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A good understanding of SUEWS is a prerequisite to the proper use of SuPy.
supy.util.calib_gΒΆ
- supy.util.calib_g(df_fc_suews, ser_ra, g_max, lai_max, wp_smd, method='cobyla', prms_init=None, debug=False)[source]ΒΆ
Calibrate parameters for modelling surface conductance over vegetated surfaces using
LMFIT
.- Parameters
df_fc_suews (pandas.DataFrame) β DataFrame in SuPy forcing format
ser_ra (pandas.Series) β Series with RA, aerodynamic resistance, [s m-1]
g_max (numeric) β Maximum surface conductance [mm s-1]
lai_max (numeric) β Maximum LAI [m2 m-2]
wp_smd (numeric) β Wilting point indicated as soil moisture deficit [mm]
method (str, optional) β Method used in minimisation by
lmfit.minimize
: details refer to itsmethod
.prms_init (lmfit.Parameters, optional) β Initial parameters for calibration
debug (bool, optional) β Option to output final calibrated
ModelResult
, by default False
- Returns
dict, or `ModelResult <lmfit β
dict: {parameter_name -> best_fit_value}
ModelResult
- Note:
Parameters for surface conductance: g_lai (LAI related), g2 (solar radiation related), g_dq_base (humidity related), g_dq_shape (humidity related), g_ta (air temperature related), g_smd (soil moisture related)
- Return type
ModelResult>` if
debug==True
Note
For calibration validity, turbulent fluxes, QH and QE, in
df_fc_suews
should ONLY be observations, i.e., interpolated values should be avoided. To do so, please placenp.nan
as missing values for QH and QE.