def region_grid(reg):
global region_grids
if reg not in region_grids:
with utils.get_lock():
fp = utils.file_downloader(region_files[reg]['vx'])
ds = salem.GeoTiff(fp)
region_grids[reg] = ds.grid
return region_grids[reg]
def process_lmr_data(gdir,
fpath_temp=None,
fpath_precip=None,
year_range=('1951', '1980'),
filesuffix='',
**kwargs):
"""Read, process and store the Last Millennium Reanalysis (LMR) data for this glacier.
LMR data: https://atmos.washington.edu/~hakim/lmr/LMRv2/
LMR data is annualised in anomaly format relative to 1951-1980. We
create synthetic timeseries from the reference data.
It stores the data in a format that can be used by the OGGM mass balance
model and in the glacier directory.
Parameters
----------
fpath_temp : str
path to the temp file (default: LMR v2.1 from server above)
fpath_precip : str
path to the precip file (default: LMR v2.1 from server above)
year_range : tuple of str
the year range for which you want to compute the anomalies. Default
for LMR is `('1951', '1980')`
filesuffix : str
append a suffix to the filename (useful for ensemble experiments).
**kwargs: any kwarg to be passed to ref:`process_gcm_data`
"""
# Get the path of GCM temperature & precipitation data
base_url = 'https://atmos.washington.edu/%7Ehakim/lmr/LMRv2/'
if fpath_temp is None:
with utils.get_lock():
fpath_temp = utils.file_downloader(
base_url + 'air_MCruns_ensemble_mean_LMRv2.1.nc')
if fpath_precip is None:
with utils.get_lock():
fpath_precip = utils.file_downloader(
base_url + 'prate_MCruns_ensemble_mean_LMRv2.1.nc')
# Glacier location
glon = gdir.cenlon
glat = gdir.cenlat
# Read the GCM files
with xr.open_dataset(fpath_temp, use_cftime=True) as tempds, \
xr.open_dataset(fpath_precip, use_cftime=True) as precipds:
# Check longitude conventions
if tempds.lon.min() >= 0 and glon <= 0:
glon += 360
# Take the closest to the glacier
# Should we consider GCM interpolation?
temp = tempds.air.sel(lat=glat, lon=glon, method='nearest')
precip = precipds.prate.sel(lat=glat, lon=glon, method='nearest')
# Currently we just take the mean of the ensemble, although
# this is probably not advised. The GCM climate will correct
# anyways
temp = temp.mean(dim='MCrun')
precip = precip.mean(dim='MCrun')
# Precip unit is kg/m^2/s we convert to mm month since we apply the anomaly after
precip = precip * 30.5 * (60 * 60 * 24)
# Back to [-180, 180] for OGGM
temp.lon.values = temp.lon if temp.lon <= 180 else temp.lon - 360
precip.lon.values = precip.lon if precip.lon <= 180 else precip.lon - 360
# OK now we have to turn these annual timeseries in monthly data
# We take the ref climate
fpath = gdir.get_filepath('climate_historical')
with xr.open_dataset(fpath) as ds_ref:
ds_ref = ds_ref.sel(time=slice(*year_range))
loc_tmp = ds_ref.temp.groupby('time.month').mean()
loc_pre = ds_ref.prcp.groupby('time.month').mean()
# Make time coord
t = np.cumsum([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] *
len(temp))
t = cftime.num2date(np.append([0], t[:-1]),
'days since 0000-01-01 00:00:00',
calendar='noleap')
temp = xr.DataArray(
(loc_tmp.data + temp.data[:, np.newaxis]).flatten(),
coords={
'time': t,
'lon': temp.lon,
'lat': temp.lat
},
dims=('time', ))
# For precip the std dev is very small - lets keep it as is for now but
# this is a bit ridiculous. We clip to zero here to be sure
precip = utils.clip_min(
(loc_pre.data + precip.data[:, np.newaxis]).flatten(), 0)
precip = xr.DataArray(precip,
dims=('time', ),
coords={
'time': t,
'lon': temp.lon,
'lat': temp.lat
})
process_gcm_data(gdir,
filesuffix=filesuffix,
prcp=precip,
temp=temp,
year_range=year_range,
calendar='noleap',
source='lmr',
**kwargs)
def _reproject_and_scale(gdir, do_error=False):
"""Reproject and scale itslive data, avoid code duplication for error"""
reg = find_region(gdir)
if reg is None:
raise InvalidWorkflowError('There does not seem to be its_live data '
'available for this glacier')
vnx = 'vx'
vny = 'vy'
if do_error:
vnx += '_err'
vny += '_err'
with utils.get_lock():
fx = utils.file_downloader(region_files[reg][vnx])
fy = utils.file_downloader(region_files[reg][vny])
# Open the files
dsx = salem.GeoTiff(fx)
dsy = salem.GeoTiff(fy)
# subset them to our map
grid_gla = gdir.grid.center_grid
proj_vel = dsx.grid.proj
x0, x1, y0, y1 = grid_gla.extent_in_crs(proj_vel)
dsx.set_subset(corners=((x0, y0), (x1, y1)), crs=proj_vel, margin=4)
dsy.set_subset(corners=((x0, y0), (x1, y1)), crs=proj_vel, margin=4)
grid_vel = dsx.grid.center_grid
# TODO: this should be taken care of by salem
# https://github.com/fmaussion/salem/issues/171
with rasterio.Env():
with rasterio.open(fx) as src:
nodata = getattr(src, 'nodata', -32767.0)
# Error files are wrong
if nodata == 0:
nodata = -32767.0
# Get the coords at t0
xx0, yy0 = grid_vel.center_grid.xy_coordinates
# Compute coords at t1
xx1 = dsx.get_vardata()
yy1 = dsy.get_vardata()
non_valid = (xx1 == nodata) | (yy1 == nodata)
xx1[non_valid] = np.NaN
yy1[non_valid] = np.NaN
orig_vel = np.sqrt(xx1**2 + yy1**2)
xx1 += xx0
yy1 += yy0
# Transform both to glacier proj
xx0, yy0 = salem.transform_proj(proj_vel, grid_gla.proj, xx0, yy0)
xx1, yy1 = salem.transform_proj(proj_vel, grid_gla.proj, xx1, yy1)
# Correct no data after proj as well (inf)
xx1[non_valid] = np.NaN
yy1[non_valid] = np.NaN
# Compute velocities from there
vx = xx1 - xx0
vy = yy1 - yy0
# Scale back velocities - https://github.com/OGGM/oggm/issues/1014
new_vel = np.sqrt(vx**2 + vy**2)
p_ok = new_vel > 1e-5 # avoid div by zero
vx[p_ok] = vx[p_ok] * orig_vel[p_ok] / new_vel[p_ok]
vy[p_ok] = vy[p_ok] * orig_vel[p_ok] / new_vel[p_ok]
# And transform to local map
vx = grid_gla.map_gridded_data(vx, grid=grid_vel, interp='linear')
vy = grid_gla.map_gridded_data(vy, grid=grid_vel, interp='linear')
# Write
with utils.ncDataset(gdir.get_filepath('gridded_data'), 'a') as nc:
vn = 'obs_icevel_x'
if do_error:
vn = vn.replace('obs', 'err')
if vn in nc.variables:
v = nc.variables[vn]
else:
v = nc.createVariable(vn, 'f4', ('y', 'x', ), zlib=True)
v.units = 'm yr-1'
ln = 'ITS LIVE velocity data in x map direction'
if do_error:
ln = 'Uncertainty of ' + ln
v.long_name = ln
v[:] = vx.filled(np.nan)
vn = 'obs_icevel_y'
if do_error:
vn = vn.replace('obs', 'err')
if vn in nc.variables:
v = nc.variables[vn]
else:
v = nc.createVariable(vn, 'f4', ('y', 'x', ), zlib=True)
v.units = 'm yr-1'
ln = 'ITS LIVE velocity data in y map direction'
if do_error:
ln = 'Uncertainty of ' + ln
v.long_name = ln
v[:] = vy.filled(np.nan)
def velocity_to_gdir(gdir):
"""Reproject the its_live files to the given glacier directory.
Variables are added to the gridded_data nc file.
Reprojecting velocities from one map proj to another is done
reprojecting the vector distances. In this process, absolute velocities
might change as well because map projections do not always preserve
distances -> we scale them back to the original velocities as per the
ITS_LIVE documentation that states that velocities are given in
ground units, i.e. absolute velocities.
We use bilinear interpolation to reproject the velocities to the local
glacier map.
Parameters
----------
gdir : :py:class:`oggm.GlacierDirectory`
where to write the data
"""
reg = find_region(gdir)
if reg is None:
raise InvalidWorkflowError('There does not seem to be its_live data '
'available for this glacier')
if not gdir.has_file('gridded_data'):
raise InvalidWorkflowError('Please run `glacier_masks` before running '
'this task')
with utils.get_lock():
fx = utils.file_downloader(region_files[reg]['vx'])
fy = utils.file_downloader(region_files[reg]['vy'])
# Open the files
dsx = salem.GeoTiff(fx)
dsy = salem.GeoTiff(fy)
# subset them to our map
grid_gla = gdir.grid.center_grid
proj_vel = dsx.grid.proj
x0, x1, y0, y1 = grid_gla.extent_in_crs(proj_vel)
dsx.set_subset(corners=((x0, y0), (x1, y1)), crs=proj_vel, margin=4)
dsy.set_subset(corners=((x0, y0), (x1, y1)), crs=proj_vel, margin=4)
grid_vel = dsx.grid.center_grid
# TODO: this should be taken care of by salem
# https://github.com/fmaussion/salem/issues/171
with rasterio.Env():
with rasterio.open(fx) as src:
nodata = getattr(src, 'nodata', -32767.0)
# Get the coords at t0
xx0, yy0 = grid_vel.center_grid.xy_coordinates
# Compute coords at t1
xx1 = dsx.get_vardata()
yy1 = dsy.get_vardata()
non_valid = (xx1 == nodata) | (yy1 == nodata)
xx1[non_valid] = np.NaN
yy1[non_valid] = np.NaN
orig_vel = np.sqrt(xx1**2 + yy1**2)
xx1 += xx0
yy1 += yy0
# Transform both to glacier proj
xx0, yy0 = salem.transform_proj(proj_vel, grid_gla.proj, xx0, yy0)
xx1, yy1 = salem.transform_proj(proj_vel, grid_gla.proj, xx1, yy1)
# Correct no data after proj as well (inf)
xx1[non_valid] = np.NaN
yy1[non_valid] = np.NaN
# Compute velocities from there
vx = xx1 - xx0
vy = yy1 - yy0
# Scale back velocities - https://github.com/OGGM/oggm/issues/1014
new_vel = np.sqrt(vx**2 + vy**2)
p_ok = new_vel > 0.1 # avoid div by zero
vx[p_ok] = vx[p_ok] * orig_vel[p_ok] / new_vel[p_ok]
vy[p_ok] = vy[p_ok] * orig_vel[p_ok] / new_vel[p_ok]
# And transform to local map
vx = grid_gla.map_gridded_data(vx, grid=grid_vel, interp='linear')
vy = grid_gla.map_gridded_data(vy, grid=grid_vel, interp='linear')
# Write
with utils.ncDataset(gdir.get_filepath('gridded_data'), 'a') as nc:
vn = 'obs_icevel_x'
if vn in nc.variables:
v = nc.variables[vn]
else:
v = nc.createVariable(vn, 'f4', (
'y',
'x',
), zlib=True)
v.units = 'm yr-1'
v.long_name = 'ITS LIVE velocity data in x map direction'
v[:] = vx
vn = 'obs_icevel_y'
if vn in nc.variables:
v = nc.variables[vn]
else:
v = nc.createVariable(vn, 'f4', (
'y',
'x',
), zlib=True)
v.units = 'm yr-1'
v.long_name = 'ITS LIVE velocity data in xy map direction'
v[:] = vy