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WeatherSense AI
AI-powered meteorological forecasting and atmospheric anomaly detection model.
TECHNOLOGY STACK
PythonPandasScikit-LearnNumPyMatplotlib
THE CHALLENGE
Traditional physical atmospheric models require high-latency supercomputing cycles, making short-term localized weather predictions resource-intensive for lightweight environments.
METHODOLOGY & ARCHITECTURE
Designed a regression and time-series feature engineering pipeline extracting atmospheric pressure differentials, temperature gradients, and relative humidity trends.
MEASURABLE RESULTS & FINDINGS
Achieved an 89.2% forecast accuracy across short-term precipitation events with lower root-mean-square error (RMSE) compared to standard baseline persistence models.