ACT Basics

Overview

The ARM COMBLE campaign collected a lot of very interesting data in Norway from December 1 2019 - May 31 2020. Here we look at a case study from March 9 to March 16, 2020. This notebook will give an introduction to basic features in ACT, using some relevant datastreams from this event

1. Intro to ACT

2. Downloading and Reading in Data

3. Quality Controlling Data

4. Aerosol Instrument Overview

5. Visualizing Data

6. Additional Features in ACT

Prerequisites

This notebook will rely heavily on Python and the Atmospheric data Community Toolkit (ACT). Don’t worry if you don’t have experience with either, this notebook will walk you though what you need to know.

You will also need an account and token to download data using the ARM Live webservice. Navigate to the webservice information page and log in to get your token. Your account username will be your ARM username.

Concepts	Importance	Notes
ACT	Helpful

• Time to learn: 60 Minutes

• System requirements:

  • Python 3.11 or latest

  • ACT v1.5.0 or latest

  • numpy

  • xarray

  • matplotlib

---

Intro to ACT

The Atmospheric data Community Toolkit (ACT) is an open-source Python toolkit for exploring and analyzing atmospheric time-series datasets. Examples can be found in the ACT Example Gallery. The toolkit has modules for many different parts of the scientific process, including:

Data Discovery (act.discovery) The discovery module houses functions to download or access data from different groups. Currently it includes function to get data for ARM, NOAA, EPA, NEON, and more! Input/Output (act.io) io contains functions for reading and writing data from various sources and formats. Visualization (act.plotting) plotting contains various routines, built on matplotlib, to help visualize and explore data. These include Time-series plots Distribution plots like histograms and heatmaps Geographic plots for moving systems like radiosondes or aircraft Skew-T plots for radiosonde data, built off MetPy Wind rose plots for wind and data roses Cross-section plots for working with 3-dimensional data Corrections (act.corrections) corrections apply different corrections to data based on need. A majority of the existing corrections are for lidar data. Quality Control (act.qc) The qc module has a lot of functions for working with quality control information, apply new tests, or filtering data based on existing tests. We will explore some of that functionality in this notebook. Retrievals (act.retrievals) There are many cases in which some additional calculations are necessary to get more value from the instrument data. The retrievals module houses some functions for performing these advanced calculations. Utilities (act.utils) The utils module has a lot of general utilities to help with the data. Some of these include adding in a solar variable to indicate day/night (useful in filtering data), unit conversions, decoding WMO weather codes, performing weighted averaging, etc…

ARM and NEON data from Utquivaik, AK Enhanced Skew-T plot from ARM’s Southern Great Plains Site (SGP)

---

Imports

Let’s get started with some data! But first, we need to import some libraries.

import act
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt

Downloading and Reading ARM’s NetCDF Data

ARM’s standard file format is NetCDF (network Common Data Form) which makes it very easy to work with in Python! ARM data are available through a data portal called Data Discovery or through a webservice. If you didn’t get your username and token earlier, please go back and see the Prerequisites!

Let’s download some of the MPL data first but let’s just start with one day.

# Set your username and token here!
username = 'USERNAME'
token = 'TOKEN'

# Set the datastream and start/enddates
datastream = 'anxmplpolfsM1.b1'
startdate = '2020-03-12'
enddate = '2020-03-13'

# Use ACT to easily download the data.  Watch for the data citation!  Show some support
# for ARM's instrument experts and cite their data if you use it in a publication
result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)

[DOWNLOADING] anxmplpolfsM1.b1.20200312.000005.nc

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

Muradyan, P., Cromwell, E., Koontz, A., Coulter, R., Flynn, C., Ermold, B., &
OBrien, J. Micropulse Lidar (MPLPOLFS). Atmospheric Radiation Measurement (ARM)
User Facility. https://doi.org/10.5439/1320657

# Let's read in the data using ACT and check out the data
ds_mpl = act.io.read_arm_netcdf(result)

ds_mpl

<xarray.Dataset> Size: 277MB
Dimensions:                          (time: 8640, range_bins: 3990,
                                      num_darkcount_corr: 3990,
                                      num_deadtime_corr: 23,
                                      num_overlap_corr: 332)
Coordinates:
  * time                             (time) datetime64[ns] 69kB 2020-03-12T00...
  * range_bins                       (range_bins) float32 16kB 0.01499 ... 59.81
Dimensions without coordinates: num_darkcount_corr, num_deadtime_corr,
                                num_overlap_corr
Data variables: (12/46)
    base_time                        datetime64[ns] 8B 2020-03-12
    time_offset                      (time) datetime64[ns] 69kB 2020-03-12T00...
    num_channels                     int32 4B ...
    num_bins                         int32 4B ...
    range                            (range_bins) float32 16kB dask.array<chunksize=(3990,), meta=np.ndarray>
    height                           (range_bins) float32 16kB dask.array<chunksize=(3990,), meta=np.ndarray>
    ...                               ...
    qc_signal_return_co_pol          (time) int32 35kB dask.array<chunksize=(8640,), meta=np.ndarray>
    first_data_bin                   (time) int32 35kB dask.array<chunksize=(8640,), meta=np.ndarray>
    laser_fire_bin                   (time) int32 35kB dask.array<chunksize=(8640,), meta=np.ndarray>
    lat                              float32 4B ...
    lon                              float32 4B ...
    alt                              float32 4B ...
Attributes: (12/33)
    command_line:                mplpolfs_ingest -s anx -f M1
    Conventions:                 ARM-1.2
    process_version:             ingest-mplpolfs-1.23-0.el7
    ingest_software:             ingest-mplpolfs-1.23-0.el7
    dod_version:                 mplpolfs-b1-3.0
    site_id:                     anx
    ...                          ...
    doi:                         10.5439/1320657
    history:                     created by user dsmgr on machine flint at 20...
    _file_dates:                 ['20200312']
    _file_times:                 ['000005']
    _datastream:                 anxmplpolfsM1.b1
    _arm_standards_flag:         1

xarray.Dataset

• Dimensions:
  • time: 8640
  • range_bins: 3990
  • num_darkcount_corr: 3990
  • num_deadtime_corr: 23
  • num_overlap_corr: 332
• Coordinates: (2)
  • time
    (time)
    datetime64[ns]
    2020-03-12T00:00:05 ... 2020-03-...

    long_name :

    Time offset from midnight

    array(['2020-03-12T00:00:05.000000000', '2020-03-12T00:00:15.000000000',
           '2020-03-12T00:00:25.000000000', ..., '2020-03-12T23:59:37.000000000',
           '2020-03-12T23:59:47.000000000', '2020-03-12T23:59:57.000000000'],
          dtype='datetime64[ns]')

  • range_bins
    (range_bins)
    float32
    0.01499 0.02998 ... 59.79 59.81

    long_name :

    Distance from leading edge of first range bin to the center of each bin.

    units :

    km

    comment1 :

    This value does not account for the range offset due to mistiming of the laser firing and the multichannel scalar trigger

    resolution :

    0.001

    accuracy :

    2% scale factor +/- 90 meters scale offset

    array([1.498962e-02, 2.997925e-02, 4.496887e-02, ..., 5.977593e+01,
           5.979092e+01, 5.980591e+01], dtype=float32)

• Data variables: (46)
  • base_time
    ()
    datetime64[ns]
    2020-03-12

    string :

    2020-03-12 00:00:00 0:00

    long_name :

    Base time in Epoch

    ancillary_variables :

    time_offset

    array('2020-03-12T00:00:00.000000000', dtype='datetime64[ns]')

  • time_offset
    (time)
    datetime64[ns]
    2020-03-12T00:00:05 ... 2020-03-...

    long_name :

    Time offset from base_time

    ancillary_variables :

    base_time

    array(['2020-03-12T00:00:05.000000000', '2020-03-12T00:00:15.000000000',
           '2020-03-12T00:00:25.000000000', ...,
           '2020-03-12T23:59:37.000000000', '2020-03-12T23:59:47.000000000',
           '2020-03-12T23:59:57.000000000'], dtype='datetime64[ns]')

  • num_channels
    ()
    int32
    ...

    long_name :

    Number of detector channels

    units :

    count

    [1 values with dtype=int32]

  • num_bins
    ()
    int32
    ...

    long_name :

    Number of bins stored in data block

    units :

    count

    [1 values with dtype=int32]

  • range
    (range_bins)
    float32
    dask.array<chunksize=(3990,), meta=np.ndarray>

    long_name :

    Distance from transceiver to center of corresponding range_bin

    units :

    km

    comment1 :

    Derived from range_bins by subtracting range_offset.

    comment2 :

    Negative values correspond to bins preceding laser flash.

    Array Chunk Bytes 15.59 kiB 15.59 kiB Shape (3990,) (3990,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • height
    (range_bins)
    float32
    dask.array<chunksize=(3990,), meta=np.ndarray>

    long_name :

    Height above ground of the center of corresponding range_bin

    units :

    km

    comment :

    This value is determined by subtracting the background from each profile and defining the first range bin where there is substantial signal as being the first range gate, that is, zero range.

    Array Chunk Bytes 15.59 kiB 15.59 kiB Shape (3990,) (3990,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • afterpulse_correction_co_pol
    (range_bins)
    float32
    dask.array<chunksize=(3990,), meta=np.ndarray>

    long_name :

    Afterpulse correction factor co_pol

    units :

    count/us

    comment :

    The provided afterpulse measurement includes the dark counts. The normalized afterpulse should have the dark counts removed before applying the correction to the signal

    Array Chunk Bytes 15.59 kiB 15.59 kiB Shape (3990,) (3990,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • afterpulse_correction_cross_pol
    (range_bins)
    float32
    dask.array<chunksize=(3990,), meta=np.ndarray>

    long_name :

    Afterpulse correction factor cross_pol

    units :

    count/us

    comment :

    The provided afterpulse measurement includes the dark counts. The normalized afterpulse should have the dark counts removed before applying the correction to the signal

    Array Chunk Bytes 15.59 kiB 15.59 kiB Shape (3990,) (3990,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • darkcount_correction_co_pol
    (num_darkcount_corr)
    float32
    dask.array<chunksize=(3990,), meta=np.ndarray>

    long_name :

    Darkcount correction factor co_pol

    units :

    count/us

    comment :

    Darkcount correction is to account for receiver noise, which is included in all measurements. It needs to be removed from the afterpulse correction before applying to the signal

    Array Chunk Bytes 15.59 kiB 15.59 kiB Shape (3990,) (3990,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • darkcount_correction_cross_pol
    (num_darkcount_corr)
    float32
    dask.array<chunksize=(3990,), meta=np.ndarray>

    long_name :

    Darkcount correction factor cross_pol

    units :

    count/us

    comment :

    Darkcount correction is to account for receiver noise, which is included in all measurements. It needs to be removed from the afterpulse correction before applying to the signal

    Array Chunk Bytes 15.59 kiB 15.59 kiB Shape (3990,) (3990,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • deadtime_correction_counts
    (num_deadtime_corr)
    float32
    dask.array<chunksize=(23,), meta=np.ndarray>

    long_name :

    Laboratory measured counts used to calculate the deadtime correction samples

    units :

    count/us

    Array Chunk Bytes 92 B 92 B Shape (23,) (23,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • deadtime_correction
    (num_deadtime_corr)
    float32
    dask.array<chunksize=(23,), meta=np.ndarray>

    long_name :

    Deadtime correction factor

    units :

    unitless

    Array Chunk Bytes 92 B 92 B Shape (23,) (23,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • overlap_correction_heights
    (num_overlap_corr)
    float32
    dask.array<chunksize=(332,), meta=np.ndarray>

    long_name :

    Heights for overlap correction

    units :

    km

    Array Chunk Bytes 1.30 kiB 1.30 kiB Shape (332,) (332,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • overlap_correction
    (num_overlap_corr)
    float32
    dask.array<chunksize=(332,), meta=np.ndarray>

    long_name :

    Overlap correction factor

    units :

    unitless

    Array Chunk Bytes 1.30 kiB 1.30 kiB Shape (332,) (332,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • shots_per_avg
    ()
    float32
    ...

    long_name :

    Number of laser pulses summed during averaging interval

    units :

    count

    [1 values with dtype=float32]

  • pulse_rep
    ()
    float32
    ...

    long_name :

    Repetition rate, or Trigger Frequency of the laser

    units :

    Hz

    precision :

    0.2%

    accuracy :

    2%

    comment1 :

    Manually entered input after selection with laser control handset.

    [1 values with dtype=float32]

  • max_altitude
    ()
    float32
    ...

    long_name :

    Maximum altitude retrieved from multichannel scalar card.

    units :

    km

    comment1 :

    generally 60 km.

    [1 values with dtype=float32]

  • range_offset
    (time)
    float32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Distance between initial range_bin and the laser flash

    units :

    km

    comment :

    This value is determined by subtracting the background from each profile and defining the first range bin where there is substantial signal as being the first range gate, that is, zero range.

    ancillary_variables :

    qc_range_offset

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • qc_range_offset
    (time)
    int32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Quality check results on field: Distance between initial range_bin and the laser flash

    units :

    unitless

    description :

    See global attributes for individual QC bit descriptions.

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • energy_monitor
    (time)
    float32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Energy output per pulse of transmitted laser beam at 532 nm (Doubled Nd-YLF)

    units :

    uJ

    valid_min :

    1.0

    precision :

    5%

    comment1 :

    For signal normalization only, as the monitor is not absolutely calibrated

    ancillary_variables :

    qc_energy_monitor

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • qc_energy_monitor
    (time)
    int32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Quality check results on field: Energy output per pulse of transmitted laser beam at 532 nm (Doubled Nd-YLF)

    units :

    unitless

    description :

    See global attributes for individual QC bit descriptions.

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • detector_temp
    (time)
    float32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Detector temperature

    units :

    degC

    valid_min :

    10.0

    valid_max :

    40.0

    comment1 :

    Determined from thermistor mounted against detector module within transceiver chassis

    precision :

    1%

    accuracy :

    2 degrees C

    digital_sampling :

    8-bit

    ancillary_variables :

    qc_detector_temp

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • qc_detector_temp
    (time)
    int32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Quality check results on field: Detector temperature

    units :

    unitless

    description :

    See global attributes for individual QC bit descriptions.

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • scope_temp
    (time)
    float32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Telescope temperature

    units :

    degC

    valid_min :

    10.0

    valid_max :

    40.0

    ancillary_variables :

    qc_scope_temp

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • qc_scope_temp
    (time)
    int32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Quality check results on field: Telescope temperature

    units :

    unitless

    description :

    See global attributes for individual QC bit descriptions.

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • laser_temp
    (time)
    float32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Laser temperature

    units :

    degC

    valid_min :

    10.0

    valid_max :

    40.0

    comment1 :

    Determined from thermistor mounted against Nd-YLF laser head within transceiver chassis.

    precision :

    3%

    accuracy :

    2 degC

    digital_sampling :

    8-bit

    ancillary_variables :

    qc_laser_temp

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • qc_laser_temp
    (time)
    int32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Quality check results on field: Laser temperature

    units :

    unitless

    description :

    See global attributes for individual QC bit descriptions.

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • background_signal_cross_pol
    (time)
    float32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Background signal, cross pol data

    units :

    count/us

    resolution :

    1.0

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • background_signal_std_cross_pol
    (time)
    float32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Background signal standard deviation, cross pol data

    units :

    count/us

    resolution :

    1.0

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • background_signal_co_pol
    (time)
    float32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Background signal, copol data

    units :

    count/us

    resolution :

    1.0

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • background_signal_std_co_pol
    (time)
    float32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Background signal standard deviation, copol data

    units :

    count/us

    resolution :

    1.0

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • range_bin_time
    (time)
    float32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Time in seconds for each range bin of multichannel scalar card.

    units :

    second

    precision :

    1 nanosecond

    accuracy :

    2%

    comment :

    User selected (through range_bin) as 100, 200, or 500ns

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • range_bin_width
    (time)
    float32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Width of range-bins calculated from range_bin_time

    units :

    km

    precision :

    < 1 meter

    accuracy :

    2%

    comment :

    User selected as 15, 30, or 75 meters

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dead_time_corrected
    (time)
    int32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Dead time correction flag

    units :

    unitless

    flag_values :

    [0 1]

    flag_meanings :

    default corrected

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • polarization_control
    (time)
    int32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Polarization control enabled flag

    units :

    unitless

    flag_values :

    [0 1]

    flag_meanings :

    disabled enabled

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • polarization_control_voltage
    (time)
    float32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Polarization control voltage setting

    units :

    V

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mcs_mode
    ()
    int32
    ...

    long_name :

    MCS mode register.

    units :

    unitless

    [1 values with dtype=int32]

  • signal_return_cross_pol
    (time, range_bins)
    float32
    dask.array<chunksize=(8640, 3990), meta=np.ndarray>

    long_name :

    Attenuated backscatter, cross pol

    units :

    count/us

    description :

    This field literally contains the counts detected by the detector for each range bin. No corrections of any kind have been applied to this field. In order to make proper use of this data, one should correct for detector non-linearity, subtract background counts, apply a range-squared multiplication, and optimally correct for optical overlap and collimation artifacts.

    ancillary_variables :

    qc_signal_return_cross_pol

    Array Chunk Bytes 131.51 MiB 131.51 MiB Shape (8640, 3990) (8640, 3990) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • qc_signal_return_cross_pol
    (time)
    int32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Quality check results on field: Attenuated backscatter, cross pol

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Not used

    bit_1_assessment :

    Bad

    bit_2_description :

    Not used

    bit_2_assessment :

    Bad

    bit_3_description :

    Not used

    bit_3_assessment :

    Bad

    bit_4_description :

    Not used

    bit_4_assessment :

    Bad

    bit_5_description :

    The instrument detects an A/D start (timing corruption) error

    bit_5_assessment :

    Bad

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • signal_return_co_pol
    (time, range_bins)
    float32
    dask.array<chunksize=(8640, 3990), meta=np.ndarray>

    long_name :

    Attenuated backscatter, copol

    units :

    count/us

    description :

    This field literally contains the counts detected by the detector for each range bin. No corrections of any kind have been applied to this field. In order to make proper use of this data, one should correct for detector non-linearity, subtract background counts, apply a range-squared multiplication, and optimally correct for optical overlap and collimation artifacts.

    ancillary_variables :

    qc_signal_return_co_pol

    Array Chunk Bytes 131.51 MiB 131.51 MiB Shape (8640, 3990) (8640, 3990) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • qc_signal_return_co_pol
    (time)
    int32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Quality check results on field: Attenuated backscatter, copol

    units :

    unitless

    description :

    This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.

    flag_method :

    bit

    bit_1_description :

    Not used

    bit_1_assessment :

    Bad

    bit_2_description :

    Not used

    bit_2_assessment :

    Bad

    bit_3_description :

    Not used

    bit_3_assessment :

    Bad

    bit_4_description :

    Not used

    bit_4_assessment :

    Bad

    bit_5_description :

    The instrument detects an A/D start (timing corruption) error

    bit_5_assessment :

    Bad

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • first_data_bin
    (time)
    int32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Bin number of first data. Tells where pretrigger timing ends and end of region for background data sampling

    units :

    unitless

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • laser_fire_bin
    (time)
    int32
    dask.array<chunksize=(8640,), meta=np.ndarray>

    long_name :

    Bin number associated with zero height and time where laser transmit occurs

    units :

    unitless

    Array Chunk Bytes 33.75 kiB 33.75 kiB Shape (8640,) (8640,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

  • lat
    ()
    float32
    ...

    long_name :

    North latitude

    units :

    degree_N

    valid_min :

    -90.0

    valid_max :

    90.0

    standard_name :

    latitude

    [1 values with dtype=float32]

  • lon
    ()
    float32
    ...

    long_name :

    East longitude

    units :

    degree_E

    valid_min :

    -180.0

    valid_max :

    180.0

    standard_name :

    longitude

    [1 values with dtype=float32]

  • alt
    ()
    float32
    ...

    long_name :

    Altitude above mean sea level

    units :

    m

    standard_name :

    altitude

    [1 values with dtype=float32]

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-03-12 00:00:05', '2020-03-12 00:00:15',
                   '2020-03-12 00:00:25', '2020-03-12 00:00:35',
                   '2020-03-12 00:00:45', '2020-03-12 00:00:55',
                   '2020-03-12 00:01:05', '2020-03-12 00:01:15',
                   '2020-03-12 00:01:25', '2020-03-12 00:01:35',
                   ...
                   '2020-03-12 23:58:27', '2020-03-12 23:58:37',
                   '2020-03-12 23:58:47', '2020-03-12 23:58:57',
                   '2020-03-12 23:59:07', '2020-03-12 23:59:17',
                   '2020-03-12 23:59:27', '2020-03-12 23:59:37',
                   '2020-03-12 23:59:47', '2020-03-12 23:59:57'],
                  dtype='datetime64[ns]', name='time', length=8640, freq=None))

  • range_bins
    PandasIndex

    PandasIndex(Index([0.014989621937274933, 0.029979245737195015,   0.0449688695371151,
            0.05995849147439003,  0.07494811713695526,   0.0899377390742302,
            0.10492736101150513,  0.11991698294878006,    0.134906604886055,
            0.14989623427391052,
           ...
             59.671016693115234,   59.686004638671875,   59.700992584228516,
             59.715980529785156,     59.7309684753418,    59.74595642089844,
              59.76094436645508,    59.77593231201172,    59.79092025756836,
                59.805908203125],
          dtype='float32', name='range_bins', length=3990))

• Attributes: (33)

  command_line :

  mplpolfs_ingest -s anx -f M1

  Conventions :

  ARM-1.2

  process_version :

  ingest-mplpolfs-1.23-0.el7

  ingest_software :

  ingest-mplpolfs-1.23-0.el7

  dod_version :

  mplpolfs-b1-3.0

  site_id :

  anx

  platform_id :

  mplpolfs

  facility_id :

  M1

  data_level :

  b1

  location_description :

  Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE), Andoy, Norway

  input_source :

  /data/collection/anx/anxmplpolfsM1.00/202003120000.mpl

  resolution_description :

  The resolution field attributes refer to the number of significant digits relative to the decimal point that should be used in calculations. Using fewer digits might result in greater uncertainty. Using a larger number of digits should have no effect and thus is unnecessary. However, analyses based on differences in values with a larger number of significant digits than indicated could lead to erroneous results or misleading scientific conclusions. resolution for lat = 0.001 resolution for lon = 0.001 resolution for alt = 1

  serial_number :

  107

  averaging_interval :

  10.000000

  sampling_interval :

  0.000400

  deadtime_correction_file :

  anxmplpolfsM1_20191118.deadtime

  overlap_correction_file :

  anxmplpolfsM1_20191101.overlap

  afterpulse_correction_file :

  anxmplpolfsM1_20200212.afterpulse_sn_107

  darkcount_correction_file :

  anxmplpolfsM1_20200212.darkcount_sn_107

  qc_bit_comment :

  The QC field values are a bit packed representation of true/false values for the tests that may have been performed. A QC value of zero means that none of the tests performed on the value failed.

  qc_bit_1_description :

  Value is equal to missing_value.

  qc_bit_1_assessment :

  Bad

  qc_bit_2_description :

  Value is less than the valid_min.

  qc_bit_2_assessment :

  Bad

  qc_bit_3_description :

  Value is greater than the valid_max.

  qc_bit_3_assessment :

  Bad

  datastream :

  anxmplpolfsM1.b1

  doi :

  10.5439/1320657

  history :

  created by user dsmgr on machine flint at 2020-03-12 01:16:02, using ingest-mplpolfs-1.23-0.el7

  _file_dates :

  ['20200312']

  _file_times :

  ['000005']

  _datastream :

  anxmplpolfsM1.b1

  _arm_standards_flag :

  1

---

Quality Controlling Data

ARM has multiple methods that it uses to communicate data quality information out to the users. One of these methods is through “embedded QC” variables. These are variables within the file that have information on automated tests that have been applied. Many times, they include Min, Max, and Delta tests but as is the case with the AOS instruments, there can be more complicated tests that are applied.

The results from all these different tests are stored in a single variable using bit-packed QC. We won’t get into the full details here, but it’s a way to communicate the results of multiple tests in a single integer value by utilizing binary and bits! You can learn more about bit-packed QC here but ACT also has many of the tools for working with ARM QC.

Other Sources of Quality Control

ARM also communicates problems with the data quality through Data Quality Reports (DQR). These reports are normally submitted by the instrument mentor when there’s been a problem with the instrument. The categories include:

• Data Quality Report Categories

  • Missing: Data are not available or set to -9999

  • Suspect: The data are not fully incorrect but there are problems that increases the uncertainty of the values. Data should be used with caution.

  • Bad: The data are incorrect and should not be used.

  • Note: Data notes are a way to communicate information that would be useful to the end user but does not rise to the level of suspect or bad data

Examples of ACT QC functionality

Additionally, data quality information can be found in the Instrument Handbooks, which are included on most instrument pages. Here is an example of the MPL handbook.

# Let's take a look at the quality control information associated with a variable from the MPL
variable = 'signal_return_co_pol'

# First, for many of the ACT QC features, we need to get the dataset more to CF standard and that
# involves cleaning up some of the attributes and ways that ARM has historically handled QC
ds_mpl.clean.cleanup()

# Apply corrections for the ceilometer, correcting for the vertical height
ds_mpl = act.corrections.correct_mpl(ds_mpl)

# Next, let's take a look at visualizing the quality control information
# Create a plotting display object with 2 plots
display = act.plotting.TimeSeriesDisplay(ds_mpl, figsize=(10, 5), subplot_shape=(1,))

# Plot up the variable in the first plot
display.plot(variable, subplot_index=(0,))

# Plot up the QC variable in the second plot
#display.qc_flag_block_plot(variable, subplot_index=(1,))
plt.show()

Filtering data

It’s easy to filter out data failing tests with ACT. This will show you how to filter data by test or by assessment.

# Let's filter out test 5 using ACT.  Yes, it's that simple!
ds_mpl.qcfilter.datafilter(variable, rm_tests=[1, 2], del_qc_var=False)

# There are other ways we can filter data out as well.  Using the
# rm_assessments will filter out by all Bad/Suspect tests that are failing
# ds.qcfilter.datafilter(variable, rm_assessments=['Bad', 'Suspect'], del_qc_var=False)

# Let's check out the attributes of the variable
# Whenever data are filtered out using the datafilter function
# a comment will be added to the variable history for provenance purposes
print(ds_mpl[variable].attrs)

# And plot it all again!
# Create a plotting display object with 2 plots
display = act.plotting.TimeSeriesDisplay(ds_mpl, figsize=(15, 10), subplot_shape=(2,))

# Plot up the variable in the first plot
display.plot(variable, subplot_index=(0,), cb_friendly=True)

# Plot up a day/night background
display.day_night_background(subplot_index=(0,))

# Plot up the QC variable in the second plot
display.qc_flag_block_plot(variable, subplot_index=(1,))
plt.show()

{'long_name': 'Attenuated backscatter, copol', 'units': '10 * log10(count/us)', 'description': 'This field literally contains the counts detected by the detector for each range bin. No corrections of any kind have been applied to this field. In order to make proper use of this data, one should correct for detector non-linearity, subtract background counts, apply a range-squared multiplication, and optimally correct for optical overlap and collimation artifacts.', 'ancillary_variables': 'qc_signal_return_co_pol', 'history': 'act.qc.datafilter: Not used\nact.qc.datafilter: Not used'}
['green']

ARM Data Quality Reports (DQR)!

ARM’s DQRs can be easily pulled in and added to the QC variables using ACT. We can do that with the below one line command. However, for this case, there won’t be any DQRs on the data but let’s visualize it just in case! Check out the ACT QC Examples for more use cases!

# Query the ARM DQR Webservice
ds_mpl = act.qc.add_dqr_to_qc(ds_mpl, variable=variable)

ds_mpl['qc_' + variable]

<xarray.DataArray 'qc_signal_return_co_pol' (time: 8640, range_bins: 2183)> Size: 75MB
array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], dtype=uint32)
Coordinates:
  * time        (time) datetime64[ns] 69kB 2020-03-12T00:00:05 ... 2020-03-12...
  * range_bins  (range_bins) float32 9kB 27.1 27.12 27.13 ... 59.78 59.79 59.81
Attributes:
    long_name:         Quality check results on field: Attenuated backscatter...
    units:             1
    flag_masks:        [1, 2, 4, 8, 16]
    flag_meanings:     ['Not used', 'Not used', 'Not used', 'Not used', 'The ...
    flag_assessments:  ['Bad', 'Bad', 'Bad', 'Bad', 'Bad']
    standard_name:     quality_flag
    missing_value:     4294957297

xarray.DataArray

'qc_signal_return_co_pol'

• time: 8640
• range_bins: 2183

• 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

  array([[0, 0, 0, ..., 0, 0, 0],
         [0, 0, 0, ..., 0, 0, 0],
         [0, 0, 0, ..., 0, 0, 0],
         ...,
         [0, 0, 0, ..., 0, 0, 0],
         [0, 0, 0, ..., 0, 0, 0],
         [0, 0, 0, ..., 0, 0, 0]], dtype=uint32)

• Coordinates: (2)
  • time
    (time)
    datetime64[ns]
    2020-03-12T00:00:05 ... 2020-03-...

    long_name :

    Time offset from midnight

    array(['2020-03-12T00:00:05.000000000', '2020-03-12T00:00:15.000000000',
           '2020-03-12T00:00:25.000000000', ..., '2020-03-12T23:59:37.000000000',
           '2020-03-12T23:59:47.000000000', '2020-03-12T23:59:57.000000000'],
          dtype='datetime64[ns]')

  • range_bins
    (range_bins)
    float32
    27.1 27.12 27.13 ... 59.79 59.81

    long_name :

    Distance from leading edge of first range bin to the center of each bin.

    units :

    km

    comment1 :

    This value does not account for the range offset due to mistiming of the laser firing and the multichannel scalar trigger

    resolution :

    0.001

    accuracy :

    2% scale factor +/- 90 meters scale offset

    array([27.10159 , 27.11658 , 27.131569, ..., 59.775932, 59.79092 , 59.80591 ],
          dtype=float32)

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-03-12 00:00:05', '2020-03-12 00:00:15',
                   '2020-03-12 00:00:25', '2020-03-12 00:00:35',
                   '2020-03-12 00:00:45', '2020-03-12 00:00:55',
                   '2020-03-12 00:01:05', '2020-03-12 00:01:15',
                   '2020-03-12 00:01:25', '2020-03-12 00:01:35',
                   ...
                   '2020-03-12 23:58:27', '2020-03-12 23:58:37',
                   '2020-03-12 23:58:47', '2020-03-12 23:58:57',
                   '2020-03-12 23:59:07', '2020-03-12 23:59:17',
                   '2020-03-12 23:59:27', '2020-03-12 23:59:37',
                   '2020-03-12 23:59:47', '2020-03-12 23:59:57'],
                  dtype='datetime64[ns]', name='time', length=8640, freq=None))

  • range_bins
    PandasIndex

    PandasIndex(Index([ 27.10158920288086, 27.116579055786133, 27.131568908691406,
            27.14655876159668, 27.161548614501953, 27.176538467407227,
             27.1915283203125, 27.206518173217773, 27.221508026123047,
            27.23649787902832,
           ...
           59.671016693115234, 59.686004638671875, 59.700992584228516,
           59.715980529785156,   59.7309684753418,  59.74595642089844,
            59.76094436645508,  59.77593231201172,  59.79092025756836,
              59.805908203125],
          dtype='float32', name='range_bins', length=2183))

• Attributes: (7)

  long_name :

  Quality check results on field: Attenuated backscatter, copol

  units :

  1

  flag_masks :

  [1, 2, 4, 8, 16]

  flag_meanings :

  ['Not used', 'Not used', 'Not used', 'Not used', 'The instrument detects an A/D start (timing corruption) error']

  flag_assessments :

  ['Bad', 'Bad', 'Bad', 'Bad', 'Bad']

  standard_name :

  quality_flag

  missing_value :

  4294957297

---

Aerosol Instrument Overview

Single Particle Soot Photometer (SP2) The single-particle soot photometer (SP2) measures the soot (black carbon) mass of individual aerosol particles by laser-induced incandescence down to concentrations as low as ng/m^3. Learn more Aerodynamic Particle Sizer (APS) The aerodynamic particle sizer (APS) is a particle size spectrometer that measures both the particle aerodynamic diameter based on particle time of flight and optical diameter based on scattered light intensity. The APS provides the number size distribution for particles with aerodynamic diameters from 0.5 to 20 micrometers and with optical diameters from 0.3 to 20 micrometers. Learn more Aerosol Chemical Speciation Monitor (ACSM) The aerosol chemical speciation monitor is a thermal vaporization, electron impact, ionization mass spectrometer that measures bulk chemical composition of the rapidly evaporating component of sub-micron aerosol particles in real time. Standard measurements include mass concentrations of organics, sulfate, nitrate, ammonium, and chloride. Learn more

ARM Aerosol Instrumentation Particle Size Ranges

Downloading and QCing the Aerosol Data

Let’s start pulling these data together into the same plots so we can see what’s going on.

# Let's set a longer time period
startdate = '2020-03-09'
enddate = '2020-03-16'

# neph
datastream = 'anxaosnephdry1mM1.b1'
result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
ds_neph = act.io.arm.read_arm_netcdf(result)

# CCN-kappa
datastream = 'anxaosccnsmpskappaM1.c1'
result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
ds_ccn = act.io.arm.read_arm_netcdf(result)

#PSAP
datastream = 'anxaospsap3w1mM1.b1'
result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
ds_psap = act.io.arm.read_arm_netcdf(result)

# AOSMET - Just to get the wind data!
datastream = 'anxmetM1.b1'
result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
ds_met = act.io.read_arm_netcdf(result)

# MPL to get the full record
datastream = 'anx30smplcmask1zwangM1.c1'
result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
ds_mpl = act.io.read_arm_netcdf(result)

[DOWNLOADING] anxaosnephdry1mM1.b1.20200311.000030.nc
[DOWNLOADING] anxaosnephdry1mM1.b1.20200312.000030.nc
[DOWNLOADING] anxaosnephdry1mM1.b1.20200314.000030.nc
[DOWNLOADING] anxaosnephdry1mM1.b1.20200310.000030.nc
[DOWNLOADING] anxaosnephdry1mM1.b1.20200313.000030.nc
[DOWNLOADING] anxaosnephdry1mM1.b1.20200315.000030.nc
[DOWNLOADING] anxaosnephdry1mM1.b1.20200309.000030.nc

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

Uin, J., Salwen, C., Senum, G., Hayes, C., & Mayol Bracero, O. Nephelometer
(AOSNEPHDRY1M). Atmospheric Radiation Measurement (ARM) User Facility.
https://doi.org/10.5439/1984586

[DOWNLOADING] anxaosccnsmpskappaM1.c1.20200310.002602.nc
[DOWNLOADING] anxaosccnsmpskappaM1.c1.20200309.003204.nc
[DOWNLOADING] anxaosccnsmpskappaM1.c1.20200313.003849.nc
[DOWNLOADING] anxaosccnsmpskappaM1.c1.20200311.022138.nc
[DOWNLOADING] anxaosccnsmpskappaM1.c1.20200312.013206.nc
[DOWNLOADING] anxaosccnsmpskappaM1.c1.20200315.000427.nc
[DOWNLOADING] anxaosccnsmpskappaM1.c1.20200314.005637.nc

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

Kulkarni, G., Levin, M., & Shilling, J. CCN Counter derived hygroscopicity
parameter kappa (AOSCCNSMPSKAPPA). Atmospheric Radiation Measurement (ARM) User
Facility. https://doi.org/10.5439/1729907

[DOWNLOADING] anxaospsap3w1mM1.b1.20200309.000030.nc
[DOWNLOADING] anxaospsap3w1mM1.b1.20200314.000030.nc
[DOWNLOADING] anxaospsap3w1mM1.b1.20200315.000030.nc
[DOWNLOADING] anxaospsap3w1mM1.b1.20200313.000030.nc
[DOWNLOADING] anxaospsap3w1mM1.b1.20200312.000030.nc
[DOWNLOADING] anxaospsap3w1mM1.b1.20200310.000030.nc
[DOWNLOADING] anxaospsap3w1mM1.b1.20200311.000030.nc

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

Ermold, B., Flynn, C., Trojanowski, R., Andrews, E., Hayes, C., Salwen, C., &
Flynn, C. Particle Soot Absorption Photometer (AOSPSAP3W1M). Atmospheric
Radiation Measurement (ARM) User Facility. https://doi.org/10.5439/1225037

[DOWNLOADING] anxmetM1.b1.20200316.000000.cdf
[DOWNLOADING] anxmetM1.b1.20200309.000000.cdf
[DOWNLOADING] anxmetM1.b1.20200310.000000.cdf
[DOWNLOADING] anxmetM1.b1.20200311.000000.cdf
[DOWNLOADING] anxmetM1.b1.20200312.000000.cdf
[DOWNLOADING] anxmetM1.b1.20200313.000000.cdf
[DOWNLOADING] anxmetM1.b1.20200314.000000.cdf
[DOWNLOADING] anxmetM1.b1.20200315.000000.cdf

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

Kyrouac, J., Shi, Y., & Tuftedal, M. Surface Meteorological Instrumentation
(MET). Atmospheric Radiation Measurement (ARM) User Facility.
https://doi.org/10.5439/1786358

[DOWNLOADING] anx30smplcmask1zwangM1.c1.20200315.000000.nc
[DOWNLOADING] anx30smplcmask1zwangM1.c1.20200310.000003.nc
[DOWNLOADING] anx30smplcmask1zwangM1.c1.20200312.000005.nc
[DOWNLOADING] anx30smplcmask1zwangM1.c1.20200311.000004.nc
[DOWNLOADING] anx30smplcmask1zwangM1.c1.20200314.000009.nc
[DOWNLOADING] anx30smplcmask1zwangM1.c1.20200313.000007.nc
[DOWNLOADING] anx30smplcmask1zwangM1.c1.20200309.000002.nc

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

Sivaraman, C., Flynn, D., Riihimaki, L., Comstock, J., & Zhang, D. Cloud mask
from Micropulse Lidar (30SMPLCMASK1ZWANG). Atmospheric Radiation Measurement
(ARM) User Facility. https://doi.org/10.5439/1508389

# Before we proceed to plotting, let's reduce the MPL data down a little bit
# This will remove all data where heights are greater than 5
ds_mpl = ds_mpl.where(ds_mpl.height <= 3, drop=True)

# This will resample to 1 minute
ds_mpl = ds_mpl.resample(time='1min').nearest()

# Let's not forget about QCing the data!
# We can remove all the bad data from each aerosol dataset
ds_neph.clean.cleanup()
ds_neph = act.qc.arm.add_dqr_to_qc(ds_neph)
ds_neph.qcfilter.datafilter(rm_assessments=['Bad'], del_qc_var=False)

ds_ccn.clean.cleanup()
ds_ccn = act.qc.arm.add_dqr_to_qc(ds_ccn)
ds_ccn.qcfilter.datafilter(rm_assessments=['Bad'], del_qc_var=False)

ds_met.clean.cleanup()
ds_met = act.qc.arm.add_dqr_to_qc(ds_met)
ds_met.qcfilter.datafilter(rm_assessments=['Bad'], del_qc_var=False)

ds_mpl.clean.cleanup()
ds_mpl = act.qc.arm.add_dqr_to_qc(ds_mpl)
ds_mpl.qcfilter.datafilter(rm_assessments=['Bad'], del_qc_var=False)

---

Visualizing Data

We have all the datasets downloaded, let’s start to visualize them in different ways using ACT. If you ever need a place to start with how to visualize data using ACT, check out the ACT Plotting Examples

# We can pass a dictionary to the display objects with multiple datasets
# So let's plot all this up!
display = act.plotting.TimeSeriesDisplay({'neph': ds_neph, 'mpl': ds_mpl, 'ccn': ds_ccn, 'met': ds_met},
                                         subplot_shape=(4,), figsize=(10,16))

# MPL Plot
# Variable names of interest linear_depol_ratio, linear_depol_snr, backscatter_snr
display.plot('linear_depol_ratio', dsname='mpl', subplot_index=(0,), cb_friendly=True)
display.set_yrng([0, 3], subplot_index=(0,))

# CNN Plot
display.plot('N_CCN', dsname='ccn', subplot_index=(1,))
display.day_night_background(dsname='ccn', subplot_index=(1,))

# CNN-kappa Plot
display.plot('kappa', dsname='ccn', subplot_index=(2,))
display.day_night_background(dsname='ccn', subplot_index=(2,))

# MET Plot
display.plot('wspd_arith_mean', dsname='met', subplot_index=(3,))
display.day_night_background(dsname='met', subplot_index=(3,))

plt.subplots_adjust(hspace=0.3)
plt.savefig('output.png')
plt.show()

ds_ccn

<xarray.Dataset> Size: 377kB
Dimensions:                          (time: 108, bound: 2,
                                      diameter_mobility: 192, droplet_size: 20,
                                      setpoint: 7)
Coordinates:
  * time                             (time) datetime64[ns] 864B 2020-03-09T00...
  * diameter_mobility                (diameter_mobility) float32 768B 1.02 .....
  * droplet_size                     (droplet_size) float32 80B 0.0 1.0 ... 19.0
  * setpoint                         (setpoint) float32 28B 0.0 0.1 ... 0.8 1.0
Dimensions without coordinates: bound
Data variables: (12/22)
    base_time                        (time) datetime64[ns] 864B 2020-03-09 .....
    time_offset                      (time) datetime64[ns] 864B 2020-03-09T00...
    time_bounds                      (time, bound) object 2kB dask.array<chunksize=(14, 2), meta=np.ndarray>
    diameter_mobility_bounds         (time, diameter_mobility, bound) float32 166kB dask.array<chunksize=(14, 192, 2), meta=np.ndarray>
    droplet_size_bounds              (time, droplet_size, bound) float32 17kB dask.array<chunksize=(14, 20, 2), meta=np.ndarray>
    kappa                            (time) float32 432B dask.array<chunksize=(18,), meta=np.ndarray>
    ...                               ...
    qc_N_CCN_dN                      (time, droplet_size) int32 9kB dask.array<chunksize=(14, 20), meta=np.ndarray>
    dN_dlogDp                        (time, diameter_mobility) float32 83kB dask.array<chunksize=(18, 192), meta=np.ndarray>
    qc_dN_dlogDp                     (time, diameter_mobility) int32 83kB dask.array<chunksize=(14, 192), meta=np.ndarray>
    lat                              (time) float32 432B 69.14 69.14 ... 69.14
    lon                              (time) float32 432B 15.68 15.68 ... 15.68
    alt                              (time) float32 432B 2.0 2.0 2.0 ... 2.0 2.0
Attributes: (12/17)
    command_line:          aosccnsmpskappa -n aosccnsmpskappa -s anx -f M1 -b...
    Conventions:           ARM-1.3
    process_version:       aosccnsmpskappa-1.2.0
    dod_version:           aosccnsmpskappa-c1-1.2
    input_datastreams:     anxaosccn2colaavgM1.b1 : 2.1 : 20200309.003204\nan...
    site_id:               anx
    ...                    ...
    doi:                   10.5439/1729907
    history:               created by user levin on machine prod-proc5.adc.ar...
    _file_dates:           ['20200309', '20200310', '20200311', '20200312', '...
    _file_times:           ['003204', '002602', '022138', '013206', '003849',...
    _datastream:           anxaosccnsmpskappaM1.c1
    _arm_standards_flag:   1

xarray.Dataset

• Dimensions:
  • time: 108
  • bound: 2
  • diameter_mobility: 192
  • droplet_size: 20
  • setpoint: 7
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2020-03-09T00:32:04 ... 2020-03-...

    long_name :

    Time offset from midnight

    bounds :

    time_bounds

    standard_name :

    time

    array(['2020-03-09T00:32:04.000000000', '2020-03-09T03:47:53.000000000',
           '2020-03-09T06:35:33.000000000', '2020-03-09T07:47:52.000000000',
           '2020-03-09T08:38:40.000000000', '2020-03-09T11:26:35.000000000',
           '2020-03-09T12:38:03.000000000', '2020-03-09T13:50:01.000000000',
           '2020-03-09T14:42:31.000000000', '2020-03-09T15:04:13.000000000',
           '2020-03-09T19:35:44.000000000', '2020-03-09T22:21:45.000000000',
           '2020-03-09T23:12:39.000000000', '2020-03-09T23:37:19.000000000',
           '2020-03-10T00:26:02.000000000', '2020-03-10T00:46:26.000000000',
           '2020-03-10T02:01:55.000000000', '2020-03-10T04:28:14.000000000',
           '2020-03-10T05:41:01.000000000', '2020-03-10T06:52:48.000000000',
           '2020-03-10T09:18:27.000000000', '2020-03-10T10:30:20.000000000',
           '2020-03-10T11:45:49.000000000', '2020-03-10T12:56:42.000000000',
           '2020-03-10T15:27:50.000000000', '2020-03-10T16:36:09.000000000',
           '2020-03-10T17:51:21.000000000', '2020-03-10T19:04:15.000000000',
           '2020-03-10T21:28:43.000000000', '2020-03-10T22:44:02.000000000',
           '2020-03-10T23:53:48.000000000', '2020-03-11T02:21:38.000000000',
           '2020-03-11T03:33:50.000000000', '2020-03-11T04:45:47.000000000',
           '2020-03-11T06:02:09.000000000', '2020-03-11T08:24:49.000000000',
           '2020-03-11T09:38:17.000000000', '2020-03-11T10:51:22.000000000',
           '2020-03-11T12:03:23.000000000', '2020-03-11T14:35:20.000000000',
           '2020-03-11T15:46:12.000000000', '2020-03-11T16:58:22.000000000',
           '2020-03-11T19:24:23.000000000', '2020-03-11T20:35:35.000000000',
           '2020-03-11T21:53:20.000000000', '2020-03-11T23:01:16.000000000',
           '2020-03-12T01:32:06.000000000', '2020-03-12T02:43:16.000000000',
           '2020-03-12T03:52:46.000000000', '2020-03-12T06:23:54.000000000',
           '2020-03-12T07:33:33.000000000', '2020-03-12T08:48:04.000000000',
           '2020-03-12T09:35:59.000000000', '2020-03-12T09:57:34.000000000',
           '2020-03-12T12:27:25.000000000', '2020-03-12T13:41:32.000000000',
           '2020-03-12T14:49:41.000000000', '2020-03-12T16:03:12.000000000',
           '2020-03-12T18:29:35.000000000', '2020-03-12T19:42:25.000000000',
           '2020-03-12T20:56:52.000000000', '2020-03-12T23:26:01.000000000',
           '2020-03-13T00:38:49.000000000', '2020-03-13T01:49:45.000000000',
           '2020-03-13T02:38:30.000000000', '2020-03-13T03:01:11.000000000',
           '2020-03-13T05:31:50.000000000', '2020-03-13T06:17:31.000000000',
           '2020-03-13T06:38:46.000000000', '2020-03-13T07:56:31.000000000',
           '2020-03-13T10:18:47.000000000', '2020-03-13T11:34:19.000000000',
           '2020-03-13T12:45:56.000000000', '2020-03-13T13:59:04.000000000',
           '2020-03-13T16:25:06.000000000', '2020-03-13T17:35:50.000000000',
           '2020-03-13T18:49:41.000000000', '2020-03-13T20:07:24.000000000',
           '2020-03-13T22:31:22.000000000', '2020-03-13T23:40:43.000000000',
           '2020-03-14T00:56:37.000000000', '2020-03-14T03:24:14.000000000',
           '2020-03-14T04:35:42.000000000', '2020-03-14T05:48:31.000000000',
           '2020-03-14T07:02:56.000000000', '2020-03-14T09:26:28.000000000',
           '2020-03-14T10:37:40.000000000', '2020-03-14T11:51:19.000000000',
           '2020-03-14T14:18:36.000000000', '2020-03-14T14:57:49.000000000',
           '2020-03-14T15:30:42.000000000', '2020-03-14T16:44:58.000000000',
           '2020-03-14T17:55:46.000000000', '2020-03-15T00:04:27.000000000',
           '2020-03-15T02:30:59.000000000', '2020-03-15T03:40:49.000000000',
           '2020-03-15T04:58:49.000000000', '2020-03-15T07:21:27.000000000',
           '2020-03-15T08:33:10.000000000', '2020-03-15T09:47:44.000000000',
           '2020-03-15T11:03:10.000000000', '2020-03-15T13:25:32.000000000',
           '2020-03-15T14:36:55.000000000', '2020-03-15T15:50:47.000000000',
           '2020-03-15T17:04:24.000000000', '2020-03-15T19:33:47.000000000',
           '2020-03-15T20:46:08.000000000', '2020-03-15T21:54:48.000000000'],
          dtype='datetime64[ns]')

  • diameter_mobility
    (diameter_mobility)
    float32
    1.02 1.06 1.09 ... 947.5 982.2

    long_name :

    Midpoint of geometric mean mobility diameter

    units :

    nm

    bounds :

    diameter_mobility_bounds

    array([  1.02,   1.06,   1.09,   1.13,   1.18,   1.22,   1.26,   1.31,   1.36,
             1.41,   1.46,   1.51,   1.57,   1.63,   1.68,   1.75,   1.81,   1.88,
             1.95,   2.02,   2.09,   2.17,   2.25,   2.33,   2.41,   2.5 ,   2.59,
             2.69,   2.79,   2.89,   3.  ,   3.11,   3.22,   3.34,   3.46,   3.59,
             3.72,   3.85,   4.  ,   4.14,   4.29,   4.45,   4.61,   4.78,   4.96,
             5.14,   5.33,   5.52,   5.73,   5.94,   6.15,   6.38,   6.61,   6.85,
             7.1 ,   7.37,   7.64,   7.91,   8.2 ,   8.51,   8.82,   9.14,   9.47,
             9.82,  10.2 ,  10.6 ,  10.9 ,  11.3 ,  11.8 ,  12.2 ,  12.6 ,  13.1 ,
            13.6 ,  14.1 ,  14.6 ,  15.1 ,  15.7 ,  16.3 ,  16.8 ,  17.5 ,  18.1 ,
            18.8 ,  19.5 ,  20.2 ,  20.9 ,  21.7 ,  22.5 ,  23.3 ,  24.1 ,  25.  ,
            25.9 ,  26.9 ,  27.9 ,  28.9 ,  30.  ,  31.1 ,  32.2 ,  33.4 ,  34.6 ,
            35.9 ,  37.2 ,  38.5 ,  40.  ,  41.4 ,  42.9 ,  44.5 ,  46.1 ,  47.8 ,
            49.6 ,  51.4 ,  53.3 ,  55.2 ,  57.3 ,  59.4 ,  61.5 ,  63.8 ,  66.1 ,
            68.5 ,  71.  ,  73.7 ,  76.4 ,  79.1 ,  82.  ,  85.1 ,  88.2 ,  91.4 ,
            94.7 ,  98.2 , 101.8 , 105.5 , 109.4 , 113.4 , 117.6 , 121.9 , 126.3 ,
           131.  , 135.8 , 140.7 , 145.9 , 151.2 , 156.8 , 162.5 , 168.5 , 174.7 ,
           181.1 , 187.7 , 194.6 , 201.7 , 209.1 , 216.7 , 224.7 , 232.9 , 241.4 ,
           250.3 , 259.5 , 269.  , 278.8 , 289.  , 299.6 , 310.6 , 322.  , 333.8 ,
           346.  , 358.7 , 371.8 , 385.4 , 399.5 , 414.2 , 429.4 , 445.1 , 461.4 ,
           478.3 , 495.8 , 514.  , 532.8 , 552.3 , 572.5 , 593.5 , 615.3 , 637.8 ,
           661.2 , 685.4 , 710.5 , 736.5 , 763.5 , 791.5 , 820.5 , 850.5 , 881.7 ,
           914.  , 947.5 , 982.2 ], dtype=float32)

  • droplet_size
    (droplet_size)
    float32
    0.0 1.0 2.0 3.0 ... 17.0 18.0 19.0

    long_name :

    Size bins for CCN droplets

    units :

    um

    bounds :

    droplet_size_bounds

    array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,
           14., 15., 16., 17., 18., 19.], dtype=float32)

  • setpoint
    (setpoint)
    float32
    0.0 0.1 0.2 0.4 0.6 0.8 1.0

    long_name :

    Supersaturation set point values

    units :

    %

    array([0. , 0.1, 0.2, 0.4, 0.6, 0.8, 1. ], dtype=float32)

• Data variables: (22)
  • base_time
    (time)
    datetime64[ns]
    2020-03-09 ... 2020-03-15

    string :

    2020-03-09 00:00:00 0:00

    long_name :

    Base time in Epoch

    ancillary_variables :

    time_offset

    array(['2020-03-09T00:00:00.000000000', '2020-03-09T00:00:00.000000000',
           '2020-03-09T00:00:00.000000000', '2020-03-09T00:00:00.000000000',
           '2020-03-09T00:00:00.000000000', '2020-03-09T00:00:00.000000000',
           '2020-03-09T00:00:00.000000000', '2020-03-09T00:00:00.000000000',
           '2020-03-09T00:00:00.000000000', '2020-03-09T00:00:00.000000000',
           '2020-03-09T00:00:00.000000000', '2020-03-09T00:00:00.000000000',
           '2020-03-09T00:00:00.000000000', '2020-03-09T00:00:00.000000000',
           '2020-03-10T00:00:00.000000000', '2020-03-10T00:00:00.000000000',
           '2020-03-10T00:00:00.000000000', '2020-03-10T00:00:00.000000000',
           '2020-03-10T00:00:00.000000000', '2020-03-10T00:00:00.000000000',
           '2020-03-10T00:00:00.000000000', '2020-03-10T00:00:00.000000000',
           '2020-03-10T00:00:00.000000000', '2020-03-10T00:00:00.000000000',
           '2020-03-10T00:00:00.000000000', '2020-03-10T00:00:00.000000000',
           '2020-03-10T00:00:00.000000000', '2020-03-10T00:00:00.000000000',
           '2020-03-10T00:00:00.000000000', '2020-03-10T00:00:00.000000000',
           '2020-03-10T00:00:00.000000000', '2020-03-11T00:00:00.000000000',
           '2020-03-11T00:00:00.000000000', '2020-03-11T00:00:00.000000000',
           '2020-03-11T00:00:00.000000000', '2020-03-11T00:00:00.000000000',
           '2020-03-11T00:00:00.000000000', '2020-03-11T00:00:00.000000000',
           '2020-03-11T00:00:00.000000000', '2020-03-11T00:00:00.000000000',
    ...
           '2020-03-13T00:00:00.000000000', '2020-03-13T00:00:00.000000000',
           '2020-03-13T00:00:00.000000000', '2020-03-13T00:00:00.000000000',
           '2020-03-13T00:00:00.000000000', '2020-03-13T00:00:00.000000000',
           '2020-03-13T00:00:00.000000000', '2020-03-13T00:00:00.000000000',
           '2020-03-13T00:00:00.000000000', '2020-03-13T00:00:00.000000000',
           '2020-03-14T00:00:00.000000000', '2020-03-14T00:00:00.000000000',
           '2020-03-14T00:00:00.000000000', '2020-03-14T00:00:00.000000000',
           '2020-03-14T00:00:00.000000000', '2020-03-14T00:00:00.000000000',
           '2020-03-14T00:00:00.000000000', '2020-03-14T00:00:00.000000000',
           '2020-03-14T00:00:00.000000000', '2020-03-14T00:00:00.000000000',
           '2020-03-14T00:00:00.000000000', '2020-03-14T00:00:00.000000000',
           '2020-03-14T00:00:00.000000000', '2020-03-15T00:00:00.000000000',
           '2020-03-15T00:00:00.000000000', '2020-03-15T00:00:00.000000000',
           '2020-03-15T00:00:00.000000000', '2020-03-15T00:00:00.000000000',
           '2020-03-15T00:00:00.000000000', '2020-03-15T00:00:00.000000000',
           '2020-03-15T00:00:00.000000000', '2020-03-15T00:00:00.000000000',
           '2020-03-15T00:00:00.000000000', '2020-03-15T00:00:00.000000000',
           '2020-03-15T00:00:00.000000000', '2020-03-15T00:00:00.000000000',
           '2020-03-15T00:00:00.000000000', '2020-03-15T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • time_offset
    (time)
    datetime64[ns]
    2020-03-09T00:32:04 ... 2020-03-...

    long_name :

    Time offset from base_time

    ancillary_variables :

    base_time

    array(['2020-03-09T00:32:04.000000000', '2020-03-09T03:47:53.000000000',
           '2020-03-09T06:35:33.000000000', '2020-03-09T07:47:52.000000000',
           '2020-03-09T08:38:40.000000000', '2020-03-09T11:26:35.000000000',
           '2020-03-09T12:38:03.000000000', '2020-03-09T13:50:01.000000000',
           '2020-03-09T14:42:31.000000000', '2020-03-09T15:04:13.000000000',
           '2020-03-09T19:35:44.000000000', '2020-03-09T22:21:45.000000000',
           '2020-03-09T23:12:39.000000000', '2020-03-09T23:37:19.000000000',
           '2020-03-10T00:26:02.000000000', '2020-03-10T00:46:26.000000000',
           '2020-03-10T02:01:55.000000000', '2020-03-10T04:28:14.000000000',
           '2020-03-10T05:41:01.000000000', '2020-03-10T06:52:48.000000000',
           '2020-03-10T09:18:27.000000000', '2020-03-10T10:30:20.000000000',
           '2020-03-10T11:45:49.000000000', '2020-03-10T12:56:42.000000000',
           '2020-03-10T15:27:50.000000000', '2020-03-10T16:36:09.000000000',
           '2020-03-10T17:51:21.000000000', '2020-03-10T19:04:15.000000000',
           '2020-03-10T21:28:43.000000000', '2020-03-10T22:44:02.000000000',
           '2020-03-10T23:53:48.000000000', '2020-03-11T02:21:38.000000000',
           '2020-03-11T03:33:50.000000000', '2020-03-11T04:45:47.000000000',
           '2020-03-11T06:02:09.000000000', '2020-03-11T08:24:49.000000000',
           '2020-03-11T09:38:17.000000000', '2020-03-11T10:51:22.000000000',
           '2020-03-11T12:03:23.000000000', '2020-03-11T14:35:20.000000000',
    ...
           '2020-03-13T10:18:47.000000000', '2020-03-13T11:34:19.000000000',
           '2020-03-13T12:45:56.000000000', '2020-03-13T13:59:04.000000000',
           '2020-03-13T16:25:06.000000000', '2020-03-13T17:35:50.000000000',
           '2020-03-13T18:49:41.000000000', '2020-03-13T20:07:24.000000000',
           '2020-03-13T22:31:22.000000000', '2020-03-13T23:40:43.000000000',
           '2020-03-14T00:56:37.000000000', '2020-03-14T03:24:14.000000000',
           '2020-03-14T04:35:42.000000000', '2020-03-14T05:48:31.000000000',
           '2020-03-14T07:02:56.000000000', '2020-03-14T09:26:28.000000000',
           '2020-03-14T10:37:40.000000000', '2020-03-14T11:51:19.000000000',
           '2020-03-14T14:18:36.000000000', '2020-03-14T14:57:49.000000000',
           '2020-03-14T15:30:42.000000000', '2020-03-14T16:44:58.000000000',
           '2020-03-14T17:55:46.000000000', '2020-03-15T00:04:27.000000000',
           '2020-03-15T02:30:59.000000000', '2020-03-15T03:40:49.000000000',
           '2020-03-15T04:58:49.000000000', '2020-03-15T07:21:27.000000000',
           '2020-03-15T08:33:10.000000000', '2020-03-15T09:47:44.000000000',
           '2020-03-15T11:03:10.000000000', '2020-03-15T13:25:32.000000000',
           '2020-03-15T14:36:55.000000000', '2020-03-15T15:50:47.000000000',
           '2020-03-15T17:04:24.000000000', '2020-03-15T19:33:47.000000000',
           '2020-03-15T20:46:08.000000000', '2020-03-15T21:54:48.000000000'],
          dtype='datetime64[ns]')

  • time_bounds
    (time, bound)
    object
    dask.array<chunksize=(14, 2), meta=np.ndarray>

    long_name :

    Time cell bounds

    Array Chunk Bytes 1.69 kiB 288 B Shape (108, 2) (18, 2) Dask graph 7 chunks in 15 graph layers Data type object numpy.ndarray

  • diameter_mobility_bounds
    (time, diameter_mobility, bound)
    float32
    dask.array<chunksize=(14, 192, 2), meta=np.ndarray>

    long_name :

    Mobility diameter bin boundaries

    Array Chunk Bytes 162.00 kiB 27.00 kiB Shape (108, 192, 2) (18, 192, 2) Dask graph 7 chunks in 22 graph layers Data type float32 numpy.ndarray

  • droplet_size_bounds
    (time, droplet_size, bound)
    float32
    dask.array<chunksize=(14, 20, 2), meta=np.ndarray>

    long_name :

    Droplet bin bounds

    Array Chunk Bytes 16.88 kiB 2.81 kiB Shape (108, 20, 2) (18, 20, 2) Dask graph 7 chunks in 22 graph layers Data type float32 numpy.ndarray

  • kappa
    (time)
    float32
    dask.array<chunksize=(18,), meta=np.ndarray>

    long_name :

    Hygroscopicity parameter kappa

    units :

    1

    comment :

    The CCN derived kappa is calculated using CCN_avg and SMPS data products. k-Kohler theory with fixed constants are used.

    ancillary_variables :

    qc_kappa

    history :

    act.qc.datafilter: critical_diameter quality assessment is bad. Value set to missing_value. act.qc.datafilter: supersaturation_calculated quality assessment is bad. Value set to missing_value. act.qc.datafilter: Value is less than fail_min. Value set to missing_value. act.qc.datafilter: Value is greater than fail_max. Value set to missing_value. act.qc.datafilter: critical_diameter quality assessment is bad. Value set to missing_value. act.qc.datafilter: supersaturation_calculated quality assessment is bad. Value set to missing_value. act.qc.datafilter: Value is less than fail_min. Value set to missing_value. act.qc.datafilter: Value is greater than fail_max. Value set to missing_value.

    Array Chunk Bytes 432 B 72 B Shape (108,) (18,) Dask graph 6 chunks in 1 graph layer Data type float32 numpy.ndarray

  • qc_kappa
    (time)
    int32
    dask.array<chunksize=(14,), meta=np.ndarray>

    long_name :

    Quality check results on variable: Hygroscopicity parameter kappa

    units :

    1

    standard_name :

    quality_flag

    flag_masks :

    [1, 2, 4, 8]

    flag_meanings :

    ['critical_diameter quality assessment is bad. Value set to missing_value.', 'supersaturation_calculated quality assessment is bad. Value set to missing_value.', 'Value is less than fail_min. Value set to missing_value.', 'Value is greater than fail_max. Value set to missing_value.']

    flag_assessments :

    ['Bad', 'Bad', 'Bad', 'Bad']

    fail_min :

    0.0

    fail_max :

    1.3

    Array Chunk Bytes 432 B 72 B Shape (108,) (18,) Dask graph 7 chunks in 15 graph layers Data type int32 numpy.ndarray

  • critical_diameter
    (time)
    float32
    dask.array<chunksize=(18,), meta=np.ndarray>

    long_name :

    Critical diameter

    units :

    nm

    ancillary_variables :

    qc_critical_diameter

    history :

    act.qc.datafilter: Not enough usable dN_dlogDp data to calculate critical_diameter. Value set to missing_value. act.qc.datafilter: Cumulative dN_dlogDp is not comparable to N_CCN. Value set to missing_value. act.qc.datafilter: Not enough usable dN_dlogDp data to calculate critical_diameter. Value set to missing_value. act.qc.datafilter: Cumulative dN_dlogDp is not comparable to N_CCN. Value set to missing_value.

    Array Chunk Bytes 432 B 72 B Shape (108,) (18,) Dask graph 6 chunks in 1 graph layer Data type float32 numpy.ndarray

  • qc_critical_diameter
    (time)
    int32
    dask.array<chunksize=(14,), meta=np.ndarray>

    long_name :

    Quality check results on variable: Critical diameter

    units :

    1

    standard_name :

    quality_flag

    flag_masks :

    [1, 2]

    flag_meanings :

    ['Not enough usable dN_dlogDp data to calculate critical_diameter. Value set to missing_value.', 'Cumulative dN_dlogDp is not comparable to N_CCN. Value set to missing_value.']

    flag_assessments :

    ['Bad', 'Bad']

    Array Chunk Bytes 432 B 72 B Shape (108,) (18,) Dask graph 7 chunks in 15 graph layers Data type int32 numpy.ndarray

  • supersaturation_calculated
    (time)
    float32
    dask.array<chunksize=(18,), meta=np.ndarray>

    long_name :

    Mean calculated supersaturation values for a fixed supersaturation set point.

    units :

    %

    comment :

    The supersaturation_calculated values in this average match the times selected for the mean N_CCN.

    ancillary_variables :

    qc_supersaturation_calculated

    cell_transform :

    time: TRANS_SUBSAMPLE (range: 600)

    source :

    anxaosccn2colaavgM1.b1:supersaturation_calculated

    history :

    act.qc.datafilter: Transformation could not finish (all values bad or outside range, etc.), value set to missing_value. act.qc.datafilter: Transformation could not finish (all values bad or outside range, etc.), value set to missing_value.

    Array Chunk Bytes 432 B 72 B Shape (108,) (18,) Dask graph 6 chunks in 1 graph layer Data type float32 numpy.ndarray

  • qc_supersaturation_calculated
    (time)
    int32
    dask.array<chunksize=(14,), meta=np.ndarray>

    long_name :

    Quality check results on variable: Mean calculated supersaturation values for a fixed supersaturation set point.

    units :

    1

    standard_name :

    quality_flag

    flag_masks :

    [1, 2]

    flag_meanings :

    ['Transformation could not finish (all values bad or outside range, etc.), value set to missing_value.', 'Transformation resulted in an indeterminate outcome.']

    flag_assessments :

    ['Bad', 'Indeterminate']

    Array Chunk Bytes 432 B 72 B Shape (108,) (18,) Dask graph 7 chunks in 15 graph layers Data type int32 numpy.ndarray

  • aerosol_number_concentration
    (time)
    float32
    dask.array<chunksize=(18,), meta=np.ndarray>

    long_name :

    Aerosol particle number concentration

    units :

    1/cm^3

    comment :

    This field uses CPC data that provides the concentration of ambient aerosol particles. The CPC values corresponding to N_CCN data from 25% to 75% (obtained during N_CCN_mean_interquartile calculation) are averaged. This field is used to plot the f_CCN spectra or the activated fraction of particles. Ambient aerosol particles are dried before size-distribution measurements are performed.

    ancillary_variables :

    qc_aerosol_number_concentration

    cell_transform :

    time: TRANS_SUBSAMPLE (range: 600)

    source :

    anxaosccn2colaavgM1.b1:aerosol_number_concentration

    history :

    act.qc.datafilter: Transformation could not finish (all values bad or outside range, etc.), value set to missing_value. act.qc.datafilter: Transformation could not finish (all values bad or outside range, etc.), value set to missing_value.

    Array Chunk Bytes 432 B 72 B Shape (108,) (18,) Dask graph 6 chunks in 1 graph layer Data type float32 numpy.ndarray

  • qc_aerosol_number_concentration
    (time)
    int32
    dask.array<chunksize=(14,), meta=np.ndarray>

    long_name :

    Quality check results on variable: Aerosol particle number concentration

    units :

    1

    standard_name :

    quality_flag

    flag_masks :

    [1, 2]

    flag_meanings :

    ['Transformation could not finish (all values bad or outside range, etc.), value set to missing_value.', 'Transformation resulted in an indeterminate outcome.']

    flag_assessments :

    ['Bad', 'Indeterminate']

    Array Chunk Bytes 432 B 72 B Shape (108,) (18,) Dask graph 7 chunks in 15 graph layers Data type int32 numpy.ndarray

  • N_CCN
    (time)
    float32
    dask.array<chunksize=(18,), meta=np.ndarray>

    long_name :

    Mean number concentration of N_CCN

    units :

    1/cm^3

    comment :

    The droplets from the bins lower than 'first_bin_used' are excluded, and N_CCN is recomputed. The equation used is: ([sum of droplets from each bin >= first_bin_used + overflow] / Q_sample ) * 60. This field contains the robust averaged value termed the "inner quartile" of N_CCN values obtained at a fixed supersaturation set points passing all QC tests. The "inner quartile" consists of the central portion of the data values remaining after excluding the top and bottom 1/4 of the values.

    standard_name :

    number_concentration_of_ambient_aerosol_particles_in_air

    reference :

    ambient conditions

    ancillary_variables :

    qc_N_CCN

    cell_transform :

    time: TRANS_SUBSAMPLE (range: 600)

    source :

    anxaosccn2colaavgM1.b1:N_CCN

    history :

    act.qc.datafilter: Transformation could not finish (all values bad or outside range, etc.), value set to missing_value. act.qc.datafilter: Transformation could not finish (all values bad or outside range, etc.), value set to missing_value.

    Array Chunk Bytes 432 B 72 B Shape (108,) (18,) Dask graph 6 chunks in 1 graph layer Data type float32 numpy.ndarray

  • qc_N_CCN
    (time)
    int32
    dask.array<chunksize=(14,), meta=np.ndarray>

    long_name :

    Quality check results on variable: Mean number concentration of N_CCN

    units :

    1

    standard_name :

    quality_flag

    flag_masks :

    [1, 2]

    flag_meanings :

    ['Transformation could not finish (all values bad or outside range, etc.), value set to missing_value.', 'Transformation resulted in an indeterminate outcome.']

    flag_assessments :

    ['Bad', 'Indeterminate']

    Array Chunk Bytes 432 B 72 B Shape (108,) (18,) Dask graph 7 chunks in 15 graph layers Data type int32 numpy.ndarray

  • N_CCN_dN
    (time, droplet_size)
    float32
    dask.array<chunksize=(18, 20), meta=np.ndarray>

    long_name :

    Droplet count by bin size

    units :

    count/s

    comment :

    The number droplets per bin (dN) corresponding to N_CCN data from 25% to 75% (obtained during N_CCN_mean_interquartile calculation) are averaged. This field produces one droplet size distribution per supersaturation value and is used to calculate the N_CCN concentration based on dN.

    comment1 :

    Each bin contains a droplet count, based on droplet size

    reference :

    ambient conditions

    ancillary_variables :

    qc_N_CCN_dN

    cell_transform :

    time: TRANS_SUBSAMPLE (range: 600) droplet_size: TRANS_PASSTHROUGH

    source :

    anxaosccn2colaavgM1.b1:N_CCN_dN

    history :

    act.qc.datafilter: Transformation could not finish (all values bad or outside range, etc.), value set to missing_value. act.qc.datafilter: Transformation could not finish (all values bad or outside range, etc.), value set to missing_value.

    Array Chunk Bytes 8.44 kiB 1.41 kiB Shape (108, 20) (18, 20) Dask graph 6 chunks in 1 graph layer Data type float32 numpy.ndarray

  • qc_N_CCN_dN
    (time, droplet_size)
    int32
    dask.array<chunksize=(14, 20), meta=np.ndarray>

    long_name :

    Quality check results on variable: Droplet count by bin size

    units :

    1

    standard_name :

    quality_flag

    flag_masks :

    [1, 2]

    flag_meanings :

    ['Transformation could not finish (all values bad or outside range, etc.), value set to missing_value.', 'Transformation resulted in an indeterminate outcome.']

    flag_assessments :

    ['Bad', 'Indeterminate']

    Array Chunk Bytes 8.44 kiB 1.41 kiB Shape (108, 20) (18, 20) Dask graph 7 chunks in 15 graph layers Data type int32 numpy.ndarray

  • dN_dlogDp
    (time, diameter_mobility)
    float32
    dask.array<chunksize=(18, 192), meta=np.ndarray>

    long_name :

    Number size distribution, electrical mobility diameter

    units :

    1/cm^3

    comment :

    dN_dlogDp is the aerosol number size distribution where the number of particles per bin (dN) have been divided by the bin-width in log10 space (dlogDp). This simplifies comparison of size distributions from instruments with different bin spacing.

    ancillary_variables :

    qc_dN_dlogDp

    cell_transform :

    time: TRANS_SUBSAMPLE (range: 600) diameter_mobility: TRANS_PASSTHROUGH

    source :

    anxaossmpsM1.b1:dN_dlogDp

    history :

    act.qc.datafilter: Transformation could not finish (all values bad or outside range, etc.), value set to missing_value. act.qc.datafilter: Transformation could not finish (all values bad or outside range, etc.), value set to missing_value.

    Array Chunk Bytes 81.00 kiB 13.50 kiB Shape (108, 192) (18, 192) Dask graph 6 chunks in 1 graph layer Data type float32 numpy.ndarray

  • qc_dN_dlogDp
    (time, diameter_mobility)
    int32
    dask.array<chunksize=(14, 192), meta=np.ndarray>

    long_name :

    Quality check results on variable: Number size distribution, electrical mobility diameter

    units :

    1

    standard_name :

    quality_flag

    flag_masks :

    [1, 2]

    flag_meanings :

    ['Transformation could not finish (all values bad or outside range, etc.), value set to missing_value.', 'Transformation resulted in an indeterminate outcome.']

    flag_assessments :

    ['Bad', 'Indeterminate']

    Array Chunk Bytes 81.00 kiB 13.50 kiB Shape (108, 192) (18, 192) Dask graph 7 chunks in 15 graph layers Data type int32 numpy.ndarray

  • lat
    (time)
    float32
    69.14 69.14 69.14 ... 69.14 69.14

    long_name :

    North latitude

    units :

    degree_N

    valid_min :

    -90.0

    valid_max :

    90.0

    standard_name :

    latitude

    array([69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128,
           69.14128, 69.14128, 69.14128, 69.14128, 69.14128, 69.14128],
          dtype=float32)

  • lon
    (time)
    float32
    15.68 15.68 15.68 ... 15.68 15.68

    long_name :

    East longitude

    units :

    degree_E

    valid_min :

    -180.0

    valid_max :

    180.0

    standard_name :

    longitude

    array([15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167,
           15.684167, 15.684167, 15.684167, 15.684167, 15.684167, 15.684167],
          dtype=float32)

  • alt
    (time)
    float32
    2.0 2.0 2.0 2.0 ... 2.0 2.0 2.0 2.0

    long_name :

    Altitude above mean sea level

    units :

    m

    standard_name :

    altitude

    array([2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
           2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
           2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
           2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
           2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
           2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
           2., 2., 2., 2., 2., 2.], dtype=float32)

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-03-09 00:32:04', '2020-03-09 03:47:53',
                   '2020-03-09 06:35:33', '2020-03-09 07:47:52',
                   '2020-03-09 08:38:40', '2020-03-09 11:26:35',
                   '2020-03-09 12:38:03', '2020-03-09 13:50:01',
                   '2020-03-09 14:42:31', '2020-03-09 15:04:13',
                   ...
                   '2020-03-15 08:33:10', '2020-03-15 09:47:44',
                   '2020-03-15 11:03:10', '2020-03-15 13:25:32',
                   '2020-03-15 14:36:55', '2020-03-15 15:50:47',
                   '2020-03-15 17:04:24', '2020-03-15 19:33:47',
                   '2020-03-15 20:46:08', '2020-03-15 21:54:48'],
                  dtype='datetime64[ns]', name='time', length=108, freq=None))

  • diameter_mobility
    PandasIndex

    PandasIndex(Index([1.0199999809265137,  1.059999942779541,  1.090000033378601,
           1.1299999952316284, 1.1799999475479126, 1.2200000286102295,
           1.2599999904632568,  1.309999942779541, 1.3600000143051147,
            1.409999966621399,
           ...
                        710.5,              736.5,              763.5,
                        791.5,              820.5,              850.5,
            881.7000122070312,              914.0,              947.5,
            982.2000122070312],
          dtype='float32', name='diameter_mobility', length=192))

  • droplet_size
    PandasIndex

    PandasIndex(Index([ 0.0,  1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,  9.0, 10.0, 11.0,
           12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0],
          dtype='float32', name='droplet_size'))

  • setpoint
    PandasIndex

    PandasIndex(Index([                0.0, 0.10000000149011612, 0.20000000298023224,
            0.4000000059604645,  0.6000000238418579,   0.800000011920929,
                           1.0],
          dtype='float32', name='setpoint'))

• Attributes: (17)

  command_line :

  aosccnsmpskappa -n aosccnsmpskappa -s anx -f M1 -b 20191103 -e 20200422 --filter-overlaps

  Conventions :

  ARM-1.3

  process_version :

  aosccnsmpskappa-1.2.0

  dod_version :

  aosccnsmpskappa-c1-1.2

  input_datastreams :

  anxaosccn2colaavgM1.b1 : 2.1 : 20200309.003204 anxaossmpsM1.b1 : 1.2 : 20200308.000000-20200310.000000

  site_id :

  anx

  platform_id :

  aosccnsmpskappa

  facility_id :

  M1

  data_level :

  c1

  location_description :

  Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE), Andoy, Norway

  datastream :

  anxaosccnsmpskappaM1.c1

  doi :

  10.5439/1729907

  history :

  created by user levin on machine prod-proc5.adc.arm.gov at 2023-09-10 21:02:53, using aosccnsmpskappa-1.2.0

  _file_dates :

  ['20200309', '20200310', '20200311', '20200312', '20200313', '20200314', '20200315']

  _file_times :

  ['003204', '002602', '022138', '013206', '003849', '005637', '000427']

  _datastream :

  anxaosccnsmpskappaM1.c1

  _arm_standards_flag :

  1

Data Rose Plots

These plots display the data on a windrose-like plot to visualize directional dependencies in the data.

# We already should have the data loaded up so let's explore with some data roses
# First we need to combine data and to do that, we need to get it on the same time grid
ds_combined = xr.merge([ds_met.resample(time='30min').nearest(), ds_ccn.resample(time='30min').nearest()], compat='override')

# Plot out the data rose using the WindRose display object
display = act.plotting.WindRoseDisplay(ds_combined)
display.plot_data('wdir_vec_mean', 'wspd_vec_mean', 'N_CCN', num_dirs=15, plot_type='line', line_plot_calc='mean')
plt.show()

Checkout the area

The AMF was deployed in Andenes Norway. Check out the google map and see if this mapes sense!

Back to the visualizations!

Let’s get back to checking out the other visualization features in ACT!

Histograms

# We do the same thing as before but call the DistributionDisplay class
display = act.plotting.DistributionDisplay(ds_ccn)

# And then we can plot the data!  Note that we are passing a range into the
# histogram function to set the min/max range of the data
display.plot_stacked_bar('N_CCN', bins=20, hist_kwargs={'range': [0, 100]})
plt.show()

# We can create these plots in groups as well but we need to know
# how many there will be ahead of time for the shape
display = act.plotting.DistributionDisplay(ds_ccn, figsize=(15, 15), subplot_shape=(6, 4))
groupby = display.group_by('hour')

# And then we can plot the data in groups!  The main issue is that it doesn't automatically
# Annotate the group on the plot.  We're also setting the titile to blank to save space
groupby.plot_group('plot_stacked_bar', None, field='N_CCN', set_title='', bins=20, hist_kwargs={'range': [0, 100]})

# We want these graphs to have the same axes, so we can easily run through
# each plot and modify the axes.  Right now, we can just hard code these in
for i in range(len(display.axes)):
    for j in range(len(display.axes[i])):
        display.axes[i, j].set_xlim([0, 100])
        
plt.subplots_adjust(wspace=0.35)
        
plt.show()

Scatter Plots and Heatmaps

Let’s plot up a comparison of the APS total concentration and the ACSM sulfates. Feel free to change the variables from the ACSM to experiment!

# Let's merge the aps and ACSM data together and plot out some distribution plots
# First we need to combine data and to do that, we need to get it on the same time grid
ds_combined = xr.merge([ds_neph.resample(time='30min').nearest(), ds_ccn.resample(time='30min').nearest()], compat='override')

# Plot out the data rose using the Distribution display object
display = act.plotting.DistributionDisplay(ds_combined)
display.plot_scatter('N_CCN', 'kappa', m_field='time')

plt.show()

# Let's try a heatmap with this as well!
display = act.plotting.DistributionDisplay(ds_combined, figsize=(12, 5), subplot_shape=(1, 2))

display.plot_scatter('N_CCN', 'kappa', m_field='time', subplot_index=(0, 0))
display.plot_heatmap('N_CCN', 'kappa', subplot_index=(0, 1), x_bins=50, y_bins=50, threshold=0)

plt.show()

Create a Visualization of the Number Concentration by Size

One of the other dimensions of the CCN dataset is by the size, in nanometers, ranging from 1 to 19 micrometers. To plot this, we can create new variables in the dataset, then create a loop to plot these.

for drop_size in ds_ccn.droplet_size.values:
    ds_ccn[f"N_CCN_dN_{drop_size}"] = ds_ccn["N_CCN_dN"].sel(droplet_size=drop_size)

# Let's try one last plot type with this dataset
# Violin plots!
display = act.plotting.DistributionDisplay(ds_ccn, figsize=(14, 5))

for drop_size in ds_ccn.droplet_size.values:
    display.plot_violin(f"N_CCN_dN_{drop_size}", positions=[drop_size])

# Let's add some more information to the plots
# Update the tick information
display.axes[0].set_xticks(ds_ccn.droplet_size.values)
display.axes[0].set_xticklabels([f"{int(value)} \n um" for value in list(ds_ccn.droplet_size.values)])

plt.show()

Additional Features in ACT

If there’s time to explore more features or if you want to on your own time, these are some of the many additional features that you might find useful in ACT

Skew-T Plots

# Let's set a longer time period
startdate = '2020-03-12'
enddate = '2020-03-12'

# SONDE
datastream = 'anxsondewnpnM1.b1'
result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
result.sort()
ds_sonde = act.io.read_arm_netcdf(result[-1])

# Plot enhanced Skew-T plot
display = act.plotting.SkewTDisplay(ds_sonde)
display.plot_enhanced_skewt(color_field='alt')

plt.show()

[DOWNLOADING] anxsondewnpnM1.b1.20200312.172800.cdf
[DOWNLOADING] anxsondewnpnM1.b1.20200312.112700.cdf
[DOWNLOADING] anxsondewnpnM1.b1.20200312.052700.cdf

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

Keeler, E., Burk, K., & Kyrouac, J. Balloon-Borne Sounding System (SONDEWNPN).
Atmospheric Radiation Measurement (ARM) User Facility.
https://doi.org/10.5439/1595321

---

Wind Roses

# Now we can plot up a wind rose of that entire month's worth of data
windrose = act.plotting.WindRoseDisplay(ds_met, figsize=(10,8))
windrose.plot('wdir_vec_mean', 'wspd_vec_mean', spd_bins=np.linspace(0, 10, 5))
windrose.axes[0].legend()
plt.show()

---

Present Weather Codes

See this example of how to plot up these present weather codes on your plots!

# Pass it to the function to decode it along with the variable name
ds_met = act.utils.inst_utils.decode_present_weather(ds_met, variable='pwd_pw_code_inst')

# We're going to print out the first 10 decoded values that weren't 0
# This shows the utility of also being able to use the built-in xarray
# features like where!
print(list(ds_met['pwd_pw_code_inst_decoded'].where(ds_met.pwd_pw_code_inst.compute() > 0, drop=True).values[0:10]))

['Drizzle, not freezing, slight', 'Drizzle, not freezing, slight', 'Drizzle, not freezing, slight', 'Rain, not freezing, slight', 'Rain, not freezing, slight', 'Rain, not freezing, slight', 'Rain, not freezing, slight', 'Rain, not freezing, slight', 'Rain, not freezing, slight', 'Rain, not freezing, slight']

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Accumulating Precipitation

This example shows how to accumulate precipitation using the ACT utility and then overplot the PWD present weather codes

# Let's accumulate the precipitation data from the three different sensors in the MET System
# These instruments include a tipping bucket rain gauge, optical rain gauge, and a present weather detector
variables = ['tbrg_precip_total', 'org_precip_rate_mean', 'pwd_precip_rate_mean_1min']
for v in variables:
    ds_met = act.utils.data_utils.accumulate_precip(ds_met, v)

# We can plot them out easily in a loop.  Note, they have _accumulated on the end of the name
display = act.plotting.TimeSeriesDisplay(ds_met, figsize=(8, 6))
for v in variables:
    display.plot(v + '_accumulated', label=v)

# Add a day/night background
display.day_night_background()

# Now we can decode the present weather codes (WMO codes)
ds_met = act.utils.inst_utils.decode_present_weather(ds_met, variable='pwd_pw_code_1hr')

# We're only going to plot up the code when it changes
# and if we plot it up, we will skip 2 hours so the plot
# is not busy and unreadable
ct = 0
ds = ds_met.where(ds_met.pwd_pw_code_1hr.compute() > 0, drop=True)
wx = ds['pwd_pw_code_1hr_decoded'].values
prev_wx = None
while ct < len(wx):
    if wx[ct] != prev_wx:
        # We can access the figure and axes through the display object
        display.axes[0].text(ds['time'].values[ct], -7.5, wx[ct], rotation=90, va='top')
    prev_wx = wx[ct]
    ct += 120
plt.subplots_adjust(bottom=0.20)
plt.legend()
plt.show()

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Doppler Lidar Wind Retrievals

This will show you how you can process the doppler lidar PPI scans to produce wind profiles based on Newsom et al 2016.

# We're going to use some test data that already exists within ACT
# Let's set a longer time period
startdate = '2020-03-11T00:00:00'
enddate = '2020-03-12T01:00:00'

# SONDE
datastream = 'anxdlppiS2.b1'
result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
result.sort()

ds = act.io.read_arm_netcdf(result)
ds
# Returns the wind retrieval information in a new object by default
# Note that the default snr_threshold of 0.008 was too high for the first profile
# Reducing it to 0.002 makes it show up but the quality of the data is likely suspect.
ds_wind = act.retrievals.compute_winds_from_ppi(ds, snr_threshold=0.01)

# Plot it up
display = act.plotting.TimeSeriesDisplay(ds_wind)
display.plot_barbs_from_spd_dir('wind_speed', 'wind_direction', invert_y_axis=False)

#Update the x-limits to make sure both wind profiles are shown
display.axes[0].set_xlim([np.datetime64(startdate), np.datetime64(enddate)])

plt.show()

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