Uncertainties in measurement and modelling : an overview Laurence Rouïl

1. Uncertainties in measurement and modelling : an overview Laurence Rouïl

2. In-situ Measurement data : main sources • Regulatory observation sites (in compliance with the Air quality directives) • “Selected” air pollutants and parameters measured • Obligations related to the choice of the observation site and the standards used for the measurement devices • Commitment of the Member States to comply with the directives in term of station number, location, quality assurance, reporting (EIONET network) • Data reported to the European Environment Agency and made routinely available • Research networks grow up in Europe : • New parameters measured : non regulatory pollutants, aerosol speciation, size distribution, physico-chemical parameters vertical profiles (Lidars, Radiosondes), aircraft measurements … • Wide range of methods can be tested and compared • Continuous measurement and/or fields campaigns (EUSAAR, EARLINET, GALION, EUCAARI, MOZAIC/IAGOS….) • Data compiled by the project partners and made available under certain constraints (publication, restrictive use…)

3. Uncertainties in measurement : • Data quality objectives (DQOs) specified in particular in AQ Directives : • Measurement uncertainty • Minimum data capture • Minimum time coverage • Metrological uncertainty : from the measurement devices; rather well managed for regulatory pollutants • Appropriate standards are developed by normalisation Committees (CEN, ISO) according to the requirements of the Air quality Directives (e.g. measurement uncertainty lower than 30% in most of the cases) • Definition of reference methods and inter-laboratory tests • Definition of common statistical procedures for uncertainty estimations • Metrological uncertainty : a field of investigation for research networks • Intercalibration campaigns (see the EUSAAR project) : EC/OC measurement, optical properties, size distribution (SPMS)....

4. Intercalibration experiments (from P. Laj) : OC/EC (J. –P. Putaud, JRC):Round-Robin intercomparison and development of artefact free sampler • Intercomparison of identical filters from several EUSAAR sites operating operating with similar thermo-optical methods • Need for homogeneizing methods -> Converging towards a EUSAAR method for thermal-optical methods and EMEP references Size distribution(A. Wiedensohler, IFT): intercalibration and improvement of SMPS • 34 CPCs (12 different models) and 16 SMPS were checked and calibrated • Intercalibration clearly needed. High variability in terms of total number and size • Improvement when using standard retrieval procedures

5. Uncertainties in measurement (ii) • Uncertainties in measurement interpretation • Which parameters are measured? • Artefacts in the measurement? • How to retrieve the expected data (concentration level) from the available measurement (AOD for instance)? • Non validated and validated data : role of the human expertise • Reporting chains (EMEP, EEA) include data flagging to qualify the status and the quality of the data • Time release of validated data must be improved in most cases (EMEP) • Access to Near Real Time (NRT) unvalidated data offers new opportunities (monitoring of air pollution episodes, air quality forecasting and short term analysis, NRT model evaluation....) but can increase uncertainties. • Uncertainty due to the measurement strategy : • Representativeness of the observations : to reduce uncertainties in maps production and air quality assessment • Performance in terms of data capture and time coverage

6. Example : sensitivity to the spatial samplingstrategy Initial data set (source: ATMO Champagne-Ardenne, 2005) Example : NO2 background concentrations over the region Champagne-Ardenne (France) – winter 2005 Dx : mesh size

7. Spatial sampling strategy Sensitivity of the estimated map to sampling density. The sampling mesh should not be larger than 15 km. Ordinary kriging – Estimated maps

8. Spatial samplingstrategy With auxiliary variables, the sampling mesh can be extended to 25-30 km.. Kriging of the residuals using population and NOx emissions density– Estimated maps

9. Uncertainties in modelling • Estimated by comparison with measurement : • Statistical scores (bias, root mean square error, gross error, correlation) • Graphical indicators (Taylors diagrams) • Contingency tables assessing the ability of the model to capture situations where thresholds are exceeded or not • Various sources of uncertainties : • input data: emissions and meteorological fields (V, temperature, . . .) ; • physical parameterizations (ci , K, . . .) ; • numerical schemes • Model resolution • Sensitivity to input data : propagating input uncertainty in the models with Monte-Carlo approaches

10. Méthodology • Probability Distribution Function (PDF) for input parameters • PDFs propagates in the CTMs with a Monte Carlo approach Hanna et al. [1998, 2001], Beekmann and Derognat [2003] • Sources : • Parole d'expert • Erreur de mesure • Ecart aux observations PDF concentrations PDFs parameters AQ model Standard deviation : measure of the output concentration uncertainty.

11. Example : CHIMERE – France results PM10 winter 2009 300 simulations Ozone august 2009500 simulations • Standard deviation : 19% for ozone daily peak et 33% PM10 daily average • Lower for highest concentrations • Uncertainty can be underestimated for PM model concentrations, the bias being also underestimated‏

12. Identification of the sensitive variables for ozone concentrations • Temperature • Lateral boundary conditions • Deposition speed

13. The ensemble approach to assess model uncertainty

14. From the individual model verification.... 14

15. … to the multi-model analysis : range of variability = a kind of model uncertainty measurement Biais RMS

16. Model intercomparison and evaluation exercises : a promising approach to assess model uncertainty The Eurodelta initiative : with JRC, CONCAWE, Next phase under the TFMM umbrella The AQMEII initiative : JRC (S. Galmarini), USEPA (S.T. Rao)

17. Emissions, modelling and measurement ….. • Close relationship : missing sources (natural) , inaccurate approximation (diffusive emissions, wood combustion...) can explain a part of uncertainty in model results • High temporal resolution for emissions can be crucial for forecasting or NRT monitoring applications • Observation should help in improving emission events ; new opportunities with earth observation • Modelling should help in assessing emission inventories • Inverse modelling : considering “reduced” uncertainties of observations to constrain models and to improve emission inventories next operational step? Impact of high resolution emission inventory MACC/TNO) on NO2 daily peak simulated by CHIMERE (RMS)