Special Issue "Machine Learning Applications in Earth System Science"

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 (29 September 2021) | Viewed by 4674

Special Issue Editors

Dr. Valentine Anantharaj
E-Mail Website
Guest Editor
National Center for Computational Sciences, Oak Ridge National Laboratory 1 Bethel Valley Road Oak Ridge, TN 37831, USA
Interests: land surface hydrology; land-atmosphere interactions; characterization of uncertainties in global climate models
Dr. Forrest M. Hoffman
E-Mail Website
Guest Editor
Computational Earth Sciences Group, Computational Sciences & Engineering Division and Climate Change Science Institute (CCSI), Oak Ridge National Laboratory Building 4500N, Room F106
Mail Stop 6301 P.O. Box 2008 Oak Ridge, TN 37831-6301, USA
Special Issues, Collections and Topics in MDPI journals
Dr. Udaysankar S. Nair
E-Mail Website
Guest Editor
Earth System Science Center, The University of Alabama in Huntsville, National Space Science and Technology Center, 320 Sparkman Drive, Huntsville, AL 35805, USA
Dr. Samantha Vanessa Adams
E-Mail Website
Guest Editor
Met Office · Science (Informatics Lab), The Informatics Lab, Met Office HPC Complex, Upper Richardson, Science Park, Exeter EX5 2FS, UK
Interests: computational neuroscience; robotics; physics simulation; python; software engineering; neural networks; artificial neural networks; artificial intelligence; machine learning; fortran

Special Issue Information

Dear Colleagues,

With the advent of the big data era, concurrently with the advances in hardware and computational technologies, machine learning (ML) is proving to be increasingly useful in synthesizing valuable information from large volumes of data from earth observations (EO) and earth system models (ESMs). One of the earliest successes in adopting ML techniques for application in atmospheric sciences dates back to 1990 when a neural network was developed to classify clouds from satellite imagery. Since then ML approaches have been widely used in earth system sciences. In fact, hybrid data-driven approaches are being employed toward developing a new generation of ESMs. We invite manuscripts regarding the application of machine learning and artificial intelligence techniques in the subject areas of earth system science encompassing Atmosphere, including meteorology, oceanography, climatology, biometeorology, land-atmosphere interactions, aerosol and air quality. Topics that are of particular interest include ML frameworks for ESMs and EO, physics informed ML, interpretable ML, and applications of ML to a broad range of problems in classification and regression, anomaly detection, spatial mapping and gap filling, geophysical retrievals, spatio-temporal prediction, downscaling for regional climate projections, characterizing extreme events, subgrid scale parameterisations, and surrogate model development for use as emulators in earth system models.

Dr. Valentine Anantharaj
Dr. Forrest M. Hoffman
Dr. Udaysankar S. Nair
Dr. Samantha Vanessa Adams
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 2000 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

  • ML frameworks for ESMs and EO
  • physics informed ML
  • applications of ML
  • characterizing extreme events
  • spatio-temporal prediction
  • regional climate projections

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
A Comparison of Machine Learning Methods to Forecast Tropospheric Ozone Levels in Delhi
Atmosphere 2022, 13(1), 46; https://doi.org/10.3390/atmos13010046 - 28 Dec 2021
Cited by 1 | Viewed by 557
Abstract
Ground-level ozone is a pollutant that is harmful to urban populations, particularly in developing countries where it is present in significant quantities. It greatly increases the risk of heart and lung diseases and harms agricultural crops. This study hypothesized that, as a secondary [...] Read more.
Ground-level ozone is a pollutant that is harmful to urban populations, particularly in developing countries where it is present in significant quantities. It greatly increases the risk of heart and lung diseases and harms agricultural crops. This study hypothesized that, as a secondary pollutant, ground-level ozone is amenable to 24 h forecasting based on measurements of weather conditions and primary pollutants such as nitrogen oxides and volatile organic compounds. We developed software to analyze hourly records of 12 air pollutants and 5 weather variables over the course of one year in Delhi, India. To determine the best predictive model, eight machine learning algorithms were tuned, trained, tested, and compared using cross-validation with hourly data for a full year. The algorithms, ranked by R2 values, were XGBoost (0.61), Random Forest (0.61), K-Nearest Neighbor Regression (0.55), Support Vector Regression (0.48), Decision Trees (0.43), AdaBoost (0.39), and linear regression (0.39). When trained by separate seasons across five years, the predictive capabilities of all models increased, with a maximum R2 of 0.75 during winter. Bidirectional Long Short-Term Memory was the least accurate model for annual training, but had some of the best predictions for seasonal training. Out of five air quality index categories, the XGBoost model was able to predict the correct category 24 h in advance 90% of the time when trained with full-year data. Separated by season, winter is considerably more predictable (97.3%), followed by post-monsoon (92.8%), monsoon (90.3%), and summer (88.9%). These results show the importance of training machine learning methods with season-specific data sets and comparing a large number of methods for specific applications. Full article
(This article belongs to the Special Issue Machine Learning Applications in Earth System Science)
Show Figures

Figure 1

Article
A Machine Learning Based Ensemble Forecasting Optimization Algorithm for Preseason Prediction of Atlantic Hurricane Activity
Atmosphere 2021, 12(4), 522; https://doi.org/10.3390/atmos12040522 - 20 Apr 2021
Cited by 4 | Viewed by 1030
Abstract
In this study, nine different statistical models are constructed using different combinations of predictors, including models with and without projected predictors. Multiple machine learning (ML) techniques are employed to optimize the ensemble predictions by selecting the top performing ensemble members and determining the [...] Read more.
In this study, nine different statistical models are constructed using different combinations of predictors, including models with and without projected predictors. Multiple machine learning (ML) techniques are employed to optimize the ensemble predictions by selecting the top performing ensemble members and determining the weights for each ensemble member. The ML-Optimized Ensemble (ML-OE) forecasts are evaluated against the Simple-Averaging Ensemble (SAE) forecasts. The results show that for the response variables that are predicted with significant skill by individual ensemble members and SAE, such as Atlantic tropical cyclone counts, the performance of SAE is comparable to the best ML-OE results. However, for response variables that are poorly modeled by individual ensemble members, such as Atlantic and Gulf of Mexico major hurricane counts, ML-OE predictions often show higher skill score than individual model forecasts and the SAE predictions. However, neither SAE nor ML-OE was able to improve the forecasts of the response variables when all models show consistent bias. The results also show that increasing the number of ensemble members does not necessarily lead to better ensemble forecasts. The best ensemble forecasts are from the optimally combined subset of models. Full article
(This article belongs to the Special Issue Machine Learning Applications in Earth System Science)
Show Figures

Figure 1

Article
Prediction of Solar Irradiance and Photovoltaic Solar Energy Product Based on Cloud Coverage Estimation Using Machine Learning Methods
Atmosphere 2021, 12(3), 395; https://doi.org/10.3390/atmos12030395 - 18 Mar 2021
Cited by 6 | Viewed by 1725
Abstract
Cloud cover estimation from images taken by sky-facing cameras can be an important input for analyzing current weather conditions and estimating photovoltaic power generation. The constant change in position, shape, and density of clouds, however, makes the development of a robust computational method [...] Read more.
Cloud cover estimation from images taken by sky-facing cameras can be an important input for analyzing current weather conditions and estimating photovoltaic power generation. The constant change in position, shape, and density of clouds, however, makes the development of a robust computational method for cloud cover estimation challenging. Accurately determining the edge of clouds and hence the separation between clouds and clear sky is difficult and often impossible. Toward determining cloud cover for estimating photovoltaic output, we propose using machine learning methods for cloud segmentation. We compare several methods including a classical regression model, deep learning methods, and boosting methods that combine results from the other machine learning models. To train each of the machine learning models with various sky conditions, we supplemented the existing Singapore whole sky imaging segmentation database with hazy and overcast images collected by a camera-equipped Waggle sensor node. We found that the U-Net architecture, one of the deep neural networks we utilized, segmented cloud pixels most accurately. However, the accuracy of segmenting cloud pixels did not guarantee high accuracy of estimating solar irradiance. We confirmed that the cloud cover ratio is directly related to solar irradiance. Additionally, we confirmed that solar irradiance and solar power output are closely related; hence, by predicting solar irradiance, we can estimate solar power output. This study demonstrates that sky-facing cameras with machine learning methods can be used to estimate solar power output. This ground-based approach provides an inexpensive way to understand solar irradiance and estimate production from photovoltaic solar facilities. Full article
(This article belongs to the Special Issue Machine Learning Applications in Earth System Science)
Show Figures

Figure 1

Back to TopTop