Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 1 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 1 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 The Center for Atmospheric Chemistry and the Environment

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 2 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 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?

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 3 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 3 Outline Model description Ensemble characteristics Meteorological assimilation Chemical assimilation

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 4 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 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

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 5 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 5 EnKF Configuration (1) Observed variable: Buoyancy or Concentration Observations: Surface observations on land Observational error: Standard deviation of ms - 2 or kg/m 3 Observation spacing:40 km (10 grid points)

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 6 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 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

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 7 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 7 The sea breeze model: The sea breeze cycle Buoyancy (ms -2 ) Vorticity (s -1 ) 123 Hour Forecast (3:00PM Local) Onset of the sea breeze Sea breze front develops at the coast

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 8 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 8 The sea breeze model: The sea breeze cycle Buoyancy (ms -2 ) Vorticity (s -1 ) 129 Hour Forecast (9:00PM Local) Peak sea breeze Sea breze front matures and penetrates inland Vertical gravity waves emanate from the PBL

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 9 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 9 The sea breeze model: The sea breeze cycle Buoyancy (ms -2 ) Vorticity (s -1 ) 135 Hour Forecast (3:00AM Local) Onset of the land breeze Sea breze front weakens Land breeze front develops

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 10 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 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

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 11 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 11 The sea breeze model: Forecast spread Buoyancy Vorticity 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

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 12 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 12 The sea breeze model: Perfect- model EnKF Results Buoyancy Vorticity 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

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 13 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 13 Mean Predicted Concentrations Sea breeze recirculation allows concentrations to build near source 3 km 500 km Sea Land Source Peak sea breeze Peak land breeze Peak heating Peak land T

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 14 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 14 Predicted Concentration Uncertainties Ensemble standard deviation has diurnal variability, grows in transition between land to sea breeze Peak sea breeze Peak land breeze Peak heating Peak land T

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 15 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 15 Evolution of Domain Average Errors EnKF assimilation of concentration observations reduces error in both meteorological variables and concentrations Concentration (kg/m 3, x10 -7 ) Bouyancy (m/s 2 ) Vorticity (s -1, x10 3 ) Error noon peak sea breeze peak land breeze peak sea breeze

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 16 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 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.

Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 17 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 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…