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

• Published in: Geophysical Research Letters
• Main Authors: Lu Yang, Bofeng Guo, Zhaoyi Zhang, Xuefeng Zhang
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2023
• Subjects: global navigation satellite system reflectometry (GNSS‐R); sea ice concentration; deep neural network; sea ice forecast; Geophysics. Cosmic physics; QC801-809; Sea ice
• Online Access: https://doi.org/10.1029/2023GL104219; https://doaj.org/article/ac1394801248494b8b3bac20d5039d8d

Abstract 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.