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EnKF Assmilation of Chemical Tracer Information in a 2-D Sea Breeze Model

Explore the effectiveness of EnKF assimilation of chemical tracers in a 2-D sea breeze model, sponsored by Texas Environmental Research Consortium. Study focuses on model setup, ensemble characteristics, and impact of chemical observations on meteorological analysis. Results show improved error reduction and potential for targeted observation optimization.

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EnKF Assmilation of Chemical Tracer Information in a 2-D Sea Breeze Model

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  1. The Center for Atmospheric Chemistry and the Environment EnKF Assmilation of Chemical Tracer Information in a 2-D Sea Breeze Model Amy L. Stuart, Altug Aksoy, Fuqing Zhang, and John W. Nielsen-Gammon Work sponsored in part by the Texas Environmental Research Consortium and the Texas Commission on Environmental Quality

  2. Research Questions • Does the EnKF perform well in a forced-dissipative dynamical system with no mechanisms for rapid error growth? • Can observations of chemical tracers effectively improve the meteorological and chemical analysis?

  3. Outline • Model description • Ensemble characteristics • Meteorological assimilation • Chemical assimilation

  4. Model Description • 2-D: 500 km x 3 km + sponge layers • Grid spacing: 4 km x 50 m • Prognostic variables: horizontal vorticity, buoyancy, concentration • Sinusoidally-varying buoyancy source over land plus stochastic white noise • Tracer source 28 km inland

  5. EnKF Configuration (1) • Observed variable: Buoyancy or Concentration • Observations: Surface observations on land • Observational error: Standard deviation of 10-3 ms-2 or 10-7 kg/m3 • Observation spacing: 40 km (10 grid points)

  6. EnKF Configuration (2) • Covariance localization: Gaspari and Cohn’s (1999) fifth-order correlation function with 100 grid-point radius of influence • Observation processing: Sequential (Snyder and Zhang 2003) with no correlation between observation errors • Filter: Square-root after Whitaker and Hamill (2002) with no perturbed observations

  7. The sea breeze model: The sea breeze cycle Buoyancy (ms-2) Vorticity (s-1) Sea breze front develops at the coast 123 Hour Forecast (3:00PM Local) Onset of the sea breeze

  8. The sea breeze model: The sea breeze cycle Buoyancy (ms-2) Vorticity (s-1) Vertical gravity waves emanate from the PBL Sea breze front matures and penetrates inland 129 Hour Forecast (9:00PM Local) Peak sea breeze

  9. The sea breeze model: The sea breeze cycle Buoyancy (ms-2) Vorticity (s-1) Sea breze front weakens 135 Hour Forecast (3:00AM Local) Onset of the land breeze Land breeze front develops

  10. The sea breeze model: The sea breeze cycle Buoyancy (ms-2) Vorticity (s-1) 141 Hour Forecast (9:00AM Local) Peak land breeze Land breeze front matures yet is not as strong as the sea breeze front

  11. The sea breeze model: Forecast spread Vorticity Buoyancy • Buoyancy spread dominated by initial error spread; little diurnal variability • Initial vorticity spread advected out of the domain; strong diurnal variability • Buoyancy power spectrum dominated by large-scale initial-condition error • Vorticity power spectrum reflects smaller-scale frontal dynamics and is flatter

  12. The sea breeze model: Perfect-model EnKF Results Vorticity Buoyancy • Buoyancy is the observed variable; its error reduction is more dramatic and faster • Buoyancy error saturates at a magnitude comparable to observational error • Unlike buoyancy, vorticity error and spread exhibit diurnal signal

  13. Mean Predicted Concentrations Peak land T Peak sea breeze • Sea breeze recirculation allows concentrations to build near source Peak land breeze Peak heating 3 km Source 500 km Land Sea

  14. Predicted Concentration Uncertainties Peak land T Peak sea breeze • Ensemble standard deviation has diurnal variability, grows in transition between land to sea breeze Peak land breeze Peak heating

  15. Evolution of Domain Average Errors Error Vorticity (s-1, x103) • EnKF assimilation of concentration observations reduces error in both meteorological variables and concentrations Bouyancy (m/s2) Concentration (kg/m3, x10-7) noon peak sea breeze peak sea breeze peak land breeze

  16. Targeted Single Observation Design • Pre- vs post- network assimilation domain uncertainty norms • Locations of promising adaptive observations are similar before and after regular network assimilation.

  17. Conclusions • EnKF works for sea breeze • Chemical data assimilation improves chemistry and meteorology • Ensemble can predict optimal locations for targeted observations • Next: imperfect model and parameter estimation…

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