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Modern Geophysical and Climate Data Analysis: Tools and Methods
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Modern Geophysical and Climate Data Analysis: Tools and Methods

A topical collection in Data (ISSN 2306-5729). This collection belongs to the section "Spatial Data Science and Digital Earth".

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Editors

Prof. Dr. Vladimir Sreckovic

E-Mail Website
Guest Editor
The Astrophysics and Ionospheric Laboratory, Institute of Physics Belgrade, University of Belgrade, 11080 Belgrade, Serbia
Interests: space weather studies of upper atmosphere; astrogeoinformatics; astroinformatics; databases; data-mining; natural hazards
Special Issues, Collections and Topics in MDPI journals
Dr. Zoran Mijic

E-Mail Website
Guest Editor
Institute of Physics Belgrade, University of Belgrade, 11080 Belgrade, Serbia
Interests: statistical modeling in atmospheric physics; multivariate receptor modeling; ground-based remote sensing for retrieval of the atmospheric composition; aerosol optical properties; aerosol physical and chemical characterization and climatic role; air quality
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

The expansion and scope of geophysical and climate data, with associated analytical tools and approaches, are changing rapidly day after day as we move into the future. The rise of “Big Data” sets deepens our understanding of such complex structured and unstructured data. In the relatively recent past of the last few decades, these datasets were relatively small and frequently disregarded by statisticians as biased data. With the availability of large geophysical and climate datasets, i.e., “Big Data”, the scientific community (statisticians, computer scientists, geophysicists, etc.) was forced to develop analytically rigorous methods in order to deal with such vast datasets. To evaluate data patterns, variable relationships, and prediction, various methods have been created, followed by emerging appropriate novel statistical theories.  

This topic seeks to offer a forum for in-depth discussion and contributions that can have either an applied or theoretical perspective and highlight different problems, with special emphasis on data analytics and methods. Manuscripts summarizing the most recent state of the art on these topics are welcome. The topics to be covered include but are not limited to the following: cutting-edge statistical learning tools for the analysis of geophysical and climate data; cutting-edge machine learning tools for the analysis of geophysical and climate data; innovative applications of existing statistical learning and/or machine learning tools for geophysical and climate data.

Prof. Dr. Vladimir Sreckovic
Dr. Zoran Mijic
Guest Editors

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 1600 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

  • data in geo and climate and related fields
  • analysis and prediction
  • new applications
  • algorithms
  • machine learning
  • visualization

Published Papers (3 papers)

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2025

Jump to: 2023

27 pages, 9197 KiB  
Data Descriptor
A Six-Year, Spatiotemporally Comprehensive Dataset and Data Retrieval Tool for Analyzing Chlorophyll-a, Turbidity, and Temperature in Utah Lake Using Sentinel and MODIS Imagery
by Kaylee B. Tanner, Anna C. Cardall and Gustavious P. Williams
Data 2025, 10(8), 128; https://doi.org/10.3390/data10080128 - 13 Aug 2025
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Abstract
Data from earth observation satellites provide unique and valuable information about water quality conditions in freshwater lakes but require significant processing before they can be used, even with the use of tools like Google Earth Engine. We use imagery from Sentinel 2 and [...] Read more.
Data from earth observation satellites provide unique and valuable information about water quality conditions in freshwater lakes but require significant processing before they can be used, even with the use of tools like Google Earth Engine. We use imagery from Sentinel 2 and MODIS and in situ data from the State of Utah Ambient Water Quality Management System (AQWMS) database to develop models and to generate a highly accessible, easy-to-use CSV file of chlorophyll-a (which is an indicator of algal biomass), turbidity, and water temperature measurements on Utah Lake. From a collection of 937 Sentinel 2 images spanning the period from January 2019 to May 2025, we generated 262,081 estimates each of chlorophyll-a and turbidity, with an additional 1,140,777 data points interpolated from those estimates to provide a dataset with a consistent time step. From a collection of 2333 MODIS images spanning the same time period, we extracted 1,390,800 measurements each of daytime water surface temperature and nighttime water surface temperature and interpolated or imputed an additional 12,058 data points from those estimates. We interpolated the data using piecewise cubic Hermite interpolation polynomials to preserve the original distribution of the data and provide the most accurate estimates of measurements between observations. We demonstrate the processing steps required to extract usable, accurate estimates of these three water quality parameters from satellite imagery and format them for analysis. We include summary statistics and charts for the resulting dataset, which show the usefulness of this data for informing Utah Lake management issues. We include the Jupyter Notebook with the implemented processing steps and the formatted CSV file of data as supplemental materials. The Jupyter Notebook can be used to update the Utah Lake data or can be easily modified to generate similar data for other waterbodies. We provide this method, tool set, and data to make remotely sensed water quality data more accessible to researchers, water managers, and others interested in Utah Lake and to facilitate the use of satellite data for those interested in applying remote sensing techniques to other waterbodies. Full article
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2023

Jump to: 2025

6 pages, 2139 KiB  
Data Descriptor
Long-Term Spatiotemporal Oceanographic Data from the Northeast Pacific Ocean: 1980–2022 Reconstruction Based on the Korea Oceanographic Data Center (KODC) Dataset
by Seong-Hyeon Kim and Hansoo Kim
Data 2023, 8(12), 175; https://doi.org/10.3390/data8120175 - 23 Nov 2023
Cited by 2 | Viewed by 2208
Abstract
The Korea Oceanographic Data Center (KODC), overseen by the National Institute of Fisheries Science (NIFS), is a pivotal hub for collecting, processing, and disseminating marine science data. By digitizing and subjecting observational data to rigorous quality control, the KODC ensures accurate information in [...] Read more.
The Korea Oceanographic Data Center (KODC), overseen by the National Institute of Fisheries Science (NIFS), is a pivotal hub for collecting, processing, and disseminating marine science data. By digitizing and subjecting observational data to rigorous quality control, the KODC ensures accurate information in line with international standards. The center actively engages in global partnerships and fosters marine data exchange. A wide array of marine information is provided through the KODC website, including observational metadata, coastal oceanographic data, real-time buoy records, and fishery environmental data. Coastal oceanographic observational data from 207 stations across various sea regions have been collected biannually since 1961. This dataset covers 14 standard water depths; includes essential parameters, such as temperature, salinity, nutrients, and pH; serves as the foundation for news, reports, and analyses by the NIFS; and is widely employed to study seasonal and regional marine variations, with researchers supplementing the limited data for comprehensive insights. The dataset offers information for each water depth at a 1 m interval over 1980–2022, facilitating research across disciplines. Data processing, including interpolation and quality control, is based on MATLAB. These data are classified by region and accessible online; hence, researchers can easily explore spatiotemporal trends in marine environments. Full article
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12 pages, 3179 KiB  
Data Descriptor
Reconstructed River Water Temperature Dataset for Western Canada 1980–2018
by Rajesh R. Shrestha and Jennifer C. Pesklevits
Data 2023, 8(3), 48; https://doi.org/10.3390/data8030048 - 26 Feb 2023
Cited by 6 | Viewed by 3220
Abstract
Continuous water temperature data are important for understanding historical variability and trends of river thermal regime, as well as impacts of warming climate on aquatic ecosystem health. We describe a reconstructed daily water temperature dataset that supplements sparse historical observations for 55 river [...] Read more.
Continuous water temperature data are important for understanding historical variability and trends of river thermal regime, as well as impacts of warming climate on aquatic ecosystem health. We describe a reconstructed daily water temperature dataset that supplements sparse historical observations for 55 river stations across western Canada. We employed the air2stream model for reconstructing water temperature dataset over the period 1980–2018, with air temperature and discharge data used as model inputs. The model was calibrated and validated by comparing with observed water temperature records, and the results indicate a reasonable statistical performance. We also present historical trends over the ice-free summer months from June to September using the reconstructed dataset, which indicate- significantly increasing water temperature trends for most stations. Besides trend analysis, the dataset could be used for various applications, such as calculation of heat fluxes, calibration/validation of process-based water temperature models, establishment of baseline condition for future climate projections, and assessment of impacts on ecosystems health and water quality. Full article
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Figure 1

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