Assessing Aerosol Assimilation Impacts on Air Quality Forecasting using EnKF and GSI

1. Experiments with Assimilation of Fine Aerosols using GSI and EnKF with WRF-Chem(on the need of assimilating satellite observations) MariuszPagowski Georg A. Grell NOAA/ESRL

2. Importance of air quality/chemical forecasting and data assimilation • Fine aerosols and ozone detrimental to health. PM2.5 is the single most critical factor affecting human mortality due to air pollution. • Interdependence of meteorology and atmospheric chemistry: e.g. aerosols affect radiation and microphysics; improved model chemistry has a potential to positively influence meteorology. • Because of these interdependence assimilation of chemistry has • a potential to improve assimilation of meteorology via regression relationships either in VAR or EnKF; and vice versa. • Potential largely unrealized yet because of the weaknesses of parameterizations, deficiencies of retrieval and data assimilation methods.

3. Challenges in air quality/chemical forecasting and data assimilation • Scarcity of observations, especially in the vertical both for model verification and data assimilation, also for aerosol species. • Multiple state variables (in tens) and parameterizations have large uncertainties – to account for such errors ensembles would benefit from stochastic parameterization. • Results very much dependent on source emissions, error estimates in surface emissions 50-200% – need for ensembles using correlated perturbations. • Air quality tied to meteorology in the boundary-layer, ensembles tend to collapse and surface observations poorly assimilated.

4. Observations AIRNow network IMPROVE and STN networks Total aerosol mass; 1-hr average available round the clock; urban, suburban, rural sites, unknown; suitable for r-t assimilation. Aerosol species: SO4, OC, EC; 24-hour averages every three days; available after several weeks/months; limited value for assimilation, possibly for FGAT in diagnostic studies.

5. Regional Model ARW WRF-Chem updated version 3.2.1 grid length ~60 km, 40 vertical levels GOCART aerosol for computational reasons; assimilate standard meteorological observations (prepbufr) and AIRNow PM2.5 in 6-hr cycle with 1-hr window; NMM lateral boundary conditions; GSI: Background Error Statistics derived from continuous forecasts in summer 2006 using NMC method; EnKF: 50 ensembles initialized from NMM using background error statistics and perturbing emissions; June 1 - July 15, 2010 Emission perturbations – an example

6. PM2.5 increment – examples 00 UTC 12 UTC

7. Experiments/Evaluations AIRNOW PM2.5

8. Conclusions • Large positive impact of assimilation, but fast error growth after the assimilation; from previous work: positive effect still present after 24 hours; potential of ensembles. • Some advantage of EnKF over GSI in terms of total PM2.5 mass. Little impact of assimilations on individual aerosols species. • Detrimental when ensemble filter determines speciation compared to a priori ratios-> GOCART physics/chemistry not reliable for this modeling domain. Still, GOCART attractive because of simplicity, speed. • (JGR, 2012: • http://www.agu.org/journals/jd/papersinpress.shtml#id2012JD018333) • Negative effect of meteorology on aerosols due to regressions derived by the filter – analysis follows.

9. EnKF_Met vs. EnKF_no_Met Time series of domain averaged PM2.5 concentration (after five-day spin-up) No_Met Met

10. EnKF_Met vs. EnKF_no_Met PM2.5 concentration model level 23 Geopotential perturbation No_Met Met

11. EnKF_Met vs. EnKF_no_Met PM2.5 concentration model level 23 + 12h No_Met Geopotential perturbation Met

12. Conclusions • Negative effect of meteorology/aerosol regressions derived by the filter: • model builds up high PM2.5 concentrations above PBL since no observations available to constrain the effect – possibly counteract with satellite observations, near future: experiments with AOD.