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Atmosphere
Special Issues
Statistical Methods in Weather Forecasting
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Statistical Methods in Weather Forecasting

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

Deadline for manuscript submissions: closed (15 May 2021) | Viewed by 12216

Special Issue Editors

Dr. Sándor Baran

E-Mail Website
Guest Editor
Faculty of Informatics, University of Debrecen. Kassai street 26, H-4028 Debrecen, Hungary
Interests: probabilistic weather forecasting; random fields; optimal design problems
Dr. Annette Möller

E-Mail Website
Guest Editor
Institute of Mathematics, Clausthal Universtiy of Technology, Erzstraße 1, 38678 Clausthal-Zellerfeld, Germany
Interests: probabilistic weather forecasting; machine learning; functional data analysis; copulas

Special Issue Information

Dear Colleagues,

Weather-related events have a deep impact on several areas of the economy and our everyday lives as well, so accurate and reliable predictions of the different weather quantities are of crucial importance. These forecasts are issued using observational data and numerical weather prediction (NWP) models, which are able to simulate atmospheric motions. Nowadays, all major meteorological services issue forecasts based on multiple runs of an NWP model with different initial conditions and/or parametrizations resulting in a forecast ensemble. The use of ensemble forecasts enables us to capture forecast uncertainty and provides information about the distribution of the predicted weather quantity, hence opening the door to probabilistic forecasting.

However, on the one hand, ensemble forecasting requires enormous computational resources; thus, to approximate high-resolution outputs of NWP models, stochastic generators using spatial and time series models are applied. On the other hand, ensemble forecasts often exhibit systematic errors, which call for some form of post-processing. In the past 15 years, a variety of different approaches to the statistical calibration of ensemble forecasts have been developed, from parametric methods to machine learning techniques. A recent focus also lies in incorporating all types of multivariate dependencies in order to obtain physically consistent forecasts.

In recognition of the importance of statistical methods in atmospheric sciences, Atmosphere is hosting a Special Issue in order to exhibit a collection of recent development in statistical calibration, weather generators, and other stochastic approaches to weather prediction.

Dr. Sándor Baran
Dr. Annette Möller
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

  • statistical ensemble post-processing
  • probabilistic weather prediction
  • stochastic generator
  • forecast verification
  • spatial modeling
  • time series models
  • machine learning techniques

Published Papers (4 papers)

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Research

13 pages, 1504 KiB  
Article
Skewed and Mixture of Gaussian Distributions for Ensemble Postprocessing
by Maxime Taillardat
Atmosphere 2021, 12(8), 966; https://doi.org/10.3390/atmos12080966 - 27 Jul 2021
Cited by 4 | Viewed by 2356
Abstract
The implementation of statistical postprocessing of ensemble forecasts is increasingly developed among national weather services. The so-called Ensemble Model Output Statistics (EMOS) method, which consists of generating a given distribution whose parameters depend on the raw ensemble, leads to significant improvements in forecast [...] Read more.
The implementation of statistical postprocessing of ensemble forecasts is increasingly developed among national weather services. The so-called Ensemble Model Output Statistics (EMOS) method, which consists of generating a given distribution whose parameters depend on the raw ensemble, leads to significant improvements in forecast performance for a low computational cost, and so is particularly appealing for reduced performance computing architectures. However, the choice of a parametric distribution has to be sufficiently consistent so as not to lose information on predictability such as multimodalities or asymmetries. Different distributions are applied to the postprocessing of the European Centre for Medium-range Weather Forecast (ECMWF) ensemble forecast of surface temperature. More precisely, a mixture of Gaussian and skewed normal distributions are tried from 3- up to 360-h lead time forecasts, with different estimation methods. For this work, analytical formulas of the continuous ranked probability score have been derived and appropriate link functions are used to prevent overfitting. The mixture models outperform single parametric distributions, especially for the longest lead times. This statement is valid judging both overall performance and tolerance to misspecification. Full article
(This article belongs to the Special Issue Statistical Methods in Weather Forecasting)
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18 pages, 5840 KiB  
Article
Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory
by Moslem Imani, Hoda Fakour, Wen-Hau Lan, Huan-Chin Kao, Chi Ming Lee, Yu-Shen Hsiao and Chung-Yen Kuo
Atmosphere 2021, 12(7), 924; https://doi.org/10.3390/atmos12070924 - 17 Jul 2021
Cited by 13 | Viewed by 2547
Abstract
Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance [...] Read more.
Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment. Full article
(This article belongs to the Special Issue Statistical Methods in Weather Forecasting)
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22 pages, 26735 KiB  
Article
Statistical Analysis and Machine Learning Prediction of Fog-Caused Low-Visibility Events at A-8 Motor-Road in Spain
by Sara Cornejo-Bueno, David Casillas-Pérez, Laura Cornejo-Bueno, Mihaela I. Chidean, Antonio J. Caamaño, Elena Cerro-Prada, Carlos Casanova-Mateo and Sancho Salcedo-Sanz
Atmosphere 2021, 12(6), 679; https://doi.org/10.3390/atmos12060679 - 26 May 2021
Cited by 15 | Viewed by 2749
Abstract
This work presents a full statistical analysis and accurate prediction of low-visibility events due to fog, at the A-8 motor-road in Mondoñedo (Galicia, Spain). The present analysis covers two years of study, considering visibility time series and exogenous variables collected in the zone [...] Read more.
This work presents a full statistical analysis and accurate prediction of low-visibility events due to fog, at the A-8 motor-road in Mondoñedo (Galicia, Spain). The present analysis covers two years of study, considering visibility time series and exogenous variables collected in the zone affected the most by extreme low-visibility events. This paper has then a two-fold objective: first, we carry out a statistical analysis for estimating the fittest probability distributions to the fog event duration, using the Maximum Likelihood method and an alternative method known as the L-moments method. This statistical study allows association of the low-visibility depth with the event duration, showing a clear relationship, which can be modeled with distributions for extremes such as Generalized Extreme Value and Generalized Pareto distributions. Second, we apply a neural network approach, trained by means of the ELM (Extreme Learning Machine) algorithm, to predict the occurrence of low-visibility events due to fog, from atmospheric predictive variables. This study provides a full characterization of fog events at this motor-road, in which orographic fog is predominant, causing important traffic problems during all year. We also show how the ELM approach is able to obtain highly accurate low-visibility events predictions, with a Pearson correlation coefficient of 0.8, within a half-hour time horizon, enough to initialize some protocols aiming at reducing the impact of these extreme events in the traffic of the A-8 motor road. Full article
(This article belongs to the Special Issue Statistical Methods in Weather Forecasting)
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20 pages, 2341 KiB  
Article
Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters
by Eslam A. Hussein, Mehrdad Ghaziasgar, Christopher Thron, Mattia Vaccari and Antoine Bagula
Atmosphere 2021, 12(5), 539; https://doi.org/10.3390/atmos12050539 - 23 Apr 2021
Cited by 6 | Viewed by 3037
Abstract
Machine learning (ML) has been utilized to predict climatic parameters, and many successes have been reported in the literature. In this paper, we scrutinize the effectiveness of five widely used ML algorithms in the monthly prediction of seasonal climatic parameters using monthly image [...] Read more.
Machine learning (ML) has been utilized to predict climatic parameters, and many successes have been reported in the literature. In this paper, we scrutinize the effectiveness of five widely used ML algorithms in the monthly prediction of seasonal climatic parameters using monthly image data. Specifically, we quantify the predictive performance of these algorithms applied to five climatic parameters using various combinations of features. We compare the predictive accuracy of the resulting trained ML models to that of basic statistical estimators that are computed directly from the training data. Our results show that ML never significantly outperforms the statistical baseline, and underperforms for most feature sets. Unlike previous similar studies, we provide error bars for the relative performance of different predictors based on jackknife estimates applied to differences in predictive error magnitudes. We also show that the practice of shuffling data sequences which was employed in some previous references leads to data leakage, resulting in over-estimated performance. Ultimately, the paper demonstrates the importance of using well-grounded statistical techniques when producing and analyzing the results of ML predictive models. Full article
(This article belongs to the Special Issue Statistical Methods in Weather Forecasting)
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