Ground-based Assessment of Snowfall Detection over Land Using Polarimetric High Frequency Microwave Measurements

Cezar Kongoli,Jun Dong,Ralph Ferraro,Huan Meng

doi:10.3390/rs12203441

Abstract

This paper explores the capability of high frequency microwave measurements at vertical and horizontal polarizations in detecting snowfall over land. Surface in-situ meteorological data were collected over Conterminous US during two winter seasons in 2014–2015 and 2015–2016. Statistical analysis of the in-situ data, matched with Global Precipitation Measurement (GPM) Microwave Imager (GMI) measurements on board NASA/JAXA Core Observatory, showed that the polarization difference at 166 GHz had the highest correlation to measured snowfall rate compared to the single channel high frequency measurements and the polarization difference at 89 GHz. A logistic regression model applied to the match-up data, using the polarization difference at 166 and 89 GHz as predictors, yielded an overall snowfall classification rate of 69.0%, with the largest contribution coming from the polarization difference at 166 GHz. Logistic regression using the four single channels as predictors (at 89 and 166 GHz, horizontal and vertical polarizations) further indicated that the horizontal polarization at 166 GHz was the most important contributor. An overall classification rate of 73% was achieved by including the 183.31 ± 3 GHz and 183.31 ± 7 GHz vertical polarization channels in the final logistic regression model. Evaluation of the final algorithm demonstrated skill in snowfall detection of two significant events.

Highlights

IntroductionNumerous investigations carried out over the last two decades have demonstrated the snowfall detection capability of high frequency passive microwave measurements in the water vapor absorption bands [1,2,3,4,5,6,7,8,9]
Satellite snowfall estimation continues to receive considerable attention
The snowfall detection capability of GMI high frequency measurements and the polarization difference at 89 and 166 GHz was explored using logistic regression technique to model the probability of snowfall

Summary

Introduction

Numerous investigations carried out over the last two decades have demonstrated the snowfall detection capability of high frequency passive microwave measurements in the water vapor absorption bands [1,2,3,4,5,6,7,8,9] They have brought new light on the physical mechanisms contributing to the retrieval of atmospheric snowfall against the background of non-precipitating clouds and the land surface. Previous studies [10,11,12] discovered that in addition to scattering, precipitating clouds exhibit an emissive behavior, i.e., the brightness temperature increases compared to the background (non-precipitating clouds or cloud free scenes) and that this response occurs more frequently in colder weather Based on this finding, they extended snowfall detection to colder snowfall conditions (down to −15.0 ◦C) from the Advanced Technology Microwave Sounder (ATMS) high frequency measurements by application of a two-regime logistic regression algorithm

Methods

Results

Conclusion