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1.1. Understanding Surface Parameters (1): Albedo¶

1.1.1. Objectives¶

Review Python programming.
Prepare plots of surface radiation and energy balance using assigned AmeriFlux observations under two scenarios: one clear day and one cloudy day.

Tips:

Check the fluxes make sense.
Think about the time of day.
Calculate the albedo through the day for both days.
How does the albedo vary through the day?
How do your site values compare to the literature?
How does the albedo vary through the year?
If you had to use one albedo value to model the site, what would it be?
For scientific background see Albedo and Reading List

1.1.2. Tasks¶

1.1.2.1. load necessary packages¶

[1]:

# These packages are necessary later on. Load all the packages in one place for consistency
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pathlib import Path

1.1.2.2. load data¶

[2]:

#The path of the directory where all AMF data are
#path_dir = Path.cwd()/'data'/'AMF-clean'
path_dir = Path.cwd()/'data'/'1'

[3]:

name_of_site = 'CA-Obs_clean.csv.gz'
path_data = path_dir/name_of_site
path_data.resolve()

[3]:

PosixPath('/Users/sunt05/Dropbox/6.Repos/BLM/data/1/CA-Obs_clean.csv.gz')

[4]:

df_data = pd.read_csv(path_data, index_col='time',parse_dates=['time'])

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-4-010038206d98> in <module>
----> 1 df_data = pd.read_csv(path_data, index_col='time',parse_dates=['time'])

/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
    683         )
    684
--> 685         return _read(filepath_or_buffer, kwds)
    686
    687     parser_f.__name__ = name

/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
    455
    456     # Create the parser.
--> 457     parser = TextFileReader(fp_or_buf, **kwds)
    458
    459     if chunksize or iterator:

/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
    893             self.options["has_index_names"] = kwds["has_index_names"]
    894
--> 895         self._make_engine(self.engine)
    896
    897     def close(self):

/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
   1133     def _make_engine(self, engine="c"):
   1134         if engine == "c":
-> 1135             self._engine = CParserWrapper(self.f, **self.options)
   1136         else:
   1137             if engine == "python":

/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
   1915         kwds["usecols"] = self.usecols
   1916
-> 1917         self._reader = parsers.TextReader(src, **kwds)
   1918         self.unnamed_cols = self._reader.unnamed_cols
   1919

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.__cinit__()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._setup_parser_source()

/anaconda3/lib/python3.7/gzip.py in __init__(self, filename, mode, compresslevel, fileobj, mtime)
    161             mode += 'b'
    162         if fileobj is None:
--> 163             fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
    164         if filename is None:
    165             filename = getattr(fileobj, 'name', '')

FileNotFoundError: [Errno 2] No such file or directory: '/Users/sunt05/Dropbox/6.Repos/BLM/data/1/CA-Obs_clean.csv.gz'

1.1.2.3. Let’s look at the site info¶

[ ]:

all_sites_info =  pd.read_csv('data/site_info.csv')
site_info=all_sites_info[all_sites_info['Site Id'] == name_of_site.split('_clean')[0]]
site_info

1.1.2.4. examine data¶

1.1.2.4.1. Loading the first 5 rows of the dataframe¶

[ ]:

df_data.head(5)

1.1.2.4.2. Let’s print some statistics of the data¶

For names used by AmeriFlux for variables see AmeriFlux Key variables for analysis

[ ]:

df_data.describe()

[ ]:

df_data.loc[:,['SWIN','LWIN','SWOUT','LWOUT']].plot(figsize=(15,4))

[ ]:

df_data.loc[:,['LE','H']].plot(figsize=(15,4))

[ ]:

df_data.loc[:,['TA']].plot(figsize=(15,4))
df_data.loc[:,['P']].plot(figsize=(15,4))
df_data.loc[:,['WS']].plot(figsize=(15,4))
df_data.loc[:,['RH']].plot(figsize=(15,4))
df_data.loc[:,['PA']].plot(figsize=(15,4))

1.1.2.5. plot data¶

1.1.2.5.1. radiation balance¶

[ ]:

date = '2001 10 21'
df_data.loc[:,['SWIN','LWIN','SWOUT','LWOUT']].dropna().loc[date].plot()

1.1.2.5.2. examining if NETRAD == (SWIN-SWOUT)+(LWIN-LWOUT)¶

[ ]:

NetSW = df_data.loc[date,'SWIN']-df_data.loc[date,'SWOUT']
NetLW = df_data.loc[date,'LWIN']-df_data.loc[date,'LWOUT']
Netrad_calc=NetSW+NetLW
df_data.loc[date,'NETRAD'].plot(color='r',label='NETRAD from data')
Netrad_calc.plot(color='b',marker='o',label='NETRAD calculated')
plt.legend()

1.1.2.5.3. surface energy balance¶

[ ]:

df_data.loc[:,['NETRAD','H','LE']].dropna().loc[date].plot()

1.1.2.6. calculate albedo¶

1.1.2.6.1. surface albedo¶

[ ]:

ser_alb=df_data['SWOUT']/df_data['SWIN']

[ ]:

ser_alb[ser_alb.between(0,1)&(df_data['SWIN']>5)].loc[date].plot(marker='o')

[ ]: