Sea Ice Concentration and thin Sea Ice Thickness in the Arctic retrieved with different configurations of an Optimal Estimation Method ...

Research on improving the prediction skill of climate models requires refining the quality of observational data used for initializing and tuning the models. This is especially true in the Polar Regions where uncertainties about the interactions between sea ice, ocean and atmosphere are driving ongo...

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Bibliographic Details
Main Authors: Scarlat, Raul Cristian, Huntemann, Marcus, Paţilea, Cătălin
Format: Dataset
Language:English
Published: PANGAEA 2020
Subjects:
AMSR2
CIMR
sea ice concentration
Sea ice thickness
Time coverage
File content
File format
File size
Uniform resource locator/link to file
Arctic
Sea ice
Online Access:https://dx.doi.org/10.1594/pangaea.912748
https://doi.pangaea.de/10.1594/PANGAEA.912748
Description
Summary:Research on improving the prediction skill of climate models requires refining the quality of observational data used for initializing and tuning the models. This is especially true in the Polar Regions where uncertainties about the interactions between sea ice, ocean and atmosphere are driving ongoing monitoring efforts.The Copernicus Imaging Microwave Radiometer (CIMR) is an European Space Agency (ESA) candidate mission which promises to offer high resolution, low uncertainty observation capabilities at the 1.4, 6.9,10.65,18.7 and 36.5 GHz frequencies. To assess the potential impact of CIMR for sea ice parameter retrieval, a comparison is made between retrievals based on present AMSR2 observations and a retrieval using future CIMR equivalent observations over a data set of validated sea ice concentration (SIC) values. An optimal estimation retrieval method (OEM) is used which can use input from different channel combinations to retrieve seven geophysical parameters (sea ice concentration, multi year ice ...

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