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Explore the critical role of data pre-processing in developing AI models for meteorological applications in this conference talk by Bipin Kumar from the Indian Institute of Tropical Meteorology. Learn how proper data preparation techniques are essential for building accurate and reliable artificial intelligence systems that can effectively analyze and predict weather patterns and atmospheric phenomena. Discover the specific challenges and considerations involved in preparing meteorological datasets, including data cleaning, normalization, feature engineering, and handling missing values in weather data. Understand how different pre-processing approaches can significantly impact the performance of machine learning models used in weather forecasting, climate modeling, and other atmospheric science applications. Gain insights into best practices for transforming raw meteorological observations into formats suitable for various AI algorithms, and examine real-world examples of how data pre-processing decisions affect model accuracy and reliability in operational meteorological systems.
