model defined on a continuous spatial domain, the finite-dimensional distributions are multivariate GEV (MGEV) with asymmetric logistic dependence function \[@Tawn-1990\].

Spatial dependence is often summarized by the extremal coefficient \[@smith-1990a\]. The pairwise extremal coefficient $\vartheta
({\mathbf{s}}_i,{\mathbf{s}}_j) \in[1,2]$ is defined by the relationship $$\label{ECdef} P\bigl(X({\mathbf{s}}_i)<c,X({\mathbf{s}}_j)<c\bigr) = P
\bigl(X({\mathbf{s}}_i)<c\bigr)^{\vartheta({\mathbf{s}}_i,{\mathbf{s}}_j)}.$$ If $X({\mathbf{s}}_i)$ and $X({\mathbf{s}}_j)$ are independent, then $\vartheta({\mathbf{s}}_i,{\mathbf{s}}_j) = 2$; in contrast, if $X({\mathbf{s}}_i)$ and $X({\mathbf{s}}_j)$ are completely dependent, then $\vartheta({\mathbf{s}}_i,{\mathbf{s}}_j) = 1$. The extremal coefficient introduced by (\[auxmodel\]) is $$\label{ECgeneral} \vartheta({\mathbf{s}}_i,{\mathbf{s}}_j) = \sum
_{l=1}^L \bigl(w_{l}(
{\mathbf{s}}_i)^{1/\alpha
}+w_{l}({\mathbf{s}}_j)^{1/\alpha}
\bigr)^{\alpha}.$$ Therefore, the extremal coefficient is the sum (over the $L$ kernels) of the $L^{1/\alpha}$ norms of the vectors $[w_l({\mathbf{s}}_i),w_l({\mathbf{s}}_j)]$.

To see how $\alpha$ controls the nugget effect, consider two observations at the same location, ${\mathbf{s}}_i={\mathbf{s}}_j$. The two observations share the same kernels, $w_l({\mathbf{s}}_i)=w_l({\mathbf{s}}_j)$ and thus $\theta({\mathbf{s}}_i)=\theta({\mathbf{s}}_j)$, but have different nugget terms $U({\mathbf{s}}_i)\ne
U({\mathbf{s}}_j)$. In this case, the extremal coefficient is $2^\alpha$. If $\alpha
=1$, then the nugget dominates and for all pairs of locations, regardless of their spatial locations \[since $\sum_{l=1}^Lw_l({\mathbf{s}})=1$ for all ${\mathbf{s}}$\]. If $\alpha=0$, then $\vartheta
({\mathbf{s}}_i,{\mathbf{s}}_j)=1$ when ${\mathbf{s}}_i={\mathbf{s}}_j$, and there is no nugget effect. The characteristics of the model are shown graphically in Figure \[fsamples\]. In these random draws from the 