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Atmosphere
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Data Mining and Machine Learning Techniques for Atmospheric and Climate-Related...
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Data Mining and Machine Learning Techniques for Atmospheric and Climate-Related Challenges at Different Time-Scales

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 (17 April 2020) | Viewed by 42259

Special Issue Editor

Dr. Rodrigo Manzanas

E-Mail Website
Guest Editor
Meteorology Group, Universidad de Cantabria, 39005 Santander, Spain
Interests: climate change and variability; evaluation of climate models; machine learning; seasonal forecasting; statistical downscaling

Special Issue Information

Dear Colleagues,

Traditionally, standard statistical methods have been used to solve many of the problems that arise in climate research. Nevertheless, the enormous volume of data that have been made available during the last decade (in situ and/or satellite records, reanalysis, ESM simulations, etc.), and the rapid development of powerful computing resources have motivated the adaptation and use of more complex and sophisticated tools, namely, data mining and machine learning techniques, which allow to extract useful knowledge by directly operating on the data.

This Special Issue of Atmosphere focuses on the application of data mining and machine learning techniques (association rules, classification/regression trees, random forests, Gaussian mixture models, artificial neural networks, support vector machines, Bayesian networks, etc.) which may help to overcome different types of problems that still constitute key challenges for the climate science community (e.g., diagnosis, classification, forecasting, downscaling, etc.).

Dr. Rodrigo Manzanas
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

  • data mining
  • machine learning
  • neural networks
  • deep learning
  • statistical forecasting
  • regional/local downscaling
  • small-scale processes identification/representation

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Published Papers (7 papers)

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Research

20 pages, 778 KiB  
Article
A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation
by Yonggwan Shin, Youngsaeng Lee and Jeong-Soo Park
Atmosphere 2020, 11(8), 775; https://doi.org/10.3390/atmos11080775 - 23 Jul 2020
Cited by 16 | Viewed by 3789
Abstract
A model weighting scheme is important in multi-model climate ensembles for projecting future changes. The climate model output typically needs to be bias corrected before it can be used. When a bias-correction (BC) is applied, equal model weights are usually derived because some [...] Read more.
A model weighting scheme is important in multi-model climate ensembles for projecting future changes. The climate model output typically needs to be bias corrected before it can be used. When a bias-correction (BC) is applied, equal model weights are usually derived because some BC methods cause the observations and historical simulation to match perfectly. This equal weighting is sometimes criticized because it does not take into account the model performance. Unequal weights reflecting model performance may be obtained from raw data before BC is applied. However, we have observed that certain models produce excessively high weights, while the weights generated in all other models are extremely low. This phenomenon may be partly due to the fact that some models are more fit or calibrated to the observations for a given applications. To address these problems, we consider, in this study, a hybrid weighting scheme including both equal and unequal weights. The proposed approach applies an “imperfect” correction to the historical data in computing their weights, while it applies ordinary BC to the future data in computing the ensemble prediction. We employ a quantile mapping method for the BC and a Bayesian model averaging for performance-based weighting. Furthermore, techniques for selecting the optimal correction rate based on the chi-square test statistic and the continuous ranked probability score are examined. Comparisons with ordinary ensembles are provided using a perfect model test. The usefulness of the proposed method is illustrated using the annual maximum daily precipitation as observed in the Korean peninsula and simulated by 21 models from the Coupled Model Intercomparison Project Phase 6. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning Techniques for Atmospheric and Climate-Related Challenges at Different Time-Scales)
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18 pages, 2164 KiB  
Article
Strong Spatiotemporal Radar Echo Nowcasting Combining 3DCNN and Bi-Directional Convolutional LSTM
by Suting Chen, Song Zhang, Huantong Geng, Yaodeng Chen, Chuang Zhang and Jinzhong Min
Atmosphere 2020, 11(6), 569; https://doi.org/10.3390/atmos11060569 - 29 May 2020
Cited by 9 | Viewed by 3921
Abstract
In order to solve the existing problems of easy spatiotemporal information loss and low forecast accuracy in traditional radar echo nowcasting, this paper proposes an encoding-forecasting model (3DCNN-BCLSTM) combining 3DCNN and bi-directional convolutional long short-term memory. The model first constructs dimensions of input [...] Read more.
In order to solve the existing problems of easy spatiotemporal information loss and low forecast accuracy in traditional radar echo nowcasting, this paper proposes an encoding-forecasting model (3DCNN-BCLSTM) combining 3DCNN and bi-directional convolutional long short-term memory. The model first constructs dimensions of input data and gets 3D tensor data with spatiotemporal features, extracts local short-term spatiotemporal features of radar echoes through 3D convolution networks, then utilizes constructed bi-directional convolutional LSTM to learn global long-term spatiotemporal feature dependencies, and finally realizes the forecast of echo image changes by forecasting network. This structure can capture the spatiotemporal correlation of radar echoes in continuous motion fully and realize more accurate forecast of moving trend of short-term radar echoes within a region. The samples of radar echo images recorded by Shenzhen and Hong Kong meteorological stations are used for experiments, the results show that the critical success index (CSI) of this proposed model for eight predicted echoes reaches 0.578 when the echo threshold is 10 dBZ, the false alarm ratio (FAR) is 20% lower than convolutional LSTM network (ConvLSTM), and the mean square error (MSE) is 16% lower than the real-time optical flow by variational method (ROVER), which outperforms the current state-of-the-art radar echo nowcasting methods. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning Techniques for Atmospheric and Climate-Related Challenges at Different Time-Scales)
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16 pages, 4063 KiB  
Article
The Comparison of Predicting Storm-Time Ionospheric TEC by Three Methods: ARIMA, LSTM, and Seq2Seq
by Rongxin Tang, Fantao Zeng, Zhou Chen, Jing-Song Wang, Chun-Ming Huang and Zhiping Wu
Atmosphere 2020, 11(4), 316; https://doi.org/10.3390/atmos11040316 - 25 Mar 2020
Cited by 76 | Viewed by 5667
Abstract
Ionospheric structure usually changes dramatically during a strong geomagnetic storm period, which will significantly affect the short-wave communication and satellite navigation systems. It is critically important to make accurate ionospheric predictions under the extreme space weather conditions. However, ionospheric prediction is always a [...] Read more.
Ionospheric structure usually changes dramatically during a strong geomagnetic storm period, which will significantly affect the short-wave communication and satellite navigation systems. It is critically important to make accurate ionospheric predictions under the extreme space weather conditions. However, ionospheric prediction is always a challenge, and pure physical methods often fail to get a satisfactory result since the ionospheric behavior varies greatly with different geomagnetic storms. In this paper, in order to find an effective prediction method, one traditional mathematical method (autoregressive integrated moving average—ARIMA) and two deep learning algorithms (long short-term memory—LSTM and sequence-to-sequence—Seq2Seq) are investigated for the short-term predictions of ionospheric TEC (Total Electron Content) under different geomagnetic storm conditions based on the MIT (Massachusetts Institute of Technology) madrigal observation from 2001 to 2016. Under the extreme condition, the performance limitation of these methods can be found. When the storm is stronger, the effective prediction horizon of the methods will be shorter. The statistical analysis shows that the LSTM can achieve the best prediction accuracy and is robust for the accurate trend prediction of the strong geomagnetic storms. In contrast, ARIMA and Seq2Seq have relatively poor performance for the prediction of the strong geomagnetic storms. This study brings new insights to the deep learning applications in the space weather forecast. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning Techniques for Atmospheric and Climate-Related Challenges at Different Time-Scales)
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17 pages, 3523 KiB  
Article
Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis
by Sevda Shabani, Saeed Samadianfard, Mohammad Taghi Sattari, Amir Mosavi, Shahaboddin Shamshirband, Tibor Kmet and Annamária R. Várkonyi-Kóczy
Atmosphere 2020, 11(1), 66; https://doi.org/10.3390/atmos11010066 - 4 Jan 2020
Cited by 125 | Viewed by 6293
Abstract
Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods [...] Read more.
Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning Techniques for Atmospheric and Climate-Related Challenges at Different Time-Scales)
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18 pages, 6022 KiB  
Article
Study on Wind Simulations Using Deep Learning Techniques during Typhoons: A Case Study of Northern Taiwan
by Chih-Chiang Wei
Atmosphere 2019, 10(11), 684; https://doi.org/10.3390/atmos10110684 - 7 Nov 2019
Cited by 8 | Viewed by 4053
Abstract
A scheme for wind-speed simulation during typhoons in Taiwan is highly desirable, considering the effects of the powerful winds accompanying the severe typhoons. The developed combination of deep learning (DL) algorithms with a weather-forecasting numerical model can be used to determine wind speed [...] Read more.
A scheme for wind-speed simulation during typhoons in Taiwan is highly desirable, considering the effects of the powerful winds accompanying the severe typhoons. The developed combination of deep learning (DL) algorithms with a weather-forecasting numerical model can be used to determine wind speed in a rapid simulation process. Here, the Weather Research and Forecasting (WRF) numerical model was employed as the numerical simulation-based model for precomputing solutions to determine the wind velocity at arbitrary positions where the wind cannot be measured. The deep neural network (DNN) was used for constructing the DL-based wind-velocity simulation model. The experimental area of Northern Taiwan was used for the simulation. Regarding the complex typhoon system, the collected data comprised the typhoon tracks, FNL (Final) Operational Global Analysis Data for the WRF model, typhoon characteristics, and ground weather data. This study included 47 typhoon events that occurred over 2000–2017. Three measures were used to analyze the models for identifying optimal performance levels: Mean absolute error, root mean squared error, and correlation coefficient. This study compared observations with the WRF numerical model and DNN model. The results revealed that (1) simulations by using the WRF-based models were satisfactorily consistent with the observed data and (2) simulations by using the DNN model were considerably consistent with those of the WRF-based model. Consequently, the proposed DNN combined with WRF model can be effectively used in simulations of wind velocity at arbitrary positions of study area. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning Techniques for Atmospheric and Climate-Related Challenges at Different Time-Scales)
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18 pages, 2324 KiB  
Article
Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units
by S. Poornima and M. Pushpalatha
Atmosphere 2019, 10(11), 668; https://doi.org/10.3390/atmos10110668 - 31 Oct 2019
Cited by 143 | Viewed by 14376
Abstract
Prediction of rainfall is one of the major concerns in the domain of meteorology. Several techniques have been formerly proposed to predict rainfall based on statistical analysis, machine learning and deep learning techniques. Prediction of time series data in meteorology can assist in [...] Read more.
Prediction of rainfall is one of the major concerns in the domain of meteorology. Several techniques have been formerly proposed to predict rainfall based on statistical analysis, machine learning and deep learning techniques. Prediction of time series data in meteorology can assist in decision-making processes carried out by organizations responsible for the prevention of disasters. This paper presents Intensified Long Short-Term Memory (Intensified LSTM) based Recurrent Neural Network (RNN) to predict rainfall. The neural network is trained and tested using a standard dataset of rainfall. The trained network will produce predicted attribute of rainfall. The parameters considered for the evaluation of the performance and the efficiency of the proposed rainfall prediction model are Root Mean Square Error (RMSE), accuracy, number of epochs, loss, and learning rate of the network. The results obtained are compared with Holt–Winters, Extreme Learning Machine (ELM), Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network and Long Short-Term Memory models in order to exemplify the improvement in the ability to predict rainfall. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning Techniques for Atmospheric and Climate-Related Challenges at Different Time-Scales)
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20 pages, 8228 KiB  
Article
Downscaling Precipitation in the Data-Scarce Inland River Basin of Northwest China Based on Earth System Data Products
by Jingping Zuo, Jianhua Xu, Yaning Chen and Chong Wang
Atmosphere 2019, 10(10), 613; https://doi.org/10.3390/atmos10100613 - 10 Oct 2019
Cited by 17 | Viewed by 3334
Abstract
Precipitation is a key climatic variable that connects the processes of atmosphere and land surface, and it plays a leading role in the water cycle. However, the vast area of Northwest China, its complex geographical environment, and its scarce observation data make it [...] Read more.
Precipitation is a key climatic variable that connects the processes of atmosphere and land surface, and it plays a leading role in the water cycle. However, the vast area of Northwest China, its complex geographical environment, and its scarce observation data make it difficult to deeply understand the temporal and spatial variation of precipitation. This paper establishes a statistical downscaling model to downscale the monthly precipitation in the inland river basin of Northwest China with the Tarim River Basin (TRB) as a typical representation. This method combines polynomial regression and machine learning, and it uses the batch gradient descent (BGD) algorithm to train the regression model. We downscale the monthly precipitation and obtain a dataset from January 2001 to December 2017 with a spatial resolution of 1 km × 1 km. The results show that the downscaling model presents a good performance in precipitation simulation with a high resolution, and it is more effective than ordinary polynomial regression. We also investigate the temporal and spatial variations of precipitation in the TRB based on the downscaling dataset. Analyses illustrate that the annual precipitation in the southern foothills of the Tianshan Mountains and the North Kunlun Mountains showed a significant upward trend during the study periods, while the annual precipitation in the central plains presented a significant downward trend. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning Techniques for Atmospheric and Climate-Related Challenges at Different Time-Scales)
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