scholarly journals EnKF and 4D-Var data assimilation with chemical transport model BASCOE (version 05.06)

2016 ◽  
Vol 9 (8) ◽  
pp. 2893-2908 ◽  
Author(s):  
Sergey Skachko ◽  
Richard Ménard ◽  
Quentin Errera ◽  
Yves Christophe ◽  
Simon Chabrillat
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Chemical Transport ◽  
Error Variance ◽  
Model Error ◽  
Chemical Processes ◽  
Observation Error ◽  
Chemical Transport Model ◽  
Error Covariance ◽  
Chemical Tracer

Abstract. We compare two optimized chemical data assimilation systems, one based on the ensemble Kalman filter (EnKF) and the other based on four-dimensional variational (4D-Var) data assimilation, using a comprehensive stratospheric chemistry transport model (CTM). This work is an extension of the Belgian Assimilation System for Chemical ObsErvations (BASCOE), initially designed to work with a 4D-Var data assimilation. A strict comparison of both methods in the case of chemical tracer transport was done in a previous study and indicated that both methods provide essentially similar results. In the present work, we assimilate observations of ozone, HCl, HNO3, H2O and N2O from EOS Aura-MLS data into the BASCOE CTM with a full description of stratospheric chemistry. Two new issues related to the use of the full chemistry model with EnKF are taken into account. One issue is a large number of error variance parameters that need to be optimized. We estimate an observation error variance parameter as a function of pressure level for each observed species using the Desroziers method. For comparison purposes, we apply the same estimate procedure in the 4D-Var data assimilation, where both scale factors of the background and observation error covariance matrices are estimated using the Desroziers method. However, in EnKF the background error covariance is modelled using the full chemistry model and a model error term which is tuned using an adjustable parameter. We found that it is adequate to have the same value of this parameter based on the chemical tracer formulation that is applied for all observed species. This is an indication that the main source of model error in chemical transport model is due to the transport. The second issue in EnKF with comprehensive atmospheric chemistry models is the noise in the cross-covariance between species that occurs when species are weakly chemically related at the same location. These errors need to be filtered out in addition to a localization based on distance. The performance of two data assimilation methods was assessed through an 8-month long assimilation of limb sounding observations from EOS Aura MLS. This paper discusses the differences in results and their relation to stratospheric chemical processes. Generally speaking, EnKF and 4D-Var provide results of comparable quality but differ substantially in the presence of model error or observation biases. If the erroneous chemical modelling is associated with moderately fast chemical processes, but whose lifetimes are longer than the model time step, then EnKF performs better, while 4D-Var develops spurious increments in the chemically related species. If, however, the observation biases are significant, then 4D-Var is more robust and is able to reject erroneous observations while EnKF does not.

scholarly journals EnKF and 4D-Var Data Assimilation with a Chemistry Transport Model

2016 ◽  
Author(s):  
Sergey Skachko ◽  
Richard Menard ◽  
Quentin Errera ◽  
Yves Christophe ◽  
Simon Chabrillat
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Model Error ◽  
The Other ◽  
Full Description ◽  
Observation Error ◽  
Tracer Transport ◽  
Chemistry Transport Model ◽  
Stratospheric Chemistry ◽  
Chemical Tracer

Abstract. We compare two optimized chemical data assimilation systems, one based on the ensemble Kalman filter (EnKF) and the other based on four-dimensional variational (4D-Var), using a comprehensive stratospheric chemistry transport model (CTM). The work is an extension of the Belgian Assimilation System for Chemical ObsErvations (BASCOE), initially designed to work with a 4D-Var data assimilation. A strict comparison of both methods in the case of chemical tracer transport was done in a previous study and indicated that both methods provide essentially similar results. In the present work, we assimilate observations of ozone, HCl, HNO3, H2O and N2O from EOS Aura-MLS data into the BASCOE CTM with a full description of stratospheric chemistry. Two new issues related to the use of full chemistry model with EnKF are taken into account. One issue concerns to a large number of error variance parameters that need to be optimized. We estimate an observation error parameter as function of pressure level for each observed species using the Desroziers' method. For comparison reasons, we apply the same estimate procedure in the 4D-Var data assimilation, where we keep both estimates: the background and observation error variances. However in EnKF, the background error covariance is modelled using the full chemistry model and a model error term. We found that it is adequate to have a single model error based on the chemical tracer formulation that is applied for all species. This is an indication that the main source of model error in chemical transport model is due to the transport. The second issue in EnKF with comprehensive atmospheric chemistry models is the sampling errors between species. When species are weakly chemically related, cross-species sampling noise errors occur at the same location. These errors need to be filtered out, in addition to a localization based on distance. The performance of two data assimilation methods was assessed through an eight-month long assimilation of limb sounding observations from EOS Aura-MLS. The paper discusses the differences in results and their relation to stratospheric chemical processes. Generally speaking, EnKF and 4D-Var provide results of comparable quality but differ substantially in presence of model error or observation biases. If the erroneous chemical modelling is associated with not too small chemical life-times, then EnKF performs better, while 4D-Var develops spurious increments in the chemically related species. If, on the other hand, the observation biases are significant, then 4D-Var is more robust and is able to reject erroneous observations, while EnKF does not.

scholarly journals Regional scale ozone data assimilation using an ensemble Kalman filter and the CHIMERE chemical transport model

2014 ◽  
Vol 7 (1) ◽  
pp. 283-302 ◽  
Author(s):  
B. Gaubert ◽  
A. Coman ◽  
G. Foret ◽  
F. Meleux ◽  
A. Ung ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Transport Model ◽  
Regional Scale ◽  
Chemical Transport ◽  
Quality Analysis ◽  
Observation Error ◽  
Chemical Transport Model ◽  
Sensitivity Tests

Abstract. An ensemble Kalman filter (EnKF) has been coupled to the CHIMERE chemical transport model in order to assimilate ozone ground-based measurements on a regional scale. The number of ensembles is reduced to 20, which allows for future operational use of the system for air quality analysis and forecast. Observation sites of the European ozone monitoring network have been classified using criteria on ozone temporal variability, based on previous work by Flemming et al. (2005). This leads to the choice of specific subsets of suburban, rural and remote sites for data assimilation and for evaluation of the reference run and the assimilation system. For a 10-day experiment during an ozone pollution event over Western Europe, data assimilation allows for a significant improvement in ozone fields: the RMSE is reduced by about a third with respect to the reference run, and the hourly correlation coefficient is increased from 0.75 to 0.87. Several sensitivity tests focus on an a posteriori diagnostic estimation of errors associated with the background estimate and with the spatial representativeness of observations. A strong diurnal cycle of both these errors with an amplitude up to a factor of 2 is made evident. Therefore, the hourly ozone background error and the observation error variances are corrected online in separate assimilation experiments. These adjusted background and observational error variances provide a better uncertainty estimate, as verified by using statistics based on the reduced centered random variable. Over the studied 10-day period the overall EnKF performance over evaluation stations is found relatively unaffected by different formulations of observation and simulation errors, probably due to the large density of observation sites. From these sensitivity tests, an optimal configuration was chosen for an assimilation experiment extended over a three-month summer period. It shows a similarly good performance as the 10-day experiment.

Estimation of Arctic O3 loss during winter 2006/2007 using data assimilation and comparison with a chemical transport model

2011 ◽  
Vol 137 (654) ◽  
pp. 118-128 ◽  
Author(s):  
O. A. Søvde ◽  
Y. J. Orsolini ◽  
D. R. Jackson ◽  
F. Stordal ◽  
I. S. A. Isaksen ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Using Data

scholarly journals Assimilation of OMI NO<sub>2</sub> retrievals into the limited-area chemical transport model DEHM (V2007.0) with a 2-D OI algorithm

2012 ◽  
Vol 5 (1) ◽  
pp. 309-346
Author(s):  
J. D. Silver ◽  
J. Brandt ◽  
M. Hvidberg ◽  
J. Frydendall
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Initial Conditions ◽  
Chemical Transport ◽  
Optimal Interpolation ◽  
Limited Area ◽  
Two Dimensional ◽  
Chemical Transport Model ◽  
Background Error ◽  
Geophysical Field

Abstract. Data assimilation is the process of combining real-world observations with a modelled geophysical field. The increasing abundance of satellite retrievals of atmospheric trace gases makes chemical data assimilation a powerful tool for improving air quality forecasts. We implemented a two-dimensional optimal interpolation (OI) algorithm to assimilate satellite-derived estimates of tropospheric NO2 column concentrations into the Danish Eulerian Hemispheric Model (DEHM, version V2007.0), a three-dimensional, European-scale, chemical transport model. In particular, we describe how we used observational data to estimate the background error covariance matrix, B. In the assimilation, the tropospheric column NO2 field was adjusted and the modelled NO2 profile was scaled accordingly; other species were only adjusted indirectly via changes to NO2 concentrations. We ran a number of experiments to compare different parameterisations of B; this involved varying the length scale used in B, the relative weighting of the background and observation errors, the errors assigned to observations and the influence of clustered observations. We assessed model performance by comparing the analysed fields to an independent set of observations: ground-based measurements of NO2 concentrations. Ozonosonde profiles were also used for verification. The analysed NO2 and O3 concentrations were more accurate than those from a reference simulation without assimilation, with lower bias for both species and improved correlation for NO2. The experiments showed that appropriately chosen parameters for the B matrix, estimated using innovation statistics, yielded more accurate surface NO2 concentrations. There was good agreement between the seasonally-averaged observed and modelled O3 profiles. The simple OI scheme was effective and computationally feasible in this context, where only a single species was assimilated and only a two-dimensional field was adjusted. However there are certain limitations to using this assimilation scheme for more highly multi-dimensional problems. Although forecast accuracy was not examined here, we discuss the potential for improving NO2 forecasts by using assimilation to generate initial conditions.

scholarly journals Investigating the Use of Ensemble Variance to Predict Observation Error of Representation

2017 ◽  
Vol 145 (2) ◽  
pp. 653-667 ◽  
Author(s):  
Elizabeth Satterfield ◽  
Daniel Hodyss ◽  
David D. Kuhl ◽  
Craig H. Bishop
Keyword(s):  
Data Assimilation ◽  
Forecast Model ◽  
Error Variance ◽  
Observation Error ◽  
Coarse Resolution ◽  
Error Covariance ◽  
Flow Dependence ◽  
Representation Error ◽  
Resolution Model ◽  
Predictive Relationship

Data assimilation schemes combine observational data with a short-term model forecast to produce an analysis. However, many characteristics of the atmospheric states described by the observations and the model differ. Observations often measure a higher-resolution state than coarse-resolution model grids can describe. Hence, the observations may measure aspects of gradients or unresolved eddies that are poorly resolved by the filtered version of reality represented by the model. This inconsistency, known as observation representation error, must be accounted for in data assimilation schemes. In this paper the ability of the ensemble to predict the variance of the observation error of representation is explored, arguing that the portion of representation error being detected by the ensemble variance is that portion correlated to the smoothed features that the coarse-resolution forecast model is able to predict. This predictive relationship is explored using differences between model states and their spectrally truncated form, as well as commonly used statistical methods to estimate observation error variances. It is demonstrated that the ensemble variance is a useful predictor of the observation error variance of representation and that it could be used to account for flow dependence in the observation error covariance matrix.

scholarly journals Multi sensor reanalysis of total ozone

2010 ◽  
Vol 10 (22) ◽  
pp. 11277-11294 ◽  
Author(s):  
R. J. van der A ◽  
M. A. F. Allaart ◽  
H. J. Eskes
Keyword(s):  
Data Assimilation ◽  
Total Ozone ◽  
Transport Model ◽  
Chemical Transport ◽  
Satellite Observations ◽  
Chemical Transport Model ◽  
Model Driven ◽  
Near Ultraviolet ◽  
Time Period ◽  
Sample Frequency

Abstract. A single coherent total ozone dataset, called the Multi Sensor Reanalysis (MSR), has been created from all available ozone column data measured by polar orbiting satellites in the near-ultraviolet Huggins band in the last thirty years. Fourteen total ozone satellite retrieval datasets from the instruments TOMS (on the satellites Nimbus-7 and Earth Probe), SBUV (Nimbus-7, NOAA-9, NOAA-11 and NOAA-16), GOME (ERS-2), SCIAMACHY (Envisat), OMI (EOS-Aura), and GOME-2 (Metop-A) have been used in the MSR. As first step a bias correction scheme is applied to all satellite observations, based on independent ground-based total ozone data from the World Ozone and Ultraviolet Data Center. The correction is a function of solar zenith angle, viewing angle, time (trend), and effective ozone temperature. As second step data assimilation was applied to create a global dataset of total ozone analyses. The data assimilation method is a sub-optimal implementation of the Kalman filter technique, and is based on a chemical transport model driven by ECMWF meteorological fields. The chemical transport model provides a detailed description of (stratospheric) transport and uses parameterisations for gas-phase and ozone hole chemistry. The MSR dataset results from a 30-year data assimilation run with the 14 corrected satellite datasets as input, and is available on a grid of 1× 1 1/2° with a sample frequency of 6 h for the complete time period (1978–2008). The Observation-minus-Analysis (OmA) statistics show that the bias of the MSR analyses is less than 1% with an RMS standard deviation of about 2% as compared to the corrected satellite observations used.

scholarly journals Multi sensor reanalysis of total ozone

2010 ◽  
Vol 10 (4) ◽  
pp. 11401-11448 ◽  
Author(s):  
R. J. van der A ◽  
M. A. F. Allaart ◽  
H. J. Eskes
Keyword(s):  
Data Assimilation ◽  
Total Ozone ◽  
Transport Model ◽  
Chemical Transport ◽  
Satellite Observations ◽  
Chemical Transport Model ◽  
Model Driven ◽  
Stratospheric Temperature ◽  
Near Ultraviolet ◽  
Time Period

Abstract. A single coherent total ozone dataset, called the Multi Sensor Reanalysis (MSR), has been created from all available ozone column data measured by polar orbiting satellites in the near-ultraviolet Huggins band in the last thirty years. Fourteen total ozone satellite retrieval datasets from the instruments TOMS (on the satellites Nimbus-7 and Earth Probe), SBUV (Nimbus-7, NOAA-9, NOAA-11 and NOAA-16), GOME (ERS-2), SCIAMACHY (Envisat), OMI (EOS-Aura), and GOME-2 (Metop-A) have been used in the MSR. As first step a bias correction scheme is applied to all satellite observations, based on independent ground-based total ozone data from the World Ozone and Ultraviolet Data Center. The correction is a function of solar zenith angle, viewing angle, time (trend), and stratospheric temperature. As second step data assimilation was applied to create a global dataset of total ozone analyses. The data assimilation method is a sub-optimal implementation of the Kalman filter technique, and is based on a chemical transport model driven by ECMWF meteorological fields. The chemical transport model provides a detailed description of (stratospheric) transport and uses parameterisations for gas-phase and ozone hole chemistry. The MSR dataset results from a 30-year data assimilation run with the 14 corrected satellite datasets as input, and is available on a grid of 1×1½ degrees with a sample frequency of 6 h for the complete time period (1978–2008). The Observation-minus-Analysis (OmA) statistics show that the bias of the MSR analyses is less than 1 percent with an RMS standard deviation of about 2 percent as compared to the corrected satellite observations used.

scholarly journals Estimation of the error covariance matrix for IASI radiances and its impact on ozone analyses

2020 ◽  
Author(s):  
Mohammad El Aabaribaoune ◽  
Emanuele Emili ◽  
Vincent Guidard
Keyword(s):  
Covariance Matrix ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Weather Prediction ◽  
Computational Cost ◽  
Observation Error ◽  
Chemical Transport Model ◽  
Error Covariance Matrix ◽  
Error Covariance ◽  
Estimated Error

Abstract. In atmospheric chemistry retrievals and data assimilation systems, observation errors associated with satellite radiances are chosen empirically and generally treated as uncorrelated. In this work, we estimate inter-channel error covariances for the Infrared Atmospheric Sounding Interferometer (IASI) and evaluate their impact on ozone assimilation with the chemical transport model MOCAGE (MOdèle de Chime Atmospheric à Grand Echelle). The method used to calculate observation errors is a diagnostic based on the observation and analysis residual statistics already adopted in numerical weather prediction centers. We used a subset of 280 channels covering the spectral range between 980 and 1100 cm−1 to estimate the observation error covariance matrix. We computed hourly 3D-Var analyses and compared the resulting O3 fields against ozonesondes and the measurements provided by the Microwave Limb Sounder (MLS). The results show significant differences between using the estimated error covariance matrix with respect to the empirical diagonal matrix employed in previous studies. The validation of the analyses against independent data reports a significant improvement especially in the tropical stratosphere. The computational cost has also been reduced when the estimated covariance is employed in the assimilation system.

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