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Atmosphere
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Atmospheric Data Prediction Using Statistical, and Machine Learning Approaches of...
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Atmospheric Data Prediction Using Statistical, and Machine Learning Approaches of Artificial Intelligence

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 (31 October 2023) | Viewed by 6088

Special Issue Editor

Dr. Sunil Jha

E-Mail Website
Guest Editor
Adani Institute of Infrastructure Engineering, Adani University, Ahmedabad 382421, Gujarat, India
Interests: atmospheric data mining; remote sensing; atmospheric data prediction; machine learning; artificial intelligence

Special Issue Information

Dear Colleagues,

The statistical and machine learning (ML) approaches of artificial intelligence (AI) methods have been successfully implemented in the predictive applications of several domains of Science and Engineering in recent years. The ML algorithms of AI are vital components for the development of an automated, accurate, and robust prediction system after analysis of the data for the specific application. The ML algorithms of AI are useful in the prediction requirements of atmospheric data, including atmospheric rive prediction, risk prediction of atmospheric emissions, turbulence, and hazard prediction, class prediction of atmospheric circulation pattern, prediction of geothermal heat flux, air quality monitoring, rainfall prediction, atmospheric aerosol prediction, global weather prediction system, prediction of the influence of atmospheric parameters on human health, etc. The improved accuracy of the statistical and ML approaches of AI is crucial in each of the applications of the predictive modeling of Atmospheric Sciences. The development of advanced ML approaches and their implementation in the analysis of atmospheric data (experimental and simulated) is challenging research at present. With this objective, the present issue invites researchers and academicians to submit their novel and unpublished research outcomes related to the current development of statistical and ML approaches to AI in predictive modeling applications of atmospheric sciences. This is the first Special Issue on atmospheric data prediction, and it will cover a broad range of topics related to applications of ML approaches in the analysis of atmospheric data with the following subtopics but not limited.

  • Atmospheric data prediction for understanding climate change
  • Role of machine learning in hydrology and meteorology
  • Forecasting and management of sustainable energy using atmospheric data modeling
  • Analysis of data of atmospheric events in the following subtopics
  • Data assimilation
  • Missing value imputation
  • Preprocessing
  • Denoising
  • Outlier detection and removal
  • Feature extraction and selection
  • Classification and clustering
  • Simulation, modeling, and optimization
  • Reliability analysis, etc.
  • Analysis of big data in big data in atmospheric science
  • Predictive modeling using transfer learning in atmospheric science
  • Ensemble learning for atmospheric data analysis
  • Atmospheric data prediction using reinforcement learning
  • Deep learning for predictive modeling in atmospheric science
  • Predictive modeling using evolutionary approaches
  • Intelligent forecasting in atmospheric sciences
  • Hybrid machine learning approaches in modeling events of atmospheric sciences
  • Machine learning approaches for soil moisture prediction
  • Other advanced approaches of machine learning and tools of artificial intelligence in atmospheric data modeling and applications.

Dr. Sunil Jha
Guest Editor

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

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

Published Papers (5 papers)

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Research

17 pages, 2740 KiB  
Article
Spatial–Temporal Temperature Forecasting Using Deep-Neural-Network-Based Domain Adaptation
by Vu Tran, François Septier, Daisuke Murakami and Tomoko Matsui
Atmosphere 2024, 15(1), 90; https://doi.org/10.3390/atmos15010090 - 10 Jan 2024
Viewed by 701
Abstract
Accurate temperature forecasting is critical for various sectors, yet traditional methods struggle with complex atmospheric dynamics. Deep neural networks (DNNs), especially transformer-based DNNs, offer potential advantages, but face challenges with domain adaptation across different geographical regions. We evaluated the effectiveness of DNN-based domain [...] Read more.
Accurate temperature forecasting is critical for various sectors, yet traditional methods struggle with complex atmospheric dynamics. Deep neural networks (DNNs), especially transformer-based DNNs, offer potential advantages, but face challenges with domain adaptation across different geographical regions. We evaluated the effectiveness of DNN-based domain adaptation for daily maximum temperature forecasting in experimental low-resource settings. We used an attention-based transformer deep learning architecture as the core forecasting framework and used kernel mean matching (KMM) for domain adaptation. Domain adaptation significantly improved forecasting accuracy in most experimental settings, thereby mitigating domain differences between source and target regions. Specifically, we observed that domain adaptation is more effective than exclusively training on a small amount of target-domain training data. This study reinforces the potential of using DNNs for temperature forecasting and underscores the benefits of domain adaptation using KMM. It also highlights the need for caution when using small amounts of target-domain data to avoid overfitting. Future research includes investigating strategies to minimize overfitting and to further probe the effect of various factors on model performance. Full article
(This article belongs to the Special Issue Atmospheric Data Prediction Using Statistical, and Machine Learning Approaches of Artificial Intelligence)
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20 pages, 2745 KiB  
Article
A Refined Zenith Tropospheric Delay Model Based on a Generalized Regression Neural Network and the GPT3 Model in Europe
by Min Wei, Xuexiang Yu, Fuyang Ke, Xiangxiang He and Keli Xu
Atmosphere 2023, 14(12), 1727; https://doi.org/10.3390/atmos14121727 - 24 Nov 2023
Viewed by 773
Abstract
An accurate model of the Zenith Tropospheric Delay (ZTD) plays a crucial role in Global Navigation Satellite System (GNSS) precise positioning, water vapor retrieval, and meteorological research. Current empirical models (such as the GPT3 model) can only reflect the approximate change trend of [...] Read more.
An accurate model of the Zenith Tropospheric Delay (ZTD) plays a crucial role in Global Navigation Satellite System (GNSS) precise positioning, water vapor retrieval, and meteorological research. Current empirical models (such as the GPT3 model) can only reflect the approximate change trend of ZTD but cannot accurately reflect nonlinear changes such as rapid fluctuations in ZTD. In recent years, the application of machine learning methods in the modeling and prediction of ZTD has gained prominence, yielding commendable results. Utilizing the ZTD products from 53 International GNSS Service (IGS) stations in Europe during the year 2021 as a foundational dataset, a Generalized Regression Neural Network (GRNN) is employed to model IGS ZTD while considering spatiotemporal factors and its association with GPT3 ZTD. This endeavor culminates in the development of a refined GRNN model. To verify the performance of the model, the prediction results are compared with two other ZTD values. One is obtained based on the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data, and the other is obtained by the GPT3 model. The results show that the bias of the GRNN refined model is almost 0 mm, and the average Root-Mean-Square Error (RMSE) and Mean Absolute Error (MAE) are 18.33 mm and 14.08 mm, respectively. Compared with ERA5 ZTD and GPT3 ZTD, the RMSE of GRNN ZTD has decreased by 19.5% and 63.4%, respectively, and the MAE of GRNN ZTD has decreased by 24.8% and 67.1%. Compared with the other two models, the GRNN refined model has better performance in reflecting the rapid fluctuations of ZTD. In addition, also discussed is the impact of spatial factors and time factors on modeling. The findings indicate that modeling accuracy within the central region of the modeling area surpasses that at the periphery by approximately 17.8%. The period from June to October is associated with the lowest accuracy, whereas the optimal accuracy is typically observed from January to April. The most substantial differences in accuracy were observed at station OP71 (Paris, France), with the highest accuracy recorded (9.51 mm) in April and the lowest (24.00 mm) in September. Full article
(This article belongs to the Special Issue Atmospheric Data Prediction Using Statistical, and Machine Learning Approaches of Artificial Intelligence)
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17 pages, 1513 KiB  
Article
Wind Speed Modeling for Wind Farms Based on Deterministic Broad Learning System
by Lin Wang and Anke Xue
Atmosphere 2023, 14(8), 1308; https://doi.org/10.3390/atmos14081308 - 18 Aug 2023
Viewed by 792
Abstract
As the penetration rate of wind power in the grid continues to increase, wind speed forecasting plays a crucial role in wind power generation systems. Wind speed prediction helps optimize the operation and management of wind power generation, enhancing efficiency and reliability. However, [...] Read more.
As the penetration rate of wind power in the grid continues to increase, wind speed forecasting plays a crucial role in wind power generation systems. Wind speed prediction helps optimize the operation and management of wind power generation, enhancing efficiency and reliability. However, wind speed is a nonlinear and nonstationary system, and traditional statistical methods and classical intelligent algorithms struggle to cope with dynamically updating operating conditions based on sampled data. Therefore, from the perspective of optimizing intelligent algorithms, a wind speed prediction model for wind farms was researched. In this study, we propose the Deterministic Broad Learning System (DBLS) algorithm for wind farm wind speed prediction. It effectively addresses the issues of data saturation and local minima that often occur in continuous-time system modeling. To adapt to the continuous updating of sample data, we improve the sample input of the Broad Learning System (BLS) by using a fixed-width input. When new samples are added, an equivalent number of old samples is removed to maintain the same input width, ensuring the feature capture capability of the model. Additionally, we construct a dataset of wind speed samples from 10 wind farms in Gansu Province, China. Based on this dataset, we conducted comparative experiments between the DBLS and other algorithms such as Random Forest (RF), Support Vector Regression (SVR), Extreme Learning Machines (ELM), and BLS. The comparison analysis of different algorithms was conducted using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Among them, the DBLS algorithm exhibited the best performance. The RMSE of the DBLS ranged from 0.762 m/s to 0.776 m/s, and the MAPE of the DBLS ranged from 0.138 to 0.149. Full article
(This article belongs to the Special Issue Atmospheric Data Prediction Using Statistical, and Machine Learning Approaches of Artificial Intelligence)
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10 pages, 673 KiB  
Article
Forecasting Turning Points of Carbon Emissions in Beijing Based on Interpretable Machine Learning
by Tianen Yao, Yaqi Wang, Xinhao Li, Xinyao Lian and Jing Li
Atmosphere 2023, 14(8), 1288; https://doi.org/10.3390/atmos14081288 - 15 Aug 2023
Viewed by 1015
Abstract
For curbing the global climate crisis, China has set an ambitious target of peak carbon emissions by 2030. Beijing, the capital of China, has implemented a carbon reduction policy since 2012. Using the reduced and generalized forms of the Environmental Kuznets Curve (EKC), [...] Read more.
For curbing the global climate crisis, China has set an ambitious target of peak carbon emissions by 2030. Beijing, the capital of China, has implemented a carbon reduction policy since 2012. Using the reduced and generalized forms of the Environmental Kuznets Curve (EKC), we deduce that both the cubic EKC and the genetic algorithm-based EKC have an N-shape. The first turning point of the three-order EKC occurs around 2011, demonstrating the effectiveness of the carbon reduction policy. However, the time series model predicts that Beijing will reach the second turning point around 2026, when the gross domestic product (GDP) is about CNY 5000 billion and carbon emissions will begin to increase again. Interpretable machine learning is proposed to explore the socio-economic drivers in carbon emissions, indicating that total energy consumption and GDP contribute the most. Therefore, we should accelerate the upgrading of energy consumption and adjust the industrial structure, thus facilitating Beijing to its peak carbon emissions and achieving carbon neutrality. Full article
(This article belongs to the Special Issue Atmospheric Data Prediction Using Statistical, and Machine Learning Approaches of Artificial Intelligence)
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19 pages, 3709 KiB  
Article
Construction and Research of Ultra-Short Term Prediction Model of Solar Short Wave Irradiance Suitable for Qinghai–Tibet Plateau
by Huimei Meng, Lingxiao Wu, Huaxia Li and Yixin Song
Atmosphere 2023, 14(7), 1150; https://doi.org/10.3390/atmos14071150 - 14 Jul 2023
Viewed by 798
Abstract
The Qinghai–Tibet Plateau region has abundant solar energy, which presents enormous potential for the development of solar power generation. Accurate prediction of solar radiation is crucial for the safe and cost-effective operation of the power grid. Therefore, constructing a suitable ultra-short-term prediction model [...] Read more.
The Qinghai–Tibet Plateau region has abundant solar energy, which presents enormous potential for the development of solar power generation. Accurate prediction of solar radiation is crucial for the safe and cost-effective operation of the power grid. Therefore, constructing a suitable ultra-short-term prediction model for the Tibetan Plateau region holds significant importance. This study was based on the autoregressive integrated moving average model (ARIMA), random forest model (RF), and long short-term memory model (LSTM) to construct a prediction model for forecasting the average irradiance for the next 10 min. By locally testing and optimizing the model parameter, the study explored the applicability of each model in different seasons and investigates the impact of factors such as training dataset and prediction time range on model accuracy. The results showed that: (1) the accuracy of the ARIMA model was lower than the persistence model used as a reference model, while both the RF model and LSTM model had higher accuracy than the persistence model; (2) the sample size and distribution of the training dataset significantly affected the accuracy of the models. When both the season (distribution) and sample size were the same, RF achieved the highest accuracy. The optimal sample sizes for ARIMA, RF, and LSTM models in each season were as follows: spring (3564, 1980, 4356), summer (2772, 4752, 2772), autumn (3564, 3564, 4752), and winter (3168, 3168, 4752). (3) The prediction forecast horizon had a significant impact on the model accuracy. As the forecast horizon increased, the errors of all models gradually increased, reaching a peak between 80 and 100 min before slightly decreasing and then continuing to rise. When both the season and forecast horizon were the same, RF had the highest accuracy, with an RMSE lower than ARIMA by 65.6–258.3 W/m2 and lower than LSTM by 3.7–83.3 W/m2. Therefore, machine learning can be used for ultra-short-term forecasting of solar irradiance in the Qinghai–Tibet Plateau region to meet the forecast requirements for solar power generation, providing a reference for similar studies. Full article
(This article belongs to the Special Issue Atmospheric Data Prediction Using Statistical, and Machine Learning Approaches of Artificial Intelligence)
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