A data-mining based methodology for wind forecasting

In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive use of this generation technology. The introduction of wind based generation in power systems poses new challenges for the power system operators. This is mainly due to the variability and uncertainty in weather conditions and, consequently, in the wind based generation. In order to deal with this uncertainty and to improve the power system efficiency, adequate wind forecasting tools must be used. This paper proposes a data-mining-based methodology for very short-term wind forecasting, which is suitable to deal with large real databases. The paper includes a case study based on a real database regarding the last three years of wind speed, and results for wind speed forecasting at 5 minutes intervals.

Essays in macro finance and monetary economics

Les questions abordées dans les deux premiers articles de ma thèse cherchent à comprendre les facteurs économiques qui affectent la structure à terme des taux d'intérêt et la prime de risque. Je construis des modèles non linéaires d'équilibre général en y intégrant des obligations de différentes échéances. Spécifiquement, le premier article a pour objectif de comprendre la relation entre les facteurs macroéconomiques et le niveau de prime de risque dans un cadre Néo-keynésien d'équilibre général avec incertitude. L'incertitude dans le modèle provient de trois sources : les chocs de productivité, les chocs monétaires et les chocs de préférences. Le modèle comporte deux types de rigidités réelles à savoir la formation des habitudes dans les préférences et les coûts d'ajustement du stock de capital. Le modèle est résolu par la méthode des perturbations à l'ordre deux et calibré à l'économie américaine. Puisque la prime de risque est par nature une compensation pour le risque, l'approximation d'ordre deux implique que la prime de risque est une combinaison linéaire des volatilités des trois chocs. Les résultats montrent qu'avec les paramètres calibrés, les chocs réels (productivité et préférences) jouent un rôle plus important dans la détermination du niveau de la prime de risque relativement aux chocs monétaires. Je montre que contrairement aux travaux précédents (dans lesquels le capital de production est fixe), l'effet du paramètre de la formation des habitudes sur la prime de risque dépend du degré des coûts d'ajustement du capital. Lorsque les coûts d'ajustement du capital sont élevés au point que le stock de capital est fixe à l'équilibre, une augmentation du paramètre de formation des habitudes entraine une augmentation de la prime de risque. Par contre, lorsque les agents peuvent librement ajuster le stock de capital sans coûts, l'effet du paramètre de la formation des habitudes sur la prime de risque est négligeable. Ce résultat s'explique par le fait que lorsque le stock de capital peut être ajusté sans coûts, cela ouvre un canal additionnel de lissage de consommation pour les agents. Par conséquent, l'effet de la formation des habitudes sur la prime de risque est amoindri. En outre, les résultats montrent que la façon dont la banque centrale conduit sa politique monétaire a un effet sur la prime de risque. Plus la banque centrale est agressive vis-à-vis de l'inflation, plus la prime de risque diminue et vice versa. Cela est due au fait que lorsque la banque centrale combat l'inflation cela entraine une baisse de la variance de l'inflation. Par suite, la prime de risque due au risque d'inflation diminue. Dans le deuxième article, je fais une extension du premier article en utilisant des préférences récursives de type Epstein -- Zin et en permettant aux volatilités conditionnelles des chocs de varier avec le temps. L'emploi de ce cadre est motivé par deux raisons. D'abord des études récentes (Doh, 2010, Rudebusch and Swanson, 2012) ont montré que ces préférences sont appropriées pour l'analyse du prix des actifs dans les modèles d'équilibre général. Ensuite, l'hétéroscedasticité est une caractéristique courante des données économiques et financières. Cela implique que contrairement au premier article, l'incertitude varie dans le temps. Le cadre dans cet article est donc plus général et plus réaliste que celui du premier article. L'objectif principal de cet article est d'examiner l'impact des chocs de volatilités conditionnelles sur le niveau et la dynamique des taux d'intérêt et de la prime de risque. Puisque la prime de risque est constante a l'approximation d'ordre deux, le modèle est résolu par la méthode des perturbations avec une approximation d'ordre trois. Ainsi on obtient une prime de risque qui varie dans le temps. L'avantage d'introduire des chocs de volatilités conditionnelles est que cela induit des variables d'état supplémentaires qui apportent une contribution additionnelle à la dynamique de la prime de risque. Je montre que l'approximation d'ordre trois implique que les primes de risque ont une représentation de type ARCH-M (Autoregressive Conditional Heteroscedasticty in Mean) comme celui introduit par Engle, Lilien et Robins (1987). La différence est que dans ce modèle les paramètres sont structurels et les volatilités sont des volatilités conditionnelles de chocs économiques et non celles des variables elles-mêmes. J'estime les paramètres du modèle par la méthode des moments simulés (SMM) en utilisant des données de l'économie américaine. Les résultats de l'estimation montrent qu'il y a une évidence de volatilité stochastique dans les trois chocs. De plus, la contribution des volatilités conditionnelles des chocs au niveau et à la dynamique de la prime de risque est significative. En particulier, les effets des volatilités conditionnelles des chocs de productivité et de préférences sont significatifs. La volatilité conditionnelle du choc de productivité contribue positivement aux moyennes et aux écart-types des primes de risque. Ces contributions varient avec la maturité des bonds. La volatilité conditionnelle du choc de préférences quant à elle contribue négativement aux moyennes et positivement aux variances des primes de risque. Quant au choc de volatilité de la politique monétaire, son impact sur les primes de risque est négligeable. Le troisième article (coécrit avec Eric Schaling, Alain Kabundi, révisé et resoumis au journal of Economic Modelling) traite de l'hétérogénéité dans la formation des attentes d'inflation de divers groupes économiques et de leur impact sur la politique monétaire en Afrique du sud. La question principale est d'examiner si différents groupes d'agents économiques forment leurs attentes d'inflation de la même façon et s'ils perçoivent de la même façon la politique monétaire de la banque centrale (South African Reserve Bank). Ainsi on spécifie un modèle de prédiction d'inflation qui nous permet de tester l'arrimage des attentes d'inflation à la bande d'inflation cible (3% - 6%) de la banque centrale. Les données utilisées sont des données d'enquête réalisée par la banque centrale auprès de trois groupes d'agents : les analystes financiers, les firmes et les syndicats. On exploite donc la structure de panel des données pour tester l'hétérogénéité dans les attentes d'inflation et déduire leur perception de la politique monétaire. Les résultats montrent qu'il y a évidence d'hétérogénéité dans la manière dont les différents groupes forment leurs attentes. Les attentes des analystes financiers sont arrimées à la bande d'inflation cible alors que celles des firmes et des syndicats ne sont pas arrimées. En effet, les firmes et les syndicats accordent un poids significatif à l'inflation retardée d'une période et leurs prédictions varient avec l'inflation réalisée (retardée). Ce qui dénote un manque de crédibilité parfaite de la banque centrale au vu de ces agents.

Quantile based entropy function in past lifetime

Di Crescenzo and Longobardi (2002) introduced a measure of uncertainty in past lifetime distributions and studied its relationship with residual entropy function. In the present paper, we introduce a quantile version of the entropy function in past lifetime and study its properties. Unlike the measure of uncertainty given in Di Crescenzo and Longobardi (2002) the proposed measure uniquely determines the underlying probability distribution. The measure is used to study two nonparametric classes of distributions. We prove characterizations theorems for some well known quantile lifetime distributions

The efficiency of direct payments versus tax reductions under uncertainty

This paper analyzes the optimal behavior of farmers in the presence of direct payments and uncertainty. In an empirical analysis for Switzerland, it confirms previously obtained theoretical results and determines the magnitude of the theoretical predicted effects. The results show that direct payments increase agricultural production between 3.7% to 4.8%. Alternatively to direct payments, the production effect of tax reductions is evaluated in order to determine its magnitude. The empirical analysis corroborates the theoretical results of the literature and demonstrates that tax reductions are also distorting, but to a substantially lesser degree if losses are not offset. However, tax reductions, independently whether losses are offset or not, lead to higher government spending than pure direct payments

Constraint satisfaction techniques under uncertain conditions for fault diagnosis in nonlinear dynamic systems

The speed of fault isolation is crucial for the design and reconfiguration of fault tolerant control (FTC). In this paper the fault isolation problem is stated as a constraint satisfaction problem (CSP) and solved using constraint propagation techniques. The proposed method is based on constraint satisfaction techniques and uncertainty space refining of interval parameters. In comparison with other approaches based on adaptive observers, the major advantage of the presented method is that the isolation speed is fast even taking into account uncertainty in parameters, measurements and model errors and without the monotonicity assumption. In order to illustrate the proposed approach, a case study of a nonlinear dynamic system is presented

Fault diagnosis by refining the parameter uncertainty space of nonlinear dynamic systems

This paper deals with fault detection and isolation problems for nonlinear dynamic systems. Both problems are stated as constraint satisfaction problems (CSP) and solved using consistency techniques. The main contribution is the isolation method based on consistency techniques and uncertainty space refining of interval parameters. The major advantage of this method is that the isolation speed is fast even taking into account uncertainty in parameters, measurements, and model errors. Interval calculations bring independence from the assumption of monotony considered by several approaches for fault isolation which are based on observers. An application to a well known alcoholic fermentation process model is presented

Conceptual design of wastewater treatment plants using multiple objectives

La implementació de la Directiva Europea 91/271/CEE referent a tractament d'aigües residuals urbanes va promoure la construcció de noves instal·lacions al mateix temps que la introducció de noves tecnologies per tractar nutrients en àrees designades com a sensibles. Tant el disseny d'aquestes noves infraestructures com el redisseny de les ja existents es va portar a terme a partir d'aproximacions basades fonamentalment en objectius econòmics degut a la necessitat d'acabar les obres en un període de temps relativament curt. Aquests estudis estaven basats en coneixement heurístic o correlacions numèriques provinents de models determinístics simplificats. Així doncs, moltes de les estacions depuradores d'aigües residuals (EDARs) resultants van estar caracteritzades per una manca de robustesa i flexibilitat, poca controlabilitat, amb freqüents problemes microbiològics de separació de sòlids en el decantador secundari, elevats costos d'operació i eliminació parcial de nutrients allunyant-les de l'òptim de funcionament. Molts d'aquestes problemes van sorgir degut a un disseny inadequat, de manera que la comunitat científica es va adonar de la importància de les etapes inicials de disseny conceptual. Precisament per aquesta raó, els mètodes tradicionals de disseny han d'evolucionar cap a sistemes d'avaluació mes complexos, que tinguin en compte múltiples objectius, assegurant així un millor funcionament de la planta. Tot i la importància del disseny conceptual tenint en compte múltiples objectius, encara hi ha un buit important en la literatura científica tractant aquest camp d'investigació. L'objectiu que persegueix aquesta tesi és el de desenvolupar un mètode de disseny conceptual d'EDARs considerant múltiples objectius, de manera que serveixi d'eina de suport a la presa de decisions al seleccionar la millor alternativa entre diferents opcions de disseny. Aquest treball de recerca contribueix amb un mètode de disseny modular i evolutiu que combina diferent tècniques com: el procés de decisió jeràrquic, anàlisi multicriteri, optimació preliminar multiobjectiu basada en anàlisi de sensibilitat, tècniques d'extracció de coneixement i mineria de dades, anàlisi multivariant i anàlisi d'incertesa a partir de simulacions de Monte Carlo. Això s'ha aconseguit subdividint el mètode de disseny desenvolupat en aquesta tesis en quatre blocs principals: (1) generació jeràrquica i anàlisi multicriteri d'alternatives, (2) anàlisi de decisions crítiques, (3) anàlisi multivariant i (4) anàlisi d'incertesa. El primer dels blocs combina un procés de decisió jeràrquic amb anàlisi multicriteri. El procés de decisió jeràrquic subdivideix el disseny conceptual en una sèrie de qüestions mes fàcilment analitzables i avaluables mentre que l'anàlisi multicriteri permet la consideració de diferent objectius al mateix temps. D'aquesta manera es redueix el nombre d'alternatives a avaluar i fa que el futur disseny i operació de la planta estigui influenciat per aspectes ambientals, econòmics, tècnics i legals. Finalment aquest bloc inclou una anàlisi de sensibilitat dels pesos que proporciona informació de com varien les diferents alternatives al mateix temps que canvia la importància relativa del objectius de disseny. El segon bloc engloba tècniques d'anàlisi de sensibilitat, optimització preliminar multiobjectiu i extracció de coneixement per donar suport al disseny conceptual d'EDAR, seleccionant la millor alternativa un cop s'han identificat decisions crítiques. Les decisions crítiques són aquelles en les que s'ha de seleccionar entre alternatives que compleixen de forma similar els objectius de disseny però amb diferents implicacions pel que respecte a la futura estructura i operació de la planta. Aquest tipus d'anàlisi proporciona una visió més àmplia de l'espai de disseny i permet identificar direccions desitjables (o indesitjables) cap on el procés de disseny pot derivar. El tercer bloc de la tesi proporciona l'anàlisi multivariant de les matrius multicriteri obtingudes durant l'avaluació de les alternatives de disseny. Específicament, les tècniques utilitzades en aquest treball de recerca engloben: 1) anàlisi de conglomerats, 2) anàlisi de components principals/anàlisi factorial i 3) anàlisi discriminant. Com a resultat és possible un millor accés a les dades per realitzar la selecció de les alternatives, proporcionant més informació per a una avaluació mes efectiva, i finalment incrementant el coneixement del procés d'avaluació de les alternatives de disseny generades. En el quart i últim bloc desenvolupat en aquesta tesi, les diferents alternatives de disseny són avaluades amb incertesa. L'objectiu d'aquest bloc és el d'estudiar el canvi en la presa de decisions quan una alternativa és avaluada incloent o no incertesa en els paràmetres dels models que descriuen el seu comportament. La incertesa en el paràmetres del model s'introdueix a partir de funcions de probabilitat. Desprès es porten a terme simulacions Monte Carlo, on d'aquestes distribucions se n'extrauen números aleatoris que es subsisteixen pels paràmetres del model i permeten estudiar com la incertesa es propaga a través del model. Així és possible analitzar la variació en l'acompliment global dels objectius de disseny per a cada una de les alternatives, quines són les contribucions en aquesta variació que hi tenen els aspectes ambientals, legals, econòmics i tècnics, i finalment el canvi en la selecció d'alternatives quan hi ha una variació de la importància relativa dels objectius de disseny. En comparació amb les aproximacions tradicionals de disseny, el mètode desenvolupat en aquesta tesi adreça problemes de disseny/redisseny tenint en compte múltiples objectius i múltiples criteris. Al mateix temps, el procés de presa de decisions mostra de forma objectiva, transparent i sistemàtica el perquè una alternativa és seleccionada en front de les altres, proporcionant l'opció que més bé acompleix els objectius marcats, mostrant els punts forts i febles, les principals correlacions entre objectius i alternatives, i finalment tenint en compte la possible incertesa inherent en els paràmetres del model que es fan servir durant les anàlisis. Les possibilitats del mètode desenvolupat es demostren en aquesta tesi a partir de diferents casos d'estudi: selecció del tipus d'eliminació biològica de nitrogen (cas d'estudi # 1), optimització d'una estratègia de control (cas d'estudi # 2), redisseny d'una planta per aconseguir eliminació simultània de carboni, nitrogen i fòsfor (cas d'estudi # 3) i finalment anàlisi d'estratègies control a nivell de planta (casos d'estudi # 4 i # 5).

Population Assessment of an Endangered Shorebird: the Piping Plover (Charadrius melodus melodus) in Eastern Canada

Small, at-risk populations are those for which accurate demographic information is most crucial to conservation and recovery, but also where data collection is constrained by logistical challenges and small sample sizes. Migratory animals in particular may experience a wide range of threats to survival and reproduction throughout each annual cycle, and identification of life stages most critical to persistence may be especially difficult for these populations. The endangered eastern Canadian breeding population of Piping Plover (Charadrius melodus melodus) was estimated at only 444 adults in 2005, and extensive effort has been invested in conservation activities, reproductive monitoring, and marking of individual birds, providing a comprehensive data set on population dynamics since 1998. We used these data to build a matrix projection model for two Piping Plover population segments that nest in eastern Canada in order to estimate both deterministic and stochastic rates of population growth (λd and λs, respectively). Annual population censuses suggested moderate growth in abundance between 1998–2003, but vital rate estimates indicated that this temporary growth may be replaced by declines in the long term, both in southern Nova Scotia (λd = 1.0043, λs = 0.9263) and in the Gulf of St. Lawrence (λd = 0.9651, λs = 0.8214). Nonetheless, confidence intervals on λ estimates were relatively wide, highlighting remaining uncertainty in future population trajectories. Differences in projected growth between regions appear to be driven by low estimated juvenile post-fledging survival in the Gulf, but threats to juveniles of both population segments following departure from nesting beaches remain unidentified. Similarly, λ in both population segments was particularly sensitive to changes in adult survival as expected for most migratory birds, but very little is understood about the threats to Piping Plover survival during migration and overwintering. Consequently, we suggest that future recovery efforts for these and other vulnerable migrants should quantify and manage the largely unknown sources of both adult and juvenile mortality during non-breeding seasons while maintaining current levels of nesting habitat protection.

Nonbreeding-Season Drivers of Population Dynamics in Seasonal Migrants: Conservation Parallels Across Taxa

For seasonal migrants, logistical constraints have often limited conservation efforts to improving survival and reproduction during the breeding season only. Yet, mounting empirical evidence suggests that events occurring throughout the migratory life cycle can critically alter the demography of many migrant species. Herein, we build upon recent syntheses of avian migration research to review the role of non-breeding seasons in determining the population dynamics and fitness of diverse migratory taxa, including salmonid fishes, marine mammals, ungulates, sea turtles, butterflies, and numerous bird groups. We discuss several similarities across these varied migrants: (i) non-breeding survivorship tends to be a strong driver of population growth; (ii) non-breeding events can affect fitness in subsequent seasons through seasonal interactions at individual- and population-levels; (iii) broad-scale climatic influences often alter non-breeding resources and migration timing, and may amplify population impacts through covariation among seasonal vital rates; and (iv) changes to both stationary and migratory non-breeding habitats can have important consequences for abundance and population trends. Finally, we draw on these patterns to recommend that future conservation research for seasonal migrants will benefit from: (1) more explicit recognition of the important parallels among taxonomically diverse migratory animals; (2) an expanded research perspective focused on quantification of all seasonal vital rates and their interactions; and (3) the development of detailed population projection models that account for complexity and uncertainty in migrant population dynamics.

An Estimate of Nest Loss in Canada Due to Industrial Forestry Operations

Annual loss of nests by industrial (nonwoodlot) forest harvesting in Canada was estimated using two avian point-count data sources: (1) the Boreal Avian Monitoring Project (BAM) dataset for provinces operating in this biome and (2) available data summarized for the major (nonboreal) forest regions of British Columbia. Accounting for uncertainty in the proportion of harvest occurring during the breeding season and in avian nesting densities, our estimate ranges from 616 thousand to 2.09 million nests. Estimates of the impact on numbers of individuals recruited into the adult breeding population were made based on the application of survivorship estimates at various stages of the life cycle. Future improvements to this estimate are expected as better and more extensive avian breeding pair density estimates become available and as provincial forestry statistics become more refined, spatially and temporally. The effect of incidental take due to forestry is not uniform and is disproportionately centered in the southern boreal. Those species whose ranges occur primarily in these regions are most at risk for industrial forestry in general and for incidental take in particular. Refinements to the nest loss estimate for industrial forestry in Canada will be achieved primarily through the provision of more accurate estimates of the area of forest harvested annually during the breeding season stratified by forest type and Bird Conservation Region (BCR). A better understanding of survivorship among life-history stages for forest birds would also allow for better modeling of the effect of nest loss on adult recruitment. Finally, models are needed to project legacy effects of forest harvesting on avian populations that take into account forest succession and accompanying cumulative effects of landscape change.

Uncertainty assessment of a process-based integrated catchment model of phosphorus

Despite the many models developed for phosphorus concentration prediction at differing spatial and temporal scales, there has been little effort to quantify uncertainty in their predictions. Model prediction uncertainty quantification is desirable, for informed decision-making in river-systems management. An uncertainty analysis of the process-based model, integrated catchment model of phosphorus (INCA-P), within the generalised likelihood uncertainty estimation (GLUE) framework is presented. The framework is applied to the Lugg catchment (1,077 km2), a River Wye tributary, on the England–Wales border. Daily discharge and monthly phosphorus (total reactive and total), for a limited number of reaches, are used to initially assess uncertainty and sensitivity of 44 model parameters, identified as being most important for discharge and phosphorus predictions. This study demonstrates that parameter homogeneity assumptions (spatial heterogeneity is treated as land use type fractional areas) can achieve higher model fits, than a previous expertly calibrated parameter set. The model is capable of reproducing the hydrology, but a threshold Nash-Sutcliffe co-efficient of determination (E or R 2) of 0.3 is not achieved when simulating observed total phosphorus (TP) data in the upland reaches or total reactive phosphorus (TRP) in any reach. Despite this, the model reproduces the general dynamics of TP and TRP, in point source dominated lower reaches. This paper discusses why this application of INCA-P fails to find any parameter sets, which simultaneously describe all observed data acceptably. The discussion focuses on uncertainty of readily available input data, and whether such process-based models should be used when there isn’t sufficient data to support the many parameters.

Uncertainties in annual riverine phosphorus load estimation: Impact of load estimation methodology, sampling frequency, baseflow index and catchment population density

Models developed to identify the rates and origins of nutrient export from land to stream require an accurate assessment of the nutrient load present in the water body in order to calibrate model parameters and structure. These data are rarely available at a representative scale and in an appropriate chemical form except in research catchments. Observational errors associated with nutrient load estimates based on these data lead to a high degree of uncertainty in modelling and nutrient budgeting studies. Here, daily paired instantaneous P and flow data for 17 UK research catchments covering a total of 39 water years (WY) have been used to explore the nature and extent of the observational error associated with nutrient flux estimates based on partial fractions and infrequent sampling. The daily records were artificially decimated to create 7 stratified sampling records, 7 weekly records, and 30 monthly records from each WY and catchment. These were used to evaluate the impact of sampling frequency on load estimate uncertainty. The analysis underlines the high uncertainty of load estimates based on monthly data and individual P fractions rather than total P. Catchments with a high baseflow index and/or low population density were found to return a lower RMSE on load estimates when sampled infrequently than those with a tow baseflow index and high population density. Catchment size was not shown to be important, though a limitation of this study is that daily records may fail to capture the full range of P export behaviour in smaller catchments with flashy hydrographs, leading to an underestimate of uncertainty in Load estimates for such catchments. Further analysis of sub-daily records is needed to investigate this fully. Here, recommendations are given on load estimation methodologies for different catchment types sampled at different frequencies, and the ways in which this analysis can be used to identify observational error and uncertainty for model calibration and nutrient budgeting studies. (c) 2006 Elsevier B.V. All rights reserved.

Integrated modelling of climate change impacts on water resources and quality in a lowland catchment: River Kennet, UK

An integrated approach to climate change impact assessment is explored by linking established models of regional climate (SDSM), water resources (CATCHMOD) and water quality (INCA) within a single framework. A case study of the River Kennet illustrates how the system can be used to investigate aspects of climate change uncertainty, deployable water resources, and water quality dynamics in upper and lower reaches of the drainage network. The results confirm the large uncertainty in climate change scenarios and freshwater impacts due to the choice of general circulation model (GCM). This uncertainty is shown to be greatest during summer months as evidenced by large variations between GCM-derived projections of future tow river flows, deployable yield from groundwater, severity of nutrient flushing episodes, and Long-term trends in surface water quality. Other impacts arising from agricultural land-use reform or delivery of EU Water Framework Directive objectives under climate change could be evaluated using the same framework. (c) 2006 Elsevier B.V. All rights reserved.

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.

When smoke gets in our eyes: The multiple impacts of atmospheric black carbon on climate, air quality and health

With both climate change and air quality on political and social agendas from local to global scale, the links between these hitherto separate fields are becoming more apparent. Black carbon, largely from combustion processes, scatters and absorbs incoming solar radiation, contributes to poor air quality and induces respiratory and cardiovascular problems. Uncertainties in the amount, location, size and shape of atmospheric black carbon cause large uncertainty in both climate change estimates and toxicology studies alike. Increased research has led to new effects and areas of uncertainty being uncovered. Here we draw together recent results and explore the increasing opportunities for synergistic research that will lead to improved confidence in the impact of black carbon on climate change, air quality and human health. Topics of mutual interest include better information on spatial distribution, size, mixing state and measuring and monitoring. (c) 2006 Elsevier Ltd. All rights reserved.