenges remain in effectively integrating heterogeneous remote sensing data, addressing observational gaps, and enhancing the synergy between observations and numerical models. The rapid evolution of artificial intelligence and big data analytics further presents new opportunities to unlock the potential of multi-source atmospheric remote sensing. By leveraging these cutting-edge technologies, researchers can advance the field of fine-scale meteorological monitoring and forecasting, ultimately supporting disaster prevention and sustainable development.

For this Special Issue, we welcome contributions from researchers in atmospheric sciences, remote sensing, data assimilation, and environmental modeling to share their latest findings on the application of multi-source atmospheric remote sensing for refined weather monitoring and forecasting.

In particular, we encourage studies investigating the following:

Multi-source remote sensing data fusion and assimilation
Novel methodologies for integrating satellite, ground-based, UAV-based, and new-generation sensor observations (e.g., miniaturized, hyperspectral, or edge-computing-enabled devices) to improve atmospheric parameter retrievals and numerical weather prediction.

High-resolution atmospheric vertical sounding for fine-scale applications
Advances in lidar, radar, hyperspectral, and microwave remote sensing for monitoring temperature, humidity, wind, and aerosol distributions, with an emphasis on applications requiring ultra-high spatiotemporal resolution (e.g., low-altitude economy, aviation safety, and urban microclimate).

AI-driven atmospheric remote sensing
Applications of machine learning and deep learning in processing, analyzing, and interpreting multi-source remote sensing data for meteorological applications, including bias correction, feature extraction, and predictive modeling.

Extreme weather monitoring
The utilization of remote sensing to study severe convective systems, typhoons, sandstorms, and other high-impact weather phenomena, with a focus on early warning systems.

Urban, regional, and low-altitude economy meteorological applications
Remote sensing approaches for monitoring urban heat islands, air quality, boundary layer dynamics, and low-altitude meteorological conditions (e.g., drone operation corridors, vertiport safety, and wind shear detection) to support city-scale weather services and emerging low-altitude economic activities.

We look forward to receiving your research contributions and reviews that will advance the understanding and application of multi-source atmospheric remote sensing in meteorology.

Prof. Dr. Xiefei Zhi
Prof. Dr. Donglian Sun
Prof. Dr. Yonghong Zhang
Dr. Yan-An Liu
Dr. Wen Huo
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

multi-source atmospheric remote sensing
meteorological monitoring
high-resolution vertical sounding
data fusion and assimilation
AI-driven analytics
extreme weather prediction
low-altitude economy meteorology
air quality monitoring
numerical weather prediction

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Published Papers (6 papers)

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Research

18 pages, 15013 KB

Open AccessArticle

Atmospheric Weighted Average Temperature Enhancement Model for the European Region Considering Daily Variations and Residual Changes in Surface Temperature

by Bingbing Zhang, Tong Wu and Yi Shen

Remote Sens. 2026, 18(1), 36; https://doi.org/10.3390/rs18010036 - 23 Dec 2025

Abstract

The retrieval of precipitable water vapor (PWV) through Global Navigation Satellite System (GNSS) meteorology critically depends on the precise determination of atmospheric weighted mean temperature (Tm). Existing empirical models for Tm retrieval over Europe offer speed but suffer accuracy limitations due to complex local environmental and climatic factors. Aiming to improve Tm model accuracy in Europe, this study constructed the European Tm Enhanced Model (EurTm). The model was constructed based on 2014–2023 radiosonde data from 40 stations across Europe, with its parameters optimized through least squares estimation. The EurTm model integrates multiple factors, including Tm from Hourly Global Pressure and Temperature 2 (HGPT2), the difference between Ts obtained by HGPT2 and Ts measured by radiosonde stations, and diurnal variation. The EurTm model’s accuracy was validated by comparing its outputs with reference values derived from 2024 radiosonde data. The EurTm model underwent comparative analysis against the widely used Bevis, ETmPoly, and HGPT2 models. The EurTm model’s accuracy was 13.2%, 4.1%, and 32.7% higher than the Bevis, ETmPoly, and HGPT2 models at 40 modeling stations. At 13 non-modeling stations, the EurTm model outperformed the Bevis, ETmPoly, and HGPT2 models with accuracy enhancements of 16.1%, 4.7%, and 30.0%, respectively. Theoretical evaluation showed that the EurTm model achieved an RMSE of 0.20 mm and a relative error of 1.11% for GNSS-derived PWV, outperforming all comparative models. In conclusion, the EurTm model not only holds significant application value for GNSS PWV retrieval in Europe but also provides a novel approach for region-specific enhancements of global empirical Tm models by addressing characteristic regional features such as diurnal variations. Full article

(This article belongs to the Special Issue Multi-Source Atmospheric Remote Sensing: Enabling High-Precision Meteorological Monitoring and Forecasting)

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18 pages, 7536 KB

Open AccessArticle

Predictability of Landfalling Typhoon Tracks in East China Based on Ensemble Sensitivity Analysis

by Jing Zhang, Shoupeng Zhu, Yan Tan and Chen Chen

Remote Sens. 2025, 17(24), 3944; https://doi.org/10.3390/rs17243944 - 5 Dec 2025

Viewed by 236

Abstract

Accurate typhoon track forecasting is vital for disaster mitigation in East China, a region frequently impacted by landfalling typhoons. Despite advances in numerical weather prediction, uncertainties remain high, especially within 48 h of landfall, due to complex interactions among tropical cyclones, the subtropical high, and mesoscale systems. This study applies Ensemble-based Sensitivity Analysis (ESA) within a high-resolution regional ensemble prediction system (Shanghai Weather And Risk Model System-Ensemble Prediction System, SWARMS-EN) to investigate forecast uncertainties of three representative typhoons—Gaemi, Bebinca, and Kong-rey—that made landfall in East China in 2024. Our results reveal consistent sensitivity patterns across diverse large-scale environments, particularly around the western flank of the subtropical high and in proximity to nearby low-pressure systems. Track uncertainty was closely tied to fluctuations in the steering flow, notably its zonal component. Moreover, binary typhoon interactions emerged as key drivers of forecast divergence. ESA effectively identified sensitive regions where small initial perturbations exert significant downstream influence on typhoon tracks. This study demonstrates the operational value of ESA for diagnosing forecast error sources and guiding targeted observations. By linking forecast uncertainty to physical mechanisms, this research enhances our understanding of typhoon predictability and supports the development of more adaptive and accurate regional forecasting systems. Full article

(This article belongs to the Special Issue Multi-Source Atmospheric Remote Sensing: Enabling High-Precision Meteorological Monitoring and Forecasting)

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20 pages, 13059 KB

Open AccessArticle

Reconstructed SWHs Based on a Deep Learning Method and the Revealed Long-Term SWH Variance Characteristics During 1993–2024

by Jingwei Xu, Yangyang Zhang, Xiefei Zhi, Ziqi Ma, Xiuzhi Zhang, Ying Xu, Yong Luo, Lisha Kong and Lin Yi

Remote Sens. 2025, 17(23), 3802; https://doi.org/10.3390/rs17233802 - 23 Nov 2025

Viewed by 527

Abstract

Long-term high-resolution spatial gridded altimeter-derived significant wave height (SWH) data with daily temporal resolution are fundamental to revealing the detailed processes through which weather systems influence the ocean. However, elucidation of those processes is hampered by the sparse coverage and narrow width of the swath of altimeter-derived SWHs. The core problem is how best to extract the spatial structure and then fill missing values around the daily swaths. Although recent developments in deep learning methods have improved the extraction of spatial features, progress regarding the reconstruction of gridded altimeter daily SWHs remains limited. This study reconstructed daily 0.25° × 0.25° gridded SWHs from 1993 to 2024 using a partial convolutional U-Net model with attention and residual blocks. Comparison between the daily reconstructed SWHs and ERA5 SWHs revealed that the reconstructed SWHs improved the accuracy for SWHs of >2.5 m, which are usually underestimated in the ERA5 data. The greatest differences were in China’s offshore waters, especially in the region of the Taiwan Strait and in waters influenced by the Huanghai Warm Current. This study highlights the importance of altimeter swath-derived SWHs in reconstructed gridded SWH datasets, particularly in complex straits and under high sea state conditions. Full article

(This article belongs to the Special Issue Multi-Source Atmospheric Remote Sensing: Enabling High-Precision Meteorological Monitoring and Forecasting)

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22 pages, 6335 KB

Open AccessArticle

Impact of Assimilating Doppler Radar Data on Short-Term Numerical Weather Forecasting at Different Spatial Scales

by Guanting Luo, Tingting Li, Ganlin Qiu, Zhizhong Su and Deqiang Liu

Remote Sens. 2025, 17(19), 3384; https://doi.org/10.3390/rs17193384 - 8 Oct 2025

Viewed by 952

Abstract

This study explores the impact of assimilating Doppler radar data on short-term numerical weather forecasting for a heavy rainfall event in Southern China, focusing on different spatial scales. Results show that radar data assimilation significantly improves the initial analysis and enhances the accuracy of hourly precipitation forecasts by providing more detailed mesoscale system information, compared to assimilating only wind profiler radar data. The Barnes filter analysis reveals that radar data assimilation has a more pronounced effect on mesoscale systems, with improvements primarily concentrated in the first 2 h of the forecast. However, this improvement diminishes rapidly beyond the 2 h lead time, indicating the inherent predictability limits of mesoscale systems. In contrast, large-scale systems exhibit a greater stability and predictability, with radar data assimilation having a relatively smaller but still positive impact. The study emphasizes the importance of radar data assimilation for short-term forecasts at different spatial scales and suggests that future work prioritize extending mesoscale predictability. Full article

(This article belongs to the Special Issue Multi-Source Atmospheric Remote Sensing: Enabling High-Precision Meteorological Monitoring and Forecasting)

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21 pages, 6709 KB

Open AccessArticle

Multi-Source Retrieval of Thermodynamic Profiles from an Integrated Ground-Based Remote Sensing System Using an EnKF1D-Var Framework

by Qi Zhang, Bin Deng, Shudong Wang, Fangyou Dong and Min Shao

Remote Sens. 2025, 17(18), 3133; https://doi.org/10.3390/rs17183133 - 10 Sep 2025

Viewed by 721

Abstract

In this study, we present a novel data assimilation framework, the Ensemble Kalman Filter One-Dimensional Variational (EnKF1D-Var) framework, which assimilates observations from a Ground-based Microwave Radiometer (GMWR), a Mie–Raman Aerosol Lidar (MRL), and a Global Navigation Satellite System Meteorology sensor (GNSS/MET). The framework integrates multi-source vertical observations of water vapor and temperature with hourly temporal and 15 m vertical resolutions, driven by GFS forecasts. Three-month-long studies from May to July 2024 at Anqing Station in subtropical China demonstrate that the EnKF1D-Var retrievals reduce biases in temperature and humidity within the low troposphere, especially for daytime retrievals, by dynamically updating the observational error covariance matrices. Maximum humidity corrections reach up to 0.075 g/kg (120 PPMV), and temperature bias reductions exceed 3%. Incremental analysis reveals that the contribution to bias correction differs across instruments. GNSS/MET plays a dominant role in temperature adjustment, while GMWR provides supplementary support. In contrast, the majority of the improvements in water vapor retrieval can be attributed to MRL observations. This study achieved a reasonable application of multiple ground-based remote sensing observations, providing a new approach for the inversion of temperature and humidity profiles in the atmospheric boundary layer. Full article

(This article belongs to the Special Issue Multi-Source Atmospheric Remote Sensing: Enabling High-Precision Meteorological Monitoring and Forecasting)

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24 pages, 18914 KB

Open AccessArticle

Canopy Chlorophyll Content Inversion of Mountainous Heterogeneous Grasslands Based on the Synergy of Ground Hyperspectral and Sentinel-2 Data: A New Vegetation Index Approach

by Yi Zheng, Yao Wang, Tayir Aziz, Ali Mamtimin, Yang Li and Yan Liu

Remote Sens. 2025, 17(13), 2149; https://doi.org/10.3390/rs17132149 - 23 Jun 2025

Cited by 2 | Viewed by 1152

Abstract

Canopy chlorophyll content (CCC) is a key indicator for assessing the carbon sequestration capacity and material cycling efficiency of ecosystems, and its accurate retrieval holds significant importance for analyzing ecosystem functioning. Although numerous destructive and remote sensing methods have been developed to estimate CCC, the accurate estimation of CCC remains a significant challenge in mountainous regions with complex terrain and heterogeneous vegetation types. Through the synergistic analysis of ground hyperspectral and Sentinel-2 data, this study employed Pearson correlation analysis and spectral resampling techniques to identify Sentinel-2 blue band B1 (443 nm) and red band B4 (665 nm) as chlorophyll-sensitive bands through spectral matching with the hyperspectral reflectance of typical grassland vegetation. Based on this, we developed a new four-band vegetation index (VI), the Dual Red-edge and Coastal Aerosol Vegetation Index (DRECAVI), for estimating the CCC of heterogeneous grasslands in the middle section of the Tianshan Mountains. DRECAVI incorporates red-edge anti-saturation modules (bands B4 and B7) and aerosol correction modules (bands B1 and B8). In order to test the performance of the new index, we compared it with eight commonly used indices and a hybrid model, the Sentinel-2 Biophysical Processor (S2BP). The results indicated the following: (1) DRECAVI demonstrated the highest accuracy in CCC retrieval for mountainous vegetation (R² = 0.74, RMSE = 16.79, MAE = 12.50) compared to other VIs and hybrid methods, effectively mitigating saturation effects in high biomass areas and capturing a weak bimodal distribution pattern of CCC in the montane meadow. (2) The blue band B1 enhances atmospheric correction robustness by suppressing aerosol scattering, and the red-edge band B7 overcomes the sensitivity limitations of conventional red-edge indices (such as NDVI705, CIred-edge, and NDRE), demonstrating the potential application of the synergy mechanism between the blue band and the red-edge band. (3) Although the S2BP achieved high accuracy (R² = 0.73, RMSE = 19.83, MAE = 14.71) without saturation effects and detected a bimodal distribution of CCC in the montane meadow of the study area, its algorithmic complexity hindered large-scale operational applications. In contrast, DRECAVI maintained similar precision while reducing algorithmic complexity, making it more suitable for regional-scale grassland dynamic monitoring. This study confirms that the synergistic use of multi-source data effectively overcomes the limitations of the spectral–spatial resolution of a single data source, providing a novel methodology for the precision monitoring of mountain ecosystems. Full article

(This article belongs to the Special Issue Multi-Source Atmospheric Remote Sensing: Enabling High-Precision Meteorological Monitoring and Forecasting)

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