Local Difference Measures between Complex Networks for Dynamical System Model Evaluation

Acknowledgments We thank Reik V. Donner for inspiring suggestions that initialized the work presented herein. Jan H. Feldhoff is credited for providing us with the STARS simulation data and for his contributions to fruitful discussions. Comments by the anonymous reviewers are gratefully acknowledged as they led to substantial improvements of the manuscript.Peer reviewedPublisher PD

Quantifying the predictability of a predictand: demonstrating the diverse roles of serial dependence in the estimation of forecast skill

Predictability varies. In geophysical systems, and related mathematical dynamical systems, variations are often expressed as serial dependence in the skill with which the system is, or can be, predicted. It is well known, of course, that estimation is more complicated in cases where the time series sample in‐hand does not reflect an independent from the target population; failure to account for this results in erroneous estimates both of the skill of the forecast system and of the statistical uncertainty in the estimated skill. This effect need not be indicated in the time series of the predictand; specifically: it is proven by example that linear correlation in the predictand is neither necessary nor sufficient to identify misestimation. Wilks [Quarterly Journal of the Royal Meteorological Society 136, 2109 (2010)] has shown that temporal correlations in forecast skill give rise to biased estimates of skill of a forecast system, and made progress on accounting for this effect in probability‐of‐precipitation forecasts. Related effects are explored in probability density forecasts of a continuous target in three different dynamical systems (demonstrating that linear correlation in the predictand is neither necessary nor sufficient), and a simple procedure is presented as a straightforward, good practice test for the effect when estimating the skill of forecast system

Declining incidence of malaria imported into the UK from West Africa

BACKGROUND: Two thirds of all falciparum malaria cases reported in the United Kingdom (UK) are acquired in West Africa (WA). To ensure recommendations and guidelines for malaria prophylaxis in travellers to West Africa correlate to the risk of infection, a study was undertaken to examine recent trends and predict future patterns of imported malaria acquired by UK residents visiting West Africa and West African visitors to the UK between 1993 and 2006. METHODS AND RESULTS: Using passenger numbers and malaria surveillance reports, the data revealed a 2.3-fold increase in travel to West Africa with a five-fold increase in travelers visiting friends and relatives (VFR). Malaria incidence fell through the study period, the greatest decline noted in VFR with a fall from 196 cases/1,000 person-years to 52 cases/1,000 person-years, 9.8% per year p < 0.0001. The risk for travellers from the UK visiting for other reasons declined 2.7 fold, at an annual decrease of 7.0%, with the incidence in West African visitors to the UK falling by 2.3 fold, a rate of 7.9% annually. DISCUSSION: The reduction in incidence among all three groups of travellers may be explained by several factors; changing chemoprophylaxis usage and/or increased travel in urban areas where malaria risk has declined over the past decade, or widespread reduction in malaria transmission in West Africa. CONCLUSION: With the reduction in malaria incidence seen in both visitors to and from West Africa, the most rational explanation for these findings is a fall in malaria transmission in West Africa, which may require a change in chemoprophylaxis policy for UK travelers over the next 5-10 years

“Semantics Inside!” But Let’s Not Tell the Data Miners: Intelligent Support for Data Mining

Knowledge Discovery in Databases (KDD) has evolved significantly over the past years and reached a mature stage offering plenty of operators to solve complex data analysis tasks. User support for building data analysis workflows, however, has not progressed sufficiently: the large number of operators currently available in KDD systems and interactions between these operators complicates successful data analysis. To help Data Miners we enhanced one of the most used open source data mining tools—RapidMiner—with semantic technologies. Specifically, we first annotated all elements involved in the Data Mining (DM) process—the data, the operators, models, data mining tasks, and KDD workflows—semantically using our eProPlan modelling tool that allows to describe operators and build a task/method decomposition grammar to specify the desired workflows embedded in an ontology. Second, we enhanced RapidMiner to employ these semantic annotations to actively support data analysts. Third, we built an Intelligent Discovery Assistant, eIda, that leverages the semantic annotation as well as HTN planning to automatically support KDD process generation. We found that the use of Semantic Web approaches and technologies in the KDD domain helped us to lower the barrier to data analysis. We also found that using a generic ontology editor overwhelmed KDD-centric users. We, therefore, provided them with problem-centric extensions to Protege. Last and most surprising, we found that our semantic modeling of the KDD domain served as a rapid prototyping approach for several hard-coded improvements of RapidMiner, namely correctness checking of workflows and quick-fixes, reinforcing the finding that even a little semantic modeling can go a long way in improving the understanding of a domain even for domain experts

Influence of the Gulf Stream on the troposphere

The Gulf Stream transports large amounts of heat from the tropics to middle and high latitudes, and thereby affects weather phenomena such as cyclogenesis and low cloud formation. But its climatic influence, on monthly and longer timescales, remains poorly understood. In particular, it is unclear how the warm current affects the free atmosphere above the marine atmospheric boundary layer. Here we consider the Gulf Stream's influence on the troposphere, using a combination of operational weather analyses, satellite observations and an atmospheric general circulation model. Our results reveal that the Gulf Stream affects the entire troposphere. In the marine boundary layer, atmospheric pressure adjustments to sharp sea surface temperature gradients lead to surface wind convergence, which anchors a narrow band of precipitation along the Gulf Stream. In this rain band, upward motion and cloud formation extend into the upper troposphere, as corroborated by the frequent occurrence of very low cloud-top temperatures. These mechanisms provide a pathway by which the Gulf Stream can affect the atmosphere locally, and possibly also in remote regions by forcing planetary waves. The identification of this pathway may have implications for our understanding of the processes involved in climate change, because the Gulf Stream is the upper limb of the Atlantic meridional overturning circulation, which has varied in strength in the past and is predicted to weaken in response to human-induced global warming in the future