wradlib Documentation - Bitbucket

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wradlib Documentation, Release 0.1.1 >>> a=[1,2,3] >>> print [x + 3 for x in a] [4, 5, 6] >>> print "a\n\nb" a b 3.9.2 wradlib.clutter.filter_gabella_a wradlib.clutter.filter_gabella_a(img, wsize, tr1) First part of the Gabella filter looking for large reflectivity gradients. This function checks for each pixel in img how many pixels surrounding it in a window of wsize are by tr1 smaller than the central pixel. See Also: Parameters img : array_like radar image to which the filter is to be applied wsize : int Size of the window surrounding the central pixel TODO check if a 5x5 window would have wsize=2 or wsize=5 tr1 : float Threshold value Returns output : array_like an array with the same shape as img, containing the filter’s results. filter_gabella_b the second part of the filter filter_gabella the complete filter Examples TODO: provide a correct example here >>> a=[1,2,3] >>> print [x + 3 for x in a] [4, 5, 6] >>> print "a\n\nb" a b 3.9.3 wradlib.clutter.filter_gabella_b wradlib.clutter.filter_gabella_b(img, thrs=0.0) Second part of the Gabella filter comparing area to circumference of contiguous echo regions. Parameters img : array_like thrs : float Threshold below which the field values will be considered as no rain 56 Chapter 3. Library Reference
wradlib Documentation, Release 0.1.1 See Also: Returns output : array_like contains in each pixel the ratio between area and circumference of the meteorological echo it is assigned to or 0 for non precipitation pixels. filter_gabella_a the first part of the filter filter_gabella the complete filter Examples TODO: provide a correct example here >>> a=[1,2,3] >>> print [x + 3 for x in a] [4, 5, 6] >>> print "a\n\nb" a b 3.9.4 wradlib.clutter.histo_cut wradlib.clutter.histo_cut(prec_accum) Histogram based clutter identification. This identification algorithm uses the histogram of temporal accumulated rainfall. It iteratively detects classes whose frequency falls below a specified percentage (1% by default) of the frequency of the class with the biggest frequency and remove the values from the dataset until the changes from iteration to iteration falls below a threshold. This algorithm is able to detect static clutter as well as shadings. It is suggested to choose a representative time periode for the input precipitation accumulation. The recommended time period should cover one year. Parameters prec_accum : array_like spatial array containing rain accumulation Returns output : array boolean array with pixels identified as clutter/shadings set to True. 3.10 Filling Missing Values 3.11 Vertical Profile of Reflectivity (VPR) UNDER DEVELOPMENT Precipitation is 3-dimensional in space. The vertical distribution of precipitation (and thus reflectivity) is typically non-uniform. As the height of the radar beam increases with the distance from the radar location (beam elevation, earth curvature), the one sweep samples from different heights. The effects of the non-uniform VPR and the differnt sampling heights need to be accounted for if we are interested in the precipiation near the ground or in defined heights. This module is intended to provide a set of tools to account for these effects. 3.10. Filling Missing Values 57
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