pysteps.feature.tstorm.detection

Contents

pysteps.feature.tstorm.detection#

pysteps.feature.tstorm.detection(input_image, max_num_features=None, minref=35, maxref=48, mindiff=6, minsize=50, minmax=41, mindis=10, output_feat=False, output_splits_merges=False, time='000000000')#

This function detects thunderstorms using a multi-threshold approach. It is recommended to use a 2-D Cartesian maximum reflectivity composite, however the function will process any 2-D array. The thunderstorm cell detection requires both scikit-image and pandas.

Parameters:
  • input_image (array-like) – Array of shape (m,n) containing input image, usually maximum reflectivity in dBZ with a resolution of 1 km. Nan values are ignored.

  • max_num_features (int, optional) – The maximum number of cells to detect. Set to None for no restriction. If specified, the most significant cells are chosen based on their area.

  • minref (float, optional) – Lower threshold for object detection. Lower values will be set to NaN. The default is 35 dBZ.

  • maxref (float, optional) – Upper threshold for object detection. Higher values will be set to this value. The default is 48 dBZ.

  • mindiff (float, optional) – Minimal difference between two identified maxima within same area to split area into two objects. The default is 6 dBZ.

  • minsize (float, optional) – Minimal area for possible detected object. The default is 50 pixels.

  • minmax (float, optional) – Minimum value of maximum in identified objects. Objects with a maximum lower than this will be discarded. The default is 41 dBZ.

  • mindis (float, optional) – Minimum distance between two maxima of identified objects. Objects with a smaller distance will be merged. The default is 10 km.

  • output_feat (bool, optional) – Set to True to return only the cell coordinates.

  • output_split_merge (bool, optional) – Set to True to return additional columns in the dataframe for describing the splitting and merging of cells. Note that columns are initialized with None, and the information needs to be analyzed while tracking.

  • time (string, optional) – Date and time as string. Used to label time in the resulting dataframe. The default is ‘000000000’.

Returns:

  • cells_id (pandas dataframe) – Pandas dataframe containing all detected cells and their respective properties corresponding to the input image. Columns of dataframe: ID - cell ID, time - time stamp, x - array of all x-coordinates of cell, y - array of all y-coordinates of cell, cen_x - x-coordinate of cell centroid, cen_y - y-coordinate of cell centroid, max_ref - maximum (reflectivity) value of cell, cont - cell contours

  • labels (array-like) – Array of shape (m,n), grid of labelled cells.