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Reseach Article

Leveraging AI and Analytics in Climate Science: Enhancing Predictions and Sustainability Practices

by Anshumali Ambasht
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 24
Year of Publication: 2024
Authors: Anshumali Ambasht
10.5120/ijca2024923694

Anshumali Ambasht . Leveraging AI and Analytics in Climate Science: Enhancing Predictions and Sustainability Practices. International Journal of Computer Applications. 186, 24 ( Jun 2024), 10-16. DOI=10.5120/ijca2024923694

@article{ 10.5120/ijca2024923694,
author = { Anshumali Ambasht },
title = { Leveraging AI and Analytics in Climate Science: Enhancing Predictions and Sustainability Practices },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2024 },
volume = { 186 },
number = { 24 },
month = { Jun },
year = { 2024 },
issn = { 0975-8887 },
pages = { 10-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number24/leveraging-ai-and-analytics-in-climate-science-enhancing-predictions-and-sustainability-practices/ },
doi = { 10.5120/ijca2024923694 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-06-27T00:56:26+05:30
%A Anshumali Ambasht
%T Leveraging AI and Analytics in Climate Science: Enhancing Predictions and Sustainability Practices
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 24
%P 10-16
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper explores the transformative potential of artificial intelligence (AI) and data analytics in climate science, specifically their roles in improving weather pattern predictions, assessing the impacts of climate change, and enhancing sustainability practices. By integrating advanced computational models, big data, and machine learning techniques, researchers and policymakers can gain deeper insights into climate dynamics, optimize resource management, and develop effective strategies to mitigate environmental degradation. The outcomes discussed in this research highlight the significant improvements in predictive accuracy and operational efficiency, underscoring the critical role of AI and analytics in addressing global climate challenges.

References
  1. Taylor, K.E., Stouffer, R.J., and Meehl, G.A. 2012. An Overview of CMIP5 and the Experiment Design. Bulletin of the American Meteorological Society, 93, 485-498.
  2. Lorenz, R., Herger, N., Sedláček, J., Eyring, V., Fischer, E.M., and Knutti, R. 2016. Prospects and Caveats of Weighting Climate Models for Summer Maximum Temperature Projections Over North America. Journal of Geophysical Research: Atmospheres, 121, 10, 5518-5538.
  3. Palmer, T.N., and Räisänen, J. 2002. Quantifying the Risk of Extreme Seasonal Precipitation Events in a Changing Climate. Nature, 415, 512-514.
  4. Hunter, A., Stephenson, D.B., and Mearns, L.O. 2012. Representation of Climate Extremes in the Community Earth System Model. Environmental Research Letters, 7, 015130.
  5. Jones, C. 1998. Climate Model Simulation of Winter Warming and Summer Cooling Following the 1991 Mount Pinatubo Volcanic Eruption. Journal of Geophysical Research, 103, 13, 837-857.
  6. Williams, P.D., and Joshi, M. 2013. Intensification of Winter Transatlantic Aviation Turbulence in Response to Climate Change. Nature Climate Change, 3, 644-648.
  7. Thompson, D.W.J., and Wallace, J.M. 1998. The Arctic Oscillation Signature in the Wintertime Geopotential Height and Temperature Fields. Geophysical Research Letters, 25, 9, 1297-1300.
  8. Franklin, J., and Youssef, A.M. 2017. Data Assimilation Methods in the Earth Sciences. Advances in Data Science and Adaptive Analysis, 9, 1750004.
  9. Li, G., and Xie, S.P. 2014. Tropical Biases in CMIP5 Multi-Model Ensemble: The Excessive Equatorial Pacific Cold Tongue and Double ITCZ Problems. Journal of Climate, 27, 1765-1780
Index Terms

Computer Science
Information Sciences
Climate Change
Sustainability
Environmental Impact
Weather Forecasting

Keywords

Machine Learning Deep Learning Climate Modeling Algorithm Bias Analytics Artificial Intelligence