1 | \documentclass[../tex_main/NEMO_manual]{subfiles} |
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2 | \begin{document} |
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3 | % ================================================================ |
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4 | % Chapter stochastic parametrization of EOS (STO) |
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5 | % ================================================================ |
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6 | \chapter{Stochastic Parametrization of EOS (STO)} |
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7 | \label{chap:STO} |
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8 | |
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9 | Authors: P.-A. Bouttier |
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10 | |
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11 | \minitoc |
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12 | |
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13 | \newpage |
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14 | |
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15 | |
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16 | The stochastic parametrization module aims to explicitly simulate uncertainties in the model. |
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17 | More particularly, \cite{Brankart_OM2013} has shown that, |
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18 | because of the nonlinearity of the seawater equation of state, unresolved scales represent |
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19 | a major source of uncertainties in the computation of the large scale horizontal density gradient |
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20 | (from T/S large scale fields), and that the impact of these uncertainties can be simulated |
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21 | by random processes representing unresolved T/S fluctuations. |
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22 | |
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23 | The stochastic formulation of the equation of state can be written as: |
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24 | \begin{equation} |
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25 | \label{eq:eos_sto} |
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26 | \rho = \frac{1}{2} \sum_{i=1}^m\{ \rho[T+\Delta T_i,S+\Delta S_i,p_o(z)] + \rho[T-\Delta T_i,S-\Delta S_i,p_o(z)] \} |
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27 | \end{equation} |
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28 | where $p_o(z)$ is the reference pressure depending on the depth and, |
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29 | $\Delta T_i$ and $\Delta S_i$ are a set of T/S perturbations defined as the scalar product |
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30 | of the respective local T/S gradients with random walks $\mathbf{\xi}$: |
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31 | \begin{equation} |
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32 | \label{eq:sto_pert} |
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33 | \Delta T_i = \mathbf{\xi}_i \cdot \nabla T \qquad \hbox{and} \qquad \Delta S_i = \mathbf{\xi}_i \cdot \nabla S |
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34 | \end{equation} |
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35 | $\mathbf{\xi}_i$ are produced by a first-order autoregressive processes (AR-1) with |
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36 | a parametrized decorrelation time scale, and horizontal and vertical standard deviations $\sigma_s$. |
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37 | $\mathbf{\xi}$ are uncorrelated over the horizontal and fully correlated along the vertical. |
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38 | |
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39 | |
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40 | \section{Stochastic processes} |
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41 | \label{sec:STO_the_details} |
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42 | |
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43 | The starting point of our implementation of stochastic parameterizations |
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44 | in NEMO is to observe that many existing parameterizations are based |
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45 | on autoregressive processes, which are used as a basic source of randomness |
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46 | to transform a deterministic model into a probabilistic model. |
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47 | A generic approach is thus to add one single new module in NEMO, |
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48 | generating processes with appropriate statistics |
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49 | to simulate each kind of uncertainty in the model |
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50 | (see \cite{Brankart_al_GMD2015} for more details). |
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51 | |
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52 | In practice, at every model grid point, independent Gaussian autoregressive |
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53 | processes~$\xi^{(i)},\,i=1,\ldots,m$ are first generated |
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54 | using the same basic equation: |
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55 | |
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56 | \begin{equation} |
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57 | \label{eq:autoreg} |
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58 | \xi^{(i)}_{k+1} = a^{(i)} \xi^{(i)}_k + b^{(i)} w^{(i)} + c^{(i)} |
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59 | \end{equation} |
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60 | |
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61 | \noindent |
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62 | where $k$ is the index of the model timestep; and |
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63 | $a^{(i)}$, $b^{(i)}$, $c^{(i)}$ are parameters defining |
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64 | the mean ($\mu^{(i)}$) standard deviation ($\sigma^{(i)}$) |
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65 | and correlation timescale ($\tau^{(i)}$) of each process: |
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66 | |
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67 | \begin{itemize} |
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68 | \item for order~1 processes, $w^{(i)}$ is a Gaussian white noise, |
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69 | with zero mean and standard deviation equal to~1, and the parameters |
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70 | $a^{(i)}$, $b^{(i)}$, $c^{(i)}$ are given by: |
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71 | |
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72 | \begin{equation} |
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73 | \label{eq:ord1} |
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74 | \left\{ |
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75 | \begin{array}{l} |
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76 | a^{(i)} = \varphi \\ |
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77 | b^{(i)} = \sigma^{(i)} \sqrt{ 1 - \varphi^2 } |
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78 | \qquad\qquad\mbox{with}\qquad\qquad |
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79 | \varphi = \exp \left( - 1 / \tau^{(i)} \right) \\ |
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80 | c^{(i)} = \mu^{(i)} \left( 1 - \varphi \right) \\ |
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81 | \end{array} |
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82 | \right. |
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83 | \end{equation} |
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84 | |
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85 | \item for order~$n>1$ processes, $w^{(i)}$ is an order~$n-1$ autoregressive process, |
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86 | with zero mean, standard deviation equal to~$\sigma^{(i)}$; correlation timescale |
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87 | equal to~$\tau^{(i)}$; and the parameters $a^{(i)}$, $b^{(i)}$, $c^{(i)}$ are given by: |
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88 | |
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89 | \begin{equation} |
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90 | \label{eq:ord2} |
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91 | \left\{ |
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92 | \begin{array}{l} |
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93 | a^{(i)} = \varphi \\ |
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94 | b^{(i)} = \frac{n-1}{2(4n-3)} \sqrt{ 1 - \varphi^2 } |
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95 | \qquad\qquad\mbox{with}\qquad\qquad |
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96 | \varphi = \exp \left( - 1 / \tau^{(i)} \right) \\ |
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97 | c^{(i)} = \mu^{(i)} \left( 1 - \varphi \right) \\ |
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98 | \end{array} |
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99 | \right. |
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100 | \end{equation} |
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101 | |
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102 | \end{itemize} |
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103 | |
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104 | \noindent |
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105 | In this way, higher order processes can be easily generated recursively using |
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106 | the same piece of code implementing (\autoref{eq:autoreg}), |
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107 | and using succesively processes from order $0$ to~$n-1$ as~$w^{(i)}$. |
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108 | The parameters in (\autoref{eq:ord2}) are computed so that this recursive application |
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109 | of (\autoref{eq:autoreg}) leads to processes with the required standard deviation |
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110 | and correlation timescale, with the additional condition that |
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111 | the $n-1$ first derivatives of the autocorrelation function |
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112 | are equal to zero at~$t=0$, so that the resulting processes |
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113 | become smoother and smoother as $n$ is increased. |
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114 | |
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115 | Overall, this method provides quite a simple and generic way of generating |
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116 | a wide class of stochastic processes. |
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117 | However, this also means that new model parameters are needed to specify each of |
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118 | these stochastic processes. As in any parameterization of lacking physics, |
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119 | a very important issues then to tune these new parameters using either first principles, |
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120 | model simulations, or real-world observations. |
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121 | |
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122 | \section{Implementation details} |
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123 | \label{sec:STO_thech_details} |
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124 | |
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125 | %---------------------------------------namsbc-------------------------------------------------- |
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126 | \forfile{../namelists/namsto} |
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127 | %-------------------------------------------------------------------------------------------------------------- |
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128 | |
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129 | The computer code implementing stochastic parametrisations can be found in the STO directory. |
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130 | It involves three modules : |
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131 | \begin{description} |
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132 | \item[\mdl{stopar}] : define the Stochastic parameters and their time evolution. |
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133 | \item[\mdl{storng}] : a random number generator based on (and includes) the 64-bit KISS |
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134 | (Keep It Simple Stupid) random number generator distributed by George Marsaglia |
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135 | (see \href{https://groups.google.com/forum/#!searchin/comp.lang.fortran/64-bit$20KISS$20RNGs}{here}) |
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136 | \item[\mdl{stopts}] : stochastic parametrisation associated with the non-linearity of the equation of seawater, |
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137 | implementing \autoref{eq:sto_pert} and specific piece of code in the equation of state implementing \autoref{eq:eos_sto}. |
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138 | \end{description} |
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139 | |
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140 | The \mdl{stopar} module has 3 public routines to be called by the model (in our case, NEMO): |
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141 | |
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142 | The first routine (\rou{sto\_par}) is a direct implementation of (\autoref{eq:autoreg}), |
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143 | applied at each model grid point (in 2D or 3D), |
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144 | and called at each model time step ($k$) to update |
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145 | every autoregressive process ($i=1,\ldots,m$). |
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146 | This routine also includes a filtering operator, applied to $w^{(i)}$, |
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147 | to introduce a spatial correlation between the stochastic processes. |
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148 | |
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149 | The second routine (\rou{sto\_par\_init}) is an initialization routine mainly dedicated |
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150 | to the computation of parameters $a^{(i)}, b^{(i)}, c^{(i)}$ |
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151 | for each autoregressive process, as a function of the statistical properties |
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152 | required by the model user (mean, standard deviation, time correlation, |
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153 | order of the process,\ldots). |
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154 | |
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155 | Parameters for the processes can be specified through the following \ngn{namsto} namelist parameters: |
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156 | \begin{description} |
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157 | \item[\np{nn\_sto\_eos}] : number of independent random walks |
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158 | \item[\np{rn\_eos\_stdxy}] : random walk horz. standard deviation (in grid points) |
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159 | \item[\np{rn\_eos\_stdz}] : random walk vert. standard deviation (in grid points) |
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160 | \item[\np{rn\_eos\_tcor}] : random walk time correlation (in timesteps) |
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161 | \item[\np{nn\_eos\_ord}] : order of autoregressive processes |
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162 | \item[\np{nn\_eos\_flt}] : passes of Laplacian filter |
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163 | \item[\np{rn\_eos\_lim}] : limitation factor (default = 3.0) |
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164 | \end{description} |
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165 | This routine also includes the initialization (seeding) of the random number generator. |
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166 | |
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167 | The third routine (\rou{sto\_rst\_write}) writes a restart file (which suffix name is |
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168 | given by \np{cn\_storst\_out} namelist parameter) containing the current value of |
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169 | all autoregressive processes to allow restarting a simulation from where it has been interrupted. |
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170 | This file also contains the current state of the random number generator. |
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171 | When \np{ln\_rststo} is set to \forcode{.true.}), the restart file (which suffix name is |
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172 | given by \np{cn\_storst\_in} namelist parameter) is read by the initialization routine |
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173 | (\rou{sto\_par\_init}). The simulation will continue exactly as if it was not interrupted |
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174 | only when \np{ln\_rstseed} is set to \forcode{.true.}, $i.e.$ when the state of |
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175 | the random number generator is read in the restart file. |
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176 | |
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177 | |
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178 | \end{document} |
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