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mmctools

A repository for mesoscale-to-microscale coupling (MMC) preprocessing, postprocessing, and analysis tools

See the dev branch for the latest code under development.

Overview

These tools are intended to:

  1. Enable offline-coupled mesoscale-to-microscale simulation between a variety of mesoscale and microscale solvers
  2. Standardize output from simulations in addition to observational data
  3. Facilitate the analysis, assessment, and reporting of MMC results

The anticipated code structure is described in the sections below.

Offline coupling methods

  • One-way internal coupling with 2D mesoscale data f(t,z)
from mmctools.coupling.sowfa import InternalCoupling
to_sowfa = InternalCoupling(output_directory,
                            dataframe_with_driving_data,
                            dateref='YYYY-MM-DD HH:MM', # t=0 in simulation
                            datefrom='YYYY-MM-DD HH:MM', # output range
                            dateto='YYYY-MM-DD HH:MM')
# create internal source terms, f(t,z), from time-height series
to_sowfa.write_timeheight('forcingTable')
# create initial vertical profile, f(z)
to_sowfa.write_ICs('initialValues')
  • One-way boundary coupling with 4D mesoscale data f(t,x,y,z)
from mmctools.coupling.sowfa import BoundaryCoupling
to_sowfa = BoundaryCoupling(output_directory,
                            xarray_with_driving_data,
                            dateref='YYYY-MM-DD HH:MM', # t=0 in simulation
                            datefrom='YYYY-MM-DD HH:MM', # output range
                            dateto='YYYY-MM-DD HH:MM')
# create inflow planes, e.g., f(t,y,z) or f(t,x,z)
to_sowfa.write_boundarydata()
# create initial field, f(x,y,z)
to_sowfa.write_solution(t=datefrom)

Data processing

  • The dataloaders module provides tools to download and read datasets from the A2e: Data Archive and Portal (DAP). read_dir() and read_date_dirs() are wrappers around file readers such as pandas.read_csv(), a measurement-specific reader provided herein, or a user-defined reader function. For example:
from mmctools.dataloaders import read_dir
from mmctools.measurements.radar import profiler
# read selected files within a directory and concatenate into dataframe
df = read_dir(dpath, file_filter='*_w*', reader=profiler)

  • The measurements.* modules provide reader functions for meteorological mast instruments and remote sensing devices.

    • measurements.metmast
    • measurements.radar
    • measurements.lidar
    • measurements.sodar

  • The datawriters module provides tools to write out data for MMC analysis with consistent, A2e-MMC standard formats.

  • Additional code-specific modules and scripts are also provided (e.g., wrf.*) where needed.

Data analysis

  • helper_functions includes atmospheric science formulae and utility functions for converting between quantities of interest

  • plotting provides routines for visualization in the A2e-MMC style

Installation

The recommended approach is to first create a new conda environment:

conda create -n mmc python=3.7
conda activate mmc
conda install -y -c conda-forge jupyterlab matplotlib scipy xarray dask pyarrow gdal rasterio elevation pyyaml netcdf4 wrf-python cdsapi cfgrib

Note: All packages after xarray are optional:

  • dask makes netcdf data processing more efficient
  • pyarrow is a dependency for the "feather" data format, an extremely efficient way to save dataframe data (in terms file I/O time and file size)
  • gdal, rasterio, and elevation are required for processing terrain data
  • netcdf4 and wrf-python are for the NCAR-provided WRF utilities, which are useful for interpolating and slicing data
  • cdsapi is needed for wrf.preprocessing to retrieve Copernicus ERA5 reanalysis data
  • cfgrib enables xarray to load grib files

Then create an "editable" installation of the mmctools repository:

cd /path/to/a2e-mmc/mmctools
pip install -e .

windtools

This repository includes a subtree from https://github.com/NREL/windtools, which provides an essential plotting library (importable as from mmctools.plotting import ...) as well as other simulation post-processing helper modules.

Code Development Principles

  • All code should be usable in, and demonstrated by, Jupyter notebooks.
  • All code should be written in Python 3.
  • PEP 8 style guidelines should be followed as much as possible.

Acknowledgements

Mesoscale-to-Microscale Coupling (MMC) is a project within the Atmosphere to Electrons (A2e) Initiative, an effort within the Wind Energy Technologies Office of the U.S. Department of Energy’s (DOE’s) Energy Efficiency and Renewable Energy Office. The MMC project is a joint collaboration between six DOE national laboratories and the National Center for Atmospheric Research.