Cloud Condensation Nuclei and Precipitation Analysis¶
Example shows how to plot up CCN droplet count in a size distribution plot, and an interactive precipitation intensity visualization.
Imports¶
import numpy as np
import act
import matplotlib.pyplot as plt
import plotly.graph_objects as goAnalyze CCN Data for BNF from the Data Archive¶
username = 'mgrover4'
token = '176e1559b67be630'
datastream = 'bnfaosccn2colaspectraM1.b1'
startdate = '2025-05-08'
enddate = '2025-05-11T23:59:59'# Download and read the data
result_ccn = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
ds_ccn = act.io.read_arm_netcdf(result_ccn)
ds_ccn.clean.cleanup()[DOWNLOADING] bnfaosccn2colaspectraM1.b1.20250508.010251.nc
[DOWNLOADING] bnfaosccn2colaspectraM1.b1.20250509.000954.nc
[DOWNLOADING] bnfaosccn2colaspectraM1.b1.20250510.002958.nc
[DOWNLOADING] bnfaosccn2colaspectraM1.b1.20250511.005002.nc
If you use these data to prepare a publication, please cite:
Koontz, A., Uin, J., Andrews, E., Enekwizu, O., Hayes, C., & Salwen, C. Cloud
Condensation Nuclei Particle Counter (AOSCCN2COLASPECTRA), 2025-05-08 to
2025-05-11, Bankhead National Forest, AL, USA; Long-term Mobile Facility (BNF),
Bankhead National Forest, AL, AMF3 (Main Site) (M1). Atmospheric Radiation
Measurement (ARM) User Facility. https://doi.org/10.5439/1323896
Visualize the CCN Data¶
if 'lat' not in ds_ccn.coords:
ds_ccn = ds_ccn.set_coords(['lat', 'lon'])
print("Variables:", list(ds_ccn.data_vars))
ccn_var = 'concentration'
# Plot
disp = act.plotting.TimeSeriesDisplay(ds_ccn, figsize=(12, 8))
disp.plot(ccn_var, label='CCN Concentration [#/cm³]')
disp.axes[0].set_title('Cloud Condensation Nuclei (CCN) Number Concentration')
disp.axes[0].set_ylabel('Supersaturation (%)')
disp.axes[0].set_xlabel('Time (UTC)')
disp.day_night_background()
disp.axes[0].legend()
plt.show()Variables: ['base_time', 'time_offset', 'time_bounds', 'setpoint_time', 'supersaturation_calculated', 'N_CCN', 'qc_N_CCN', 'N_CCN_fit_coefs', 'N_CCN_fit_error', 'N_CCN_fit_value', 'concentration', 'f_CCN', 'qc_f_CCN', 'alt']
/tmp/ipykernel_3934/132956031.py:15: UserWarning: Legend does not support handles for QuadMesh instances.
See: https://matplotlib.org/stable/tutorials/intermediate/legend_guide.html#implementing-a-custom-legend-handler
disp.axes[0].legend()
/tmp/ipykernel_3934/132956031.py:15: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
disp.axes[0].legend()
#Get supersaturation levels and time
supersat = ds_ccn['supersaturation_setpoint'].values
times = ds_ccn['time'].values
#Create the plot
fig, ax = plt.subplots(figsize=(12, 5))
colors = plt.cm.viridis(np.linspace(0, 1, len(supersat)))
#Plot one trace per supersaturation level
for i, ss in enumerate(supersat):
ccn = ds_ccn['concentration'].sel(supersaturation_setpoint=ss)
valid = ~ccn.isnull()
ax.scatter(times[valid], ccn.values[valid], s=10, color=colors[i], label=f'SS = {ss:.2f}')
#Plot makeup
ax.set_title('CCN Number Concentration vs Time at Different Supersaturations')
ax.set_xlabel('Time (UTC)')
ax.set_ylabel('CCN Concentration [#/cm³]')
ax.set_yscale('log')
ax.legend(title='Supersaturation (%)')
ax.grid(True)
plt.tight_layout()
plt.show()
Precipitation Analysis with the Pluvio¶
# Set the datastream and start/enddates
datastream = 'bnfwbpluvio2M1.a1'
startdate = '2025-05-08'
enddate = '2025-05-11T23:59:59'# 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_rain = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
# Let's read in the data using ACT and check out the data
ds_rain = act.io.read_arm_netcdf(result_rain)
# Apply quality control checks
ds_rain.clean.cleanup()[DOWNLOADING] bnfwbpluvio2M1.a1.20250508.000000.nc
[DOWNLOADING] bnfwbpluvio2M1.a1.20250509.000000.nc
[DOWNLOADING] bnfwbpluvio2M1.a1.20250510.000000.nc
[DOWNLOADING] bnfwbpluvio2M1.a1.20250511.000000.nc
If you use these data to prepare a publication, please cite:
Zhu, Z., Wang, D., Jane, M., Cromwell, E., Sturm, M., Irving, K., & Delamere, J.
Weighing Bucket Precipitation Gauge (WBPLUVIO2), 2025-05-08 to 2025-05-11,
Bankhead National Forest, AL, USA; Long-term Mobile Facility (BNF), Bankhead
National Forest, AL, AMF3 (Main Site) (M1). Atmospheric Radiation Measurement
(ARM) User Facility. https://doi.org/10.5439/1338194
#printing output label
print("Available rain-related variables:")
#printing list of variables
print(list(ds_rain.data_vars))
#saving rain variables
rain_rate_var = 'intensity_rt'
#giving a shortcut
ds = ds_rainAvailable rain-related variables:
['base_time', 'time_offset', 'intensity_rt', 'accum_rtnrt', 'accum_nrt', 'accum_total_nrt', 'bucket_rt', 'bucket_nrt', 'load_cell_temp', 'heater_status', 'pluvio_status', 'elec_unit_temp', 'supply_volts', 'orifice_temp', 'maintenance_flag', 'reset_flag', 'volt_min', 'ptemp', 'intensity_rtnrt', 'lat', 'lon', 'alt']
Interactive Visualizion of the Pluvio Data¶
#store data
rain_rate_var = 'intensity_rt'
df_rain = ds_rain[rain_rate_var].to_dataframe().dropna()
fig = go.Figure()
fig.add_trace(go.Scatter(
x=df_rain.index,
y=df_rain[rain_rate_var],
mode='lines+markers',
name='Rain Rate [mm/hr]',
line=dict(color='blue')
))
fig.update_layout(
title='Rain Rate (Pluvio2)',
xaxis_title='Time (UTC)',
yaxis_title='Rain Rate [mm/hr]',
hovermode='x unified',
template='plotly_white',
height=500,
)
fig.show()Loading...