Machine Learning for Advancing Spaceborne Passive Microwave Remote Sensing of Snowfall

Abstract

Falling snow is one of the key elements of the water and energy cycle that occurs in response to a complex cascade of macro and microphysical processes. While an accurate observation of spatiotemporal variability of snowfall is lacking due to the sparse network of ground-based gauges and their intrinsic challenges, remote sensing from spaceborne satellites has provided a global picture of snowfall through near-global observations. Bayesian passive microwave (PMW) retrievals of snowfall have been developed to detect precipitation phase and retrieve its rate using coincident data from the active and passive sensors onboard the CloudSat and the Global Precipitation Measurement (GPM) satellites. These Bayesian techniques often rely on mathematical matching of the observed vectors of brightness temperature (TB) with an a priori database of precipitation profiles and their corresponding TBs. In the present dissertation, we analyzed the effects of surface and atmospheric state variables on the PMW retrievals through Silhouette Coefficient (SC) analysis. The Neyman-Pearson (NP) hypothesis testing is employed to improve these retrievals by conditioning them to the associated physical variables that affect the PMW signatures including the cloud total liquid (LWP) and ice water path (IWP). The presented approach determines thresholds for IWP and LWP that enable identification of non-snowing and snowing clouds, which can mislead the retrieval algorithms to falsely detect or miss the snowfall events. Inspired by advances in deep learning approaches, a dense and deep neural network architecture is proposed. The presented framework first detects the precipitation occurrence and its phase, and then estimates its intensity using key physical variables including those capturing cloud microphysical properties. The results suggest the proposed framework could effectively reduce the uncertainties in the retrievals and improve their accuracy compared to the existing reanalysis data and official GPM products.