swirlspy.qpe package

Submodules

swirlspy.qpe.cal_zr module

swirlspy.qpe.cal_zr.cal_zr(reflectivity_rf, duration)

Calibrate the Z=aR^b relationship through deriving (a,b) by linear regression, also compute the correlation of 2 time series of reflectivity and rainfall

Parameters

  • reflectivity_rf (array of tuples (reflectivity, rainfall_amount)) – reflectivity in dBZ, rainfall amount in mm

  • duration (integer) – duration of rainfall accumulation in minutes, default to 60

Returns

  • a (float)

  • b (float) – a,b value in the Z=aR^b relationship

  • correlation (float) – correlation of 2 time series of reflectivity and rainfall

swirlspy.qpe.rfmap module

swirlspy.qpe.rfmap.overlay_pil(filename_rfcontour, filename_overlay, dimension, position)

Overlay rainfall map with frame. then save as figure.

Parameters

  • filename_rfcontour (string) – Filename of rainfall contour, returned from rfmap.savefig.

  • filename_overlay (string) – Filename of overlaying frame/ basemap.

  • dimension (tuples (width, height)) – dimension of the desired rainfall contour. Should be customised to fit the size overlaying frame/ basemap.

  • position (tuples (x,y)) – position of the overlaying frame/ basemap, where the resized rainfall contour will be pasted.

swirlspy.qpe.rfmap.rg_interpolate(rg_object, area_def, interp_type, coord_label=['x', 'y'], gamma_value=2, minimum_neighbors=3, kappa_star=0.01, search_radius=None, rbf_func='multiquadric', rbf_smooth=0)

Interpolate the rainfall on an area, based on metpy.interpolate.interpolate_to_points.

Parameters

  • rg_object (Rain) – Rain object.

  • area_def (pyresample.geometry.AreaDefinition) – AreaDefinition of the target grid.

  • interp_type (str) – Type of interpolation to be used. Available options include: 1)”linear”, “nearest”, “cubic”, or “rbf” from scipy.interpolate. 2)”natural_neighbor”, “barnes”, or “cressman” from metpy.interpolate.

  • coord_label (list of strings) – Coordinate labels. Defaults to [‘x’, ‘y’].

  • gamma_value(optional) (float) – Adjustable smoothing parameter for the barnes interpolation. Default 2.0.

  • minimum_neighbors(optional) (int) – Minimum number of neighbors needed to perform barnes or cressman interpolation for a point. Default is 3. Adjustable.

  • kappa_star(optional) (float) – Response parameter for barnes interpolation, specified non-dimensionally in terms of the Nyquist. Default 0.01.

  • search_radius(optional) (float) – A search radius to use for the barnes and cressman interpolation values. If search_radius is not specified, it will default to the average spacing of observations.

  • rbf_func(optional) (str) – rbf_func (str) – Specifies which function to use for Rbf interpolation. Options include: ‘multiquadric’, ‘inverse’, ‘gaussian’, ‘linear’, ‘cubic’, ‘quintic’, and ‘thin_plate’. Default to ‘multiquadric’.

  • rbf_smooth(optional) (float) – Smoothing value applied to rbf interpolation. Higher values result in more smoothing. Defaults to 0.

Returns

interpolated_rf – Interpolated values in 2D.

Return type

xarray.DataArray

Notes

Default values set in ‘minimum_neighbors’, ‘gamma’ and ‘kappa_star’, shows best effect for Hong Kong rainfall map. May not be suitable for other countries / regions. Try other values to get the best effect.

swirlspy.qpe.rfmap.show_raingauge(rg_object, show_value=True, color='k', fontsize=12, markersize=12)

Generates a scatter plot showing location and rainfall of raingauges.

Parameters

  • rg_object (Rain) – Rain object.

  • show_value (bool) – Show rainfall values of raingauges. Defaults to True.

  • color (str) – Color of text and markers. Defaults to ‘k’.

  • fontsize (int) – Font size. Defaults to 12.

  • markersize (int) – Marker size. Defaults to 12.

swirlspy.qpe.utils module

swirlspy.qpe.utils.acc(rf, ts, acc_period)

Calculate the past rainfall by accumulating rainfall amount For example: to calculate the past 60 minute rainfall amount at 15:00 1. Create past 6-min rainfall amount at 14:06, 14:12 … by rfn() 2. Concatenate all xarray’s returned by rfn() 3. Call acc(rf, datetime64(‘15:00:00’), timedelta(6, ‘m’))

Parameters

  • rf (xarray.DataArray) – Rainfall amount at various time

  • ts (datetime64[ns] or pandas.Timestamp) – Time to accumulate rainfall

  • acc_period (datetime64[ns]) – Rainfall accumulation period.

Returns

acc_rf – accumulated rainfall amount over the designated period

Return type

xarray.DataArray

swirlspy.qpe.utils.dbz2rr(dbz, a=200.0, b=1.6)

Convert reflectivity to rainfall rate by the Z-R relationship. Default a & b are Marshall-Plalmer. You may wish to refer to the Z-R relationship page for more information on the choice of a & b.

Parameters

  • dbz (xarray.DataArray) – xarray containing the reflectivity xarray in dBZ to convert

  • a (float) – a in Z = aR^b relationship

  • b (float) – b in Z = aR^b relationship

Returns

rainrate – xarray containing the rainfall rate converted by the Z=aR^b relationship

Return type

xarray.DataArray

swirlspy.qpe.utils.locate_file(directory, pattern)

Locate a file under path that contains the timestamp string. Certain radar systems generates radar output files with names other than fixed prefix/suffix and timestamp, e.g. TMS180822171807.RAWULST. This utility helps locate the file. If more than one file is located, only the first one is returned.

Parameters

  • directory (str) – Directory in which the file is located

  • pattern (str) – Pattern in the filename, typically a timestamp

Returns

path – Path to the located file

Return type

str

swirlspy.qpe.utils.multiple_acc(rainfalls, basetime, end_time, times=Timedelta('0 days 00:30:00'), acc_period=Timedelta('0 days 01:00:00'))

Obtain accumulated rainfall over a time period from a start_time to an end_time at certain times. E.g. Hourly accumulated rainfall every 30 minutes from start_time = x to end_time = y.

Parameters

  • rainfalls (xarray.DataArray) – Contains observed and/or forecasted rainfall every n minutes, n being the interval (in minutes) between two timestamps.

  • basetime (pandas.Timestamp) – The basetime of the forecast.

  • end_time (pandas.Timestamp) – The last forecasted timestep.

  • times (pandas.Timedelta) – Times at which accumulated rainfall are to be calculated. Defaults to every 30 minutes.

  • acc_period (pandas.Timedelta) – Rainfall accumulation period. Defaults to 1 hour.

Returns

macc_rf – Contains accumulated rainfall over [acc_period] every [times].

Return type

xarray,DataArray

swirlspy.qpe.utils.rr2rf(rainrate, scan_duration=6, time_shift=0, drift=0)

Convert from radar rainfall rate to accumulated rainfall.

Parameters

  • rainrate (xarray.DataArray) – rainfall rate, likely outputs from dbz2rr()

  • scan_duration (int) – number of minutes a radar scan represents, typically 5, 6 or 10

  • time_shift (timedelta) – temporal shift to account for time required for rain droplets to fall to the ground, and also in case radar data are labelled at the start of the scan while rainfall are labelled at the end (e.g. past 60-minute rainfall)

  • drift (dict) – spatial displacement to account for rain droplets advected by wind, unit same as that of the xarray

Returns

rf – rainfall amount accumulated during the past n minutes

Return type

xarray.DataArray

swirlspy.qpe.utils.temporal_interp(rainfalls, start_time, end_time, intvl=Timedelta('0 days 00:06:00'), interp_type='linear')

Interpolate rainfall xarray along the time dimension.

Parameters

  • rainfalls (xarray.DataArray) – Contains observed and/or forecasted rainfall.

  • start_time (pandas.Timestamp) – User defined start-time.

  • end_time (pandas.Timestamp) – The user-defined end time of the forecasted period.

  • intvl (pandas.Timedelta) – The time interval between interpolated rainfalls. Defaults to 6 minutes.

  • interp_type (str) – The interpolation type for interpolating data in the time axis. Options include: ‘linear’, ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘previous’, ‘next’. Interpolation based on scipy.interpolate.interp1d. Default is linear.

Returns

interp_rainfalls – Contains observed and/or forecasted rainfall, interpolated along the time axis.

Return type

xarray.DataArray

swirlspy.qpe.utils.timestamps_ending(end_time, duration=Timedelta('0 days 01:00:00'), interval=Timedelta('0 days 00:06:00'), exclude_end=True, format='%y%m%d%H%M')

Generate a list of timestamp strings for the duration ending at time

Parameters

  • end_time (datetime64[ns]) – time at the end of the period (excluding end)

  • duration (timedelta64) – duration of the accumulation

  • interval (timedelta64) – intervals between each timestamp

  • exclude_end (Boolean) – If true, no timestamp for the end time

  • format (str) – format of the datetime string, referencing time.strftime()

Returns

timestamps – list of timestamps

Return type

a tuple of strings

swirlspy.qpe.utils.timestamps_period(start_time, end_time, interval, exclude_end=True, format='%y%m%d%H%M')

Generate a list of timestamp strings during the user-defined period

Parameters

  • start_time (pandas.Timestamp) – start time of the accumulated rainfall

  • end_time (pandas.Timestamp) – end time of the accumulated rainfall

  • interval (pandas.Timedelta) – time intervals between each xarray object

  • format (str) – format of the datetime string, referencing time.strftime()

Returns

timestamps – list of filenames

Return type

a tuple of strings