def test_rso_daily(ra=d_args['ra'],
ea=d_args['ea'],
pair=s_args['pair'],
doy=d_args['doy'],
lat=s_args['lat'],
rso=d_args['rso']):
assert float(calcs._rso_daily(ra, ea, pair, doy,
lat)) == pytest.approx(rso)
def calc_rso_and_refet(lat, elev, wind_anemom, doy, month, tmax, tmin, ea, uz,
rs):
"""
Calculates all of the following variables using the refet package (https://github.com/DRI-WSWUP/RefET):
rso : clear sky solar radiation
monthly_rs : : monthly averaged solar radiation (12 values total) values across all of record
eto : grass reference evapotranspiration in units mm/day
etr : alfalfa reference evapotranspiration in units mm/day
monthly_eto : monthly averaged grass reference ET (12 values total) values across all of record
monthly_etr : monthly averaged alfalfa reference ET (12 values total) values across all of record
Parameters:
lat : station latitude in decimal degrees
elev: station elevation in meters
wind_anemom : height of windspeed anemometer in meters
doy : 1D numpy array of day of year in record
month : 1D numpy array of current month in record
tmax : 1D numpy array of maximum temperature values
tmin : 1D numpy array of minimum temperature values
ea : 1D numpy array of vapor pressure in kPa
uz : 1D numpy array of average windspeed values
rs : 1D numpy array of solar radiation values
Returns:
Returns all variables listed above as 1D numpy arrays
"""
data_size = month.shape[0] # Size of data set
ra = np.empty(
data_size) # Extraterrestrial solar radiation, MJ/m2, ASCE eq. 21
rso = np.empty(data_size) # Clear sky solar radiation, MJ/m2, ASCE eq. 16
eto = np.empty(data_size)
etr = np.empty(data_size)
monthly_rs = np.empty(12)
monthly_eto = np.empty(12)
monthly_etr = np.empty(12)
pressure = 101.3 * ((
(293 -
(0.0065 * elev)) / 293)**5.26) # units kPa, EQ 3 in ASCE RefET manual
# refet package expects rs in MJ/m2 and latitude in radians
refet_input_rs = np.array(rs * 0.0864) # convert W/m2 to MJ/m2
refet_input_lat = lat * (np.pi / 180.0) # convert latitude into radians
# Calculate daily values
for i in range(data_size):
ra[i] = _ra_daily(lat=refet_input_lat, doy=doy[i], method='asce')
rso[i] = _rso_daily(ra=ra[i],
ea=ea[i],
pair=pressure,
doy=doy[i],
lat=refet_input_lat)
# Calculating ETo in mm using refET package
eto[i] = Daily(tmin=tmin[i],
tmax=tmax[i],
ea=ea[i],
rs=refet_input_rs[i],
uz=uz[i],
zw=wind_anemom,
elev=elev,
lat=lat,
doy=doy[i],
method='asce').eto()
# Calculating ETr in mm using refET package
etr[i] = Daily(tmin=tmin[i],
tmax=tmax[i],
ea=ea[i],
rs=refet_input_rs[i],
uz=uz[i],
zw=wind_anemom,
elev=elev,
lat=lat,
doy=doy[i],
method='asce').etr()
# Calculate mean monthly values
j = 1
for k in range(12):
temp_indexes = [ex for ex, ind in enumerate(month) if ind == j]
temp_indexes = np.array(temp_indexes, dtype=int)
monthly_rs[k] = np.nanmean(rs[temp_indexes])
monthly_eto[k] = np.nanmean(eto[temp_indexes])
monthly_etr[k] = np.nanmean(etr[temp_indexes])
j += 1
rso *= 11.574 # Convert rso from MJ/m2 to w/m2
return rso, monthly_rs, eto, etr, monthly_eto, monthly_etr
def test_rso_daily_ra_zero(ea=d_args['ea'], pair=s_args['pair']):
# Rso can go to zero for winter DOY and/or high latitudes when Ra is zero
rso = calcs._rso_daily(calcs._ra_daily(80 * math.pi / 180, 1), ea, pair, 1,
80 * math.pi / 180)
assert float(rso) == 0