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 an...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2019-10, Vol.11 (19), p.2200
Main Authors: Edel, Léo, Rysman, Jean-François, Claud, Chantal, Palerme, Cyril, Genthon, Christophe
Format: Article
Language:English
Subjects:
Algorithms
Altitude
Arctic
Atmosphere
Brightness temperature
Climate change
Clouds
CloudSat
Cold
Decision trees
Environmental conditions
Environmental Sciences
Humidity
Hydrology
machine learning
Mountain regions
Mountainous areas
passive microwaves
Precipitation
Remote sensing
Satellites
Sciences of the Universe
Snow
Snowfall
Temperature
Water vapor
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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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs11192200