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The classification of weather data involves categorizing meteorologicalphenomena into classes, thereby facilitating nuanced analyses and precisepredictions for various sectors such as agriculture, aviation, and disastermanagement. This involves utilizing machine learning models to analyze large,multidimensional weather datasets for patterns and trends. These datasets mayinclude variables such as temperature, humidity, wind speed, and pressure,contributing to meteorological conditions. Furthermore, it's imperative thatclassification algorithms proficiently navigate challenges such as dataimbalances, where certain weather events (e.g., storms or extreme temperatures)might be underrepresented. This empirical study explores data augmentationmethods to address imbalanced classes in tabular weather data in centralizedand federated settings. Employing data augmentation techniques such as theSynthetic Minority Over-sampling Technique or Generative Adversarial Networkscan improve the model's accuracy in classifying rare but critical weatherevents. Moreover, with advancements in federated learning, machine learningmodels can be trained across decentralized databases, ensuring privacy and dataintegrity while mitigating the need for centralized data storage andprocessing. Thus, the classification of weather data stands as a criticalbridge, linking raw meteorological data to actionable insights, enhancing ourcapacity to anticipate and prepare for diverse weather conditions.

A Distributed Approach to Meteorological Predictions: Addressing Data  Imbalance in Precipitation Prediction Models through Federated Learning and  GANs