Enhancing Weather Pattern Recognition through Multi-Stage Sensor Data Fusion and Smoothing Regression

Abstract

It is often harder to make accurate predictions with large, various intelligent sensor systems because of data noise and the high cost of traditional modeling methods like Numerical Weather Prediction (NWP). This paper presents a comprehensive framework that integrates multi-stage sensor data fusion and preprocessing with machine-learning-based pattern recognition to produce highly precise, computationally efficient analog weather forecasts. Our method uses a strict three-stage pipeline to clean up and combine raw sensor inputs. It uses historical data from a distributed network of 79 NOAA weather stations. There are three phases: filling in missing values, addressing outliers, and smoothing moving averages. Subsequently, a decision tree model is used to identify similar weather patterns and predict critical weather variables. Our method is effective because it makes predictions that are much more likely to be accurate than incorrect. The Root Mean Squared Error (RMSE) declined by over 99%, from 0.1715 to an amazing 0.0011014. The results show that combining targeted sensor data preprocessing with complex pattern recognition is a suitable methodology for making smart weather forecasting systems work. Rather than serving as a replacement for physics-based NWP models, the proposed approach should be regarded as a lightweight complementary tool for localized, short-term analog weather pattern recognition.