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 _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 make_layers() -> list[Layer]:
return [
Layer(
id=f'continental_shelf_{key}',
title=params['title'],
description=params['description'],
tags=[],
input=LayerInput(
dataset=dataset,
asset=dataset.assets[key],
),
steps=[
decompress_step(input_file='{input_dir}/*.zip', ),
CommandStep(args=[
'ogr2ogr',
*STANDARD_OGR2OGR_ARGS,
'-makevalid',
'{output_dir}/final.gpkg',
'{input_dir}/*.shp',
], ),
],
) for key, params in LAYER_PARAMS.items()
]
def compressed_vector(
*,
input_file: str,
output_file: str,
vector_filename: str = '*.shp',
decompress_step_kwargs=default_decompress_step_kwargs,
ogr2ogr_args: StepArgs = (),
boundary_filepath: EvalFilePath = project.boundaries['background'].
filepath,
) -> list[CommandStep]:
"""Unzip a vector data file and reproject."""
return [
decompress_step(
input_file=input_file,
**decompress_step_kwargs,
),
*ogr2ogr(
input_file='{input_dir}/' + vector_filename,
output_file=output_file,
boundary_filepath=boundary_filepath,
ogr2ogr_args=ogr2ogr_args,
),
]
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
ice_thickness_change = Layer(
id='ice_thickness_change',
title='Ice column thickness rate of change 2003-2019 (5km)',
description=(
"""Ice column thickness-change-rate estimates in meters per year based
on data from NASA's ICESat and ICESat-2 satellites."""
),
tags=[],
style='ice_thickness_change',
input=LayerInput(
dataset=dataset,
asset=dataset.assets['only'],
),
steps=[
decompress_step(
input_file='{input_dir}/ICESat1_ICESat2_mass_change.zip',
decompress_contents_mask=SOURCE_FP,
),
*warp(
input_file='{input_dir}/' + SOURCE_FP,
output_file='{output_dir}/warped.tif',
cut_file=project.boundaries['data'].filepath,
),
*compress_and_add_overviews(
input_file='{input_dir}/warped.tif',
output_file='{output_dir}/final.tif',
dtype_is_float=True,
),
],
)
soil_types = Layer(
id='soil_types',
title='Soil characteristics',
description=(
"""Polygons representing dominant soil characteristics with percentage
polygon area for each soil type. Data coverage limited to Greenland."""
),
tags=[],
style='soil_types',
input=LayerInput(
dataset=dataset,
asset=dataset.assets['only'],
),
steps=[
decompress_step(
decompress_type='gzip',
input_file='{input_dir}/*.gz',
),
*ogr2ogr(
input_file='{input_dir}/ggd602_soils_greenland.shp',
output_file='{output_dir}/soil_types.gpkg',
boundary_filepath=project.boundaries['data'].filepath,
ogr2ogr_args=(
'-s_srs',
'"+proj=laea +a=6370997.00 +b=6370997.00 +lat_0=90 +lon_0=180 +x_0=0 +y_0=0"',
),
),
],
)
layers = [
Layer(
id=layer_id,
title=params['title'],
description=params['description'],
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,
]
background = Layer(
id='background',
title='Background (500m)',
description='Stylized shaded-relief map for providing a general sense of geography.',
tags=['background', 'shaded-relief'],
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
