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 _make_masked_racmo_layer(
*,
layer_id: str,
title: str,
description: str,
style: str,
input_filename: str,
decompress_contents_mask: str,
variable: str,
nodata: int = -9999,
gdal_edit_args=(),
) -> 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}/RACMO_QGreenland_Jan2021.zip',
decompress_contents_mask=decompress_contents_mask,
),
# Apply the promice mask. The `Promicemask` values are 3 = Greenland ice
# sheet; 2,1 = Greenland peripheral ice caps; 0 = Ocean. This step masks
# out the ocean as 'nodata'.
CommandStep(args=[
'gdal_calc.py',
f'--calc="numpy.where((B != 0), A, {nodata})"',
f'--NoDataValue={nodata}',
'--outfile={output_dir}/' + f'{variable}.tif',
'-A',
'NETCDF:{input_dir}/' + f'{input_filename}:{variable}',
'-B',
('NETCDF:{input_dir}/'
'Icemask_Topo_Iceclasses_lon_lat_average_1km_GrIS.nc:Promicemask'
),
], ),
*gdal_edit(
input_file='{input_dir}/' + f'{variable}.tif',
output_file='{output_dir}/edited.tif',
gdal_edit_args=[
'-a_srs',
project.crs,
*gdal_edit_args,
],
),
*compress_and_add_overviews(
input_file='{input_dir}/edited.tif',
output_file='{output_dir}/' + f'racmo_{variable}.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 surface_elevation_layer(
*,
array_index: int,
start_year: int,
end_year: int,
variable: SurfaceElevVar,
) -> Layer:
if variable == 'SEC':
description = 'Rate of surface elevation change in meters per year.'
style = 'surface_elevation_change'
else:
description = 'Error of rate of surface elevation change in meters per year.'
style = 'surface_elevation_change_errors'
return Layer(
id=f'surface_elevation_change_{variable.lower()}_{start_year}_{end_year}',
title=f'Surface elevation change {start_year}-{end_year}',
description=description,
tags=[],
style=style,
input=LayerInput(
dataset=dataset,
asset=dataset.assets['only'],
),
steps=[
CommandStep(
args=[
'gdalmdimtranslate',
'-co', 'COMPRESS=DEFLATE',
'-array', f'name={variable},view=[:,:,{array_index}]',
'{input_dir}/Release/CCI_GrIS_RA_SEC_5km_Vers2.0_2020-08-26.nc',
'{output_dir}/' + f'{variable.lower()}_{start_year}_{end_year}.tif',
],
),
*compress_and_add_overviews(
input_file=(
'{input_dir}/'
f'{variable.lower()}_{start_year}_{end_year}.tif'
),
output_file='{output_dir}/overviews.tif',
dtype_is_float=True,
),
],
)
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,
),
],
)
cut_file=project.boundaries['data'].filepath,
),
CommandStep(args=[
'gdal_calc.py',
'--calc',
'"A * 100.0"',
'--NoDataValue',
'-9999',
'--type',
'Int32',
'-A',
'{input_dir}/arctic_dem.tif',
'--outfile',
'{output_dir}/arctic_dem_scaled.tif',
], ),
*gdal_edit(
input_file='{input_dir}/arctic_dem_scaled.tif',
output_file='{output_dir}/arctic_dem.tif',
gdal_edit_args=[
'-scale',
'0.01',
],
),
*compress_and_add_overviews(
input_file='{input_dir}/arctic_dem.tif',
output_file='{output_dir}/arctic_dem.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
]
the year 3007. Values less than or equal to 16% are masked."""),
tags=[],
style='future_ice_sheet_coverage',
input=LayerInput(
dataset=dataset,
asset=dataset.assets[f'rcp_{rcp}'],
),
steps=[
*warp(
input_file=(
'NETCDF:{input_dir}/'
f'percent_gris_g1800m_v3a_rcp_{rcp}_0_1000.nc:sftgif'),
output_file='{output_dir}/extracted.tif',
warp_args=(
'-srcnodata',
'0',
'-tr',
'1800',
'1800',
),
cut_file=project.boundaries['data'].filepath,
),
*compress_and_add_overviews(
input_file='{input_dir}/extracted.tif',
output_file='{output_dir}/final.tif',
dtype_is_float=True,
),
],
) for rcp in LAYER_RCPS
]
*warp(
input_file='NETCDF:{input_dir}/IBCAO_v4_400m_ice.nc:z',
output_file='{output_dir}/bathymetric_chart.tif',
warp_args=(
'-s_srs', '"+proj=stere +lat_0=90 +lat_ts=75 +datum=WGS84"',
'-dstnodata', '-9999',
'-tr', '400', '400',
# 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['background'].filepath,
),
*compress_and_add_overviews(
input_file='{input_dir}/bathymetric_chart.tif',
output_file='{output_dir}/bathymetric_chart.tif',
dtype_is_float=True,
),
],
)
bathymetric_contours = make_layer(
layer_id=bathymetric_contours_params['id'],
layer_params=bathymetric_contours_params,
)
CommandStep(
id='merge_bands',
args=[
'gdal_merge.py',
'-o', '{output_dir}/merged.tif',
'-co', 'COMPRESS=DEFLATE',
'-separate',
'{input_dir}/b1.tif',
'{input_dir}/b2.tif',
'{input_dir}/b3.tif',
'{input_dir}/mask.tif',
],
),
# Finally, add compression with overviews and use the newly added band 4
# as a mask.
*compress_and_add_overviews(
input_file='{input_dir}/merged.tif',
output_file='{output_dir}/overviews.tif',
compress_type='JPEG',
compress_args=[
'-co', 'JPEG_QUALITY=90',
'-co', 'PHOTOMETRIC=YCBCR',
'-b', '1', '-b', '2', '-b', '3',
# The `-mask 4` option sets band 4 as the mask.
'-mask', '4',
'--config', 'GDAL_TIFF_INTERNAL_MASK', 'YES',
],
),
],
)
def make_racmo_supplemental_layers() -> list[Layer]:
layers = []
_racmo_mask_layer_params = {
'racmo_promicemask': {
'title':
'PROMICE mask (1km)',
'description':
("""Mask of categorized Greenland ice. 3 = Greenland ice sheet; 2,1 = Greenland
peripheral ice caps; 0 = Ocean."""),
'extract_filename':
'Icemask_Topo_Iceclasses_lon_lat_average_1km_GrIS.nc',
'variable':
'Promicemask',
},
'racmo_grounded_ice': {
'title': 'Grounded ice mask (1km)',
'description': 'Mask of grounded ice. 1 = grounded.',
'extract_filename':
'Icemask_Topo_Iceclasses_lon_lat_average_1km_Aug2020.nc',
'variable': 'grounded_ice',
},
}
for layer_id, params in _racmo_mask_layer_params.items():
layers.append(
Layer(
id=layer_id,
title=params['title'],
description=params['description'],
tags=[],
style='racmo_promicemask',
input=LayerInput(
dataset=dataset,
asset=dataset.assets['only'],
),
steps=[
decompress_step(
input_file='{input_dir}/RACMO_QGreenland_Jan2021.zip',
decompress_contents_mask=params['extract_filename'],
),
CommandStep(
args=[
'gdal_translate',
'-a_srs',
project.crs,
'-a_ullr',
RACMO_ULLR,
# Data is stored as Float32 but uses integers for mask values.
'-ot',
'Byte',
'-a_nodata',
'none',
('NETCDF:{input_dir}/' +
f"{params['extract_filename']}:{params['variable']}"
),
'{output_dir}/' + f"{params['variable']}.tif",
], ),
*compress_and_add_overviews(
input_file='{input_dir}/' +
f"{params['variable']}.tif",
output_file='{output_dir}/' + f'{layer_id}.tif',
dtype_is_float=False,
),
],
), )
racmo_topography = _make_masked_racmo_layer(
layer_id='racmo_topography',
title='Ice surface topography (1km)',
description=
("""Ice sheet surface elevation in meters upscaled from the Greenland Mapping
Project (GIMP) Digital Elevation Model."""),
style='racmo_topography',
decompress_contents_mask=
'Icemask_Topo_Iceclasses_lon_lat_average_1km_GrIS.nc',
input_filename='Icemask_Topo_Iceclasses_lon_lat_average_1km_GrIS.nc',
variable='Topography',
gdal_edit_args=[
'-a_ullr',
RACMO_ULLR,
],
)
layers.append(racmo_topography)
return layers
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
heat flow map (10km)' layer."""),
tags=[],
input=LayerInput(
dataset=dataset,
asset=dataset.assets['heat_flow_measurements'],
id='vegetation_biomass_2010',
title='Vegetation biomass 2010 (12.4km)',
description=(
"""Estimated above ground plant biomass for the tundra biome in
kilograms per square meter. Based on trans-Arctic field data and AVHRR
NDVI. Data provided by ORNL DAAC."""),
tags=[],
style='vegetation_biomass',
input=LayerInput(
dataset=dataset,
asset=dataset.assets['only'],
),
steps=[
*warp(
input_file='{input_dir}/aga_circumpolar_avhrr_biomass_2010.tif',
output_file='{output_dir}/warped.tif',
cut_file=project.boundaries['data'].filepath,
warp_args=(
'-tr',
'12400',
'12400',
),
),
*compress_and_add_overviews(
input_file='{input_dir}/warped.tif',
output_file='{output_dir}/compressed.tif',
dtype_is_float=True,
),
],
)
'gdal_calc.py',
'--type',
'Int16',
# Set a nodata value of 3. This value does not occur in the data
# (valid values are -1, 0, 1).
'--NoDataValue',
'3',
# This dataset contains nans. Replace them with the nodata value (3)
'--calc="nan_to_num(A, nan=3)"',
'-A',
'{input_dir}/basal_thermal_state.tif',
'--outfile={output_dir}/basal_thermal_state.tif',
], ),
*gdal_edit(
input_file='{input_dir}/basal_thermal_state.tif',
output_file='{output_dir}/basal_thermal_state.tif',
gdal_edit_args=[
'-a_ullr',
'-632500.0 -667500.0 847500.0 -3342500.0',
'-a_srs',
'EPSG:3413',
],
),
*compress_and_add_overviews(
input_file='{input_dir}/basal_thermal_state.tif',
output_file='{output_dir}/basal_thermal_state.tif',
dtype_is_float=False,
),
],
)
("""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()
]
'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."""),
in_package=False,
tags=[],
input=LayerInput(
dataset=dataset,
asset=dataset.assets['only'],
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',
output_file='{output_dir}/' + f'{layer_id}.tif',
dtype_is_float=True,
),
],
) for layer_id, params in _layer_params.items()
]
from qgreenland.config.datasets.dms_gtk import (
danish_agency_for_data_supply_and_efficiency_gtk_topo_map as dataset, )
from qgreenland.config.helpers.steps.compress_and_add_overviews import compress_and_add_overviews
from qgreenland.models.config.layer import Layer, LayerInput
dms_gtk_topo = Layer(
id='dms_gtk_topo',
title='Topographic map (1 to 500,000)',
description=("""Digitized and geolocated topographic map of Greenland."""),
tags=[],
input=LayerInput(
dataset=dataset,
asset=dataset.assets['only'],
),
steps=[
# NOTE: This layer is not reprojected. We had some issues in the distant
# past reprojecting this layer correctly. Its internal CRS is
# `EPSG:32624`.
*compress_and_add_overviews(
input_file='{input_dir}/dms_gtk_topo.tif',
output_file='{output_dir}/final.tif',
dtype_is_float=False,
),
],
)
compress_and_add_overviews,
)
from qgreenland.models.config.layer import Layer, LayerInput
layers = [Layer(
id=f'albedo_{year}_07',
title=f'July {year} albedo (1km)',
description=(
f"""Average broadband planar albedo for July {year} derived from
the Ocean and Land Colour Instrument (OLCI) on board the European Union
Copernicus Sentinel-3A satellite.
Albedo is a fractional value ranging from 0-1 representing the amount of
incident solar radiation reflected from the surface."""
),
tags=[],
style='albedo',
input=LayerInput(
dataset=dataset,
asset=dataset.assets[f'{year}_07'],
),
steps=[
*compress_and_add_overviews(
input_file='{input_dir}/*.tif',
output_file='{output_dir}/' + f'albedo_{year}_07.tif',
dtype_is_float=True,
),
],
) for year in (2018, 2019)]