Surface Properties Linked to Retrieval Uncertainty of Satellite Sea-Ice Thickness with Upward-Looking Sonar Measurements.

One of the key sources of uncertainties in sea ice freeboard and thickness estimates derived from satellite radar altimetry results from changes in sea ice surface properties. In this study, we analyse this effect, comparing upward-looking sonar (ULS) measurements in the Beaufort Sea over the period 2003–2018 to sea ice draft derived from Envisat and Cryosat-2 data. We show that the sea ice draft growth underestimation observed for the most of winter seasons depends on the surface properties preconditioned by the melt intensity during the preceding summer. The comparison of sea ice draft time series in the Cryosat-2 era indicates that applying 50% retracker thresholds, used to produce the European Space Agency’s Climate Change Initiative (CCI) product, provide better agreement between satellite retrievals and ULS data than the 80% threshold that is closer to the expected physical waveform interpretation. Our results, therefore, indicate compensating error contributions in the full end-to-end sea-ice thickness processing chain, which prevents the quantification of individual factors with sea-ice thickness/draft validation data alon

Analysis of CryoSat’s radar altimeter waveforms over different Arctic sea ice regimes

Satellite altimetry has been used to derive information about sea ice thickness in the Arctic already for several decades. As part of the algorithms applied the shape of the radar signal is used to identify leads, the open water between ice floes. Analysis of airborne altimeter data reveals that the waveform shape can additionally be used to identify different sea ice types. In this study we analyze signal waveforms from ESA’s CryoSat-2 satellite, to test the possibility of sea ice classification based on radar altimeter waveforms on an Arctic wide scale. We define six parameters to account for the difference in the shape of the radar waveforms obtained over First- and Multi-Year-Ice and find significant differences for several of these parameters. The Pulse Peakiness, Stack Standard Deviation and Leading Edge Width show the largest difference. These waveform parameters can thus be used to classify First- and Multi-Year-Ice over large areas of the Arctic Ocean. However, analyzing the spatial distribution we find some discrepancies compared to other retrievals of sea ice type. CryoSat waveform parameters have values typical for Multi-Year-Ice over large areas classified as First-Year-Ice. These areas are co-located with strong gradients in drift speed, indicating, that the radar signal is mainly sensitive to surface roughness. Potentially this information could be used to reduce biases in the freeboard retrievals and to improve estimates of sea ice thickness

An Advanced Algorithm to Retrieve Total Atmospheric Water Vapor Content From the Advanced Microwave Scanning Radiometer Data Over Sea Ice and Sea Water Surfaces in the Arctic

An advanced algorithm for atmospheric water vapor column (WVC) retrieval from the Advanced Microwave Scanning Radiometer (AMSR) measurements over the Arctic sea ice (SI) and open ocean waters is presented. The algorithm is built on the physical modeling of the brightness temperature (BT) of the microwave radiation of the SI-open ocean-atmosphere system at the AMSR frequencies and polarizations. The BTs are calculated using a data set of the SI, atmospheric, and oceanic parameters changing in the range of their natural variability in the Arctic, and using the SI microwave emission coefficients varied according to the published experimental data. The inverse operator explores neural networks (NNs), trained on an ensemble of modeled BTs. The algorithm is applied both to the AMSR-E and to the AMSR2 measurement data. Validation of the algorithm is performed with radiosonde (r/s) WVC measurements from the four Arctic coastal stations at different SI conditions during 2014-2017. The results of the application of the new algorithm to satellite radiometer measurements are also compared with the Era-Interim reanalysis WVC, as well as with other satellite WVC products, based on the data of the Moderate Resolution Imaging Spectrometer (MODIS) and on the data of the Advanced Microwave Sounding Unit-B (AMSU-B) for 2008 and 2015. To justify the usage of the Era-Interim WVC as a reference data set for the algorithm accuracy estimation in the Arctic area, Era-Interim WVC is also compared with the r/s WVC measurements