Estimating the snow depth, the snow-ice interface temperature, and the effective temperature of Arctic sea ice using Advanced Microwave Scanning Radiometer 2 and Ice Mass Balance buoys data

Abstract

Abstract. Mapping Sea Ice Concentration (SIC) and understanding sea ice properties and variability is important especially today with the recent Arctic sea ice decline. Moreover, accurate estimation of the sea ice effective temperature (Teff) at 50 GHz is needed for atmospheric sounding applications over sea ice and for noise reduction in SIC estimates. At low microwave frequencies, the sensitivity to atmosphere is low, and it is possible to derive sea ice parameters due to the penetration of microwaves in the snow and ice layers. In this study, we propose simple algorithms to derive the snow depth, the snow-ice interface temperature (TSnow-Ice) and the Teff of Arctic sea ice from microwave brightness temperatures (TBs). This is achieved using the Round Robin Data Package of the ESA sea ice CCI project, which contains TBs from the Advanced Microwave Scanning Radiometer 2 (AMSR2) collocated with measurements from Ice Mass Balance (IMB) buoys and the NASA Operation Ice Bridge (OIB) airborne campaigns over the Arctic sea ice. The snow depth over sea ice is estimated with an error of ~ 6 cm using a multilinear regression with the TBs at 6 V, 18 V, and 36 V. The TSnow-Ice is retrieved using a linear regression as a function of the snow depth and the TBs at 10 V or 6 V. The Root Mean Square Errors (RMSEs) obtained are 1.69 and 1.95 K respectively, with the 10 V and 6 V TBs. The Teff at microwave frequencies between 6 and 89 GHz is expressed as a function of TSnow-Ice using data from a thermodynamical model combined with the Microwave Emission Model of Layered Snow-packs. Teffs are estimated from the TSnow-Ice with a RMSE of less than 1 K.

Estimating Snow Depth on Arctic Sea Ice using Satellite Microwave Radiometry and a Neural Network

“…In this section we measure the performance of our neural networks and compare the results to the algorithms proposed by Markus and Cavalieri (1998), Rostosky et al (2018) and Kilic et al (2018b). For this evaluation we employ the test data part of the OIB snow depth measurements.…”

“…2). Kilic et al Kilic et al (2018b) developed a simple multilinear regression approach using vertically polarised brightness temperatures at 6.9 GHz, 18.7 GHz and 36.5 GHz. These three channels were identified as the best predictor combination in a forward selection method with the OIB data of 2013.…”

“…More recently, Kilic et al (2018b) make use of the low frequency 6.9 GHz channel, but instead of using the gradient ratio, they fit a multilinear regression between microwave radiometer data and snow depth using data from Ice Mass Balance (IMB) buoys in the Arctic.…”

Estimating snow depth on Arctic sea ice based on reanalysis reconstruction and particle filter assimilation

“…Finally, the brightness temperature, sea ice type and NSIDC snow depth data are resampled to 50 km × 50 km. Kilic et al (2019) developed a multilinear regression approach for snow depth estimation based on four IMB buoys, i.e., 2012G, 2012H, 2012J and 2012L. The multilinear regression relationship between the vertically polarized brightness temperatures of AMSR2 (7, 19 and 37 GHz) and the IMB-measured snow depth was established, and Eq.…”

“…Rostosky et al (2018) successfully extended the snow depth model to determine the snow depth atop FYI and multiyear ice (MYI) with the GRV (19/7) indicator. In contrast to snow depth models relying on linear regression analysis, Kilic et al (2019) obtained the snow depth atop FYI and MYI via multiple linear regression analysis.…”