def estimate_fao56_daily(self, day_of_year, temp_c, temp_c_min, temp_c_max,
tdew, elevation, latitude, rh, wind_m_s,
atmos_pres):
""" Estimate fao56 from weather """
sha = pyeto.sunset_hour_angle(pyeto.deg2rad(latitude),
pyeto.sol_dec(day_of_year))
daylight_hours = pyeto.daylight_hours(sha)
sunshine_hours = 0.8 * daylight_hours
ird = pyeto.inv_rel_dist_earth_sun(day_of_year)
et_rad = pyeto.et_rad(pyeto.deg2rad(latitude),
pyeto.sol_dec(day_of_year), sha, ird)
sol_rad = pyeto.sol_rad_from_sun_hours(daylight_hours, sunshine_hours,
et_rad)
net_in_sol_rad = pyeto.net_in_sol_rad(sol_rad=sol_rad, albedo=0.23)
cs_rad = pyeto.cs_rad(elevation, et_rad)
avp = pyeto.avp_from_tdew(tdew)
net_out_lw_rad = pyeto.net_out_lw_rad(
pyeto.convert.celsius2kelvin(temp_c_min),
pyeto.convert.celsius2kelvin(temp_c_max), sol_rad, cs_rad, avp)
net_rad = pyeto.net_rad(net_in_sol_rad, net_out_lw_rad)
eto = pyeto.fao56_penman_monteith(
net_rad=net_rad,
t=pyeto.convert.celsius2kelvin(temp_c),
ws=wind_m_s,
svp=pyeto.svp_from_t(temp_c),
avp=avp,
delta_svp=pyeto.delta_svp(temp_c),
psy=pyeto.psy_const(atmos_pres))
return eto
def get_evap_i(lat, elev, wind, srad, tmin, tmax, tavg, month):
if month == 1:
J = 15
else:
J = 15 + (month - 1) * 30
latitude = pyeto.deg2rad(lat)
atmosphericVapourPressure = pyeto.avp_from_tmin(tmin)
saturationVapourPressure = pyeto.svp_from_t(tavg)
ird = pyeto.inv_rel_dist_earth_sun(J)
solarDeclination = pyeto.sol_dec(J)
sha = [pyeto.sunset_hour_angle(l, solarDeclination) for l in latitude]
extraterrestrialRad = [
pyeto.et_rad(x, solarDeclination, y, ird)
for x, y in zip(latitude, sha)
]
clearSkyRad = pyeto.cs_rad(elev, extraterrestrialRad)
netInSolRadnet = pyeto.net_in_sol_rad(srad * 0.001, albedo=0.23)
netOutSolRadnet = pyeto.net_out_lw_rad(tmin, tmax, srad * 0.001,
clearSkyRad,
atmosphericVapourPressure)
netRadiation = pyeto.net_rad(netInSolRadnet, netOutSolRadnet)
tempKelvin = pyeto.celsius2kelvin(tavg)
windSpeed2m = pyeto.wind_speed_2m(wind, 10)
slopeSvp = pyeto.delta_svp(tavg)
atmPressure = pyeto.atm_pressure(elev)
psyConstant = pyeto.psy_const(atmPressure)
return pyeto.fao56_penman_monteith(netRadiation,
tempKelvin,
windSpeed2m,
saturationVapourPressure,
atmosphericVapourPressure,
slopeSvp,
psyConstant,
shf=0.0)
tmaxk=pyeto.celsius2kelvin(tmax) t=(Tmean["0"][j]) tk=pyeto.celsius2kelvin(t) rh_mean=(HR["0"][j]) ws=(VV["0"][j]) lat=pyeto.deg2rad(lat) day=day+1 sunshine_hours=(BS["0"][j]) #Radiacion neta sol_dec=pyeto.sol_dec(day) sha=pyeto.sunset_hour_angle(lat,sol_dec) daylight_hours=pyeto.daylight_hours(sha) ird=pyeto.inv_rel_dist_earth_sun(day) et_rad=pyeto.et_rad(lat, sol_dec, sha, ird) sol_rad=pyeto.sol_rad_from_sun_hours(daylight_hours,sunshine_hours,et_rad) ni_sw_rad=pyeto.net_in_sol_rad(sol_rad, albedo=0.23) cs_rad=pyeto.cs_rad(altitude, et_rad) svp_tmin=pyeto.svp_from_t(tmin) svp_tmax=pyeto.svp_from_t(tmax) avp=pyeto.avp_from_rhmean(svp_tmin, svp_tmax, rh_mean) no_lw_rad=pyeto.net_out_lw_rad(tmink, tmaxk, sol_rad, cs_rad, avp) net_rad=pyeto.net_rad(ni_sw_rad, no_lw_rad) #Presion de vapor de saturacion svp=pyeto.svp_from_t(t) #Delta presion de vapor de saturacion delta_svp=pyeto.delta_svp(t) #Constante psicrométrica atmos_pres=pyeto.atm_pressure(altitude) psy=pyeto.psy_const(atmos_pres) #Calculo ETo Fao Penman Monteith ETo=pyeto.fao56_penman_monteith(net_rad, tk, ws, svp, avp, delta_svp, psy, shf=0.0)
def exec(self):
log.info('[START] {}'.format("exec"))
try:
if (platform.system() == 'Windows'):
# 옵션 설정
sysOpt = {
# 시작/종료 시간
'srtDate': '2020-09-01',
'endDate': '2020-09-03'
# 경도 최소/최대/간격
,
'lonMin': 0,
'lonMax': 360,
'lonInv': 0.5
# 위도 최소/최대/간격
,
'latMin': -90,
'latMax': 90,
'latInv': 0.5
}
else:
# 옵션 설정
sysOpt = {
# 시작/종료 시간
# 'srtDate': globalVar['srtDate']
# , 'endDate': globalVar['endDate']
}
# globalVar['outPath'] = 'F:/Global Temp/aski'
modelList = ['MRI-ESM2-0']
for i, modelInfo in enumerate(modelList):
log.info("[CHECK] modelInfo : {}".format(modelInfo))
inpFile = '{}/{}/{} ssp585 2015-2100_*.nc'.format(
globalVar['inpPath'], serviceName, modelInfo)
fileList = sorted(glob.glob(inpFile))
dsData = xr.open_mfdataset(fileList)
# 월별 시간 변환
dsData['time'] = pd.to_datetime(pd.to_datetime(
dsData['time'].values).strftime("%Y-%m"),
format='%Y-%m')
# 단위 설정
dsData['tasmin'].attrs['units'] = 'degC'
dsData['tasmax'].attrs['units'] = 'degC'
dsData['tas'].attrs['units'] = 'degC'
# 단위 환산을 위한 매월 마지막 날 계산
lon1D = dsData['lon'].values
lat1D = dsData['lat'].values
time1D = dsData['time'].values
timeEndMonth = []
timeYear = dsData['time.year'].values
timeMonth = dsData['time.month'].values
for i in range(0, len(timeYear)):
timeEndMonth.append(
calendar.monthrange(timeYear[i], timeMonth[i])[1])
latRad1D = pyeto.deg2rad(dsData['lat'])
dayOfYear1D = dsData['time.dayofyear']
latRad3D = np.tile(
np.transpose(np.tile(latRad1D, (len(lon1D), 1))),
(len(time1D), 1, 1))
dayOfYear3D = np.transpose(
np.tile(dayOfYear1D, (len(lon1D), len(lat1D), 1)))
timeEndMonth3D = np.transpose(
np.tile(timeEndMonth, (len(lon1D), len(lat1D), 1)))
tmpData = xr.Dataset(
{
'timeEndMonth':
(('time', 'lat', 'lon'), (timeEndMonth3D).reshape(
len(time1D), len(lat1D), len(lon1D))),
'latRad': (('time', 'lat', 'lon'), (latRad3D).reshape(
len(time1D), len(lat1D), len(lon1D))),
'dayOfYear':
(('time', 'lat', 'lon'), (dayOfYear3D).reshape(
len(time1D), len(lat1D), len(lon1D)))
},
coords={
'lat': lat1D,
'lon': lon1D,
'time': time1D
})
# ********************************************************************************************
# Hargreaves 방법
# ********************************************************************************************
# https://xclim.readthedocs.io/en/stable/indicators_api.html 매뉴얼 참조
harRes = xclim.indices.potential_evapotranspiration(
dsData['tasmin'],
dsData['tasmax'],
dsData['tas'],
dsData['lat'],
method='hargreaves85')
# 1 kg/m2/s = 86400 mm/day를 기준으로 mm/month 변환
harResL1 = harRes * 86400.0 * tmpData['timeEndMonth']
# ********************************************************************************************
# Thornthwaite 방법
# ********************************************************************************************
# https://xclim.readthedocs.io/en/stable/indicators_api.html 매뉴얼 참조
thwRes = xclim.indices.potential_evapotranspiration(
dsData['tasmin'],
dsData['tasmax'],
dsData['tas'],
dsData['lat'],
method='thornthwaite48')
# 1 kg/m2/s = 86400 mm/day를 기준으로 mm/month 변환
thwResL1 = thwRes * 86400.0 * tmpData['timeEndMonth']
# ********************************************************************************************
# FAO-56 Penman-Monteith 방법
# ********************************************************************************************
# https://pyeto.readthedocs.io/en/latest/fao56_penman_monteith.html 매뉴얼 참조
# 1 W/m2 = 1 J/m2를 기준으로 MJ/day 변환
dsData['rsdsMJ'] = dsData['rsds'] * 86400 / (10**6)
# 섭씨 to 켈빈
dsData['tasKel'] = dsData['tas'] + 273.15
dsData['tasminKel'] = dsData['tasmin'] + 273.15
dsData['tasmaxKel'] = dsData['tasmax'] + 273.15
dsData['svp'] = svp_from_t(dsData['tas'])
dsData['svpMax'] = svp_from_t(dsData['tasmax'])
dsData['svpMin'] = svp_from_t(dsData['tasmin'])
tmpData['solDec'] = sol_dec(tmpData['dayOfYear'])
tmpData['sha'] = sunset_hour_angle(tmpData['latRad'],
tmpData['solDec'])
tmpData['ird'] = inv_rel_dist_earth_sun(tmpData['dayOfYear'])
tmpData['etRad'] = et_rad(tmpData['latRad'], tmpData['solDec'],
tmpData['sha'], tmpData['ird'])
tmpData['csRad'] = fao.cs_rad(altitude=1.5,
et_rad=tmpData['etRad'])
dsData['deltaSvp'] = delta_svp(dsData['tas'])
# 대기 온도 1.5 m 가정
psy = fao.psy_const(atmos_pres=fao.atm_pressure(altitude=15))
dsData['avp'] = fao.avp_from_rhmin_rhmax(
dsData['svpMax'], dsData['svpMin'], dsData['hurs'].min(),
dsData['hurs'].max())
niSwRad = pyeto.net_in_sol_rad(dsData['rsdsMJ'], albedo=0.23)
niLwRad = net_out_lw_rad(dsData['tasminKel'],
dsData['tasmaxKel'], dsData['rsdsMJ'],
tmpData['csRad'], dsData['avp'])
dsData['net_rad'] = fao.net_rad(ni_sw_rad=niSwRad,
no_lw_rad=niLwRad)
faoRes = fao.fao56_penman_monteith(dsData['net_rad'],
dsData['tasKel'],
dsData['sfcWind'],
dsData['svp'],
dsData['avp'],
dsData['deltaSvp'],
psy,
shf=0)
# ********************************************************************************************
# 데이터 병합
# ********************************************************************************************
data = xr.Dataset(
{
'hargreaves':
(('time', 'lat', 'lon'), (harResL1.values).reshape(
len(time1D), len(lat1D), len(lon1D))),
'thornthwaite':
(('time', 'lat', 'lon'), (thwResL1.values).reshape(
len(time1D), len(lat1D), len(lon1D))),
'penman-monteith':
(('time', 'lat', 'lon'), (faoRes.values).reshape(
len(time1D), len(lat1D), len(lon1D)))
},
coords={
'lat': lat1D,
'lon': lon1D,
'time': time1D
})
dataL1 = xr.merge([data, dsData])
# # NetCDF 파일 저장
saveFile = '{}/{}/{}_eto.nc'.format(globalVar['outPath'],
serviceName, modelInfo)
os.makedirs(os.path.dirname(saveFile), exist_ok=True)
dataL1.to_netcdf(saveFile)
log.info('[CHECK] saveFile : {}'.format(saveFile))
except Exception as e:
log.error("Exception : {}".format(e))
raise e
finally:
log.info('[END] {}'.format("exec"))
def calculate_precipitation(self, d):
if "rain" in d:
self.rain_day = float(d["rain"])
if "snow" in d:
self.snow_day = float(d["snow"])
# def calculate_ev_fao56_factor(self, d):
dt = d['dt']
factor = 0.0
if dt > d['sunrise']:
if dt < d['sunset']:
factor = min(float(dt - d['sunrise'])/3600.0, 1.0)
else:
if dt > d['sunset']:
factor = (dt - d['sunrise'])/3600.0
if factor < 1.0:
factor = 1.0 - factor
return factor
#def estimate_fao56_hourly(self, day_of_year, temp_c, tdew, elevation, latitude, rh, wind_m_s, atmos_pres):
""" Estimate fao56 from weather """
sha = pyeto.sunset_hour_angle(pyeto.deg2rad(latitude),
pyeto.sol_dec(day_of_year))
daylight_hours = pyeto.daylight_hours(sha)
sunshine_hours = 0.8 * daylight_hours
ird = pyeto.inv_rel_dist_earth_sun(day_of_year)
et_rad = pyeto.et_rad(pyeto.deg2rad(latitude),
pyeto.sol_dec(day_of_year), sha, ird)
sol_rad = pyeto.sol_rad_from_sun_hours(daylight_hours, sunshine_hours,
et_rad)
net_in_sol_rad = pyeto.net_in_sol_rad(sol_rad=sol_rad, albedo=0.23)
cs_rad = pyeto.cs_rad(elevation, et_rad)
avp = pyeto.avp_from_tdew(tdew)
#not sure if I trust this net_out_lw_rad calculation here!
net_out_lw_rad = pyeto.net_out_lw_rad(temp_c-1, temp_c, sol_rad,
cs_rad, avp)
net_rad = pyeto.net_rad(net_in_sol_rad, net_out_lw_rad)
eto = pyeto.fao56_penman_monteith(
net_rad=net_rad,
t=pyeto.convert.celsius2kelvin(temp_c),
ws=wind_m_s,
svp=pyeto.svp_from_t(temp_c),
avp=avp,
delta_svp=pyeto.delta_svp(temp_c),
psy=pyeto.psy_const(atmos_pres))
return eto
#def calculate_fao56_hourly(self, d):
day_of_year = datetime.datetime.now().timetuple().tm_yday
T_hr = d['temp']
t_dew = float(d["dew_point"])
pressure = d['pressure']
RH_hr = d['humidity']
u_2 = d['wind_speed']
#print("CALCULATE_FAO56:")
#print("T_hr: {}".format(T_hr))
#print("t_dew: {}".format(t_dew))
#print("RH_hr: {}".format(RH_hr))
#print("u_2: {}".format(u_2))
#print("pressure: {}".format(pressure))
fao56 = self.estimate_fao56_hourly(day_of_year,
T_hr,
t_dew,
self.elevation,
LAT,
RH_hr,
u_2,
pressure)
return fao56
coastal = True altitude = 147 rh_min = 13 rh_max = 88 ws = 1.3 tmean = pyeto.daily_mean_t(tmin, tmax) atmos_pres = pyeto.atm_pressure(altitude) psy = pyeto.psy_const(atmos_pres) # Humidity svp_tmin = pyeto.svp_from_t(tmin) svp_tmax = pyeto.svp_from_t(tmax) delta_svp = pyeto.delta_svp(tmean) svp = pyeto.mean_svp(tmin, tmax) avp = pyeto.avp_from_rhmin_rhmax(svp_tmin, svp_tmax, rh_min, rh_max) # Radiation sol_dec = pyeto.sol_dec(day_of_year) sha = pyeto.sunset_hour_angle(latitude, sol_dec) ird = pyeto.inv_rel_dist_earth_sun(day_of_year) et_rad = pyeto.et_rad(latitude, sol_dec, sha, ird) cs_rad = pyeto.cs_rad(altitude, et_rad) sol_rad = pyeto.sol_rad_from_t(et_rad, cs_rad, tmin, tmax, coastal) ni_sw_rad = pyeto.net_in_sol_rad(sol_rad) no_lw_rad = pyeto.net_out_lw_rad(pyeto.celsius2kelvin(tmin), pyeto.celsius2kelvin(tmax), sol_rad, cs_rad, avp) net_rad = pyeto.net_rad(ni_sw_rad, no_lw_rad) eto = pyeto.fao56_penman_monteith(net_rad, pyeto.celsius2kelvin(tmean), ws, svp, avp, delta_svp, psy) print eto
def exec(self):
log.info('[START] {}'.format("exec"))
try:
if (platform.system() == 'Windows'):
# 옵션 설정
sysOpt = {
# 시작/종료 시간
'srtDate': '2020-09-01',
'endDate': '2020-09-03'
# 경도 최소/최대/간격
,
'lonMin': 0,
'lonMax': 360,
'lonInv': 0.5
# 위도 최소/최대/간격
,
'latMin': -90,
'latMax': 90,
'latInv': 0.5
}
else:
# 옵션 설정
sysOpt = {
# 시작/종료 시간
# 'srtDate': globalVar['srtDate']
# , 'endDate': globalVar['endDate']
}
# globalVar['outPath'] = 'F:/Global Temp/aski'
modelList = ['MRI-ESM2-0']
for i, modelInfo in enumerate(modelList):
log.info("[CHECK] modelInfo : {}".format(modelInfo))
inpFile = '{}/{}/{} ssp585 2015-2100_*.nc'.format(
globalVar['inpPath'], serviceName, modelInfo)
fileList = sorted(glob.glob(inpFile))
dsData = xr.open_mfdataset(fileList)
# dsData = dsData.sel(lon=slice(120, 150), time=slice('2015-01', '2016-12'))
# dsData = dsData.sel(lat=slice(50, 50), lon=slice(120, 120), time=slice('2015-01', '2015-12'))
# 월별 시간 변환
dsData['time'] = pd.to_datetime(pd.to_datetime(
dsData['time'].values).strftime("%Y-%m"),
format='%Y-%m')
# 단위 설정
dsData['tasmin'].attrs['units'] = 'degC'
dsData['tasmax'].attrs['units'] = 'degC'
dsData['tas'].attrs['units'] = 'degC'
# 단위 환산을 위한 매월 마지막 날 계산
lon1D = dsData['lon'].values
lat1D = dsData['lat'].values
time1D = dsData['time'].values
timeEndMonth = []
timeYear = dsData['time.year'].values
timeMonth = dsData['time.month'].values
for i in range(0, len(timeYear)):
timeEndMonth.append(
calendar.monthrange(timeYear[i], timeMonth[i])[1])
latRad1D = pyeto.deg2rad(dsData['lat'])
# 2022.08.13 doy 순서 변경
# 시작 순서 1, 32, ...
dayOfYear1D = dsData['time.dayofyear']
latRad3D = np.tile(
np.transpose(np.tile(latRad1D, (len(lon1D), 1))),
(len(time1D), 1, 1))
dayOfYear3D = np.transpose(
np.tile(dayOfYear1D, (len(lon1D), len(lat1D), 1)))
timeEndMonth3D = np.transpose(
np.tile(timeEndMonth, (len(lon1D), len(lat1D), 1)))
tmpData = xr.Dataset(
{
'timeEndMonth':
(('time', 'lat', 'lon'), (timeEndMonth3D).reshape(
len(time1D), len(lat1D), len(lon1D))),
'latRad': (('time', 'lat', 'lon'), (latRad3D).reshape(
len(time1D), len(lat1D), len(lon1D)))
# , 'dayOfYear': (('time', 'lat', 'lon'), (dayOfYear3D).reshape(len(time1D), len(lat1D), len(lon1D)))
,
'doy': (('time', 'lat', 'lon'), (dayOfYear3D).reshape(
len(time1D), len(lat1D), len(lon1D)))
},
coords={
'lat': lat1D,
'lon': lon1D,
'time': time1D
})
# 마지막 순서 31, 59, ...
# tmpData['dayOfYear'] = tmpData['doy']
tmpData['dayOfYear'] = tmpData['doy'] + timeEndMonth3D
# ********************************************************************************************
# FAO-56 Penman-Monteith 방법
# ********************************************************************************************
# https://pyeto.readthedocs.io/en/latest/fao56_penman_monteith.html 매뉴얼 참조
# 1 W/m2 = 1 J/m2를 기준으로 MJ/day 변환
# dsData['rsdsMJ'] = dsData['rsds'] * 86400 / (10 ** 6)
dsData['rsdsMJ'] = dsData['rsds'] * 2592000 / (10**6)
# 섭씨 to 켈빈
dsData['tasKel'] = dsData['tas'] + 273.15
dsData['tasminKel'] = dsData['tasmin'] + 273.15
dsData['tasmaxKel'] = dsData['tasmax'] + 273.15
dsData['svp'] = svp_from_t(dsData['tas'])
dsData['svpMax'] = svp_from_t(dsData['tasmax'])
dsData['svpMin'] = svp_from_t(dsData['tasmin'])
tmpData['solDec'] = sol_dec(tmpData['dayOfYear'])
tmpData['sha'] = sunset_hour_angle(tmpData['latRad'],
tmpData['solDec'])
tmpData['dayLightHour'] = daylight_hours(tmpData['sha'])
tmpData['ird'] = inv_rel_dist_earth_sun(tmpData['dayOfYear'])
# tmpData['etRad'] = et_rad(tmpData['latRad'], tmpData['solDec'], tmpData['sha'], tmpData['ird'])
tmpData['etRad'] = dsData['rsdsMJ']
tmpData['csRad'] = fao.cs_rad(altitude=1.5,
et_rad=tmpData['etRad'])
dsData['deltaSvp'] = delta_svp(dsData['tas'])
# 대기 온도 1.5 m 가정
psy = fao.psy_const(atmos_pres=fao.atm_pressure(altitude=15))
dsData['avp'] = fao.avp_from_rhmin_rhmax(
dsData['svpMax'], dsData['svpMin'], dsData['hurs'].min(),
dsData['hurs'].max())
niSwRad = pyeto.net_in_sol_rad(dsData['rsdsMJ'], albedo=0.23)
niLwRad = net_out_lw_rad(dsData['tasminKel'],
dsData['tasmaxKel'], dsData['rsdsMJ'],
tmpData['csRad'], dsData['avp'])
dsData['net_rad'] = fao.net_rad(ni_sw_rad=niSwRad,
no_lw_rad=niLwRad)
# 2022.08.13
# 상수
# shfData = 0.336
# 이전, 현재 대기온도를 통해 계산
timeList = dsData['time'].values
shfDataL1 = xr.Dataset()
for i, timeInfo in enumerate(timeList):
prevYmd = (pd.to_datetime(timeInfo) +
pd.DateOffset(months=-1)).strftime('%Y-%m-%d')
nowYmd = pd.to_datetime(timeInfo).strftime('%Y-%m-%d')
prevData = dsData['tas'].interp(
time=prevYmd,
method='nearest',
kwargs={'fill_value': 'extrapolate'})
nowData = dsData['tas'].interp(
time=nowYmd,
method='nearest',
kwargs={'fill_value': 'extrapolate'})
shfData = pyeto.monthly_soil_heat_flux2(
prevData, nowData).rename('shf')
shfData['time'] = pd.to_datetime(timeInfo)
if (i == 0):
shfDataL1 = shfData
else:
shfDataL1 = xr.concat([shfDataL1, shfData], dim='time')
dsData = xr.merge([dsData, shfDataL1])
# Daily eto
# faoRes = fao.fao56_penman_monteith(dsData['net_rad'], dsData['tasKel'], dsData['sfcWind'], dsData['svp'], dsData['avp'], dsData['deltaSvp'], psy, shf = 0)
# Monthly eto
faoRes = fao.fao56_penman_monteith(dsData['net_rad'],
dsData['tasKel'],
dsData['sfcWind'],
dsData['svp'],
dsData['avp'],
dsData['deltaSvp'],
psy,
shf=dsData['shf'])
# ********************************************************************************************
# Hargreaves 방법
# ********************************************************************************************
# https://xclim.readthedocs.io/en/stable/indicators_api.html 매뉴얼 참조
# harRes = xclim.indices.potential_evapotranspiration( dsData['tasmin'], dsData['tasmax'], dsData['tas'], dsData['lat'], method='hargreaves85')
# 1 kg/m2/s = 86400 mm/day를 기준으로 mm/month 변환
# harResL1 = harRes * 86400.0 * tmpData['timeEndMonth']
# https://pyeto.readthedocs.io/en/latest/thornthwaite.html 매뉴얼 참조
harRes = pyeto.hargreaves(dsData['tasmin'], dsData['tasmax'],
dsData['tas'], tmpData['etRad'])
harResL1 = harRes
# ********************************************************************************************
# Thornthwaite 방법
# ********************************************************************************************
# https://xclim.readthedocs.io/en/stable/indicators_api.html 매뉴얼 참조
# thwRes = xclim.indices.potential_evapotranspiration(dsData['tasmin'], dsData['tasmax'], dsData['tas'], dsData['lat'], method ='thornthwaite48')
# 1 kg/m2/s = 86400 mm/day를 기준으로 mm/month 변환
# thwResL1 = thwRes * 86400.0 * tmpData['timeEndMonth']
dsData['tasAdj'] = xr.where((dsData['tas'] >= 0),
dsData['tas'], 0)
dsData['heatIdx'] = (dsData['tasAdj'] / 5.0)**1.514
sumHeatIdx = dsData['heatIdx'].groupby('time.year').sum(
skipna=True)
sumHeatIdxData = xr.Dataset()
timeList = dsData['time'].values
for i, timeInfo in enumerate(timeList):
iYear = int(pd.to_datetime(timeInfo).strftime('%Y'))
selHeatIdx = sumHeatIdx.sel(year=iYear).rename(
{'year': 'time'})
selHeatIdx['time'] = timeInfo
if (i == 0):
sumHeatIdxData = selHeatIdx
else:
sumHeatIdxData = xr.concat(
[sumHeatIdxData, selHeatIdx], dim='time')
# 2022.08.15
# dsData['thwConst'] = (6.75e-07 * dsData['heatIdx'] ** 3) - (7.71e-05 * dsData['heatIdx'] ** 2) + (1.792e-02 * dsData['heatIdx']) + 0.49239
# thwRes = 1.6 * (tmpData['dayLightHour'] / 12.0) * (tmpData['timeEndMonth'] / 30.0) * ((10.0 * dsData['tasAdj'] / dsData['heatIdx']) ** dsData['thwConst']) * 10.0
dsData['thwConst'] = (6.75e-07 * sumHeatIdxData**3) - (
7.71e-05 * sumHeatIdxData**2) + (1.792e-02 *
sumHeatIdxData) + 0.49239
thwRes = 1.6 * (tmpData['dayLightHour'] /
12.0) * (tmpData['timeEndMonth'] / 30.0) * (
(10.0 * dsData['tasAdj'] / sumHeatIdxData)
**dsData['thwConst']) * 10.0
# 0보다 작은 경우 0으로 대체
thwRes = xr.where((thwRes >= 0), thwRes, 0)
# ********************************************************************************************
# 데이터 병합
# ********************************************************************************************
etoData = xr.Dataset(
{
'hargreaves':
(('time', 'lat', 'lon'), (harResL1.values).reshape(
len(time1D), len(lat1D), len(lon1D))),
'thornthwaite':
(('time', 'lat', 'lon'), (thwRes.values).reshape(
len(time1D), len(lat1D), len(lon1D))),
'penman-monteith':
(('time', 'lat', 'lon'), (faoRes.values).reshape(
len(time1D), len(lat1D), len(lon1D)))
},
coords={
'lat': lat1D,
'lon': lon1D,
'time': time1D
})
# NetCDF 파일 저장
saveFile = '{}/{}/{}_eto.nc'.format(globalVar['outPath'],
serviceName, modelInfo)
os.makedirs(os.path.dirname(saveFile), exist_ok=True)
etoData.to_netcdf(saveFile)
log.info('[CHECK] saveFile : {}'.format(saveFile))
except Exception as e:
log.error("Exception : {}".format(e))
raise e
finally:
log.info('[END] {}'.format("exec"))
def test_net_in_sol_rad(self):
# Test based on example 12, p.87 of FAO paper
# Note, there seems to be a rounding error in the answer given
# in the FAO paper!
rad = pyeto.net_in_sol_rad(14.5)
self.assertAlmostEqual(rad, 11.1, 0)
# Humidity
svp_tmin = pyeto.svp_from_t(tmin)
svp_tmax = pyeto.svp_from_t(tmax)
delta_svp = pyeto.delta_svp(tmean)
svp = pyeto.mean_svp(tmin, tmax)
avp = pyeto.avp_from_rhmin_rhmax(svp_tmin, svp_tmax, rh_min, rh_max)
# Radiation
sol_dec = pyeto.sol_dec(day_of_year)
sha = pyeto.sunset_hour_angle(latitude, sol_dec)
ird = pyeto.inv_rel_dist_earth_sun(day_of_year)
et_rad = pyeto.et_rad(latitude, sol_dec, sha, ird)
cs_rad = pyeto.cs_rad(altitude, et_rad)
sol_rad = pyeto.sol_rad_from_t(et_rad, cs_rad, tmin, tmax, coastal)
ni_sw_rad = pyeto.net_in_sol_rad(sol_rad)
no_lw_rad = pyeto.net_out_lw_rad(pyeto.celsius2kelvin(tmin), pyeto.celsius2kelvin(tmax), sol_rad, cs_rad, avp)
net_rad = pyeto.net_rad(ni_sw_rad, no_lw_rad)
eto = pyeto.fao56_penman_monteith(net_rad, pyeto.celsius2kelvin(tmean), ws, svp, avp, delta_svp, psy)
print eto
Forever
check the time
if not running
if eto is <0 then
turn on water
var ontime = time.time()
else
time running = ontime - time.time()
if time running > eto then
def test_net_in_sol_rad(self):
# Test based on example 12, p.87 of FAO paper
# Note, there seems to be a rounding error in the answer given
# in the FAO paper!
rad = pyeto.net_in_sol_rad(14.5)
self.assertAlmostEqual(rad, 11.1, 0)