Arctic sea ice and snow affect the energy balance of the global climate system through the radiation budget. Accurate determination of the snow cover over Arctic sea ice is significant for the retrieval of the sea ice thickness (SIT). In this study, we developed a new snow depth retrieval method over Arctic sea ice with a long short-term memory (LSTM) deep learning algorithm based on Operation IceBridge (OIB) snow depth data and brightness temperature data of AMSR-2 passive microwave radiometers. We compared climatology products (modified W99 and AWI), altimeter products (Kwok) and microwave radiometer products (Bremen, Neural Network and LSTM). The climatology products and altimeter products are completely independent of the OIB data used for training, while microwave radiometer products are not completely independent of the OIB data. We also compared the SITs retrieved from the above different snow depth products based on Cryosat-2 radar altimeter data. First, the snow depth spatial patterns for all products are in broad agreement, but the temporal evolution patterns are distinct. Snow products of microwave radiometers, such as Bremen, Neural Network and LSTM snow depth products, show thicker snow in early winter with respect to the climatology snow depth products and the altimeter snow depth product, especially in the multiyear ice (MYI) region. In addition, the differences in all snow depth products are relatively large in the early winter and relatively small in spring. Compared with the OIB and IceBird observation data (April 2019), the snow depth retrieved by the LSTM algorithm is better than that retrieved by the other algorithms in terms of accuracy, with a correlation of 0.55 (0.90), a root mean square error (RMSE) of 0.06 m (0.05 m) and a mean absolute error (MAE) of 0.05 m (0.04 m). The spatial pattern and seasonal variation of the SITs retrieved from different snow depths are basically consistent. The total sea ice decreases first and then thickens as the seasons change. Compared with the OIB SIT in April 2019, the SIT retrieved by the LSTM snow depth is superior to that retrieved by the other SIT products in terms of accuracy, with the highest correlation of 0.46, the lowest RMSE of 0.59 m and the lowest MAE of 0.44 m. In general, it is promising to retrieve Arctic snow depth using the LSTM algorithm, but the retrieval of snow depth over MYI still needs to be verified with more measured data, especially in early winter.

Abstract. As a kind of one-dimensional nanostructure, silicon nanowires have a very wide range of applications in photovoltaic devices, electrical devices, and biosensors. The morphology control of nanowire arrays greatly affects the performance of nanowire arrays. In this paper, we introduce the light-assisted MACE method to prepare nanowire arrays. Through a large number of experiments, the effects of different light intensities and light wavelengths on the morphology of the formed silicon nanowire arrays are analyzed. At the same time, the degree of aggregation of nanowires is taken as the characterization parameter of the morphological characteristics, the change rule of clustering density and the change rule of the etching rate under different illumination power and wavelength conditions are qualitatively analyzed. Finally, we make a preliminary exploration and analysis of the reasons for this appearance.

Abstract. In the context of global warming, sea ice changes have received increasing attention as "indicators" and "amplifiers" of climate change. With the development of satellite altimeters, satellite altimeter technologies have been increasingly used to retrieve Arctic sea ice thicknesses and have achieved rapid development and application. At present, the CryoSat-2 radar altimeter and Ice, Cloud and land Elevation Satellite-2 (ICESat-2) laser altimeter are the main data sources used in Arctic sea ice thickness retrievals. With the continuous development of the China Ocean Dynamic Environment Satellite Series (HY-2), it is of great significance to explore the potential application of this dataset in Arctic sea ice thickness retrievals. In this study, we first estimated the Arctic radar freeboard and sea ice thickness values during two sea ice growing cycles (from October 2019 to April 2020 and from October 2020 to April 2021) using the China HY-2B radar altimeter and then compared the results with the Alfred Wegener Institute (AWI) CryoSat-2 sea ice freeboard and sea ice thickness products recorded during the same period. The accuracies of the HY-2B radar freeboard and sea ice thickness were then verified with the Operation IceBridge (OIB) airborne data and ICESat-2 laser altimeter data, and the random uncertainties in the HY-2B sea ice freeboard and sea ice thickness results were finally estimated. Although the spatial distributions of the HY-2B radar freeboard and sea ice thickness results agreed well with those of AWI CryoSat-2, the deviation between the HY-2B radar freeboard and CryoSat-2 radar freeboard data was within 2 cm, while the deviation between the HY-2B sea ice thickness data and CryoSat-2 sea ice thickness data was within 0.2 m. In addition, the growth trends of the HY-2B radar freeboard and sea ice thickness were slower than those of AWI CryoSat-2. This finding was related to the applied sea surface height anomaly (SSHA) extraction method. Comparisons with the OIB sea ice freeboard and sea ice thickness values recorded in April 2019 showed that the correlation between the HY-2B sea ice freeboard retrievals and OIB sea ice freeboard data was 0.58, the root mean square error (RMSE) was 0.17 m, and the mean absolute error (MAE) was 0.14 m. The correlation between the HY-2B sea ice thickness retrieval and OIB sea ice thickness data was 0.41, the RMSE was 2.05 m, and the MAE was 1.91 m. Based on the Gaussian error propagation theory, we estimated the uncertainties of the HY-2B sea ice freeboard and sea ice thickness data: the uncertainty of the former ranged from 8.5 cm to 12.0 cm, while the uncertainty of the latter ranged from 26.8 cm to 37.7 cm. Due to the influence of the SSHA uncertainty (σSSA) and the number of observation points inside the grid, the uncertainties in the HY-2B sea ice freeboard and sea ice thickness data were higher at low latitudes than at high latitudes.