v0.43.0

Support experimental interpolation against gridded specific humidity. Add new data-driven humidity scaling models.

Features

• Add new experimental interpolation_q_method field to the ModelParams data class. This parameter controls the interpolation methodology when interpolation against gridded specific humidity. The possible values are:

  • None: Interpolate linearly against specific humidity. This is the default behavior and is the same as the previous behavior.
  • "cubic-spline": Apply cubic-spline scaling to the interpolation table vertical coordinate before interpolating linearly against specific humidity.
  • "log-q-log-p": Interpolate in the log-log domain against specific humidity and pressure.

  This interpolation parameter is used when calling pycontrails.core.models.interpolate_met. It can also be used directly with the new lower-level pycontrails.core.models.interpolate_gridded_specific_humidity function.

• Add new experimental HistogramMatching humidity scaling model to match RHi values against IAGOS observations. The previous HistogramMatchingWithEckel scaling is still available when working with ERA5 ensemble members.

• Add new tutorial discussing the new specific humidity interpolation methodology.

Breaking changes

• Add an optional q_method parameter to the pycontrails.core.models.interpolate_met function. The default value None agrees with the previous behavior.
• Change function signatures in the cocip.py module for consistency. The interp_kwargs parameter is now unpacked in the calc_timestep_meterology signature. Rename kwargs to interp_kwargs where appropriate.
• Remove the cache_size parameter in MetDataset.from_zarr. Previously this parameter allowed the user to wrap the Zarr store in a LRUCacheStore to improve performance. Changes to Zarr internals have broken this approach. Custom Zarr patterns should now be handled outside of pycontrails.

Fixes

• Recompute and extend quantiles for histogram matching humidity scaling. Quantiles are now available for each combination of q_method and the following ERA5 data products: reanalysis and ensemble members 0-9. This data is available as a parquet file and is packaged with pycontrails.
• Fix the precomputed Eckel coefficients. Previous values where computed for different interpolation assumptions and were not correct for the default interpolation method.
• Clip the scaled humidity values computed by the humidity_scaling.eckel_scaling function to ensure that they are non-negative. Previously, both relative and specific humidity values arising from Eckel scaling could be negative.
• Handle edge case of all NaN values in the T_critical_sac function in the sac.py module.
• Avoid extraneous copy when calling VectorDataset.sum.
• Officially support numpy v1.25.0.
• Set a pytest-timeout limit for tests in tests/unit/test_ecmwf.py to avoid hanging tests.
• Add forecast_step parameter to the ACCF model.

Internals

• Refactor auxillary functions used by HistogramMatchingWithEckel to better isolated histogram matching from Eckel scaling.
• Refactor intersect_met method in pycontrails.core.models to handle experimental q_method parameter.
• Include a q_method field in Model.interp_kwargs.
• Include precomputed humidity lapse rate values in the new pycontrails.core.models._load_spline function.
• Move the humidity_scaling.py module into its own subdirectory within pycontrails/models.