wind_profile package¶
Created on Tue Sep 13 15:50:52 2016
@author: Hector Nieto (hnieto@ias.csic.es)
DESCRIPTION¶
This package contains the main routines for estimating the wind profile above and within a canopy. It requires the following package.
- MO_similarity package for the estimation of adiabatic correctors.
Wind profile functions¶
calc_u_C()
[Norman1995] canopy wind speed.calc_u_C_star()
MOST canopy wind speed.calc_u_Goudriaan()
[Goudriaan1977] wind speed profile below the canopy.calc_A_Goudriaan()
[Goudriaan1977] wind attenuation coefficient below the canopy.
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pyTSEB.wind_profile.
calc_u_C
(u_friction, h_C, d_0, z_0M)[source]¶ [Norman1995] wind speed at the canopy, reformulated to use u_friction
Parameters: Returns: u_C – wind speed at the canop interface (m s-1).
Return type: References
[Norman1995] J.M. Norman, W.P. Kustas, K.S. Humes, Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature, Agricultural and Forest Meteorology, Volume 77, Issues 3-4, Pages 263-293, http://dx.doi.org/10.1016/0168-1923(95)02265-Y.
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pyTSEB.wind_profile.
calc_u_C_star
(u_friction, h_C, d_0, z_0M, L=inf)[source]¶ MOST wind speed at the canopy
Parameters: Returns: u_C – wind speed at the canop interface (m s-1).
Return type:
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pyTSEB.wind_profile.
calc_u_Goudriaan
(u_C, h_C, LAI, leaf_width, z)[source]¶ Estimates the wind speed at a given height below the canopy.
Parameters: Returns: u_z – wind speed at height z (m s-1).
Return type: References
[Norman1995] J.M. Norman, W.P. Kustas, K.S. Humes, Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature, Agricultural and Forest Meteorology, Volume 77, Issues 3-4, Pages 263-293, http://dx.doi.org/10.1016/0168-1923(95)02265-Y. [Goudriaan1977] Goudriaan (1977) Crop micrometeorology: a simulation study
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pyTSEB.wind_profile.
calc_A_Goudriaan
(h_C, LAI, leaf_width)[source]¶ Estimates the extinction coefficient factor for wind speed
Parameters: Returns: a – exctinction coefficient for wind speed through the canopy
Return type: References
[Goudriaan1977] Goudriaan (1977) Crop micrometeorology: a simulation study
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pyTSEB.wind_profile.
calc_u_Massman
(u_c, h_c, lai, z, canopy_distribution, xi_soil=0.0001, c_d=0.2)[source]¶ ‘ Canopy wind speed. From Eq. 11 of [Massman2017] and implemented in TSEB by [Nieto2019]. :param u_c: Wind speed at the top of the canopy. :type u_c: float :param h_c: canopy height :type h_c: float :param lai: Leaf Area Index :type lai: float :param z: height above the ground :type z: float :param canopy_distribution: relative cummulative canopy distribution function :type canopy_distribution: array_like :param xi_soil: ground surface roughness length. Default = 0.00101m. :type xi_soil: float :param c_d: Equivalent drag coefficient of the individual foliage elements. Default = 0.2. :type c_d: float
Returns: u_z – Canopy wind speed. Return type: float References
[Nieto2019] Nieto, Héctor, et al. “Impact of different within-canopy wind attenuation formulations on modelling sensible heat flux using TSEB.” Irrigation Science 37.3 (2019): 315-331. https://doi.org/10.1007/s00271-018-0611-y [Massman2017] W. J. Massman, J. M. Forthofer, M. A. Finney. An Improved Canopy Wind Model for Predicting Wind Adjustment Factors and Wildland Fire Behavior, Canadian Journal of Forest Research, Pages 594-603, http://dx.doi.org/10.1139/cjfr-2016-0354
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pyTSEB.wind_profile.
calc_U_b
(z, h_c, xi_soil=0.0025)[source]¶ ‘ Logarithmic wind profile. Dominant near the ground From Eq. 6 of [Massman2017]. :param z: height above the ground :type z: float :param h_c: canopy height :type h_c: float :param xi_soil: ground surface roughness length. Default = 0.00101m. :type xi_soil: float
Returns: U_b – Non dimensional logarithmic wind profile. Return type: float References
[Massman2017] W. J. Massman, J. M. Forthofer, M. A. Finney. An Improved Canopy Wind Model for Predicting Wind Adjustment Factors and Wildland Fire Behavior, Canadian Journal of Forest Research, Pages 594-603, http://dx.doi.org/10.1139/cjfr-2016-0354
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pyTSEB.wind_profile.
calc_U_t
(z, lai, h_c, canopy_distribution, xi_soil=0.0025, c_d_equiv=0.2)[source]¶ ‘ hyperbolic cosine wind profile. Dominant near the top of the canopy From Eq. 7 of [Massman2017]. :param z: height above the ground :type z: float :param lai: Leaf Area Index :type lai: float :param h_c: canopy height :type h_c: float :param canopy_distribution: relative cummulative canopy distribution function :type canopy_distribution: array_like :param xi_soil: ground surface roughness length. Default = 0.00101m. :type xi_soil: float :param c_d_equiv: Equivalent drag coefficient of the individual foliage elements. Default = 0.2. :type c_d_equiv: float
Returns: u_t – Non dimensional hyperbolic cosine wind profile. Return type: float References
[Massman2017] W. J. Massman, J. M. Forthofer, M. A. Finney. An Improved Canopy Wind Model for Predicting Wind Adjustment Factors and Wildland Fire Behavior, Canadian Journal of Forest Research, Pages 594-603, http://dx.doi.org/10.1139/cjfr-2016-0354
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pyTSEB.wind_profile.
calc_u_star_ratio
(zeta_h, xi_0_soil)[source]¶ Ratio of friction velocity and wind speed at the canopy height. From Eq. 10 of [Massman2017].
Parameters: Returns: u_star_ratio – Ratio of friction velocity and wind speed at the canopy height.
Return type: float or array
References
[Massman2017] W. J. Massman, J. M. Forthofer, M. A. Finney. An Improved Canopy Wind Model for Predicting Wind Adjustment Factors and Wildland Fire Behavior, Canadian Journal of Forest Research, Pages 594-603, http://dx.doi.org/10.1139/cjfr-2016-0354
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pyTSEB.wind_profile.
cummulative_drag_area
(lai, foliage_distribution, upper_limit, c_d_equiv=0.2)[source]¶ Cummulative drag area below a normzalized height, from Eq. 4 or 5 in [Massman2017].
Parameters: - lai (array_like) – Leaf Area Index
- foliage_distribution (array_like) – cummulative canopy distribution function
- upper_limit (array_like) – Upper heigh normalized value below which the drag area drag_area_distribution will be computed. Default=1, i.e. top of the canopy.
- c_d_equiv (float) – drag coefficient Cd described in Eq. 3 of [MassmanXX]. Default 0.2, 0, 0
Returns: zeta_xi – Cummulative drag area. By default returns the drag area index, see Eq. 4 of [MassmanXX].
Return type: References
[Massman2017] W. J. Massman, J. M. Forthofer, M. A. Finney. An Improved Canopy Wind Model for Predicting Wind Adjustment Factors and Wildland Fire Behavior, Canadian Journal of Forest Research, Pages 594-603, http://dx.doi.org/10.1139/cjfr-2016-0354
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pyTSEB.wind_profile.
drag_area_index
(lai, c_d_equiv=0.2)[source]¶ Cummulative drag area below a normzalized height, from Eq. 4 or 5 in [Massman2017].
Parameters: Returns: Zeta_h – Cummulative drag area. By default returns the drag area index, see Eq. 4 of [MassmanXX].
Return type: float or array
References
[Massman2017] W. J. Massman, J. M. Forthofer, M. A. Finney. An Improved Canopy Wind Model for Predicting Wind Adjustment Factors and Wildland Fire Behavior, Canadian Journal of Forest Research, Pages 594-603, http://dx.doi.org/10.1139/cjfr-2016-0354
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pyTSEB.wind_profile.
calc_cummulative_canopy_distribution
(f_a)[source]¶ Calculates the non-dimensional cummulative canopy distribution. From Eq. 1 in [Massman2017].
Parameters: f_a (float) – Non-dimensional canopy distribution at a normalized height. Returns: f_a – cummulative canopy density. Return type: float References
[Massman2017] W. J. Massman, J. M. Forthofer, M. A. Finney. An Improved Canopy Wind Model for Predicting Wind Adjustment Factors and Wildland Fire Behavior, Canadian Journal of Forest Research, Pages 594-603, http://dx.doi.org/10.1139/cjfr-2016-0354
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pyTSEB.wind_profile.
calc_canopy_distribution
(Xi_max, sigma_u, sigma_l)[source]¶ Calculates the non-dimensional canopy distribution at a normalized height. From Eq. 1 in [Massman2017].
Parameters: Returns: f_a – non-dimensional foliage density at a normalized height Xi.
Return type: References
[Massman2017] W. J. Massman, J. M. Forthofer, M. A. Finney. An Improved Canopy Wind Model for Predicting Wind Adjustment Factors and Wildland Fire Behavior, Canadian Journal of Forest Research, Pages 594-603, http://dx.doi.org/10.1139/cjfr-2016-0354
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pyTSEB.wind_profile.
assimetrical_gaussian_distribution
(Xi_max, sigma_u, sigma_l, upper_Xi=1)[source]¶ Double assimetrical Gaussian distribution function. From Eq. 2 of [Massman2017].
Parameters: Returns: f_a – Distribution function at equidisitant bins between 0 and upper_Xi.
Return type: array
References
[Massman2017] W. J. Massman, J. M. Forthofer, M. A. Finney. An Improved Canopy Wind Model for Predicting Wind Adjustment Factors and Wildland Fire Behavior, Canadian Journal of Forest Research, Pages 594-603, http://dx.doi.org/10.1139/cjfr-2016-0354
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pyTSEB.wind_profile.
canopy_shape
(h_c, h_b, h_max=0.5)[source]¶ Asymmetrical Gaussian foliage distribution.
Parameters: Returns: - Xi_max (float) – Value of the peak distribution
- sigma_u (float) – upper standard deviation.
- sigma_l (float) – lower standard deviation.
References
[Massman2017] W. J. Massman, J. M. Forthofer, M. A. Finney. An Improved Canopy Wind Model for Predicting Wind Adjustment Factors and Wildland Fire Behavior, Canadian Journal of Forest Research, Pages 594-603, http://dx.doi.org/10.1139/cjfr-2016-0354