Integrative Pollutant Analysis Notes for HTAP 2007 Interim ReportHTAP 2007 Interim Report by R. Husar, May 2007R. Husar 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 Emissions Emission 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 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