import os
import netCDF4
from mpas_tools.logging import check_call
from mpas_tools.scrip.from_mpas import scrip_from_mpas
from compass.landice.mesh import (
add_bedmachine_thk_to_ais_gridded_data,
build_cell_width,
build_mali_mesh,
clean_up_after_interp,
interp_gridded2mali,
make_region_masks,
preprocess_ais_data,
)
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 Antarctica 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)
self.mesh_filename = 'Antarctica.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]}_imbie_regionMasks.nc')
self.add_output_file(
filename=f'{self.mesh_filename[:-3]}_ismip6_regionMasks.nc')
self.add_input_file(
filename='antarctica_8km_2024_01_29.nc',
target='antarctica_8km_2024_01_29.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_ais = config['antarctica']
nProcs = section_ais.get('nProcs')
src_proj = section_ais.get("src_proj")
data_path = section_ais.get('data_path')
measures_filename = section_ais.get("measures_filename")
bedmachine_filename = section_ais.get("bedmachine_filename")
measures_dataset = os.path.join(data_path, measures_filename)
bedmachine_dataset = os.path.join(data_path, bedmachine_filename)
section_name = 'mesh'
# TODO: do we want to add this to the config file?
source_gridded_dataset = 'antarctica_8km_2024_01_29.nc'
bm_updated_gridded_dataset = add_bedmachine_thk_to_ais_gridded_data(
self, source_gridded_dataset, bedmachine_dataset)
logger.info('calling build_cell_width')
cell_width, x1, y1, geom_points, geom_edges, floodFillMask = \
build_cell_width(
self, section_name=section_name,
gridded_dataset=bm_updated_gridded_dataset)
# 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=bm_updated_gridded_dataset,
projection=src_proj, geojson_file=None)
# Now that we have base mesh with standard interpolation
# perform advanced interpolation for specific fields
# that require more careful treatment
# Add iceMask for later trimming if not already in file.
# It should be automatically added as of MPAS-Tools commit
# df90de2c434ed24bbbaf9ca353c2a91de1140654
# Aug 8, 2022, but safest to double check here.
data = netCDF4.Dataset(self.mesh_filename, 'r+')
if 'iceMask' not in data.variables:
data.createVariable('iceMask', 'f', ('Time', 'nCells'))
data.variables['iceMask'][:] = 0.
data.close()
# Preprocess the gridded AIS source datasets to work
# with the rest of the workflow
logger.info('calling preprocess_ais_data')
preprocessed_gridded_dataset = preprocess_ais_data(
self, bm_updated_gridded_dataset, floodFillMask)
# interpolate fields from *preprocessed* composite dataset
# NOTE: while this has already been done in `build_mali_mesh()`
# we are using an updated version of the gridded dataset here,
# which has had unit conversion and extrapolation done.
# Also, it should be assessed if bilinear or
# barycentric used here is preferred for this application.
# Current thinking is they are both equally appropriate.
logger.info('calling interpolate_to_mpasli_grid.py')
args = ['interpolate_to_mpasli_grid.py', '-s',
preprocessed_gridded_dataset,
'-d',
self.mesh_filename,
'-m', 'd', '-v',
'floatingBasalMassBal', 'basalHeatFlux', 'sfcMassBal',
'surfaceAirTemperature', 'observedThicknessTendency',
'observedThicknessTendencyUncertainty', 'thickness']
check_call(args, logger=logger)
# 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, 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, 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 a region mask
mask_filename = f'{self.mesh_filename[:-3]}_imbie_regionMasks.nc'
make_region_masks(self, self.mesh_filename, mask_filename,
self.cpus_per_task,
tags=['EastAntarcticaIMBIE',
'WestAntarcticaIMBIE',
'AntarcticPeninsulaIMBIE'],
all_tags=False)
mask_filename = f'{self.mesh_filename[:-3]}_ismip6_regionMasks.nc'
make_region_masks(self, self.mesh_filename, mask_filename,
self.cpus_per_task,
tags=['ISMIP6_Basin'])