Bias correction process

This example shows how to call the bias-correction service based on the Kernel Density Distribution Mapping. We first connect to the WPS server and get some information on the service, identified by id kddm_bc.

from owslib.wps import WebProcessingService
url = 'https://pavics.ouranos.ca/twitcher/ows/proxy/flyingpigeon/wps'
wps = WebProcessingService(url=url)
proc = wps.describeprocess('kddm_bc')
print(proc.abstract)

Bias correction method using Kernel Density Distribution Mapping (KDDM).

For the next step, we’ll create small synthetic files and run the process.

import numpy as np
import xarray as xr
import pandas as pd

obs_time_index = pd.date_range(start='2000-01-01', end='2000-12-31', freq='D')
obs = xr.DataArray(np.arange(len(obs_time_index)), coords={'time': obs_time_index}, dims='time')
ref = obs + 1

fut_time_index = pd.date_range(start='2050-01-01', end='2050-12-31', freq='D')
fut = xr.DataArray(np.arange(len(fut_time_index))+10, coords={'time': fut_time_index}, dims='time')

fn = {'obs': '/tmp/obs.nc',
      'ref': '/tmp/ref.nc',
      'fut': '/tmp/fut.nc'}

obs.to_netcdf(fn['obs'])
ref.to_netcdf(fn['ref'])
fut.to_netcdf(fn['fut'])

resp = wps.execute('kddm_bc', inputs=[('obs', fn['obs']), ('ref', fn['ref']), ('fut', fn['fut'])])

print(resp.status)

ProcessAccepted