Feed-Forward Neural Network for ERS-2 Retracker Threshold Computation

• Main Authors: Stephan Paul, Stefan Hendricks
• Format: Other/Unknown Material
• Language: unknown
• Published: Zenodo 2023
• Subjects: Arctic Basin; Climate change; Sea ice
• Online Access: https://doi.org/10.5281/zenodo.8335298

Current release candidate v2.0 (RC2) of the ERS-2 retracker threshold model used for producing the satellite-altimetry-based sea-ice thickness climate data record (CDR) v3.0 ofthe European Space Agencies (ESA) Climate Change Initiative+ (CCI+) on sea ice. Model training is based on orbit trajectory matches between ENVISAT and ERS-2 within the mission overlap period between 2002/10 and 2003/04. All training data was generated from trajectory matches within the Arctic basin and within a radius of 1.5 km around the each individual ERS-2 waveform. Initial optimal-retracker thresholds were then computed from ENVISAT average reference freeboards per ERS-2 waveform. Model input are individual echo-waveform subsets of 35 range bins around the first-maximum index used the by the Threshold First Maximum Retracker Algorithm (TFMRA; 5bins before and 30bins after the first-maximum index)– threshold computations are therefore independent on any auxiliary data or associated waveform parameters. Model architecture (pyTorch implementation): <code>fnn_envisat_rc1 ( (fc1): Linear(in_features=45, out_features=2048, bias=True) (fc2): Linear(in_features=2048, out_features=2048, bias=True) (fc3): Linear(in_features=2048, out_features=2048, bias=True) (fc4): Linear(in_features=2048, out_features=2048, bias=True) (fc5): Linear(in_features=2048, out_features=2048, bias=True) (fc6): Linear(in_features=2048, out_features=1, bias=True) )</code>