diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py index ac19ce5e82..8057704ac2 100644 --- a/pyart/retrieve/echo_class.py +++ b/pyart/retrieve/echo_class.py @@ -612,8 +612,10 @@ def hydroclass_semisupervised( radar_freq=None, ): """ - Classifies precipitation echoes following the approach by Besic et al - (2016). + Classifies precipitation echoes into hydrometeor types. + + The `hydroclass_semisupervised` function classifies precipitation echoes in the polarimetric radar data + into 9 hydrometeor types using a semi-supervised approach (Besic et al., 2016). Parameters ---------- @@ -642,7 +644,17 @@ def hydroclass_semisupervised( Returns ------- hydro : dict - Hydrometeor classification field. + Hydrometeor classification. + - 0: Not classified + - 1: Aggregates + - 2: Ice crystals + - 3: Light rain + - 4: Rimed particles + - 5: Rain + - 6: Vertically oriented ice + - 7: Wet snow + - 8: Melting hail + - 9: Dry hail or high-density graupel References ---------- @@ -651,6 +663,18 @@ def hydroclass_semisupervised( of polarimetric radar measurements: a semi-supervised approach, Atmos. Meas. Tech., 9, 4425-4445, doi:10.5194/amt-9-4425-2016, 2016 + Usage + ----- + .. code-block:: python + hydro_class = pyart.retrieve.hydroclass_semisupervised( + radar, + refl_field="corrected_reflectivity", + zdr_field="corrected_differential_reflectivity", + kdp_field="specific_differential_phase", + rhv_field="uncorrected_cross_correlation_ratio", + temp_field="temperature", + ) + Notes ----- The default hydrometeor classification is valid for C-band radars. For X-band radars, @@ -660,8 +684,8 @@ def hydroclass_semisupervised( If the radar frequency information is missing from the radar object, you can add it in `radar.instrument_parameters`, as follows: - .. code-block:: python + .. code-block:: python radar.instrument_parameters["frequency"] = { "long_name": "Radar frequency", "units": "Hz",