Problem
The ERA5 field closures (wind, wave) use jax.scipy.ndimage.map_coordinates with mode="nearest". When t exceeds the temporal extent of the loaded NetCDF file, the query is silently clamped to the last available timestep.
Example: A route departing Dec 28 2024 with a 10-day crossing needs weather data into Jan 7 2025. If only 2024 data is loaded, segments arriving in 2025 silently use Dec 31 2024 18:00 weather — frozen in time, no error, no warning.
Impact
- Optimization produces routes that ignore actual weather conditions for the later portion of the voyage
- Performance/cost calculations are based on stale data
- The user has no indication this is happening
Proposed solution
- Download script: Accept a date range (not just a single year) so overlapping data can be fetched:
uv run scripts/download_era5.py --start 2024-12-01 --end 2025-02-01
- Loader: Support loading and concatenating multiple NetCDF files along the time axis
- Boundary warning: Log a warning (or raise) when
t exceeds the dataset's temporal range
Workaround
For now, download a second year and concatenate manually with xarray, or ensure departures are early enough that the full voyage fits within the downloaded year.
Problem
The ERA5 field closures (wind, wave) use
jax.scipy.ndimage.map_coordinateswithmode="nearest". Whentexceeds the temporal extent of the loaded NetCDF file, the query is silently clamped to the last available timestep.Example: A route departing Dec 28 2024 with a 10-day crossing needs weather data into Jan 7 2025. If only 2024 data is loaded, segments arriving in 2025 silently use Dec 31 2024 18:00 weather — frozen in time, no error, no warning.
Impact
Proposed solution
texceeds the dataset's temporal rangeWorkaround
For now, download a second year and concatenate manually with xarray, or ensure departures are early enough that the full voyage fits within the downloaded year.