Design of a forecasting system for ECMWF

As laid out in its convention there are 8 different objectives for ECMWF. One of the major objectives will consist of the preparation, on a regular basis, of the data necessary for the preparation of medium-range weather forecasts. The interpretation of this item is that the Centre will make forecasts once a day for a prediction period of up to 10 days. It is also evident that the Centre should not carry out any real weather forecasting but merely disseminate to the member countries the basic forecasting parameters with an appropriate resolution in space and time. It follows from this that the forecasting system at the Centre must from the operational point of view be functionally integrated with the Weather Services of the Member Countries. The operational interface between ECMWF and the Member Countries must be properly specified in order to get a reasonable flexibility for both systems. The problem of making numerical atmospheric predictions for periods beyond 4-5 days differs substantially from 2-3 days forecasting. From the physical point we can define a medium range forecast as a forecast where the initial disturbances have lost their individual structure. However we are still interested to predict the atmosphere in a similar way as in short range forecasting which means that the model must be able to predict the dissipation and decay of the initial phenomena and the creation of new ones. With this definition, medium range forecasting is indeed very difficult and generally regarded as more difficult than extended forecasts, where we usually only predict time and space mean values. The predictability of atmospheric flow has been extensively studied during the last years in theoretical investigations and by numerical experiments. As has been discussed elsewhere in this publication (see pp 338 and 431) a 10-day forecast is apparently on the fringe of predictability.

Technical Note: The normal quantile transformation and its application in a flood forecasting system.

The Normal Quantile Transform (NQT) has been used in many hydrological and meteorological applications in order to make the Cumulated Distribution Function (CDF) of the observed, simulated and forecast river discharge, water level or precipitation data Gaussian. It is also the heart of the meta-Gaussian model for assessing the total predictive uncertainty of the Hydrological Uncertainty Processor (HUP) developed by Krzysztofowicz. In the field of geo-statistics this transformation is better known as the Normal-Score Transform. In this paper some possible problems caused by small sample sizes when applying the NQT in flood forecasting systems will be discussed and a novel way to solve the problem will be outlined by combining extreme value analysis and non-parametric regression methods. The method will be illustrated by examples of hydrological stream-flow forecasts.

Wall materials and techniques available for low cost housing in Khartoum from a sustainability point of view

Khartoum like many cities in least developing countries (LDCs) still witnesses huge influx of people. Accommodation of the new comers leads to encroachment on the cultivation land leads to sprawl expansion of Greater Khartoum. The city expanded in diameter from 16.8 km in 1955 to 802.5 km in 1998. Most of this horizontal expansion was residential. In 2008 Khartoum accommodated 29% of the urban population of Sudan. Today Khartoum is considered as one of 43 major cities in Africa that accommodates more than 1 million inhabitants. Most of new comers live in the outskirts of the city e.g. Dar El-Salam and Mayo neighbourhoods. The majority of those new comers built their houses especially the walls from mud, wood, straw and sacks. Selection of building materials usually depends on its price regardless of the environmental impact, quality, thermal performance and life of the material. Most of the time, this results in increasing the cost with variables of impacts over the environment during the life of the building. Therefore, consideration of the environmental impacts, social impacts and economic impacts is crucial in the selection of any building material. Decreasing such impacts could lead to more sustainable housing. Comparing the sustainability of the available wall building materials for low cost housing in Khartoum is carried out through the life cycle assessment (LCA) technique. The purpose of this paper is to compare the most available local building materials for walls for the urban poor of Khartoum from a sustainability point of view by going through the manufacturing of the materials, the use of these materials and then the disposal of the materials after their life comes to an end. Findings reveal that traditional red bricks couldn’t be considered as a sustainable wall building material that will draw the future of the low cost housing in Greater Khartoum. On the other hand, results of the comparison lead to draw attention to the wide range of the soil techniques and to its potentials to be a promising sustainable wall material for urban low cost housing in Khartoum.

Synchronisation and commonalities in metropolitan housing market cycles

This paper examines the degree of commonalities present in the cyclical behavior of the eight largest metropolitan housing markets in Australia. Using two techniques originally in the business cycle literature we consider the degree of synchronization present and secondly decompose the series’ into their permanent and cyclical components. Both empirical approaches reveal similar results. Sydney and Melbourne are closely related to each other and are relatively segmented from the smaller metropolitan areas. In contrast, there is substantial evidence of commonalities in the cyclical behavior of the remaining cities, especially those on the Eastern and Southern coasts of Australia.

Forecasting VaR using analytic higher moments for GARCH processes

It is widely accepted that some of the most accurate Value-at-Risk (VaR) estimates are based on an appropriately specified GARCH process. But when the forecast horizon is greater than the frequency of the GARCH model, such predictions have typically required time-consuming simulations of the aggregated returns distributions. This paper shows that fast, quasi-analytic GARCH VaR calculations can be based on new formulae for the first four moments of aggregated GARCH returns. Our extensive empirical study compares the Cornish–Fisher expansion with the Johnson SU distribution for fitting distributions to analytic moments of normal and Student t, symmetric and asymmetric (GJR) GARCH processes to returns data on different financial assets, for the purpose of deriving accurate GARCH VaR forecasts over multiple horizons and significance levels.

An evolutionary innovation perspective on the selection of low and zero carbon technologies in new housing

The Code for Sustainable Homes (the Code) will require new homes in the United Kingdom to be ‘zero carbon’ from 2016. Drawing upon an evolutionary innovation perspective, this paper contributes to a gap in the literature by investigating which low and zero carbon technologies are actually being used by house builders, rather than the prevailing emphasis on the potentiality of these technologies. Using the results from a questionnaire three empirical contributions are made. First, house builders are selecting a narrow range of technologies. Second, these choices are made to minimise the disruption to their standard design and production templates (SDPTs). Finally, the coalescence around a small group of technologies is expected to intensify with solar-based technologies predicted to become more important. This paper challenges the dominant technical rationality in the literature that technical efficiency and cost benefits are the primary drivers for technology selection. These drivers play an important role but one which is mediated by the logic of maintaining the SDPTs of the house builders. This emphasises the need for construction diffusion of innovation theory to be problematized and developed within the context of business and market regimes constrained and reproduced by resilient technological trajectories.

Forecasting by factors, by variables, by both or neither?

We consider forecasting with factors, variables and both, modeling in-sample using Autometrics so all principal components and variables can be included jointly, while tackling multiple breaks by impulse-indicator saturation. A forecast-error taxonomy for factor models highlights the impacts of location shifts on forecast-error biases. Forecasting US GDP over 1-, 4- and 8-step horizons using the dataset from Stock and Watson (2009) updated to 2011:2 shows factor models are more useful for nowcasting or short-term forecasting, but their relative performance declines as the forecast horizon increases. Forecasts for GDP levels highlight the need for robust strategies, such as intercept corrections or differencing, when location shifts occur as in the recent financial crisis.