def test_download_icon(self):
"""Value Error if date to old"""
try:
with self.assertRaises(ValueError):
download_icon_grib(dt.datetime(2020, 1, 1))
except IOError:
pass
def test_icon_eu_forecast_download(self):
"""Test availability of DWD icon forecast."""
run_datetime = dt.datetime.utcnow() - dt.timedelta(hours=5)
run_datetime = run_datetime.replace(hour=run_datetime.hour // 12 * 12,
minute=0,
second=0,
microsecond=0)
icon_file = download_icon_grib(run_datetime, max_lead_time=1)
self.assertEqual(len(icon_file), 1)
delete_icon_grib(run_datetime, max_lead_time=1) #deletes icon_file
self.assertFalse(Path(icon_file[0]).exists())
def from_icon_grib(cls,
run_datetime,
event_date=None,
model_name='icon-eu-eps',
description=None,
grib_dir=None,
delete_raw_data=True,
intensity_thres=None):
"""Create new StormEurope object from DWD icon weather forecast footprints.
New files are available for 24 hours on
https://opendata.dwd.de, old files can be processed if they are
already stored in grib_dir.
One event is one full day in UTC. Current setup works for runs
starting at 00H and 12H. Otherwise the aggregation is inaccurate,
because of the given file structure with 1-hour, 3-hour and
6-hour maxima provided.
The frequency for one event is 1/(number of ensemble members)
Parameters
----------
run_datetime : datetime
The starting timepoint of the forecast run
of the icon model
event_date : datetime, optional
one day within the forecast
period, only this day (00H-24H) will be included in the hazard
model_name : str,optional
select the name of the icon model to
be downloaded. Must match the url on https://opendata.dwd.de
(see download_icon_grib for further info)
description : str, optional
description of the events, defaults
to a combination of model_name and run_datetime
grib_dir : str, optional
path to folder, where grib files are
or should be stored
delete_raw_data : bool,optional
select if downloaded raw data in
.grib.bz2 file format should be stored on the computer or
removed
intensity_thres : float, optional
Intensity threshold for storage in m/s. Default: class attribute
StormEurope.intensity_thres (same as used by WISC SSI calculations)
Returns
-------
haz : StormEurope
StormEurope object with data from DWD icon weather forecast footprints.
"""
intensity_thres = cls.intensity_thres if intensity_thres is None else intensity_thres
haz = cls()
if not (run_datetime.hour == 0 or run_datetime.hour == 12):
LOGGER.warning('The event definition is inaccuratly implemented ' +
'for starting times, which are not 00H or 12H.')
# download files, if they don't already exist
file_names = download_icon_grib(run_datetime,
model_name=model_name,
download_dir=grib_dir)
# create centroids
nc_centroids_file = download_icon_centroids_file(model_name, grib_dir)
haz.centroids = haz._centroids_from_nc(nc_centroids_file)
# read intensity from files
for ind_i, file_i in enumerate(file_names):
gripfile_path_i = Path(file_i[:-4])
with open(file_i, 'rb') as source, open(gripfile_path_i,
'wb') as dest:
dest.write(bz2.decompress(source.read()))
ds_i = xr.open_dataset(gripfile_path_i, engine='cfgrib')
if ind_i == 0:
stacked = ds_i
else:
stacked = xr.concat([stacked, ds_i], 'valid_time')
# create intensity matrix with max for each full day
stacked = stacked.assign_coords(
date=('valid_time', stacked["valid_time"].dt.floor("D").values))
if event_date:
try:
stacked = stacked.sel(valid_time=event_date.strftime(
'%Y-%m-%d')).groupby('date').max()
except KeyError:
raise ValueError('Extraction of date and coordinates failed. '
'This is most likely because '
'the selected event_date {} is not contained'
' in the weather forecast selected by '
'run_datetime {}. Please adjust event_date'
' or run_datetime.'.format(
event_date.strftime('%Y-%m-%d'),
run_datetime.strftime('%Y-%m-%d %H:%M')))
considered_dates = np.datetime64(event_date)
else:
time_covered_step = stacked['valid_time'].diff('valid_time')
time_covered_day = time_covered_step.groupby('date').sum()
# forecast run should cover at least 18 hours of a day
considered_dates_bool = time_covered_day >= np.timedelta64(18, 'h')
stacked = stacked.groupby('date').max().sel(
date=considered_dates_bool)
considered_dates = stacked['date'].values
stacked = stacked.stack(date_ensemble=('date', 'number'))
stacked = stacked.where(stacked > intensity_thres)
stacked = stacked.fillna(0)
# fill in values from netCDF
haz.intensity = sparse.csr_matrix(stacked.gust.T)
haz.event_id = np.arange(stacked.date_ensemble.size) + 1
# fill in default values
haz.units = 'm/s'
haz.fraction = haz.intensity.copy().tocsr()
haz.fraction.data.fill(1)
haz.orig = np.ones_like(haz.event_id) * False
haz.orig[(stacked.number == 1).values] = True
haz.date = np.repeat(np.array(datetime64_to_ordinal(considered_dates)),
np.unique(stacked.number).size)
haz.event_name = [
date_i + '_ens' + str(ens_i) for date_i, ens_i in zip(
date_to_str(haz.date), stacked.number.values)
]
haz.frequency = np.divide(np.ones_like(haz.event_id),
np.unique(stacked.number).size)
if not description:
description = ('icon weather forecast windfield ' +
'for run startet at ' +
run_datetime.strftime('%Y%m%d%H'))
haz.tag = TagHazard(HAZ_TYPE,
'Hazard set not saved, too large to pickle',
description=description)
haz.check()
# delete generated .grib2 and .4cc40.idx files
for ind_i, file_i in enumerate(file_names):
gripfile_path_i = Path(file_i[:-4])
idxfile_path_i = next(
gripfile_path_i.parent.glob(
str(gripfile_path_i.name) + '.*.idx'))
gripfile_path_i.unlink()
idxfile_path_i.unlink()
if delete_raw_data:
#delete downloaded .bz2 files
delete_icon_grib(run_datetime,
model_name=model_name,
download_dir=grib_dir)
return haz