Coldpool and Downdraft Analysis

import act
import pyart
import metpy
from metpy.units import units
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import glob

# this is pulling individual May 20 C-SAPR2 radar data
csacr = "/data/project/ARM_Summer_School_2025/bnf/bnfcsapr2cfrS3.a1/bnfcsapr2cfrS3.a1.20250520.233012.nc"
ds_csacr = xr.open_dataset(csacr)
ds_csacr

# # pull in met data using ACT
# # Set your username and token here!
# username = 'abchui'
# token = '3279f3ac6800743d'

# # Set the datastream and start/enddates
# datastream = 'bnfsondewnpnM1.b1'
# startdate = '2025-05-20T23:00:00'
# enddate = '2025-05-20T23:59:59'

# # 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_sonde = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)

# sonde = pyart.io.read_arm_sonde("/data/project/ARM_Summer_School_2025/bnf/bnfsondewnpnM1.b1/bnfsondewnpnM1.b1.20250520.233000.cdf")

# pull in met data using ACT
# Set your username and token here!
username = 'abchui'
token = '3279f3ac6800743d'

# Set the datastream and start/enddates
datastream = 'bnfmetM1.b1'
startdate = '2025-05-20'
enddate = '2025-05-21T23:59:59' #notice can give date and time or leave it blank (like with start date)

# 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_met = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
ds_met = act.io.read_arm_netcdf(result_met)
ds_met

# thetae = metpy.calc.equivalent_potential_temperature(ds_met.atmos_pressure, ds_met.temp_mean, ds_met.dewpoint)

# # Create a plotting display object with 2 plots - do I want 
# display = act.plotting.TimeSeriesDisplay(ds_met, figsize=(15, 10), subplot_shape=(2,))

# # Plot up the MET btemperature and precipitation
# display.plot('temp_mean', subplot_index=(1,))
# ax2 = display.axes[1].twinx() #copies the x axis onto both plots
# ax2.plot(ds_met.time, ds_met.tbrg_precip_total, color='orange')

# # Plot up a day/night background
# display.day_night_background(subplot_index=(1,))

# plt.show()

# C-SACR data
csacr = sorted(glob.glob("/data/project/ARM_Summer_School_2025/bnf/bnfcsapr2cfrS3.a1/bnfcsapr2cfrS3.a1.20250315.233012.nc"))

# Set your username and token here!
username = 'abchui'
token = '3279f3ac6800743d'

# Set the datastream and start/enddates
datastream = 'bnfkazr2cfrgeM1.a1'
startdate = '2025-05-01'
enddate = '2025-05-01T23:59:59' #notice can give date and time or leave it blank (like with start date)

# 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_kazr2 = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
ds_kazr2 = act.io.read_arm_netcdf(result_kazr2)

# pull in met data using ACT
# Set your username and token here!
username = 'abchui'
token = '3279f3ac6800743d'

# Set the datastream and start/enddates
datastream = 'bnfmetM1.b1'
startdate = '2025-05-01'
enddate = '2025-05-01T23:59:59' #notice can give date and time or leave it blank (like with start date)

# 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_met = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
ds_met = act.io.read_arm_netcdf(result_met)
ds_met

# pull in met data using ACT
# Set your username and token here!
username = 'abchui'
token = '3279f3ac6800743d'

# Set the datastream and start/enddates
datastream = 'bnf915rwpwindavgM1.a1'
startdate = '2025-05-01'
enddate = '2025-05-01T23:59:59' #notice can give date and time or leave it blank (like with start date)

# 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_rwp = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)
ds_rwp = act.io.read_arm_netcdf(result_rwp)
ds_rwp

# two ways to calculate theta-e: 1) pull maws data dewpoint temp and combine time or 2) calculate dewpoint temp from RH in the met data
# method 2) is better because method 1) will cause loss of resolution

# 1) to calculate theta-e, need met and maws data but by looking at the time from above you can see the time is slightly off, both are 1 min but maws if at 4 sec into the minute
# ds_combined = xr.merge([ds_met.resample(time='1min').nearest(), ds_maws.resample(time='1min').nearest()], compat='override')
# thetae = metpy.calc.equivalent_potential_temperature(ds_combined.atmos_pressure, ds_combined.temp_mean, ds_combined.atmospheric_dew_point)

# 2) 
dewpt_temp = metpy.calc.dewpoint_from_relative_humidity(ds_met.temp_mean, ds_met.rh_mean)
thetaeK = metpy.calc.equivalent_potential_temperature(ds_met.atmos_pressure, ds_met.temp_mean, dewpt_temp)
thetaeC = thetaeK - 273.15*units.kelvin

# Create a plotting display object with 3 plots - do I want 
display = act.plotting.TimeSeriesDisplay({'KAZR2': ds_kazr2, 'MET': ds_met, 'RWP' : ds_rwp}, figsize=(15, 15), subplot_shape=(3,))

# top (first) subplot (index of 0)
display.plot('reflectivity', dsname='KAZR2', subplot_index=(0,))

# middle (second) subplot (index of 1)
# Plot up the MET temperature and theta-e (calculated from met mean temp and RH)
display.plot('temp_mean', dsname='MET', subplot_index=(1,))
ax2 = display.axes[1].twinx() #copies the x axis onto both plots
ax2.plot(ds_met.time, thetaeC, color='orange')
ax2.set_ylabel('Equivalent Potential Temperature (degC)')

# bottom (third) subplot (index of 2)
# display.plot_barbs_from_spd_dir('wind_speed', num_barbs_x=20), dsname="RWP", subplot_index=(2,1))

# Plot up a day/night background
display.day_night_background(dsname='MET', subplot_index=(1,))

plt.show()

csacr = sorted(glob.glob("/data/project/ARM_Summer_School_2025/bnf/bnfcsapr2cfrS3.a1/bnfcsapr2cfrS3.a1.20250512.233012.nc"))