EVALUATING UNROLLED OPTIMIZATION MODEL ARCHITECTURES FOR OCEANOGRAPHIC DATA ASSIMILATION
Author(s) : Chaima Bounadra, Paul De Nailly
In response to the reviewer's feedback, we made several revisions to improve accessibility and technical clarity. We simplified the title to ``Evaluating Unrolled Optimization Model Architectures for Oceanographic Data Assimilation,'' removing jargon such as ``SWOT-era.'' We rewrote the abstract to explicitly define unrolled optimization models for a non-expert audience. In the Background section, we removed the unsourced ``90\% of oceanic kinetic energy'' statistic and expanded the description of 4DVarNet with the variational cost function $J(x) = J_{\text{obs}}(x, y) + J_{\text{prior}}(x; \Phi)$ and iterative update rule $x^{(k+1)} = x^{(k)} - \alpha \nabla_x J(x^{(k)})$. In the Methods section, we specified the network architecture (UNet with 128 channels, bilinear interpolation, 0.1 dropout), replacing the vague ``multi-scale residual design'' phrase. We converted ``Expected Results'' to ``Results'' presenting actual obtained metrics, and added new statistical analyses: a spatial effective resolution map (Figure 1) and a regional variance score table (Table 1) covering coastal, offshore, equatorial, and Arctic regions.