Special Issue "Data Mining and Machine Learning Techniques for Seasonal Forecasting and Climate Change"

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

Deadline for manuscript submissions: 17 April 2020.

Special Issue Editor

Dr. Rodrigo Manzanas
E-Mail Website
Guest Editor
Department of Applied Mathematics and Computer Science, University of Cantabria, 39005 Santander, Spain
Interests: climate change; climate variability; statistical downscaling; seasonal forecasting; climate services; bias correction

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.) in the context of seasonal forecasting and climate change projections, with interest in a number of problems of different nature that constitute key challenges for the climate science community (e.g., diagnosis, classification, forecasting, downscaling). Topics of interest include, but are not limited to:

(i) Representation of clouds and other small-scale aspects of the atmosphere and the ocean that can help to better predict global and regional climate’s response to rising greenhouse gas concentrations

(ii) Identification of relevant processes and their associated responses (i.e., atmospheric teleconnections)

(iii) Proper model weighting to improve ensemble forecasts

(iv) Automatic predictor selection for statistical downscaling which helps to reduce the uncertainty in local/regional predictions

Dr. Rodrigo Manzanas
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 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

  • Statistical forecasts
  • Climate change projections
  • Data mining
  • Machine learning
  • Neural networks
  • Small-scale processes representation
  • Prediction uncertainty
  • Teleconnections
  • Model weighting
  • Statistical downscaling

Published Papers (4 papers)

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Research

Open AccessArticle
Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis
Atmosphere 2020, 11(1), 66; https://doi.org/10.3390/atmos11010066 - 04 Jan 2020
Cited by 2
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
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Open AccessArticle
Study on Wind Simulations Using Deep Learning Techniques during Typhoons: A Case Study of Northern Taiwan
Atmosphere 2019, 10(11), 684; https://doi.org/10.3390/atmos10110684 - 07 Nov 2019
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
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Open AccessArticle
Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units
Atmosphere 2019, 10(11), 668; https://doi.org/10.3390/atmos10110668 - 31 Oct 2019
Cited by 2
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
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Open AccessArticle
Downscaling Precipitation in the Data-Scarce Inland River Basin of Northwest China Based on Earth System Data Products
Atmosphere 2019, 10(10), 613; https://doi.org/10.3390/atmos10100613 - 10 Oct 2019
Cited by 1
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
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