penmon - Implementation of weather station class in Python that supports Penman-Monteith ETo Equation.
$ pip install penmon
import penmon as pm
### create a station class with known location and elevation
station = pm.Station(latitude=41.42, altitude=109)
station.anemometer_height = 10
### getting a day instance for August 16th
day = station.day_entry(238,
temp_min = 19.5,
temp_max = 25.6,
wind_speed = 2.5,
humidity_mean = 65,
radiation_s =25.6
)
print("ETo for this day is", day.eto() )
import pandas as pd
import penmon as pm
# Assuming 'your_data.csv' contains your data
import_data_frame = pd.read_csv('import_test.csv')
# Create an instance of the Station class
station = pm.Station(latitude=41.42, altitude=109)
# Import data into the Station instance
day = station.import_data(import_data_frame)
print(station.days[1].eto)
for day_number, day_entry in station.days.items():
eto_value = day_entry.eto()
print(f"Day {day_number}: ETo = {eto_value:.2f} mm")
Full implementation of Penman-Monteith ETo equation based on UAN-FAO Irrigation and Drainage Paper 56.
Penman-Monteith equation is used to calculate reference crop evapotranspiration ETo for a given location using available climate data. This method provides many ways of estimating missing climate data.
Following are the least data required to estimate ETo (But the more data you provide the better the accuracy gets):
* latitude
* elevation
* date
* daily minimum temperature
* daily maximum temperature
It can do this by making certain assumptions about the climate. These assumptions can be fine-tuned. Climate-class is responsible for setting these assumptions. We'll talk more about it later (see Climate class below)
To calculate ETo, including intermediate atmospheric data you first need to define an instance of a Station with a known latitude and altitude. Then you request the station to create an instance of a DayEntry, which represents a single day with a known date. We then set whatever data we know about that particular day, and ask the day to calculate information that we do not know, including ETo.
This is pre-release version of the library and intended for review only. API of the class may change in future releases. Do not assume backwards compatability in future releases. Consults CHANGES file before upgrading!
import penmon as pm
station = pm.Station(latitude=41.42, altitude=109)
latitude must be a float between -90 and 90. altitudu must be a positive integer. These arguments values are of utmost importance. Please make sure these data are as accurate as you can get them be. latitude is used to calculate sunset hour angle (Eq. 25) and Extraterrestrial radiation (Eq. 21), which in turn, along with date, is used to calculate solar radiation!
altitude is used to calculate atmospheric pressure, which in turn is used to calculate psychrometric constant, which in turn is used to calculate vapour pressure, which is used to calculate net longwave radiation. As you can see, these very innocent looking numbers are pretty much backbone of the whole equation. Show them respect!
Above station assumes its anemometer height is 2m above ground surface. If it's not, you can set the height as:
station.anemometer_height = 10
Now all the wind_speed information is assumed to be wind speed at 10m height. This is important information, since ETo is calculated with speed close the crop surface, which is 2m. Library uses logarithmic algorithm to convert the data accordingly. Again, important setting! Shoud you wish to access calculated wind speed at 2m use wind_speed_2m() method:
day.wind_speed=2.0;
u2m = day.wind_speed_2m();
In the above example u2m returns 2.0 if the anemometer was set to 2 meters. If it was set to 10m, it returns 1.5. If it was set to 50 meters, it reads 1.2 m/s.
Station also makes certain assumptions about its climate. You can set this by creating a new climate instance (see Climate class) and set is as:
humid_climate = pm.Climate().humid().coastal().strong_winds()
arid_climate = pm.Climate().arid().interior().moderare_winds()
station.climate = humid_climate
By default it assumes we are in arid, interior location, with moderate winds. If it's in arid climate, it makes certain assumption about the dew point temperature. This temperature will be used to calculate relative humidity if humidity data is missing. It deduces dew_point temperature by looking at the temp_min of the record. In particulare, for arid location it substracts 2.0 degrees from temp_min. In humid location it treats temp_min as temp_dew. In the following example we set dew_point temperature 4.0 below temp_min
climate=pm.Climate()
# above is the same as saying:
climate=pm.Climate().arid().interior().moderate_winds()
climate.dew_point_difference=4.0
station.climate=climate;
# from now on if humidity data is missing it substtracts 4.0 degrees
from temp_min to take a guess at temp_dew
It assumes it will be calculating ETo for a refernce crop. According to the original paper reference crop is assumed to be grass of 0.12m height, aerodynamic resistance of 70 and albedo of 0.23. It you wish to calculate ETo for a different reference crop you may do so as:
different_crop = pm.Crop(resistance_a=208, albedo=0.23, height=0.12)
station.ref_crop = different_crop
Based on the above example this station has no climate data available at this moment. But if it were you would've been able to iterate through these records like following:
for a_day in station.days:
# do stuff with a_day
Once we have station data available we work with a day at a time. We first need to get a single day, identified by a day number:
day = station.day_entry(238)
day is an instance of DayEntry class. 238 above represents August 26th - it is 238th day of the year. Day number can only be an integer in 1-366 range. It also supports a date string:
day = station.day_entry("2020-08-26")
day.day_number # returns 238
If you have to pass a date string that has a different template than "%Y-%m-%d", you can pass your template string to the method as follows. Above example assumes following default date_template value:
day = station.day_entry("2020-08-26", date_template="%Y-%m-%d")
To learn more about date template placeholders refer to strptime() manual either from datetime module, or by refering to your system's strptime manual (available in all linux/unix machines).
Based on this day number alone library is able to calculate inverse of the relative Sun-Earth distance (Eq. 23), solar declination (Eq. 24), which is used to calculate possible daylight hours for that particular day for that particular latitude. It's amazing how much information we can deduce based on this single number!
Once you have day instance now all the fun begins!
Everyday has following attributes:
- day_number (whatever we passed to the constructor)
- station (set automatically by station)
# following are required to be set:
- temp_min
- temp_max
# following are better to be set:
- temp_dew
- wind_speed
- humidity_min
- humidity_max
- radiation_s - Solar Radiation
# following are optional, if above attributes are known
- radiation_a
- temp_mean
- temp_dry - dry bulb temperature
- temp_wet - wet bulb temperature
Before setting any of the above attributes following information is available for us. These information do not use any recorded data, but only uses mostly astronomical calculations.
Returns atmostpheric pressure in kPa for station elevation. For the above example altitude (109m) returns 100.0kpa. Atmospheric pressure is also available through station.atmospheric_pressure() call. In fact, DayEntry.atmospheric_pressure() is just an alias to it.
Value returned is a pressure in kPa. If you wish to convert it to mercury scale multiply it by 7.50 to get mm Hg, or 0.295 to get in Hg.
Returns 2.45
Returns 0.001013
For the elevation in this example returns 0.0665.
Given T - temperature returns saturation vapour pressure. For example for T=25.0 returns 3.168
Given T - temperature returns slope of saturation vapour pressure curve. For example for T=25.0 returns 0.188684
Returns inverse of relative Earth-Sun distance. For this example returns 0.981
For this example returns 0.172. This is angle in radians. To convert this to degrees multiply by 180 and devide to math.pi(). For example, 0.172 rad is the same as 9.8 degrees. So Sun's declination was 9.8 degrees north relative to equatorial plane. If the value were negative it would've meant the Sun is declined to the south of the equatorial plane.
For this example, returns 1.725. See day.solar_declination to convert this to degrees.
Returns extraterrestrial radiation in MJ/m2/day. For this given example returns 40.3.
The same as above but in mm. 16.4 for this example.
Possible daylight hours for this day.
Depends on the value of day.sunshine_hours attribute. Returns solar radiation in MJ/m2/day
Same as above, but returns in mm.
Calculates Clear-Sky solar radiation.
Given hours of direct sunlight (in day.sunshine_hours attribute) calculates net solar radiation (or shortwave radiation) Takes into account albedo of the crop.
Returns 0.00 for daily calculations.
At this point we still cannot calculate ETo. some vital information are still missing. Let's record some data for this day:
day.temp_min = 19.5
day.tmep_max = 25.6
Now we can calculate ETo for the first time:
day.eto() # returns 3.2mm
To calculate ETo with the current recorded data the library did lots of assumptions and calculations based on empirical data. Let's help it further by recording other important information:
day.temp_dew = 15.0
day.eto() # returns 3.58mm
day.wind_speed=2.50
day.eto()# returns 3.82mm
Recording solar radiation gets us the most accurate ETo:
day.radiation_s = 25.0
day.eto() # returns 5.04m
See: Issues at github.com
libpenmon - port of the current module into C++. See github.com/sherzodr/libpenmon
Sherzod Ruzmetov [email protected]