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Atmosphere
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Artificial Intelligence for Meteorology Applications
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Artificial Intelligence for Meteorology Applications

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (15 September 2022) | Viewed by 12793

Special Issue Editors

Prof. Dr. Wei Fang

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Guest Editor
1. School of Computer and Software, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China
2. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
3. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: artificial intelligence applied in the atmospheric science; artificial intelligence applied in severe weather predict; artificial intelligence applied in climate change; convective weather; data mining and knowledge discovery; cloud computing; applied meteorology; big data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Victor S. Sheng

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Guest Editor
Department of Computer, Texas Tech University, Lubbock, TX 79409, USA
Interests: data science; machine learning; computational intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Qiguang Wang

E-Mail Website
Guest Editor
China Meteorological Administration Training Center, China Meteorological Administration, Beijing 100081, China
Interests: artificial Intelligence, numerical modeling；extended-range forecast；nonlinear dynamics；extreme events；complex network
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Artificial Intelligence (AI) is playing a more and more essential role in the industrial revolution and we are seeking a lot of evolution in various machine learning methodologies. Forecast of meteorological disasters is an important and challenging worldwide problem. Various techniques have been used to solve it, but the accuracy of them is not high due to the highly nonlinear, random, and complex nature of precipitation. In recent years, with the rapid development of artificial intelligence technology, it has gradually penetrated into all aspects of people's lives, and the meteorological field are no exception. This Special Issue aims at bring together top academic scientists, researchers and research scholars to exchange and share their experience and research results in all aspects of the application of meteorology based on artificial intelligence. It also provides an important interdisciplinary platform for researchers, practitioners and educators to show and discuss the latest innovations, trends and concerns in the field of meteorological applications, as well as the practical challenges and solutions.

Dr. Wei Fang
Prof. Dr. Victor S. Sheng
Dr. Qiguang Wang
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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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

  • artificial intelligence applied in the atmospheric science
  • using artificial intelligence to better predict severe weather
  • machine learning in weather forecasting
  • meteorological satellite studies
  • observation networks and weather forecasting
  • forecasting different types of convective weather
  • applications of meteorology
  • agricultural meteorology
  • weather impact modelling
  • uncertainty quantification
  • advanced machine learning algorithms for satellite-based precipitation assimilation

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Published Papers (4 papers)

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Research

18 pages, 3076 KiB  
Article
Tropical Cyclone Detection from the Thermal Infrared Sensor IASI Data Using the Deep Learning Model YOLOv3
by Lisa Lam, Maya George, Sébastien Gardoll, Sarah Safieddine, Simon Whitburn and Cathy Clerbaux
Atmosphere 2023, 14(2), 215; https://doi.org/10.3390/atmos14020215 - 19 Jan 2023
Cited by 10 | Viewed by 2708
Abstract
Tropical cyclone (TC) detection is essential to mitigate natural disasters, as TCs can cause significant damage to life, infrastructure and economy. In this study, we applied the deep learning object detection model YOLOv3 to detect TCs in the North Atlantic Basin, using data [...] Read more.
Tropical cyclone (TC) detection is essential to mitigate natural disasters, as TCs can cause significant damage to life, infrastructure and economy. In this study, we applied the deep learning object detection model YOLOv3 to detect TCs in the North Atlantic Basin, using data from the Thermal InfraRed (TIR) Atmospheric Sounding Interferometer (IASI) onboard the Metop satellites. IASI measures the outgoing TIR radiation of the Earth-Atmosphere. For the first time, we provide a proof of concept of the possibility of constructing images required by YOLOv3 from a TIR remote sensor that is not an imager. We constructed a dataset by selecting 50 IASI radiance channels and using them to create images, which we labeled by constructing bounding boxes around TCs using the hurricane database HURDAT2. We trained the YOLOv3 on two settings, first with three “best” selected channels, then using an autoencoder to exploit all 50 channels. We assessed its performance with the Average Precision (AP) metric at two different intersection over union (IoU) thresholds (0.1 and 0.5). The model achieved promising results with AP at IoU threshold 0.1 of 78.31%. Lower performance was achieved with IoU threshold 0.5 (31.05%), showing the model lacks precision regarding the size and position of the predicted boxes. Despite that, we show YOLOv3 demonstrates great potential for TC detection using TIR instruments data. Full article
(This article belongs to the Special Issue Artificial Intelligence for Meteorology Applications)
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20 pages, 4581 KiB  
Article
Short-Term Regional Temperature Prediction Based on Deep Spatial and Temporal Networks
by Shun Wu, Fengchen Fu, Lei Wang, Minhang Yang, Shi Dong, Yongqing He, Qingqing Zhang and Rong Guo
Atmosphere 2022, 13(12), 1948; https://doi.org/10.3390/atmos13121948 - 23 Nov 2022
Cited by 9 | Viewed by 1823
Abstract
Accurate prediction of air temperature is of great significance to outdoor activities and daily life. However, it is important and more challenging to predict air temperature in complex terrain areas because of prevailing mountain and valley winds and variable wind directions. The main [...] Read more.
Accurate prediction of air temperature is of great significance to outdoor activities and daily life. However, it is important and more challenging to predict air temperature in complex terrain areas because of prevailing mountain and valley winds and variable wind directions. The main innovation of this paper is to propose a regional temperature prediction method based on deep spatiotemporal networks, designing a spatiotemporal information processing module to align temperature data with regional grid points and further transforming temperature time series data into image sequences. Long Short-Term Memory network is constructed on the images to extract the depth features of the data to train the model. The experiments demonstrate that the deep learning prediction model containing the spatiotemporal information processing module and the deep learning prediction module is fully feasible in short-term regional temperature prediction. The comparison experiments show that the model proposed in this paper has better prediction results for classical models, such as convolutional neural networks and LSTM networks. The experimental conclusion shows that the method proposed in this paper can predict the distribution and change trend of temperature in the next 3 h and the next 6 h on a regional scale. The experimental result RMSE reached 0.63, showing high stability and accuracy. The model provides a new method for local regional temperature prediction, which can support the planning of production and life in advance and tend to save energy and reduce consumption. Full article
(This article belongs to the Special Issue Artificial Intelligence for Meteorology Applications)
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18 pages, 4628 KiB  
Article
A Novel Tropical Cyclone Track Forecast Model Based on Attention Mechanism
by Wei Fang, Wenhe Lu, Jiaxin Li and Liyao Zou
Atmosphere 2022, 13(10), 1607; https://doi.org/10.3390/atmos13101607 - 30 Sep 2022
Cited by 2 | Viewed by 3407
Abstract
Tropical cyclones are one of the most powerful and destructive weather systems on Earth. Accurately forecasting the landing time, location and moving paths of tropical cyclones are of great significance to mitigate the huge disasters it produces. However, with the continuous accumulation of [...] Read more.
Tropical cyclones are one of the most powerful and destructive weather systems on Earth. Accurately forecasting the landing time, location and moving paths of tropical cyclones are of great significance to mitigate the huge disasters it produces. However, with the continuous accumulation of meteorological monitoring data and the application of multi-source data, traditional tropical cyclone track forecasting methods face many challenges in forecasting accuracy. Recently, deep learning methods have proven capable of learning spatial and temporal features from massive datasets. In this paper, we propose a new spatiotemporal deep learning model for tropical cyclone track forecasting, which adopts spatial location and multiple meteorological factors to forecast the tracks of tropical cyclones. The model proposes a multi-layer ConvGRU to extract the nonlinear spatial features of tropical cyclones, while Spatial and Channel Attention Mechanism (CBAM) is adopted to overcome the large-scale problem of high response isobaric surface affecting the tropical cyclones. Meanwhile, this model utilizes a Deep and Cross framework to combine the traditional CNN model with the multi-ConvGRU model. Experiments were conducted on the China Meteorological Administration Tropical Cyclone Best Track Dataset (CMA) from 2000 to 2020, and the EAR-Interim dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). The experimental results show that the proposed model is superior to the deep learning tropical cyclone forecasting methods. Full article
(This article belongs to the Special Issue Artificial Intelligence for Meteorology Applications)
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16 pages, 5338 KiB  
Article
Classification and Estimation of Typhoon Intensity from Geostationary Meteorological Satellite Images Based on Deep Learning
by Shuailong Jiang and Lijun Tao
Atmosphere 2022, 13(7), 1113; https://doi.org/10.3390/atmos13071113 - 14 Jul 2022
Cited by 5 | Viewed by 2850
Abstract
In this paper, a novel typhoon intensity classification and estimation network (TICAENet) is constructed to recognize typhoon intensity. The TICAENet model is based on the LeNet-5 model, which uses weight sharing to reduce the number of training parameters, and the VGG16 model, which [...] Read more.
In this paper, a novel typhoon intensity classification and estimation network (TICAENet) is constructed to recognize typhoon intensity. The TICAENet model is based on the LeNet-5 model, which uses weight sharing to reduce the number of training parameters, and the VGG16 model, which replaces a large convolution kernel with multiple small kernels to improve feature extraction. Satellite cloud images of typhoons over the Northwest Pacific Ocean and the South China Sea from 1995–2020 are taken as samples. The results show that the classification accuracy of this model is 10.57% higher than that of the LeNet-5 model; the classification accuracy of the TICAENet model is 97.12%, with a classification precision of 97.00% for tropical storms, severe tropical storms and super typhoons. The mean absolute error (MAE) and root mean square error (RMSE) of the samples estimation in 2019 are 4.78 m/s and 6.11 m/s, and the estimation accuracy are 18.98% and 20.65% higher than that of the statistical method, respectively. Additionally, the model takes less memory and runs faster due to the weight sharing and multiple small kernels. The results show that the proposed model performs better than other methods. In general, the proposed model can be used to accurately classify typhoon intensity and estimate the maximum wind speed by extracting features from geostationary meteorological satellite images. Full article
(This article belongs to the Special Issue Artificial Intelligence for Meteorology Applications)
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