Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

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

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Related Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 14698

Share This Special Issue

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 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

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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Remote Sensing Data Application, Data Reanalysis and Advances for Mesoscale Numerical Weather Models in Remote Sensing (12 articles)

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

26 pages, 8779 KB

Open AccessArticle

TAUT: A Remote Sensing-Based Terrain-Adaptive U-Net Transformer for High-Resolution Spatiotemporal Downscaling of Temperature over Southwest China

by Zezhi Cheng, Jiping Guan, Li Xiang, Jingnan Wang and Jie Xiang

Remote Sens. 2026, 18(3), 416; https://doi.org/10.3390/rs18030416 - 27 Jan 2026

Viewed by 727

Abstract

High-precision temperature prediction is crucial for dealing with extreme weather events under the background of global warming. However, due to the limitations of computing resources, numerical weather prediction models are difficult to directly provide high spatio-temporal resolution data that meets the specific application requirements of a certain region. This problem is particularly prominent in areas with complex terrain. The use of remote sensing data, especially high-resolution terrain data, provides key information for understanding and simulating the interaction between land and atmosphere in complex terrain, making the integration of remote sensing and NWP outputs to achieve high-precision meteorological element downscaling a core challenge. Aiming at the challenge of temperature scaling in complex terrain areas of Southwest China, this paper proposes a novel deep learning model—Terrain Adaptive U-Net Transformer (TAUT). This model takes the encoder–decoder structure of U-Net as the skeleton, deeply integrates the global attention mechanism of Swin Transformer and the local spatiotemporal feature extraction ability of three-dimensional convolution, and innovatively introduces the multi-branch terrain adaptive module (MBTA). The adaptive integration of terrain remote sensing data with various meteorological data, such as temperature fields and wind fields, has been achieved. Eventually, in the complex terrain area of Southwest China, a spatio-temporal high-resolution downscaling of 2 m temperature was realized (from 0.1° in space to 0.01°, and from 3 h intervals to 1 h intervals in time). The experimental results show that within the 48 h downscaling window period, the TAUT model outperforms the comparison models such as bilinear interpolation, SRCNN, U-Net, and EDVR in all evaluation metrics (MAE, RMSE, COR, ACC, PSNR, SSIM). The systematic ablation experiment verified the independent contributions and synergistic effects of the Swin Transformer module, the 3D convolution module, and the MBTA module in improving the performance of each model. In addition, the regional terrain verification shows that this model demonstrates good adaptability and stability under different terrain types (mountains, plateaus, basins). Especially in cases of high-temperature extreme weather, it can more precisely restore the temperature distribution details and spatial textures affected by the terrain, verifying the significant impact of terrain remote sensing data on the accuracy of temperature downscaling. The core contribution of this study lies in the successful construction of a hybrid architecture that can jointly leverage the local feature extraction advantages of CNN and the global context modeling capabilities of Transformer, and effectively integrate key terrain remote sensing data through dedicated modules. The TAUT model offers an effective deep learning solution for precise temperature prediction in complex terrain areas and also provides a referential framework for the integration of remote sensing data and numerical model data in deep learning models. 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))

► Show Figures

Figure 1

21 pages, 12311 KB

Open AccessArticle

Added Value of Assimilating FY-4B AGRI Water Vapor Radiances on Analyses and Forecasts for “23 · 7” Heavy Rainfall

by Tingting Zhong, Chun Yang, Jinzhong Min, Bingying Shi and Qiongbo Sun

Remote Sens. 2025, 17(23), 3808; https://doi.org/10.3390/rs17233808 - 24 Nov 2025

Viewed by 878

Abstract

The infrared satellite data have become an important source of assimilated data in numerical weather prediction (NWP) models. With the self-constructed assimilated module in the Weather Research and Forecasting model’s Data Assimilation (WRFDA) system, a set of cycling assimilation experiments is conducted to evaluate the added value of assimilating the Fengyun-4B (FY-4B) Advanced Geostationary Radiation Imager (AGRI) water vapor channels clear-sky data on analyses and forecasts for “23 · 7” heavy rainfall. The results show a notable reduction (50~60%) in the root mean square error (RMSE) of observed and simulated brightness temperature after assimilating AGRI and the positive analysis increments in temperature and humidity fields, which are conducive to precipitation formation. Furthermore, changes in humidity analysis caused by AGRI assimilation propagate from the upper to lower levels with assimilation cycling. Compared to the benchmark experiment, the AGRI assimilation experiments produce higher humidity conditions and more pronounced ascending motion, resulting in more realistic rainfall predictions at both location and intensity with higher rainfall scores, especially with the two-step assimilation scheme. Moreover, based on the results from sensitivity experiments, it is proven that the addition of a new channel 11 can further improve humidity and enhance rainfall location and intensity predictions. Overall, the clear-sky assimilation of the FY-4B AGRI water vapor channel data brings notable improvements to “23 · 7” heavy rainfall 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))

► Show Figures

Figure 1

24 pages, 7931 KB

Open AccessArticle

Impact of FY-3D MWRI and MWHS-2 Radiance Data Assimilation in WRFDA System on Forecasts of Typhoon Muifa

by Feifei Shen, Jiahao Zhang, Si Cheng, Changchun Pei, Dongmei Xu and Xiaolin Yuan

Remote Sens. 2025, 17(17), 3035; https://doi.org/10.3390/rs17173035 - 1 Sep 2025

Cited by 1 | Viewed by 1585

Abstract

This study investigates the impact of assimilating FY-3D Microwave Radiation Imager (MWRI) radiance data into the Weather Research and Forecasting (WRF) model, utilizing a 3D-Var data assimilation system, on the forecast accuracy of Typhoon Muifa (2022). The research focuses on the selection of data from different channels, land/ocean coverage, and orbits of the MWRI, along with the synergistic assimilation strategy with MWHS-2 data. Ten assimilation experiments were conducted, starting from 0600 UTC on 14 September 2022, covering a 42 h forecast period. The results show that after assimilating the microwave radiometer data, the brightness temperature deviation in the ocean area was significantly reduced compared to the simulation without data assimilation. This led to an improvement in the accuracy of typhoon track and intensity predictions, particularly for predictions beyond 24 h. Furthermore, the assimilation of land data and single-orbit data (particularly from the western orbit) further enhanced forecast accuracy, while the joint assimilation of MWHS-2 and MWRI data yielded additional error reductions. These findings underscore the potential of satellite data assimilation in improving typhoon forecasting and highlight the need for optimal land observation and channel selection techniques. 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))

► Show Figures

Figure 1

20 pages, 10486 KB

Open AccessArticle

Improving the Assimilation of T-TREC-Retrieved Wind Fields with Iterative Smoothing Constraints During Typhoon Linfa

by Huimin Bian, Haiyan Fei, Yuqing Mao, Cong Li, Aiqing Shu and Jiajun Chen

Remote Sens. 2025, 17(16), 2821; https://doi.org/10.3390/rs17162821 - 14 Aug 2025

Cited by 1 | Viewed by 831

Abstract

Enhancing radar data assimilation at cloud-resolving scales is essential for advancing typhoon analysis and forecasting. This study focuses on Typhoon Linfa, the 10th Pacific Typhoon of 2015, and proposes T-TREC-IS (Typhoon Circulation Tracking Radar Echo by Correlations with Iterative Smoothing), an enhanced version of the T-TREC algorithm. The enhancement incorporates an iterative smoothing constraint into the T-TREC algorithm, which improves the continuity of the retrieved wind field and mitigates the effects of velocity aliasing in radar data, thereby increasing the operational feasibility of the method. Building on this improvement, we evaluate the effectiveness of assimilating the T-TREC-IS-retrieved wind field for analyzing and forecasting Typhoon Linfa. The results demonstrate that the iterative smoothing constraint effectively filters out velocity de-aliasing errors during radar data quality control, enhances wind field intensity near the typhoon core, and retrieves the typhoon circulation more accurately. The refined wind field exhibits improved consistency and continuity, resulting in superior performance in subsequent assimilation analyses and forecasts. 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))

► Show Figures

Graphical abstract

21 pages, 8601 KB

Open AccessArticle

Impact of Cloud Microphysics Initialization Using Satellite and Radar Data on CMA-MESO Forecasts

by Lijuan Zhu, Yuan Jiang, Jiandong Gong and Dan Wang

Remote Sens. 2025, 17(14), 2507; https://doi.org/10.3390/rs17142507 - 18 Jul 2025

Viewed by 1558

Abstract

High-resolution numerical weather prediction requires accurate cloud microphysical initial conditions to enhance forecasting capabilities for high-impact severe weather events such as convective storms. This study integrated Fengyun-2 (FY-2) geostationary satellite data (equivalent blackbody temperature and total cloud cover) and next-generation 3D weather radar reflectivity from the China Meteorological Administration (CMA) to construct cloud microphysical initial fields and evaluate their impact on the CMA-MESO 3 km regional model. An analysis of the catastrophic rainfall event in Henan on 20 July 2021, and a 92-day continuous experiment (May–July 2024) revealed that assimilating cloud microphysical variables significantly improved precipitation forecasting: the equitable threat scores (ETSs) for 1 h forecasts of light, moderate, and heavy rain increased from 0.083, 0.043, and 0.007 to 0.41, 0.36, and 0.217, respectively, with average hourly ETS improvements of 21–71% for 2–6 h forecasts and increases in ETSs for light, moderate, and heavy rain of 7.5%, 9.8%, and 24.9% at 7–12 h, with limited improvement beyond 12 h. Furthermore, the root mean square error (RMSE) of the 2 m temperature forecasts decreased across all 1–72 h lead times, with a 4.2% reduction during the 1–9 h period, while the geopotential height RMSE reductions reached 5.8%, 3.3%, and 2.0% at 24, 48, and 72 h, respectively. Additionally, synchronized enhancements were observed in 10 m wind prediction accuracy. These findings underscore the critical role of cloud microphysical initialization in advancing mesoscale numerical weather prediction systems. 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))

► Show Figures

Figure 1

21 pages, 5160 KB

Open AccessArticle

A Spatiotemporal Sequence Prediction Framework Based on Mask Reconstruction: Application to Short-Duration Precipitation Radar Echoes

by Zhi Yang, Changzheng Liu, Ping Mei and Lei Wang

Remote Sens. 2025, 17(13), 2326; https://doi.org/10.3390/rs17132326 - 7 Jul 2025

Cited by 1 | Viewed by 1287

Abstract

Short-term precipitation forecasting is a core task in meteorological science, aiming to achieve accurate predictions by modeling the spatiotemporal evolution of radar echo sequences, thereby supporting meteorological services and disaster warning systems. However, existing spatiotemporal sequence prediction methods still struggle to disentangle complex spatiotemporal dependencies effectively and fail to capture the nonlinear chaotic characteristics of precipitation systems. This often results in ambiguous predictions, attenuation of echo intensity, and spatial localization errors. To address these challenges, this paper proposes a unified spatiotemporal sequence prediction framework based on spatiotemporal masking, which comprises two stages: self-supervised pre-training and task-oriented fine-tuning. During pre-training, the model learns global structural features of meteorological systems from sparse contexts by randomly masking local spatiotemporal regions of radar images. In the fine-tuning stage, considering the importance of the temporal dimension in short-term precipitation forecasting and the complex long-range dependencies in spatiotemporal evolution of precipitation systems, we design an RNN-based cyclic temporal mask self-encoder model (MAE-RNN) and a transformer-based spatiotemporal attention model (STMT). The former focuses on capturing short-term temporal dynamics, while the latter simultaneously models long-range dependencies across space and time via a self-attention mechanism, thereby avoiding the smoothing effects on high-frequency details that are typical of conventional convolutional or recurrent structures. The experimental results show that STMT improves 3.73% and 2.39% in CSI and HSS key indexes compared with the existing advanced models, and generates radar echo sequences that are closer to the real data in terms of air mass morphology evolution and reflection intensity grading. 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))

► Show Figures

Figure 1

21 pages, 5785 KB

Open AccessArticle

Impacts of the Assimilation of Radar Radial Velocity Data Using the Ensemble Kalman Filter (EnKF) on the Analysis and Forecast of Typhoon Lekima (2019)

by Jiping Guan, Jiajun Chen, Xinya Li, Mengting Liu and Mingyang Zhang

Remote Sens. 2025, 17(13), 2258; https://doi.org/10.3390/rs17132258 - 30 Jun 2025

Viewed by 1201

Abstract

High-resolution radar observations are essential to improving the numerical predictions of high-impact weather systems with data assimilation techniques. The numerical simulations of the landfall of Typhoon Lekima (2019) are conducted in the framework of the WRF model, investigating the impact of assimilating radar radial velocity observations via the Ensemble Kalman Filter (EnKF) on the typhoon’s analysis and forecast performance. The results demonstrate that the EnKF method significantly improves forecast accuracy for Typhoon Lekima, including track, intensity and the 24 h cumulative precipitation. To be specific, the control experiment significantly underestimated typhoon intensity, while EnKF-based radar radial velocity assimilation markedly improved near-surface winds (>48 m/s) in the typhoon core, refined vortex structure and reduced track forecast errors by 50–60%. Compared with the control and 3DVAR experiments, EnKF assimilation better captured typhoon precipitation patterns, with the highest ETS scores, especially for moderate-to-high precipitation intensities. Moreover, the detailed analysis and diagnostics of Lekima show that the warm core structure is better captured in the assimilation experiment. The typhoon system is also improved, as reflected by enhanced potential temperature and a more robust wind field analysis. 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))

► Show Figures

Figure 1

23 pages, 7824 KB

Open AccessArticle

Impact of All-Sky Assimilation of Multichannel Observations from Fengyun-3F MWHS-II on Typhoon Forecasting

by Tianheng Wang, Wei Sun and Fan Ping

Remote Sens. 2025, 17(12), 2056; https://doi.org/10.3390/rs17122056 - 14 Jun 2025

Viewed by 1670

Abstract

All-sky radiance assimilation can increase the utilization of satellite observations in cloudy regions and improve typhoon forecasts. This study focuses on the newly launched FengYun-3F satellite equipped with the Microwave Humidity Sounder II (MWHS-II) and develops an all-sky assimilation capability for its radiance data. A series of assimilation experiments were conducted to evaluate their impacts on the forecast of Typhoon Yagi (2024), demonstrating that all-sky assimilation leads to reductions in track error (23.14%) and improvements in precipitation forecasts (Equitable Threat Score increase of 16.92%) compared to clear-sky assimilation. Furthermore, a detailed comparison of assimilation experiments shows that using only the 183 GHz humidity channels yields limited improvement in tropospheric humidity, whereas assimilating the 118 GHz temperature channels significantly enhances temperature and wind forecasts. Combined assimilation of both frequency bands synergistically maintains accurate track and intensity predictions while further improving precipitation prediction. These findings demonstrate the value of multichannel all-sky assimilation and inform future satellite data assimilation strategies. 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))

► Show Figures

Figure 1

18 pages, 20560 KB

Open AccessArticle

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

Cited by 2 | Viewed by 1393

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 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))

► Show Figures

Figure 1

18 pages, 10136 KB

Open AccessArticle

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 2 | Viewed by 2259

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. 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))

► Show Figures

Journal Menu

Journal Browser

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

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Related Special Issue

Published Papers (10 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI