Simultaneous forecasts of air quality and weather using WRF/Chem & Ensemble air quality modeling and its economic v

1. Simultaneous forecasts of air quality and weather using WRF/Chem&Ensemble air quality modeling and its economic value Georg A. Grell, Steven E. Peckham, Mariusz Pagowski The Cooperative Institute for Research in Environmental Sciences, The Cooperative Institute for Research in the Atmosphere and NOAA Earth System Research Laboratory, Global System Division

2. Outline • WRF Chemistry model (WRF/Chem) • Current status, latest additions • Ensemble modeling during ICARTT/NEAQS experiment

3. WRF/Chem “Online” (also called “inline”) chemistry • Consistent: all transport done by meteorology model • Same vertical and horizontal coordinates (no horizontal and vertical interpolation) • Same physics parameterization for subgrid scale • No interpolation in time • Easy handling (Data management) • Least amount of computing time if only doing one simulation

4. Directly involved in major WRF/Chem development NOAA/ESRL Georg Grell, Steven Peckham, Stuart McKeen PNNL Jerome Fast, Bill Gustafson, Rahul A. Zaveri, James C. Barnard NCAR Bill Skamarock Rainer Schmitz (University of Chile – Santiago, Chile) Marc Salzmann (Max Planck Institute for Chemistry – Mainz, Germany) And Many more national and international collaborators About 250 registered users

5. WRF/Chem Chemistry Package – V2.1.2 • Chemical mechanisms: • RADM2, Carbon Bond (CBMZ) • Photolysis (coupled with hydrometeors and aerosols): • Madronich, Fast-j (coupled to aerosols and microphysics) • Deposition: • Dry deposition (coupled with soil/veg scheme, “flux-resistance” analogy) • Simplified wet deposition by convective parameterization • Biogenic emissions: • Guenther – online calculation based on USGS landuse, T and radiation • BEISv3.11 (modify reference fields produced from complex landuse data)

6. WRF/Chem Aerosols – V2.1.2 • Modal approach – Binkowski and Shankar 1995, Ackermann et al. 1998, Schell et al. 2001 • Modal Aerosol Dynamics Model for Europe (MADE) modified to include Secondary Organic Aerosols (SOA) • Sectional approach - Zaveri et al., 2005, 2006 • Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) (4 or 8 bins)

7. WRF/ChemV2.1.2: Aerosol direct effect Aerosols coupled to the Goddard atmospheric radiation scheme • July 23, 2004, 2100Z From Chung et al. 2007, submitted Surface temperature difference from run with/without aerosols Aerosol absorption induce cloud changes via radiation but no account for effect on nucleation in clouds

8. Comparison with 350 Ozone monitors during ICARTT/NEAQS experiment – 8hr peak Evaluation period: July 12 – July 31, 2004 Continuous model improvement over the last 2 years

9. Additions in WRF/Chem V2.2 • Kinetic PreProcessor (KPP), MPI Mainz • Improved convective (non-resolved) transport, coupling of convective parameterization with atmospheric and photolysis radiation (ESRL/GSD) • Non-resolved and resolved aqueous phase chemistry, wet deposition, (NOAA/ARL/EPA, PNNL,ESRL/GSD) • 2-way nesting (PNNL, ESRL/GSD) • Cloud-aerosol interaction (indirect effect) with Lin et al. 6-class microphysics scheme (PNNL) • Lateral boundary conditions from global models (U of Chile, ESRL/GSD) • Urban parameterizations (NCAR, coming soon Spain) • Positive definite advection (NCAR) • NMM and ARW dynamic cores (ESRL/GSD) • Offline version for the ARW core (to be released shortly, C-DAC, India and ESRL/GSD))

10. ICARTT/NEAQS(International Consortium for Atmospheric Research on Transport and Transformation/ New England Air Quality Study) • From 6 July to 30 August 2004. • Over 350 observation sites measuring 1-hr average ozone concentrations - 16480 observations. • Only 87 exceedances of 85 ppbv threshold, no exceedances of 125 ppbv threshold.

11. ICARTT/NEAQS • Models participating • AURAMS (MSC, Canada) • CHRONOS (MSC, Canada) • CMAQ (EPA, USA) • MAQSIP at two resolutions (BAMS, USA) • STEM-2K3 (U of Iowa, USA) • WRF/Chem (NOAA/ESRL, USA) • Different chemical mechanisms, meteorological drivers, emission inventories, horizontal and vertical resolutions. • 24-hour forecasts issued at 0000 UTC (0600 UTC – CMAQ)

12. Economic Value of Forecasts Contingency table: Observed Yes No Yes a (C) b (C) Forecast No c (L) d (0) C - cost of preventive action L - loss due to lack of prevention Probabilistic forecasts: need to assign thresholds for probability. Decision analytic model: Murphy (1977)

13. Economic Value of Forecasts Relative economic value of forecasts - Richardson (2000, 2003)

14. Economic Value of Forecasts (ECV) Optimum decision level (ECV maximized) when forecast probability equals cost-loss ratio.

15. Economic Value of Forecasts 50 ppbv 70 ppbv 85 ppbv 8hr daily max O3 black - individual models, green - ensemble average, blue - deterministic ensemble DLR, red – probabilistic, purple – probabilistic DLR 1hr daily max O3

16. Conclusions • Economic value of deterministic forecasts derived from the ensemble of models is superior to the results obtained for the individual models. • Maximum economic value is achieved by converting forecasts of ensemble members to probabilistic framework. The economic value of probabilistic forecasts is superior to the deterministic forecasts over a wide range of cost-loss ratios and for nearly all thresholds analyzed.

17. Outlook • Data Assimilation (3DVAR, 4DVAR) • Global WRF/Chem • SMOKE emission module • Aerosols – direct and indirect effects Contact: www.wrf-model.org/WG11, User Group and Support