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
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