NEON Data

This example shows how to download data from NEON and ARM 2m surface meteorology stations on the North Slope and plot them

[DOWNLOADING] nsametC1.b1.20221001.000000.cdf
[DOWNLOADING] nsametC1.b1.20221002.000000.cdf
[DOWNLOADING] nsametC1.b1.20221003.000000.cdf
[DOWNLOADING] nsametC1.b1.20221004.000000.cdf
[DOWNLOADING] nsametC1.b1.20221005.000000.cdf
[DOWNLOADING] nsametC1.b1.20221006.000000.cdf
[DOWNLOADING] nsametC1.b1.20221007.000000.cdf

If you use these data to prepare a publication, please cite:

Kyrouac, J., Shi, Y., & Tuftedal, M. Surface Meteorological Instrumentation
(MET), 2022-10-01 to 2022-10-07, North Slope Alaska (NSA), Central Facility,
Barrow AK (C1). Atmospheric Radiation Measurement (ARM) User Facility.
https://doi.org/10.5439/1786358

/home/runner/work/ACT/ACT/act/io/arm.py:155: FutureWarning: In a future version of xarray the default value for data_vars will change from data_vars='all' to data_vars=None. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set data_vars explicitly.
  ds = xr.open_mfdataset(filenames, **kwargs)
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.variables.20230220T172200Z.csv
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.sensor_positions.20230220T172200Z.csv
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.000.010.030.SAAT_30min.2022-10.basic.20230220T172200Z.csv
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.EML.20221001-20221101.20250129T000730Z.xml
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.000.010.001.SAAT_1min.2022-10.basic.20230220T172200Z.csv
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.000.020.001.SAAT_1min.2022-10.basic.20230220T172200Z.csv
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.000.030.001.SAAT_1min.2022-10.basic.20230220T172200Z.csv
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.readme.20250129T000730Z.txt
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.000.030.030.SAAT_30min.2022-10.basic.20230220T172200Z.csv
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.000.020.030.SAAT_30min.2022-10.basic.20230220T172200Z.csv
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.variables.20230220T172200Z.csv
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.000.020.030.SAAT_30min.2022-10.expanded.20230220T172200Z.csv
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.000.020.001.SAAT_1min.2022-10.expanded.20230220T172200Z.csv
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.EML.20221001-20221101.20250129T000730Z.xml
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.sensor_positions.20230220T172200Z.csv
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.000.010.030.SAAT_30min.2022-10.expanded.20230220T172200Z.csv
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.000.010.001.SAAT_1min.2022-10.expanded.20230220T172200Z.csv
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.readme.20250129T000730Z.txt
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.000.030.001.SAAT_1min.2022-10.expanded.20230220T172200Z.csv
[DOWNLOADING]  NEON.D18.BARR.DP1.00002.001.000.030.030.SAAT_30min.2022-10.expanded.20230220T172200Z.csv

import os
import glob
import matplotlib.pyplot as plt

import act

# Place your username and token here
username = os.getenv('ARM_USERNAME')
token = os.getenv('ARM_PASSWORD')

if token is not None and len(token) > 0:
    # Download ARM data if a username/token are set
    files = act.discovery.download_arm_data(
        username, token, 'nsametC1.b1', '2022-10-01', '2022-10-07'
    )
    ds = act.io.arm.read_arm_netcdf(files)

    # Download NEON Data
    # NEON sites can be found through the NEON website
    # https://www.neonscience.org/field-sites/explore-field-sites
    site_code = 'BARR'
    product_code = 'DP1.00002.001'
    result = act.discovery.neon.download_neon_data(site_code, product_code, '2022-10')

    # A number of files are downloaded and further explained in the readme file that's downloaded.
    # These are the files we will need for reading 1 minute NEON data
    file = glob.glob(
        os.path.join(
            '.',
            'BARR_DP1.00002.001',
            'NEON.D18.BARR.DP1.00002.001.000.010.001.SAAT_1min.2022-10.expanded.*.csv',
        )
    )
    variable_file = glob.glob(
        os.path.join('.', 'BARR_DP1.00002.001', 'NEON.D18.BARR.DP1.00002.001.variables.*.csv')
    )
    position_file = glob.glob(
        os.path.join(
            '.',
            'BARR_DP1.00002.001',
            'NEON.D18.BARR.DP1.00002.001.sensor_positions.*.csv',
        )
    )
    # Read in the data using the ACT reader, passing with it the variable and position files
    # for added information in the dataset
    ds2 = act.io.read_neon_csv(file, variable_files=variable_file, position_files=position_file)

    # Plot up the two datasets
    display = act.plotting.TimeSeriesDisplay({'ARM': ds, 'NEON': ds2})
    display.plot('temp_mean', 'ARM', marker=None, label='ARM')
    display.plot('tempSingleMean', 'NEON', marker=None, label='NEON')
    display.day_night_background('ARM')
    plt.show()