a novel computational model of the wheat global market with an application to the 2010 russian federation case

In this paper, we build a computational model for the analysis of international wheat spot price formation, its dynamics and the dynamics of quantities traded internationally. The model has been calibrated using FAOSTAT data to evaluate its in-sample predictive power. The model is able to generate wheat prices in twelve international markets and traded wheat quantities in twenty-four world regions. The time span considered is from 1992 to 2013. In our study, particular attention was paid to the impact of the Russian Federation's 2010 grain export ban on wheat price and quantities traded internationally. Among other results, we found that the average weighted world wheat price in 2013 would have been 3.55% lower than the observed one if the Russian Federation had not imposed the export ban in 2010

Competencies based innovative learning solutions for co-development of climate services in West Africa

Abstract. In developing countries, and particularly in West Africa, the role of Climate Services (CS) for sustainable development is growing thanks to wide spreading collaboration among European institutions, including National Meteorological and Hydrological Services (NMHS) research centers, universities, and homologue local institutions. Operationally, the implementation of CSs in developing countries is mainly pivoted on NMHS, which, according to the World Meteorological Organization (WMO), are dramatically affected by unmet learning demand. The global scale of learning needs for co-development of CSs calls for innovative solutions and a range of flexible modalities to reach learners in a variety of ways, and for sharing resources and successful strategies within the global education and training community. In order to harmonize expected learning outcomes, WMO defined a competency framework (CF) for CSs to be used in the implementation of training initiatives and knowledge sharing tools. This paper presents the strategic and methodological approach adopted in the implementation of the TOPaCS, a new knowledge-based distance learning initiative, aiming to provide a flexible learning environment within the CSs CF of WMO ensuring coherence with other WMO education initiatives (Global Campus, other RTCs, etc.). The methodological approach adopted is based on the competency-based approach to training, where competencies are composed by elements of knowledge and skill. TOPaCS integrates the WMO CF for CSs into a taxonomy co-designed with stakeholders at different levels, and allows the definition of learning paths, which are a further interactive opportunity for co-development of CSs within the TOPaCS learning ecosystem. Indeed, the approach aims also to guide further instructional strategies and assessments and becomes a starting point to build a common language enabling a better cooperation and exchange between the different CSs training initiatives

Statistical methods for the analysis of climatic phenomena

Statistical Climatology investigates the application of statistics to atmospheric and climate science. This Thesis is intended to contribute some developments in this field. The Thesis is composed of two distinct applied problems: the first presents a functional clustering procedure applied to meteorological time series to obtain homogeneous climate zones; the second builds a hierarchical Bayesian model aiming at the prediction of 15- and 30-minutes cumulated precipitation at unknown locations and time using information on lightnings in the same area

A Counting Process Approach for Trend Assessment of Drought Condition

This paper discusses some methodological aspects of the historical analysis of drought, particularly the trend assessment. The Standardized Evapotranspiration Index (SPEI) is widely used as a measure of drought condition. Since different SPEI thresholds allow classifying the risk into moderate, severe, and extreme, the drought occurrence becomes a counting process. In this framework, would a statistical trend test based on a Non-Homogeneous Poisson Process (NHPP) give a similar result of the nonparametric Mann–Kendall (M-K) test? In this paper, we demonstrate that the NHPP approach is able to characterize the information given by the classical M-K approach in term of drought risk classes. Furthermore, we show how it can be used to reinforce the framework of drought trend analysis in combination with a standard non-parametric approach. At a global scale, we find that: (1) areas under increasing risk of drought identified by the NHPP approach are considerably larger in comparison to those identified by M-K; and (2) the results of the two tests are different during crucial periods such as hydrological droughts in winter and spring

Functional clustering for Italian climate zones identification

This work presents a functional clustering procedure applied to meteorological time series. Our proposal combines time series interpolation with smoothing penalized B-spline and the partitioning around medoids clustering algorithm. Our final goal is to obtain homogeneous climate zones of Italy. We compare this approach to standard methods based on a combination of principal component analysis and Cluster Analysis (CA) and we discuss it in relation to other functional clustering approaches based on Fourier analysis and CA. We show that a functional approach is simpler than the standard methods from a methodological and interpretability point of view. Indeed, it becomes natural to find a clear connection between mathematical results and physical variability mechanisms. We discuss how the choice of the basis expansion (splines, Fourier) affects the analysis and propose some comments on their use. The basis for classification is formed by monthly values of temperature and precipitation recorded during the period 1971-2000 over 95 and 94 Italian monitoring stations, respectively. An assessment based on climatic patterns is presented to prove the consistency of the clustering and a comparison of results obtained with different methods is used to judge the functional data approach. © 2012 Springer-Verlag Wien

Semi-Automatic Operational Service for Drought Monitoring and Forecasting in the Tuscany Region

A drought-monitoring and forecasting system developed for the Tuscany region was improved in order to provide a semi-automatic, more detailed, timely and comprehensive operational service for decision making, water authorities, researchers and general stakeholders. Ground-based and satellite data from different sources (regional meteorological stations network, MODIS Terra satellite and CHIRPS/CRU precipitation datasets) are integrated through an open-source, interoperable SDI (spatial data infrastructure) based on PostgreSQL/PostGIS to produce vegetation and precipitation indices that allow following of the occurrence and evolution of a drought event. The SDI allows the dissemination of comprehensive, up-to-date and customizable information suitable for different end-users through different channels, from a web page and monthly bulletins, to interoperable web services, and a comprehensive climate service. The web services allow geospatial elaborations on the fly, and the geo-database can be increased with new input/output data to respond to specific requests or to increase the spatial resolution