Integrative Pollutant Analysis

Notes for HTAP 2007 Interim Report

by R. Husar, May 2007

Models

Observations

Emissions

Forecast

Characterization

Processes

Collect, Compare, Integrate/Reconcile

• Emissions, Observations and Models are contributed by diverse, distributed providers

• These data need to be accessed, integrated and/or reconciled

Comparison studies are conducted for Observations, Emissions and Models

Model Outputs

Observations

EmissionsEmission Integration

Emission comparisons, reconciliation

Model Comparisons

Model-model comparison, ensemble,

Data Integration

Data homogenization and integration

Interdependence of Emissions, Models and Observations

• Emissions arise from bottom-up ‘bean counting’ (man-made) or through inverse modeling (natural)

• Forward modeling performance depends heavily on the quality of the (uncertain) emissions inventory

• Field observations include all sources; so model evaluation is only possible if emissions are correct

Observations, emissions and modeling require iterative development and linking

Model Outputs

Emissions

Inverse Modeling

Emissions retrieval from observations;

Model Evaluation, Data Assimilation

Performance testing, improved formulation,

model nudging

Forward Modeling

Process-based simulation; source-receptor relationship

Observations

Emission Integration

Emission comparisons, reconciliation

Model Comparisons

Model-model comparison, ensemble,

Data Integration

Data homogenization and integration

Observations

Process Studies

• Characterization – creating the best available pollutant pattern as distributed in space-time-parameter• Characterization - achievable by Reanalysis with the ‘best available’ model and assimilated observations• Understanding gained from the model processes and applying previous/tacit knowledge

Goal: Pollutant Characterization and Understanding

Models

Emissions

Assimilation

Diagnostics

Use observations and model to extract process insights

Assimilation

Processes

Real-Time Pollutant Forecasting

• Characterization – creating the best available pollutant pattern as distributed in space-time-parameter• Characterization - achievable by Reanalysis with the ‘best available’ model and assimilated observations• Understanding gained from the model processes and applying previous/tacit knowledge

Goal: Pollutant Characterization and Understanding

Models

Observations

Emissions

Assimilation

Assimilation

Forecast

Forecasting

Meteorology(s), emissions(s) and chem-

model(s)

Pollutant Characterization, Understanding

• Characterization – creating the best available pollutant pattern as distributed in space-time-parameter• Characterization - achievable by Reanalysis with the ‘best available’ model and assimilated observations• Understanding gained from the model processes and applying previous/tacit knowledge

Goal: Pollutant Characterization and Understanding

Models

Observations

Emissions

Reanalysis

Forward model with assimilated observations

Data Interpretation

Use of previous & tacit knowledge to explain data

GOAL:

Knowledge Creation

Characterization of pattern; understating of processes

Characterization

Integrative Air Pollution Analysis

• In the past, most of these activities were conducted separately with little mutual support/benefit

• Dynamically linking these activities for specific analyses would benefit each

Model Outputs

Observations

Emissions

Inverse Modeling

Model Evaluation Data Assimilation

Forward Modeling

Reanalysis

Data Interpretation

Use of previous & tacit knowledge to explain data

Data Integration

Emission Integration

Model Comparisons

Forecast

Forecasting

Characterization

Diagnostics

Use observations and model to extract process insights

Diagnostics

Processes

Data Interpretation

Integrative Air Pollution Analysis

• In the past, most of these activities were conducted separately with little mutual support/benefit

• Dynamically linking these activities for specific analyses would benefit each

Models

Observations

Emissions

Inverse Modeling

Emissions retrieval from observations;

Model Evaluation

Data Assimilation

Performance testing, improved formulation

Forward Modeling

Process-based simulation; source-receptor relationship

Reanalysis

Data Interpretation

Use of previous & tacit knowledge to explain data

Data Integration

Data homogenization and integration

Emission Integration

Emission comparisons, reconciliation

Model Comparisons

Model-model comparison, ensemble,

Forecast

Forecasting

Characterization

Diagnostics

Use observations and model to extract process insights

Diagnostics

Processes

Data Interpretation

Integrative Air Pollution Analysis

• In the past, most of these activities were conducted separately with little mutual support/benefit

• Dynamically linking these activities for specific analyses would benefit each

Models

Observations

Emissions Characterization

Understanding

Inverse Modeling

Emissions retrieval from observations;

Model Evaluation

Performance testing, improved formulation

Forward Modeling

Process-based simulation; source-receptor relationship

Reanalysis

Forward model with assimilated observations

Data Interpretation

Use of previous & tacit knowledge to explain data

Data Integration

Data homogenization and integration

Emission Integration

Emission comparisons, reconciliation

Model Comparisons

Model-model comparison, ensemble,

GOAL:

Knowledge Creation

Characterization of pattern; understating of processes

Incorporations of HTAP Projects

• HTAP incorporates a number of projects (individual ‘systems’) relevant to Integrated Analysis• The projects could be connected into a ‘System of Systems’ and act as a coordinated unit

The information infrastructure for integration is available

Models

Observations

Emissions Characterization

Understanding

Inverse Modeling

???

Model Evaluation

HTAP Phase II ?

Forward Modeling

HTAP Phase II

Data Integration

Juelich, DataFed?

Emission Integration

NEISGEI, GEIA

Model Comparisons

AeroCom, HTAP

GOAL:

Knowledge Creation

Characterization of pattern; understating of processes

H. Eskes, P. Levelt, KNMI