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Global Warming In Ghana's Major Cities Based on Statistical Analysis of NASA's POWER Over 3-Decades

arXiv 2308.10909 ↗First posted 20 Aug 2023physics.ao-ph cs.LGcited by 0

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Abstract (arXiv)

Global warming's impact on high temperatures in various parts of the world has raised concerns. This study investigates long-term temperature trends in four major Ghanaian cities representing distinct climatic zones. Using NASA's Prediction of Worldwide Energy Resource (POWER) data, statistical analyses assess local climate warming and its implications. Linear regression trend analysis and eXtreme Gradient Boosting (XGBoost) machine learning predict temperature variations. Land Surface Temperature (LST) profile maps generated from the RSLab platform enhance accuracy. Results reveal local warming trends, particularly in industrialized Accra. Demographic factors aren't significant. XGBoost model's low Root Mean Square Error (RMSE) scores demonstrate effectiveness in capturing temperature patterns. Wa unexpectedly has the highest mean temperature. Estimated mean temperatures for mid-2023 are: Accra 27.86degC, Kumasi 27.15degC, Kete-Krachi 29.39degC, and Wa 30.76degC. These findings improve understanding of local climate warming for policymakers and communities, aiding climate change strategies.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 46 references that are in the map. First posted on arXiv 20 Aug 2023.

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References · 3

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2022Why do tree-based models still outperform deep learning on tabular data?cited by 199
  3. 2018Using the Jupyter Notebook as a Tool for Open Science: An Empirical Studycited by 9

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