A Novel Approach for Improving Cloud Liquid Water Content Profiling with Machine Learning

Abstract

Accurate prediction of Cloud Liquid Water Content (CLWC) is critical for understanding and forecasting weather phenomena, particularly in regions with complex microclimates. This study integrates high-resolution ERA5 climatic data from the European Centre for Medium-Range Weather Forecasts (ECMWF) with radiosonde observations from the Hong Kong area to address data accuracy and resolution challenges. Machine learning (ML) models—specifically Fine Tree regressors—were employed to interpolate radiosonde data, resolving temporal and spatial discrepancies and enhancing data coverage. A metaheuristic algorithm was also applied for data cleansing, significantly improving correlations between input features (temperature, pressure, and humidity) and CLWC. The methodology was tested across multiple ML algorithms, with ensemble models such as Bagged Trees demonstrating superior predictive accuracy and robustness. The approach substantially improved CLWC profile reliability, outperforming traditional methods and addressing the nonlinear complexities of atmospheric data. Designed for scalability, this methodology extends beyond Hong Kong’s unique conditions, offering a flexible framework for improving weather prediction models globally. By advancing CLWC estimation techniques, this work contributes to enhanced weather forecasting and atmospheric science in diverse climatic regions.