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The geo-statistical Kriging method is conventionally used in the spatial dimension to predict missing values in a series by utilizing information from neighbouring data, supported by the hypothesis that mathematical expectation is a function of distance between observations. By using a data-driven machine learning-based inferencing and exploration framework, this research applies a Kriging-based interpolation in the temporal dimension to fill in data gaps in time-series of air temperatures. It assesses its performance for artificial gap scenarios (ranging in length from single one to six consecutive data points) generated using data with both daily and hourly resolutions from five sites in Europe (Laqueuille, France; Grillenburg, Germany; Monte Bondone, Italy; Oensingen, Switzerland; Rothamsted, United Kingdom) and one in France overseas (Sedael, Réunion Island). Results show that the method is capable of predicting missing temperatures with acceptable accuracy, especially with the hourly resolution and for non-high elevation sites: modeling efficiency (EF ≤ 1, optimum) >0.8, with the exception of Monte Bondone, placed at >2000 m a.s.l. (EF < 0). With daily data, maximum temperature was correctly predicted at all sites (0.6 ≤ EF < 0.9), while some less accuracy (down to EF < 0.4) was noted when predicting missing daily minimum temperatures. In conclusion, the method appears suitable to be applied to fill in hourly temperature gaps, requiring more stringent hypotheses concerning daily data and mountain sites (but further studies are required to draw concluding recommendations). [ABSTRACT FROM AUTHOR]

Abstract: Wind is a library providing a collection of stochastic approaches to generate wind speed data on daily and hourly time steps. Daily generation refers to as mean, maximum and minimum daily wind speed. The library is made available as software component, inclusive of hypertext help file, allowing the development of language-independent clients under Windows operating systems. The component includes advanced features for re-use in custom developed applications, and it allows independent extensions by third parties without requiring its re-compilation. Illustrative examples on how to extend and re-use the library are provided as C# code projects. [Copyright &y& Elsevier]

We document the release of webXTREME , a new online tool for the evaluation of indices of climatic extremes (extreme temperatures and aridity) having impact on agricultural production. The tool is globally available and can be operated with either observed weather data or time series representing future climatic conditions. It is thus suitable for risk evaluation under climate change. webXTREME was implemented using Shiny , an open-source programming framework for creating web applications on the basis of the R Statistical Language. [ABSTRACT FROM AUTHOR]

Highlights: [•] We simulate grassland processes over large areas for climate change impact studies. [•] We parallelize simulations with pixel-based Pasture Simulation model (PaSim). [•] We perform simulations with 8×8km resolution for metropolitan France (∼10,000pixels). [•] We report 25h (speedup of 200) for the simulation on a 200-processor cluster. [ABSTRACT FROM AUTHOR]