def METHOD_NAME(self, open_dataset, init_kbdi, limiting_func):
    """Test the FFDI indicators using real data"""
    # I couldn't find any high quality data or code to test against. I considered
    # the CEMS GEFF dataset, and the R packages ClimInd and ecbtools but all use
    # older definitions of the KBDI and DF that differ from our code and I don't
    # think reflect the modern literature.
    # For now, we just test that the indicators run using real data and that the
    # outputs look sensible
    test_data = open_dataset(data_url)
    pr_annual = test_data["pr"].resample(time="A").mean().mean("time")
    pr_annual.attrs["units"] = test_data["pr"].attrs["units"]
    if init_kbdi:
        kbdi0 = xr.ones_like(pr_annual) + 203.2
        kbdi0.attrs["units"] = test_data["pr"].attrs["units"]
    else:
        kbdi0 = None
    kbdi = atmos.keetch_byram_drought_index(
        test_data["pr"], test_data["tasmax"], pr_annual, kbdi0
    )
    assert (kbdi >= 0).all()
    assert (kbdi <= 203.2).all()
    assert kbdi.shape == test_data["pr"].shape
    if limiting_func == "xlim":
        df_max = 10.7216381
    else:
        df_max = 10
    df = atmos.griffiths_drought_factor(test_data["pr"], kbdi)
    assert (df.isel(time=slice(19, None)) >= 0).all()
    assert (df.isel(time=slice(19, None)) <= df_max).all()
    assert df.shape == test_data["pr"].shape
    ffdi = atmos.mcarthur_forest_fire_danger_index(
        df, test_data["tasmax"], test_data["hurs"], test_data["sfcWindmax"]
    )
    assert (ffdi.isel(time=slice(19, None)) >= 0).all()
    assert ffdi.shape == test_data["pr"].shape