def _make_racmo_wind_speed() -> Layer:
return Layer(
id='racmo_wind_speed',
title='Annual mean wind speed 1958-2019 (5km)',
description=
("""Averaged annual mean wind speed in meters per second from RACMO2.3p2
for the period 1958-2019."""),
tags=[],
style='racmo_wind_speed',
input=LayerInput(
dataset=dataset,
asset=dataset.assets['only'],
),
steps=[
decompress_step(
input_file='{input_dir}/RACMO_QGreenland_Jan2021.zip',
decompress_contents_mask='magnitudes.nc',
),
*warp_and_cut(
input_file='{input_dir}/magnitudes.nc',
output_file='{output_dir}/racmo_wind_speed.tif',
cut_file=project.boundaries['data'].filepath,
),
*compress_and_add_overviews(
input_file='{input_dir}/racmo_wind_speed.tif',
output_file='{output_dir}/racmo_wind_speed.tif',
dtype_is_float=True,
),
],
)
def sea_ice_age_layer(year: int, age_type: AgeType) -> Layer:
layer_info = seaice_age_layers[year][age_type]
return Layer(
id=f'seaice_{age_type}_age_{year}',
title=f"{layer_info['date_range']} {year}",
description=(
f"""Age of sea ice derived from weekly averaged ice motion vectors. A
value of N indicates ice aged N-1 to N years. A value of 20 represents
land; 21 represents ocean cells where ice age was not calculated. Week
of {age_type} extent chosen based on NSDIC's Sea Ice Index 5-day
average."""
),
tags=[],
style='sea_ice_age',
input=LayerInput(
dataset=dataset,
asset=dataset.assets[str(year)],
),
steps=[
CommandStep(
args=[
'gdal_translate',
'-b', layer_info['band_num'],
(
'NETCDF:{input_dir}/'
f'iceage_nh_12.5km_{year}0101_{year}1231_v4.1.nc:age_of_sea_ice'
),
'{output_dir}/age_of_sea_ice.tif',
],
),
*gdal_edit(
input_file='{input_dir}/age_of_sea_ice.tif',
output_file='{output_dir}/edited.tif',
gdal_edit_args=[
'-a_ullr', '-4518421 4518421 4506579 -4506579',
],
),
*warp_and_cut(
input_file='{input_dir}/edited.tif',
output_file='{output_dir}/warped_and_cut.tif',
cut_file=project.boundaries['background'].filepath,
reproject_args=[
'-tr', '12500', '12500',
],
),
*compress_and_add_overviews(
input_file='{input_dir}/warped_and_cut.tif',
output_file='{output_dir}/overviews.tif',
dtype_is_float=False,
),
],
)
def _make_layer(
*,
layer_id: str,
title: str,
description: str,
style: str,
filename: str,
dataset: Dataset,
) -> Layer:
return Layer(
id=layer_id,
title=title,
description=description,
tags=[],
style=style,
input=LayerInput(
dataset=dataset,
asset=dataset.assets['only'],
),
steps=[
decompress_step(
input_file='{input_dir}/archive.zip',
decompress_contents_mask=filename,
),
*warp_and_cut(
input_file='{input_dir}/' + filename,
output_file='{output_dir}/' + filename,
reproject_args=[
# Source data is 0.02x-0.02 degrees resolution. Rene noted in
# his email to QGreenland on 2021-01-22 that the geoid and
# gravity anomaly grids are 2km resolution.
'-tr',
'2000',
'2000',
],
cut_file=project.boundaries['data'].filepath,
),
*compress_and_add_overviews(
input_file='{input_dir}/' + filename,
output_file='{output_dir}/' + filename,
dtype_is_float=True,
),
],
)
def _layer(year) -> Layer:
month = conc_max_month(year)
month_name = calendar.month_name[month]
return Layer(
id=f'seaice_maximum_concentration_{year}',
title=f'{month_name} {year}',
description=CONCENTRATION_DESCRIPTION,
tags=[],
style=CONCENTRATION_STYLE,
input=LayerInput(
dataset=dataset,
asset=dataset.assets[f'maximum_concentration_{year}'],
),
# TODO: Extract to helper
steps=[
CommandStep(
args=[
'gdal_calc.py',
'--calc', "'A / 10.0'",
'-A', '{input_dir}/*.tif',
'--outfile={output_dir}/downscaled.tif',
],
),
*warp_and_cut(
input_file='{input_dir}/downscaled.tif',
output_file='{output_dir}/warped_and_cut.tif',
cut_file=project.boundaries['background'].filepath,
),
*compress_and_add_overviews(
input_file='{input_dir}/warped_and_cut.tif',
output_file='{output_dir}/overviews.tif',
dtype_is_float=False,
),
],
)
title=f'September {year}',
description=CONCENTRATION_DESCRIPTION,
tags=[],
style=CONCENTRATION_STYLE,
input=LayerInput(
dataset=dataset,
asset=dataset.assets[f'minimum_concentration_{year}'],
),
steps=[
CommandStep(args=[
'gdal_calc.py',
'--calc',
"'A / 10.0'",
'-A',
'{input_dir}/*.tif',
'--outfile={output_dir}/downscaled.tif',
], ),
*warp_and_cut(
input_file='{input_dir}/downscaled.tif',
output_file='{output_dir}/warped_and_cut.tif',
cut_file=project.boundaries['background'].filepath,
),
*compress_and_add_overviews(
input_file='{input_dir}/warped_and_cut.tif',
output_file='{output_dir}/overviews.tif',
dtype_is_float=False,
),
],
) for year in MIN_CONCENTRATION_YEARS
]
dataset=dataset,
asset=dataset.assets['only'],
),
steps=[
CommandStep(args=[
'gdal_calc.py',
'--calc="A*B"',
'--outfile={output_dir}/' + f'masked_{layer_id}.tif',
'-A',
'NETCDF:{input_dir}/GRE_G0120_0000.nc:v',
'-B',
'NETCDF:{input_dir}/GRE_G0120_0000.nc:ice',
], ),
*warp_and_cut(
input_file='{input_dir}/' + f'masked_{layer_id}.tif',
output_file='{output_dir}/' + f'{layer_id}.tif',
cut_file=project.boundaries['data'].filepath,
),
*compress_and_add_overviews(
input_file='{input_dir}/' + f'{layer_id}.tif',
output_file='{output_dir}/' + f'{layer_id}.tif',
dtype_is_float=True,
),
],
) for layer_id, params in _masked_velocity_mosaic_params.items()
]
velocity_mosaic_ice_mask = Layer(
id='velocity_mosaic_ice_mask',
title='Ice mask (120m)',
description=("""Ice mask used for ITS_LIVE velocity mosiac."""),
),
steps=[
*warp_and_cut(
input_file=(
'NETCDF:{input_dir}/UiO_PEX_5.0_20181127_2000_2016_10km.nc:'
+ params['variable']),
output_file='{output_dir}/' + f'{layer_id}.tif',
reproject_args=[
# Webpage for this data
# (https://doi.pangaea.de/10.1594/PANGAEA.888600) notes All
# files are provided in Arctic Polar Stereographic projection
# (EPSG:3995 WGS 84)
'-s_srs',
'EPSG:3995',
'-tr',
'10000',
'10000',
# This dataset does not contain CF-compliant fields or
# geotransform array. Set
# `GDAL_NETCDF_IGNORE_XY_AXIS_NAME_CHECKS` to `true` to use
# the provided `x` and `y` dims as coordinate values so that
# gdal can compute the transform on its own. See
# https://github.com/OSGeo/gdal/issues/4075
'--config',
'GDAL_NETCDF_IGNORE_XY_AXIS_NAME_CHECKS',
'true',
],
cut_file=project.boundaries['data'].filepath,
),
*compress_and_add_overviews(
input_file='{input_dir}/' + f'{layer_id}.tif',
'-b',
DEPTHS_BANDS[depth],
'NETCDF:{input_dir}/' + f'{SEASONS_FNS[season]}:t_an',
'{output_dir}/extracted.tif',
], ),
*warp_and_cut(
input_file='{input_dir}/extracted.tif',
output_file='{output_dir}/warped_and_cut.tif',
cut_file=project.boundaries['data'].filepath,
reproject_args=(
'-tr',
'25000',
'25000',
# A "target extent" bounding box is required to reproject
# this correctly, or we receive an error like:
# ERROR 1: Attempt to create 0x1 dataset is
# illegal,sizes must be larger than zero.
'-te',
*(
project.boundaries['data'].bbox.min_x,
project.boundaries['data'].bbox.min_y,
project.boundaries['data'].bbox.max_x,
project.boundaries['data'].bbox.max_y,
),
),
),
*compress_and_add_overviews(
input_file='{input_dir}/warped_and_cut.tif',
output_file='{output_dir}/final.tif',
dtype_is_float=True,
),
tags=[],
style=params['style'],
input=LayerInput(
dataset=dataset,
asset=dataset.assets['only'],
),
steps=[
decompress_step(
input_file=
'{input_dir}/greenland_iv_250m_s1_20191214_20200131_v1_3.zip',
decompress_contents_mask=(
'greenland_iv_250m_s1_20191214_20200131_v1_3'
'/greenland_iv_250m_s1_20191214_20200131_v1_3.nc'),
),
*warp_and_cut(
input_file=
('NETCDF:{input_dir}/greenland_iv_250m_s1_20191214_20200131_v1_3/'
'greenland_iv_250m_s1_20191214_20200131_v1_3.nc'
f":{params['variable']}"),
output_file='{output_dir}/' + f'{layer_id}.tif',
cut_file=project.boundaries['data'].filepath,
),
*compress_and_add_overviews(
input_file='{input_dir}/' + f'{layer_id}.tif',
output_file='{output_dir}/' + f'{layer_id}.tif',
dtype_is_float=True,
),
],
) for layer_id, params in _esa_cci_velocity_params.items()
]
geothermal_heat_flow = Layer(
id='geothermal_heat_flow_map',
title='Flow from multiple observations (55km)',
style='geothermal_heat_flow_map',
description=(
"""Geothermal heat flow map from machine-learning algorithm (55 km
native resolution)."""),
tags=[],
input=LayerInput(
dataset=dataset,
asset=dataset.assets['55km_map'],
),
steps=[
*warp_and_cut(
input_file=
'{input_dir}/geothermal_heat_flow_map_55km_without_NGRIP.nc',
output_file='{output_dir}/geothermal_heat_flow_map_55km.tif',
cut_file=project.boundaries['data'].filepath,
),
*compress_and_add_overviews(
input_file='{input_dir}/geothermal_heat_flow_map_55km.tif',
output_file='{output_dir}/geothermal_heat_flow_map_55km.tif',
dtype_is_float=True,
),
],
)
geothermal_heat_flow_measurements = Layer(
id='geothermal_heat_flow_measurements',
title='Flow measurement locations',
description=
("""Heat flow measurement database used in the creation of the 'Geothermal
show=True,
input=LayerInput(
dataset=background_dataset,
asset=background_dataset.assets['high_res'],
),
steps=[
decompress_step(
input_file='{input_dir}/*.zip',
),
*warp_and_cut(
input_file='{input_dir}/NE2_HR_LC_SR_W.tif',
output_file='{output_dir}/warped_and_cut.tif',
reproject_args=[
'-tr', '500', '500',
# TODO import project config and access correct boundary.
'-te', '-5774572.727595 -5774572.727595 5774572.727595 5774572.727595',
'-dstnodata', '0',
'-wo', 'SOURCE_EXTRA=100',
'-wo', 'SAMPLE_GRID=YES',
],
cut_file='{assets_dir}/latitude_shape_40_degrees.geojson',
),
# Because the background image is large, we use JPEG compression
# (`compress_and_add_overviews` step below). To do so without JPEG
# artifacts around the curved clip boundary the image (appears as black
# pixels around the outside edges of the image), a mask band can be
# used. This step creates the mask file that will be added as a fourth
# band to the RGB background image.
CommandStep(
id='create_mask',
args=[
("""Error estimate for Greenland bed elevation and ice thickness in
meters."""),
},
}
layers = [
Layer(
id=f'bedmachine_{key}',
title=f'{params["title"]} (150m)',
description=params['description'],
tags=['terrain_model'],
style=f'bedmachine_{key}',
input=LayerInput(
dataset=bedmachine.bedmachine,
asset=bedmachine.bedmachine.assets['only'],
),
steps=[
*warp_and_cut(
input_file='NETCDF:{input_dir}/' + f'{bedmachine_fn}:{key}',
output_file='{output_dir}/warped_and_cut.tif',
cut_file='{assets_dir}/greenland_rectangle.geojson',
),
*compress_and_add_overviews(
input_file='{input_dir}/warped_and_cut.tif',
output_file='{output_dir}/overviews.tif',
dtype_is_float=False if key == 'errbed' else True,
),
],
) for key, params in bed_datasets.items()
]
