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The reconstruction problem

geo2wf asks whether colocated GEO imagery and environmental context can recover a plausible tropical-cyclone surface wind field where SAR provides sparse supervision.

Observations, context, and target

Each sample is aligned to one 256 × 256 EPSG:4326 grid:

  • GEO observation: ten GOES ABI or Himawari AHI infrared / water-vapor channels;
  • ERA5 context: seven exported atmospheric/surface fields plus derived wind speed and relative vorticity;
  • derived context: distance to the IBTrACS center and three solar-time fields;
  • SAR target: one near-surface wind-speed channel in m/s; and
  • validity masks: explicit support for the condition, ERA5 anchor, and sparse SAR swath.

See Model inputs and training targets for real examples and exact channel assembly.

GEO and SAR do not observe the same physical quantity, so this is not ordinary super-resolution. The model infers surface-wind structure compatible with cloud-top, water-vapor, environmental, and storm-relative context.

Main two-stage formulation

Stage 1 makes one deterministic commitment:

\[ \hat v_{\mathrm{base}} = v_{\mathrm{ERA5}} + f_\theta(x_{\mathrm{GEO}}, x_{\mathrm{ERA5}}, x_{\mathrm{derived}}, m) \]

Stage 2 models the distribution of signed SAR corrections around that frozen field:

\[ p_\phi\left( v_{\mathrm{SAR}} - \hat v_{\mathrm{base}} \mid x_{\mathrm{GEO}}, x_{\mathrm{ERA5}}, x_{\mathrm{derived}}, \hat v_{\mathrm{base}}, m \right) \]

This separates the broad, stable reconstruction from probabilistic fine-structure refinement. Read the full two-stage workflow.

Scientific constraints reflected in code

Sparse SAR coverage
Losses and metrics use target_mask. A weak off-swath anchor can constrain corrections where ERA5 is valid without calling those pixels SAR observations.
Physical scale
The dataset retains target_physical and reversible normalization parameters. The deterministic model predicts in m/s; diffusion residuals are inverted and added in m/s.
Storm geometry
Manifests carry IBTrACS center coordinates and raster bounds. The dataset derives a distance input, while evaluation uses physical coordinates for eye, inner-core, radial-profile, and radius-of-maximum-wind metrics.
Illumination
Pixelwise local-solar-time sine/cosine and solar zenith help interpret daylight-sensitive GEO structure.
Reproducible sampling
Validation noise is derived from the global validation seed and sample_id, so the same sample starts from the same latent across epochs.
Vector-aware augmentation
Flips transform ERA5 wind components and vorticity according to their physical parity instead of treating every channel as a generic scalar image.

Explicit non-goals

The current system does not represent arbitrary heterogeneous observation sets, multi-temporal windows, learned availability policies, or complete tracks as model inputs. Source download is also outside prepare_data(); export is an explicit preprocessing step.