Experimenting with the LETKF in a dispersion model coupled with the Lorenz 96 model Author: Félix Carrasco, PhD Student at University of Buenos Aires, Department of Atmospheric and Oceanic Sciences World Weather Open Science Conference. Montreal, Canada, 16 to 21 August 2014 In collaboration with: Juan Ruiz - Celeste Saulo - Axel Osses

Outline Introduction. The coupled Lorenz-Dispersion model. Experiment Setup and definitions. LETKF for model variables. Comparison between online and offline. LETKF to estimate Emissions. Conclusion and future work.

Introduction - We deal with two important data in the atmosphere/chemistry community: Model and Observations, yet both of them contains errors. Using both information in an optimal sense: Data Assimilation. - There has been great improves in order to estimate the emission (Inventory) which usually have great uncertainties. Bocquet, 2011 (4Dvar); Kang et al. (LETKF), 2011; Saide et al., 2011 (non Gaussian distribution). - Chemical weather forecast has improved greatly the last decade using data assimilation techniques also including operational implementations. Kukkonen et al., 2012 (review Europe); Uno et al., 2003 (Japan); Constantinescu et al., Data Assimilation has been widely used in weather forecast and it has been lately used in Atmospheric Chemistry for both chemical weather forecast and source estimation.

Test the ability of the LETKF in simple transport model to improve estimation of concentration and sources of atmospheric constituents in the context of online and offline model. - A good approach to test the ability of the technique is use simple models to evaluate the performance before to implement in a more complex model. Objective - LETKF (Hunt et al. 2007) is a highly efficient and almost model independent state-of-the art data assimilation technique that has been successfully applied to several models. Some ideas

The coupled Lorenz-Dispersion model -The idea is to coupled a trace compound using the Lorenz variables as the “wind” (Bocquet & Sakov, 2013)

Experiments setups and definitions -Observations are generated from a long time model integration adding a randomly distributed noise with STD equal to 1. Observations are assimilated every five steps. -The coupled model is resolved using a Four order Rungge-Kutta method with a dT=0.01. We used N=40 variables for concentration and Lorenz variables (total equal to 80) with the following parameter value for the model: - We test the LETKF using a constant inflation factor and a localization scale.... Localization Length Assimilated Variable - To evaluate the performance we used the RMSE using the truth.

Experiments setups and definitions Offline Model Lorenz 96 Assimilation Cycle Transport model Assimilation Cycle MEAN ENSEMBLE Online Model Lorenz 96 + Transport Model Assimilation Cycle - Two configuration model:

LETKF for optimal setup - Optimization of inflation and localization scales for the concentration variables - Optimal values for the wind variables also good for the concentration variables Online Concentration - Concentration and “wind” observations are available at each grid point. - Variables shows high sensitivity to the inflation parameter and localization scale. Online Concentration Ens Size=20 - Less sensitivity to the inflation parameter and to the localization scale.

- In the offline case, the RMSE values for concentration variables are much higher than the online case yet minor than the observation deviation. - If the wind is not perturbed then a large part of the uncertainty is missed ---> Higher optimal inflation factor LETKF for optimal setup - When we resolve the assimilation cycle using the ensemble wind, the performance is almost as good as in the online case. Offline ENS Offline Mean Ens Size =10

LETKF for model variables Online Inflation factor: 1.02 Ensemble size: 20 Localization length: 6 - Large differences in the RMSE even using the optimal parameters configuration -Impact of concentration upon wind analysis is small (At least when observation density is high) Offline MEAN Inflation factor: 1.8 Ensemble size: 10 Localization length: 4 Offline ENSEMBLE Inflation factor: 1.02 Ensemble size: 10 Localization length: 2

LETKF for model variables - We evaluated the performace of the three model for different observation densities experiments where performed for each observations densities randomly varying the distribution of the observations. - The large variability that is observed at low observation densities, is because the position of the observation grid impacts directly on the performance of the data assimilation cycle

LETKF: Estimating sources - Using the online model, we perform three experiments to test the ability of the LETKF estimating the emission. Inflation: 1.02 (Emission and Concentration) Localization: 6 Ensemble Size: 20

LETKF: Estimating sources Inflation: 1.02 (Model); 1.01 (Emiss) Localization: 6 (Wind); 3 (Concentration) Ensemble Size: 20 Two different emission scenarios: Smooth spatial variabilty High spatial variabilty Inflation: 1.02; 1.01 Localization: 6; 3 Ensemble Size: 20 Time Serie of RMSE.

Conclusion and Future work: -We explore the abilities of one data assimilation technique (LETKF) in a simple transport model for two model configuration. - Results shows a good perfomance in estimating concentrations and wind in both configuration with better perfomance when the uncertainty in the wind is considered (Online and offline using ensemble). - Results also shows a good perfomance in estimating emissions within concentrations and wind with the online configuration. However the performance of the filter is strongly sensitive to the spatial distribution of the sources. - Future work with this model is to explore using the rapid frequency Lorenz variables model to study the impact of turbulence in the transport equation not included in this formulation.

Thank You ! Questions? Suggestions? I like to thank the organizers for the travel grant that allow me to participate in this WWOSC Conference.