How well does the ECMWF Ensemble Prediction System predict blocking?

Forecasting atmospheric blocking is one of the main problems facing medium-range weather forecasters in the extratropics. The European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) provides an excellent basis for medium-range forecasting as it provides a number of different possible realizations of the meteorological future. This ensemble of forecasts attempts to account for uncertainties in both the initial conditions and the model formulation. Since 18 July 2000, routine output from the EPS has included the field of potential temperature on the potential vorticity (PV) D 2 PV units (PVU) surface, the dynamical tropopause. This has enabled the objective identification of blocking using an index based on the reversal of the meridional potential-temperature gradient. A year of EPS probability forecasts of Euro-Atlantic and Pacific blocking have been produced and are assessed in this paper, concentrating on the Euro-Atlantic sector. Standard verification techniques such as Brier scores, Relative Operating Characteristic (ROC) curves and reliability diagrams are used. It is shown that Euro-Atlantic sector-blocking forecasts are skilful relative to climatology out to 10 days, and are more skilful than the deterministic control forecast at all lead times. The EPS is also more skilful than a probabilistic version of this deterministic forecast, though the difference is smaller. In addition, it is shown that the onset of a sector-blocking episode is less well predicted than its decay. As the lead time increases, the probability forecasts tend towards a model climatology with slightly less blocking than is seen in the real atmosphere. This small under-forecasting bias in the blocking forecasts is possibly related to a westerly bias in the ECMWF model. Copyright © 2003 Royal Meteorological Society

Metrics for solar wind prediction models: Comparison of empirical, hybrid, and physics-based schemes with 8 years of L1 observations

Space weather effects on technological systems originate with energy carried from the Sun to the terrestrial environment by the solar wind. In this study, we present results of modeling of solar corona-heliosphere processes to predict solar wind conditions at the L1 Lagrangian point upstream of Earth. In particular we calculate performance metrics for (1) empirical, (2) hybrid empirical/physics-based, and (3) full physics-based coupled corona-heliosphere models over an 8-year period (1995–2002). L1 measurements of the radial solar wind speed are the primary basis for validation of the coronal and heliosphere models studied, though other solar wind parameters are also considered. The models are from the Center for Integrated Space-Weather Modeling (CISM) which has developed a coupled model of the whole Sun-to-Earth system, from the solar photosphere to the terrestrial thermosphere. Simple point-by-point analysis techniques, such as mean-square-error and correlation coefficients, indicate that the empirical coronal-heliosphere model currently gives the best forecast of solar wind speed at 1 AU. A more detailed analysis shows that errors in the physics-based models are predominately the result of small timing offsets to solar wind structures and that the large-scale features of the solar wind are actually well modeled. We suggest that additional “tuning” of the coupling between the coronal and heliosphere models could lead to a significant improvement of their accuracy. Furthermore, we note that the physics-based models accurately capture dynamic effects at solar wind stream interaction regions, such as magnetic field compression, flow deflection, and density buildup, which the empirical scheme cannot.

Progress on “Changing coastlines: data assimilation for morphodynamic prediction and predictability”

The task of assessing the likelihood and extent of coastal flooding is hampered by the lack of detailed information on near-shore bathymetry. This is required as an input for coastal inundation models, and in some cases the variability in the bathymetry can impact the prediction of those areas likely to be affected by flooding in a storm. The constant monitoring and data collection that would be required to characterise the near-shore bathymetry over large coastal areas is impractical, leaving the option of running morphodynamic models to predict the likely bathymetry at any given time. However, if the models are inaccurate the errors may be significant if incorrect bathymetry is used to predict possible flood risks. This project is assessing the use of data assimilation techniques to improve the predictions from a simple model, by rigorously incorporating observations of the bathymetry into the model, to bring the model closer to the actual situation. Currently we are concentrating on Morecambe Bay as a primary study site, as it has a highly dynamic inter-tidal zone, with changes in the course of channels in this zone impacting the likely locations of flooding from storms. We are working with SAR images, LiDAR, and swath bathymetry to give us the observations over a 2.5 year period running from May 2003 – November 2005. We have a LiDAR image of the entire inter-tidal zone for November 2005 to use as validation data. We have implemented a 3D-Var data assimilation scheme, to investigate the improvements in performance of the data assimilation compared to the previous scheme which was based on the optimal interpolation method. We are currently evaluating these different data assimilation techniques, using 22 SAR data observations. We will also include the LiDAR data and swath bathymetry to improve the observational coverage, and investigate the impact of different types of observation on the predictive ability of the model. We are also assessing the ability of the data assimilation scheme to recover the correct bathymetry after storm events, which can dramatically change the bathymetry in a short period of time.

HESS Opinions ``Ensembles, uncertainty and flood prediction''

Ensemble predictions are being used more frequently to model the propagation of uncertainty through complex, coupled meteorological, hydrological and coastal models, with the goal of better characterising flood risk. In this paper, we consider the issues that we judge to be important when designing and evaluating ensemble predictions, and make recommendations for the guidance of future research.

A data collection case study supporting requirements oriented prediction and management in software developments

Consider the statement "this project should cost X and has risk of Y". Such statements are used daily in industry as the basis for making decisions. The work reported here is part of a study aimed at providing a rational and pragmatic basis for such statements. Of particular interest are predictions made in the requirements and early phases of projects. A preliminary model has been constructed using Bayesian Belief Networks and in support of this, a programme to collect and study data during the execution of various software development projects commenced in May 2002. The data collection programme is undertaken under the constraints of a commercial industrial regime of multiple concurrent small to medium scale software development projects. Guided by pragmatism, the work is predicated on the use of data that can be collected readily by project managers; including expert judgements, effort, elapsed times and metrics collected within each project.

"Racism in a Melting Pot...?" Trinidadian mid-life transnational migrants' views on race and colour-class on return to their homes of descent

This paper deals with second-generation, one-and-a-half generation and ‘‘prolonged sojourner” Trinidadian transnational migrants, who have decided to ‘return’ to the birthplace of their parents. Based on 40 in-depth interviews, the paper considers both the positive and critical things that these youthful transnational migrants report about returning to, and living in, this multi-ethnic plural society and the salience of racial and colour-class stratification as part of their return migration experiences. Our qualitative analysis is based on the narratives provided by these youthful returnees, as relayed ‘‘in their own words”, presenting critical reflections on racism, racial identities and experiences as transnational Trinidadians. It is clear that it is contexts such as contemporary working environments, family and community that act as the reference points for the adaptation ‘‘back home” of this strongly middle-class cohort. We accordingly encounter a diverse, sometimes contesting set of racial issues that emerge as salient concerns for these returnees. The consensus is that matters racial remain as formidable legacies in the hierarchical stratification of Trinidadian society for a sizeable number. Many of our respondents reported the positive aspects of racial affirmation on return. But for another sub-set, the fact that multi-ethnic and multi-cultural mixing are proudly embraced in Trinidad meant that it was felt that return experiences were not overly hindered, or blighted by obstacles of race and colour-class. For these returnees, Trinidad and Tobago is seen as representing a 21st century ‘‘Melting Pot”. But for others the continued existence of racial divisions within society – between ethnic groups and among those of different skin shades – was lamented. In the views of these respondents, too much racial power is still ascribed to ‘near-whiteness’. But for the most part, the returnees felt that where race played a part in their new lives, this generally served to advantage them. However, although the situation in Trinidad appears to have been moderated by assumptions that it remains a racial ‘Melting Pot’, the analysis strongly suggests that the colour-class system of stratification is still playing an essential role, along with racial stereotyping in society at large.

Estimating drizzle drop size and precipitation rate using two-colour lidar measurements

A method to estimate the size and liquid water content of drizzle drops using lidar measurements at two wavelengths is described. The method exploits the differential absorption of infrared light by liquid water at 905 nm and 1.5 μm, which leads to a different backscatter cross section for water drops larger than ≈50 μm. The ratio of backscatter measured from drizzle samples below cloud base at these two wavelengths (the colour ratio) provides a measure of the median volume drop diameter D0. This is a strong effect: for D0=200 μm, a colour ratio of ≈6 dB is predicted. Once D0 is known, the measured backscatter at 905 nm can be used to calculate the liquid water content (LWC) and other moments of the drizzle drop distribution. The method is applied to observations of drizzle falling from stratocumulus and stratus clouds. High resolution (32 s, 36 m) profiles of D0, LWC and precipitation rate R are derived. The main sources of error in the technique are the need to assume a value for the dispersion parameter μ in the drop size spectrum (leading to at most a 35% error in R) and the influence of aerosol returns on the retrieval (≈10% error in R for the cases considered here). Radar reflectivities are also computed from the lidar data, and compared to independent measurements from a colocated cloud radar, offering independent validation of the derived drop size distributions.

Data assimilation for marine monitoring and prediction: The MERCATOR operational assimilation systems and the MERSEA developments

During the past 15 years, a number of initiatives have been undertaken at national level to develop ocean forecasting systems operating at regional and/or global scales. The co-ordination between these efforts has been organized internationally through the Global Ocean Data Assimilation Experiment (GODAE). The French MERCATOR project is one of the leading participants in GODAE. The MERCATOR systems routinely assimilate a variety of observations such as multi-satellite altimeter data, sea-surface temperature and in situ temperature and salinity profiles, focusing on high-resolution scales of the ocean dynamics. The assimilation strategy in MERCATOR is based on a hierarchy of methods of increasing sophistication including optimal interpolation, Kalman filtering and variational methods, which are progressively deployed through the Syst`eme d’Assimilation MERCATOR (SAM) series. SAM-1 is based on a reduced-order optimal interpolation which can be operated using ‘altimetry-only’ or ‘multi-data’ set-ups; it relies on the concept of separability, assuming that the correlations can be separated into a product of horizontal and vertical contributions. The second release, SAM-2, is being developed to include new features from the singular evolutive extended Kalman (SEEK) filter, such as three-dimensional, multivariate error modes and adaptivity schemes. The third one, SAM-3, considers variational methods such as the incremental four-dimensional variational algorithm. Most operational forecasting systems evaluated during GODAE are based on least-squares statistical estimation assuming Gaussian errors. In the framework of the EU MERSEA (Marine EnviRonment and Security for the European Area) project, research is being conducted to prepare the next-generation operational ocean monitoring and forecasting systems. The research effort will explore nonlinear assimilation formulations to overcome limitations of the current systems. This paper provides an overview of the developments conducted in MERSEA with the SEEK filter, the Ensemble Kalman filter and the sequential importance re-sampling filter.

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.

Analytical potentials for triatomic molecules IX. The prediction of anharmonic force constants from potential energy surfaces based on harmonic force fields and dissociation energies for SO2 and O3

Analytical potential energy functions which are valid at all dissociation limits have been derived for the ground states of SO2 and O3. The procedure involves minimizing the errors between the observed vibrational spectra and spectra calculated by a variational procedure. Good agreement is obtained between the observed and calculated spectra for both molecules. Comparisons are made between anharmonic force fields, previously determined from the spectral data, and the force fields obtained by differentiating the derived analytical functions at the equilibrium configurations.

Singular vector ensemble forecasting systems and the prediction of flow dependent uncertainty

The ECMWF ensemble weather forecasts are generated by perturbing the initial conditions of the forecast using a subset of the singular vectors of the linearised propagator. Previous results show that when creating probabilistic forecasts from this ensemble better forecasts are obtained if the mean of the spread and the variability of the spread are calibrated separately. We show results from a simple linear model that suggest that this may be a generic property for all singular vector based ensemble forecasting systems based on only a subset of the full set of singular vectors.

The colour, light and contrast manual - designing and managing inclusive built environments

Endorsed by the Society of Light and Lighting, this practical book offers comprehensive guidance on how colour, light and contrast can be incorporated within buildings to enhance their usability. The book provides state-of-the-art, clear guidance as well as a valuable information source for busy professionals involved in the design or management of new and existing environments. The ways colour, light and contrast are used within built environments are critical in determining how people interact with the space, and how confident, safe, and secure they will feel when doing so. They also have a major influence on a person’s sense of well-being and their ability to use the environment independently and without undue effort. Understanding how to use colour and contrast and how they are influenced by both natural and artificial lighting is vital for all those involved in the design and management of the environments and spaces we all use. In recent years there has been a considerable amount of work undertaken to further our understanding of how colour, light and contrast affect emotion and sensory abilities, and how they can assist or hinder people in their everyday lives. Other publications consider these issues individually but The Colour, Light and Contrast Manual: designing and managing inclusive built environments draws knowledge and information together to produce a unique, comprehensive and informative guide to how the three elements can work together to improve the design and management of environments for us all.

Whole crop cereals 2. Prediction of apparent digestibility and energy value from in vitro digestion techniques and near infrared reflectance spectroscopy and of chemical composition by near infrared reflectance spectroscopy

Samples of whole crop wheat (WCW, n = 134) and whole crop barley (WCB, n = 16) were collected from commercial farms in the UK over a 2-year period (2003/2004 and 2004/2005). Near infrared reflectance spectroscopy (NIRS) was compared with laboratory and in vitro digestibility measures to predict digestible organic matter in the dry matter (DOMD) and metabolisable energy (ME) contents measured in vivo using sheep. Spectral models using the mean spectra of two scans were compared with those using individual spectra (duplicate spectra). Overall NIRS accurately predicted the concentration of chemical components in whole crop cereals apart from crude protein. ammonia-nitrogen, water-soluble carbohydrates, fermentation acids and solubility values. In addition. the spectral models had higher prediction power for in vivo DOMD and ME than chemical components or in vitro digestion methods. Overall there Was a benefit from the use of duplicate spectra rather than mean spectra and this was especially so for predicting in vivo DOMD and ME where the sample population size was smaller. The spectral models derived deal equally well with WCW and WCB and Would he of considerable practical value allowing rapid determination of nutritive value of these forages before their use in diets of productive animals. (C) 2008 Elsevier B.V. All rights reserved.