The seasonal forecast of electricity demand: A hierarchical Bayesian model with climatological weather generator

In this paper we focus on the one year ahead prediction of the electricity peak-demand daily trajectory during the winter season in Central England and Wales. We define a Bayesian hierarchical model for predicting the winter trajectories and present results based on the past observed weather. Thanks to the flexibility of the Bayesian approach, we are able to produce the marginal posterior distributions of all the predictands of interest. This is a fundamental progress with respect to the classical methods. The results are encouraging in both skill and representation of uncertainty. Further extensions are straightforward at least in principle. The main two of those consist in conditioning the weather generator model with respect to additional information like the knowledge of the first part of the winter and/or the seasonal weather forecast. Copyright (C) 2006 John Wiley & Sons, Ltd.

A Bayesian approach for multi-model downscaling: Seasonal forecasting of regional rainfall and river flows in South America

This study addresses three issues: spatial downscaling, calibration, and combination of seasonal predictions produced by different coupled ocean-atmosphere climate models. It examines the feasibility Of using a Bayesian procedure for producing combined, well-calibrated downscaled seasonal rainfall forecasts for two regions in South America and river flow forecasts for the Parana river in the south of Brazil and the Tocantins river in the north of Brazil. These forecasts are important for national electricity generation management and planning. A Bayesian procedure, referred to here as forecast assimilation, is used to combine and calibrate the rainfall predictions produced by three climate models. Forecast assimilation is able to improve the skill of 3-month lead November-December-January multi-model rainfall predictions over the two South American regions. Improvements are noted in forecast seasonal mean values and uncertainty estimates. River flow forecasts are less skilful than rainfall forecasts. This is partially because natural river flow is a derived quantity that is sensitive to hydrological as well as meteorological processes, and to human intervention in the form of reservoir management.

Cone model-based SEP event calculations for applications to multipoint observations

The problem of modeling solar energetic particle (SEP) events is important to both space weather research and forecasting, and yet it has seen relatively little progress. Most important SEP events are associated with coronal mass ejections (CMEs) that drive coronal and interplanetary shocks. These shocks can continuously produce accelerated particles from the ambient medium to well beyond 1 AU. This paper describes an effort to model real SEP events using a Center for Integrated Space weather Modeling (CISM) MHD solar wind simulation including a cone model of CMEs to initiate the related shocks. In addition to providing observation-inspired shock geometry and characteristics, this MHD simulation describes the time-dependent observer field line connections to the shock source. As a first approximation, we assume a shock jump-parameterized source strength and spectrum, and that scatter-free transport occurs outside of the shock source, thus emphasizing the role the shock evolution plays in determining the modeled SEP event profile. Three halo CME events on May 12, 1997, November 4, 1997 and December 13, 2006 are used to test the modeling approach. While challenges arise in the identification and characterization of the shocks in the MHD model results, this approach illustrates the importance to SEP event modeling of globally simulating the underlying heliospheric event. The results also suggest the potential utility of such a model for forcasting and for interpretation of separated multipoint measurements such as those expected from the STEREO mission.

Predicting magnetospheric dynamics with a coupled sun-to-Earth model: challenges and first results

Results from the first Sun-to-Earth coupled numerical model developed at the Center for Integrated Space Weather Modeling are presented. The model simulates physical processes occurring in space spanning from the corona of the Sun to the Earth's ionosphere, and it represents the first step toward creating a physics-based numerical tool for predicting space weather conditions in the near-Earth environment. Two 6- to 7-d intervals, representing different heliospheric conditions in terms of the three-dimensional configuration of the heliospheric current sheet, are chosen for simulations. These conditions lead to drastically different responses of the simulated magnetosphere-ionosphere system, emphasizing, on the one hand, challenges one encounters in building such forecasting tools, and on the other hand, emphasizing successes that can already be achieved even at this initial stage of Sun-to-Earth modeling.

Global scale water stages from SAR imagery (GlosSARi) to support global flood forecasting

Despite the success of studies attempting to integrate remotely sensed data and flood modelling and the need to provide near-real time data routinely on a global scale as well as setting up online data archives, there is to date a lack of spatially and temporally distributed hydraulic parameters to support ongoing efforts in modelling. Therefore, the objective of this project is to provide a global evaluation and benchmark data set of floodplain water stages with uncertainties and assimilation in a large scale flood model using space-borne radar imagery. An algorithm is developed for automated retrieval of water stages with uncertainties from a sequence of radar imagery and data are assimilated in a flood model using the Tewkesbury 2007 flood event as a feasibility study. The retrieval method that we employ is based on possibility theory which is an extension of fuzzy sets and that encompasses probability theory. In our case we first attempt to identify main sources of uncertainty in the retrieval of water stages from radar imagery for which we define physically meaningful ranges of parameter values. Possibilities of values are then computed for each parameter using a triangular ‘membership’ function. This procedure allows the computation of possible values of water stages at maximum flood extents along a river at many different locations. At a later stage in the project these data are then used in assimilation, calibration or validation of a flood model. The application is subsequently extended to a global scale using wide swath radar imagery and a simple global flood forecasting model thereby providing improved river discharge estimates to update the latter.

The social impacts of heat waves

The effects of the 2003 European heat wave have highlighted the need for society to prepare itself for and cope more effectively with heat waves. This is particularly important in the context of predicted climate change and the likelihood of more frequent extreme climate events; to date, heat as a natural hazard has been largely ignored. In order to develop better coping strategies, this report explores the factors that shape the social impacts of heat waves, and sets out a programme of research to address the considerable knowledge gaps in this area. Heat waves, or periods of anomalous warmth, do not affect everyone; it is the vulnerable individuals or sectors of society who will most experience their effects. The main factors of vulnerability are being elderly, living alone, having a pre-existing disease, being immobile or suffering from mental illness and being economically disadvantaged. The synergistic effects of such factors may prove fatal for some. Heat waves have discernible impacts on society including a rise in mortality, an increased strain on infrastructure (power, water and transport) and a possible rise in social disturbance. Wider impacts may include effects on the retail industry, ecosystem services and tourism. Adapting to more frequent heat waves should include soft engineering options and, where possible, avoid the widespread use of air conditioning which could prove unsustainable in energy terms. Strategies for coping with heat include changing the way in which urban areas are developed or re-developed, and setting up heat watch warning systems based around weather and seasonal climate forecasting and intervention strategies. Although heat waves have discernible effects on society, much remains unknown about their wider social impacts, diffuse health issues and how to manage them.

TIGGE: Comparison of the prediction of northern hemisphere extratropical cyclones by different ensemble prediction systems

The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) is a World Weather Research Programme project. One of its main objectives is to enhance collaboration on the development of ensemble prediction between operational centers and universities by increasing the availability of ensemble prediction system (EPS) data for research. This study analyzes the prediction of Northern Hemisphere extratropical cyclones by nine different EPSs archived as part of the TIGGE project for the 6-month time period of 1 February 2008–31 July 2008, which included a sample of 774 cyclones. An objective feature tracking method has been used to identify and track the cyclones along the forecast trajectories. Forecast verification statistics have then been produced [using the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis as the truth] for cyclone position, intensity, and propagation speed, showing large differences between the different EPSs. The results show that the ECMWF ensemble mean and control have the highest level of skill for all cyclone properties. The Japanese Meteorological Administration (JMA), the National Centers for Environmental Prediction (NCEP), the Met Office (UKMO), and the Canadian Meteorological Centre (CMC) have 1 day less skill for the position of cyclones throughout the forecast range. The relative performance of the different EPSs remains the same for cyclone intensity except for NCEP, which has larger errors than for position. NCEP, the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), and the Australian Bureau of Meteorology (BoM) all have faster intensity error growth in the earlier part of the forecast. They are also very underdispersive and significantly underpredict intensities, perhaps due to the comparatively low spatial resolutions of these EPSs not being able to accurately model the tilted structure essential to cyclone growth and decay. There is very little difference between the levels of skill of the ensemble mean and control for cyclone position, but the ensemble mean provides an advantage over the control for all EPSs except CPTEC in cyclone intensity and there is an advantage for propagation speed for all EPSs. ECMWF and JMA have an excellent spread–skill relationship for cyclone position. The EPSs are all much more underdispersive for cyclone intensity and propagation speed than for position, with ECMWF and CMC performing best for intensity and CMC performing best for propagation speed. ECMWF is the only EPS to consistently overpredict cyclone intensity, although the bias is small. BoM, NCEP, UKMO, and CPTEC significantly underpredict intensity and, interestingly, all the EPSs underpredict the propagation speed, that is, the cyclones move too slowly on average in all EPSs.