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supy.util.calib_g¶
-
supy.util.
calib_g
(df_fc_suews, ser_ra, g_max, lai_max, wp_smd, method='cobyla', prms_init=None, debug=False)[source]¶ Calibrate parameters for modelling surface conductance over vegetated surfaces using
LMFIT
.Parameters: - df_fc_suews (pandas.DataFrame) – DataFrame in SuPy forcing format
- ser_ra (pandas.Series) – Series with RA, aerodynamic resistance, [s m-1]
- g_max (numeric) – Maximum surface conductance [mm s-1]
- lai_max (numeric) – Maximum LAI [m2 m-2]
- wp_smd (numeric) – Wilting point indicated as soil moisture deficit [mm]
- method (str, optional) – Method used in minimisation by
lmfit.minimize
: details refer to itsmethod
. - prms_init (lmfit.Parameters, optional) – Initial parameters for calibration
- debug (bool, optional) – Option to output final calibrated
ModelResult
, by default False
Returns: dict, or `ModelResult <lmfit –
- dict: {parameter_name -> best_fit_value}
ModelResult
- Note:
Parameters for surface conductance: g_lai (LAI related), g2 (solar radiation related), g_q1 (humidity related), g_q2 (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.