import os
import numpy as np
import xarray as xr
from mpas_tools.scrip.from_mpas import scrip_from_mpas
from compass.landice.mesh import (
build_cell_width,
build_mali_mesh,
clean_up_after_interp,
interp_gridded2mali,
make_region_masks,
)
from compass.model import make_graph_file
from compass.step import Step
[docs]
class Mesh(Step):
"""
A step for creating a mesh and initial condition for greenland test cases
Attributes
----------
mesh_filename : str
File name of the MALI mesh
"""
[docs]
def __init__(self, test_case):
"""
Create the step
Parameters
----------
test_case : compass.TestCase
The test case this step belongs to
"""
super().__init__(test_case=test_case, name='mesh', cpus_per_task=128,
min_cpus_per_task=1)
# output files
self.mesh_filename = 'GIS.nc'
self.add_output_file(filename='graph.info')
self.add_output_file(filename=self.mesh_filename)
self.add_output_file(
filename=f'{self.mesh_filename[:-3]}_ismip6_regionMasks.nc')
self.add_output_file(
filename=f'{self.mesh_filename[:-3]}_zwally_regionMasks.nc')
# input files
self.add_input_file(
filename='greenland_1km_2024_01_29.epsg3413.icesheetonly.nc',
target='greenland_1km_2024_01_29.epsg3413.icesheetonly.nc',
database='')
self.add_input_file(filename='greenland_2km_2024_01_29.epsg3413.nc',
target='greenland_2km_2024_01_29.epsg3413.nc',
database='')
# no setup() method is needed
[docs]
def run(self):
"""
Run this step of the test case
"""
logger = self.logger
config = self.config
section_gis = config['greenland']
parallel_executable = config.get('parallel', 'parallel_executable')
nProcs = section_gis.get('nProcs')
src_proj = section_gis.get("src_proj")
data_path = section_gis.get('data_path')
measures_filename = section_gis.get("measures_filename")
bedmachine_filename = section_gis.get("bedmachine_filename")
measures_dataset = os.path.join(data_path, measures_filename)
bedmachine_dataset = os.path.join(data_path, bedmachine_filename)
section_name = 'mesh'
source_gridded_dataset_1km = 'greenland_1km_2024_01_29.epsg3413.icesheetonly.nc' # noqa: E501
source_gridded_dataset_2km = 'greenland_2km_2024_01_29.epsg3413.nc'
logger.info('calling build_cell_width')
cell_width, x1, y1, geom_points, geom_edges, floodMask = \
build_cell_width(
self, section_name=section_name,
gridded_dataset=source_gridded_dataset_2km,
flood_fill_start=[100, 700])
# Now build the base mesh and perform the standard interpolation
build_mali_mesh(
self, cell_width, x1, y1, geom_points, geom_edges,
mesh_name=self.mesh_filename, section_name=section_name,
gridded_dataset=source_gridded_dataset_1km,
projection=src_proj, geojson_file=None)
# Create scrip file for the newly generated mesh
logger.info('creating scrip file for destination mesh')
dst_scrip_file = f"{self.mesh_filename.split('.')[:-1][0]}_scrip.nc"
scrip_from_mpas(self.mesh_filename, dst_scrip_file)
# Now perform bespoke interpolation of geometry and velocity data
# from their respective sources
interp_gridded2mali(self, bedmachine_dataset, dst_scrip_file,
parallel_executable, nProcs,
self.mesh_filename, src_proj, variables="all")
# only interpolate a subset of MEaSUREs variables onto the MALI mesh
measures_vars = ['observedSurfaceVelocityX',
'observedSurfaceVelocityY',
'observedSurfaceVelocityUncertainty']
interp_gridded2mali(self, measures_dataset, dst_scrip_file,
parallel_executable, nProcs,
self.mesh_filename, src_proj,
variables=measures_vars)
# perform some final cleanup details
clean_up_after_interp(self.mesh_filename)
# create graph file
logger.info('creating graph.info')
make_graph_file(mesh_filename=self.mesh_filename,
graph_filename='graph.info')
# create region masks
mask_filename = f'{self.mesh_filename[:-3]}_ismip6_regionMasks.nc'
make_region_masks(self, self.mesh_filename, mask_filename,
self.cpus_per_task,
tags=["Greenland", "ISMIP6", "Shelf"],
component='ocean')
mask_filename = f'{self.mesh_filename[:-3]}_zwally_regionMasks.nc'
make_region_masks(self, self.mesh_filename, mask_filename,
self.cpus_per_task,
tags=['eastCentralGreenland',
'northEastGreenland',
'northGreenland',
'northWestGreenland',
'southEastGreenland',
'southGreenland',
'southWestGreenland',
'westCentralGreenland'],
all_tags=False)
# Do some final validation of the mesh
ds = xr.open_dataset(self.mesh_filename)
# Ensure basalHeatFlux is positive
ds["basalHeatFlux"] = np.abs(ds.basalHeatFlux)
# Ensure reasonable dHdt values
dHdt = ds["observedThicknessTendency"]
# Arbitrary 5% uncertainty; improve this later
dHdtErr = np.abs(dHdt) * 0.05
# Use threshold of |dHdt| > 1.0 to determine invalid data
mask = np.abs(dHdt) > 1.0
# Assign very large uncertainty where data is missing
dHdtErr = dHdtErr.where(~mask, 1.0)
# Remove ridiculous values
dHdt = dHdt.where(~mask, 0.0)
# Put the updated fields back in the dataset
ds["observedThicknessTendency"] = dHdt
ds["observedThicknessTendencyUncertainty"] = dHdtErr
# Write the data to disk
ds.to_netcdf(self.mesh_filename, 'a')