‘‘Model age-based” and ‘‘copy when uncertain” biases in children’s social learning of a novel task

Theoretical models of social learning predict that individuals can benefit from using strategies that specify when and whom to copy. Here the interaction of two social learning strategies, model age-based biased copying and copy when uncertain, was investigated. Uncertainty was created via a systematic manipulation of demonstration efficacy (completeness) and efficiency (causal relevance of some actions). The participants, 4- to 6-year-old children (N = 140), viewed both an adult model and a child model, each of whom used a different tool on a novel task. They did so in a complete condition, a near-complete condition, a partial demonstration condition, or a no-demonstration condition. Half of the demonstrations in each condition incorporated causally irrelevant actions by the models. Social transmission was assessed by first responses but also through children’s continued fidelity, the hallmark of social traditions. Results revealed a bias to copy the child model both on first response and in continued interactions. Demonstration efficacy and efficiency did not affect choice of model at first response but did influence solution exploration across trials, with demonstrations containing causally irrelevant actions decreasing exploration of alternative methods. These results imply that uncertain environments can result in canalized social learning from specific classes of mode

Lidar uncertainty in complex terrain development of a bias correction methodology

El autor ha trabajado como parte del equipo de investigación en mediciones de viento en el Centro Nacional de Energías Renovables (CENER), España, en cooperación con la Universidad Politécnica de Madrid y la Universidad Técnica de Dinamarca. El presente reporte recapitula el trabajo de investigación realizado durante los últimos 4.5 años en el estudio de las fuentes de error de los sistemas de medición remota de viento, basados en la tecnología lidar, enfocado al error causado por los efectos del terreno complejo. Este trabajo corresponde a una tarea del paquete de trabajo dedicado al estudio de sistemas remotos de medición de viento, perteneciente al proyecto de intestigación europeo del 7mo programa marco WAUDIT. Adicionalmente, los datos de viento reales han sido obtenidos durante las campañas de medición en terreno llano y terreno complejo, pertenecientes al también proyecto de intestigación europeo del 7mo programa marco SAFEWIND. El principal objetivo de este trabajo de investigación es determinar los efectos del terreno complejo en el error de medición de la velocidad del viento obtenida con los sistemas de medición remota lidar. Con este conocimiento, es posible proponer una metodología de corrección del error de las mediciones del lidar. Esta metodología está basada en la estimación de las variaciones del campo de viento no uniforme dentro del volumen de medición del lidar. Las variaciones promedio del campo de viento son predichas a partir de los resultados de las simulaciones computacionales de viento RANS, realizadas para el parque experimental de Alaiz. La metodología de corrección es verificada con los resultados de las simulaciones RANS y validadas con las mediciones reales adquiridas en la campaña de medición en terreno complejo. Al inicio de este reporte, el marco teórico describiendo el principio de medición de la tecnología lidar utilizada, es presentado con el fin de familiarizar al lector con los principales conceptos a utilizar a lo largo de este trabajo. Posteriormente, el estado del arte es presentado en donde se describe los avances realizados en el desarrollo de la la tecnología lidar aplicados al sector de la energía eólica. En la parte experimental de este trabajo de investigación se ha estudiado los datos adquiridos durante las dos campañas de medición realizadas. Estas campañas has sido realizadas en terreno llano y complejo, con el fin de complementar los conocimiento adquiridos en casa una de ellas y poder comparar los efectos del terreno en las mediciones de viento realizadas con sistemas remotos lidar. La primer campaña experimental se desarrollo en terreno llano, en el parque de ensayos de aerogeneradores H0vs0re, propiedad de DTU Wind Energy (anteriormente Ris0). La segunda campaña experimental se llevó a cabo en el parque de ensayos de aerogeneradores Alaiz, propiedad de CENER. Exactamente los mismos dos equipos lidar fueron utilizados en estas campañas, haciendo de estos experimentos altamente relevantes en el contexto de evaluación del recurso eólico. Un equipo lidar está basado en tecnología de onda continua, mientras que el otro está basado en tecnología de onda pulsada. La velocidad del viento fue medida, además de con los equipos lidar, con anemómetros de cazoletas, veletas y anemómetros verticales, instalados en mástiles meteorológicos. Los sensores del mástil meteorológico son considerados como las mediciones de referencia en el presente estudio. En primera instancia, se han analizado los promedios diez minútales de las medidas de viento. El objetivo es identificar las principales fuentes de error en las mediciones de los equipos lidar causadas por diferentes condiciones atmosféricas y por el flujo no uniforme de viento causado por el terreno complejo. El error del lidar ha sido estudiado como función de varias propiedades estadísticas del viento, como lo son el ángulo vertical de inclinación, la intensidad de turbulencia, la velocidad vertical, la estabilidad atmosférica y las características del terreno. El propósito es usar este conocimiento con el fin de definir criterios de filtrado de datos. Seguidamente, se propone una metodología para corregir el error del lidar causado por el campo de viento no uniforme, producido por la presencia de terreno complejo. Esta metodología está basada en el análisis matemático inicial sobre el proceso de cálculo de la velocidad de viento por los equipos lidar de onda continua. La metodología de corrección propuesta hace uso de las variaciones de viento calculadas a partir de las simulaciones RANS realizadas para el parque experimental de Alaiz. Una ventaja importante que presenta esta metodología es que las propiedades el campo de viento real, presentes en las mediciones instantáneas del lidar de onda continua, puede dar paso a análisis adicionales como parte del trabajo a futuro. Dentro del marco del proyecto, el trabajo diario se realizó en las instalaciones de CENER, con supervisión cercana de la UPM, incluyendo una estancia de 1.5 meses en la universidad. Durante esta estancia, se definió el análisis matemático de las mediciones de viento realizadas por el equipo lidar de onda continua. Adicionalmente, los efectos del campo de viento no uniforme sobre el error de medición del lidar fueron analíticamente definidos, después de asumir algunas simplificaciones. Adicionalmente, durante la etapa inicial de este proyecto se desarrollo una importante trabajo de cooperación con DTU Wind Energy. Gracias a esto, el autor realizó una estancia de 1.5 meses en Dinamarca. Durante esta estancia, el autor realizó una visita a la campaña de medición en terreno llano con el fin de aprender los aspectos básicos del diseño de campañas de medidas experimentales, el estudio del terreno y los alrededores y familiarizarse con la instrumentación del mástil meteorológico, el sistema de adquisición y almacenamiento de datos, así como de el estudio y reporte del análisis de mediciones. ABSTRACT The present report summarizes the research work performed during last 4.5 years of investigation on the sources of lidar bias due to complex terrain. This work corresponds to one task of the remote sensing work package, belonging to the FP7 WAUDIT project. Furthermore, the field data from the wind velocity measurement campaigns of the FP7 SafeWind project have been used in this report. The main objective of this research work is to determine the terrain effects on the lidar bias in the measured wind velocity. With this knowledge, it is possible to propose a lidar bias correction methodology. This methodology is based on an estimation of the wind field variations within the lidar scan volume. The wind field variations are calculated from RANS simulations performed from the Alaiz test site. The methodology is validated against real scale measurements recorded during an eight month measurement campaign at the Alaiz test site. Firstly, the mathematical framework of the lidar sensing principle is introduced and an overview of the state of the art is presented. The experimental part includes the study of two different, but complementary experiments. The first experiment was a measurement campaign performed in flat terrain, at DTU Wind Energy H0vs0re test site, while the second experiment was performed in complex terrain at CENER Alaiz test site. Exactly the same two lidar devices, based on continuous wave and pulsed wave systems, have been used in the two consecutive measurement campaigns, making this a relevant experiment in the context of wind resource assessment. The wind velocity was sensed by the lidars and standard cup anemometry and wind vanes (installed on a met mast). The met mast sensors are considered as the reference wind velocity measurements. The first analysis of the experimental data is dedicated to identify the main sources of lidar bias present in the 10 minute average values. The purpose is to identify the bias magnitude introduced by different atmospheric conditions and by the non-uniform wind flow resultant of the terrain irregularities. The lidar bias as function of several statistical properties of the wind flow like the tilt angle, turbulence intensity, vertical velocity, atmospheric stability and the terrain characteristics have been studied. The aim of this exercise is to use this knowledge in order to define useful lidar bias data filters. Then, a methodology to correct the lidar bias caused by non-uniform wind flow is proposed, based on the initial mathematical analysis of the lidar measurements. The proposed lidar bias correction methodology has been developed focusing on the the continuous wave lidar system. In a last step, the proposed lidar bias correction methodology is validated with the data of the complex terrain measurement campaign. The methodology makes use of the wind field variations obtained from the RANS analysis. The results are presented and discussed. The advantage of this methodology is that the wind field properties at the Alaiz test site can be studied with more detail, based on the instantaneous measurements of the CW lidar. Within the project framework, the daily basis work has been done at CENER, with close guidance and support from the UPM, including an exchange period of 1.5 months. During this exchange period, the mathematical analysis of the lidar sensing of the wind velocity was defined. Furthermore, the effects of non-uniform wind fields on the lidar bias were analytically defined, after making some assumptions for the sake of simplification. Moreover, there has been an important cooperation with DTU Wind Energy, where a secondment period of 1.5 months has been done as well. During the secondment period at DTU Wind Energy, an important introductory learning has taken place. The learned aspects include the design of an experimental measurement campaign in flat terrain, the site assessment study of obstacles and terrain conditions, the data acquisition and processing, as well as the study and reporting of the measurement analysis.

Error and uncertainty of adult age estimation of the pubic symphysis in an Australian sub-population using computed tomography

After attending this presentation, attendees will gain awareness of: (1) the error and uncertainty associated with the application of the Suchey-Brooks (S-B) method of age estimation of the pubic symphysis to a contemporary Australian population; (2) the implications of sexual dimorphism and bilateral asymmetry of the pubic symphysis through preliminary geometric morphometric assessment; and (3) the value of three-dimensional (3D) autopsy data acquisition for creating forensic anthropological standards. This presentation will impact the forensic science community by demonstrating that, in the absence of demographically sound skeletal collections, post-mortem autopsy data provides an exciting platform for the construction of large contemporary ‘virtual osteological libraries’ for which forensic anthropological research can be conducted on Australian individuals. More specifically, this study assesses the applicability and accuracy of the S-B method to a contemporary adult population in Queensland, Australia, and using a geometric morphometric approach, provides an insight to the age-related degeneration of the pubic symphysis. Despite the prominent use of the Suchey-Brooks (1990) method of age estimation in forensic anthropological practice, it is subject to intrinsic limitations, with reports of differential inter-population error rates between geographical locations1-4. Australian forensic anthropology is constrained by a paucity of population specific standards due to a lack of repositories of documented skeletons. Consequently, in Australian casework proceedings, standards constructed from predominately American reference samples are applied to establish a biological profile. In the global era of terrorism and natural disasters, more specific population standards are required to improve the efficiency of medico-legal death investigation in Queensland. The sample comprises multi-slice computed tomography (MSCT) scans of the pubic symphysis (slice thickness: 0.5mm, overlap: 0.1mm) on 195 individuals of caucasian ethnicity aged 15-70 years. Volume rendering reconstruction of the symphyseal surface was conducted in Amira® (v.4.1) and quantitative analyses in Rapidform® XOS. The sample was divided into ten-year age sub-sets (eg. 15-24) with a final sub-set of 65-70 years. Error with respect to the method’s assigned means were analysed on the basis of bias (directionality of error), inaccuracy (magnitude of error) and percentage correct classification of left and right symphyseal surfaces. Morphometric variables including surface area, circumference, maximum height and width of the symphyseal surface and micro-architectural assessment of cortical and trabecular bone composition were quantified using novel automated engineering software capabilities. The results of this study demonstrated correct age classification utilizing the mean and standard deviations of each phase of the S-B method of 80.02% and 86.18% in Australian males and females, respectively. Application of the S-B method resulted in positive biases and mean inaccuracies of 7.24 (±6.56) years for individuals less than 55 years of age, compared to negative biases and mean inaccuracies of 5.89 (±3.90) years for individuals greater than 55 years of age. Statistically significant differences between chronological and S-B mean age were demonstrated in 83.33% and 50% of the six age subsets in males and females, respectively. Asymmetry of the pubic symphysis was a frequent phenomenon with 53.33% of the Queensland population exhibiting statistically significant (χ2 - p<0.01) differential phase classification of left and right surfaces of the same individual. Directionality was found in bilateral asymmetry, with the right symphyseal faces being slightly older on average and providing more accurate estimates using the S-B method5. Morphometric analysis verified these findings, with the left surface exhibiting significantly greater circumference and surface area than the right (p<0.05). Morphometric analysis demonstrated an increase in maximum height and width of the surface with age, with most significant changes (p<0.05) occurring between the 25-34 and 55-64 year age subsets. These differences may be attributed to hormonal components linked to menopause in females and a reduction in testosterone in males. Micro-architectural analysis demonstrated degradation of cortical composition with age, with differential bone resorption between the medial, ventral and dorsal surfaces of the pubic symphysis. This study recommends that the S-B method be applied with caution in medico-legal death investigations of unknown skeletal remains in Queensland. Age estimation will always be accompanied by error; therefore this study demonstrates the potential for quantitative morphometric modelling of age related changes of the pubic symphysis as a tool for methodological refinement, providing a rigor and robust assessment to remove the subjectivity associated with current pelvic aging methods.

Managing uncertainty to improve the cost performance of complex infrastructure projects

There has been a recent spate of high profile infrastructure cost overruns in Australia and internationally. This is just the tip of a longer-term and more deeply-seated problem with initial budget estimating practice, well recognised in both academic research and industry reviews: the problem of uncertainty. A case study of the Sydney Opera House is used to identify and illustrate the key causal factors and system dynamics of cost overruns. It is conventionally the role of risk management to deal with such uncertainty, but the type and extent of the uncertainty involved in complex projects is shown to render established risk management techniques ineffective. This paper considers a radical advance on current budget estimating practice which involves a particular approach to statistical modelling complemented by explicit training in estimating practice. The statistical modelling approach combines the probability management techniques of Savage, which operate on actual distributions of values rather than flawed representations of distributions, and the data pooling technique of Skitmore, where the size of the reference set is optimised. Estimating training employs particular calibration development methods pioneered by Hubbard, which reduce the bias of experts caused by over-confidence and improve the consistency of subjective decision-making. A new framework for initial budget estimating practice is developed based on the combined statistical and training methods, with each technique being explained and discussed.

Systematic bias in MODIS dust aerosol retrieval at Kanpur (AERONET), Indo-Gangetic Basin - art.no.640816

We have compared the spectral aerosol optical depth (AOD) and aerosol fine mode fraction (AFMF) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) with those of Aerosol Robotic Network (AERONET) at Kanpur (26.45N, 80.35E), northern India for the pre-monsoon season (March to June, 2001-2005). We found that MODIS systematically overestimates AOD during pre-monsoon season (known to be influenced by dust transport from north-west of India). The errors in AOD were correlated with the MODIS top-of-atmosphere apparent surface reflectance in 2.1 mu m channel (rho*(2.1)). MODIS aerosol algorithm uses p*(2.1) to derive the surface reflectance in visible channels (rho(0.47), rho(0.66)) using an empirical mid IR-visible correlation (rho(0.47) = rho(2.1)/4, rho(0.66) = rho(2.1)/2). The large uncertainty in estimating surface reflectance in visible channels (Delta rho(0.66)+/- 0.04, Delta rho(0.47)+/- 0.02) at higher values of p*(2.1) (p*(2.1) > 0.18) leads to higher aerosol contribution in the total reflected radiance at top-of atmosphere to compensate for the reduced surface reflectance in visible channels and thus leads to overestimation of AOD. This was also reflected in the very low values of AFMF during pre-monsoon whose accuracy depends on the aerosol path radiance in 0.47 and 0.66 mu m channels and aerosol models. The errors in AOD were also high in the scattering angle range 110 degrees-140 degrees, where the effect of dust non-spherity on its optical properties is significant. The direct measurements of spectral surface reflectance are required over the Indo-Gangetic basin in order to validate the mid IR-visible relationship. MODIS aerosol models should also be modified to incorporate the effect of non-spherity of dust aerosols.

Uncertainty estimates of remote sensing reflectance derived from comparison of ocean color satellite data sets

Assigning uncertainty to ocean-color satellite products is a requirement to allow informed use of these data. Here, uncertainty estimates are derived using the comparison on a 12th-degree grid of coincident daily records of the remote-sensing reflectance RRS obtained with the same processing chain from three satellite missions, MERIS, MODIS and SeaWiFS. The approach is spatially resolved and produces σ, the part of the RRS uncertainty budget associated with random effects. The global average of σ decreases with wavelength from approximately 0.7– 0.9 10−3 sr−1 at 412 nm to 0.05–0.1 10−3 sr−1 at the red band, with uncertainties on σ evaluated as 20–30% between 412 and 555 nm, and 30–40% at 670 nm. The distribution of σ shows a restricted spatial variability and small variations with season, which makes the multi-annual global distribution of σ an estimate applicable to all retrievals of the considered missions. The comparison of σ with other uncertainty estimates derived from field data or with the support of algorithms provides a consistent picture. When translated in relative terms, and assuming a relatively low bias, the distribution of σ suggests that the objective of a 5% uncertainty is fulfilled between 412 and 490 nm for oligotrophic waters (chlorophyll-a concentration below 0.1 mg m−3). This study also provides comparison statistics. Spectrally, the mean absolute relative difference between RRS from different missions shows a characteristic U-shape with both ends at blue and red wavelengths inversely related to the amplitude of RRS. On average and for the considered data sets, SeaWiFS RRS tend to be slightly higher than MODIS RRS, which in turn appear higher than MERIS RRS. Biases between mission-specific RRS may exhibit a seasonal dependence, particularly in the subtropical belt.

Using interviewer random effects to remove selection bias from HIV prevalence estimates

Background: Selection bias in HIV prevalence estimates occurs if non-participation in testing is correlated with HIV status. Longitudinal data suggests that individuals who know or suspect they are HIV positive are less likely to participate in testing in HIV surveys, in which case methods to correct for missing data which are based on imputation and observed characteristics will produce biased results. Methods: The identity of the HIV survey interviewer is typically associated with HIV testing participation, but is unlikely to be correlated with HIV status. Interviewer identity can thus be used as a selection variable allowing estimation of Heckman-type selection models. These models produce asymptotically unbiased HIV prevalence estimates, even when non-participation is correlated with unobserved characteristics, such as knowledge of HIV status. We introduce a new random effects method to these selection models which overcomes non-convergence caused by collinearity, small sample bias, and incorrect inference in existing approaches. Our method is easy to implement in standard statistical software, and allows the construction of bootstrapped standard errors which adjust for the fact that the relationship between testing and HIV status is uncertain and needs to be estimated. Results: Using nationally representative data from the Demographic and Health Surveys, we illustrate our approach with new point estimates and confidence intervals (CI) for HIV prevalence among men in Ghana (2003) and Zambia (2007). In Ghana, we find little evidence of selection bias as our selection model gives an HIV prevalence estimate of 1.4% (95% CI 1.2% – 1.6%), compared to 1.6% among those with a valid HIV test. In Zambia, our selection model gives an HIV prevalence estimate of 16.3% (95% CI 11.0% - 18.4%), compared to 12.1% among those with a valid HIV test. Therefore, those who decline to test in Zambia are found to be more likely to be HIV positive. Conclusions: Our approach corrects for selection bias in HIV prevalence estimates, is possible to implement even when HIV prevalence or non-participation is very high or very low, and provides a practical solution to account for both sampling and parameter uncertainty in the estimation of confidence intervals. The wide confidence intervals estimated in an example with high HIV prevalence indicate that it is difficult to correct statistically for the bias that may occur when a large proportion of people refuse to test.

The Impact of Uncertainty Shocks under Measurement Error: A Proxy SVAR approach

A growing literature considers the impact of uncertainty using SVAR models that include proxies for uncertainty shocks as endogenous variables. In this paper we consider the impact of measurement error in these proxies on the estimated impulse responses. We show via a Monte-Carlo experiment that measurement error can result in attenuation bias in impulse responses. In contrast, the proxy SVAR that uses the uncertainty shock proxy as an instrument does not su¤er from this bias. Applying this latter method to the Bloom (2009) data-set results in impulse responses to uncertainty shocks that are larger in magnitude and more persistent than those obtained from a recursive SVAR.

Does uncertainty cause inertia in decision making? An experimental study of the role of regret aversion and indecisiveness

Previous research has shown that often there is clear inertia in individual decision making---that is, a tendency for decision makers to choose a status quo option. I conduct a laboratory experiment to investigate two potential determinants of inertia in uncertain environments: (i) regret aversion and (ii) ambiguity-driven indecisiveness. I use a between-subjects design with varying conditions to identify the effects of these two mechanisms on choice behavior. In each condition, participants choose between two simple real gambles, one of which is the status quo option. I find that inertia is quite large and that both mechanisms are equally important.

Sampling uncertainty properties of cloud fraction estimates from random transect observations

[1] Cloud cover is conventionally estimated from satellite images as the observed fraction of cloudy pixels. Active instruments such as radar and Lidar observe in narrow transects that sample only a small percentage of the area over which the cloud fraction is estimated. As a consequence, the fraction estimate has an associated sampling uncertainty, which usually remains unspecified. This paper extends a Bayesian method of cloud fraction estimation, which also provides an analytical estimate of the sampling error. This method is applied to test the sensitivity of this error to sampling characteristics, such as the number of observed transects and the variability of the underlying cloud field. The dependence of the uncertainty on these characteristics is investigated using synthetic data simulated to have properties closely resembling observations of the spaceborne Lidar NASA-LITE mission. Results suggest that the variance of the cloud fraction is greatest for medium cloud cover and least when conditions are mostly cloudy or clear. However, there is a bias in the estimation, which is greatest around 25% and 75% cloud cover. The sampling uncertainty is also affected by the mean lengths of clouds and of clear intervals; shorter lengths decrease uncertainty, primarily because there are more cloud observations in a transect of a given length. Uncertainty also falls with increasing number of transects. Therefore a sampling strategy aimed at minimizing the uncertainty in transect derived cloud fraction will have to take into account both the cloud and clear sky length distributions as well as the cloud fraction of the observed field. These conclusions have implications for the design of future satellite missions. This paper describes the first integrated methodology for the analytical assessment of sampling uncertainty in cloud fraction observations from forthcoming spaceborne radar and Lidar missions such as NASA's Calipso and CloudSat.

Measuring the response of macroeconomic uncertainty to shocks

Recent research documents the importance of uncertainty in determining macroeconomic outcomes, but little is known about the transmission of uncertainty across such outcomes. This paper examines the response of uncertainty about inflation and output growth to shocks documenting statistically significant size and sign bias and spillover effects. Uncertainty about inflation is a determinant of output uncertainty, whereas higher growth volatility tends to raise inflation volatility. Both inflation and growth volatility respond asymmetrically to positive and negative shocks. Negative growth and inflation shocks lead to higher and more persistent uncertainty than shocks of equal magnitude but opposite sign.

Calibration and bias correction of climate projections for crop modelling: an idealised case study over Europe

Producing projections of future crop yields requires careful thought about the appropriate use of atmosphere-ocean global climate model (AOGCM) simulations. Here we describe and demonstrate multiple methods for ‘calibrating’ climate projections using an ensemble of AOGCM simulations in a ‘perfect sibling’ framework. Crucially, this type of analysis assesses the ability of each calibration methodology to produce reliable estimates of future climate, which is not possible just using historical observations. This type of approach could be more widely adopted for assessing calibration methodologies for crop modelling. The calibration methods assessed include the commonly used ‘delta’ (change factor) and ‘nudging’ (bias correction) approaches. We focus on daily maximum temperature in summer over Europe for this idealised case study, but the methods can be generalised to other variables and other regions. The calibration methods, which are relatively easy to implement given appropriate observations, produce more robust projections of future daily maximum temperatures and heat stress than using raw model output. The choice over which calibration method to use will likely depend on the situation, but change factor approaches tend to perform best in our examples. Finally, we demonstrate that the uncertainty due to the choice of calibration methodology is a significant contributor to the total uncertainty in future climate projections for impact studies. We conclude that utilising a variety of calibration methods on output from a wide range of AOGCMs is essential to produce climate data that will ensure robust and reliable crop yield projections.

Assessing levels of uncertainty in recent temperature time series

We examine to what degree we can expect to obtain accurate temperature trends for the last two decades near the surface and in the lower troposphere. We compare temperatures obtained from surface observations and radiosondes as well as satellite-based measurements from the Microwave Soundings Units (MSU), which have been adjusted for orbital decay and non-linear instrument-body effects, and reanalyses from the European Centre for Medium-Range Weather Forecasts (ERA) and the National Centre for Environmental Prediction (NCEP). In regions with abundant conventional data coverage, where the MSU has no major influence on the reanalysis, temperature anomalies obtained from microwave sounders, radiosondes and from both reanalyses agree reasonably. Where coverage is insufficient, in particular over the tropical oceans, large differences are found between the MSU and either reanalysis. These differences apparently relate to changes in the satellite data availability and to differing satellite retrieval methodologies, to which both reanalyses are quite sensitive over the oceans. For NCEP, this results from the use of raw radiances directly incorporated into the analysis, which make the reanalysis sensitive to changes in the underlying algorithms, e.g. those introduced in August 1992. For ERA, the bias-correction of the one-dimensional variational analysis may introduce an error when the satellite relative to which the correction is calculated is biased itself or when radiances change on a time scale longer than a couple of months, e.g. due to orbit decay. ERA inhomogeneities are apparent in April 1985, October/November 1986 and April 1989. These dates can be identified with the replacements of satellites. It is possible that a negative bias in the sea surface temperatures (SSTs) used in the reanalyses may have been introduced over the period of the satellite record. This could have resulted from a decrease in the number of ship measurements, a concomitant increase in the importance of satellite-derived SSTs, and a likely cold bias in the latter. Alternately, a warm bias in SSTs could have been caused by an increase in the percentage of buoy measurements (relative to deeper ship intake measurements) in the tropical Pacific. No indications for uncorrected inhomogeneities of land surface temperatures could be found. Near-surface temperatures have biases in the boundary layer in both reanalyses, presumably due to the incorrect treatment of snow cover. The increase of near-surface compared to lower tropospheric temperatures in the last two decades may be due to a combination of several factors, including high-latitude near-surface winter warming due to an enhanced NAO and upper-tropospheric cooling due to stratospheric ozone decrease.

Do location specific forecasts pose a new challenge for communicating uncertainty?

In the last decade, the growth of local, site-specific weather forecasts delivered by mobile phone or website represents arguably the fastest change in forecast consumption since the beginning of Television weather forecasts 60 years ago. In this study, a street-interception survey of 274 members of the public a clear first preference for narrow weather forecasts above traditional broad weather forecasts is shown for the first time, with a clear bias towards this preference for users under 40. The impact of this change on the understanding of forecast probability and intensity information is explored. While the correct interpretation of the statement ‘There is a 30% chance of rain tomorrow’ is still low in the cohort, in common with previous studies, a clear impact of age and educational attainment on understanding is shown, with those under 40 and educated to degree level or above more likely to correctly interpret it. The interpretation of rainfall intensity descriptors (‘Light’, ‘Moderate’, ‘Heavy’) by the cohort is shown to be significantly different to official and expert assessment of the same descriptors and to have large variance amongst the cohort. However, despite these key uncertainties, members of the cohort generally seem to make appropriate decisions about rainfall forecasts. There is some evidence that the decisions made are different depending on the communication format used, and the cohort expressed a clear preference for tabular over graphical weather forecast presentation.