tams.identify#

tams.identify(ctt, *, ctt_threshold=235, ctt_core_threshold=219, size_filter=True, size_threshold=4000, parallel=False)#

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.
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.
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 (default), this also identifies the 219s (if any) that are within each 235. Only 235s with enough 219 area (size_threshold) are kept.
size_threshold (float) – Area threshold (units: km²) to use when size_filter is enabled.
parallel (bool) – Identify in parallel along 'time' dimension for 3-D ctt (requires joblib).

Returns:

css235 – List of dataframes of 235 K contour polygons (CEs). If size_filter is enabled (default), an area_km2 column is included, column cs219 gives the cold cores for each CE as a multi-polygon, and those rows that don’t meet the size filtering criteria are dropped.
css219 – List of dataframes of 219 K contour polygons (cold cores). If size_filter is enabled (default), an area_km2 column is included, but all rows are included, regardless of the area value.

Return type:

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

tams.identify

Contents

tams.identify#