Potential of Passive Microwave around 183 GHz for Snowfall Detection in the Arctic

This study evaluates the potential use of the Microwave Humidity Sounder (MHS) for snowfall detection in the Arctic. Using two years of colocated MHS and CloudSat observations, we develop an algorithm that is able to detect up to 90% of the most intense snowfall events (snow water path ≥400 g m −2 a...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Léo Edel, Jean-François Rysman, Chantal Claud, Cyril Palerme, Christophe Genthon
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2019
Subjects:
snowfall
Arctic
passive microwaves
CloudSat
machine learning
Science
Q
Greenland
Online Access:https://doi.org/10.3390/rs11192200
https://doaj.org/article/ff2b82ecabe94949bfddaefb56869023
Description
Summary:This study evaluates the potential use of the Microwave Humidity Sounder (MHS) for snowfall detection in the Arctic. Using two years of colocated MHS and CloudSat observations, we develop an algorithm that is able to detect up to 90% of the most intense snowfall events (snow water path ≥400 g m −2 and 50% of the weak snowfall rate events (snow water path ≤50 g m −2 . The brightness temperatures at 190.3 GHz and 183.3 ± 3 GHz, the integrated water vapor, and the temperature at 2 m are identified as the most important variables for snowfall detection. The algorithm tends to underestimate the snowfall occurrence over Greenland and mountainous areas (by as much as −30%), likely due to the dryness of these areas, and to overestimate the snowfall occurrence over the northern part of the Atlantic (by up to 30%), likely due to the occurrence of mixed phase precipitation. An interpretation of the selection of the variables and their importance provides a better understanding of the snowfall detection algorithm. This work lays the foundation for the development of a snowfall rate quantification algorithm.

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