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Brian Golding, Nigel Roberts, Giovanni Leoncini, Ken Mylne, and Richard Swinbank

ABSTRACT

Flooding is one of the costliest hazards in the United Kingdom. A large part of the annual flood damage is caused by surface water flooding that is a direct result of intense rainfall. Traditional catchment-based approaches to flood prediction are not applicable for surface water floods. However, given sufficiently accurate forecasts of rainfall intensity, with sufficient lead time, actions can be taken to reduce their impact. These actions require reliable information about severity and areas at risk that is clear and easily interpreted. The accuracy requirements, in particular, are very challenging, as they relate to prediction of intensities that occur only infrequently and that typically affect only small areas. In this paper, forecasts of intense rainfall from a new convection-permitting ensemble prediction system are evaluated using radar observations of intense rain and surface water flooding reports. An urban flooding case that occurred in Edinburgh in 2011 is first investigated and then a broader look is taken at performance through a 3-month period during the London Olympic and Paralympic Games in 2012. Conclusions are drawn about the value of the ensemble and the particular means of presenting the forecasts, and areas requiring further work are highlighted.

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Pricilla Marimo, Todd R. Kaplan, Ken Mylne, and Martin Sharpe

Abstract

Experimental economics is used to test whether undergraduate students presented with a temperature forecast with uncertainty information in a table and bar graph format were able to use the extra information to interpret a given forecast. Participants were asked to choose the most probable temperature-based outcome between a set of “lotteries.” Both formats with uncertainty information were found on average to significantly increase the probability of choosing the correct outcome. However, in some cases providing uncertainty information was damaging. Factors that influence understanding are statistically determined. 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. Over time, participants improve in speed and initially improve in accuracy of choosing the correct outcome.

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Philippe Bougeault, Zoltan Toth, Craig Bishop, Barbara Brown, David Burridge, De Hui Chen, Beth Ebert, Manuel Fuentes, Thomas M. Hamill, Ken Mylne, Jean Nicolau, Tiziana Paccagnella, Young-Youn Park, David Parsons, Baudouin Raoult, Doug Schuster, Pedro Silva Dias, Richard Swinbank, Yoshiaki Takeuchi, Warren Tennant, Laurence Wilson, and Steve Worley

Ensemble forecasting is increasingly accepted as a powerful tool to improve early warnings for high-impact weather. Recently, ensembles combining forecasts from different systems have attracted a considerable level of interest. The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Globa l Ensemble (TIGGE) project, a prominent contribution to THORPEX, has been initiated to enable advanced research and demonstration of the multimodel ensemble concept and to pave the way toward operational implementation of such a system at the international level. The objectives of TIGGE are 1) to facilitate closer cooperation between the academic and operational meteorological communities by expanding the availability of operational products for research, and 2) to facilitate exploring the concept and benefits of multimodel probabilistic weather forecasts, with a particular focus on high-impact weather prediction. Ten operational weather forecasting centers producing daily global ensemble forecasts to 1–2 weeks ahead have agreed to deliver in near–real time a selection of forecast data to the TIGGE data archives at the China Meteorological Agency, the European Centre for Medium-Range Weather Forecasts, and the National Center for Atmospheric Research. The volume of data accumulated daily is 245 GB (1.6 million global fields). This is offered to the scientific community as a new resource for research and education. The TIGGE data policy is to make each forecast accessible via the Internet 48 h after it was initially issued by each originating center. Quicker access can also be granted for field experiments or projects of particular interest to the World Weather Research Programme and THORPEX. A few examples of initial results based on TIGGE data are discussed in this paper, and the case is made for additional research in several directions.

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Mathias W. Rotach, Paolo Ambrosetti, Christof Appenzeller, Marco Arpagaus, Lionel Fontannaz, Felix Fundel, Urs Germann, Alessandro Hering, Mark A. Liniger, Marco Stoll, Andre Walser, Felix Ament, Hans-Stefan Bauer, Andreas Behrendt, Volker Wulfmeyer, François Bouttier, Yann Seity, Andrea Buzzi, Silvio Davolio, Matteo Corazza, Michael Denhard, Manfred Dorninger, Theresa Gorgas, Jacqueline Frick, Christoph Hegg, Massimiliano Zappa, Christian Keil, Hans Volkert, Chiara Marsigli, Andrea Montaini, Ron McTaggart-Cowan, Ken Mylne, Roberto Ranzi, Evelyne Richard, Andrea Rossa, Daniel Santos-Muñoz, Christoph Schär, Michael Staudinger, Yong Wang, and Johannes Werhahn

Abstract

No Abstract available.

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Mathias W. Rotach, Paolo Ambrosetti, Felix Ament, Christof Appenzeller, Marco Arpagaus, Hans-Stefan Bauer, Andreas Behrendt, François Bouttier, Andrea Buzzi, Matteo Corazza, Silvio Davolio, Michael Denhard, Manfred Dorninger, Lionel Fontannaz, Jacqueline Frick, Felix Fundel, Urs Germann, Theresa Gorgas, Christoph Hegg, Alessandro Hering, Christian Keil, Mark A. Liniger, Chiara Marsigli, Ron McTaggart-Cowan, Andrea Montaini, Ken Mylne, Roberto Ranzi, Evelyne Richard, Andrea Rossa, Daniel Santos-Muñoz, Christoph Schär, Yann Seity, Michael Staudinger, Marco Stoll, Hans Volkert, Andre Walser, Yong Wang, Johannes Werhahn, Volker Wulfmeyer, and Massimiliano Zappa

Demonstration of probabilistic hydrological and atmospheric simulation of flood events in the Alpine region (D-PHASE) is made by the Forecast Demonstration Project in connection with the Mesoscale Alpine Programme (MAP). Its focus lies in the end-to-end flood forecasting in a mountainous region such as the Alps and surrounding lower ranges. Its scope ranges from radar observations and atmospheric and hydrological modeling to the decision making by the civil protection agents. More than 30 atmospheric high-resolution deterministic and probabilistic models coupled to some seven hydrological models in various combinations provided real-time online information. This information was available for many different catchments across the Alps over a demonstration period of 6 months in summer/fall 2007. The Web-based exchange platform additionally contained nowcasting information from various operational services and feedback channels for the forecasters and end users. D-PHASE applications include objective model verification and intercomparison, the assessment of (subjective) end user feedback, and evaluation of the overall gain from the coupling of the various components in the end-to-end forecasting system.

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