Detecting improvements in forecast correlation skill: Statistical testing and power analysis

This is the final version. Available from the American Meteorological Society via the DOI in this recordThe skill of weather and climate forecast systems is often assessed by calculating the correlation coefficient between past forecasts and their verifying observations. Improvements in forecast skill can thus be quantified by correlation differences. The uncertainty in the correlation difference needs to be assessed to judge whether the observed difference constitutes a genuine improvement, or is compatible with random sampling variations. A widely used statistical test for correlation difference is known to be unsuitable, because it assumes that the competing forecasting systems are independent. In this paper, appropriate statistical methods are reviewed to assess correlation differences when the competing forecasting systems are strongly correlated with one another. The methods are used to compare correlation skill between seasonal temperature forecasts that differ in initialization scheme and model resolution. A simple power analysis framework is proposed to estimate the probability of correctly detecting skill improvements, and to determine the minimum number of samples required to reliably detect improvements. The proposed statistical test has a higher power of detecting improvements than the traditional test. The main examples suggest that sample sizes of climate hindcasts should be increased to about 40 years to ensure sufficiently high power. It is found that seasonal temperature forecasts are significantly improved by using realistic land surface initial conditions.The authors acknowledge support by the European Union Program FP7/2007-13 under Grant Agreement 3038378 (SPECS). The work of O. Bellprat was funded by ESA under the Climate Change Initiative (CCI) Living Planet Fellowship VERITAS-CCI

Kinetic Control over Self-Assembly of Semiconductor Nanoplatelets

Semiconductor nanoplatelets exhibit spectrally pure, directional fluorescence. To make polarized light emission accessible and the charge transport effective, nanoplatelets have to be collectively oriented in the solid state. We discovered that the collective nanoplatelets orientation in monolayers can be controlled kinetically by exploiting the solvent evaporation rate in self-assembly at liquid interfaces. Our method avoids insulating additives such as surfactants, making it ideally suited for optoelectronics. The monolayer films with controlled nanoplatelets orientation (edge-up or face-down) exhibit long-range ordering of transition dipole moments and macroscopically polarized light emission. Furthermore, we unveil that the substantial in-plane electronic coupling between nanoplatelets enables charge transport through a single nanoplatelets monolayer, with an efficiency that strongly depends on the orientation of the nanoplatelets. The ability to kinetically control the assembly of nanoplatelets into ordered monolayers with tunable optical and electronic properties paves the way for new applications in optoelectronic devices

Kinetic Control over Self-Assembly of Semiconductor Nanoplatelets

Semiconductor nanoplatelets exhibit spectrally pure, directional fluorescence. To make polarized light emission accessible and the charge transport effective, nanoplatelets have to be collectively oriented in the solid state. We discovered that the collective nanoplatelets orientation in monolayers can be controlled kinetically by exploiting the solvent evaporation rate in self-assembly at liquid interfaces. Our method avoids insulating additives such as surfactants, making it ideally suited for optoelectronics. The monolayer films with controlled nanoplatelets orientation (edge-up or face-down) exhibit long-range ordering of transition dipole moments and macroscopically polarized light emission. Furthermore, we unveil that the substantial in-plane electronic coupling between nanoplatelets enables charge transport through a single nanoplatelets monolayer, with an efficiency that strongly depends on the orientation of the nanoplatelets. The ability to kinetically control the assembly of nanoplatelets into ordered monolayers with tunable optical and electronic properties paves the way for new applications in optoelectronic devices

Bias adjustment and ensemble recalibration methods for seasonal forecasting: a comprehensive intercomparison using the C3S dataset

This work presents a comprehensive intercomparison of diferent alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)-e.g. quantile mapping-to more sophisticated ensemble recalibration (RC) methods- e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account diferent aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Ofce-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with diferent skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods efectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value-with respect to the raw model outputs-beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly afects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.This work has been funded by the C3S activity on Evaluation and Quality Control for seasonal forecasts. JMG was partially supported by the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER). FJDR was partially funded by the H2020 EUCP project (GA 776613)

Estimation of annual average daily traffic with optimal adjustment factors

This study aimed to estimate the annual average daily traffic in inter-urban networks determining the best correlation (affinity) between the short period traffic counts and permanent traffic counters. A bi-level optimisation problem is proposed in which an agent in an upper level prefixes the affinities between short period traffic counts and permanent traffic counters stations and looks to minimise the annual average daily traffic calculation error while, in a lower level, an origin–destination (O–D) trip matrix estimation problem from traffic counts is solved. The proposed model is tested over the well-known Sioux-Falls network and applied to a real case of Cantabria (Spain) regional road network. The importance of determining appropriate affinity and the effect of localisation of permanent traffic counters stations are discussed

Robust skill of decadal climate predictions

There is a growing need for skilful predictions of climate up to a decade ahead. Decadal climate predictions show high skill for surface temperature, but confidence in forecasts of precipitation and atmospheric circulation is much lower. Recent advances in seasonal and annual prediction show that the signal-to-noise ratio can be too small in climate models, requiring a very large ensemble to extract the predictable signal. Here, we reassess decadal prediction skill using a much larger ensemble than previously available, and reveal significant skill for precipitation over land and atmospheric circulation, in addition to surface temperature. We further propose a more powerful approach than used previously to evaluate the benefit of initialisation with observations, improving our understanding of the sources of skill. Our results show that decadal climate is more predictable than previously thought and will aid society to prepare for, and adapt to, ongoing climate variability and change.D.M.S., A.A.S., N.J.D., L.H. and R.E. were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). L.P.C. was supported by the Spanish MINECO HIATUS (CGL2015-70353-R) project. F.J.D.R. was supported by the H2020 EUCP (GA 776613) and the Spanish MINECO CLINSA (CGL2017-85791-R) projects. W.A. M. and H.P. were supported by the German Ministry of Education and Research (BMBF) under the project MiKlip (grant 01LP1519A). The NCAR contribution was supported by the US National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program Grant NA13OAR4310138 and by the US National Science Foundation (NSF) Collaborative Research EaSM2 Grant OCE-1243015. The NCAR contribution is also based upon work supported by NCAR, which is a major facility sponsored by the US NSF under Cooperative Agreement No. 1852977. The Community Earth System Model Decadal Prediction Large Ensemble (CESM-DPLE) was generated using computational resources provided by the US National Energy Research Scientific Computing Center, which is supported by the Office of Science of the US Department of Energy under Contract DE-AC02-05CH11231, as well as by an Accelerated Scientific Discovery grant for Cheyenne (https://doi.org/10.5065/D6RX99HX) that was awarded by NCAR’s Computational and Information System Laboratory.Peer ReviewedPostprint (published version

What have we learnt from EUPORIAS climate service prototypes?

The international effort toward climate services, epitomised by the development of the Global Framework for Climate Services and, more recently the launch of Copernicus Climate Change Service has renewed interest in the users and the role they can play in shaping the services they will eventually use. Here we critically analyse the results of the five climate service prototypes that were developed as part of the EU funded project EUPORIAS. Starting from the experience acquired in each of the projects we attempt to distil a few key lessons which, we believe, will be relevant to the wider community of climate service developers

Seasonal prediction skill of winter temperature over North India

This document is the Accepted Manuscript version of the following article: Tiwari, P.R., Kar, S.C., Mohanty, U.C. et al. Theor Appl Climatol (2016) 124: 15. The final publication is available at Springer via https://doi.org/10.1007/s00704-015-1397-y. © Springer-Verlag Wien 2015.The climatology, amplitude error, phase error, and mean square skill score (MSSS) of temperature predictions from five different state-of-the-art general circulation models (GCMs) have been examined for the winter (December–January– February) seasons over North India. In this region, temperature variability affects the phenological development processes of wheat crops and the grain yield. The GCM forecasts of temperature for a whole season issued in November from various organizations are compared with observed gridded temperature data obtained from the India Meteorological Department (IMD) for the period 1982–2009. The MSSS indicates that the models have skills of varying degrees. Predictions of maximum and minimum temperature obtained from the National Centers for Environmental Prediction (NCEP) climate forecast system model (NCEP_CFSv2) are compared with station level observations from the Snow and Avalanche Study Establishment (SASE). It has been found that when the model temperatures are corrected to account the bias in the model and actual orography, the predictions are able to delineate the observed trend compared to the trend without orography correction.Peer reviewedFinal Accepted Versio

Reducing residual thrombotic risk in patients with peripheral artery disease : impact of the COMPASS trial

Altres ajuts: Writing and editorial assistance was funded by Bayer Hispania.Patients with peripheral artery disease (PAD) are at a high risk not only for the classical cardiovascular (CV) outcomes (major adverse cardiovascular events; MACE) but also for vascular limb events (major adverse limb events; MALE). Therefore, a comprehensive approach for these patients should include both goals. However, the traditional antithrombotic approach with only antiplatelet agents (single or dual antiplatelet therapy) does not sufficiently reduce the risk of recurrent thrombotic events. Importantly, the underlying cause of atherosclerosis in patients with PAD implies both platelet activation and the initiation and promotion of coagulation cascade, in which Factor Xa plays a key role. Therefore, to reduce residual vascular risk, it is necessary to address both targets. In the Cardiovascular Outcomes for People Using Anticoagulation Strategies (COMPASS) trial that included patients with stable atherosclerotic vascular disease, the rivaroxaban plus aspirin strategy (versus aspirin) markedly reduced the risk of both CV and limb outcomes, and related complications, with a good safety profile. In fact, the net clinical benefit outcome composed of MACE; MALE, including major amputation, and fatal or critical organ bleeding was significantly reduced by 28% with the COMPASS strategy, (hazard ratio: 0.72; 95% confidence interval: 0.59-0.87). Therefore, the rivaroxaban plus aspirin approach provides comprehensive protection and should be considered for most patients with PAD at high risk of such events

Will Arctic sea ice thickness initialization improve seasonal forecast skill?

Arctic sea ice thickness is thought to be an important predictor of Arctic sea ice extent. However, coupled seasonal forecast systems do not generally use sea ice thickness observations in their initialization and are therefore missing a potentially important source of additional skill. To investigate how large this source is, a set of ensemble potential predictability experiments with a global climate model, initialized with and without knowledge of the sea ice thickness initial state, have been run. These experiments show that accurate knowledge of the sea ice thickness field is crucially important for sea ice concentration and extent forecasts up to 8 months ahead, especially in summer. Perturbing sea ice thickness also has a significant impact on the forecast error in Arctic 2 m temperature a few months ahead. These results suggest that advancing capabilities to observe and assimilate sea ice thickness into coupled forecast systems could significantly increase skill