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Atmosphere
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Application of Non-linear Approaches and Frequency Analysis in Characterization and...
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Application of Non-linear Approaches and Frequency Analysis in Characterization and Prediction of Rainfall Data

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

Deadline for manuscript submissions: closed (10 August 2021) | Viewed by 7607

Special Issue Editor

Dr. Javier Estévez

E-Mail Website
Guest Editor
Department of Rural Engineering, Universidad de Cordoba, 5016 Cordoba, Spain
Interests: agrometeorology; quality control; validation procedures; precipitation; reference evapotranspiration; solar radiation

Special Issue Information

Dear Colleagues,

The main aim of this Special Issue is to gather recent studies on rainfall modeling and prediction using frequency analysis and nonlinear approaches such as spectral, wavelet or multifractal analyses, as well as neural networks techniques.

This Special Issue aims to summarize the state of the science in the characterization and forecasting of this crucial meteorological variable, having a great impact on different sectors such as energy, agriculture, civil engineering or tourism, among others. Relevant contributions that study this topic using novel and well-known approaches are welcome. We particularly seek contributions on, but not limited to, the following topics:

- Rainfall forecasting at different timescales. Quantitative forecasts are highly desired. What advances have been made in this area? How can machine learning and deep learning approaches enhance forecasts compared to other methods? Multiscale analysis, wavelet analysis, as well as other techniques that can be combined with neural networks modeling, can be studied;

- Rainfall extreme values. Intensity–duration–frequency models and the impact of climate change in these relationships. Scale-invariance methods, multifractal analysis and other techniques. What improvements can be carried out?

- Rainfall behavior characterization. Multiscale approaches, temporal and spatial trends at different timescales. Application of this knowledge to crucial sectors such as agriculture, energy or civil engineering.

Dr. Javier Estévez
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

  • Precipitation
  • Neural network models
  • Frequency analysis
  • Multiscale modeling
  • Multifractal analysis
  • Forecasting
  • Rainfall
  • Prediction
  • Nonlinear modeling

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Published Papers (2 papers)

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Research

25 pages, 3256 KiB  
Article
Assessing Machine Learning Models for Gap Filling Daily Rainfall Series in a Semiarid Region of Spain
by Juan Antonio Bellido-Jiménez, Javier Estévez Gualda and Amanda Penélope García-Marín
Atmosphere 2021, 12(9), 1158; https://doi.org/10.3390/atmos12091158 - 9 Sep 2021
Cited by 19 | Viewed by 3689
Abstract
The presence of missing data in hydrometeorological datasets is a common problem, usually due to sensor malfunction, deficiencies in records storage and transmission, or other recovery procedures issues. These missing values are the primary source of problems when analyzing and modeling their spatial [...] Read more.
The presence of missing data in hydrometeorological datasets is a common problem, usually due to sensor malfunction, deficiencies in records storage and transmission, or other recovery procedures issues. These missing values are the primary source of problems when analyzing and modeling their spatial and temporal variability. Thus, accurate gap-filling techniques for rainfall time series are necessary to have complete datasets, which is crucial in studying climate change evolution. In this work, several machine learning models have been assessed to gap-fill rainfall data, using different approaches and locations in the semiarid region of Andalusia (Southern Spain). Based on the obtained results, the use of neighbor data, located within a 50 km radius, highly outperformed the rest of the assessed approaches, with RMSE (root mean squared error) values up to 1.246 mm/day, MBE (mean bias error) values up to −0.001 mm/day, and R2 values up to 0.898. Besides, inland area results outperformed coastal area in most locations, arising the efficiency effects based on the distance to the sea (up to an improvement of 63.89% in terms of RMSE). Finally, machine learning (ML) models (especially MLP (multilayer perceptron)) notably outperformed simple linear regression estimations in the coastal sites, whereas in inland locations, the improvements were not such significant. Full article
(This article belongs to the Special Issue Application of Non-linear Approaches and Frequency Analysis in Characterization and Prediction of Rainfall Data)
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18 pages, 4819 KiB  
Article
Enhancing the Encoding-Forecasting Model for Precipitation Nowcasting by Putting High Emphasis on the Latest Data of the Time Step
by Chang Hoo Jeong, Wonsu Kim, Wonkyun Joo, Dongmin Jang and Mun Yong Yi
Atmosphere 2021, 12(2), 261; https://doi.org/10.3390/atmos12020261 - 16 Feb 2021
Cited by 12 | Viewed by 2929
Abstract
Nowcasting is an important technique for weather forecasting because sudden weather changes significantly affect human life. The encoding-forecasting model, which is a state-of-the-art architecture in the field of data-driven radar extrapolation, does not particularly focus on the latest data when forecasting natural phenomena. [...] Read more.
Nowcasting is an important technique for weather forecasting because sudden weather changes significantly affect human life. The encoding-forecasting model, which is a state-of-the-art architecture in the field of data-driven radar extrapolation, does not particularly focus on the latest data when forecasting natural phenomena. This paper proposes a weighted broadcasting method that emphasizes the latest data of the time step to improve the nowcasting performance. This weighted broadcasting method allows the most recent rainfall patterns to have a greater impact on the forecasting network by extending the architecture of the existing encoding-forecasting model. Experimental results show that the proposed model is 1.74% and 2.20% better than the existing encoding-forecasting model in terms of mean absolute error and critical success index, respectively. In the case of heavy rainfall with an intensity of 30 mm/h or higher, the proposed model was more than 30% superior to the existing encoding-forecasting model. Therefore, applying the weighted broadcasting method, which explicitly places a high emphasis on the latest information, to the encoding-forecasting model is considered as an improvement that is applicable to the state-of-the-art implementation of data-driven radar-based precipitation nowcasting. Full article
(This article belongs to the Special Issue Application of Non-linear Approaches and Frequency Analysis in Characterization and Prediction of Rainfall Data)
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