Publication Date:
2021-10-15
Description:
Within a rapidly changing Arctic climate system, snow on sea ice is an important climate parameter. A common method to derive snow depth on an Arctic-wide scale is based on passive microwave satellite observations. However, the uncertainties of this method are not well constrained. In this study, we estimate the influence of geophysical parameters, including ice, snow, and atmospheric properties on passive microwave snow depth retrievals using a Monte Carlo uncertainty estimation. The results are based on model simulations from the Microwave Emission Model for Layered Snowpacks, the SNOWPACK model, and from the Passive and Active Microwave TRAnsfer model. All simulations are based on in situ observations obtained during the N-ICE2015 campaign. The average uncertainty in potential snow depth retrievals is between 11% and 19%, depending on the microwave frequencies used and increases with increasing snow depth. For lower-frequency retrievals (including 6.9 GHz), unknown snow properties are the strongest source of uncertainty while for higher-frequency retrievals (including 36.5 GHz), the contribution of ice, snow properties, and clouds is equally strong.
Keywords:
551.5
;
snow
;
remote sensing
;
modeling
;
Arctic
Language:
English
Type:
map
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