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Recent Advances and Future Prospects of Machine Learning in Predictive...
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Recent Advances and Future Prospects of Machine Learning in Predictive Modeling of Atmospheric Sciences

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 33390

Special Issue Editors

Dr. Sunil Kumar Jha

E-Mail Website
Guest Editor
1. Nanjing University of Information Science and Technology, Ningliu Road, P.R.C. 210044 Nanjing, Jiangsu, China
2. University of Information Technology and Management, Sucharskiego 2, 35-225 Rzeszów, Poland
Interests: pattern recognition; machine learning; atmospheric data mining; remote sensing; atmospheric data prediction
Dr. Xiaorui Zhang

E-Mail Website
Guest Editor
Nanjing University of Information Science & Technology, No.219, Ningliu Road, Nanjing, Jiangsu, China
Interests: Human-Computer Interaction; Virtual Reality Technology
Dr. Limao Zhang

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, Nanyang Technological University, N1-01a-29, 50 Nanyang Avenue, Singapore 639798, Singapore
Interests: artificial intelligence; tunnelling excavation; bim data analytics; structural health monitoring; data-driven simulation; uncertainty modelling and risk analysis; decision support systems
Special Issues, Collections and Topics in MDPI journals
Dr. Nilesh Patel

E-Mail Website
Guest Editor
Oakland University, 318 Meadow Brook Rd, Rochester, MI 48309, USA

Special Issue Information

Dear Colleagues,

In recent years, machine learning (ML) algorithms have been widely used in predictive modeling of different research domains of science and engineering. ML algorithms design an automated, accurate, and robust decision-making system by extracting the meaningful conclusion from the observations. ML algorithms have been implemented successfully in predictive modeling applications of atmospheric sciences in past research, such as circulation pattern classification, risk assessment of atmospheric emissions, atmospheric rive forecast, prediction of geothermal heat flux, diagnosis of cold atmospheric plasma sources, turbulence forecasting, hazard assessment, and forecasting of rainfall changes, etc. Due to the growing demand for ML in most of the aspect of our life, it is sensible to use it in enhancing the prediction efficiency in predictive modeling of atmospheric sciences research and applications. Extracting the meaningful conclusion by using the advanced ML approaches in the analysis of the experimental and simulated observations of atmospheric phenomena is the demanding research at present. With this objective, the present issue invites researchers to submit their novel and unpublished research related to the current advancement of ML in predictive modeling research and applications of atmospheric sciences. The present issue will cover a broad range of topics related to applications of ML approaches in the analysis of atmospheric data with the following subtopics.

  • Climate change modeling using machine learning
  • Machine learning in meteorology and hydrology applications
  • Role of machine learning in renewable energy
  • Analysis of data of atmospheric events in following subtopics but not limited
    • Data assimilation
    • Missing value imputation, preprocessing, and denoising
    • Outlier detection and removal
    • Feature extraction and selection
    • Classification and clustering
    • Simulation, modeling, and optimization
    • Reliability analysis
  • Big data in atmospheric sciences and its analysis
  • Transfer and deep learning in predictive modeling in atmospheric sciences
  • Intelligent forecasting in atmospheric sciences
  • Reinforcement and ensemble learning uses in atmospheric sciences
  • Predictive modeling in atmospheric sciences using evolutionary approaches
  • Hybrid ML approaches in efficient modeling of events of atmospheric sciences
  • Other advanced ML approaches and tools in atmospheric data modeling and applications.

Dr. Sunil Kumar Jha
Dr. Xiaorui Zhang
Dr. Limao Zhang
Dr. Nilesh Patel
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

  • machine learning
  • atmospheric sciences
  • predictive modelling
  • atmospheric data mining
  • intelligent forecasting

Published Papers (7 papers)

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Research

19 pages, 5436 KiB  
Article
An Overall Uniformity Optimization Method of the Spherical Icosahedral Grid Based on the Optimal Transformation Theory
by Fuli Luo, Xuesheng Zhao, Wenbin Sun, Yalu Li and Yuanzheng Duan
Atmosphere 2021, 12(11), 1516; https://doi.org/10.3390/atmos12111516 - 17 Nov 2021
Viewed by 1653
Abstract
The improvement of overall uniformity and smoothness of spherical icosahedral grids, the basic framework of atmospheric models, is a key to reducing simulation errors. However, most of the existing grid optimization methods have optimized grid from different aspects and not improved overall uniformity [...] Read more.
The improvement of overall uniformity and smoothness of spherical icosahedral grids, the basic framework of atmospheric models, is a key to reducing simulation errors. However, most of the existing grid optimization methods have optimized grid from different aspects and not improved overall uniformity and smoothness of grid at the same time, directly affecting the accuracy and stability of numerical simulation. Although a well-defined grid with more than 12 points cannot be constructed on a sphere, the area uniformity and the interval uniformity of the spherical grid can be traded off to enhance extremely the overall grid uniformity and smoothness. To solve this problem, an overall uniformity and smoothness optimization method of the spherical icosahedral grid is proposed based on the optimal transformation theory. The spherical cell decomposition method has been introduced to iteratively update the grid to minimize the spherical transportation cost, achieving an overall optimization of the spherical icosahedral grid. Experiments on the four optimized grids (the spring dynamics optimized grid, the Heikes and Randall optimized grid, the spherical centroidal Voronoi tessellations optimized grid and XU optimized grid) demonstrate that the grid area uniformity of our method has been raised by 22.60% of SPRG grid, −1.30% of HR grid, 38.30% of SCVT grid and 38.20% of XU grid, and the grid interval uniformity has been improved by 2.50% of SPRG grid, 2.80% of HR grid, 11.10% of SCVT grid and 11.00% of XU grid. Although the grid uniformity of the proposed method is similar with the HR grid, the smoothness of grid deformation has been enhanced by 79.32% of grid area and 24.07% of grid length. To some extent, the proposed method may be viewed as a novel optimization approach of the spherical icosahedral grid which can improve grid overall uniformity and smoothness of grid deformation. Full article
(This article belongs to the Special Issue Recent Advances and Future Prospects of Machine Learning in Predictive Modeling of Atmospheric Sciences)
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14 pages, 4701 KiB  
Article
Spatial and Temporal Characteristics of Rainfall Anomalies in 1961–2010 in the Yangtze River Basin, China
by Shuying Bai, Jixi Gao, Yu Xue and Romany Mansour
Atmosphere 2021, 12(8), 960; https://doi.org/10.3390/atmos12080960 - 27 Jul 2021
Cited by 8 | Viewed by 1694
Abstract
Understanding rainfall anomalies and their relationship with floods in the Yangtze River Basin (YRB) is essential for evaluating flood disasters, which have a great impact on the development of agriculture and the economy. On the basis of daily rainfall data from 1961 to [...] Read more.
Understanding rainfall anomalies and their relationship with floods in the Yangtze River Basin (YRB) is essential for evaluating flood disasters, which have a great impact on the development of agriculture and the economy. On the basis of daily rainfall data from 1961 to 2010 from 178 meteorological stations, the temporal and spatial characteristics of rainfall anomalies in the YRB were studied on an annual scale, seasonal scale, and monthly scale. The annual rainfall of the YRB showed a generally increasing trend from 1961 to 2010 (14.22 mm/10 a). By means of the Bernaola–Galvan abrupt change test and Redfit spectrum analysis, it was found that the annual average rainfall increased abruptly after 1979 and had a cycle of 2–3 years. On the seasonal scale, the rainfall in spring and autumn showed a gradually decreasing trend, especially in September, while it showed a significant increasing trend in summer and winter in the YRB. As for the monthly scale, the rainfall in the rainy season from June to July presented a clear increasing trend during the study period, which greatly enhanced the probability of floods in the YRB. Additionally, through the analysis of the spatial distribution characteristics of rainfall in the entire YRB from 1961 to 2010, it was observed that the annual rainfall amount in the YRB presented an “increase–decrease–increase” tendency from east to west, accompanied by a rain belt that continuously moved from west to east. Moreover, the rainfall characteristics in flood years were summarized, and the results revealed that the years with rainfall anomalies were more likely to have flood disasters. However, anomalies alone would not result in big floods; the spatially and temporally inhomogeneous rainfall distribution might be the primary reason for flood disasters in the entire YRB. Full article
(This article belongs to the Special Issue Recent Advances and Future Prospects of Machine Learning in Predictive Modeling of Atmospheric Sciences)
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20 pages, 3708 KiB  
Article
An Empirical Atmospheric Density Calibration Model Based on Long Short-Term Memory Neural Network
by Yan Zhang, Jinjiang Yu, Junyu Chen and Jizhang Sang
Atmosphere 2021, 12(7), 925; https://doi.org/10.3390/atmos12070925 - 17 Jul 2021
Cited by 4 | Viewed by 2378
Abstract
The accuracy of the atmospheric mass density is one of the most important factors affecting the orbital precision of spacecraft at low Earth orbits (LEO). Although there are a number of empirical density models available to use in the orbit determination and prediction [...] Read more.
The accuracy of the atmospheric mass density is one of the most important factors affecting the orbital precision of spacecraft at low Earth orbits (LEO). Although there are a number of empirical density models available to use in the orbit determination and prediction of LEO spacecraft, all of them suffer from errors of various degrees. A practical way to reduce the error of a particular model is to calibrate the model using precise density data or tracking data. In this paper, a long short-term memory (LSTM) neural network is proposed to calibrate the NRLMSISE-00 density model, in which the densities derived from spaceborne accelerometer data are the main input. The resulted LSTM-NRL model, calibrated using the accelerometer data from Challenging Minisatellite Payload (CHAMP) satellite, is extensively experimented to evaluate the calibration performance. With data in one month to train the LSTM-NRL model, the model is shown to effectively reduce the root mean square error of the model densities outside the training window by more than 40% in various time spans and space weather environment. The LSTM-NRL model is also shown to have remarkable transferring performance when it is applied along the GRACE satellite orbits. Full article
(This article belongs to the Special Issue Recent Advances and Future Prospects of Machine Learning in Predictive Modeling of Atmospheric Sciences)
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17 pages, 3410 KiB  
Article
K-Means and C4.5 Decision Tree Based Prediction of Long-Term Precipitation Variability in the Poyang Lake Basin, China
by Dan Lou, Mengxi Yang, Dawei Shi, Guojie Wang, Waheed Ullah, Yuanfang Chai and Yutian Chen
Atmosphere 2021, 12(7), 834; https://doi.org/10.3390/atmos12070834 - 28 Jun 2021
Cited by 9 | Viewed by 1892
Abstract
The machine learning algorithms application in atmospheric sciences along the Earth System Models has the potential of improving prediction, forecast, and reconstruction of missing data. In the current study, a combination of two machine learning techniques namely K-means, and decision tree (C4.5) algorithms, [...] Read more.
The machine learning algorithms application in atmospheric sciences along the Earth System Models has the potential of improving prediction, forecast, and reconstruction of missing data. In the current study, a combination of two machine learning techniques namely K-means, and decision tree (C4.5) algorithms, are used to separate observed precipitation into clusters and classified the associated large-scale circulation indices. Observed precipitation from the Chinese Meteorological Agency (CMA) during 1961–2016 for 83 stations in the Poyang Lake basin (PLB) is used. The results from K-Means clusters show two precipitation clusters splitting the PLB precipitation into a northern and southern cluster, with a silhouette coefficient ~0.5. The PLB precipitation leading cluster (C1) contains 48 stations accounting for 58% of the regional station density, while Cluster 2 (C2) covers 35, accounting for 42% of the stations. The interannual variability in precipitation exhibited significant differences for both clusters. The decision tree (C4.5) is employed to explore the large-scale atmospheric indices from National Climate Center (NCC) associated with each cluster during the preceding spring season as a predictor. The C1 precipitation was linked with the location and intensity of subtropical ridgeline position over Northern Africa, whereas the C2 precipitation was suggested to be associated with the Atlantic-European Polar Vortex Area Index. The precipitation anomalies further validated the results of both algorithms. The findings are in accordance with previous studies conducted globally and hence recommend the applications of machine learning techniques in atmospheric science on a sub-regional and sub-seasonal scale. Future studies should explore the dynamics of the K-Means, and C4.5 derived indicators for a better assessment on a regional scale. This research based on machine learning methods may bring a new solution to climate forecast. Full article
(This article belongs to the Special Issue Recent Advances and Future Prospects of Machine Learning in Predictive Modeling of Atmospheric Sciences)
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17 pages, 27491 KiB  
Article
Survey on the Application of Deep Learning in Extreme Weather Prediction
by Wei Fang, Qiongying Xue, Liang Shen and Victor S. Sheng
Atmosphere 2021, 12(6), 661; https://doi.org/10.3390/atmos12060661 - 21 May 2021
Cited by 16 | Viewed by 5717
Abstract
Because of the uncertainty of weather and the complexity of atmospheric movement, extreme weather has always been an important and difficult meteorological problem. Extreme weather events can be called high-impact weather, the ‘extreme’ here means that the probability of occurrence is very small. [...] Read more.
Because of the uncertainty of weather and the complexity of atmospheric movement, extreme weather has always been an important and difficult meteorological problem. Extreme weather events can be called high-impact weather, the ‘extreme’ here means that the probability of occurrence is very small. Deep learning can automatically learn and train from a large number of sample data to obtain excellent feature expression, which effectively improves the performance of various machine learning tasks and is widely used in computer vision, natural language processing, and other fields. Based on the introduction of deep learning, this article makes a preliminary summary of the existing extreme weather prediction methods. These include the ability to use recurrent neural networks to predict weather phenomena and convolutional neural networks to predict the weather. They can automatically extract image features of extreme weather phenomena and predict the possibility of extreme weather somewhere by using a deep learning framework. Full article
(This article belongs to the Special Issue Recent Advances and Future Prospects of Machine Learning in Predictive Modeling of Atmospheric Sciences)
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17 pages, 4597 KiB  
Article
A Short-Term Wind Speed Forecasting Model Based on a Multi-Variable Long Short-Term Memory Network
by Anqi Xie, Hao Yang, Jing Chen, Li Sheng and Qian Zhang
Atmosphere 2021, 12(5), 651; https://doi.org/10.3390/atmos12050651 - 19 May 2021
Cited by 29 | Viewed by 3717
Abstract
Accurately forecasting wind speed on a short-term scale has become essential in the field of wind power energy. In this paper, a multi-variable long short-term memory network model (MV-LSTM) based on Pearson correlation coefficient feature selection is proposed to predict the short-term wind [...] Read more.
Accurately forecasting wind speed on a short-term scale has become essential in the field of wind power energy. In this paper, a multi-variable long short-term memory network model (MV-LSTM) based on Pearson correlation coefficient feature selection is proposed to predict the short-term wind speed. The proposed method utilizes multiple historical meteorological variables, such as wind speed, temperature, humidity, and air pressure, to predict the wind speed in the next hour. Hourly data collected from two ground observation stations in Yanqing and Zhaitang in Beijing were divided into training and test sets. The training sets were used to train the model, and the test sets were used to evaluate the model with the root-mean-square error (RMSE), mean absolute error (MAE), mean bias error (MBE), and mean absolute percentage error (MAPE) metrics. The proposed method is compared with two other forecasting methods (the autoregressive moving average model (ARMA) method and the single-variable long short-term memory network (LSTM) method, which inputs only historical wind speed data) based on the same dataset. The experimental results prove the feasibility of the MV-LSTM method for short-term wind speed forecasting and its superiority to the ARMA method and the single-variable LSTM method. Full article
(This article belongs to the Special Issue Recent Advances and Future Prospects of Machine Learning in Predictive Modeling of Atmospheric Sciences)
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18 pages, 1378 KiB  
Article
Atlantic Hurricane Activity Prediction: A Machine Learning Approach
by Tanmay Asthana, Hamid Krim, Xia Sun, Siddharth Roheda and Lian Xie
Atmosphere 2021, 12(4), 455; https://doi.org/10.3390/atmos12040455 - 03 Apr 2021
Cited by 11 | Viewed by 14878
Abstract
Long-term hurricane predictions have been of acute interest in order to protect the community from the loss of lives, and environmental damage. Such predictions help by providing an early warning guidance for any proper precaution and planning. In this paper, we present a [...] Read more.
Long-term hurricane predictions have been of acute interest in order to protect the community from the loss of lives, and environmental damage. Such predictions help by providing an early warning guidance for any proper precaution and planning. In this paper, we present a machine learning model capable of making good preseason-prediction of Atlantic hurricane activity. The development of this model entails a judicious and non-linear fusion of various data modalities such as sea-level pressure (SLP), sea surface temperature (SST), and wind. A Convolutional Neural Network (CNN) was utilized as a feature extractor for each data modality. This is followed by a feature level fusion to achieve a proper inference. This highly non-linear model was further shown to have the potential to make skillful predictions up to 18 months in advance. Full article
(This article belongs to the Special Issue Recent Advances and Future Prospects of Machine Learning in Predictive Modeling of Atmospheric Sciences)
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