Towards a Machine Learning Snowfall Retrieval Algorithm for GPM-IMERG

Abstract

Remote sensing of snowfall has been proved to be a great challenge since the start of the satellite era. Several techniques have been applied to satellite data to estimate the fraction of frozen precipitation that reaches the surface. This study aims at investigating the efficacy of machine learning (ML), and especially deep learning (DL), in estimating the precipitation phase of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM-IMERG). To achieve this, a training phase with hourly high-resolution numerical model outputs and in situ data was chosen for the period of late-2020 and 2021. Preliminary results show that ML models can estimate the precipitation phase with relatively high accuracy based on several case studies. The findings suggest that ML models offer a promising approach for advancing the nowcasting of snowfall and building a long-term archive dataset of IMERG-based snowfall using conventional real-time data.

Keywords

• snowfall
• precipitation phase
• GPM IMERG
• machine learning
• deep learning
• neural networks