Mesoscale Modelling for Meteorological and Air Pollution Applications

By Anthem Press

‘Mesoscale Modelling for Meteorological and Air Pollution Applications’ combines the fundamental and practical aspects of mesoscale air pollution and meteorological modelling. Providing an overview of the fundamental concepts of air pollution and meteorological modelling, including parameterization of key atmospheric processes, the book also considers equally important aspects such as model integration, evaluation concepts, performance evaluation, policy relevance and user training. Based on research topics that are the most relevant to the development, with models for high resolution meteorology and air quality simulations, and also based on the experience of a large number of meteorological services and air pollution modelling research and user groups, mainly from Europe and North America, ‘Mesoscale Modelling for Meteorological and Air Pollution Applications’ encapsulates the basic concepts of numerical modelling of air quality, model structures, operational characteristics and applications of air pollution mesoscale models for research as well as operational tasks.

• Language: English
• Publisher: Anthem Press
• Release date: Nov 15, 2018
• ISBN: 9781783088287

Book preview

Praise for Mesoscale Modelling for Meteorological and Air Pollution Applications

‘This new book is a prerequisite reading for anyone starting in the field of air pollution modelling. There are few such comprehensive references tackling both the theory and the challenges of transforming these theoretical concepts into actual applications. Mesoscale Modelling for Meteorological and Air Pollution Applications does not shy away from addressing the use of mesoscale models in the context of policy decision support, which few have done; another distinguishing feature is a welcome review of current techniques for model performance evaluation, an area that has seen much evolution. A definite must-read.’

—Veronique Bouchet, Director, Canadian Meteorological Centre Development Division, Meteorological Service of Canada, and Chair, WMO GURME SAG

‘This exceptional volume represents a superb tutorial on chemical transport modelling and will soon become a classic not only for future generations of students and graduates but also for practitioners of weather and air quality forecasts. It is an impressive document that will become a companion to atmospheric chemists and meteorologists, and will serve as a reference to many researchers worldwide.’

—Guy P. Brasseur, Chair of the Joint Scientific Committee, World Climate Research Programme

Abatement of air pollution is an urgent environmental policy concern. This book summarizes the legacy of the international collaborative effort in Europe and North America over several decades to develop the science effort to model mesoscale meteorology and air pollution. The accumulated expert knowledge presented here in a textbook format is a key to solving the air pollution issues in a cost effective way. Fundamental aspects and well as numerous practical applications are reviewed.

—Øystein Hov, Secretary General, The Norwegian Academy of Science and Letters, and President, WMO Commission for Atmospheric Sciences

‘Mesoscale Modelling for Meteorological and Air Pollution Applications is a welcome new addition to this important topic. Mesoscale models coupling meteorology and air quality are important tools in atmospheric science research and are widely used in forecast-mode to support a growing spectrum of applications. This book combines both fundamental and practical aspects of mesoscale modelling in a seamless manner. It is a great resource for the meteorological and air pollution communities, and especially useful in the training of contemporary model users.’

—Gregory R. Carmichael, Karl Kammermeyer Professor of Chemical and Biochemical Engineering, University of Iowa, Iowa City, IA USA and Chair, WMO GAW Science Steering Committee

‘If you are new to the field of regional scale modelling of air pollution or meteorology, or if you have not been able to follow research developments over the past decade, this book will be an invaluable source of information. It is comprehensive, clear and practical. All the main processes at play are described both from a theoretical standpoint and by using actual results and applications. The book is written by the best domain experts in Europe (and a few other international specialists), so this is first-hand information. Finally and importantly, it testifies that mesoscale meteorology and air pollution modelling are no longer separate research areas, but that the way forward for improved meteorological and air quality forecasts is to perform them in a fully integrated manner.’

—Vincent-Henri Peuch, Deputy Director for Copernicus Services and Head of the Copernicus Atmosphere Monitoring Service, European Centre for Medium-Range Weather Forecasts and Chair, WMO GAW APP SAG

‘A comprehensive review of the key atmospheric processes influencing the transport and fate of air pollutants that could be used as a textbook for a course on air pollution meteorology. A valuable reference for graduate students and researchers interested in air quality modelling and its application.’

—S. T. Rao, Former Director, U.S. EPA’s Atmospheric Modeling and Analysis Division

Mesoscale Modelling for Meteorological and Air Pollution Applications

Mesoscale Modelling for Meteorological and Air Pollution Applications

Edited by

Ranjeet S. Sokhi, Alexander Baklanov and K. Heinke Schlünzen

Anthem Press

An imprint of Wimbledon Publishing Company

www.anthempress.com

This edition first published in UK and USA 2018

by ANTHEM PRESS

75–76 Blackfriars Road, London SE1 8HA, UK

or PO Box 9779, London SW19 7ZG, UK

and

244 Madison Ave #116, New York, NY 10016, USA

© 2018 Ranjeet S. Sokhi, Alexander Baklanov and K. Heinke Schlünzen editorial matter and selection; individual chapters © individual contributors

The moral right of the authors has been asserted.

All rights reserved. Without limiting the rights under copyright reserved above,

no part of this publication may be reproduced, stored or introduced into

a retrieval system, or transmitted, in any form or by any means

(electronic, mechanical, photocopying, recording or otherwise),

without the prior written permission of both the copyright

owner and the above publisher of this book.

British Library Cataloguing-in-Publication Data

A catalogue record for this book is available from the British Library.

ISBN-13: 978-1-78308-826-3 (Hbk)

ISBN-10: 1-78308-826-5 (Hbk)

This title is also available as an e-book.

CONTENTS

List of Illustrations

Preface

Acknowledgements

List of Abbreviations

List of Contributors

References

Index

ILLUSTRATIONS

Figures

2.1Main components of a mesoscale air pollution modelling system

2.2Interconnections between the main physical processes represented in mesoscale meteorological models

2.3Schematic of the chemical and transport process related to the composition of atmosphere and air pollution

2.4Comparison of measured and modelled ozone concentrations between 24 and 28 June 2001 in the vicinity of London

2.5Comparison of nitrate, ammonium and sulfate aerosol concentrations at Melpitz, Germany, derived for the complete year 2000 with MM5-CMAQ

2.6Winter (top panels) and summer (bottom panels) surface 2010 means for O3 over the European domain showing the MACC reanalysis (right) and average over the 16 regional models (left). Ground measurement stations are shown with green dots.

2.7Winter (top panels) and summer (bottom panels) surface 2010 means for O3 over the North America domain showing MACC reanalysis (right) and the average over five regional models. Ground measurement stations are shown with green dots.

3.1Vertical profiles of mean wind speed from measurements (left) and LES simulations (right). Lines represent: full line, no waves and neutral; dotted line, no waves and convective conditions; dashed line, swell waves and neutral; dash-dotted line, swell waves and convective conditions. Inset figures use a log vertical scale.

4.1Cloud processes important for air quality and weather forecasting

4.2Three aspects of the cumulus parameterization problem: control, feedback and parameterized convective clouds (static control)

4.3Sketch of larger-scale organized updrafts (thicker lines) embedded in smaller-scale turbulence. The left panel, (a), shows a dry convective boundary layer with inversion height zinv as the top, and the right panel, (b), shows a dry subcloud layer with the top of a cloudy layer from the cloud base, zb, up to ztop.

4.4Left panel, (a): Schematic diagram of a bulk updraft with mass flux M, fractional entrainment ε and detrainment δ, at an arbitrary height in the cloud layer. Right panel, (b): A schematic picture of a cloud ensemble according to de Rooy and Siebesma (2010) with massive entrainment, εdyn, at cloud base zbot and massive detrainment, δdyn, at the top of individual clouds. From the cloud base to the top of individual clouds turbulent lateral mixing takes place, presented by εturb and δturb. For individual clouds, the mass flux is constant with height. The deepest cloud reaches height ztop, the top of the cloud layer.

4.5A schematic diagram of T and qt, where the variation (e.g. within a grid box) is presented by isolines of a joint PDF (dashed lines) around the grid box mean ( and ). The solid line shows the saturation-specific humidity (qsat= f(T)). The shaded area is the part of the PDF with cloud cover and liquid water.

5.1Schematic diagram of interactions of various physical, biological and chemical processes in the atmosphere

5.2Schematic diagram of the offline and online modelling approaches. Online coupling can be achieved through the use of various available coupling tools or through directly including chemical and aerosol modules into the NWP models.

5.3Energy power spectrum from a WRF forecast with 10-km horizontal resolution (dashed black line) and analytic results

5.4Dynamically small and dynamically large systems. Dependence on horizontal length scale and Rossby radius of deformation.

5.5Difference in bias for predicted 8-hr ozone peak values – produced by comparing an average of 16 runs using WRF/Chem offline with a 1-hr coupling interval and using WRF/Chem online. Both model simulations were compared to observations over a two-week period. Mean bias to observations is determined for each model simulation, and then the displayed differences are calculated.

5.6Measured and modelled time development of concentration (ngm-3) at ETEX stations DK02 (a) and F15 (b) for the online and offline simulations with coupling intervals 10 (online), 30, 60, 120, 240 and 360 minutes

5.7Integrated meteorological and air quality modelling system conceptual scheme

5.8Comparison among O3 observed (black dots) and computed concentration with KV minimum value set to 0.1 m²s-1 (grey line) and 1 m²s-1 (black line) at urban (a) and rural (b) stations

5.9Comparison among NO2 observed (black dots) and computed concentration with KV minimum value set to 0.1 m²s-1 (grey line) and 1 m²s-1 (black line) at urban (a) and rural (b) stations

5.10Sensible heat flux (a) and KZ (b) computed by RAMS (blue line) and SURFPRO (red line) during summer thunderstorm episode in Torino

5.11Concentrations of O3 (a) and NO2 (b) computed using RAMS (blue line) and SURFPRO (red line) turbulent fluxes and scaling parameters versus observations (green line)

5.12Current regulatory (dash line) and suggested (solid line) ways for forecasting systems of urban meteorology within urban air quality information and forecasting systems (UAQIFSs) by downscaling from the adequate meteorological or numerical weather prediction (NWP) models to the urban/micro-scale obstacle-resolved models

6.1Comparison between observed and modelled daily average PM10 values at Melpitz (station code DE33)

6.2Statistics of the comparison between observed and modelled daily average values of SO4 and NO3 and 5 measurement stations. Shown are fractional bias (FB), normalized mean square error (NMSE), fraction within a factor of 2 (FAC2) and the correlation coefficient (r) for nine model systems.

6.3Mean vertical profiles for the period 24 February 2003–11 March 2003 of SO4 and NO3 at DE33

6.4Time series of ground values, vertically integrated values and the derived scale height for SO4 in three models (GKSS-CLM, FMI, UPM-WRF) for the period 24 February 2003–11 March 2003 at DE33

6.5Mean scale heights of SO4 as represented by the different model systems at DE33

6.6Wind speed and wind direction measured by a boundary layer wind profiler at Lindenberg, Germany, in approx. 300 m together with model results from four selected model systems

6.7Comparison between model results and observed wind speed and direction: hit rates (+/- 1 m/s and +/- 10 °) for hourly values in the period 24 February–11 March 2003

6.8Total daily AOD from MODIS (a) and SILAM (b) for 3 May 2006.

6.9(a) Time series of daily spatially averaged values of total AOD from MODIS (light grey line) and SILAM (dark grey line). (b) Daily relative and absolute deviations (dark grey and light grey bars respectively) between MODIS and SILAM AODs.

6.10Daily figures of merit in space, spatial correlation coefficients and root mean square errors (light grey, dark grey and white bars respectively) for MODIS and SILAM AOD.

6.11Contribution from different species and fire emissions to AOD at 550nm.

6.12Relative bias, correlation coefficient and RMSE for modelled and measured concentrations.

6.13Estimated RMSE for BS (light grey) and FS (dark grey) simulations for PM10 and August 2003.

6.14Estimated BIAS for BS (light grey) and FS (dark grey) simulations for PM10 and August 2003.

6.15Estimated r for BS (light grey) and FS (dark grey) simulations for PM10 and August 2003.

6.16Estimated RMSE for BS (light grey) and FS (dark grey) simulations for O3 and August 2003.

6.17Estimated BIAS for BS (light grey) and FS (dark grey) simulations for O3 and August 2003.

6.18Estimated r for BS (light grey) and FS (dark grey) simulations for O3 and August 2003.

6.19Spatial differences (µg/m³) between MM5-CMAQ and WRF-chem simulation results with (FS) and without (BS) forest fire emissions, for PM10 daily averages on 3 August 2003.

6.20Spatial differences (µg/m³) between MM5-CMAQ and WRF-chem simulations results with (FS) and without (BS) forest fire emissions, for O3 daily maximum values on 3 August 2003.

6.2110 m wind verification for August–September 2003 (sum) and January–February 2004 (win).

6.22Example of 10 m wind roses for August–September 2003. Observations (top), ECMWF and Hirlam (2nd row), COSMO and MM5-CH (3rd row), MM5-E and WRF (last row).

6.2310 m wind verification for Apr–Sep 2003 (sum) and Oct 2003–Mar 2004 (win).

7.1AQ simulation chain; the models can either be online coupled (denoted by dotted square), or chemistry and meteorology are offline coupled.

7.2Scheme of the coupling between the different processes and scales relevant for air pollution transport and transformation. The synergy of all these components (processes plus meteorological scales) results in the ‘Regional Air Quality’.

7.3Structure of inventories introduced within COST728

7.4Schematic representation of the working of the ENSEMBLE system.

7.5Model results. (a) Average ensemble for NO2, and (b) 50th percentile values of boundary-layer (PBL) height obtained from the ensemble of five model results.

7.6Structure of a generic evaluation protocol

7.7Interrelation of the different parts involved in an evaluation. Grey arrows show the ways to gain knowledge using both observations and numerical models – and by an evaluation employed. White arrows show feedbacks which allow the improvement of both the modelling system and also the observational strategies (meteorological and air quality monitoring and surveillance).

8.1Conceptual model evaluation or assessment framework for model developer or reviewer

8.2Model evaluation framework for a policymaker, showing the relationship to the model user

8.3Figure illustrating number of runs for a fixed computing resource as a function of the degree of observed data dependence. (This assumes that models can be classified into generalized types of model, such as Eulerian, statistical and empirical models. The latter class relies on incorporating data from an extensive modelling network.)

8.4NO2 concentrations (ppb) in 2003 from a power station in southern England using the CMAQ model with a 15-km grid resolution

8.5Ozone concentrations (ppb) in 2003 produced by a power station in southern England from the CMAQ model using a 15-km grid resolution (note: concentrations are negative)

8.6PM10 concentrations (µg/m³) in 2003 from a southern England power station using the CMAQ model with a 15-km grid resolution

8.7Schematic diagram showing how the results of regional air pollution models may be represented by a series of first-order or second-order transfer coefficients in an integrated assessment

8.8Role of aerosols in climate-air quality interactions. Aerosols significantly influence the Earth’s surface and atmospheric energy budgets, surface temperature, evaporation and sensible heat flux. Aerosols may both increase and decrease cloud cover, as well as cloud and surface albedo, which, in turn, affect air quality. Aerosols may significantly alter the extent or patterns of precipitation, which, in turn, affect air quality. Aerosols include potent climate warming agents (most notably black carbon), which may rival CO2 in their warming impact. Hence one needs an integrated meteorological and chemical model.

9.1Chain showing steps towards a decision (consequences and actions) starting from meteorological observations and information on anthropogenic emissions. This chain is valid for emergency response and also in the case of air pollution assessment studies. The boxes with full lines around them denote the focus of this chapter.

9.2Left plot shows the 14 October 2009 18 UTC 300 hPa wind (black lines and filled contours) and surface pressure (red lines). For wind speeds exceeding 30 m/s, the contours are filled with light yellow, through bright yellow (>50 m/s), orange (>60 m/s) to red (>70 m/s). The right panel gives an impression of the representation of the jet stream in three dimensions. The isosurface mapping velocity is set at 35 m/s.

9.3The technical set-up of the virtual reality laboratory at KNMI. Passive depolarizing glasses are used to look into a virtual world.

Tables

5.1Models contained in the model inventory initiated by COST728

5.2Online coupled meso-meteorology and chemistry and transport models

5.3Overview of European online models displaying advection, vertical diffusion and convection schemes

5.4Chemistry, aerosol and deposition in European online models

5.5Examples of interface approach of selected offline coupled MetMs and CTMs

5.6Key parameters for urban models of different scales (elaborated by COST715, 2003)

6.1Participating groups and their model systems

6.2List of participating models

6.3Measurements stations by pollutants used for model performance comparison (Spring 2006)

6.4Model-measurement comparison for the fire-influenced stations

6.5Model-measurement comparison for the stations influenced by anthropogenic pollution

6.6Model-measurement comparison for the two same-grid cell located stations

6.7Summary of statistics for SB, RB and UB stations during summer and winter periods

6.8Summary of model configurations used in the intercomparison

7.1Typical performances of mesoscale meteorological models

7.2Data sets described in the COST728 meta-database

7.3Example for scientific evaluation: needed qualities of a mesoscale meteorology model applied for emergency response, in forecast or hindcast mode for episodic or long-term simulations

8.1Source-receptor tables indicating the effect of 15 per cent reductions in NOx or VOCs in different countries on a health-related metric, SOMO35 (positive values indicate improvements, i.e. reductions), and on an ecosystem damage-related metric, AOT40, taken from EMEP (2005) for the year 2003

9.1Meteorological parameters to be evaluated for all time scales (episode, short term, long term) with respect to air pollution forecast and impact studies

9.2Meteorological parameters relevant for emergency response

9.3Examples of parameterization schemes and dependence of output parameters on these schemes

PREFACE

This book has resulted from ongoing efforts from a large section of the international mesoscale modelling community represented by the COST 728 Action on Enhancing Mesoscale Meteorological Modelling Capabilities for Air Pollution and Dispersion Applications. Two organizations have been instrumental in bringing together the community of mesoscale modelling scientist and users, namely, the European Cooperation in Science and Technology (COST) and the World Meteorological Organization through the Global Atmosphere Watch (GAW) Urban Research Meteorology and Environment (GURME) Project.

The development and use of sophisticated meteorological models for air pollution applications has been gradually increasing over the last few decades. This move has been stimulated by a number of factors, including the recognition that air pollution is a multiscale problem and is influenced by complex non-linear meteorological and chemical processes and interactions that affect air pollutants in the atmosphere on short and long time scales. Mesoscale meteorological and chemistry transport models are fast becoming an essential tool not only for research and scientific investigation of atmospheric processes but also for supporting policies to protect human health and the environment.

With the advent of ‘one atmosphere’ models combining meteorological and air pollution predictive capabilities, researchers and users have the opportunity to investigate a number of problems within a consistent and seamless modelling framework. This is particularly important as air pollution at any given place is determined by a combination of local-, urban-, regional- and even global-scale contributions. While mesoscale models offer considerable advantages over simpler approaches, there still remain important areas of research in terms of model development, evaluation and applications.

The growth of mesoscale modelling and the associated community of modellers and users have increased the need for information and training in the development and application of such models. It is common to find meteorological and air pollution modelling taught in university programmes, and many numerical weather model institutes and other research organizations offer training courses. There are a number of books available that provide a thorough treatment of meteorological and air pollution modelling. Notable examples include Jacobson (2005) and Pielke (2013 and earlier editions). The question arises, why yet another book on this topic?

The answer to the question lies in the fact that over the past decade, interaction and communication between the numerical weather prediction and air pollution modelling communities has increased. Over time, mesoscale modelling systems have adopted a more integrated approach incorporating both meteorological and air pollution predictive and forecasting capabilities, which are now part of more consistent and seamless modelling systems. There are very few, if any, comprehensive books that bring together the science, operational characteristics and practical aspects of mesoscale modelling for meteorological and air pollution applications.

This book addresses the above need by adopting a different approach to previous published texts. It treats meteorological and air pollution modelling as an integrated topic and takes a combined scientific and practical approach with real examples demonstrating the current state of mesoscale modelling capabilities. After providing an overview of the fundamental scientific aspects of mesoscale modelling, the book continues with chapters that benefit from the experience of a large section of meteorological services and air pollution modelling researchers and user groups from Europe and North America. The content includes an introduction to the basic principles and parametrizations, description of the model structure and interfaces, review of evaluation approaches and operational characteristics, and discussion of research and policy applications of meteorological and air pollution mesoscale models. Unusually for a book of this type, we discuss aspects of training for users and model developers. This is one aspect that is normally neglected. However, there is sufficient experience in the community to document the main requirements of effective training in the use of mesoscale models. Our hope is that this book will prove to be useful to a wide section of the meteorological and air pollution science communities and environmental consultants as well as university graduates and research students.

It is important to note that this work would not have been possible if it were not for the dedication, creativity and ingenuity of a large number of scientists who have contributed to the development and use of mesoscale meteorological and air pollution models for research and policy support.

Ranjeet S. Sokhi, Alexander Baklanov and

K. Heinke Schlünzen (Editors)

May 2018

ACKNOWLEDGEMENTS

We are grateful to the European Cooperation in Science and Technology (COST) and the World Meteorological Organization through the GAW Urban Research Meteorology and Environment (GURME) Programme for supporting this work. In particular this book would not have been possible if it were not for the dedication of all the scientists who contributed to the Action COST 728 on Enhancing Mesoscale Meteorological Modelling Capabilities for Air Pollution and Dispersion Applications. The involvement and collaboration with other COST Actions and major projects is also gratefully acknowledged. In particular we thank the support and contributions from the following:

ES0602 – Towards a European Network on Chemical Weather Forecasting and Information Systems (ENCWF)

ES1004 – European framework for online integrated air quality and meteorology modelling (EuMetChem)

MEGAPOLI (FP7 Project) – Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation

TRANSPHORM (FP7 Project) – Transport related Air Pollution and Health impacts – Integrated Methodologies for Assessing Particulate Matter

Funds have been provided to support this work of Grimmond by UK Met Office (P001550) and EU FP 7 project BRIDGE (211345)

Gryning and Batchvarova were supported by the Danish Council for Strategic Research, Sagsnr 2104-08-0025 and the EU FP7 Marie Curie Fellowship PIEF-GA-2009-237471-VSABLA

Financial support for Schlünzen came for this work through the Cluster of Excellence CliSAP (EXC177) funded through the German Research Foundation (DFG), and the City of Hamburg funded research project UrbMod (Cities in Change—Development of a multi-sectoral urban development impact model).

Yang Zhang was supported the US EPA-Science to Achieve Results (STAR) program (Grant# RD833376)

Permission to reprint figures in this book has been granted by J. L. Palau (Figure 7.2) and Pérez-Landa (Figure 7.7)

Sabine Ehrenreich is acknowledged for assisting in the preparation of the list of abbreviations.

The work of Matthew Blackett and Catherine Souch for Chapter 3 is gratefully acknowledged.

ABBREVIATIONS

CONTRIBUTORS

Alexander Baklanov

Research Department, World Meteorological Organization (WMO)

7 bis, Avenue de la Paix, BP2300, 1211 Geneva, Switzerland Formerly at Danish Meteorological Institute (DMI), Copenhagen, Denmark

Iakovos Barmpadimos

SCOR Global P&C

General Guisan-Quai 26

CH-8022 Zurich, Switzerland

Ekaterina Batchvarova

National Institute of Meteorology and Hydrology

Bulgarian Academy of Sciences

Sofia, Bulgaria

and

DTU Wind Energy

Technical University of Denmark, Risø Campus

Roskilde, Denmark

Peter Builtjes

Institute of Meteorology

Freie Universität Berlin

Carl-Heinrich-Becker-Weg 6-10

12165 Berlin

Charles Chemel

Centre for Atmospheric and Climate Physics (CACP)School of Physics, Astronomy and Mathematics

University of Hertfordshire

College Lane, Hatfield AL10 9AB, United Kingdom

Wim C. de Rooy

Royal Netherlands Meteorological Institute

Utrechtseweg 297

3731 GA De Bilt, The Netherlands

Marco Deserti

Regional Agency for Prevention and Environment (ARPA)

Hydrometeorological service (SIM)

Bologna, Emilia Romagna, Italy

John Douros

Laboratory of Heat Transfer and Environmental Engineering

Aristotle University University Campus

Box 483, 54124 Thessaloniki, Greece

Barbara Fay

Deutscher Wetterdienst

Frankfurter Str. 135, 63067 Offenbach, Germany

Sandro Finardi

ARIANET, via Gilino 9, 20128 Milano, Italy

Giovanna Finzi

Department of Information Engineering

University of Brescia

Via Branze 38, 25123 Brescia, Italy

Bernard Fisher

Little Beeches, Headley Road, Leatherhead, Surrey KT22 8PT, United Kingdom

Evangelia Fragkou

Laboratory of Heat Transfer and Environmental Engineering

Aristotle University University Campus

Box 483, 54124 Thessaloniki, Greece

Xavier Francis

formerly at Centre for Atmospheric and Climate Physics (CACP)

School of Physics, Astronomy and Mathematics

University of Hertfordshire

College Lane, Hatfield, AL10 9AB, United Kingdom

Current address: Plymouth University, Drake Circus, Plymouth, PL4 8AA, United Kingdom

Elmar Friese

Rhenish Institute for Environmental Research

University of Cologne

Aachener Straße 209, 50931 Cologne, Germany

Stefano Galmarini

JRC's Institute for Environment and Sustainability (JRC-IES)

Ispra, Via Enrico Fermi 2749, 21027 Ispra (VA), Italy

Gertie Geertsema

Royal Netherlands Meteorological Institute

Utrechtseweg 297, 3731 GA De Bilt, The Netherlands

Georg A. Grell

National Oceanic and Atmospheric Administration

Earth System Research Laboratory

325 Broadway

Boulder, Colorado 80303 United States

C. S. B. Grimmond

Department of Meteorology

University of Reading

Reading, RG6 6BB, United Kingdom

Sven-Erik Gryning

DTU Wind Energy

Technical University of Denmark, Risø Campus

Roskilde, Denmark

Liisa Jalkanen

formerly at World Meteorological Organization (WMO)

7 bis, Avenue de la Paix, BP2300, 1211 Geneva, Switzerland

Jacek W. Kaminski

Centre for Research in Earth and Space Science

York University, Toronto, Canada

Johannes Keller

Paul Scherrer Institut

5232 Villigen PSI, Switzerland

Mark Kelly

DTU Wind Energy

Technical University of Denmark, Risø Campus

Roskilde, Denmark

Kaisa Kesanurm

University of Tartu

Ülikooli 18, 50090, Tartu, Estonia

Xin Kong

formerly at Centre for Atmospheric and Climate Physics (CACP)

School of Physics, Astronomy and Mathematics

University of Hertfordshire

College Lane, Hatfield, AL10 9AB, United Kingdom

Ulrik Korsholm

Danish Meteorological Institute

Lyngbyvej 100, Copenhagen, Denmark

Jose Luis Palau

Centro de Estudios Ambientales del Mediterraneo

c/o Charles R. Darwin 14, 46980, Paterna, Spain

Alexander Mahura

Institute for Atmospheric and Earth System ResearchUniversity of HelsinkiGustaf Hällströminkatu 2a, P.O. Box 64, FI-00014, FinlandFormerly at Danish Meteorological Institute

Lyngbyvej 100, Copenhagen, Denmark

Vera Martins

CESAM, Department of Environment and Planning

University of Aveiro, 3810-193 Aveiro, Portugal

Volker Matthias

Helmholtz-Zentrum Geesthacht

Centre for Materials and Coastal Research (HZG), Max-Planck-Straße 1, 21502 Geesthacht, Germany

Enrico Minguzzi

Arpa Emilia-Romagna - Servizio Idro-Meteo-Clima

Viale Silvani, 6

40122, Bologna, Italy

Ana Isabel Miranda

CESAM, Department of Environment and Planning

University of Aveiro

3810-193 Aveiro, Portugal

Alexandra Monteiro

CESAM, Department of Environment and Planning

University of Aveiro

3810-193 Aveiro, Portugal

Markus Quante

Helmholtz-Zentrum Geesthacht, Centre for Materials and Coastal Research (HZG)

Max-Planck-Straße 1, 21502 Geesthacht, Germany

Juan L. Pérez

Environmental Software and Modelling Group

Computer Science School – Technical University of Madrid

Campus de Montegancedo – Boadilla del Monte

28660 Madrid, Spain

Heleen ter Pelkwijk

Royal Netherlands Meteorological Institute

Utrechtseweg 297, 3731 GA De Bilt, The Netherlands

Marje Prank

Finnish Meteorological Institute

Erik Palménin aukio 1, 00560 Helsinki, Finland

Anna Rutgersson

Department of Earth Sciences

Uppsala University

Villav. 16

75236 Uppsala

Sweden

Elisa Sá

CESAM, Department of Environment and Planning

University of Aveiro

3810-193 Aveiro, Portugal

Roberto San Jose

Environmental Software and Modelling Group

Computer Science School

Technical University of Madrid

Campus de Montegancedo

Boadilla del Monte, 28660 Madrid, Spain

Martijn Schaap

TNO, Dept. of Climate, Air and Sustainability

P.O. Box 80015, NL-3508TA, Utrecht, Netherlands

Kenneth Schere*

Atmospheric Modeling and Analysis Division

U.S. Environmental Protection Agency

Research Triangle Park, NC 27711, USA

* retired

K. Heinke Schlünzen

Meteorologisches Institut, CEN, Universität Hamburg

Bundesstr. 55, 20146 Hamburg, Germany

Mikhail Sofiev

Finnish Meteorological Institute

Erik Palménin aukio 1, 00560 Helsinki, Finland

Ranjeet S. Sokhi

Centre for Atmospheric and Climate Physics (CACP)

School of Physics, Astronomy and Mathematics

University of