def Calc_Humidity(Temp_format,
P_format,
Hum_format,
output_format,
Startdate,
Enddate,
freq="D"):
folder_dir_out = os.path.dirname(output_format)
if not os.path.exists(folder_dir_out):
os.makedirs(folder_dir_out)
Dates = pd.date_range(Startdate, Enddate, freq="D")
for Date in Dates:
print(Date)
Day = Date.day
Month = Date.month
Year = Date.year
Tempfile_one = Temp_format.format(yyyy=Year, mm=Month, dd=Day)
Presfile_one = P_format.format(yyyy=Year, mm=Month, dd=Day)
Humfile_one = Hum_format.format(yyyy=Year, mm=Month, dd=Day)
out_folder_one = output_format.format(yyyy=Year, mm=Month, dd=Day)
geo_out, proj, size_X, size_Y = RC.Open_array_info(Tempfile_one)
Tdata = RC.Open_tiff_array(Tempfile_one)
if "MERRA_K" in Temp_format:
Tdata = Tdata - 273.15
Tdata[Tdata < -900] = -9999
Pdata = RC.Open_tiff_array(Presfile_one)
Hdata = RC.Open_tiff_array(Humfile_one)
Pdata[Pdata < 0] = -9999
Hdata[Hdata < 0] = -9999
# gapfilling
Tdata = RC.gap_filling(Tdata, -9999)
Pdata = RC.gap_filling(Pdata, -9999)
Hdata = RC.gap_filling(Hdata, -9999)
Esdata = 0.6108 * np.exp((17.27 * Tdata) / (Tdata + 237.3))
HumData = np.minimum((1.6077717 * Hdata * Pdata / Esdata), 1) * 100
HumData = HumData.clip(0, 100)
DC.Save_as_tiff(out_folder_one, HumData, geo_out, "WGS84")
return ()
def Calc_Humidity(Temp_format,
P_format,
Hum_format,
output_format,
Startdate,
Enddate,
freq="D"):
folder_dir_out = os.path.dirname(output_format)
if not os.path.exists(folder_dir_out):
os.makedirs(folder_dir_out)
Dates = pd.date_range(Startdate, Enddate, freq="D")
for Date in Dates:
print(Date)
Day = Date.day
Month = Date.month
Year = Date.year
Windyfile_one = wind_y_format.format(yyyy=Year, mm=Month, dd=Day)
Windxfile_one = wind_x_format.format(yyyy=Year, mm=Month, dd=Day)
out_folder_one = output_format.format(yyyy=Year, mm=Month, dd=Day)
geo_out, proj, size_X, size_Y = RC.Open_array_info(Windxfile_one)
Windxdata = RC.Open_tiff_array(Windxfile_one)
Windxdata[Windxdata < -900] = -9999
Windydata = RC.Open_tiff_array(Windyfile_one)
Windydata[Windxdata < -900] = -9999
# gapfilling
Windydata = RC.gap_filling(Windydata, -9999)
Windxdata = RC.gap_filling(Windxdata, -9999)
Wind = np.sqrt(Windxdata**2 + Windydata**2)
DC.Save_as_tiff(out_folder_one, Wind, geo_out, "WGS84")
return ()
def __init__(self,
input_folder,
input_format,
Dates=None,
Conversion=1,
Example_Data=None,
Mask_Data=None,
gap_filling=None,
reprojection_type=2,
Variable='',
Product='',
Description='',
Unit='',
Dimension_description=''):
if Mask_Data != None:
dest_MASK = RC.reproject_dataset_example(Mask_Data, Example_Data,
1)
MASK = dest_MASK.GetRasterBand(1).ReadAsArray()
MASK = np.where(MASK == 1, 1, np.nan)
else:
MASK = 1
if Dates != None:
# Get Ordinal time
time_or = np.array(list(map(lambda i: i.toordinal(), Dates)))
i = 1
# Define start and enddate string
Startdate_str = '%d-%02d-%02d' % (Dates[0].year, Dates[0].month,
Dates[0].day)
Enddate_str = '%d-%02d-%02d' % (Dates[-1].year, Dates[-1].month,
Dates[-1].day)
# Define dimensions
size_z = len(Dates)
for Date in Dates:
try:
sys.stdout.write("\rLoading Data %s %i/%i (%f %%)" %
(Variable, i, len(Dates), i /
(len(Dates)) * 100))
sys.stdout.flush()
# Create input filename
filename_in = os.path.join(
input_folder,
input_format.format(yyyy=Date.year,
mm=Date.month,
dd=Date.day))
dest, Array, proj, geo = Get_Data(filename_in,
Example_Data,
reprojection_type)
if Date == Dates[0]:
size_x = dest.RasterXSize
size_y = dest.RasterYSize
proj = dest.GetProjection()
geo = dest.GetGeoTransform()
Array_end = np.ones([len(Dates), size_y, size_x
]) * np.nan
if gap_filling != None and ~np.isnan(np.nanmean(Array)):
Array[np.isnan(Array)] = -9999
Array = RC.gap_filling(Array, -9999, gap_filling)
Array_end[time_or == Date.toordinal(
), :, :] = Array * Conversion * MASK
i += 1
except:
print("Was not able to collect %s %s" % (Variable, Date))
shape = [size_z, size_y, size_x]
else:
sys.stdout.write("\rLoading Constant Data %s" % (Variable))
sys.stdout.flush()
# Define start and enddate string
Startdate_str = ''
Enddate_str = ''
# Get constant data
filename_in = os.path.join(input_folder, input_format)
dest, Array_end, proj, geo = Get_Data(filename_in, Example_Data,
reprojection_type)
# Get dimension
size_x = dest.RasterXSize
size_y = dest.RasterYSize
shape = [size_y, size_x]
time_or = ''
# Apply gapfilling if needed
if gap_filling != None and ~np.isnan(np.nanmean(Array_end)):
Array_end[np.isnan(Array_end)] = -9999
Array_end = RC.gap_filling(Array_end, -9999, gap_filling)
Array_end = Array_end * MASK
self.Projection = proj
self.Size = shape
self.GeoTransform = geo
self.Data = Array_end.astype(np.float64)
self.NoData = np.nan
self.Ordinal_time = time_or
# Origin Of Data
self.Directory = input_folder
self.Format = input_format
# Describtion of dataset
self.Variable = Variable
self.Product = Product
self.Description = Description
self.Unit = Unit
self.Dimension_description = Dimension_description
# Time Series
self.Startdate = Startdate_str
self.Enddate = Enddate_str
self.Timestep = ''
print(
" "
)
def calc_ETref(Dir, tmin_str, tmax_str, humid_str, press_str, wind_str,
down_short_str, down_long_str, up_long_str, DEMmap_str, DOY):
"""
This function calculates the ETref by using all the input parameters (path)
according to FAO standards
see: http://www.fao.org/docrep/x0490e/x0490e08.htm#TopOfPage
Keyword arguments:
tmin_str -- 'C:/' path to the minimal temperature tiff file [degrees Celcius], e.g. from GLDAS
tmax_str -- 'C:/' path to the maximal temperature tiff file [degrees Celcius], e.g. from GLDAS
humid_str -- 'C:/' path to the humidity tiff file [kg/kg], e.g. from GLDAS
press_str -- 'C:/' path to the air pressure tiff file [kPa], e.g. from GLDAS
wind_str -- 'C:/' path to the wind velocity tiff file [m/s], e.g. from GLDAS
down_short_str -- 'C:/' path to the downward shortwave radiation tiff file [W*m-2], e.g. from CFSR/LANDSAF
down_long_str -- 'C:/' path to the downward longwave radiation tiff file [W*m-2], e.g. from CFSR/LANDSAF
up_long_str -- 'C:/' path to the upward longwave radiation tiff file [W*m-2], e.g. from CFSR/LANDSAF
DEMmap_str -- 'C:/' path to the DEM tiff file [m] e.g. from HydroSHED
DOY -- Day of the year
"""
# Get some geo-data to save results
GeoT, Projection, xsize, ysize = RC.Open_array_info(DEMmap_str)
#NDV, xsize, ysize, GeoT, Projection, DataType = GetGeoInfo(DEMmap_str)
raster_shape = [xsize, ysize]
# Create array to store results
ETref = np.zeros(raster_shape)
# gap fill
tmin_str_GF = RC.gap_filling(tmin_str, -9999)
tmax_str_GF = RC.gap_filling(tmax_str, -9999)
humid_str_GF = RC.gap_filling(humid_str, -9999)
press_str_GF = RC.gap_filling(press_str, -9999)
wind_str_GF = RC.gap_filling(wind_str, -9999)
down_short_str_GF = RC.gap_filling(down_short_str, np.nan)
down_long_str_GF = RC.gap_filling(down_long_str, np.nan)
if up_long_str is not 'not':
up_long_str_GF = RC.gap_filling(up_long_str, np.nan)
else:
up_long_str_GF = 'nan'
#dictionary containing all tthe paths to the input-maps
inputs = dict({
'tmin': tmin_str_GF,
'tmax': tmax_str_GF,
'humid': humid_str_GF,
'press': press_str_GF,
'wind': wind_str_GF,
'down_short': down_short_str_GF,
'down_long': down_long_str_GF,
'up_long': up_long_str_GF
})
#dictionary containing numpy arrays of al initial and intermediate variables
input_array = dict({
'tmin': None,
'tmax': None,
'humid': None,
'press': None,
'wind': None,
'albedo': None,
'down_short': None,
'down_long': None,
'up_short': None,
'up_long': None,
'net_radiation': None,
'ea': None,
'es': None,
'delta': None
})
#APPLY LAPSE RATE CORRECTION ON TEMPERATURE
tmin = lapse_rate(Dir, inputs['tmin'], DEMmap_str)
tmax = lapse_rate(Dir, inputs['tmax'], DEMmap_str)
#PROCESS PRESSURE MAPS
press = adjust_P(Dir, inputs['press'], DEMmap_str)
#PREPARE HUMIDITY MAPS
dest = RC.reproject_dataset_example(inputs['humid'], DEMmap_str, method=2)
humid = dest.GetRasterBand(1).ReadAsArray()
dest = None
#CORRECT WIND MAPS
dest = RC.reproject_dataset_example(inputs['wind'], DEMmap_str, method=2)
wind = dest.GetRasterBand(1).ReadAsArray() * 0.75
dest = None
#PROCESS GLDAS DATA
input_array['ea'], input_array['es'], input_array['delta'] = process_GLDAS(
tmax, tmin, humid, press)
ea = input_array['ea']
es = input_array['es']
delta = input_array['delta']
if up_long_str == 'not':
#CORRECT WIND MAPS
dest = RC.reproject_dataset_example(down_short_str,
DEMmap_str,
method=2)
Short_Net_data = dest.GetRasterBand(1).ReadAsArray() * 0.75
dest = None
dest = RC.reproject_dataset_example(down_long_str,
DEMmap_str,
method=2)
Short_Clear_data = dest.GetRasterBand(1).ReadAsArray() * 0.75
dest = None
# Calculate Long wave Net radiation
Rnl = 4.903e-9 * (
((tmin + 273.16)**4 +
(tmax + 273.16)**4) / 2) * (0.34 - 0.14 * np.sqrt(ea)) * (
1.35 * Short_Net_data / Short_Clear_data - 0.35)
# Calulate Net Radiation and converted to MJ*d-1*m-2
net_radiation = (Short_Net_data * 0.77 + Rnl) * 86400 / 10**6
else:
#OPEN DOWNWARD SHORTWAVE RADIATION
dest = RC.reproject_dataset_example(inputs['down_short'],
DEMmap_str,
method=2)
down_short = dest.GetRasterBand(1).ReadAsArray()
dest = None
down_short, tau, bias = slope_correct(down_short, press, ea,
DEMmap_str, DOY)
#OPEN OTHER RADS
up_short = down_short * 0.23
dest = RC.reproject_dataset_example(inputs['down_long'],
DEMmap_str,
method=2)
down_long = dest.GetRasterBand(1).ReadAsArray()
dest = None
dest = RC.reproject_dataset_example(inputs['up_long'],
DEMmap_str,
method=2)
up_long = dest.GetRasterBand(1).ReadAsArray()
dest = None
#OPEN NET RADIATION AND CONVERT W*m-2 TO MJ*d-1*m-2
net_radiation = ((down_short - up_short) +
(down_long - up_long)) * 86400 / 10**6
#CALCULATE ETref
ETref = (0.408 * delta * net_radiation + 0.665 * 10**-3 * press *
(900 / ((tmax + tmin) / 2 + 273)) * wind *
(es - ea)) / (delta + 0.665 * 10**-3 * press * (1 + 0.34 * wind))
# Set limits ETref
ETref[ETref < 0] = 0
ETref[ETref > 400] = np.nan
#return a reference ET map (numpy array), a dictionary containing all intermediate information and a bias of the slope correction on down_short
return ETref
Dates = pd.date_range(Startdate, Enddate, freq="D")
for Date in Dates:
Day = Date.day
Month = Date.month
Year = Date.year
Tempfile_one = Temp_folder.format(yyyy=Year, mm=Month, dd=Day)
Presfile_one = Pres_folder.format(yyyy=Year, mm=Month, dd=Day)
Humfile_one = Hum_folder.format(yyyy=Year, mm=Month, dd=Day)
out_folder_one = out_folder.format(yyyy=Year, mm=Month, dd=Day)
geo_out, proj, size_X, size_Y = RC.Open_array_info(Tempfile_one)
Tdata = RC.Open_tiff_array(Tempfile_one)
Tdata[Tdata < -900] = -9999
Pdata = RC.Open_tiff_array(Presfile_one)
Hdata = RC.Open_tiff_array(Humfile_one)
Pdata[Pdata < 0] = -9999
Hdata[Hdata < 0] = -9999
# gapfilling
Tdata = RC.gap_filling(Tdata, -9999)
Pdata = RC.gap_filling(Pdata, -9999)
Hdata = RC.gap_filling(Hdata, -9999)
Esdata = 0.6108 * np.exp((17.27 * Tdata) / (Tdata + 237.3))
HumData = np.minimum((1.6077717 * Hdata * Pdata / Esdata), 1) * 100
HumData = HumData.clip(0, 100)
DC.Save_as_tiff(out_folder_one, HumData, geo_out, "WGS84")