Mesoscale Modelling for Meteorological and Air Pollution Applications - Hardcover

Hardcover

ISBN 10: 1783088265 ISBN 13: 9781783088263

Publisher: Anthem Press, 2018

Synopsis

‘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.

About this title

About the Author

Ranjeet S. Sokhi is director of the Centre for Atmospheric and Climate Physics Research, School of Physics, Astronomy and Mathematics, University of Hertfordshire, UK. He was the coordinator of the COST 728 Action on Enhancing Mesoscale Meteorological Modelling for Air Pollution and Dispersion Applications and is a PI for the National Centre for Atmospheric Science, UK.

Alexander Baklanov is scientific officer of Research Department, World Meteorological Organization, Geneva, Switzerland, and affiliated professor at the Niels Bohr Institute of the University of Copenhagen, Denmark. He was the vice coordinator for the COST 728 Action on Enhancing Mesoscale Meteorological Modelling for Air Pollution and Dispersion Applications.

K. Heinke Schlünzen is professor for meteorology, head of the Mesoscale and Microscale Modelling group at Meteorological Institute, Center for Earth System Research and Sustainability, Universität Hamburg, Germany. She was the vice coordinator for the COST 728 Action on Enhancing Mesoscale Meteorological Modelling for Air Pollution and Dispersion Applications. Since 2016 she has been a member of the review board on Atmospheric Science, Oceanography and Climate Research of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation).

Excerpt. © Reprinted by permission. All rights reserved.

Mesoscale Modelling for Meteorological and Air Pollution Applications

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

Wimbledon Publishing Company

Copyright © 2018 Ranjeet S. Sokhi, Alexander Baklanov and K. Heinke Schlünzen editorial matter and selection
All rights reserved.

ISBN: 978-1-78308-826-3

Contents

List of Illustrations, xi, 
Preface, xvii, 
Acknowledgements, xix, 
List of Abbreviations, xxi, 
List of Contributors, xxxiii, 
Chapter One Introduction Ranjeet S. Sokhi, 1, 
Chapter Two Basic Concepts of Mesoscale Modelling for Air Pollution Applications Ranjeet S. Sokhi, Xavier Francis, Xin Kong, Ana Isabel Miranda, Volker Matthias and Kenneth Schere, 7, 
Chapter Three Representation of Surface Processes in Mesoscale Models E. Batchvarova, Sven-Erik Gryning, C. S. B. Grimmond, M. Kelly, A. Rutgersson, T. Vihma, Alexander Baklanov and Ranjeet S. Sokhi, 41, 
Chapter Four Representation of Boundary-Layer, Radiation, Cloud and Aerosol Processes in Mesoscale Models TC1[Sven-Erik Gryning, E. Batchvarova, Alexander Baklanov, Georg Grell, W. C. de Rooy, R. San Jose, J. Struzewska, Maria Tombrou and Ranjeet S.Sokhi, 69, 
Chapter Five Integration and Implementation of Models and Interfaces Alexander Baklanov, K. Heinke Schlunzen, Georg Grell, Ulrik Korsholm, Sandro Finardi, Barbara Fay, Kenneth Schere, Yang Zhang, Maria Tombrou, Jacek W. Kaminski, Alexander Mahura and RanjeetS. Sokhi, 107, 
Chapter Six Applications of Mesoscale Models for Air Pollution Research John Douros, Evangelia Fragkou, Iakovos Barmpadimos, Charles Chemel, Marco Deserti, Giovanna Finzi, Elmar Friese, Gertie Geertsema, Johannes Keller, Kaisa Kesanurm, Vera Martins, Volker Matthias, Enrico Minguzzi, Ana Isabel Miranda, Alexandra Monteiro, Juan L. Perez, Marje Prank, Markus Quante, Elisa Sa, Roberto San Jose, Martijn Schaap, K. Heinke Schlunzen, Mikhail Sofiev, Ranjeet S. Sokhi, Rainer Stern, Joanna Struzewska, Robert Vautard and Ralf Wolke, 161, 
Chapter Seven Evaluating the Performance of Mesoscale Meteorology Models Used for Air Quality Simulations K. Heinke Schlunzen, Peter Builtjes, Marco Deserti, John Douros, Stefano Galmarini, Ana Isabel Miranda, Jose Luis Palau, Kenneth Schere and Ranjeet S. Sokhi, 199, 
Chapter Eight Policy Relevance and Support Provided by Mesoscale Models Bernard Fisher, 227, 
Chapter Nine User Training for Mesoscale Modelling Applications to Air Pollution Gertie Geertsema, K. Heinke Schlunzen, Heleen ter Pelkwijk, Liisa Jalkanen, Alexander Baklanov, Bernard Fisher and Ranjeet S. Sokhi, 251, 
References, 273, 
Index, 325, 

CHAPTER 1

INTRODUCTION

Ranjeet S. Sokhi

1.1 Advent of Mesoscale Models for Air Pollution Applications

Over the last four decades, mesoscale modelling of meteorology and air pollution has developed into an essential tool for research and the scientific investigation of atmospheric processes and for designing emission control strategies to protect human health and the environment (Anthes and Warner, 1978; Shuman, 1989; Dudhia, 2014). Developments in computational methods and techniques, particularly in numerical weather predictions (NWP), has provided the opportunity to employ sophisticated modelling approaches for operational and policy applications (McGregor et al., 1978; Tapp and White, 1976; Grell et al., 1994).

The scale of the air pollution problem has moved away from considering only the impact of a local source or even emissions just within a city region. The appreciation that air pollution at a given point is determined by a combination of local, urban, regional and even global influences has grown with our understanding of atmospheric processes and source contributions (e.g. NRC, 1991; Hov et al., 1978; Rao et al., 1997 and 2003). While measurements play a key role in monitoring the current state and the past trends of air pollutants, they also have a key role in model evaluation and development (e.g. Porter et al., 2015). Multiscale mesoscale models now offer a number of unique capabilities, which include providing comprehensive spatial and temporal description of air pollution, providing the ability to analyse scenarios, explaining changes in pollutant concentrations and their behaviour in terms of the underlying atmospheric processes and producing forecasts of air pollution in the short and long term.

Mesoscale models for air pollution applications have a history that intertwines developments in numerical weather prediction and air pollution dispersion modelling approaches. Since the 1970s, these two stands have gradually merged with stronger cooperation. While simpler modelling approaches still have a role in air quality assessment, there has been a greater shift towards full process-oriented 'comprehensive' or 'one atmosphere' models (e.g. Russell, 1997). These types of models were a major shift, for example from simpler Gaussian approaches, and include more sophisticated treatment of meteorology and chemistry as well as emissions processing. Earlier models were developed to study long-range transport leading to acid deposition (e.g. Elliassen, 1980; Fisher, 1982) and photochemical pollution affecting urban areas (McRae et al., 1982; Seinfeld, 1988; Scheffe and Morris, 1993; Peters et al., 1995).

A significant advantage of mesoscale models is their modular and flexible structure. The main components involve the following:

(i) sources and emissions – anthropogenic and natural including biogenic species

(ii) meteorological processes, such as advection and diffusion, land-surface exchange, radiation, turbulence, microphysics and precipitation

(iii) geophysical processes involving terrain and land cover;

(iv) chemical transformations, such as, gas, aqueous and heterogeneous reactions and pathways

(v) physical transformations including deposition and aerosol processes

(vi) numerical solvers

(vii) analysis and visualization tools

The term 'model' implies a single computer software or a core set of algorithms that lead to a mathematical prediction of a quantity after taking account of the inputs, governing processes and any post-processing to deliver the output fields. However, in the case of mesoscale models, a more appropriate term would be 'mesoscale modelling system' as they are a collection of models and modules for physical and chemical parameterization schemes. While they offer considerable flexibility and robustness of their scientific basis, the number of components within a system also adds to their complexity and hence requires a greater level of expertise to operate them effectively for analysis air pollution problems.

1.2 Growth of Air Pollution Mesoscale Models

The move towards more sophisticated process models has been motivated by the need for more reliable predictions taking account of complex non-linear dynamical and chemical processes and interactions that affect air pollutants in the atmosphere. For example, emission rates of biogenic species will be affected by meteorological conditions and have to be calculated and integrated into the emission inputs before running the model. Terrain characteristics will affect meteorological fields, and both land cover and meteorology will influence surface exchange processes. Meteorological interactions with chemistry also have to be treated within models, for example, where clouds affect the radiation in the atmosphere (Ramanathan et al., 1989; Kylling et al., 2005) and hence change photolysis rates (Thompson, 1984; Madronich, 1987).

There are numerous meteorological and air pollution mesoscale models. For example, see the reviews by Schlunzen and Sokhi (2008), Kukkonen et al. (2012) and Baklanov et al. (2014). These surveys have established an international overview of model capabilities, model developments and meteorological and air pollution dispersion applications. These modelling systems all share common features listed above and solve the main governing equations, combine meteorological and chemistry-transport models and have modules to process emissions. Many published studies have also compared the performance of a large number of mesoscale models and revealed where they exhibit common behaviour and where they show significant differences between their outputs because of the different operational configurations (e.g. grid resolution, vertical levels), numerical approaches employed to solve the governing equations and the choice of physics and chemical schemes (Stern et al., 2008; Appel et al., 2012; Solazzo et al., 2012; Brunner et al., 2015).

1.3 Requirements for Air Pollution Applications

Current mesoscale models are able to provide high-resolution spatial and temporal distributions of concentrations of pollutants over urban to regional scales. Typically, concentrations are output for every hour with spatial grid size of a few kilometres, although grid sizes of 1 km x 1 km are also possible. The accuracy of the concentrations critically depends on the accuracy of the simulated wind fields, temperature, humidity, turbulent mixing, boundary-layer features and emission inventories. There are also several other aspects that are important, such as the chemical schemes, transformation of aerosols, clouds, radiation and convective processes and feedbacks between chemistry and meteorology.

We should note that the meteorological regimes that are important during air pollution situations can be different from those that are usually relevant for weather forecasting applications. In operational meteorological forecasting applications, atmospheric situations involving strong synoptic or mesoscale forcing can lead to severe weather and heavy precipitation events. For air quality applications, there are a number of local and regional scale situations that are particularly important. For example, weak forcing where light winds and large diurnal changes in boundary-layer stability occur, can lead to high air pollution episodes (Kukkonen et al., 2005a, b; Francis et al., 2011). Nocturnal transport aloft in the residual layer and downward entrainment on the following day can also influence diurnal variations of air pollutant concentrations (Zhang and Rao, 1999). Terrain-forced circulations are critical to describing the transport and diffusion of pollutants and for predicting their ground level concentrations. From a policy perspective, models need to be able to correctly describe the behaviour of air pollutants affected by a combination of synoptic and local forcing, and baseline concentrations which are largely determined by emissions loading and seasonal and longer-term forcing (Hogrefe et al., 2000; Rao et al., 2011).

A prerequisite to predicting air pollution concentrations on a wide range of scales and situations is the ability to produce spatially and temporally resolved meteorological fields (Seaman, 2000; Hogrefe et al., 2011). A vital advance in the 1970s was to employ three-dimensional high-frequency mesoscale data sets derived from dynamical numerical weather prediction (NWP) models to simulate the transport, diffusion, dry and wet deposition and chemical transformations for airborne chemical species, aerosols and particulate matter. The use of NWP models to drive chemical transport models (CTMs) for air quality predictions has now become routine for research applications (Brunner et al., 2015). Over recent years, this trend has continued, particularly because computational power is continually increasing along with improvements in physics parameterization and the numerics of dynamical models (Stensrud 2007; Madala et al., 2014; Cohen et al., 2015). The accuracy of NWP predictions depends on the parameterization of surface fluxes of heat, moisture and momentum as they constitute the energy source for the atmosphere. For both weather and air pollution prediction applications, NWP models have to meet core forecast objectives, taking account of mass, energy and momentum exchange between the atmosphere and surface (e.g. Yano, 2014). It follows that representation of the processes in the boundary layer is fundamental for numerical modelling of the weather, air pollution and atmospheric dispersion.

1.4 Evaluation of Model Performance

Applications of mesoscale modelling systems for analysing and explaining conditions leading to high air pollution levels have continued to grow. Many cases have similarities, such as prevalence of stable conditions, but many applications also show different causes of the onset of air pollution events (e.g. Kukkonen et al., 2005a, b; Sofiev et al., 2009a; Liu et al., 2010; Francis et al., 2011; Kong et al., 2014). Complexity in explaining air pollution events arises as the causes can be due to a large number of factors related to emissions, prevailing meteorology and chemical transformations that occur in the atmosphere. Related to this issue is the requirement to have a verifiable performance evaluation of the mesoscale modelling systems for specific or representative air pollution situations (Schlunzen and Sokhi, 2008).

Methods for evaluating mesoscale models have developed into sophisticated approaches dealing with meteorological and chemical variables, offline and online modelling approaches, the tracking of model development and input data sets. In addition to the operational evaluation of model outputs with observations, methods have been developed to examine model responses to dynamical changes (e.g. emissions), diagnostic analysis to test for sensitivity to process treatment within the models and probabilistic analysis of model outputs (Schlunzen and Sokhi, 2008; Dennis et al., 2010). The evaluation of models is no longer a purely scientific exercise. It is now a requirement by users and policymakers to have confidence in the use of the outputs of the model for policy support and response.

1.5 Policy Relevance of Mesoscale Models

Often there is no alternative but to use sophisticated mesoscale models, especially where the situation involves secondary atmospheric pollutants, such as particulate matter (PM10, PM2.5) and ozone (O3), where air pollution arises from long-range transport and when broader concerns over air quality and climate policy are involved. Inevitably, users of mesoscale models and their outputs need guidance and have access to methods for quantifying model performance so that they can assess the potential implications for their responses. Also it is common for policy applications that additional processing of mesoscale model outputs is needed, for example, source-receptor relationships, integrated assessments and pollutant footprints (Napelenok et al., 2011; Chemel et al., 2014). While scientists are interested in the underlying processes and improving their representation in models, policy users will want to know if the model can address questions that are relevant to their needs and give them the confidence to inform their decision-making process. In particular, city and policy users will want the model to output not just concentration fields. Often they are interested in the maximum concentrations and exceedances of national or regional air quality limit values or World Health Organization (WHO) guidelines, quantifying the impact of local and regional policy implementation on regulated pollutant concentrations and assessing the implications of emissions and air quality policies on human exposure and health impact.

1.6 Scope of the Book

There are already commendable published works that provide a very thorough treatment of meteorological and air pollution modelling (see, e.g. Jacobson, 2005; Pielke, 2013). This book combines the fundamental and practical aspects of mesoscale modelling. It provides an overview of the fundamental concepts of air pollution and meteorological modelling, including parameterization of key unresolved processes and then considers equally important aspects such as model integration, performance evaluation, policy relevance and user training. It does not purport to cover all aspects of mesoscale modelling but instead its content has been developed around topics that build upon the experience of a large number of meteorological services and air pollution modelling research and user groups, mainly from Europe and North America. The focus of this book is more on the basic principles, structure, operational characteristics and applications of air pollution mesoscale models. For example, the processing of emissions of air pollutants is not covered, although it is a critical input component of air pollution modelling.

Chapter 2 provides an overview of the fundamental concepts that form the basis of meteorological and air pollution modelling. After reviewing the key scientific basis of gridded Eulerian mesoscale meteorological and air pollution modelling on urban to mesoscales, it considers some of the operational aspects of mesoscale modelling from the perspective of a model user and provides examples of actual applications within Europe.

Chapter 3 focusses on the parameterization of surface processes in mesoscale models, for example, to represent homogeneous terrain, water surface, urban areas and forests. It also considers subgrid issues related to the parameterization of processes. Chapter 4 continues examining the scientific fundamentals of mesoscale models and discusses the representation of boundary-layer processes including radiation, cloud and aerosol processes.

The next set of chapters moves to topics that are more operational and practical in nature, with particular reference to air pollution modelling applications on urban to regional scales. Chapter 5 reviews the structure, classification and integration of mesoscale models. Both offline-and online-coupled modelling systems are considered, including requirements for interfacing meteorological and chemistry transport models. The use of nesting to address multiple scale problems and the improvement of model outputs through data assimilation approaches are also examined. To illustrate the principles discussed in the previous chapters, examples of air pollution applications of mesoscale models are presented in Chapter 6. The selected cases have arisen from international cooperation to examine and document the performance of mesoscale models for air pollution applications. Methods of evaluating the performance of mesoscale models used for air quality simulations are discussed in Chapter 7. As part of the evaluation process, the chapter highlights relevant meteorological parameters for chemical transport models and essential data sets that are necessary for evaluating the performance of mesoscale models. Policy applications of mesoscale models are considered in Chapter 8. The use of mesoscale models for policy analysis is increasing, and for many problems there is no alternative to a full-process modelling system. Unlike most other books on air pollution modelling, we include the topic of training in the use of mesoscale models as an important element for understanding air pollution and dispersion modelling systems. It is hoped that the information provided in Chapter 9 will encourage the uptake of more advanced modelling systems, especially when analysing complex air pollution situations.

(Continues...)

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Edition details

• Publisher: Anthem Press
• Publication date: 2018
• Language: English
• ISBN 10: 1783088265
• ISBN 13: 9781783088263
• Binding: Hardcover
• Number of pages: 380
• Editor: Sokhi Ranjeet S., Baklanov Alexander, Schlünzen K. Heinke