# 10Exercises

We will calculate evapotranspiration using two methods: Thornthwaite and Penman. After that, we will compare these estimates with measurements of pan evaporation.

1. 10-min data
• On the navigation bar on the left, choose “10 Minutes Observations”
• Clock: Local time winter (UTC +2)
• Choose the following date range: 01/01/2020 00:00 to 01/01/2021 00:00
• Choose station Bet Dagan
• Select units: Celcius, m/s, KJ/m^2
• Under “Select parameters”, choose “Check All”
• Choose option “by station”, then “Submit”
• “Download Result as” CSV, call it bet-dagan-10min.csv
• Clock: Local time winter (UTC +2)
• Choose the following date range: 01/01/2020 00:00 to 01/01/2021 00:00
• Select hours: Check all hours
• Choose station Bet Dagan
• Select units: KJ/m^2
• Under “Select parameters”, choose “Check All”
• “Download Result as” CSV, call it bet-dagan-radiation.csv
1. pan evaporation data
• On the navigation bar on the left, choose “Daily Observations”
• Choose the following date range: 01/01/2020 00:00 to 01/01/2021 00:00
• Choose station Bet Dagan Man
• Select units: Celcius
• Under “Select parameters”, choose “Check All”
• “Download Result as” CSV, call it bet-dagan-pan.csv

## 10.2 Install and import relevant packages

We will need to use two new packages:

If you don’t have them installed yet, run this:

!pip install pyet
!pip install noaa_ftp

Once they are installed, import all the necessary packages for today’s exercises.

import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import pandas as pd
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()  # datetime converter for a matplotlib
import seaborn as sns
sns.set(style="ticks", font_scale=1.5)
import pyet
from noaa_ftp import NOAA

## 10.3 import 10-minute data

df = pd.read_csv('bet-dagan-10min.csv',
#  encoding = "ISO-8859-8",  # this shows hebrew characters properly
na_values=["-"]           # substitute "-" for NaN
)
df['timestamp'] = pd.to_datetime(df['Date & Time (Winter)'], dayfirst=True)
df = df.set_index('timestamp')
# resample to daily data according to "mean"
df = df.resample('D').mean()
# convert hecto pascals (hPa) to kilo pascals (kPa)
df["Pressure (kPa)"] = df["Pressure at station level (hPa)"] / 10.0
df
/var/folders/c3/7hp0d36n6vv8jc9hm2440__00000gn/T/ipykernel_16976/111106941.py:8: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
df = df.resample('D').mean()
Pressure at station level (hPa) Relative humidity (%) Temperature (°C) Maximum temperature (°C) Minimum temperature (°C) Grass temperature (°C) Wind direction (°) Gust wind direction (°) Wind speed (m/s) Maximum 1 minute wind speed (m/s) Maximum 10 minutes wind speed (m/s) Gust wind speed (m/s) Standard deviation wind direction (°) Rainfall (mm) Pressure (kPa)
timestamp
2020-01-01 1013.263889 80.590278 12.375000 12.486806 12.268750 13.061111 166.069444 166.673611 1.552083 2.013889 1.706250 2.325694 12.588194 0.000000 101.326389
2020-01-02 1011.922917 85.631944 12.020833 12.104861 11.921528 11.669444 149.333333 150.062500 2.207639 2.877083 2.438194 3.423611 12.726389 0.140278 101.192292
2020-01-03 1013.757639 60.756944 12.962500 13.086111 12.838889 13.389583 190.513889 191.277778 4.763194 5.940278 4.995139 7.355556 10.436111 0.000000 101.375764
2020-01-04 1011.581250 76.909722 10.849306 10.938194 10.772222 10.311806 163.958333 164.118056 5.439583 6.996528 5.829167 8.632639 14.309028 0.317361 101.158125
2020-01-05 1012.361806 79.583333 12.956250 13.053472 12.864583 13.135417 195.784722 197.326389 4.765278 6.120833 5.097917 7.763889 12.976389 0.112500 101.236181
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2020-12-28 1014.429861 58.729167 14.797917 14.915972 14.659722 14.653472 93.375887 96.758865 2.631915 3.440426 2.842553 4.218440 11.494326 0.000000 101.442986
2020-12-29 1015.031944 71.215278 14.146528 14.315278 13.986111 14.176389 97.777778 94.631944 1.493750 1.951389 1.630556 2.277083 14.328472 0.000000 101.503194
2020-12-30 1013.234028 68.923611 14.186806 14.336111 14.047222 14.187500 63.916667 63.722222 1.776389 2.275000 1.936111 2.646528 9.754861 0.000000 101.323403
2020-12-31 1011.840972 75.465278 14.915278 15.068056 14.763194 15.154167 165.895833 165.062500 1.395833 1.803472 1.546528 2.054861 10.363194 0.000000 101.184097
2021-01-01 1012.748760 85.115702 14.980992 15.133884 14.817355 15.890083 182.247934 178.190083 1.242975 1.691736 1.396694 1.938843 15.280992 0.000000 101.274876

367 rows × 15 columns

df_rad = pd.read_csv('bet-dagan-radiation.csv',
na_values=["-"]
)
df_rad
Date
2020-01-01 Bet Dagan Rad 01/1991-04/2023 Global 0.0 10.8 270.0 594.0 1252.8 1407.6 1800.0 1587.6 1443.6 1123.2 482.4 57.6 0.0 0.0
2020-01-01 Bet Dagan Rad 01/1991-04/2023 Direct 0.0 3.6 72.0 428.4 1393.2 1281.6 1911.6 1414.8 1112.4 1083.6 752.4 0.0 0.0 0.0
2020-01-01 Bet Dagan Rad 01/1991-04/2023 Diffused 0.0 10.8 216.0 403.2 543.6 586.8 590.4 684.0 770.4 637.2 252.0 57.6 0.0 0.0
2020-01-02 Bet Dagan Rad 01/1991-04/2023 Global 0.0 10.8 241.2 518.4 1018.8 93.6 129.6 345.6 720.0 673.2 478.8 82.8 0.0 0.0
2020-01-02 Bet Dagan Rad 01/1991-04/2023 Direct 0.0 3.6 57.6 100.8 471.6 0.0 0.0 32.4 140.4 334.8 680.4 79.2 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2020-12-31 Bet Dagan Rad 01/1991-04/2023 Direct 0.0 0.0 892.8 1998.0 2455.2 2696.4 2710.8 2545.2 2386.8 2066.4 1328.4 169.2 0.0 0.0
2020-12-31 Bet Dagan Rad 01/1991-04/2023 Diffused 0.0 14.4 158.4 270.0 320.4 360.0 388.8 403.2 378.0 316.8 219.6 54.0 0.0 0.0
2021-01-01 Bet Dagan Rad 01/1991-04/2023 Global 0.0 14.4 302.4 882.0 1432.8 1814.4 1962.0 1897.2 1602.0 1170.0 572.4 75.6 0.0 0.0
2021-01-01 Bet Dagan Rad 01/1991-04/2023 Direct 0.0 0.0 662.4 1576.8 2181.6 2412.0 2422.8 2343.6 2188.8 1980.0 1350.0 126.0 0.0 0.0
2021-01-01 Bet Dagan Rad 01/1991-04/2023 Diffused 0.0 14.4 172.8 352.8 421.2 478.8 525.6 540.0 478.8 381.6 237.6 57.6 0.0 0.0

1098 rows × 16 columns

Choose only “Global” radiation. Then sum all hours of the day, and convert from kJ to MJ.

df_rad = df_rad[df_rad["Radiation type"] == "Global "]
.sum(axis=1) /  # sum all columns
1000            # divide by 1000 to convert from kJ to MJ
)
df_rad
/var/folders/c3/7hp0d36n6vv8jc9hm2440__00000gn/T/ipykernel_16976/3934990453.py:2: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_rad['daily_radiation_MJ_per_m2_per_day'] = (df_rad.iloc[:, 3:]    # take all rows, columns 3 to end
Date
2020-01-01 Bet Dagan Rad 01/1991-04/2023 Global 0.0 10.8 270.0 594.0 1252.8 1407.6 1800.0 1587.6 1443.6 1123.2 482.4 57.6 0.0 0.0 10.0296
2020-01-02 Bet Dagan Rad 01/1991-04/2023 Global 0.0 10.8 241.2 518.4 1018.8 93.6 129.6 345.6 720.0 673.2 478.8 82.8 0.0 0.0 4.3128
2020-01-03 Bet Dagan Rad 01/1991-04/2023 Global 0.0 10.8 334.8 1040.4 1612.8 1846.8 1904.4 1947.6 1296.0 964.8 669.6 46.8 0.0 0.0 11.6748
2020-01-04 Bet Dagan Rad 01/1991-04/2023 Global 0.0 3.6 97.2 237.6 208.8 208.8 93.6 79.2 352.8 144.0 183.6 36.0 0.0 0.0 1.6452
2020-01-05 Bet Dagan Rad 01/1991-04/2023 Global 0.0 7.2 118.8 226.8 421.2 882.0 1296.0 1090.8 1242.0 1101.6 388.8 79.2 0.0 0.0 6.8544
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2020-12-28 Bet Dagan Rad 01/1991-04/2023 Global 0.0 14.4 349.2 1000.8 1551.6 1810.8 2048.4 1796.4 1627.2 993.6 482.4 68.4 0.0 0.0 11.7432
2020-12-29 Bet Dagan Rad 01/1991-04/2023 Global 0.0 14.4 342.0 936.0 1530.0 1926.0 2088.0 1994.4 1702.8 1216.8 604.8 64.8 0.0 0.0 12.4200
2020-12-30 Bet Dagan Rad 01/1991-04/2023 Global 0.0 21.6 302.4 986.4 1526.4 1922.4 2080.8 2019.6 1720.8 1238.4 612.0 68.4 0.0 0.0 12.4992
2020-12-31 Bet Dagan Rad 01/1991-04/2023 Global 0.0 14.4 324.0 954.0 1476.0 1861.2 2012.4 1908.0 1627.2 1159.2 565.2 72.0 0.0 0.0 11.9736
2021-01-01 Bet Dagan Rad 01/1991-04/2023 Global 0.0 14.4 302.4 882.0 1432.8 1814.4 1962.0 1897.2 1602.0 1170.0 572.4 75.6 0.0 0.0 11.7252

366 rows × 17 columns

## 10.5 import pan evaporation data

df_pan = pd.read_csv('bet-dagan-pan.csv',
na_values=["-"]           # substitute "-" for NaN
)
df_pan['Date'] = pd.to_datetime(df_pan['Date'], dayfirst=True)
df_pan = df_pan.set_index('Date')
df_pan
Station Maximum Temperature (°C) Minimum Temperature (°C) Grass Temperature (°C) Hail Frost Snow Fog Mist Dew Thunder Lightening Sand storm Gale Accumulated 6 hr evaporation (mm) Accumulated 12 hr evaporation (mm) Daily evaporation type A (mm) Daily evaporation type A code (code) Sunshine duration (minutes)
Date
2020-01-01 Bet Dagan Man 01/1964-03/2023 NaN NaN NaN 0 NaN 0 0 NaN NaN 0 0 0 NaN NaN NaN 0.8 0.0 NaN
2020-01-02 Bet Dagan Man 01/1964-03/2023 NaN NaN NaN 0 NaN 0 0 NaN NaN 1 0 0 NaN NaN NaN NaN NaN NaN
2020-01-03 Bet Dagan Man 01/1964-03/2023 NaN NaN NaN 0 NaN 0 0 NaN NaN 0 0 0 NaN NaN NaN NaN NaN NaN
2020-01-04 Bet Dagan Man 01/1964-03/2023 NaN NaN NaN 0 NaN 0 0 NaN NaN 1 0 0 NaN NaN NaN NaN NaN NaN
2020-01-05 Bet Dagan Man 01/1964-03/2023 NaN NaN NaN 0 NaN 0 0 NaN NaN 1 0 0 NaN NaN NaN 2.4 0.0 NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2020-12-28 Bet Dagan Man 01/1964-03/2023 NaN NaN NaN 0 NaN 0 0 NaN NaN 0 0 0 NaN NaN NaN 3.0 0.0 NaN
2020-12-29 Bet Dagan Man 01/1964-03/2023 NaN NaN NaN 0 NaN 0 0 NaN NaN 0 0 0 NaN NaN NaN 1.8 0.0 NaN
2020-12-30 Bet Dagan Man 01/1964-03/2023 NaN NaN NaN 0 NaN 0 0 NaN NaN 0 0 0 NaN NaN NaN 2.4 0.0 NaN
2020-12-31 Bet Dagan Man 01/1964-03/2023 NaN NaN NaN 0 NaN 0 0 NaN NaN 0 0 0 NaN NaN NaN 1.7 0.0 NaN
2021-01-01 Bet Dagan Man 01/1964-03/2023 NaN NaN NaN 0 NaN 0 0 NaN NaN 0 0 0 NaN NaN NaN 1.5 0.0 NaN

367 rows × 19 columns

## 10.6 calculate penman

We need some data about the Bet Dagan Station. See here.

• Latitude: 32.0073°
• Elevation: 31 m
# average day temperature [°C]
tmean = df["Temperature (°C)"]
# mean day wind speed [m/s]
wind = df["Wind speed (m/s)"]
# mean daily relative humidity [%]
rh = df["Relative humidity (%)"]
# incoming solar radiation [MJ m-2 d-1]
# atmospheric pressure [kPa]
pressure = df["Pressure (kPa)"]
# the site elevation [m]
elevation = 31.0
penm = pyet.combination.penman(tmean=tmean,
wind=wind,
pressure=pressure,
elevation=elevation,
rh=rh,
rs=rs,
lat=latitude,
)
fig, ax = plt.subplots(1)
ax.plot(penm, label="penman")
ax.plot(df_pan["Daily evaporation type A (mm)"], label="pan")
ax.legend()
plt.gcf().autofmt_xdate()  # makes slated dates
ax.set_ylabel("ET (mm)")
Text(0, 0.5, 'ET (mm)')

## 10.7 Thornthwaite

E = 16\left[ \frac{10\,T^\text{monthly mean}}{I} \right]^a,

where

I = \sum_{i=1}^{12} \left[ \frac{T_i^\text{monthly mean}}{5} \right]^{1.514},

and

\begin{split} a &= 6.75\times 10^{-7}I^3 \\ &- 7.71\times 10^{-5}I^2 \\ &+ 1.792\times 10^{-2}I \\ &+ 0.49239 \nonumber \end{split}

• E is the monthly potential ET (mm)
• T_\text{monthly mean} is the mean monthly temperature in °C
• I is a heat index
• a is a location-dependent coefficient

From df, make a new dataframe, df_th, that stores monthly temperatures means. Use resample function.

# monthly data
df_th = (df['Temperature (°C)'].resample('MS')  # MS assigns mean to first day in the month
.mean()
.to_frame()
)

# we now add 14 days to the index, so that all monthly data is in the middle of the month
# not really necessary, makes plot look better
df_th.index = df_th.index + pd.DateOffset(days=14)
df_th
Temperature (°C)
timestamp
2020-01-15 12.484812
2020-02-15 14.044349
2020-03-15 16.371381
2020-04-15 18.476339
2020-05-15 23.177769
2020-06-15 24.666423
2020-07-15 27.380466
2020-08-15 28.099328
2020-09-15 28.421644
2020-10-15 25.058944
2020-11-15 19.266082
2020-12-15 15.915031
2021-01-15 14.980992

Calculate I, then a, and finally E_p. Add E_p as a new column in df_th.

# Preparing "I" for the Thornthwaite equation
I = np.sum(
(
df_th['Temperature (°C)'] / 5
) ** (1.514)
)

# Preparing "a" for the Thornthwaite equation
a = (+6.75e-7 * I**3
-7.71e-5 * I**2
+1.792e-2 * I
+ 0.49239)

# The final Thornthwaite model for monthly potential ET (mm)
df_th['Ep (mm/month)'] = 16*(
(
10 * df_th['Temperature (°C)'] / I
) ** a
)
df_th
Temperature (°C) Ep (mm/month)
timestamp
2020-01-15 12.484812 20.018337
2020-02-15 14.044349 26.999656
2020-03-15 16.371381 39.865158
2020-04-15 18.476339 54.213825
2020-05-15 23.177769 96.461505
2020-06-15 24.666423 112.997152
2020-07-15 27.380466 147.331024
2020-08-15 28.099328 157.362480
2020-09-15 28.421644 161.990981
2020-10-15 25.058944 117.623700
2020-11-15 19.266082 60.299205
2020-12-15 15.915031 37.101123
2021-01-15 14.980992 31.814464
fig, ax = plt.subplots(1)
ax.plot(penm, label="penman")
ax.plot(df_pan["Daily evaporation type A (mm)"], label="pan")
ax.plot(df_th['Ep (mm/month)']/30, label="thornthwaite")

ax.legend()
plt.gcf().autofmt_xdate()  # makes slated dates
ax.set_ylabel("ET (mm/day)")
Text(0, 0.5, 'ET (mm/day)')

## 10.8 Data from NOAA

Let’s download data from a different repository. More specifically, we will retrieve sub-hourly data from the U.S. Climate Reference Network. We can see all the sites in this map. Besides the sub-hourly data, we can find other datasets (monthly, daily, etc).

As an example, we will choose the statin in Austin, Texas. In order to download, we will access the data from the FTP client using the python package noaa_ftp.

The dir command list everything in the folder:

noaa_dir = NOAA("ftp.ncdc.noaa.gov", 'pub/data/uscrn/products/subhourly01').dir()
drwxrwsr-x   2 ftp      ftp          8192 Oct  7  2020 2006
drwxrwsr-x   2 ftp      ftp          8192 Nov 10  2021 2007
drwxrwsr-x   2 ftp      ftp          8192 Dec  1  2020 2008
drwxrwsr-x   2 ftp      ftp         12288 May 25  2021 2009
drwxrwsr-x   2 ftp      ftp         12288 Nov 10  2021 2010
drwxrwsr-x   2 ftp      ftp         12288 Nov 12  2021 2011
drwxrwsr-x   2 ftp      ftp         12288 Nov 12  2021 2012
drwxrwsr-x   2 ftp      ftp         12288 Nov 15  2021 2013
drwxrwsr-x   2 ftp      ftp         12288 Nov 15  2021 2014
drwxrwsr-x   2 ftp      ftp         12288 Nov 12  2021 2015
drwxrwsr-x   2 ftp      ftp         12288 Nov 12  2021 2016
drwxrwsr-x   2 ftp      ftp         12288 Nov 15  2021 2017
drwxrwsr-x   2 ftp      ftp         12288 Nov 12  2021 2018
drwxrwsr-x   2 ftp      ftp         12288 Nov 24  2021 2019
drwxrwsr-x   2 ftp      ftp         12288 Nov 30  2021 2020
drwxrwsr-x   2 ftp      ftp         12288 Jan 29  2022 2021
drwxrwsr-x   2 ftp      ftp         12288 Aug 23  2022 2022
drwxrwsr-x   2 ftp      ftp         12288 Feb  2 16:55 2023
-rw-rw-r--   1 ftp      ftp          2157 Feb 18  2022 headers.txt
-rw-rw-r--   1 ftp      ftp           456 Oct  7  2020 HEADERS.txt
-rw-rw-r--   1 ftp      ftp         14892 Feb 18  2022 readme.txt
-rw-rw-r--   1 ftp      ftp         14936 Sep 21  2021 README.txt
drwxrwsr-x   2 ftp      ftp          4096 Apr 24 01:50 snapshots

Let’s download two files: * sub-hourly data for the year 2022 for Austin, TX. * the HEADERS.txt contains the names of the columns in the csv.

noaa = NOAA("ftp.ncdc.noaa.gov", 'pub/data/uscrn/products/subhourly01').download('HEADERS.txt')
noaa = NOAA("ftp.ncdc.noaa.gov", 'pub/data/uscrn/products/subhourly01/2022').download('CRNS0101-05-2022-TX_Austin_33_NW.txt')
Downloading:   0% [                                  ] ETA:  --:--:--   0.0 s/B
Downloading:  99% [############################### ] ETA:   0:00:00 616.4 KiB/s
# Read column names from another file
delim_whitespace=True,
)
# Read CSV file using column names from another file
delim_whitespace=True,  # use (any number of) white spaces as delimiter between columns
names=column_names.iloc[1],  # column names from row i=1 of "column_names"
na_values=[-99, -9999, -99999],  # substitute these values by NaN
)
# make integer column LST_DATE as string
df['LST_DATE'] = df['LST_DATE'].astype(str)#.apply(lambda x: f'{x:0>4}')
# make integer column LST_DATE as string
# pad numbers with 0 from the left, such that 15 becomes 0015
df['LST_TIME'] = df['LST_TIME'].apply(lambda x: f'{x:0>4}')
# combine both DATE and TIME
df['datetime'] = pd.to_datetime(df['LST_DATE'] + df['LST_TIME'], format='%Y%m%d%H%M')
df = df.set_index('datetime')
df
WBANNO UTC_DATE UTC_TIME LST_DATE LST_TIME CRX_VN LONGITUDE LATITUDE AIR_TEMPERATURE PRECIPITATION ... ST_TYPE ST_FLAG RELATIVE_HUMIDITY RH_FLAG SOIL_MOISTURE_5 SOIL_TEMPERATURE_5 WETNESS WET_FLAG WIND_1_5 WIND_FLAG
datetime
2021-12-31 18:05:00 23907 20220101 5 20211231 1805 2.623 -98.08 30.62 23.3 0.0 ... C 0 66.0 0 NaN NaN 964.0 0 1.48 0
2021-12-31 18:10:00 23907 20220101 10 20211231 1810 2.623 -98.08 30.62 23.3 0.0 ... C 0 66.0 0 NaN NaN 964.0 0 1.48 0
2021-12-31 18:15:00 23907 20220101 15 20211231 1815 2.623 -98.08 30.62 23.2 0.0 ... C 0 66.0 0 NaN NaN 964.0 0 1.01 0
2021-12-31 18:20:00 23907 20220101 20 20211231 1820 2.623 -98.08 30.62 23.1 0.0 ... C 0 66.0 0 NaN NaN 964.0 0 0.51 0
2021-12-31 18:25:00 23907 20220101 25 20211231 1825 2.623 -98.08 30.62 22.7 0.0 ... C 0 68.0 0 NaN NaN 964.0 0 0.67 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2022-12-31 17:40:00 23907 20221231 2340 20221231 1740 2.623 -98.08 30.62 20.8 0.0 ... C 0 37.0 0 NaN NaN 964.0 0 2.92 0
2022-12-31 17:45:00 23907 20221231 2345 20221231 1745 2.623 -98.08 30.62 20.7 0.0 ... C 0 37.0 0 NaN NaN 963.0 0 2.94 0
2022-12-31 17:50:00 23907 20221231 2350 20221231 1750 2.623 -98.08 30.62 20.6 0.0 ... C 0 37.0 0 NaN NaN 962.0 0 3.61 0
2022-12-31 17:55:00 23907 20221231 2355 20221231 1755 2.623 -98.08 30.62 20.4 0.0 ... C 0 38.0 0 NaN NaN 962.0 0 3.03 0
2022-12-31 18:00:00 23907 20230101 0 20221231 1800 2.623 -98.08 30.62 20.1 0.0 ... C 0 39.0 0 NaN NaN 961.0 0 2.32 0

105120 rows × 23 columns

The provided data is not the same as what is provided by the IMS.

• Now we don’t have air pressure values, so we need to provide elevation.
• We do have R_n (net radiation), so there is no need to provide latitude.

Attention! According to the headers file, net radiation provided by NOAA is in W m^{-2}, but pyet requires it to be MJ m^{-2} d^{-1}. We need to agreggate the downloaded data into daily radiation.

Data comes in 5-minute intervals, so if we have a value x W m^{-2} over a 5-minute interval, the total amount of energy is:

x \frac{W}{m^{-2}}\times 5\, min = x \frac{J}{m^{-2}\cdot s}\times 5\cdot 60\, s = x\cdot 5 \cdot 60 \frac{J}{m^{-2}}

Let’s call X=\sum_{day}x. Then, summing all energy in 1 day:

\sum_{day}x \cdot 5 \cdot 60 \frac{J}{m^{-2}\cdot day} = \sum_{day}x \cdot 5 \cdot 60\cdot 10^{-6} \frac{MJ}{m^{-2}\cdot day}

Make a new dataframe with daily means for temperature, relative humidity and wind speed.

df_TX = df[['AIR_TEMPERATURE',
'RELATIVE_HUMIDITY',
'WIND_1_5']].resample('D').mean()
df_TX
AIR_TEMPERATURE RELATIVE_HUMIDITY WIND_1_5
datetime
2021-12-31 21.721127 79.985915 2.113662
2022-01-01 18.336806 65.833333 2.586840
2022-01-02 -0.222222 46.187500 3.849757
2022-01-03 4.592708 36.927083 0.892778
2022-01-04 9.964583 41.996528 2.428924
... ... ... ...
2022-12-27 6.534375 50.871528 1.881597
2022-12-28 14.418056 65.090278 3.289167
2022-12-29 16.878125 72.934028 2.068750
2022-12-30 13.047917 51.006944 1.180729
2022-12-31 15.010138 58.387097 2.590276

366 rows × 3 columns

X = df['SOLAR_RADIATION'].resample('D').sum()
df_TX['SOLAR_RADIATION'] = X * 5 * 60 * 1e-6
df_TX
datetime
2021-12-31 21.721127 79.985915 2.113662 0.0000
2022-01-01 18.336806 65.833333 2.586840 7.2870
2022-01-02 -0.222222 46.187500 3.849757 14.2536
2022-01-03 4.592708 36.927083 0.892778 14.4309
2022-01-04 9.964583 41.996528 2.428924 14.2056
... ... ... ... ...
2022-12-27 6.534375 50.871528 1.881597 11.5200
2022-12-28 14.418056 65.090278 3.289167 10.9917
2022-12-29 16.878125 72.934028 2.068750 5.9829
2022-12-30 13.047917 51.006944 1.180729 2.9967
2022-12-31 15.010138 58.387097 2.590276 12.7248

366 rows × 4 columns

tmean = df_TX["AIR_TEMPERATURE"]
wind = df_TX["WIND_1_5"]
rh = df_TX["RELATIVE_HUMIDITY"]
elevation = 358
penm2 = pyet.combination.penman(tmean=tmean,
wind=wind,
elevation=elevation,
rh=rh,
rn=rn,
)
fig, ax = plt.subplots(1)
ax.plot(penm2, label="penman")
# ax.legend()
plt.gcf().autofmt_xdate()  # makes slated dates
ax.set_ylabel("ET (mm/day)")
Text(0, 0.5, 'ET (mm/day)')

Does this make sense? How can we know?