The relationship between wind power, electricity demand and winter weather patterns in Great Britain

Wind power generation in Great Britain has increased markedly in recent years. However due to its intermittency its ability to provide power during periods of high electricity demand has been questioned. Here we characterise the winter relationship between electricity demand and the availability of wind power. Although a wide range of wind power capacity factors is seen for a given demand, the average capacity factor reduces by a third between low and high demand. However, during the highest demand average wind power increases again, due to strengthening easterly winds. The nature of the weather patterns affecting Great Britain are responsible for this relationship. High demand is driven by a range of high pressure weather types, each giving cold conditions, but variable wind power availability. Offshore wind power is sustained at higher levels and offers a more secure supply compared to that onshore. However, during high demand periods in Great Britain neighbouring countries may struggle to provide additional capacity due to concurrent low temperatures and low wind power availability

Simple statistical probabilistic forecasts of the winter NAO

The variability of the North Atlantic Oscillation is a key aspect of Northern Hemisphere atmospheric circulation and has a profound impact upon the weather of the surrounding land masses. Recent success with dynamical forecasts predicting the winter NAO at lead times of a few months has the potential to deliver great socio-economic impacts. Here we find that a linear regression model can provide skillful predictions of the winter NAO based on a limited number of statistical predictors. Identified predictors include El-Niño, Arctic sea ice, Atlantic SSTs and tropical rainfall. These statistical models can show significant skill when used to make out-of-sample forecasts and we extend the method to produce probabilistic predictions of the winter NAO. The statistical hindcasts can achieve similar levels of skill to state-of the art dynamical forecast models, although out-of-sample predictions are less skillful, albeit over a small period. Forecasts over a longer out-of-sample period suggest there is true skill in the statistical models, comparable with that of dynamical forecasting models. They can be used both to help evaluate, and to offer insight into sources of predictability and limitations of, dynamical models

Comparative analysis of the transcriptome across distant species

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a 'universal model' based on a single set of organism-independent parameters