AI-assisted analysis of temperature and precipitation trends in climate studies

Abstract:

Climate change has impacted on global temperature and rain distributions drastically, causing a rise in climate variability, weather extremes as well as environmental instability. The nonlinear interactions and long-term dependencies within climate systems are usually difficult to represent traditional statistical and physical climate models. The applications of recent improvements in artificial intelligence (AI) and the combination of large-scale climatic data provided by radiometric techniques and remote sensing and ground-based monitoring have provided new opportunities to compete in analyzing the trends in temperatures and precipitation with higher complexity and precision. This paper examines how AI methods, such as machine learning and deep learning algorithms, can be used to identify patterns, anomalies and long-term trends in climate data. The processing and analysis of the methodology involves multi-source datasets of satellite-derived temperatures, rainfalls, and atmospheric variables, that are processed and analyzed with the help of such advanced AI models like convolutional neural networks (CNNs), recurrent neural networks (RNNs) and hybrid architectures. Findings show that the use of AI-supported methods is much more effective in identifying temporal trends, spatial changes, and extreme climate events than the conventional techniques. Nonetheless, issues like data inconsistency, interpretability of the models, and computation needs are still pertinent factors. The research identifies the opportunities of AI-based climate analytics in aiding climate policy, environmental planning, and sustainable development plans.