Unraveling atmosphere and sea ice in the Arctic : advancements in a multi-parameter retrieval using satellite microwave radiometer data

The Arctic undergoes accelerated warming compared to Global Warming, known as Arctic amplification. To understand this phenomenon, studying key variables at various scales is crucial. Every day, satellite radiometers measure Arctic-wide emissions of microwave radiation in terms of brightness temperatures. We use the distinct sensitivities of observations at different frequencies to atmospheric and surface parameters, aiming to disentangle the satellite signal and improve a multi-parameter retrieval. The retrieval involves inverting a forward model with an optimal estimation method to attribute satellite measurements (6.9 to 89 GHz) to a specific geophysical state.
For that, surface emissions need to be well represented in the forward model.
We study surface emissions by considering the theoretical concept of emissivity followed by an analysis of brightness temperature measurements and derived emissivities that we obtained during a summer ship campaign.
In a case study, we examine the impact of changing surface emissions caused by warm air intrusions on sea ice concentration (SIC) satellite retrievals.
We improve the multi-parameter retrieval by a better representation of the sea ice and snow emissions in the forward model for non-melting conditions. Both forward model and retrieval are evaluated against ground truth, including MOSAiC expedition data. The forward model succeeds in simulating realistic brightness temperatures. The retrieval output agrees well with reference data with regard to SIC. While the retrieval of cloud liquid water path and snow depth are promising, some disagreements are identified. Focusing on atmospheric total water vapor, our analysis demonstrates a good agreement with numerous reference datasets. We find a substantial improvement over the previous version of the method.
After applying the retrieval to satellite data from the last two decades we further analyze the spatio-temporal variability of atmospheric water vapor in winter.