DNN-Based Retrieval of Arctic Sea Ice Concentration From GNSS-R and Its Effects on the Synoptic-Scale Forecasting as Supplementary Observation Source

Using delay-Doppler maps of Global Navigation Satellite Systems Reflectometry (GNSS-R) from the TechDemoSat-1 satellite and considering sea ice and ocean interaction, an innovative method for retrieval of Arctic sea ice concentration (SIC) based on a deep neural network is proposed. This retrieval method shows the potential of future GNSS-R applications for Arctic missions. Compared with SIC products from Hamburg University, the root mean square errors (RMSE) of retrieved results in March and June 2016 are 0.0284 and 0.0415, respectively. When the retrieved GNSS-R SIC data are added into the assimilation as supplementary passive microwave remote-sensing data, it has a positive influence on improving the accuracy of the Arctic SIC forecast. Especially in some edge regions of sea ice, when compared to only assimilating the remote-sensing data, the regional RMSE of joint assimilation has a maximum decrease of approximately 17% in the 24-hr forecast time, and over 5% in 72-hr.

Automated summary

Using delay-Doppler maps of GNSS-R from TechDemoSat-1 and considering sea ice and ocean interaction, a deep neural network based method for retrieving Arctic sea ice concentration (SIC) is proposed. The retrieval method shows potential for future GNSS-R applications in Arctic missions. Compared to Hamburg University's SIC products, the root mean square errors (RMSE) of retrieved results in March and June 2016 are 0.0284 and 0.0415, respectively. When the retrieved GNSS-R SIC data are assimilated as supplementary passive microwave remote-sensing data, it improves the accuracy of Arctic SIC forecast, especially in edge regions of sea ice, with a maximum decrease of approximately 17% in the 24-hr forecast time and over 5% in 72-hr.