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Atmosphere
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Application of Statistical Methods and Machine Learning to Large-Scale Climate...
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Application of Statistical Methods and Machine Learning to Large-Scale Climate Informatics

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 June 2019) | Viewed by 7390

Special Issue Editor

Dr. Andrew Mercer

E-Mail Website
Guest Editor
Department of Geosciences, Mississippi State University, 108 Hilbun Hall, P. O. Box 5448, Mississippi State, MS 39759, USA
Interests: statistical climatology; machine learning; atmospheric teleconnections; synoptic meteorology; tropical meteorology; severe weather outbreaks; hydrometeorological applications; numerical weather prediction

Special Issue Information

Dear Colleagues,

As our understanding of the climate system has advanced, so has the need for more detailed analyses into the interrelationships among its components. The increasing popularity of sophisticated statistical methods and machine learning in climate science has afforded a unique opportunity to bridge some of these gaps in understanding within the climate system. We invite researchers to contribute original research articles, as well as review articles, that help address the current limitations in climate system understanding utilizing sophisticated statistical methods and machine learning. Topics of interest include, but are not limited to:

  • Teleconnections and their relationships to large-scale climate system problems
  • Climate downscaling studies implementing machine learning techniques
  • Coupling of climate systems using machine learning methods
  • Applications of advanced statistical methods and machine learning in climate modelling studies
  • Regional climate studies that implement state-of-the-art statistical methods
  • Relationships between climate processes and smaller-scale atmospheric phenomena

Other topics, as well as review articles addressing possible future lines of investigation will also be considered. 

Dr. Andrew Mercer
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

  • Machine Learning
  • Climate System Coupling
  • Advanced Statistical Analysis Techniques
  • Teleconnections
  • Climate Change
  • Climate Modeling and Downscaling

Published Papers (1 paper)

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Research

16 pages, 6902 KiB  
Article
Temperature Prediction Using the Missing Data Refinement Model Based on a Long Short-Term Memory Neural Network
by Inyoung Park, Hyun Soo Kim, Jiwon Lee, Joon Ha Kim, Chul Han Song and Hong Kook Kim
Atmosphere 2019, 10(11), 718; https://doi.org/10.3390/atmos10110718 - 16 Nov 2019
Cited by 43 | Viewed by 7088
Abstract
In this paper, we propose a new temperature prediction model based on deep learning by using real observed weather data. To this end, a huge amount of model training data is needed, but these data should not be defective. However, there is a [...] Read more.
In this paper, we propose a new temperature prediction model based on deep learning by using real observed weather data. To this end, a huge amount of model training data is needed, but these data should not be defective. However, there is a limitation in collecting weather data since it is not possible to measure data that have been missed. Thus, the collected data are apt to be incomplete, with random or extended gaps. Therefore, the proposed temperature prediction model is used to refine missing data in order to restore missed weather data. In addition, since temperature is seasonal, the proposed model utilizes a long short-term memory (LSTM) neural network, which is a kind of recurrent neural network known to be suitable for time-series data modeling. Furthermore, different configurations of LSTMs are investigated so that the proposed LSTM-based model can reflect the time-series traits of the temperature data. In particular, when a part of the data is detected as missing, it is restored by using the proposed model’s refinement function. After all the missing data are refined, the LSTM-based model is retrained using the refined data. Finally, the proposed LSTM-based temperature prediction model can predict the temperature through three time steps: 6, 12, and 24 h. Furthermore, the model is extended to predict 7 and 14 day future temperatures. The performance of the proposed model is measured by its root-mean-squared error (RMSE) and compared with the RMSEs of a feedforward deep neural network, a conventional LSTM neural network without any refinement function, and a mathematical model currently used by the meteorological office in Korea. Consequently, it is shown that the proposed LSTM-based model employing LSTM-refinement achieves the lowest RMSEs for 6, 12, and 24 h temperature prediction as well as for 7 and 14 day temperature prediction, compared to other DNN-based and LSTM-based models with either no refinement or linear interpolation. Moreover, the prediction accuracy of the proposed model is higher than that of the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) for 24 h temperature predictions. Full article
(This article belongs to the Special Issue Application of Statistical Methods and Machine Learning to Large-Scale Climate Informatics)
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