`approach='first'` for TC's producing no results

[aaTman]

All outputs when using approach='first' for tropical cyclones produce an empty row and an init_time of 0.

Code to reproduce:

import xarray as xr
import numpy as np
from extremeweatherbench import inputs, metrics, cases, derived, calc, evaluate

# Preprocessing function for CIRA data that includes geopotential thickness calculation
# required for tropical cyclone tracks
def _preprocess_bb_cira_tc_forecast_dataset(ds: xr.Dataset) -> xr.Dataset:
    """An example preprocess function that renames the time coordinate to lead_time,
    creates a valid_time coordinate, and sets the lead time range and resolution not
    present in the original dataset.

    Args:
        ds: The forecast dataset to rename.

    Returns:
        The renamed forecast dataset.
    """
    ds = ds.rename({"time": "lead_time"})
    # The evaluation configuration is used to set the lead time range and resolution.
    ds["lead_time"] = np.array(
        [i for i in range(0, 241, 6)], dtype="timedelta64[h]"
    ).astype("timedelta64[ns]")
    ds["geopotential_thickness"] = calc.geopotential_thickness(
        ds["z"], top_level_value=300, bottom_level_value=500, geopotential=True
    )
    return ds

# Load the case collection from the YAML file
case_yaml = cases.load_ewb_events_yaml_into_case_collection()

# Select single case
case_yaml.select_cases(by="case_id_number", value=151, inplace=True)

# Define IBTrACS target, no arguments needed as defaults are sufficient
ibtracs_target = inputs.IBTrACS()

# Define FCNv2 forecast
fcnv2_forecast = inputs.KerchunkForecast(
    name="fcn_forecast",
    source="gs://extremeweatherbench/FOUR_v200_GFS.parq",
    variables=[derived.TropicalCycloneTrackVariables()],
    # Define metadata variable mapping for FCNv2 forecast
    variable_mapping=inputs.CIRA_metadata_variable_mapping,
    # Preprocess the FCNv2 forecast to include geopotential thickness calculation
    preprocess=_preprocess_bb_cira_tc_forecast_dataset,
    storage_options={"remote_protocol": "s3", "remote_options": {"anon": True}},
)

composite_landfall_metrics = [
    metrics.LandfallMetric(
        metrics=[
            metrics.LandfallIntensityMeanAbsoluteError,
            metrics.LandfallTimeMeanError,
            metrics.LandfallDisplacement,
        ],
        approach="first",
    )
]

tc_evaluation_object = [
    # FCNv2 forecast
    inputs.EvaluationObject(
        event_type="tropical_cyclone",
        metric_list=composite_landfall_metrics,
        target=ibtracs_target,
        forecast=fcnv2_forecast,
    ),
]

ewb = evaluate.ExtremeWeatherBench(
    case_metadata=case_yaml,
    evaluation_objects=tc_evaluation_object,
)

# Run the workflow with parallel_config backend set to dask
outputs = ewb.run(
    parallel_config={"backend": "loky", "n_jobs": 1},
)
outputs.to_csv("tc_metric_test_results.csv")

[linear]

EWB-87