varwg.meteo package¶

Submodules¶

varwg.meteo.avrwind module¶

written by Raphael Lutz 2010

varwg.meteo.avrwind.angle2component(angle, norm, wind=True)[source]¶

Converts wind data with the format (direction, speed) into the velocity components (u,v).

If wind == True, 0 degree is wind blowing from north, 90 degree is wind from east, etc. If False (if data is not wind data but ship movement or anything else), 0 degree is pointing to north, 90 to east etc.

Parameters:

anglefloat or np.array of floats: direction [0,360] where the wind is coming from
normfloat or np.array of floats: wind speed
windboole, optional: input data is wind data, default is True

Returns:

ufloat or np.array of floats: component u pointing from west to east
vfloat or np.array of floats: component v pointing from south to north

See also

component2angle: vice versa

Examples

>>> angle2component(90, 1)
(-1.0, -6.123233995736766e-17)

>>> angle2component([90,180],[1,1], wind=False)
(array([  1.00000000e+00,   1.22464680e-16]),
 array([  6.12323400e-17,  -1.00000000e+00]))

varwg.meteo.avrwind.avrwind(u, v, date_time, new_timeres, method='vector', verbose=True, sec=16, wind_=True)[source]¶

function to convert measured data from one time resolution to another you can decide which method you will use, sector=sector-wise or vector=vector-adding polygon trace

Parameters:

ufloat or np.array of floats: component u pointing from west to east
vfloat or np.array of floats: component v pointing from south to north
date_timetimeinfo as list/array of string ‘01.01.2010 12:30:40’: or datetime-object, or single timedelta in seconds(e.g: 60)
new_timeresint: [seconds] new time resolution of output data
method{‘vector’,’sector’}
verbose{True, False}, optional: give information about problems during calculation
secint, optional: number of sectors used for sector interpolation. Default is 16
wind_boole, optional: input data is wind data, default is True (for details: see documentation to angle2component)

Returns:

avr_ufloat: averaged u velocity
avr_vfloat: averaged v velocity
avr_wind_directionfloat: direction of averaged wind (‘vector’) or most common wind direction (‘sector’)
average_speed_maxsectorfloat: speed of averaged wind (‘vector’) or average speed in most commen wind direction (‘sector’)
return_timelist or array if str or datetime objects, depending in input

See also

avrwind_vec: wind averaging using vector method
avrwind_sec: wind averaging using sector method

Examples

>>> date_str = ['01.01.2010 12:00:00', '01.01.2010 12:01:00']
>>> avrwind([1,2], [3,4], date_str, 60*2, 'vector')
[[1.5],
 [3.5],
 [203.19859051364818],
 [3.8078865529319543],
 array(['01.01.2010 12:01:00'],
      dtype='|S19')]

>>> avrwind([1,2], [3,4], date_str, 60*2, 'vector', wind_=False)
[[1.5],
 [3.5],
 [23.198590513648185],
 [3.8078865529319543],
 array(['01.01.2010 12:01:00'],
      dtype='|S19')]

>>> avrwind([1,2], [3,4], 60, 120, 'sector', sec=16)
[[1.4607818031736237],
 [3.526639240889589],
 [202.5],
 [3.8172068075839798],
 [datetime.datetime(1900, 1, 1, 0, 1)]]

varwg.meteo.avrwind.avrwind_sec(u, v, sec=16, verbose=True, timeinfo=None, return_secdata=False, wind=True)[source]¶

function to calculate the average windspeed and directions via sectors

looks for sector with most wind, gives the sector as dir and the average of wind speed in this sector as speed

Parameters:

ufloat or np.array of floats: component u pointing from west to east
vfloat or np.array of floats: component v pointing from south to north
secint, optional: number of sectors used for interpolation. Default is 16
verbose{True, False}, optional: give information about problems during calculation
timeinfoNone or string, optional: time string, fmt=”%d.%m.%Y %H:%M:%S”, only for problem information
return_secdata{False, True}, optional: print information about sectors to screen
windboole, optional: input data is wind data, default is True (for details: see documentation to angle2component)

Returns:

avr_ufloat: averaged u velocity
avr_vfloat: averaged v velocity
avr_wind_directionfloat: most common wind direction
average_speed_maxsectorfloat: average speed in most commen wind direction
norm_uv_directfloat: speed: averaged input wind speeds

See also

avrwind_vec: wind averaging using vector method
avrwind: convienience function

Examples

>>> avrwind_sec([1,10,10],[-1,5,5])
(10.329287188579819, 4.2785308431564255, 247.5, 11.180339887498949, 0.6666666666666666, 7.924964445790331)

>>> avrwind_sec([1,10,10],[-1,5,5], wind=False)
(10.329287188579821, 4.2785308431564202, 67.5, 11.180339887498949, 0.6666666666666666, 7.924964445790331)

varwg.meteo.avrwind.avrwind_vec(u, v, sec=16, verbose=True, timeinfo=None, return_secdata=False, wind=True)[source]¶

calculates the average windspeed and direction using vector averaging

Parameters:

ufloat or np.array of floats: component u pointing from west to east
vfloat or np.array of floats: component v pointing from south to north
sec, verbose, timeinfo, return_secdatakwargs: kwargs for avrwind_sec, not used here. Only for easier making the function call of the two functions the same
windboole, optional: input data is wind data, default is True (for details: see documentation to angle2component)

Returns:

avr_ufloat: averaged u velocity
avr_vfloat: averaged v velocity
angle_avrfloat: direction of averaged wind
norm_avrfloat: speed of averaged wind
norm_uv_directfloat: speed: averaged input wind speeds

See also

avrwind_sec: wind averaging using sector method
avrwind: convienience function

Examples

>>> avrwind_vec([1,23],[-1,5])
(12.0, 2.0, 260.53767779197437, 12.165525060596439, 12.475709077126368)

varwg.meteo.avrwind.component2angle(u, v, wind=True)[source]¶

converts a tuple of (u,v) to (angle,norm) in deg 0=north 90=east

Parameters:

ufloat or np.array of floats: component u pointing from west to east
vfloat or np.array of floats: component v pointing from south to north
windboole, optional: input data is wind data, default is True

Returns:

anglefloat or np.array of floats: direction [0,360] where the wind is coming from
normfloat or np.array of floats: wind speed

See also

angle2component: vice versa

Examples

>>> component2angle(1,0)
(270.0, 1.0)

>>> component2angle([1,-1,0,0],[0,0,1,-1], wind=False)
(array([  90.,  270.,    0.,  180.]), array([ 1.,  1.,  1.,  1.]))

varwg.meteo.avrwind.phi_main(u, v)[source]¶

varwg.meteo.avrwind.plothist(x, legend)[source]¶

varwg.meteo.avrwind.turn_uv(u, v, phi)[source]¶

varwg.meteo.meteox2y module¶

Meteorological Conversions (`meteo.meteox2y`)¶

The meteox2y module provides functions to calculate derived meteorological variables from measured meteorological values.

`sat_vap_p`	saturation vapour pressure from air temperature
`rel2vap_p`	vapour pressure from relative humidity and air temperature
`vap_p2rel`	relative humidity from vapour pressure and air temperature
`dewpoint`	dewpoint from air temperature and humidity
`dew2rel`	relative humidity from dewpoint and air temperature
`norm_pressure`	normalize pressure to sealevel
`iziomon`	incident long wave radiation following Iziomon et al (2003)[R6331e1085f99-1]_
`lw2clouds`	Cloud cover from incident long wave radiation (Iziomon et al (2003)[Rd8835ed535f1-1]_)
`lw_tennessee`	incident long wave radiation
`haude`	calculates the evapotranspiration following Haude (1955) [1] as described in the Hydrologie-I-Skript
`turc`	calculates the potential evapotranspiration following Turc [1] as described in the Hydrologie-I-Skript (p.
`turc_rad`	calculates the potential evapotranspiration following Turc
`hargreaves`	calculates the potential evapotranspiration rate [mm/d] following Hargreaves & Samani 1985 according to THE ASCE STANDARDIZED REFERENCE EVAPOTRANSPIRATION EQUATION
`penman_monteith`	Calculates the reference crop evaporation following the Penman-Monteith method and the FAO-56 determinations published in ASCE Standardized Reference Evapotranspiration Equation
`pot_s_rad`	theoretical maximal potential solar radiation outside of atmosphere
`sunshine`	sunshine or not?
`blackbody_rad`	Stefan-Boltzmann law
`altitude`	Converts continous meassured vertical pressure and temperature data to altitude.
`spec_hum`	Calculates the specific humidity
`psychro2e`	Vapour pressure from dry and wet temperature from Assmann psychrometer using Sprung's [1] formula (as seen on wikipedia)
`slope_sat_p`	slope of the saturation vapor pressure function, depends only on air temperature

varwg.meteo.meteox2y.SPI_ar(obs_ar, weeks=1, reference=None)[source]¶

varwg.meteo.meteox2y.SPI_ds(obs_ds, weeks=1, reference=None)[source]¶

varwg.meteo.meteox2y.STI_ar(obs_ar, weeks=1, reference=None)[source]¶

varwg.meteo.meteox2y.STI_ds(obs_ds, weeks=1, reference=None)[source]¶

varwg.meteo.meteox2y.abs_hum2rel(abs_hum, at)[source]¶

Relative humidity from absolute humidity and air temperature.

Parameters:

abs_humfloat or numpy.array of floats: absolute humidity [g / m^3]
atfloat or numpy.array of floats: air temperature [deg C]

Returns:

rhfloat or numpy.array of floats: relative humidity with values between 0 and 1

Examples

>>> abs_hum2rel(13.831072059995932, 20)
0.8

varwg.meteo.meteox2y.altitude(temp1, temp0, pres1, pres0, alt0)[source]¶

Converts continous meassured vertical pressure and temperature data to altitude. # 1 means i and 0 means i-1

Parameters:

temp1float or array_like: Temperature value at timestep i.
temp0float or array_like: Temperature value at timestep i-1.
pres1float or array_like: Pressure value at timestep i.
pres0float or array_like: Pressure value at timestep i-1.
alt0float or array_like: Altitude value at timestep i-1.

Returns:

altitudefloat or array_like: Altitude at timestep i

Notes

This is the implemented equation:

g : Gravity acceleration 9.81 m/s^2 T_0 : Temperature 273.16 K

$altitude = ((\frac{\ln(pres0)}{pres1})*287*((\frac{(\frac{(temp1+temp0)}{2})+T_0)}/{g}))+alt0$

References

* -> Ask Felix!!! *

Examples

>>> alt = np.nan*np.ones(40)
>>> alt[0] = 600
>>> for i,element in enumerate(alt):
...    if str(element) == 'nan':
...        element = altitude(temp[i], temp[i-1], pres[i], pres[i-1], alt[i-1])

varwg.meteo.meteox2y.apparent_temperature(Ta, rh, ws, Q)[source]¶: Australian Bureau of Meteorology formulation of Steadman (1984).

varwg.meteo.meteox2y.blackbody_rad(rad=None, temp=None, eps=1.0)[source]¶

Stefan-Boltzmann law

There are two ways to use this funtion:

1) If input is radiation, then the function calculates the absolute temperature of the body emitting the radiation

2) If input is temperature, then the function calculates the radiation emitted by the body of this temperature

Parameters:

radfloat or numpy.array of floats or None: radiation [W/m**2]
tempfloat or numpy.array of floats or None: temperature [deg C]
epsfloat or numpy.array of floats, optional: emissivity of a grey body, values between 0 and 1, default=1

Returns:

either:
tempfloat or numpy.array of floats or None: temperature [deg C]
or:
radfloat or numpy.array of floats or None: radiation [W/m**2]

Examples

>>> "%.9f" % blackbody_rad(rad=350)
'7.138805085'

>>> "%.9f" % blackbody_rad(temp=0)
'315.683203500'

varwg.meteo.meteox2y.brunner_compound(sti, spi, sequential=False, progress=False)[source]¶

Rank-based hot-dry index.

Notes

Brunner 2021 uses E-GPD for precipitation and a STI index for temperature. This implementation just uses empirical ranks.

References

Brunner, Manuela I., Eric Gilleland, and Andrew W. Wood. “Space–Time Dependence of Compound Hot–Dry Events in the United States: Assessment Using a Multi-Site Multi-Variable Weather Generator.” Earth System Dynamics 12, no. 2 (May 19, 2021): 621–34. https://doi.org/10.5194/esd-12-621-2021.

varwg.meteo.meteox2y.dew2rel(dew, at)[source]¶

relative humidity from dewpoint and air temperature

Parameters:

atfloat or numpy.array of floats: air temperature [deg C]
dewfloat or numpy.array of floats: dewpoint [deg C]

Returns:

rhfloat or numpy.array of floats: relative humidity with values between 0 and 1 [-]

Examples

>>> dew2rel(15.,16.0)
0.9378863357566809

>>> at = np.array((0.0, 2.5, 5.0, 7.5, 10.0))
>>> dew = np.array((0.0, 3., 5.0, 7.7, 10.0))
>>> dew2rel(at,dew)
array([1.        ,  0.96506519,  1.        ,  0.98642692,  1.        ])

varwg.meteo.meteox2y.dewpoint(at, rh=None, e=None)[source]¶

dewpoint from air temperature and humidity

As input is required: air temperature (at) and EITHER relative humidity (rh) OR vapour pressure (e).

Parameters:

atfloat or numpy.array of floats: air temperature [deg C]
rhfloat or numpy.array of floats or None: relative humidity with values between 0 and 1 [-]
efloat or numpy.array of floats or None: vapour pressure [hPa]

Returns:

dewfloat or numpy.array of floats: dewpoint [deg C]

Raises:

Warning: If relative humidity is > 1.1 (e. g. if vapour pressure is taken as relative humidity)

Examples

>>> dewpoint(20.,rh=0.5)
9.269628637124908

>>> dewpoint(20.,e=10.0)
6.968196840688138

>>> at = np.array((0.0, 2.5, 5.0, 7.5, 10.0))
>>> rh = np.array((0.9, 0.8, 0.7, 0.6, 0.5))
>>> dewpoint(at, rh=rh)
array([-1.43893707, -0.59071344, -0.0041022 ,  0.25144096,  0.07140267])

>>> at = np.array((0.0, 2.5, 5.0, 7.5, 10.0))
>>> e = np.array((5.5, 5.8, 6.1, 6.2, 6.3))
>>> dewpoint(at, e=e)
array([-1.43646874, -0.71330622, -0.02250498,  0.20109231,  0.42152362])

varwg.meteo.meteox2y.esi(at, rh, sw)[source]¶

Environmental stress index (wet bulb globe temperature substitute)

Parameters:

atfloat or numpy.array of floats: air temperature [deg C]
rhfloat or numpy.array of floats: relative humidity [%]
swfloat or numpy.array of floats: solar radiation [W / m²]

Returns:

ESIenvironmental stress index

References

[1]

Moran et al., “An Environmental Stress Index (ESI) as a Substitute for the Wet Bulb Globe Temperature (WBGT).”

varwg.meteo.meteox2y.get_tz_offset(dates, longitude, latitude)[source]¶

varwg.meteo.meteox2y.hargreaves(tmax, tmin, date, in_format='%Y-%m-%dT%H:%M:%S')[source]¶

calculates the potential evapotranspiration rate [mm/d] following Hargreaves & Samani 1985 according to THE ASCE STANDARDIZED REFERENCE EVAPOTRANSPIRATION EQUATION

Parameters:

tmaxnp.array of floats: maximum of the daily air temperature [deg C]
tminnp.array of floats: minimum of the daily air temperature [deg C]
datenp.array of strings: date strings in format in_format
in_formatformat string: default: ‘%Y-%m-%dT%H:%M:%S’

Returns:

etpnumpy.array of floats: potential evapotranspiration [mm/d]

Notes

equation needs the extraterrestrial radiation Ra, which depends on:
- inverse relative distance factor for the earth-sun dr []
- solar declination delta [rad]
- sunset hour angle [rad] with latitude Lauchaecker lat=48.738 deg => pi/180 * 48.738 deg = 0.8506 rad
conversion of date in day of year (j)
ETPmax = 7.0 mm/d due to energetic limit (Germany)

varwg.meteo.meteox2y.haude(svp, vp, mon)[source]¶

calculates the evapotranspiration following Haude (1955) [1] as described in the Hydrologie-I-Skript

Parameters:

svpfloat or numpy.array of floats: saturation vapour pressure [hPa], measured at 2pm
vpfloat or numpy.array of floats: vapour pressure [hPa], measured at 2pm
monint or numpy.array of ints: month: 1=january, 12=december

Returns:

etpfloat or numpy.array of floats: evapotranspiration [mm] daily values

See also

turc: potential evapotranspiration following Turc

Notes

The Haude formula is only valid in temperate humid climate

References

[1] (1,2)

Haude, W. (1955): Zur Bestimmung der Verdunstung auf moeglichst einfache Weise. - Mitt. Dt. Wetterd. 2 (11), Bad Kissingen (Dt. Wetterd.)

Examples

>>> haude(12.28,8.83,5)
1.0005

>>> haude(12.28,8.83,12)
0.759

>>> svp = np.array([11.87809345,  12.28364703,  12.70132647])
>>> vp = np.array([8.55222728,  8.84422586,  9.14495506])
>>> haude(svp,vp,12)
array([0.73169056,  0.75667266,  0.78240171])

>>> mon = np.array([2,3,4])
>>> haude(svp,vp,mon)
array([0.73169056,  0.75667266,  1.03134771])

varwg.meteo.meteox2y.humidex(at, rh)[source]¶

varwg.meteo.meteox2y.iziomon(temp, clouds, rh=None, dew=None, e=None, site='low')[source]¶

incident long wave radiation following Iziomon et al (2003)[R6331e1085f99-1]_

As input is required: air temperature (temp), cloud cover (clouds) and EITHER relative humidity (rh) OR dewpoint (dew) OR vapour pressure (e).

Parameters:

tempfloat or numpy.array of floats: air temperature [deg C]
cloudsfloat or numpy.array of floats: cloud cover with values between 0 and 1 [-]
rhfloat or numpy.array of floats or None: relative humidity with values between 0 and 1 [-]
dewfloat or numpy.array of floats or None: dewpoint [deg C]
efloat or numpy.array of floats or None: vapour pressure [hPa]
site{‘low’, ‘high’}: parameterisation for lowland or highland site

Returns:

lwfloat or numpy.array of floats: incident longwave radiation [W/m**2]

See also

lw2clouds: reverse (get cloud cover out of long wave, temperature and humidity)

Notes

Empirical formula, found for experiments in Bremgarten (47deg54’35’’N; 7deg37’18’’E) in the Upper Rhine plain in Germany (lowland site) and Feldberg, 1489 m asl, 47deg52’31’’N, 8deg00’11’’E, Black Forest, Germany (highland site)

References

[1]

Iziomon, M.G., Mayer H, Matzarakis A. (2003): Downward atmospheric longwave irradiance under clear and cloudy skies: Measurement and parameterization, Journal of Atmospheric and Solar-Terrestrial Physics 65 (2003) 1107 - 1116

Examples

>>> iziomon(15.,0.5,rh=0.89)
327.52426791875763

>>> temp = np.array((0.0, 2.5, 5.0, 7.5, 10.0))
>>> clouds = np.array((0.0, 0.1, 0.8, 0.5, 1.0))
>>> e = np.array((5.5, 5.8, 6.1, 6.2, 6.3))
>>> iziomon(temp,clouds,e=e)
array([225.34414848,  235.0777488 ,  279.01405432,  267.25348612,
        321.15448959])

varwg.meteo.meteox2y.lw2clouds(lw, temp, rh=None, dew=None, e=None, site='low')[source]¶

Cloud cover from incident long wave radiation (Iziomon et al (2003)[Rd8835ed535f1-1]_)

As input is required: incident long wave radiation (lw), air temperature (temp) and EITHER relative humidity (rh) OR dewpoint (dew) OR vapour pressure (e)

Parameters:

lwfloat or numpy.array of floats: incident longwave radiation [W/m**2]
tempfloat or numpy.array of floats: air temperature [deg C]
rhfloat or numpy.array of floats or None: relative humidity with values between 0 and 1 [-]
dewfloat or numpy.array of floats or None: dewpoint [deg C]
efloat or numpy.array of floats or None: vapour pressure [hPa]
site{‘low’, ‘high’}: parameterisation for lowland or highland site

Returns:

cloudsfloat or numpy.array of floats: cloud cover with values between 0 and 1 [-]

See also

iziomon: incident long wave radiation from air temperature, cloud cover and humidity

Notes

Resulting cloud cover is always between 0 and 1. Gives 0 for unrealistic low and 1 for unrealistic high values of lw.

References

[1]

Iziomon, M.G., Mayer H, Matzarakis A. (2003): Downward atmospheric longwave irradiance under clear and cloudy skies: Measurement and parameterization, Journal of Atmospheric and Solar-Terrestrial Physics 65 (2003) 1107 - 1116

Examples

>>> lw2clouds(328., 15., rh=0.9)
0.49959967427213553

>>> lw = np.array((225.5, 235.1, 279, 267, 321.2))
>>> temp = np.array((0.0, 2.5, 5.0, 7.5, 10.0))
>>> e = np.array((5.5, 5.8, 6.1, 6.2, 6.5))
>>> lw2clouds(lw,temp,e=e)
array([0.0555659 ,  0.1020956 ,  0.79983929,  0.49550839,  0.99289638])

varwg.meteo.meteox2y.lw_tennessee(temp, clouds)[source]¶

incident long wave radiation

Parameters:

tempfloat or numpy.array of floats: air temperature [deg C]
cloudsfloat or numpy.array of floats: cloud cover with values between 0 and 1 [-]

Returns:

lwfloat or numpy.array of floats: incident longwave radiation [W/m**2]

See also

iziomon: other empirical formula, from southwestern germany

References

[1]

Tennessee Valley Authority 1972. Heat and mass transfer between a water surface and the atmosphere Water Resources Research Laboratory Report 14, Report No. 0-6803. (I didn’t find it. But it’s mentioned in:)

[2]

ELCOM Science manual

varwg.meteo.meteox2y.max_sunshine_minutes(dates, longitude, latitude, tz_offset=None)[source]¶

varwg.meteo.meteox2y.norm_pressure(p, at, h=454.0)[source]¶

normalize pressure to sealevel

formula from wikipedia.de for linear temperature gradient 0.0065K/m

Parameters:

pfloat or numpy.array of floats: pressure [hPa]
atfloat or numpy.array of floats: air temperature [deg C]
hfloat, optional: height above sea level of measuring station [m] default value = 454.0 (height of station Stuttgart-Lauchaecker)

Returns:

p_nnfloat or numpy.array of floats: sea level pressure [hPa]

Examples

>>> "%.9f" % norm_pressure(930,10.0)
'982.074383073'

>>> "%.9f" % norm_pressure(930,10.0,h=765)
'1019.090035371'

>>> at = np.array((0.0, 2.5, 5.0, 7.5, 10.0))
>>> p = np.array((925, 930, 935, 932, 931))
>>> norm_pressure(p,at,h=765)
array([1016.98077228,  1021.60706988,  1026.24032953,  1022.10690872,
        1020.18583111])

varwg.meteo.meteox2y.penman_monteith(at, u, Rn, rh)[source]¶

Calculates the reference crop evaporation following the Penman-Monteith method and the FAO-56 determinations published in ASCE Standardized Reference Evapotranspiration Equation

Parameters:

atnp.array of floats: mean daily air temperature at 2m-height [deg C]
unp.array of floats: mean daily wind speed at 2m-height [m/s]
Rnnp.array of floats: measured net radiation at the crop surface [MJ m^-2 d^-1]
rhnp.array of floats: relative humidity [%]

Returns:

etonp.array of floats

FAO Penman-Monteith standardized reference crop evapotranspiration for: short (~=0.12m) surfaces [mm d^-1]

Notes

Units for the 0.408 coefficient are m^2 mm MJ^-1
The FAO-56 Penman-Monteith equation is a grass reference equation that was derived from the Penman-Monteith form of the combination equation (Monteith 1965, 1981) by fixing h = 0.12 m for clipped grass and by assuming measurement heights of z = 2 m (at, rh, u) and using a latent heat of vaporization of 2.45 MJ kg-1. The result is an equation that defines the reference evapotranspiration from a hypothetical grass surface having a fixed height of 0.12 m, bulk surface resistance of 70 s m-1, and albedo of 0.23.
in relationship to the net radiation, the soil heat flux is very small and is fixed with G = 0.1*Rn

varwg.meteo.meteox2y.pot_s_rad(date, lat=48.738, longt=9.099, in_format='%Y-%m-%dT%H:%M', tz_mer=15.0, wog=-1)[source]¶

theoretical maximal potential solar radiation outside of atmosphere

Parameters:

datenumpy.array of time strings (format: in_format) or datetime objects: or floats (doys)
latfloat, optional: latitude of station in decimal degrees, default: Stuttgart Lauchaecker
longtfloat, optional: longitude of station in decimal degrees, default: Stuttgart Lauchaecker
in_formattime format string, optional: format of date if date is string, default ‘%Y-%m-%dT%H:%M’
tz_merint, optional: central meridian of time zone, default: 15 (CET)
wog{-1, 1}, optional: west of greenwich, 1 if west, -1 if east, default = -1

Returns:

smaxnumpy.array of floats: maximal potential solar radiation in W/m^2

Notes

from campbell technical note 18 [1], except declination of sun (d): formula of Spencer (1971) [2]

WARNING: Campbell Scientific recommends the use of a high quality sun screen lotion when exposing your skin to solar radiation for large values of sunshine hours!

References

[1]

Campbell Scientific (2005) technical note 18: CALCULATING SUNSHINE HOURS FROM PYRANOMETER / SOLARIMETER DATA

[2]

Spencer JW (1971) Fourier series representation of the position of the Sun. Search 2: 172.

Examples

>>> date_str = np.array(["2011-09-28T11:27"])
>>> pot_s_rad(date_str)
array([855.35624182])
>>> from varwg import times
>>> dt = times.str2datetime(date_str, "%Y-%m-%dT%H:%M")
>>> pot_s_rad(dt)
array([855.35624182])
>>> pot_s_rad(times.datetime2doy(dt))
array([855.35624182])

varwg.meteo.meteox2y.pot_s_rad_daily(date, lat=48.738, longt=9.099, in_format='%Y-%m-%d', tz_mer=15.0, wog=-1)[source]¶: daily average values of –> pot_s_rad

varwg.meteo.meteox2y.psychro2e(t_dry, t_wet, p=None)[source]¶

Vapour pressure from dry and wet temperature from Assmann psychrometer using Sprung’s [1] formula (as seen on wikipedia)

Parameters:

t_dryfloat or np.array of floats: dry temperature [deg C]
t_wetfloat or np.array of floats: wet temperature [deg C]
pfloat or np.array of floats or None, optional: air pressure [hPa], if None: use the simplified version of the formula, default is None

Returns:

efloat or np.array of floats: vapour pressure [hPa]

Notes

If p is None, a simplification is used which can be used below 500m above sea level

References

[1] (1,2)

Sprung, A.: Ueber die Bestimmung der Luftfeuchtigkeit mit Hilfe des Assmannschen Aspirationspsychrometers, Z. Angew. Meteorol., Das Wetter, 5 (1888), S. 105?108

Examples

>>> psychro2e(np.array([17.5,18.9]),np.array([12.3,12.1]))
array([10.82619823,   9.56701424])

>>> p = np.array([800,800])
>>> psychro2e(np.array([17.5,18.9]),np.array([12.3,12.1]),p=p)
array([11.57630294,  10.54309631])

varwg.meteo.meteox2y.rel2abs_hum(rh, at)[source]¶

Absolute humidity from relative humidity and air temperature.

Parameters:

rhfloat or numpy.array of floats: relative humidity with values between 0 and 1
atfloat or numpy.array of floats: air temperature [deg C]

Returns:

abs_humfloat or numpy.array of floats: absolute humidity [g / m^3]

Examples

>>> rel2abs_hum(0.8, 20)
13.831072059995932

varwg.meteo.meteox2y.rel2vap_p(rh, at)[source]¶

vapour pressure from relative humidity and air temperature

Parameters:

rhfloat or numpy.array of floats: relative humidity with values between 0 and 1 [-]
atfloat or numpy.array of floats: air temperature [deg C]

Returns:

efloat or numpy.array of floats: vapour pressure [hPa]

Examples

>>> rel2vap_p(0.5,20.0)
11.695234581996313

>>> rh = np.array((0.9, 0.8, 0.7, 0.6, 0.5))
>>> at = np.array((0.0, 2.5, 5.0, 7.5, 10.0))
>>> rel2vap_p(rh,at)
array([5.499     ,  5.85226692,  6.10817613,  6.22271646,  6.14182351])

varwg.meteo.meteox2y.sat_vap_p(at)[source]¶

saturation vapour pressure from air temperature

Parameters:

atfloat or numpy.array of floats: air temperature [deg C]

Returns:

c_efloat or numpy.array of floats: saturation vapour pressure [hPa]

References

Hydrologie-Skript I, p. 17

Examples

>>> sat_vap_p(25.0)
31.688149728170984

>>> at = np.array((0.0, 2.5, 5.0, 7.5, 10.0))
>>> sat_vap_p(at[:3])
array([6.11      ,  7.31533365,  8.72596589])
>>> sat_vap_p(at[3:])
array([10.3711941 ,  12.28364703])

varwg.meteo.meteox2y.slope_sat_p(at)[source]¶

slope of the saturation vapor pressure function, depends only on air temperature

Parameters:

atfloat or numpy.array of floats: (mean daily) air temperature [deg C]

Returns:

slopefloat or numpy.array of floats: slope of the saturation vapor pressure function [kPa/deg C]

Notes

A polynomial is used to evaluate the slope and is only valid for -5 < ‘at’ > 45 [deg C].

References

[1]

Campbell Scientific (1995) application note 4-D: On-Line Estimation of Grass Reference Evapotranspiration with the Campbell Scientific Automated Weather Station

varwg.meteo.meteox2y.sonnenscheindauer(date, sw, del_t=60)[source]¶

bestimmt Sonnenscheindauer anhand der kurzwelligen Solarstrahlung

Parameters:

datestring: datetime.datetime(2040, 1, 1, 0, 0, 0)
swstring
del_tint, optional: timestep in minutes: 1-min-data is averaged to this timestep. Default is 60

Returns:

sunshine_hour

varwg.meteo.meteox2y.spec_hum(e, p)[source]¶

Calculates the specific humidity

Parameters:

pressurefloat: air pressure in hPa
efloat: vapour pressure in hPa

Returns:

spec_humfloat

Notes

The formula is: s = ((0.623*e)/(p-0.377*e))*1000 where M_w/M_tL=0.622 and 0.378 = 1-0.622

varwg.meteo.meteox2y.sunshine(sw, date, lat=48.738, longt=9.099, in_format='%Y-%m-%dT%H:%M', tz_mer=15.0, wog=-1)[source]¶

sunshine or not?

calculates maximum potential solar radiation depending on latitude, longitude, and time and compares it to actual solar radiation. Sunshine if actual solar radiation > 0.4*maximum potential solar radiation accurate enough for normal non-scientific use of sunshine hour data

Parameters:

swnumpy.array of floats: solar (short wave) radiation in W/m^2
datenumpy.array of time strings: date and time in format in_format
latfloat, optional: latitude of station in decimal degrees, default: Stuttgart Lauchaecker
longtfloat, optional: longitude of station in decimal degrees, default: Stuttgart Lauchaecker
in_formattime format string, optional: format of date, default ‘%Y-%m-%dT%H:%M’
tz_merint, optional: central meridian of time zone, default: 15 (CET)
wog{-1, 1}, optional: west of greenwich, 1 if west, -1 if east, default = -1

Returns:

shiningnumpy.array containing 0 and 1: 1: sun is shining in corresponding time step, 0: sun is not shining in corresponding time step

Examples

>>> sunshine(np.array([450]),np.array(["2011-09-28T11:27"]))
array([1])
>>> sunshine(np.array([200]),np.array(["2011-09-28T11:27"]))
array([0])

varwg.meteo.meteox2y.sunshine_hours(dates, longitude, latitude, tz_offset=None)[source]¶

Calculates hours from sunrise to sunset.

Notes

see: https://en.wikipedia.org/wiki/Sunrise_equation https://michelanders.blogspot.com/2010/12/calulating-sunrise-and-sunset-in-python.html

Examples

>>> from datetime import date
>>> sunshine_hours([date(2018, 6, 1)], 8.848, 48.943)  # mühlacker
array([15.89961046])

varwg.meteo.meteox2y.sunshine_pot(doys, lat=48.738, longt=9.099, tz_mer=15.0, wog=-1)[source]¶: Maximum daily sunshine hours based on evaluating pot_s_rad per minute.

varwg.meteo.meteox2y.sunshine_riseset(dates, longitude, latitude, tz_offset=None)[source]¶: Calculates sunrise and sunset hours.

varwg.meteo.meteox2y.temp2lw(temp)[source]¶

Incident long-wave radiation from air temperature (Gal pc).

Parameters:

tempfloat or numpy.array of floats: air temperature [deg C]

Returns:

lwfloat or numpy.array of floats: incident longwave radiation [W/m**2]

varwg.meteo.meteox2y.turc(at, ts, mon)[source]¶

calculates the potential evapotranspiration following Turc [1] as described in the Hydrologie-I-Skript (p. 55)

Parameters:

atfloat or numpy.array of floats: daily average air temperature [degC]
tsfloat or numpy.array of floats: number of sunshine hours per day
monint or numpy.array of ints: month: 1=january, 12=december

Returns:

etpfloat or numpy.array of floats: potential evapotranspiration [mm] daily values

See also

haude: evapotranspiration following Haude

Notes

The Turc formula is only valid for at > 0 deg C

References

[1] (1,2)

TURC??

Examples

>>> "%.9f" % turc(10,0,5)
'1.104000000'

>>> "%.9f" % turc(10,0,12)
'0.408000000'

varwg.meteo.meteox2y.turc_rad(at, rh, G)[source]¶

calculates the potential evapotranspiration following Turc

with the global radiation instead of the empiric factors according to Hydrologie und Wasserwirtschaft by Maniak

Parameters:

atnumpy.array of floats: mean daily air temperature [deg C]
rhnumpy.array of floats: relative humidity [%]
Gnumpy.array of floats: global radiation [W/m^2]

Returns:

etpnumpy.array of floats: potential evapotranspiration [mm/d]

Notes

For etp < 0.1 mm/d the evapotranspiration rate is set to 0.1 mm/d. [DVWK 1996]
According to the energetic limit of 7 mm/d in Germany, ETPmax is set to 7 mm/d.

varwg.meteo.meteox2y.vap_p2rel(e, at)[source]¶

relative humidity from vapour pressure and air temperature

Parameters:

efloat or numpy.array of floats: vapour pressure [hPa]
atfloat or numpy.array of floats: air temperature [deg C]

Returns:

rhfloat or numpy.array of floats: relative humidity with values between 0 and 1 [-]

Examples

>>> vap_p2rel(6.22,20.0)
0.26592027532201407

>>> e = np.array((5.5, 5.8, 6.1, 6.2, 6.3))
>>> at = np.array((0.0, 2.5, 5.0, 7.5, 10.0))
>>> vap_p2rel(e,at)
array([0.90016367, 0.79285516, 0.69906301, 0.59780966, 0.512877  ])

varwg.meteo.meteox2y.wet_bulb_stull(at, rh)[source]¶

varwg.meteo.windrose module¶

Provides functions to plot windroses when given wind directions.

varwg.meteo.windrose.equal_num_bins(values, n_bins, round=True)[source]¶: Returns bins so that values is cut into n_bins bins that each contain the same number of values’ values.

varwg.meteo.windrose.mind_the_gap(hist, overlap_i)[source]¶

varwg.meteo.windrose.rel_ranks(values)[source]¶: Returns ranks of values in the range [0,1].

varwg.meteo.windrose.scatter(wind_dirs, speed)[source]¶

varwg.meteo.windrose.seasonal_windroses(datetimes, wind_dirs, n_sectors=32, time_format_str='%m', fig=None, speed=None, title=None, normalize=True, separate=False, *args, **kwds)[source]¶: Plots a figure with subplots of windroses. Each subplot is a windrose containing only directions from the measurement times signified by “time_format_str”. The default (“%m”) plots one windrose for each distinct month.

varwg.meteo.windrose.windrose(wind_dirs, n_sectors=32, speed=None, speed_bins=6, fig=None, subfig=False, ticks=True, normalize=True, *args, **kwds)[source]¶: Plots a windrose (histogram with polar axes) of the given wind_dirs. If speed_bins is an int, equally filled speed_bins will be created.

varwg.meteo package¶

Submodules¶

varwg.meteo.avrwind module¶

varwg.meteo.meteox2y module¶

Meteorological Conversions (`meteo.meteox2y`)¶

varwg.meteo.windrose module¶

Module contents¶

Table of Contents

This Page

varwg.meteo package¶

Submodules¶

varwg.meteo.avrwind module¶

varwg.meteo.meteox2y module¶

Meteorological Conversions (meteo.meteox2y)¶

varwg.meteo.windrose module¶

Module contents¶

Meteorological Conversions (`meteo.meteox2y`)¶