Kryštof Eben

A rigorous inter-comparison of ground-level ozone predictions

Novel statistical approaches to prediction have recently been shown to perform well in several scientific fields but have not, until now, been comprehensively evaluated for predicting air pollution. In this paper we report on a model inter-comparison exercise in which 15 different statistical techniques for ozone forecasting were applied to ten data sets representing different meteorological and emission conditions throughout Europe. We also attempt to compare the performance of the statistical techniques with a deterministic chemical trajectory model. Likewise, our exercise includes comparisons of sites, performance indices, forecasting horizons, etc. The comparative evaluation of forecasting performance (benchmarking) produced 1340 yearly time series of daily predictions and the results are described in terms of predefined performance indices. Through analysing associations between the performance indices, we found that the success index is of outstanding significance. For models that are excellent in predicting threshold exceedances and have a high success index, we also observe high performance in the overall goodness of fit. The 8-h average ozone concentration forecast accuracy was found to be superior to the 1-h mean ozone concentration forecast, which makes the former very significant for operational forecasting. The best forecasts were achieved for sites located in rural and suburban areas in Central Europe unaffected by extreme emissions (e.g. from industries). Our results demonstrate that a particular technique is often excellent in some respects but poor in others. For most situations, we recommend neural network and generalised additive models as the best compromise, as these can handle nonlinear associations and can be easily adapted to site specific conditions. In contrast, nonlinear modelling of the dynamical development of univariate ozone time-series was not profitable.

Statistical modeling of energy production by photovoltaic farms

This paper introduces several alternative statistical approaches to modeling and prediction of electric energy generated by photovoltaic farms. The statistical models use outputs of a numerical weather prediction model as their inputs. Presented statistical models allow for easy-to-compute predictions, both in temporal sense and for out-of-sample individual farms. Model performance is illustrated on a sample of real photovoltaic farms located in the Czech Republic.

Support Vector Regression of multiple predictive models of downward short-wave radiation

Accurate forecasts of weather conditions are of the utmost importance for the management and operation of renewable energy sources with intermittent (stochastic) production. With the growing amount of intermittent energy sources, the need for precise weather predictions increases. Production of energy from renewable power sources, such as wind and solar, can be predicted using numerical weather prediction models. These models can provide high-resolution, localized forecast of wind speed and solar irradiation. However, different instances of numerical weather prediction models may provide different forecasts, depending on their properties and parameterizations. To alleviate this problem, it is possible to employ multiple models and to combine their outputs to obtain more accurate localized forecasts. This work uses the machine-learning tool of Support Vector Regression to amalgamate downward short-wave radiation forecasts of several numerical weather prediction models. Results of SVR-based multi-model forecasts of irradiation at a large set of locations show a significant improvement of prediction accuracy.

Air pollution episodes: Modelling tools for improved smog management (APPETISE)

Most ambient air quality models are deterministic models or rely upon simple regression based statistics. Their success, however, is limited either by their failure to capture the non-linear behaviour of air pollutants, or the incomplete understanding of the physical and chemical processes involved. The APPETISE project aims to develop and test the suitability of novel non-linear statistical methods to improve the ability to accurately forecast variations in air quality. It also aims to develop methods for handling missing data, which will have generic applications for other real data situations. The work is being carried out over a period of 2 years by a consortium from 9 institutions from 5 different European countries and is funded under the European Union Fifth Framework Programme. The project concentrates on 4 key pollutants; nitrogen oxides, particulates, ground level ozone and sulphur dioxide. Since it is likely that different methods and models will work best under different situations an ensemble approach will be utilised to improve the confidence held in any given prediction. The project will work towards the construction of a prototype air quality prediction and warning system the performance of which will be tested against existing systems.

Inverse modeling of emissions and their time profiles

The paper presents a version of the 4DVar method, capable of optimizing diurnal time profiles of emissions. It is a generalization of existing inverse methods that optimize emission daily totals. The core of the method is formed by the CMAQ adjoint model with SAPRC99 mechanism. Measurements from both ground–level stations (NO2 and O3), and satellites (retrieved columns of NO2 from GOME2 and OMI and the lowest layer of O3 retrieved from IASI) have been used as a data source for the inverse modeling procedure. The method can be used for detection of bias or errors in the emission model. It also can assist in development of data–driven emission model with location–specific time profiles of emissions. Different aspects of the method are illustrated on simulation experiments. Forecasting performance of the optimized model is evaluated for O3 and NO2 concentrations.

Nonlinearity and Prediction of Air Pollution

A presence of nonlinearity in time series of concentrations of air pollutants and in their relations to time series of meteorological variables is tested using information-theoretic functionals and the surrogate data approach. The results are discussed in relation to predictability of the pollutant concentrations aimed to alert smog episodes.

High resolution modelling of anthropogenic heat from traffic in urban canopy: A sensitivity study

Impact of climate change is often amplified in urban areas - particularly during the heat waves, the extreme temperatures are even more pronounced in cities due to the effect urban heat island (UHI). It is therefore important to improve our understanding of heat fluxes and energy balance in urbanized areas. We investigate the possibility of high resolution urban canopy modelling using PALM model. To account for the realistic implementation of urban canopy processes in complex urban geometry we enhanced PALM model including some of the most important urban canopy mechanisms including detailed description of physical properties of urban surfaces, calculation of shape view factors and plant canopy sink factor to model accurately both shortwave and longwave radiation budgets, and heat transfer within urban surfaces and on the interfaces of surfaces and atmosphere or ground. Such approach allows for very detailed modelling in high spatial and temporal scale. The simulation of the impact of anthropogenic heat from transportation has been conducted as one of the pilot experiments to test feasibility of this approach and also sensitivity of highly unstable turbulent flow heat exchange to a relatively small perturbation of input parameters.

On the development of urban adaptation strategies using ecosystem-based approaches to adaptation

Selected aspects of currently running project UrbanAdapt are described. The project deals with the adaptation of cities on changing climatic conditions. The main project objective is to start the process of preparation of cities adaptation strategies, developing adaptation scenarios and testing the effects and benefits of particular measures. Previously developed models of adaptation impacts are used with available real data and according to several prepared scenarios to provide necessary decision making tools. The final policies have to take into account climatic conditions, available means, and devices to propose necessary amendments and solutions. Project team involves groups from different fields that cover various aspects of adaptation measures including economic analyses, policy making processes, education and dissemination to the public. The presented paper deals with the part of activities which are focused on modelling of adaptation measures and climatic impacts for the city of Prague. These activities include assessment of energy balance of city, in terms of interactions of solar radiation, atmosphere and urban environment. Urban environment includes not only buildings, street surfaces and vegetation, but also the processes having impact on energy balance such as the traffic, air conditioning and industry that produce anthropogenic heat which can play a role for example in summer heat waves. The ultimate goal is to assess the impact of different adaptation measures on citizens who live in environmental conditions of growing effect of urban heat island. Thus the connection between objective meteorological variables and subjective biological indices has to be investigated. The concept of Physiological Equivalent Temperature (PET) is adopted. In comparison to single values of air temperature, air humidity, global horizontal irradiance, wind speed, and other meteorological indexes, concept of PET has added value in determining the value of important biometeorological index in.

On the Comparison of Nesting of Lagrangian Air-Pollution Model Smog to Numerical Weather Prediction Model ETA and Eulerian CTM CAMX to NWP Model MM5: Ozone Episode Simulation

711 The spatial distribution of air pollution on the local scale of parts of the territory in Czech Republic is simulated by means of Charles University Lagrangian puff model SMOG nested in NWP model ETA. The results are used for the assessment of the concentration fields of ozone, nitrogen oxides and other ozone precursors. A current improved version of the model based on Bednar et al. (2001) covers up to 18 groups of basic compounds and it is based on trajectory computation and puff interaction both by means of Gaussian diffusion, mixing and chemical reactions of basic species. Results of summer photochemical smog episode simulations are compared to results obtained by another couple adopted in the framework of the national project as a basis for further development of data assimilation techniques, Eulerian CTM CAMx nested in NWP model MM5. There are measured data from field campaigns for some episodes as well as air-quality monitoring station data available for comparison of model results with reality. Usually, there is a problem with emission data for the simulations and definitely they are far from actual instantaneous data. Both the couples have rather older databases of emissions available with many uncertainities, for

Multilevel maximum likelihood estimation with application to covariance matrices

ABSTRACT The asymptotic variance of the maximum likelihood estimate is proved to decrease when the maximization is restricted to a subspace that contains the true parameter value. Maximum likelihood estimation allows a systematic fitting of covariance models to the sample, which is important in data assimilation. The hierarchical maximum likelihood approach is applied to the spectral diagonal covariance model with different parameterizations of eigenvalue decay, and to the sparse inverse covariance model with specified parameter values on different sets of nonzero entries. It is shown computationally that using smaller sets of parameters can decrease the sampling noise in high dimension substantially.