experiments with assimilation of fine aerosols using gsi and enkf with wrf-chem (on the need of...
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Experiments with Assimilation of Fine Aerosols using GSI and EnKF with WRF-Chem
(on the need of assimilating satellite observations)
Mariusz Pagowski
Georg A. Grell
NOAA/ESRL
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.
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.
Observations
AIRNow network IMPROVE and STN networks
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.
Total aerosol mass;
1-hr average available round the clock;
urban, suburban, rural sites, unknown;
suitable for r-t assimilation.
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, 2010Emission perturbations – an example
PM2.5 increment – examples
00 UTC 12 UTC
Experiments/EvaluationsAIRNOW PM2.5
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.
EnKF_Met vs. EnKF_no_Met
No_Met
Met
Time series of domain averaged PM2.5 concentration (after five-day spin-up)
EnKF_Met vs. EnKF_no_Met
No_Met
Met
PM2.5 concentration model level 23
Geopotential perturbation
EnKF_Met vs. EnKF_no_Met
No_Met
Met
PM2.5 concentration model level 23 + 12h
Geopotential perturbation
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.