tams.identify

Contents

tams.identify#

tams.identify(ctt, *, size_filter=True, parallel=False, ctt_threshold=235, ctt_core_threshold=219)#

Identify clouds in 2-D (lat/lon) or 3-D (lat/lon + time) cloud-top temperature data ctt. The 235 K contours returned (first list) serve to identify cloud elements (CEs). In a given frame from this list, each row corresponds to a certain CE.

This is the first step in a TAMS workflow.

Parameters
  • ctt (xarray.DataArray) – Cloud-top temperature array.

  • size_filter (bool) –

    Whether to apply size-filtering (using 235 K and 219 K areas to filter out CEs that are not MCS material). Filtering at this stage makes TAMS more computationally efficient overall. Disable this option to return all identified CEs. Note that all 219s are returned regardless of this setting.

    When enabled, this also identifies the 219s (if any) that are within each 235.

  • parallel (bool) – Identify in parallel along 'time' dimension for 3-D ctt (requires joblib).

  • ctt_threshold (float) – Used to identify the edges of cloud elements.

  • ctt_core_threshold (float) – Used to identify deep convective cloud regions within larger cloud areas. This is used to determine whether or not a system is eligible for being classified as an organized system. It helps target raining clouds.

Return type

tuple[list[geopandas.GeoDataFrame], list[geopandas.GeoDataFrame]]