Assimilation of ground versus lidar observations for PM10 forecasting

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Table of contents

1 Introduction 
1.1 Atmospheric particulate matter
1.1.1 Health effects
1.1.2 Visibility effects
1.1.3 Climate effects
1.2 Aerosol monitoring
1.2.1 Surface measurements
1.2.2 Satellite remote sensing
1.2.3 Ground-based lidar networks
1.3 Air quality modelling of aerosols
1.3.1 Historical model development
1.3.2 Important processes
1.3.3 Numerical approach
1.3.4 Model performance evaluation
1.4 Data assimilation for aerosol forecasting
1.4.1 Background
1.4.2 OI/3D-Var
1.4.3 4D-Var
1.4.4 Ensemble Kalman filter
1.4.5 Choice of DA method
1.5 Objectives and plan of thesis
2 Assimilation of ground versus lidar observations for PM10 forecasting
2.1 Introduction
2.2 Choice of DA method
2.3 Experimental setup
2.3.1 Model
2.3.2 Input data
2.3.3 Observational data
2.4 Observing system simulation experiment
2.4.1 Nature run
2.4.2 Simulated observations and error modelling
2.4.3 Control run
2.4.4 Parameters of the DA runs
2.5 Choice of the horizontal and vertical correlation lengths
2.6 Comparison between AirBase and 12 lidars network DA
2.7 Sensitivity to the number and position of lidars
2.8 Conclusions
3 Modelling and assimilation of lidar signals over Greater Paris
3.1 Introduction
3.2 Experiment setup
3.2.1 POLAIR3D model
3.2.2 Modelling setup and observational data
3.3 Methodology
3.3.1 Modelling of lidar signals
3.3.2 Estimation of zref
3.4 Model evaluation
3.4.1 Model evaluation with Airparif data
3.4.2 Model evaluation with AERONET data
3.5 Comparisons with lidar vertical profiles
3.6 Assimilation test of lidar observations
3.6.1 Basic formulation
3.6.2 Construction of error covariances
3.6.3 DA setup
3.6.4 Results and discussions
3.7 Conclusions
4 Assimilation of lidar signals: Application to the Mediterranean basin 
4.1 Introduction
4.2 Modelling system
4.3 Observations
4.3.1 Lidar observations
4.3.2 Observations for validation
4.3.3 Case study
4.4 Assimilation parameter tests
4.4.1 Assimilation period length
4.4.2 Assimilation correlation length
4.4.3 Assimilation altitude range
4.5 Results and discussions
4.5.1 Validation with the BDQA network
4.5.2 Validation with the Barcelona network
4.5.3 Validation with the EMEP-Spain/Portugal network
4.5.4 Validation with the AERONET network
4.6 Conclusions
5 Summary 
5.1 Conclusions
5.2 Outlook
5.2.1 Aerosol modelling
5.2.2 Data assimilation
5.2.3 Lidar observations