1. Assimilating chemical compound with a regional chemical model Chu-Chun Chang 1, Shu-Chih Yang 1, Mao-Chang Liang 2, ShuWei Hsu 1, Yu-Heng Tseng 3 and Ji-Sung Kang 4 1 Institute of Atmospheric Sciences, National Central University, Taiwan. 2 Research Center for Environmental Changes, Academia Sinica, Taiwan. 3 Institute of Atmospheric Sciences, National Taiwan University, Taiwan. 4 Department of Atmospheric and Oceanic Science, University of Maryland Reference and Acknowledgement 6. Summary To constrain the sink and source of the chemical compounds at surface during model simulation, chemical compound assimilation with Local Ensemble Transform Kalman Filter (LETKF) has been implemented to the WRF-ChemT model. Previously related studies on chemical assimilation have been focusing on the global impact using global atmospheric models (Kang et al. 2011 and Liu et al. 2010). In this study, we investigate the impact of the chemical compounds assimilation for estimating the regional surface emission under an OSSE framework. 1. Background We have successfully implement the variable localization scheme to estimate the regional surface flux by assimilating the meteorological and chemical observations with WRF-ChemT-LETKF system in the OSSE framework. In the constant emission case, the system can estimate the unobserved surface flux and improve the distribution of tracer significantly. In the diurnal emission case, the problem of surface flux estimation will become more complex and difficult. By using the time- varying information derived from the tracer variation in the first day, the diurnal emission pattern can be simulated successfully. We found that in the diurnal emission case, flux estimation is sensitive to the quality of initial tracer and surface flux ensembles. Therefore, how to generate reliable ensembles is an important issue in the diurnal emission case. In the future work, we will try to add real emission profile and apply to more realistic system. Liu, J., I. Fung, E. Kalnay, and J. Kang (2011), CO 2 transport uncertainties from the uncertainties in meteorological fields, Geophys. Res. Lett., 38, L12808, doi:10.1029/2011GL047213. Kang, J.-S., E. Kalnay, J. Liu, I. Fung, T. Miyoshi, and K. Ide (2011), “Variable localization” in an ensemble Kalman filter: Application to carbon cycle data assimilation, J. Geophys. Res., 116, D09110,doi:10.1029/2010JD01467. Yang, S-C, E. Kalnay and T. Miyoshi (2012), Improving EnKF spin-up for typhoon assimilation and prediction. Wea. Forecasting. 27, 878-897. We greatly appreciate comments and suggestions from Prof. Julius Chang. We also acknowledge the computational support from Academia Sinica. 3. OSSE Experiment setup 2. Variable localization in LETKF ATM variables WRFChemT simulation Surface Emission Surface Emission ATM assim ATM vars tracer vars Tracer assim Tracer OBS ATM OBS ATM variables + Surface Flux + tracer vars ATM variables + Surface Flux + tracer vars updated Coupled assimilation with meteorological and chemical components Fig. 1. The flow chart of the meteorology- chemistry coupled assimilation system. WRF-ChemT model domain: The grid dimension is 168×152×24 layers, centered at (141°E, 37.4°N). The horizontal resolution is 45 Km. Truth run: an 18-day simulation with initial meteorology field from the FNL reanalysis and surface emission with a Gaussian distribution 1. Constant emission 2. Emission with a diurnal cycle varies with a step function Analysis variables: Atmospheric prognostic variables, CO 2 tracer, surface flux. Observations: add Gaussian noises to the true evolution. The OSSE experiment is performed with 32 ensemble members from 07/03/2009 00Z to 07/07/2009 00Z The ensemble is spun-up with a set of randomly perturbed flux forcing from 07/01/2009 00Z to 07/03/2009 00Z. EXPsConstant emission caseDiurnal emission case DA cycle6-hour4-hour Observation densitySounding: Realistic radiosonde network Tracer(CO 2 concentration): 4 grids for each Initial tracer perturbationRandomly chosen from nature-runs with different emission. Modified temporal profileConstant (1.0)Time-varying (0~1.0) Fig. 4. The time profile of truth run in the diurnal emission case. The blue spots are the corresponded DA times. Table 1. The OSSE experiment setup The LETKF scheme (Hunt et al. 2007) performs an analysis locally in space using local information, including the background ensemble and observations. The meteorology-chemistry coupled LETKF system is implemented in the WRF-ChemT model based on the WRF-LETKF system (Yang et al., 2012). The analysis variables include variables of meteorology and chemical compounds and surface flux. At the analysis time, not only does the new coupled assimilation system assimilate meteorological observations, it also assimilates observations of chemical compounds. The variable localization scheme (Kang et al. 2001) for performing univariate or one-way multivairate CO 2 assimilation is used to obtain either the univariate (ATM only) or multi-variate background error covariance. The goal is to estimate the “unobserved” surface flux of the CO 2. 4. Impact on constant emission case Fig. 2. (a)The true surface flux.(b) East- West cross-section of the background and analysis ensemble of the surface flux at 00Z 03 July (the 1st analysis cycle). With the correlation between tracer distribution and surface flux, the pattern of the surface flux can be correctly captured! 4. Impact on diurnal emission case I. Surface flux estimation In the setup of diurnal emission, a initial guess of the time profile will be first estimated and provided for later assimilation. II. Surface flux estimation Results at both emission and non-emission times show that with the modified time profile, the system is able to estimate the time-varying surface flux and also improve the distribution of tracer fields. Fig. 5. Domain-wise total emission from the truth, analysis with a time-constant profile and with an estimated time-varying profile. I. Modified time profile II. RMSE and spread of Uwind and tracer field The improvement of surface flux estimation improve the distribution of tracer fields not only at the assimilated level but also at the off-observing level. Fig. 3. Root-Mean-Square error and ensemble spread for (a) zonal wind at the 5th model level, (b) CO 2 concentration at the 1st model level and (c) CO 2 concentration at the 5th model level. (d) is the total emission derived from the truth and analysis. Fig. 6. The estimated surface flux derived (a) with a time-constant profile (no strategy) and (c) with an estimated time-varying profile. East- West cross-section from the truth and the mean of the estimated surface flux ensemble at 00Z 05 Jul from (b) with a time-constant profile (no strategy) and (d) with an estimated time-varying profile. EXPControlWith strategy DA level/ cycleLevel 1/ 4 hrs ATM/CHEM obsReal setup/dense setup Estimate profileNoYes Table 2. EXPs for diurnal emission case (a) (b) In the WRF-ChemT model, the emission amount on the grid (x,y) at time t = spatial flux(x,y)* profile(t)