The Chinese Meteorological Society
Article Information
 YAN, Bing, Sixun HUANG, Jing FENG, et al., 2019.
 The Distribution and Uncertainty Quantification of Wind Profile in the Stochastic General Ekman Momentum Approximation Model. 2019.
 J. Meteor. Res., 33(2): 336348
 http://dx.doi.org/10.1007/s1335101980763
Article History
 Received June 12, 2018
 in final form December 26, 2018
2. Center for Computational Science and Finance, Shanghai University of Finance and Economics, Shanghai 200433
Boundarylayer issues have received considerable and critical attention in the fields of meteorology and oceanography (Mahrt, 1998; Tan, 2001; Baklanov et al., 2011). The boundary layer is a complex system that consists of complicated dynamic and thermodynamic processes (Tan and Wu, 1994). Ekman (1905) derived the Ekman model, although the Ekman model is a simple boundarylayer model, it can capture the main physical characteristics of the atmosphere and ocean. The Ekman model has been widely applied to theoretical studies of the atmosphere and ocean dynamics (Blumen and Wu, 1983; Twigg and Bannon, 1998; Beare and Cullen, 2010). The deficiency of this approach is that it fails to consider nonlinear characteristics, whereas the nonlinear features in the actual atmospheric boundary layer are not negligible. Intermediate models have been developed over time. These models are simpler than complete boundarylayer model but have less dynamic constrains than the Ekman model (Tan and Wang, 2002). Wu and Blumen (1982) revised the Ekman model with the geostrophic momentum approximation, in which the inertial term is taken into consideration. The modified boundarylayer model with the Ekman momentum approximation was proposed by Tan and Wu (1994). Cullen (1989) developed a semigeostrophic boundarylayer model that is more accurate than the Ekman model for the twodimensional boundarylayer problem. Tan and Wang (2002) extended the Ekman momentum approximation to the general Ekman momentum approximation (GEM), in which the turbulent friction term is complete. Recently, many new achievements have been made by scholars in the field. Beare and Cullen (2010) incorporated wellmixed boundary layers into a semigeostrophic model, which broadened the applications of the model. Marlatt et al. (2012) evaluated two closure models with a direct numerical simulation at a Reynolds number of 1000. Of these two models, the O’Brien model that requires data both at the surface layer and at the top of the boundary layer proved superior.
For the case of eddy viscosity, Berger and Grisogono (1998) provided an approximate solution for the baroclinic Ekman boundary layer with variable eddy viscosity using the WKB (Wentzel–Kramers–Brillouin) method. Tan and Wang (2002) analysed the wind structure of the baroclinic and variable eddy diffusivity semigeostrophic Ekman boundary layer. The approximate analytical solution for the weakly nonlinear Prandtl model for simple slope flows with variable eddy diffusivities was derived by Grisogono et al. (2015). The retrieval of the eddy thermal conductivity in the weakly nonlinear Prandtl model for katabatic flows was recently discussed by Yan et al. (2017).
Because errors are unavoidable, including the model error and the observation error, we consider the eddy viscosity as an uncertain quantity, which satisfies some distribution and subsequently obtains the distribution of wind profiles by uncertainty quantification. Berner et al. (2017) reviewed the development of stochastic parameterized scheme and proposed that the introduction of the stochastic parameterization schemes better shows the uncertainty of the model, which can improve the quantification of forecast uncertainty. The methods of uncertainty quantification have been extensively developed and are still being widely researched. Such methods include the Monte Carlo method, perturbation methods, moment equations, and generalized polynomial chaos (gPC; Xiu, 2010).
Monte Carlo sampling is one of the most widely used uncertainty methods. The realizations of the random variables are sampled based on the given probability distribution function. For each sample, the data are fixed, and the model is deterministic. Statistical information, e.g., the mean and variance, can be extracted from the ensemble of the deterministic model solutions. The advantages of the Monte Carlo method are that it is simple and straightforward, and there is no need to modify the deterministic solvers. However, a large number of samples and computationally intensive deterministic codes are required to obtain accurate statistical information. Recently, Monte Carlo techniques have been developed to accelerate convergence, and such techniques include Latin hypercube sampling (Loh, 1996; Helton and Davis, 2003), quasiMonte Carlo sampling (Fox, 1999; Xiu, 2009), and so on. However, their applicability is limited because of the inherent limitations of these methods. Perturbation methods are prevalent nonsampling methods (D’Onofrio et al., 2017). In perturbation methods, the random variables are expanded via a Taylor series, and the series is truncated at a certain order, which is usually the second order, because the system becomes extremely complicated beyond the second order. This method has been applied in various engineering fields. A natural restriction of this method is that the magnitude of the uncertainties cannot be overly large (typically less than 10%). In the moment equation approach, the moments of the random output are directly computed (Singer, 2006; Barzel and Biham, 2011). The closure problem of the equations is the largest limitation, and this issue often requires information about higher moments. A recently developed method, the generalized polynomial chaos expansion, has become one of the most commonly used methods (Li and Xiu, 2009; Zeng and Zhang, 2010; Li et al., 2014; Wang et al., 2018; Yan et al., 2018). The random output can be expanded as orthogonal polynomials of the random input parameters using gPC. Essentially, this process is a spectral expansion in random space. When the solution smoothly depends on the random input parameters, the convergence rate of this method will be fast.
To solve for the undetermined gPC coefficients of the solutions, two types of methods are commonly adopted: intrusive (e.g., stochastic Galerkin method) (Marzouk and Najm, 2009; Najm, 2009) and nonintrusive methods (e.g., regression method and stochastic collocation method) (Li et al., 2009; Lin et al., 2010). The advantage of the intrusive approach is that it can directly solve the coefficients. The main limitation of the intrusive approach, however, is the modification of the existing deterministic code. Generally, the code used to solve the deterministic models is complex, which makes the implementation of the intrusive method difficult. Unlike the intrusive method, the deterministic code does not need to be revised during the implementation of the nonintrusive method. In this study, the nonintrusive method is used to ascertain the undetermined gPC coefficients of the solutions.
In the present study, we analyze the randomness of eddy viscosity and quantify the uncertainty of wind profiles by the GEM. Generalized polynomial chaos is selected for the uncertainty quantification of the stochastic boundarylayer model, and the regression method is used to determine the gPC coefficients. The overall structure of the study is covered in the following five sections: Section 1 is an introductory chapter; Section 2 presents the GEM; Section 3 focuses on generalized polynomial chaos and the implementation of the regression method; Section 4 presents the results of the numerical experiment; and Section 5 provides a summary and discussion.
2 The GEMThe classic Ekman model has been widely used in theoretical research of the atmosphere and ocean dynamics (Blumen and Wu, 1983; Twigg and Bannon, 1998; Beare and Cullen, 2010). Tan and Wang (2002) improved the classic Ekman model by including the effect of the inertia terms and variable eddy viscosity. This modification maintains the computational simplicity and the inherent nonlinearity of the system. The GEM is given as follows:
$\left\{ \begin{array}{l} \dfrac{\partial }{{\partial z}}(K\dfrac{{\partial u}}{{\partial z}}) + {a_1}u + {b_1}v = {c_1}, \\ \dfrac{\partial }{{\partial z}}(K\dfrac{{\partial v}}{{\partial z}}) + {a_2}u + {b_2}v = {c_2}, \\ \end{array} \right.$  (1) 
where
$\left\{ \begin{array}{l} {a_1} =  \lambda \dfrac{{\partial {u_{\rm{e}}}}}{{\partial x}}, {b_1} = f  \lambda \dfrac{{\partial {u_{\rm{e}}}}}{{\partial y}}, {c_1} = f{v_{\rm{g}}} + \lambda \dfrac{{\partial {u_{\rm{e}}}}}{{\partial t}}, \\ {a_2} =  (f + \lambda \dfrac{{\partial {v_{\rm{e}}}}}{{\partial x}}), {b_2} =  \lambda \dfrac{{\partial {v_{\rm{e}}}}}{{\partial y}}, {c_2} =  f{u_{\rm{g}}} + \lambda \dfrac{{\partial {v_{\rm{e}}}}}{{\partial t}}. \\ \end{array} \right.$  (2) 
In Eq. (2),
This study focuses on the randomness of the stochastic eddy viscosity, and we change the form of
The original concept of polynomial chaos in uncertainty quantification was proposed by Wiener (1938). Inspired by this idea, Ghanem and Spanos (1991) expanded polynomial chaos to generalized polynomial chaos and applied it to stochastic linear equations for the first time. This method has become one of the most widely used techniques in uncertainty analysis. A brief description of generalized polynomial chaos is in the Appendix.
We can expand
$\begin{array}{l} u( \xi) = \displaystyle\sum\limits_{i = 0}^P {{u_i}{\varPsi _i}( \xi)}, \\ v( \xi) = \displaystyle\sum\limits_{i = 0}^P {{v_i}{\varPsi _i}( \xi)}, \\ \end{array} $ 
where
The coefficients of the output variables can be determined by various methods. Numerical methods include intrusive methods, such as the stochastic Galerkin method (Marzouk and Najm, 2009; Najm, 2009), and nonintrusive methods. We substitute the input parameters and output variables with the form of gPC expansion. Then, using the feature of the inner product of the orthogonal polynomials, we apply a Galerkin projection to each basis polynomial. Because the basis polynomials are fixed according to the probability distribution of the input parameters, the key problem is the determination of coefficients. After the coefficients are determined, the approximate statistics of the solution can be computed. One major drawback of the stochastic Galerkin method is that the implementation is cumbersome. Additionally, the formula needs to be derived; hence, the associated codes must be rewritten for the larger, coupled system. When the origin model is complex and highly nonlinear, the explicit derivation of the stochastic Galerkin approach can be nontrivial and sometimes impossible. Thus, another numerical scheme is adopted in this study, which is the nonintrusive method (Li et al., 2009; Lin et al., 2010). A common nonintrusive method is the regression method. In this approach, the algorithms are straightforward and easy to implement. In addition, the model is not affected by the complexity or nonlinearity of the original model as long as the deterministic code is reliable. The regression procedures for the stochastic GEM are given as follows.
The stochastic GEM is given in Eq. (3):
$\left\{ \begin{array}{l} \dfrac{\partial }{{\partial z}}(K(z, \xi)\dfrac{{\partial u}}{{\partial z}}) + {a_1}u + {b_1}v = {c_1}, \\ \dfrac{\partial }{{\partial z}}(K(z, \xi)\dfrac{{\partial v}}{{\partial z}}) + {a_2}u + {b_2}v = {c_2}, \\ \end{array} \right.$  (3) 
where
Step 1: generate the realizations
Step 2: calculate each realization of input parameter
Step 3: establish the overdetermined equations based on the results of Step 2.
$\left({\begin{array}{*{20}{c}} {{\varPsi _0}({{ \xi }^1})}& \ldots &{{\varPsi _P}({{ \xi }^1})} \\ \vdots & \ddots & \vdots \\ {{\varPsi _0}({{ \xi }^N})}& \cdots &{{\varPsi _P}({{ \xi }^N})} \end{array}} \right)\left(\begin{array}{l} {u_0} \\ \vdots \\ {u_P} \\ \end{array} \right) = \left(\begin{array}{l} u({{ \xi }^1}) \\ \vdots \\ u({{ \xi }^N}) \\ \end{array} \right).$ 
The number of collocation points/realizations should be at least twice of the number terms retained in the expansion [i.e.,
Step 4: obtain the gPC expansion coefficients by solving the overdetermined equations in Step 3. The least squares method is used to solve the equations.
It is worth noting that its corresponding coefficients
A flow chart of the calculation procedure is provided in Fig. 1.
4 Numerical experimentTo examine the reliability and computational efficiency and make the procedure convenient to implement, a typical surface geostrophic shear flow and three types of random eddy viscosities are chosen for analysis. Each type is assessed based on the Monte Carlo method and the deterministic model.
The geostrophic shear flow is given as follows (Tan and Wang, 2002):
${u_{\rm{g}}} = {u_0}  \alpha y, \, \, {v_{\rm{g}}} = 0,$ 
where
Three different types of random eddy viscosities are considered. In Type I, eddy viscosity is assumed to be a function that varies with height, and some parameters in the function are uncertain. In Type II, eddy viscosity is a vertically varying random parameter, and the mean is constant and height independent. In Type III, eddy viscosity is a vertically varying random parameter, and the mean is a function that varies with height. The Gaussian distribution is selected in this paper. The corresponding optimal orthogonal polynomials are Hermite polynomials. The order of truncated gPC expansion is
We assume
The orthogonal Hermite polynomials of the twodimensional Gaussian random variables truncated at the third order are given as follows (Sun and Sun, 2015) (more details about derivation process is in the Appendix):
$\begin{align} & {\varPsi _{\rm{0}}}{\rm{ = 1;}}\\ & {\varPsi _{\rm{1}}}{\rm{ = }}{\xi _{\rm{1}}},{\varPsi _{\rm{2}}}{\rm{ = }}{\xi _{\rm{2}}};\\ & {\varPsi _{\rm{3}}}{\rm{ = }}\xi _{\rm{1}}^{\rm{2}}{\rm{  1}},{\varPsi _{\rm{4}}}{\rm{ = }}{\xi _{\rm{1}}}{\xi _{\rm{2}}},{\varPsi _{\rm{5}}}{\rm{ = }}\xi _{\rm{2}}^{\rm{2}}{\rm{  1}},{\varPsi _6} \!= \xi _{\rm{1}}^3{\rm{  3}}{\xi _1;} \\ & {\varPsi _7} \!= {\xi _2}({\xi _1}^2  1),{\varPsi _8} \!=\! {\xi _1}({\xi _2}^2  1), \!{\varPsi _9} \!=\! \xi _2^3{\rm{  3}}{\xi _2}, \end{align}$ 
where
$\begin{array}{l} \delta = 0.2{\varPsi _{\rm{0}}} + 0.05{\varPsi _{\rm{1}}}, \\ {z_{\rm{m}}} = 500{\varPsi _{\rm{0}}} + 50{\varPsi _{\rm{2}}}. \\ \end{array} $ 
We conduct the experiment according to the procedure shown in Fig. 1. The result is shown in Fig. 3.
The average of wind speed distributions calculated by gPC and Monte Carlo methods are consistent with the rule of Ekman spiral. The average Ekman spirals of two methods are visually indistinguishable.
The central processing unit (CPU) running time (s) and rootmeansquare error (RMSE; m s^{–1}) are given in Table 1.
Method  Cyclonic  Anticyclonic  
CPU time  RMSE  CPU time  RMSE  
gPC  0.12  0.069  0.12  0.079  
Monte Carlo  5.85  0.078  5.90  0.085 
Table 1 displays the RMSE of the two methods, and the two circulation types are approximately equal. The order of the RMSE is 10^{–2}, and the errors of cyclonic type are slightly smaller than the anticyclonic ones. The CPU time for the gPC is much less than that required by the Monte Carlo method (approximately 1/50 the time of the latter). These results validate the reliability and speed of the gPC method in solving the GEM with stochastic eddy viscosity.
Figures 4 and 5 illustrate that the standard deviation of the wind speed is less than 1. There are some differences between the two methods, but these differences are small. This result indicates that gPC is an efficient method for solving the GEM with a random eddy viscosity function. The maximum value of standard deviation of
Figures 6 and 7 illustrate that large uncertainty of wind speed appear in the middle of the boundary layer. The uncertainty of zonal wind is less than that of meridional wind; the uncertainty of wind speed in cyclonic shear flow is less than that in anticyclonic shear flow.
4.2 Experiment ⅡWe set the eddy viscosity to a vertically varying random parameter with a constant mean of
$\bar K(z) = 8, \quad {C_K}({z_1}, {z_2}) = {{\rm e}^{  {{\left {{z_1}  {z_2}} \right} / c}}}, \quad {z_1}, {z_2} \in [  a, a], $  (4) 
where
We represent
$K(z, \omega) = \bar K(z) + \sum\limits_{j = 1}^N {\sqrt {{\lambda _j}} {\phi _j}(z){\xi _j}}. $ 
The eigenpairs
$\int\limits_{  a}^a {{{\rm e}^{  {{\left {{z_1}  {z_2}} \right} / c}}}{\phi _j}({z_2}){\rm d}{z_2}} = {\lambda _j}{\phi _j}({z_1}).$  (5) 
The detailed procedure for solving Eq. (11) can be seen in the associated literatures (Le Maître and Knio, 2010; Xiu, 2010). Through this process, we can decrease the high dimensionality of the random variables.
In this study, we use threedimensional random variables to represent
In Fig. 9, the average value of wind speed distribution is conform to Ekman spiral, and the results produced by the gPC and Monte Carlo methods are very consistent.
The CPU running time and RMSE can be seen in Table 2. As presented in Table 2, the error order of the two methods is 10^{–1}, which is slightly larger than that from experiment Ⅰ. However, the complexity of experiment Ⅱ is greater than that of experiment Ⅰ because of the higher dimension of the random variables and the truncation error produced by the truncated KL expansion. The CPU running time for experiment Ⅱ slightly increases compared to that of experiment I, and the computational efficiency of the gPC method is better than that of the Monte Carlo method.
Method  Cyclonic  Anticyclonic  
CPU time  RMSE  CPU time  RMSE  
gPC  0.18  0.117  0.19  0.143  
Monte Carlo  6.06  0.126  5.94  0.148 
Similar to experiment Ⅰ, Figs. 10 and 11 illustrate that the standard deviations are less than one. The standard deviation in experiment Ⅱ is less smooth than that in experiment I, which is largely because of the complexity of experiment Ⅱ noted in the analysis of Table 2. Generally, the standard deviations solved by the two methods are very similar, and this result validates the effectiveness of the gPC method. The large values of standard deviations mainly lie at the bottom of the boundary layer. For zonal wind, the standard deviation varies sharply in the lower layer, and for meridional wind, the standard deviation varies sharply in the middle layer of the boundary layer. The standard deviation of cyclones is greater than that of anticyclones. The greater the standard deviation, the higher of error in the deterministic mode.
As can be seen from Figs. 12 and 13, the uncertainty of wind speed distribution is small and changes little with height.
4.3 Experiment ⅢConsider
$\bar \alpha (z) = 0, \quad {C_\alpha }({z_1}, {z_2}) = {{\rm e}^{  {{\left {{z_1}  {z_2}} \right} / c}}}, \quad {z_1}, {z_2} \in [  a, a], $  (6) 
The other parameters in Eq. (12) are the same as those in Eq. (9).
We represent
The level of random eddy viscosity deviation from the average can be seen in Fig. 14. The results are shown below. In Fig. 15, the average value of wind speed distribution is coincidence with Ekman spiral, and the results of the gPC method are very similar to those of the Monte Carlo method.
The CPU running time and RMSE are given in Table 3. The order of the RMSE is 10^{–2}. The CPU running time of the gPC method is far less than that of the Monte Carlo method. This finding indicates that the gPC can be used to effectively solve for the mean value of the stochastic GEM.
Method  Cyclonic  Anticyclonic  
CPU time  RMSE  CPU time  RMSE  
gPC  0.19  0.051  0.18  0.070  
Monte Carlo  6.26  0.054  6.22  0.069 
Figures 16 and 17 illustrate that the differences in the standard deviations between the gPC and Monte Carlo methods are small. The gPC can effectively provide the statistical information required by the GEM with stochastic eddy viscosity. The large values of standard deviations of
It can be seen from Figs. 18 and 19 that the uncertainties of distributions are small. The large uncertainty of
In this paper, the eddy viscosity is replaced with a random eddy viscosity in the GEM. We quantify the uncertainty of wind in the Ekman layer through the GEM. The approaches adopted for this study are the gPC and regression method. The regression method is used to determine the expansion coefficients in the gPC expansion. Three different types of random eddy viscosities are used in the numerical tests. The results of these experiments show that the gPC combined with the regression method is a fast and accurate technique for solving the GEM with random parameter.
In this study, the random variables are assumed to be Gaussian random variables, and the range of Gaussian random variable is
Let
$\begin{array}{l} \zeta (\omega) = {\zeta _0}{\varPsi _0} + \displaystyle\sum\limits_{k = 1}^\infty {{\zeta _k}{\varPsi _1}({\xi _k})} \\ + \;\displaystyle\sum\limits_{k = 1}^\infty {\displaystyle\sum\limits_{j = 1}^k {{\zeta _{kj}}{\varPsi _2}({\xi _k}, {\xi _j})} } + \;\displaystyle\sum\limits_{k = 1}^\infty {\displaystyle\sum\limits_{j = 1}^k {\displaystyle\sum\limits_{l = 1}^j {{\zeta _{kjl}}{\varPsi _3}({\xi _k}, {\xi _j}, {\xi _l})} } } + \cdots \\ \end{array} , \tag{A1}$  (A1) 
where
$\zeta (\omega) = \sum\limits_{k = 0}^P {{\zeta _k}{\varPsi _k}( \xi)} , \tag{A2}$  (A2) 
where
$P + 1 = \dfrac{{(n + p)!}}{{n!p!}}, \tag{A3}$  (A3) 
where
Distribution (

Polynomial (

Support  
Continuous  Gamma  Laguerre 

Beta  Jacobi 


Uniform  Legendre 


Gaussian  Hermite 


Discrete  Negative binomial  Meixner 

Binomial  Krawtchouk 


Poisson  Charlier 


Hypergeometric  Hahn 

The following is an example of Hermite polynomial chaos corresponding to normal distribution. It is well known that orthogonal polynomials satisfy a threeterm recurrence relation
${\varPsi _{n + 1}}(\xi) = \xi \, {\varPsi _n}(\xi)  n{\varPsi _{n  1}}(\xi), \quad n > 0, \tag{A4}$  (A4) 
where
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