Nested wind retrievals

PyDDA supports nested wind retrievals via pydda.retrieval.get_dd_wind_field_nested(). A nested retrieval performs the optimization on a coarse outer grid first, then uses the result to initialize progressively finer inner grids. This approach can reduce computational cost while still resolving fine-scale features in a region of interest.

Building the nested grid tree

Nested grids are organized in an xarray.DataTree. Each node of the tree represents one nesting level and contains one child dataset per radar (named radar_0, radar_1, …). Per-level retrieval parameters can be stored as node attributes to override the top-level keyword arguments for that nesting level.

import xarray as xr
from xarray import DataTree
import pydda

# Assume grid_ktlx_coarse, grid_kict_coarse, grid_ktlx_fine, grid_kict_fine
# have already been created with pyart.map.grid_from_radars and converted
# with pydda.io.read_from_pyart_grid.

tree = DataTree.from_dict(
    {
        "/nest_0/radar_0": grid_ktlx_coarse,
        "/nest_0/radar_1": grid_kict_coarse,
        "/nest_1/radar_0": grid_ktlx_fine,
        "/nest_1/radar_1": grid_kict_fine,
    }
)

output_tree = pydda.retrieval.get_dd_wind_field_nested(
    tree,
    Cm=256.0,
    Co=1e-2,
    Cx=100,
    Cy=100,
    Cz=1,
    model_fields=["hrrr"],
    refl_field="DBZ",
    engine="scipy",
)

The function returns the same xarray.DataTree with an output_grids entry added to each node containing the retrieved wind fields for that nesting level.

Note

All grids within the same nesting level must share the same grid shape, origin latitude/longitude, and coordinate arrays. The fine grid does not need to match the coarse grid.

For full API details see pydda.retrieval.get_dd_wind_field_nested().