Regional Chemical Modeling in Support of ICARTT Topics:  How good were the regional forecasts?  What are we learning about the emissions?  What are.

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Presentation transcript:

Regional Chemical Modeling in Support of ICARTT Topics:  How good were the regional forecasts?  What are we learning about the emissions?  What are our plans for integrating models with observations?

Our Analysis Framework Mesoscale Meteorological Model (RAMS or MM5) MOZART Global Chemical Transport Model STEM Prediction Model with on-line TUV & SCAPE Anthropogenic & biomass burning Emissions TOMS O 3 Chemistry & Transport Analysis Meteorological Dependent Emissions (biogenic, dust, sea salt) STEM Tracer Model (classified tracers for regional and emission types) STEM Data- Assimilation Model Observations Airmasses and their age & intensity Analysis Influence Functions Emission Biases/ Inversion

NEI-1999 emission in 60km (below) and 12km (right) domains. Analysis Done at 60 and 12 km Horizontal Resolution

Extensive Real-Time Evaluation of Regional Forecasts – Stu McKeen

Complimentary Actions to Improve Our Ability to Forecast Pollution are Needed * Persistence * Single Forward Model w/o assimilation * Ensemble forecast (8 models) w/o assimilation

Ensemble Techniques Help !

Ensemble Methods Also Work for PM2.5 Forecasting

But take little comfort…….. We have a long way to go !!

Emission Issues Slope 1.7

Species 60 km simulation with original NEI-1999v3 emission 60 km simulation with half CO, NO x and SO 2 emissions SlopeR R CO NO y PILS SO SAGA SO PILS NH SAGA NH O3O Ethyne URI HCHO H2O2H2O Sensitivity Runs Using Reduced Emissions -- Correlations between STEM simulations and Measurements for All DC-8 flights

Clear Improvement in Surface Predictions

Integration of Measurements & Models Data: Larc Cost functional measures the model- observation gap. Goal: produce an optimal state of the atmosphere using:  Model information consistent with physics/chemistry represented  Measurement information consistent with reality  within errors

Reanalysis of Ozone using Surface as Well as Ozone Profile and Aircraft Data 60 km!!! O3 data: Tom Ryerson

Getting the Vertical Distributions Right is Critical Current models have a difficult time…so data assimilation is important IONS O3 data: Anne Thompson & John Merrill

Ozone Forecasts (left) and Reanalysis (Right) Circles represent observations (locations and values)

4dVar can be used to recover emissions Forward Inverse k

Adjoint Tools Can Also Help in the Characterization of Emissions Preliminary Results: CO emission scaling factor ~ 0.7. Influence Function

Observed (PILS)Predicted Regional Distributions of Aerosols

Observed and Predicted Submicron Scattering

STEM Source Region Tracers Can Be Used to Sort Data & Complement Trajectories

Future Plans  Improve Base Emissions -- Update base year inventory (Streets and Vukovich), Biomass burning (others)  Emission inversions  Re-analysis using aircraft, surface, satellites, sondes (Ozone, CO, NOy, HCHO)  Analysis of aerosols and optical properties, by better linking observations and models  Better understand and constrain physical removal processes (dry and wet) We will submit our model products along the flight tracks