Abstract: Very large climate data repositories provide a consistent view of weather conditions over long time periods. In some applications and studies, given a current weather pattern (e.g. today's weather), it is useful to identify similar ones (weather analogues) in the past. Looking for simi-lar patterns in an archive using a brute force approach requires data to be retrieved from the ar-chive and the compared to the query, using a chosen similarity measure. Such operation would be very long and costly. In this work a wavelet-based fingerprinting scheme is proposed to in-dex all weather patterns from the archive. The scheme allows to answer queries by computing the fingerprint of the query pattern, then comparing them to the index of all fingerprints more ef-ficiently, in order to then retrieve only the corresponding selected data from the archive. The ex-perimental analysis is carried out on the ECMWF's ERA-Interim reanalyses data representing the global state of the atmosphere over seral decades. Results shows that 32 bits fingerprints are sufficient to represent metrological fields over a 1700 km×1700 km region and allow the quasi instantaneous retrieval of weather analogues.

Abstract: Assimilation of data into a fire-spread model is formulated as an optimization problem. The level set equation, which relates the fire arrival time and the rate of spread, is allowed to be satisfied only approximately, and we minimize a norm of the residual. Our previous methods based on modification of the fire arrival time either used an additive correction to the fire arrival time, or made a position correction. Unlike additive fire arrival time corrections, the new method respects the dependence of the fire rate of spread on diurnal changes of fuel moisture and on weather changes, and, unlike position corrections, it respects the dependence of the fire spread on fuels and terrain as well. The method is used to interpolate the fire arrival time between two perimeters by imposing the fire arrival time at the perimeters as constraints.

Abstract: Complex systems (e.g. volcanos, debris flows, climate) com- monly have many models advocated by different modelers and incorpo- rating different modeling assumptions. Limited and sparse data on the modeled phenomena does not permit a clean discrimination among mod- els for fitness of purpose and heuristic choices are usually made especially for critical predictions of behavior that has not been experienced. We advocate in recent work for characterizing models and the modeling as- sumptions they represent using a statistical approach over the full range of applicability of the models. Such a characterization may then be used to decide the appropriateness of a model for use and as needed weighted compositions of models for better predictive power. We use the example of dense granular representations of natural mass flows in volcanic debris avalanches to illustrate our approach.

Abstract: With the development of technology, the technology foresight becomes more and more important. Delphi method as the core method of technology foresight is increasingly questioned. This paper propose a new technology foresight method based on intelligent convergence in open network environment. We put a large number of scientific and technological innovation topics into the open network technology community. Through the supervision and guidance to stimulate the discussion of expert groups, a lot of interactive information can be generated. Based on the accurate topic delivery, effective topic monitoring, reasonable topic guiding, comprehensive topic recovering, and interactive data mining, we get the technology foresight result and further look for the expert or team engaged in relevant research.