Lab 3-1: Surface Energy Balance at Kettle Ponds - Radiation Balance

Lab 3-1: Surface Energy Balance at Kettle Ponds - Radiation Balance#

Written by Daniel Hogan - April, 2023.

Modified by Jessica Lundquist - April, 2023.

Modified by Eli Schwat - January 2024.

import xarray as xr
import numpy as np
import os 
import urllib
import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt

# Install a package called pysolar
!pip install pysolar

Collecting pysolar

  Downloading pysolar-0.13-py3-none-any.whl.metadata (358 bytes)
Requirement already satisfied: numpy in /opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages (from pysolar) (2.4.2)
Downloading pysolar-0.13-py3-none-any.whl (46 kB)

Installing collected packages: pysolar
Successfully installed pysolar-0.13

# Now that we've installed that package, we need to import all of its functions
from pysolar.solar import radiation, get_altitude

SOS Data#

sos_file = "../data/sos_full_dataset_30min.nc"
sos_dataset = xr.open_dataset(sos_file)

Select a subset of days and plot all four radiometer variables#

Rsw_in_9m_d - Incoming shortwave radiation, measured at 9 meters on tower

Rsw_out_9m_d - Outgoing shortwave radiation, measured at 9 meters on tower

Rlw_in_9m_d - Incoming longwave radiation, measured at 9 meters on tower

Rlw_out_9m_d - Outgoing longwave radiation, measured at 9 meters on tower

sos_dataset[[
    'Rsw_in_9m_d',
    'Rsw_out_9m_d',
    'Rlw_in_9m_d',
    'Rlw_out_9m_d'
]].sel(time = 
    slice('20221201', '20221206')
).to_dataframe().plot() 

#Note: converting to_dataframe here allows use to use a simple convenience function to plot all 4 variables at once

<Axes: xlabel='time'>
../../_images/d554958e9133f763a021d52317edb5da88137c54ad163dd13daf9aed6ef495f1.png

Exercise: calculate and plot net radiation#

\[R_n = SW_{in} - SW_{out} + LW_{in} - LW_{out}\]
# Write this code yourself

Use Pysolar to get clear sky radiation time series#

Note that the radiation.get_radiation_direct function requires that we provide a datetime object that is “timezone aware”. See the annotations in the code below.

import pytz
kettle_ponds_lat_lon = [-106.97298,38.94182]
dates = pd.date_range(dt.datetime(2022,12, 1), dt.datetime(2022,12, 6),  freq='30Min')

slice('20221201', '20221206')

clear_sky_rad = []
for date in dates:
    date = (pytz.utc.localize(date)).to_pydatetime() # This assigns the UTC timezone to the date
    altitude_deg = get_altitude(kettle_ponds_lat_lon[1], kettle_ponds_lat_lon[0], date)
    clear_sky_rad.append(radiation.get_radiation_direct(date, altitude_deg))

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/pysolar/numeric.py:62: UserWarning: no explicit representation of timezones available for np.datetime64
  dd = numpy.array(d, dtype='datetime64[D]')
/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/pysolar/numeric.py:63: UserWarning: no explicit representation of timezones available for np.datetime64
  dy = numpy.array(d, dtype='datetime64[Y]')

plt.plot(dates,clear_sky_rad)

[<matplotlib.lines.Line2D at 0x7f815eb929d0>]
../../_images/b98c81c2c3207e845086c012fe9f052129039978aa2fd16512ea4324125eb36c.png

Note how “solar noon” appears late in the day. This is because the timezone is UTC, which is ahead of US/Mountain Time. We can convert the dates we have to US/Mountain Time, so that we can compare with our own dataset.

dates_index = pd.DatetimeIndex(dates, tz='UTC')
dates_index = dates_index.tz_convert('US/Mountain').tz_localize(None)

fig, ax = plt.subplots()
ax.plot(dates_index, clear_sky_rad, color='black', label='Ideal SW_IN')
sos_dataset[[
    'Rsw_in_9m_d',
    'Rsw_out_9m_d',
    'Rlw_in_9m_d',
    'Rlw_out_9m_d'
]].sel(time = 
    slice('20221201', '20221206')
).to_dataframe().plot(ax=ax) 

/opt/hostedtoolcache/Python/3.11.14/x64/lib/python3.11/site-packages/pandas/plotting/_matplotlib/core.py:981: UserWarning: This axis already has a converter set and is updating to a potentially incompatible converter
  return ax.plot(*args, **kwds)

<Axes: xlabel='time'>
../../_images/9f20e1ce9abdad53247c8a50ed99e6a1fea0a5649e2667c7ae9bcf7d768b79fd.png
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