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Remote Sensing
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Application of Remote Sensing Data in Data Assimilation, Reanalysis and...
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Application of Remote Sensing Data in Data Assimilation, Reanalysis and Artificial Intelligence for Mesoscale Numerical Weather Models (Second Edition)

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2303

Special Issue Editors

Dr. Feifei Shen

E-Mail Website
Guest Editor
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Nanjing University of Information Science & Technology, Nanjing 211544, China
Interests: doppler weather radar data assimilation; satellite remote sensing observation data assimilation; integrated variational hybrid assimilation system development; wind, solar and other renewable energy research
Special Issues, Collections and Topics in MDPI journals
Dr. Yunheng Wang

E-Mail Website
Guest Editor
Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO), University of Oklahoma, Norman, OK, USA
Interests: radar data assimilation for short-term severe weather forecasting; high performance computing in data assimilation and numerical weather prediction
Special Issues, Collections and Topics in MDPI journals
Dr. Xin Li

E-Mail Website
Guest Editor
Nanjing Joint Institute for Atmospheric Sciences, Nanjing, China
Interests: satellite data assimilation; radar data assimilation; ensemble–variational data assimilation; satellite data application; numerical model prediction; severe weather simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Lipeng Jiang

E-Mail Website
Guest Editor
CMA Earth System Modeling and Prediction Centre, China Meteorological Administration (CMA), Beijing, China
Interests: global and regional reanalysis; satellite remote sensing data assimilation; coupled chemistry-meteorology data assimilation
Special Issues, Collections and Topics in MDPI journals
Dr. Yuyan Cui

E-Mail Website
Guest Editor
Regional Air Quality Modeling Section, Air Quality Planning and Science Division, California Air Resources Board (CARB), Sacramento, CA, USA
Interests: atmospheric numerical and statistical modeling (application and development); boundary layer and turbulence; earth-atmosphere interactions; atmospheric composition; trace gas (greenhouse gas) emissions; machine learning application of atmospheric sciences
Special Issues, Collections and Topics in MDPI journals
Dr. Dongmei Xu

E-Mail Website
Guest Editor
School of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 211544, China
Interests: satellite remote sensing observation data assimilation; radiance data application for cloud retrievals; ensemble–variational data assimilation; radar data assimilation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent progress in computer technology and computing capabilities has facilitated more advanced applications of remote sensing data in mesoscale numerical weather models. Furthermore, the developments in remote sensing technology continuously provide new data types. Such advances will benefit both numerical weather prediction (NWP) for severe and high-impact weather events and the quality of regional/global data reanalysis. This Special Issue seeks innovative submissions that are related to improving the accuracy of mesoscale weather models through remote sensing data assimilations, artificial intelligence and machine learning algorithms, new remote sensing networks, and other remote sensing data applications that improve the prediction of high-impact weather events, air quality research, land and water monitoring, and the decision making involved in such predictions; we also welcome applications of and enhancements in regional or global data reanalysis with remote sensing data. This Special Issue is the second edition based on the first edition’s success.

Dr. Feifei Shen
Dr. Yunheng Wang
Dr. Xin Li
Dr. Lipeng Jiang
Dr. Yuyan Cui
Dr. Dongmei Xu
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • advances in remote sensing data assimilation
  • new types of remote sensing observations
  • network design or data analysis with numerical models
  • convective-allowing and/or regional numerical model developments
  • probabilistic prediction methods
  • verification methods and statistical modelling
  • new developments in artificial intelligence for numerical models
  • regional and global data reanalysis techniques
  • coupled data assimilation
  • artificial intelligence
  • machine learning
  • air quality research

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Related Special Issue

Published Papers (2 papers)

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Research

18 pages, 20560 KiB  
Article
The Impacts of Assimilating Radar Reflectivity for the Analysis and Forecast of “21.7” Henan Extreme Rainstorm Within the Gridpoint Statistical Interpolation–Ensemble Kalman Filter System: Issues with Updating Model State Variables
by Aiqing Shu, Dongmei Xu, Jinzhong Min, Ling Luo, Haiyan Fei, Feifei Shen, Xiaojun Guan and Qilong Sun
Remote Sens. 2025, 17(3), 501; https://doi.org/10.3390/rs17030501 - 31 Jan 2025
Viewed by 617
Abstract
Based on the “21.7” Henan extreme rainstorm case, this study investigates the influence of updating model state variables in the GSI-EnKF (Gridpoint Statistical Interpolation–ensemble Kalman filter) system with the Thompson microphysics scheme. Six sensitivity experiments are conducted to assess the impact of updating [...] Read more.
Based on the “21.7” Henan extreme rainstorm case, this study investigates the influence of updating model state variables in the GSI-EnKF (Gridpoint Statistical Interpolation–ensemble Kalman filter) system with the Thompson microphysics scheme. Six sensitivity experiments are conducted to assess the impact of updating different model state variables on the EnKF analysis and subsequent forecast. The experiments include the Z_ALL experiment (updating all variables), the Z_NoEnv experiment (excluding dynamical and thermodynamical variables), the Z_NoNr experiment (excluding rainwater number concentration), and three additional experiments that examine the removal of updating horizontal wind (U, V), vertical wind (W), and perturbation potential temperature (T), which are marked as Z_NoUV, Z_NoW, and Z_NoT. The results indicate that updating different model state variables leads to various effects on dynamical, thermodynamical, and hydrometeor fields. Specifically, excluding the update of vertical wind or perturbation potential temperature has little effect on the rainwater mixing ratio, whereas excluding the update of the rainwater number concentration causes a significant increase in the rainwater mixing ratio, particularly in the northern region of Zhengzhou. Not updating horizontal wind or environmental variables shifts the rainwater mixing ratio northward, deviating from the observed rainfall center. The analysis of near-surface divergence and vertical wind also reveals that not updating certain variables could result in weaker or less detailed wind structures. Although radar reflectivity, which is mainly influenced by the mixing ratios of hydrometeors, shows consistent spatial distribution across experiments, their intensity varies, with the Z_ALL experiment showing the most accurate prediction. The 4 h deterministic forecasts based on the ensemble mean analysis demonstrate that updating all variables provides the best improvement in predicting the “21.7” Henan extreme rainstorm. These results emphasize the importance of updating all relevant model variables for improving predictions of extreme rainstorms. Full article
(This article belongs to the Special Issue Application of Remote Sensing Data in Data Assimilation, Reanalysis and Artificial Intelligence for Mesoscale Numerical Weather Models (Second Edition))
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18 pages, 10136 KiB  
Article
The Combination Application of FY-4 Satellite Products on Typhoon Saola Forecast on the Sea
by Chun Yang, Bingying Shi and Jinzhong Min
Remote Sens. 2024, 16(21), 4105; https://doi.org/10.3390/rs16214105 - 2 Nov 2024
Cited by 1 | Viewed by 1237
Abstract
Satellite data play an irreplaceable role in global observation data systems. Effective comprehensive application of satellite products will inevitably improve numerical weather prediction. FengYun-4 (FY-4) series satellites can provide not only radiance data but also retrieval data with high temporal and spatial resolutions. [...] Read more.
Satellite data play an irreplaceable role in global observation data systems. Effective comprehensive application of satellite products will inevitably improve numerical weather prediction. FengYun-4 (FY-4) series satellites can provide not only radiance data but also retrieval data with high temporal and spatial resolutions. To evaluate the potential benefits of the combination application of FY-4 Advanced Geostationary Radiance Imager (AGRI) products on Typhoon Saola analysis and forecast, two group of experiments are set up with the Weather Research and Forecasting model (WRF). Compared with the benchmark experiment, whose sea surface temperature (SST) is from the National Centers for Environmental Prediction (NCEP) reanalysis data, the SST replacement experiments with FY-4 A/B SST products significantly improve the track and precipitation forecast, especially with the FY-4B SST product. Based on the above results, AGRI clear-sky and all-sky assimilations with FY-4B SST are implemented with a self-constructed AGRI assimilation module. The results show that the AGRI all-sky assimilation experiment can obtain better analyses and forecasts. Furthermore, it is proven that the combination application of AGRI radiance and SST products is beneficial for typhoon prediction. Full article
(This article belongs to the Special Issue Application of Remote Sensing Data in Data Assimilation, Reanalysis and Artificial Intelligence for Mesoscale Numerical Weather Models (Second Edition))
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