# Meteorology and Evaporation Function Modules for Python¶

## meteolib.py: Meteorological functions¶

Library of functions for meteorology.

### Meteorological function names¶

• cp_calc: Calculate specific heat
• Delta_calc: Calculate slope of vapour pressure curve
• es_calc: Calculate saturation vapour pressures
• ea_calc: Calculate actual vapour pressures
• gamma_calc: Calculate psychrometric constant
• L_calc: Calculate latent heat of vapourisation
• pottemp: Calculate potential temperature (1000 hPa reference pressure)
• rho_calc: Calculate air density
• sun_NR: Maximum sunshine duration [h] and extraterrestrial radiation [J/day]
• vpd_calc: Calculate vapour pressure deficits
• windvec: Calculate average wind direction and speed

Module requires and imports math and scipy modules.

Tested for compatibility with Python 2.7.

### Function descriptions¶

meteolib.Delta_calc(airtemp=array([], dtype=float64))[source]

Function to calculate the slope of the temperature - vapour pressure curve (Delta) from air temperature T:

$\Delta = 1000 \cdot \frac{e_s \cdot 4098}{(T + 237.3)^2}$

where es is the saturated vapour pressure at temperature T.

Parameters:
• airtemp: (array of) air temperature [Celsius].
Returns:
• Delta: (array of) slope of saturated vapour curve [Pa K-1].

References

Technical regulations 49, World Meteorological Organisation, 1984. Appendix A. 1-Ap-A-3.

Examples

>>> Delta_calc(30.0)
243.34309166827094
>>> x = [20, 25]
>>> Delta_calc(x)
array([ 144.6658414 ,  188.62504569])

meteolib.L_calc(airtemp=array([], dtype=float64))[source]

Function to calculate the latent heat of vapourisation from air temperature.

Parameters:
• airtemp: (array of) air temperature [Celsius].
Returns:
• L: (array of) lambda [J kg-1 K-1].

References

J. Bringfelt. Test of a forest evapotranspiration model. Meteorology and Climatology Reports 52, SMHI, Norrköpping, Sweden, 1986.

Examples

>>> L_calc(25)
2440883.8804625
>>> t=[10, 20, 30]
>>> L_calc(t)
array([ 2476387.3842125,  2452718.3817125,  2429049.3792125])

meteolib.cp_calc(airtemp=array([], dtype=float64), rh=array([], dtype=float64), airpress=array([], dtype=float64))[source]

Function to calculate the specific heat of air:

$c_p = 0.24 \cdot 4185.5 \cdot \left(1 + 0.8 \cdot \frac{0.622 \cdot e_a}{p - e_a}\right)$

where ea is the actual vapour pressure calculated from the relative humidity and p is the ambient air pressure.

Parameters:
• airtemp: (array of) air temperature [Celsius].
• rh: (array of) relative humidity data [%].
• airpress: (array of) air pressure data [Pa].
Returns:
cp: array of saturated c_p values [J kg-1 K-1].

References

R.G. Allen, L.S. Pereira, D. Raes and M. Smith (1998). Crop Evaporation Guidelines for computing crop water requirements, FAO - Food and Agriculture Organization of the United Nations. Irrigation and drainage paper 56, Chapter 3. Rome, Italy. (http://www.fao.org/docrep/x0490e/x0490e07.htm)

Examples

>>> cp_calc(25,60,101300)
1014.0749457208065
>>> t = [10, 20, 30]
>>> rh = [10, 20, 30]
>>> airpress = [100000, 101000, 102000]
>>> cp_calc(t,rh,airpress)
array([ 1005.13411289,  1006.84399787,  1010.83623841])

meteolib.ea_calc(airtemp=array([], dtype=float64), rh=array([], dtype=float64))[source]

Function to calculate actual vapour pressure from relative humidity:

$e_a = \frac{rh \cdot e_s}{100}$

where es is the saturated vapour pressure at temperature T.

Parameters:
• airtemp: array of measured air temperatures [Celsius].
• rh: Relative humidity [%].
Returns:
• ea: array of actual vapour pressure [Pa].

Examples

>>> ea_calc(25,60)
1900.0946514729308

meteolib.es_calc(airtemp=array([], dtype=float64))[source]

Function to calculate saturated vapour pressure from temperature.

For T<0 C the saturation vapour pressure equation for ice is used accoring to Goff and Gratch (1946), whereas for T>=0 C that of Goff (1957) is used.

Parameters:
• airtemp : (data-type) measured air temperature [Celsius].
Returns:
• es : (data-type) saturated vapour pressure [Pa].

References

• Goff, J.A.,and S. Gratch, Low-pressure properties of water from -160 to 212 F. Transactions of the American society of heating and ventilating engineers, p. 95-122, presented at the 52nd annual meeting of the American society of heating and ventilating engineers, New York, 1946.
• Goff, J. A. Saturation pressure of water on the new Kelvin temperature scale, Transactions of the American society of heating and ventilating engineers, pp 347-354, presented at the semi-annual meeting of the American society of heating and ventilating engineers, Murray Bay, Quebec. Canada, 1957.

Examples

>>> es_calc(30.0)
4242.725994656632
>>> x = [20, 25]
>>> es_calc(x)
array([ 2337.08019792,  3166.82441912])

meteolib.gamma_calc(airtemp=array([], dtype=float64), rh=array([], dtype=float64), airpress=array([], dtype=float64))[source]

Function to calculate the psychrometric constant gamma.

$\gamma = \frac{c_p \cdot p}{0.66 \cdot \lambda}$

where p is the air pressure and lambda the latent heat of vapourisation.

Parameters:
• airtemp: array of measured air temperature [Celsius].
• rh: array of relative humidity values[%].
• airpress: array of air pressure data [Pa].
Returns:
• gamma: array of psychrometric constant values [Pa K-1].

References

J. Bringfelt. Test of a forest evapotranspiration model. Meteorology and Climatology Reports 52, SMHI, Norrköpping, Sweden, 1986.

Examples

>>> gamma_calc(10,50,101300)
66.26343318657227
>>> t = [10, 20, 30]
>>> rh = [10, 20, 30]
>>> airpress = [100000, 101000, 102000]
>>> gamma_calc(t,rh,airpress)
array([ 65.25518798,  66.65695779,  68.24239285])

meteolib.meteolib()[source]

A libray of functions for calculation of micrometeorological parameters.

This is the help function which prints a list of functions and contact information about the author, version and last modification date.

meteolib.pottemp(airtemp=array([], dtype=float64), rh=array([], dtype=float64), airpress=array([], dtype=float64))[source]

Function to calculate the potential temperature air, theta, from air temperatures, relative humidity and air pressure. Reference pressure 1000 hPa.

Parameters:
• airtemp: (array of) air temperature data [Celsius].
• rh: (array of) relative humidity data [%].
• airpress: (array of) air pressure data [Pa].
Returns:
• theta: (array of) potential air temperature data [Celsius].

Examples

>>> t = [5, 10, 20]
>>> rh = [45, 65, 89]
>>> airpress = [101300, 102000, 99800]
>>> pottemp(t,rh,airpress)
array([  3.97741582,   8.40874555,  20.16596828])
>>> pottemp(5,45,101300)
3.977415823848844

meteolib.rho_calc(airtemp=array([], dtype=float64), rh=array([], dtype=float64), airpress=array([], dtype=float64))[source]

Function to calculate the density of air, rho, from air temperatures, relative humidity and air pressure.

$\rho = 1.201 \cdot \frac{290.0 \cdot (p - 0.378 \cdot e_a)}{1000 \cdot (T + 273.15)} / 100$
Parameters:
• airtemp: (array of) air temperature data [Celsius].
• rh: (array of) relative humidity data [%].
• airpress: (array of) air pressure data [Pa].
Returns:
• rho: (array of) air density data [kg m-3].

Examples

>>> t = [10, 20, 30]
>>> rh = [10, 20, 30]
>>> airpress = [100000, 101000, 102000]
>>> rho_calc(t,rh,airpress)
array([ 1.22948419,  1.19787662,  1.16635358])
>>> rho_calc(10,50,101300)
1.2431927125520903

meteolib.sun_NR(doy=array([], dtype=float64), lat=<type 'float'>)[source]

Function to calculate the maximum sunshine duration [h] and incoming radiation [MJ/day] at the top of the atmosphere from day of year and latitude.

Parameters:
• doy: (array of) day of year.
• lat: latitude in decimal degrees, negative for southern hemisphere.

Returns: - N: (float, array) maximum sunshine hours [h]. - Rext: (float, array) extraterrestrial radiation [J day-1].

Notes

Only valid for latitudes between 0 and 67 degrees (i.e. tropics and temperate zone).

References

R.G. Allen, L.S. Pereira, D. Raes and M. Smith (1998). Crop Evaporation - Guidelines for computing crop water requirements, FAO - Food and Agriculture Organization of the United Nations. Irrigation and drainage paper 56, Chapter 3. Rome, Italy. (http://www.fao.org/docrep/x0490e/x0490e07.htm)

Examples

>>> sun_NR(50,60)
(9.1631820597268163, 9346987.824773483)
>>> days = [100,200,300]
>>> latitude = 52.
>>> sun_NR(days,latitude)
(array([ 13.31552077,  15.87073276,   9.54607624]), array([ 29354803.66244921,  39422316.42084264,  12619144.54566777]))

meteolib.vpd_calc(airtemp=array([], dtype=float64), rh=array([], dtype=float64))[source]

Function to calculate vapour pressure deficit.

Parameters:
• airtemp: measured air temperatures [Celsius].
• rh: (array of) rRelative humidity [%].
Returns:
• vpd: (array of) vapour pressure deficits [Pa].

Examples

>>> vpd_calc(30,60)
1697.090397862653
>>> T=[20,25]
>>> RH=[50,100]
>>> vpd_calc(T,RH)
array([ 1168.54009896,     0.        ])

meteolib.windvec(u=array([], dtype=float64), D=array([], dtype=float64))[source]

Function to calculate the wind vector from time series of wind speed and direction.

Parameters:
• u: array of wind speeds [m s-1].
• D: array of wind directions [degrees from North].
Returns:
• uv: Vector wind speed [m s-1].
• Dv: Vector wind direction [degrees from North].

Examples

>>> u = scipy.array([[ 3.],[7.5],[2.1]])
>>> D = scipy.array([,,])
>>> windvec(u,D)
(4.162354202836905, array([ 353.2118882]))
>>> uv, Dv = windvec(u,D)
>>> uv
4.162354202836905
>>> Dv
array([ 353.2118882])


## evaplib.py: Evaporation functions¶

Functions for calculation of potential and actual evaporation from meteorological data.

### Potential and actual evaporation functions¶

• E0: Calculate Penman (1948, 1956) open water evaporation.
• Em: Calculate evaporation according to Makkink (1965).
• Ept: Calculate evaporation according to Priestley and Taylor (1972).
• ET0pm: Calculate Penman Monteith reference evaporation short grass.
• Epm: Calculate Penman-Monteith evaporation (actual evapotranspiration).
• ra: Calculate aerodynamic resistance from windspeed and roughnes parameters.
• tvardry: calculate sensible heat flux from temperature variations.
• gash79: Gash (1979) analytical rainfall interception model.

Requires and imports scipy and meteolib modules. Compatible with Python 2.7.3.

### Function descriptions¶

evaplib.E0(airtemp=array([], dtype=float64), rh=array([], dtype=float64), airpress=array([], dtype=float64), Rs=array([], dtype=float64), Rext=array([], dtype=float64), u=array([], dtype=float64), alpha=0.08, Z=0.0)[source]

Function to calculate daily Penman (open) water evaporation estimates:

$E_0 = \frac{R_n \cdot \Delta}{\lambda \cdot (\Delta + \gamma)} + \frac{6430000 \cdot E_a \cdot \gamma}{\lambda \cdot (\Delta+\gamma)}$
Parameters:
• airtemp: (array of) daily average air temperatures [Celsius].
• rh: (array of) daily average relative humidity [%].
• airpress: (array of) daily average air pressure data [Pa].
• Rs: (array of) daily incoming solar radiation [J m-2 day-1].
• Rext: (array of) daily extraterrestrial radiation [J m-2 day-1].
• u: (array of) daily average wind speed at 2 m [m s-1].
• alpha: albedo [-] set at 0.08 for open water by default.
• Z: (array of) site elevation, default is 0 m a.s.l.
Returns:
• E0: (array of) Penman open water evaporation values [mm day-1].

Notes

Meteorological parameters measured at 2 m above the surface. Albedo alpha set by default at 0.08 for open water (Valiantzas, 2006).

References

• H.L. Penman (1948). Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences 193: 120-145.
• H.L. Penman (1956). Evaporation: An introductory survey. Netherlands Journal of Agricultural Science 4: 9-29.
• J.D. Valiantzas (2006). Simplified versions for the Penman evaporation equation using routine weather data. J. Hydrology 331: 690-702.

Examples

>>> # With single values and default albedo/elevation
>>> E0(20.67,67.0,101300.0,22600000.,42000000.,3.2)
6.6029208786994467
>>> # With albedo is 0.18 instead of default and default elevation
>>> E0(20.67,67.0,101300.0,22600000.,42000000.,3.2,alpha=0.18)
5.9664248091431968
>>> # With standard albedo and Z= 250.0 m
>>> E0(20.67,67.0,101300.0,22600000.,42000000.,3.2,Z=250.0)
6.6135588207586284
>>> # With albedo alpha = 0.18 and elevation Z = 1000 m a.s.l.
>>> E0(20.67,67.0,101300.0,22600000.,42000000.,3.2,0.18,1000.)
6.00814764682986

evaplib.ET0pm(airtemp=array([], dtype=float64), rh=array([], dtype=float64), airpress=array([], dtype=float64), Rs=array([], dtype=float64), Rext=array([], dtype=float64), u=array([], dtype=float64), Z=0.0)[source]

Function to calculate daily Penman Monteith reference evaporation estimates.

Parameters:
• airtemp: (array of) daily average air temperatures [Celsius].
• rh: (array of) daily average relative humidity values [%].
• airpress: (array of) daily average air pressure data [hPa].
• Rs: (array of) total incoming shortwave radiation [J m-2 day-1].
• Rext: Incoming shortwave radiation at the top of the atmosphere [J m-2 day-1].
• u: windspeed [m s-1].
• Z: elevation [m], default is 0 m a.s.l.
Returns:
• ET0pm: (array of) Penman Monteith reference evaporation (short grass with optimum water supply) values [mm day-1].

Notes

Meteorological measuements standard at 2 m above soil surface.

References

R.G. Allen, L.S. Pereira, D. Raes and M. Smith (1998). Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56. FAO - Food and Agriculture Organization of the United Nations, Rome, 1998. (http://www.fao.org/docrep/x0490e/x0490e07.htm)

Examples

>>> ET0pm(20.67,67.0,101300.0,22600000.,42000000.,3.2)
4.7235349721073039

evaplib.Em(airtemp=array([], dtype=float64), rh=array([], dtype=float64), airpress=array([], dtype=float64), Rs=array([], dtype=float64))[source]

Function to calculate Makkink evaporation (in mm/day):

$E_m = 0.65 \frac{R_s}{\lambda} \cdot \frac{\Delta}{\Delta + \gamma}$

The Makkink evaporation is a reference crop evaporation. It is a reference crop evaporation equation based on the Penman open water equation and represents evapotranspiration from short, well-watered grassland under Dutch climate conditions. Makkink reference evaporation values are published daily by the Royal Netherlands Meteorological Institute (KNMI) in the Netherlands. Values are used in combination with crop factors to provide daily estimates of actual crop evaporation for many crop types.

Parameters:
• airtemp: (array of) daily average air temperatures [Celsius].
• rh: (array of) daily average relative humidity values [%].
• airpress: (array of) daily average air pressure data [Pa].
• Rs: (array of) average daily incoming solar radiation [J m-2 day-1].
Returns:
• Em: (array of) Makkink evaporation values [mm day-1].

Notes

Meteorological measurements standard at 2 m above soil surface.

References

H.A.R. de Bruin (1987). From Penman to Makkink, in Hooghart, C. (Ed.), Evaporation and Weather, Proceedings and Information. Comm. Hydrological Research TNO, The Hague. pp. 5-30.

Examples

>>> Em(21.65,67.0,101300.,24200000.)
4.503830479197991

evaplib.Epm(airtemp=array([], dtype=float64), rh=array([], dtype=float64), airpress=array([], dtype=float64), Rn=array([], dtype=float64), G=array([], dtype=float64), ra=array([], dtype=float64), rs=array([], dtype=float64))[source]

Function to calculate the Penman Monteith evaporation.

$E_{pm} = \frac{\Delta \cdot (R_n-G)+\rho \cdot c_p \cdot (e_s-e_a)/r_a}{\lambda \cdot (\Delta + \gamma \cdot (1+\frac{r_s}{r_a}))}$

The function can be used with different time intervals, such as commonly used hourly or daily time intervals are used. When a plant canopy is wet, the surface resistance (rs) becomes zero (stomatal resistance irrelevant, as evaporation is directly from wet leaf surface). Function ra() in this module can be used to calculate the aerodynamic resistance (ra) from wind speed and height parameters.

Parameters:
• airtemp: (array of) daily average air temperatures [Celsius].
• rh: (array of) daily average relative humidity values [%].
• airpress: (array of) daily average air pressure data [hPa].
• Rn: (array of) net radiation input over time interval t [J t-1].
• G: (array of) soil heat flux input over time interval t [J t-1].
• ra: aerodynamic resistance [s m-1].
• rs: surface resistance [s m-1].
Returns:
• Epm: (array of) Penman Monteith evaporation values [mm t-1].

References

J.L. Monteith (1965). Evaporation and environment. Symp. Soc. Exp. Biol. 19: 205-224.

Examples

>>> Epm(21.67,67.0,1013.0,14100000.,500000.,104.,70.)
3.243341146049407

evaplib.Ept(airtemp=array([], dtype=float64), rh=array([], dtype=float64), airpress=array([], dtype=float64), Rn=array([], dtype=float64), G=array([], dtype=float64))[source]

Function to calculate daily Priestley - Taylor evaporation:

$E_{pt} = \alpha \frac{R_n - G}{\lambda} \cdot \frac{\Delta}{\Delta + \gamma}$

where alpha is set to 1.26.

Parameters:
• airtemp: (array of) daily average air temperatures [Celsius].
• rh: (array of) daily average relative humidity values [%].
• airpress: (array of) daily average air pressure data [Pa].
• Rn: (array of) average daily net radiation [J m-2 day-1].
• G: (array of) average daily soil heat flux [J m-2 day-1].
Returns:
• Ept: (array of) Priestley Taylor evaporation values [mm day-1].

Notes

Meteorological parameters normally measured at 2 m above the surface.

References

Priestley, C.H.B. and R.J. Taylor, 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather Rev. 100:81-82.

Examples

>>> Ept(21.65,67.0,101300.,18200000.,600000.)
6.349456116128078

evaplib.evaplib()[source]

Evaplib: A libray with Python functions for calculation of evaporation from meteorological data.

evaplib.gash79(Pg=array([], dtype=float64), ER=<type 'float'>, S=<type 'float'>, St=<type 'float'>, p=<type 'float'>, pt=<type 'float'>)[source]

Function to calculate precipitation interception loss from daily precipitation values and and vegetation parameters.

Parameters:
• Pg: daily rainfall data [mm].
• ER: evaporation percentage of total rainfall [mm h-1].
• S: storage capacity canopy [mm].
• St: stem storage capacity [mm].
• p: direct throughfall [mm].
• pt: stem precipitation [mm].
Returns:
• Pg: Daily rainfall [mm].
• Ei: Interception [mm].
• TF: through fall [mm].
• SF: stemflow [mm].

References

J.H.C. Gash, An analytical model of rainfall interception by forests, Quarterly Journal of the Royal Meteorological Society, 1979, 105, pp. 43-55.

Examples

>>> gash79(12.4,0.15,1.3,0.2,0.2,0.02)
(12.4, 8.4778854123725971, 0, 3.9221145876274024)
>>> gash79(60.0,0.15,1.3,0.2,0.2,0.02)
(60.0, 47.033885412372598, 0, 12.966114587627404)

evaplib.ra(z=<type 'float'>, z0=<type 'float'>, d=<type 'float'>, u=array([], dtype=float64))[source]

Function to calculate aerodynamic resistance from windspeed:

$r_a = \frac{\left[\ln\frac{z-d}{z_0}\right]^2}{k^2 \cdot u_z}$

where k is the von Karman constant set at 0.4.

Parameters:
• z: measurement height [m].
• z0: roughness length [m].
• d: displacement length [m].
• u: (array of) wind speed measured at height z [m s-1].
Returns:
• ra: (array of) aerodynamic resistance [s m-1].

References

A.S. Thom (1075), Momentum, mass and heat exchange of plant communities, In: Monteith, J.L. Vegetation and the Atmosphere, Academic Press, London. p. 57–109.

Examples

>>> ra(3,0.12,2.4,5.0)
3.2378629924752942
>>> u=([2,4,6])
>>> ra(3,0.12,2.4,u)
array([ 8.09465748,  4.04732874,  2.69821916])

evaplib.tvardry(rho=array([], dtype=float64), cp=array([], dtype=float64), T=array([], dtype=float64), sigma_t=array([], dtype=float64), z=0.0, d=0.0, C1=2.9, C2=28.4)[source]

Function to calculate the sensible heat flux from high frequency temperature measurements and their standard deviation:

$H= \rho c_p \left(k g (z-d) \frac{C_2}{C_1^3}\right)^\frac{1}{2} \left( \frac{\sigma_T^3}{T}\right)^\frac{1}{2}$
Parameters:
• rho: (array of) air density values [kg m-3].
• cp: (array of) specific heat at constant temperature values [J kg-1 K-1].
• T: (array of) temperature data [Celsius].
• sigma_t: (array of) standard deviation of temperature data [Celsius].
• z: height [m] above the surface of the temperature measurement.
• d: displacement height due to vegetation, default set to zero [m].
• C1: Constant, default set to 2.9 [-] for unstable conditions (de Bruin et al., 1993).
• C2: Constant, default set to 28.4 [-] for unstable conditions (de Bruin et al., 1993).
Returns:
• H: (array of) sensible heat flux [W m-2].

Notes

This function holds only for free convective conditions when C2*z/L >>1, where L is the Obhukov length.

References

• H.A.R. de Bruin and W. Kohsiek and B.J.J.M. van den Hurk (1993). A verification of some methods to determine the fluxes of momentum, sensible heat andwWater vapour using standard seviation and structure parameter of scalar meteorological quantities. Boundary-Layer Meteorology 63(3): 231-257.
• J.E. Tillman (1972), The indirect determination of stability, heat and momentum fluxes in the atmosphere boundary layer from simple scalar variables during dry unstable conditions, Journal of Applied Meteorology 11: 783-792.
• H.F. Vugts, M.J. Waterloo, F.J. Beekman, K.F.A. Frumau and L.A. Bruijnzeel. The temperature variance method: a powerful tool in the estimation of actual evaporation rates. In J. S. Gladwell, editor, Hydrology of Warm Humid Regions, Proc. of the Yokohama Symp., IAHS Publication No. 216, pages 251-260, July 1993.

Examples

>>> tvardry(1.25,1035.0,25.3,0.25,3.0)
34.658669290185287
>>> displ_len=0.25
>>> tvardry(1.25,1035.0,25.3,0.25,3.0,d=displ_len)
33.183149497185511
>>> tvardry(1.25,1035.0,25.3,0.25,3.0,d=displ_len,C2=30)
34.10507908798597


## meteo_util.py: Utility functions¶

A library of miscellaneous functions for meteorological data processing.

### Miscellaneous functions¶

• event2time: Convert (event) based measurements into equidistant time spaced data for a selected interval
• date2doy: Calculates day of year from day, month and year data.

The module requires and imports scipy and datetime modules. Tested for compatibility with Python 2.7.3.

#### Function descriptions¶

meteo_util.date2doy(dd=array([], dtype=float64), mm=array([], dtype=float64), yyyy=array([], dtype=float64))[source]

Function to calculate the julian day (day of year) from day, month and year.

Parameters:
• dd: (array of) day of month
• mm: (array of) month
• yyyy: (array of) year
Returns:
• jd: (array of) julian day of year

Examples

>>> date2doy(04,11,2006)
308
>>> date2doy(04,11,2008)
309
>>> day=[10,10,10]
>>> month=[1,2,3]
>>> year=[2007,2008,2009]
>>> date2doy(day,month,year)
array([ 10.,  41.,  69.])
>>>

meteo_util.event2time(yyyy=array([], dtype=float64), doytime=array([], dtype=float64), X=array([], dtype=float64), method=<type 'str'>, interval=None)[source]

Function to convert (event-based) time series data to equidistant time spaced data at a specified interval.

The maximum interval for processing is 86400 s, resulting in daily values. You can choose to sum (e.g. for event-based rainfall measurements) or average the input data over a given time interval. If you choose to average, a -9999 value (missing value) will be output if there are no data in the specified interval. For summation, a zero will be output, as required for event-based rainfall measurements.

Parameters:
• yyyy: Array of year values (e.g. 2008)
• doytime: Array of day of year (doy, 1-366) with decimal time values (0-1) (e.g. 133.4375).
• X: Array of data values (e.g. 0.2). for event-based precipitation data, data should be zero at start and end times of the event data record.
• method: Enter sum to sum data (e.g. precipitation), and avg to average data over the selected time interval.
• interval: Optional: interval in seconds (integer value, e.g. 900 for a 15-minute interval). A default value of 1800 s is assumed when interval is not specified as a function argument.
Returns:
• YEAR: Array of year.
• DOY_TIME: Array of day of year (1-366) + decimal time values (0-1), e.g. 153.5 for noon on day 153.
• Y: Array of corresponding summed or averaged data values, where -9999 indicates missing values when avg is selected and 0 when sum is selected.

Examples

>>> year=[2008,2008,2008]
>>> daytime=[153.5,153.9,154.1]
>>> vals=[0,0.4,2.]
>>> event2time(year,daytime,vals,'sum',3600)
(array([2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008,
2008, 2008]), array([ 153.58333333,  153.625     ,  153.66666667,  153.70833333,
153.75      ,  153.79166667,  153.83333333,  153.875     ,
153.91666667,  153.95833333,  154.        ,  154.04166667,
154.08333333]), array([ 0.4,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  2. ,  0. ,  0. ,
0. ,  0. ]))
>>> yr,day_time,sum_P = event2time(year,daytime,vals,'sum',3600)
>>> yr
array([2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008,
2008, 2008])
>>> sum_P
array([ 0.4,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  0. ,  2. ,  0. ,  0. ,
0. ,  0. ])