Source code for mpas_analysis.ocean.climatology_map_eke

# This software is open source software available under the BSD-3 license.
#
# Copyright (c) 2020 Triad National Security, LLC. All rights reserved.
# Copyright (c) 2020 Lawrence Livermore National Security, LLC. All rights
# reserved.
# Copyright (c) 2020 UT-Battelle, LLC. All rights reserved.
#
# Additional copyright and license information can be found in the LICENSE file
# distributed with this code, or at
# https://raw.githubusercontent.com/MPAS-Dev/MPAS-Analysis/master/LICENSE
from __future__ import absolute_import, division, print_function, \
    unicode_literals

import xarray as xr
from pyremap import LatLonGridDescriptor

from mpas_analysis.shared import AnalysisTask

from mpas_analysis.shared.io.utility import build_obs_path

from mpas_analysis.shared.climatology import RemapMpasClimatologySubtask, \
    RemapObservedClimatologySubtask

from mpas_analysis.ocean.plot_climatology_map_subtask import \
    PlotClimatologyMapSubtask


[docs]class ClimatologyMapEKE(AnalysisTask): # {{{ """ An analysis task for comparison of eddy kinetic energy (eke) against observations """ # Authors # ------- # Kevin Rosa
[docs] def __init__(self, config, mpasClimatologyTask, controlConfig=None): # {{{ """ Construct the analysis task. Parameters ---------- config : ``MpasAnalysisConfigParser`` Configuration options mpasClimatologyTask : ``MpasClimatologyTask`` The task that produced the climatology to be remapped and plotted controlConfig : ``MpasAnalysisConfigParser``, optional Configuration options for a control run (if any) """ # Authors # ------- # Xylar Asay-Davis, Kevin Rosa fieldName = 'eke' # call the constructor from the base class (AnalysisTask) super(ClimatologyMapEKE, self).__init__( config=config, taskName='climatologyMapEKE', componentName='ocean', tags=['climatology', 'horizontalMap', fieldName, 'publicObs']) mpasFieldName = 'eke' iselValues = {'nVertLevels': 0} sectionName = self.taskName # read in what seasons we want to plot seasons = config.getExpression(sectionName, 'seasons') # EKE observations are annual climatology so only accept annual # climatology **should move this to setup_and_check() if seasons != ['ANN']: raise ValueError('config section {} does not contain valid list ' 'of seasons. For EKE, may only request annual ' 'climatology'.format(sectionName)) comparisonGridNames = config.getExpression(sectionName, 'comparisonGrids') if len(comparisonGridNames) == 0: raise ValueError('config section {} does not contain valid list ' 'of comparison grids'.format(sectionName)) # the variables in variableList will be added to mpasClimatologyTask # along with the seasons. variableList = ['timeMonthly_avg_velocityZonal', 'timeMonthly_avg_velocityMeridional', 'timeMonthly_avg_velocityZonalSquared', 'timeMonthly_avg_velocityMeridionalSquared'] remapClimatologySubtask = RemapMpasEKEClimatology( mpasClimatologyTask=mpasClimatologyTask, parentTask=self, climatologyName=fieldName, variableList=variableList, comparisonGridNames=comparisonGridNames, seasons=seasons, iselValues=iselValues) # to compare to observations: if controlConfig is None: refTitleLabel = \ 'Observations (Surface EKE from Drifter Data)' observationsDirectory = build_obs_path( config, 'ocean', '{}Subdirectory'.format(fieldName)) obsFileName = \ "{}/drifter_variance_20180804.nc".format( observationsDirectory) refFieldName = 'eke' outFileLabel = 'ekeDRIFTER' galleryName = 'Observations: EKE from Drifters' remapObservationsSubtask = RemapObservedEKEClimatology( parentTask=self, seasons=seasons, fileName=obsFileName, outFilePrefix=refFieldName, comparisonGridNames=comparisonGridNames) self.add_subtask(remapObservationsSubtask) diffTitleLabel = 'Model - Observations' # compare with previous run: else: remapObservationsSubtask = None controlRunName = controlConfig.get('runs', 'mainRunName') galleryName = None refTitleLabel = 'Control: {}'.format(controlRunName) refFieldName = mpasFieldName outFileLabel = 'eke' diffTitleLabel = 'Main - Control' for comparisonGridName in comparisonGridNames: for season in seasons: # make a new subtask for this season and comparison grid subtask = PlotClimatologyMapSubtask( self, season, comparisonGridName, remapClimatologySubtask, remapObservationsSubtask, controlConfig=controlConfig) subtask.set_plot_info( outFileLabel=outFileLabel, fieldNameInTitle='EKE', mpasFieldName=mpasFieldName, refFieldName=refFieldName, refTitleLabel=refTitleLabel, diffTitleLabel=diffTitleLabel, unitsLabel=r'cm$^2$/s$^2$', imageCaption='Mean Surface Eddy Kinetic Energy', galleryGroup='Eddy Kinetic Energy', groupSubtitle=None, groupLink='eke', galleryName=galleryName) self.add_subtask(subtask)
# }}} # }}} # adds to the functionality of RemapDepthSlicesSubtask class RemapMpasEKEClimatology(RemapMpasClimatologySubtask): # {{{ """ A subtask for computing climatologies of eddy kinetic energy from means of velocity and velocity-squared. """ # Authors # ------- # Kevin Rosa def customize_masked_climatology(self, climatology, season): # {{{ """ Construct velocity magnitude as part of the climatology Parameters ---------- climatology : ``xarray.Dataset`` object the climatology data set season : str The name of the season to be masked Returns ------- climatology : ``xarray.Dataset`` object the modified climatology data set """ # Authors # ------- # Xylar Asay-Davis # first, call the base class's version of this function so we extract # the desired slices. climatology = super(RemapMpasEKEClimatology, self).customize_masked_climatology(climatology, season) # calculate mpas eddy kinetic energy scaleFactor = 100 * 100 # m2/s2 to cm2/s2 eke = 0.5 * scaleFactor * \ (climatology.timeMonthly_avg_velocityZonalSquared - climatology.timeMonthly_avg_velocityZonal ** 2 + climatology.timeMonthly_avg_velocityMeridionalSquared - climatology.timeMonthly_avg_velocityMeridional ** 2) # drop unnecessary fields before re-mapping climatology.drop_vars(['timeMonthly_avg_velocityZonal', 'timeMonthly_avg_velocityMeridional', 'timeMonthly_avg_velocityZonalSquared', 'timeMonthly_avg_velocityMeridionalSquared']) # this creates a new variable eke in climatology (like netcdf) climatology['eke'] = eke climatology.eke.attrs['units'] = 'cm$^[2]$ s$^{-2}$' climatology.eke.attrs['description'] = 'eddy kinetic energy' return climatology # }}} # }}} class RemapObservedEKEClimatology(RemapObservedClimatologySubtask): # {{{ """ A subtask for reading and remapping EKE observations """ # Authors # ------- # Kevin Rosa def get_observation_descriptor(self, fileName): # {{{ ''' get a MeshDescriptor for the observation grid Parameters ---------- fileName : str observation file name describing the source grid Returns ------- obsDescriptor : ``MeshDescriptor`` The descriptor for the observation grid ''' # Authors # ------- # Kevin Rosa # create a descriptor of the observation grid using the lat/lon # coordinates obsDescriptor = LatLonGridDescriptor.read(fileName=fileName, latVarName='Lat', lonVarName='Lon') return obsDescriptor # }}} def build_observational_dataset(self, fileName): # {{{ ''' read in the data sets for observations, and possibly rename some variables and dimensions Parameters ---------- fileName : str observation file name Returns ------- dsObs : ``xarray.Dataset`` The observational dataset ''' # Authors # ------- # Kevin Rosa, Xylar Asay-Davis dsIn = xr.open_dataset(fileName) scaleFactor = 100 * 100 # m2/s2 to cm2/s2 eke = 0.5 * scaleFactor * \ (dsIn['Up2bar'].values + dsIn['Vp2bar'].values) # create a new dataset for the observations. solves transpose issues. dsObs = xr.Dataset({'eke': (['latitude', 'longitude'], eke.T)}, coords={'Lat': (['latitude'], dsIn.Lat.values), 'Lon': (['longitude'], dsIn.Lon.values)} ) # update attributes dsObs.eke.attrs['units'] = 'cm$^2$ s$^{-2}$' dsObs.eke.attrs['long_name'] = 'Eddy kinetic energy' return dsObs # }}} # }}} # vim: foldmethod=marker ai ts=4 sts=4 et sw=4 ft=python