Interpretation of the waveform observed from satellite microwave radar altimeters is essential for the estimation of the thickness of snow and sea ice. Previous studies have relied on the radar equation to interpret backscattered signals from the snow-covered ice surface; however, that equation does not account for refraction at the snow surface, which changes the direction of the radar pulse. Therefore, this study derived a modified radar equation for a snow-covered sea ice surface that explicitly considers refraction. Compared to the ordinary equation, the modified equation produces different return powers and waveform shapes. Two primary mechanisms drive these differences: (1) changes in wavefront geometry, which reduce the return power by the square of the snow refractive index, and (2) decreased incidence angles at the ice surface, which amplify the return power with increasing distance from nadir. The combined effect resulted in a dampened and broadened waveform, which can influence the interpretation and retracking of the waveform. Therefore, it is recommended that this modified radar equation be implemented to update the existing waveform simulators.

The retrieval of sea ice concentration (SIC) using passive microwave satellite sensors has become a well-established field over several decades. Various algorithms employing slightly different approaches, such as utilizing diverse microwave satellite channels with the ability to combine frequencies and polarizations, have been tested in previous studies. This paper further explores the possibility of using the Radiative Transfer Model (RTM) to remove atmospheric effects on Brightness Temperature (TB) to improve SIC retrievals. This study specifically falls within the framework of the OSI SAF Near Real Time (NRT) SIC products, actively developed and maintained by the Danish Meteorological Institute (DMI). The findings indicated the feasibility of using the RTTOV package of NWP SAF for the atmospheric corrections. The RTTOV-based atmospheric correction could remove warm TB signals over the ocean due to atmospheric effects, resulting in the removal of false sea ice signatures without using empirical filters. The proposed atmospheric correction scheme is straightforward to adopt for the upcoming passive microwave sensors without intensive sensor-specific calibration (e.g., EUMETSAT’s MWI, JAXA’s AMSR-3, and ESA’s CIMR).

The thermal structure of the Arctic sea ice is a critical indicator in the atmosphere–sea ice–ocean energy budget and, thus, for understanding Arctic warming and associated climate change. Therefore, understanding this thermal structure and its monitoring should be vital. However, it is challenging to obtain a 3-D view of the thermal structure of the sea ice (such as the temperature profile) through satellite measurements because of the lack of understanding of the nonlinear relationship between sea ice emission and measured radiance at the top of the atmosphere. In this study, a model was developed to estimate the temperature profile within the Arctic sea ice during winter using satellite-borne passive microwave measurements. An artificial neural network (ANN) technique based on deep learning was introduced, and the nonlinear relationship between satellite-measured brightness temperatures and buoy-measured sea ice temperature profiles was learned. The ANN model was mapped and verified using the tenfold cross-validation technique. The developed ANN model was able to restore the sea ice temperatures at all specified levels with correlation coefficients > 0.95, absolute biases < 0.1 K, and root mean square errors < 1.6 K. The retrieved temperature results well represent expected thermal structures, in addition to the snow–sea ice interface temperature similar to that in the published literature. Besides the data for validating climate model simulations, the results also promise applications for improving the sea ice growth model performance by tightly constraining the vertical thermal structure in the sea ice growth model.

Information on snow depth on sea ice and bulk sea ice density is required to convert CryoSat-2 radar freeboard (hf) into sea ice thickness (SIT). It is difficult to obtain their information on an Arctic basin scale; therefore, most CryoSat-2 SIT products largely rely on the distributions of snow depth and bulk sea ice density derived from parameterizations, which are based on sea ice type and climatological values. Several observational studies have found that the distributions of parameterized variables are inaccurate compared to the actual distributions. This study aims to develop a new type of retrieval algorithm for snow depth, SIT and bulk density, and ice freeboard in the Arctic winter by synergizing active CryoSat-2 with passive microwave and infrared measurements. Two parameterizations for the snow-ice thickness ratio and bulk sea ice density were combined with the hydrostatic balance and radar wave speed correction equations. Consequently, solutions for the four target variables were obtained and applied to different CryoSat-2hf, derived from empirical and waveform-fitting retracker algorithms. The retrieved thickness-related parameters based onhffrom the lognormal waveform-fitting retracker algorithm showed good agreement with the airborne snow depth, total freeboard, and mooring ice draft measurements. The retrieved multiyear sea ice bulk density was significantly higher than the value of 882 kg m-3, which was used in the previous density parameterization, showing a higher agreement with values from in-situ measurements. The spatial and interannual variabilities of SIT increased when the results from this study were compared with those based on previous parameterizations.

Retrievals of sea ice thickness from passive microwave measurements have been limited to thin ice because microwaves penetrate at most the upper 50 cm of sea ice. To overcome such a limitation, a method of retrieving Arctic basin-scale ice thickness is developed. The physical background of this method is that the scattering optical thickness at microwave frequencies within the freeboard layer is linearly proportional to the physical thickness of the ice freeboard. In this study, we relate the optical thickness estimated from the Advanced Microwave Scanning Radiometer 2 (AMSR2) with ice freeboard estimated from the CryoSat-2 (CS2) by employing a piecewise linear fit. The results show a strong linear relationship between the AMSR2-estimated and CS2-measured ice freeboards with a correlation coefficient of 0.85 and bias and RMSE of 0.0001 and 0.04 m, respectively; this evidence suggests that the method can provide Arctic basin-scale ice freeboard with a comparable accuracy level of CS2. The method is also applied to estimate ice freeboard for the periods of the Scanning Multichannel Microwave Radiometer (SMMR) (1978-1987) and AMSR-E (2002-2011). It is shown that the area-averaged ice freeboard has decreased significantly with the linear trends of 1.5 cm/decade. In addition, there seems to be a change of ice freeboard distributions over the Arctic. Furthermore, the algorithm is extended to the ice thickness retrieval by using the hydrostatic balance equation, showing that operational basin-scale ice thickness retrieval will be possible from satellite passive microwave measurements if a realistic snow depth on sea ice is employed.