Abstract.The recently increasing development of whole sky imagers enables temporal and spatial high-resolution sky observations. One application already performed in most cases is the estimation of fractional sky cover. A distinction between different cloud types, however, is still in progress. Here, an automatic cloud classification algorithm is presented, based on a set of mainly statistical features describing the color as well as the texture of an image. The k-nearestneighbour classifier is used due to its high performance in solving complex issues, simplicity of implementation and low computational complexity. Seven different sky conditions are distinguished: high thin clouds (cirrus and cirrostratus), high patched cumuliform clouds (cirrocumulus and altocumulus), stratocumulus clouds, low cumuliform clouds, thick clouds (cumulonimbus and nimbostratus), stratiform clouds and clear sky. Based on the Leave-One-Out CrossValidation the algorithm achieves an accuracy of about 97%. In addition, a test run of random images is presented, still outperforming previous algorithms by yielding a success rate of about 75%, or up to 88% if only "serious" errors with respect to radiation impact are considered. Reasons for the decrement in accuracy are discussed, and ideas to further improve the classification results, especially in problematic cases, are investigated.

Abstract. Global biogeochemical ocean models contain a variety of different biogeochemical components and often much simplified representations of complex dynamical interactions, which are described by many ( ≈ 10 to  ≈ 100) parameters. The values of many of these parameters are empirically difficult to constrain, due to the fact that in the models they represent processes for a range of different groups of organisms at the same time, while even for single species parameter values are often difficult to determine in situ. Therefore, these models are subject to a high level of parametric uncertainty. This may be of consequence for their skill with respect to accurately describing the relevant features of the present ocean, as well as their sensitivity to possible environmental changes. We here present a framework for the calibration of global biogeochemical ocean models on short and long timescales. The framework combines an offline approach for transport of biogeochemical tracers with an estimation of distribution algorithm (Covariance Matrix Adaption Evolution Strategy, CMA-ES). We explore the performance and capability of this framework by five different optimizations of six biogeochemical parameters of a global biogeochemical model, simulated over 3000 years. First, a twin experiment explores the feasibility of this approach. Four optimizations against a climatology of observations of annual mean dissolved nutrients and oxygen determine the extent to which different setups of the optimization influence model fit and parameter estimates. Because the misfit function applied focuses on the large-scale distribution of inorganic biogeochemical tracers, parameters that act on large spatial and temporal scales are determined earliest, and with the least spread. Parameters more closely tied to surface biology, which act on shorter timescales, are more difficult to determine. In particular, the search for optimum zooplankton parameters can benefit from a sound knowledge of maximum and minimum parameter values, leading to a more efficient optimization. It is encouraging that, although the misfit function does not contain any direct information about biogeochemical turnover, the optimized models nevertheless provide a better fit to observed global biogeochemical fluxes.

Artocarpin, a tetrameric lectin of molecular mass 65 kDa, is one of the two lectins extracted from the seeds of jackfruit. The structures of the complexes of artocarpin with mannotriose and mannopentose reported here, together with the structures of artocarpin and its complex with Me-amannose reported earlier, show that the lectin possesses a deep-seated binding site formed by three loops. The binding site can be considered as composed of two subsites; the primary site and the secondary site. Interactions at the primary site composed of two of the loops involve mainly hydrogen bonds, while those at the secondary site comprising the third loop are primarily van der Waals in nature. Mannotriose in its complex with the lectin interacts through all the three mannopyranosyl residues; mannopentose interacts with the protein using at least three of the five mannose residues. The complexes provide a structural explanation for the carbohydrate specificities of artocarpin. A detailed comparison with the sugar complexes of heltuba, the only other mannose-specific jacalinlike lectin with known three-dimensional structure in sugar-bound form, establishes the role of the sugar-binding loop constituting the secondary site, in conferring different specificities at the oligosaccharide level. This loop is four residues longer in artocarpin than in heltuba, providing an instance where variation in loop length is used as a strategy for generating carbohydrate specificity.

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a b s t r a c tThe well-known star discrepancy is a common measure for the uniformity of point distributions. It is used, e.g., in multivariate integration, pseudo random number generation, experimental design, statistics, or computer graphics.We study here the complexity of calculating the star discrepancy of point sets in the d-dimensional unit cube and show that this is an NP-hard problem.To establish this complexity result, we first prove NP-hardness of the following related problems in computational geometry: Given n points in the d-dimensional unit cube, find a subinterval of minimum or maximum volume that contains k of the n points.Our results for the complexity of the subinterval problems settle a conjecture of E. Thiémard [E. Thiémard, Optimal volume subintervals with k points and star discrepancy via integer programming, Math. Meth. Oper. Res. 54 (2001) 21-45].