experimenting with the letkf in a dispersion model coupled with the lorenz 96 model author: félix...
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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 [email protected]
World Weather Open Science Conference.Montreal, Canada, 16 to 21 August 2014
In collaboration with: Juan Ruiz - Celeste Saulo - Axel Osses
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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.
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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., 2007.
- 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.
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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
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The coupled Lorenz-Dispersion model
-The idea is to coupled a trace compound using the Lorenz variables as the “wind” (Bocquet & Sakov, 2013)
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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.
......
LocalizationLength
AssimilatedVariable
- To evaluate the performance we used the RMSE using the truth.
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Experiments setups and definitions
Offline Model
Lorenz 96
Assimilation Cycle
Transport model
Assimilation CycleMEAN
ENSEMBLE
Online Model
Lorenz 96 +Transport Model
Assimilation Cycle
- Two configuration model:
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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
OnlineConcentration
- Concentration and “wind” observations are available at each grid point.
- Variables shows high sensitivity to the inflation parameter and localization scale.
OnlineConcentrationEns Size=20
- Less sensitivity to the inflation parameter and to the localization scale.
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- 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.
OfflineENS
OfflineMeanEns Size =10
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LETKF for model variables
OnlineInflation factor: 1.02Ensemble size: 20Localization 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: 1.8Ensemble size: 10Localization length: 4
Offline ENSEMBLEInflation factor: 1.02Ensemble size: 10Localization length: 2
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LETKF for model variables
- We evaluated the performace of the three model for different observation densities.
- 100 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
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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
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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.
Time Serie of RMSE.
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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.
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Thank You !
Questions?
Suggestions?
I like to thank the organizers for the travel grant that allow me to participate in this WWOSC Conference.