Atmospheric Humidity Estimation From Wind Profiler Radar Using a Cascaded Machine Learning Approach

Abstract

A method for estimating atmospheric relative humidity using wind profiler radar and a “cascaded” machine learning algorithm is introduced. Unlike existing methods in the literature, the proposed approach uses only I/Q or moment data from the profiler radar to generate an intermediate pressure profile, which serves as training data for humidity estimations without requiring temperature as an input feature. The study examines the potential of various machine learning algorithms and evaluates their performance using field data collected by the Hong Kong Observatory between January and June 2021. Importantly, this is the first time a cascading machine-learning solution has been successfully applied to the humidity estimation problem, resulting in a simplified model with reduced complexity and fewer required features.

Keywords

• Decision tree
• ensemble tree
• machine learning (ML)
• neural network (NN)
• profiler radar
• relative humidity (RH)