An Appraisal of the Progress in Utilizing Radiosondes and Satellites for Monitoring Upper Air Temperature Profiles

Author:

Mashao Frederick M.12ORCID,Demoz Belay3ORCID,Kifle Yehenew4ORCID,Klopper Danitza1,Chikoore Hector12ORCID,Sakai Ricardo K.5,Ayisi Kingsley K.2

Affiliation:

1. Department of Geography and Environmental Studies, University of Limpopo, Sovenga 0727, South Africa

2. Risk and Vulnerability Science Center, University of Limpopo, Sovenga 0727, South Africa

3. Physics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA

4. Department of Math and Statistics, University of Maryland, Baltimore County, Baltimore, MD 21250, USA

5. Program in Atmospheric Sciences, Howard University, Beltsville, MD 20705, USA

Abstract

Upper air temperature measurements are critical for understanding weather patterns, boundary-layer processes, climate change, and the validation of space-based observations. However, there have been growing concerns over data discrepancies, the lack of homogeneity, biases, and discontinuities associated with historical climate data records obtained using these technologies. Consequently, this article reviews the progress of utilizing radiosondes and space-based instruments for obtaining upper air temperature records. A systematic review process was performed and focused on papers published between 2000 and 2023. A total of 74,899 publications were retrieved from the Google Scholar, Scopus, and Web of Science databases using a title/abstract/keyword search query. After rigorous screening processes using relevant keywords and the elimination of duplicates, only 599 papers were considered. The papers were subjected to thematic and bibliometric analysis to comprehensively outline the progress, gaps, challenges, and opportunities related to the utilization of radiosonde and space-based instruments for monitoring upper air temperature. The results show that in situ radiosonde measurements and satellite sensors have improved significantly over the past few decades. Recent advances in the bias, uncertainty, and homogeneity correction algorithms (e.g., machine learning approaches) for enhancing upper air temperature observations present great potential in improving numerical weather forecasting, atmospheric boundary studies, satellite data validation, and climate change research.

Funder

National Research Foundation

Publisher

MDPI AG

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