El SPC de UK Met Office MOGREPS

El Servicio Meteorológico británico, United Kingdom Met Office o, abreviadamente, Met Office disfruta, entre otros sistemas de predicción numérica, de un sistema de predicción por conjuntos (SPC) que abarca todas las escalas tanto espaciales como, por ende, temporales. El así llamado Met Office Global and Regional Ensemble Prediction System, MOGREPS, es decir, SPC global y regional de Met Office, consta de una componente global y otra regional para generar información sobre la incertidumbre atmosférica, principalmente enfocado a las previsiones a corto plazo. El sistema está diseñado especialmente para ayudar en la predicción del desarrollo de tormentas rápidas, viento, lluvia, nieve y niebla

Met Office Weather Game Survey 2011

This dataset contains game play results and demographic data collected from participants in the 2011 Met Office weather game. The game was designed to determine the best methods of communicating uncertainty in rainfall and temperature forecasts, and to widen public engagement in uncertainty in weather forecasting. Within the ‘ice-cream seller’ scenario of the game participants were asked to make decisions based on rainfall and temperature forecasts presented in different ways. The game was designed with a randomised structure to enable participants to experience being ‘lucky’ or ‘unlucky’ when the most likely forecast scenario did not occur. The database contains the game play selections from over 8000 unique participants and the scores that they achieved in the game. Data were also collected on participant age, gender, location and educational attainment

On the predictability of extremes: Does the butterfly effect ever decrease?

This is the peer reviewed version of the following article: Sterk, A. E., Stephenson, D. B., Holland, M. P. and Mylne, K. R. (2015), On the predictability of extremes: Does the butterfly effect ever decrease?. Quarterly Journal of the Royal Meteorological Society, which has been published in final form at http://dx.doi.org/10.1002/qj.2627. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving: http://olabout.wiley.com/WileyCDA/Section/id-820227.html#termsThis study investigates whether or not predictability always decreases for more extreme events. Predictability is measured by the Mean Squared Error (MSE), estimated here from the difference of pairs of ensemble forecasts, conditioned on one of the forecast variables (the 'pseudo-observation') exceeding a threshold. Using an exchangeable linear regression model for pairs of forecast variables, we show that the MSE can be decomposed into the sum of three terms: a threshold-independent constant, a mean term that always increases with threshold, and a variance term that can either increase, decrease, or stay constant with threshold. Using the generalised Pareto distribution to model wind speed excesses over a threshold, we show that MSE always increases with threshold at sufficiently high threshold. However, MSE can be a decreasing function of threshold at lower thresholds but only if the forecasts have finite upper bounds. The methods are illustrated by application to daily wind speed forecasts for London made using the 24 member Met Office Global and Regional Ensemble Prediction System from 1 January 2009 to 31 May 2011. For this example, the mean term increases faster than the variance term decreases with increasing threshold, and so predictability decreases for more extreme events.Engineering and Physical Sciences Research Council (EPSRC)Netherlands Organisation for Scientific Research (NWO

The Met Office weather game: investigating how different methods for presenting probabilistic weather forecasts influence decision-making

To inform the way probabilistic forecasts would be displayed on their website, the UK Met Office ran an online game as a mass participation experiment to highlight the best methods of communicating uncertainty in rainfall and temperature forecasts, and to widen public engagement in uncertainty in weather forecasting. The game used a hypothetical “ice-cream seller” scenario and a randomized structure to test decision-making ability using different methods of representing uncertainty and to enable participants to experience being “lucky” or “unlucky” when the most likely forecast scenario did not occur. Data were collected on participant age, gender, educational attainment, and previous experience of environmental modelling. The large number of participants (n > 8000) that played the game has led to the collation of a unique large dataset with which to compare the impact on the decision-making ability of different weather forecast presentation ormats. This analysis demonstrates that within the game the provision of information regarding forecast uncertainty greatly improved decision-making ability and did not cause confusion in situations where providing the uncertainty added no further information

Communication of uncertainty in weather forecasts

Experimental economics methods were used to assess public understanding of information in weather forecasts and test whether the participants were able to make better decisions using the probabilistic information presented in table or bar graph formats than if they are presented with a deterministic forecast. We asked undergraduate students from the University of Exeter to choose the most probable temperature outcome between a set of “lotteries” based on the temperature up to five days ahead. If they choose a true statement, participants were rewarded with a cash reward. Results indicate that on average participants provided with uncertainty information make better decisions than those without. Statistical analysis indicates a possible learning effect as the experiment progressed. Furthermore, participants who were shown the graph with uncertainty information took on average less response time compared to those who were shown a table with uncertainty information

Communication of uncertainty in weather forecasts

Experimental economics methods were used to assess public understanding of information in weather forecasts and test whether the participants were able to make better decisions using the probabilistic information presented in table or bar graph formats than if they are presented with a deterministic forecast. We asked undergraduate students from the University of Exeter to choose the most probable temperature outcome between a set of “lotteries” based on the temperature up to five days ahead. If they choose a true statement, participants were rewarded with a cash reward. Results indicate that on average participants provided with uncertainty information make better decisions than those without. Statistical analysis indicates a possible learning effect as the experiment progressed. Furthermore, participants who were shown the graph with uncertainty information took on average less response time compared to those who were shown a table with uncertainty information

Ensemble forecasting of storm surges

The overtopping of flood defenses by coastal storm surges constitutes a significant threat to life and property. Like all forecasts, storm surge predictions have an associated uncertainty, but this is not directly predicted by current operational systems. The dominant source of this uncertainty is thought to be uncertainty in the driving atmospheric forecast of conditions at the sea surface, which can vary substantially depending on the meteorological situation. Ensemble prediction is a technique used to assess uncertainty in forecasts of complex nonlinear systems such as weather, where small errors can quickly grow to produce significantly different outcomes. It works by running not one but several forecasts, using slightly different initial conditions, boundary conditions, and/or model physics. These are chosen to sample the range of uncertainty in model inputs and formulation so that the corresponding forecasts will sample the range of possible results that are consistent with those uncertainties. The United Kingdom Met Office has recently developed the Met Office Global and Regional Ensemble Prediction System (MOGREPS), which provides 24 different predictions of meteorological evolution over a North Atlantic and European domain with a 24 km grid length. The aim of the present project is to run a barotropic storm surge prediction for each MOGREPS ensemble member, and thereby estimate the risk of damaging events given the forecast uncertainties which are sampled by the ensemble. The system forecasts 54 hours ahead and runs twice per day. In most situations, the ensemble develops rather little spread, suggesting a fairly predictable situation and a high degree of confidence in the forecast. On some occasions, however, the spread is much larger, suggesting a greater degree of uncertainty. Initial verification results are encouraging, although statistical evaluation suggests the ensemble spread is generally too smal

Making sense of uncertainty: why uncertainty is part of science

Scientific uncertainty is prominent in research that has big implications for our society: could the Arctic be ice-free in summer by 2080? Will a new cancer drug be worth its side effects? Is this strain of ‘flu going to be a dangerous epidemic? Uncertainty is normal currency in scientific research. Research goes on because we don’t know everything. Researchers then have to estimate how much of the picture is known and how confident we can all be that their findings tell us what’s happening or what’s going to happen. This is uncertainty