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 Assimilation of OMI NO<sub>2</sub> retrievals into the limited-area chemistry-transport model DEHM (V2009.0) with a 3-D OI algorithm

2013 ◽  
Vol 6 (1) ◽  
pp. 1-16 ◽  
Author(s):  
J. D. Silver ◽  
J. Brandt ◽  
M. Hvidberg ◽  
J. Frydendall ◽  
J. H. Christensen
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Transport Model ◽  
Initial Conditions ◽  
Three Dimensional ◽  
Optimal Interpolation ◽  
Limited Area ◽  
Background Error ◽  
Geophysical Field ◽  
Chemistry Transport Model

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 an increasingly viable method for deriving more accurate analysed fields and initial conditions for air quality forecasts. We implemented a three-dimensional optimal interpolation (OI) scheme to assimilate retrievals of NO2 tropospheric columns from the Ozone Monitoring Instrument into the Danish Eulerian Hemispheric Model (DEHM, version V2009.0), a three-dimensional, regional-scale, offline chemistry-transport model. The background error covariance matrix, B, was estimated based on differences in the NO2 concentration field between paired simulations using different meteorological inputs. Background error correlations were modelled as non-separable, horizontally homogeneous and isotropic. Parameters were estimated for each month and for each hour to allow for seasonal and diurnal patterns in NO2 concentrations. Three experiments were run to compare the effects of observation thinning and the choice of observation errors. Model performance was assessed by comparing the analysed fields to an independent set of observations: ground-based measurements from European air-quality monitoring stations. The analysed NO2 and O3 concentrations were more accurate than those from a reference simulation without assimilation, with increased temporal correlation for both species. Thinning of satellite data and the use of constant observation errors yielded a better balance between the observed increments and the prescribed error covariances, with no appreciable degradation in the surface concentrations due to the observation thinning. Forecasts were also considered and these showed rather limited influence from the initial conditions once the effects of the diurnal cycle are accounted for. The simple OI scheme was effective and computationally feasible in this context, where only a single species was assimilated, adjusting the three-dimensional field for this compound. Limitations of the assimilation scheme are discussed.

scholarly journals Regional scale ozone data assimilation using an Ensemble Kalman Filter and the CHIMERE Chemical-Transport Model

2013 ◽  
Vol 6 (2) ◽  
pp. 3033-3083
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 ◽  
Random Variable ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Background Error ◽  
Filter Performance

Abstract. The Ensemble Kalman Filter is an efficient algorithm for data assimilation; it allows for an estimation of forecast and analysis error by updating the model error covariance matrices at the analysis step. This algorithm has been coupled to the CHIMERE chemical transport model in order to assimilate ozone ground measurements at the regional scale. The analyzed ozone field is evaluated using a consistent set of observations and shows a reduction of the quadratic error by about a third and an improvement of the hourly correlation coefficient despite of a low ensemble size designed for operational purposes. A classification of the European observation network is derived from the ozone temporal variability in order to qualitatively determine the observation spatial representativeness. Then, an estimation of the temporal behavior of both model and observations error variances of the assimilated stations is checked using a posteriori Desroziers diagnostics. The amplitude of the additive noise applied to the ozone fields can be diagnosed and tuned online. The evaluation of the obtained background error variance distribution through the Reduced Centered Random Variable standard deviation shows improved statistics. The use of the diagnostics indicates a strong diurnal cycle of both the model and the representativeness errors. Another design of the ensemble is constructed by perturbing model parameter, but does not allow creating enough variability if used solely. Finally, the overall filter performance over evaluation stations is found to be relatively unaffected by different formulations of observation and simulation errors.

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 Inversion of long-lived trace gas emissions using combined Eulerian and Lagrangian chemical transport models

2011 ◽  
Vol 11 (18) ◽  
pp. 9887-9898 ◽  
Author(s):  
M. Rigby ◽  
A. J. Manning ◽  
R. G. Prinn
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Transport Model ◽  
Initial Conditions ◽  
Dispersion Model ◽  
Chemical Transport ◽  
Particle Dispersion ◽  
Global Network ◽  
Lagrangian Model ◽  
Chemical Transport Model

Abstract. We present a method for estimating emissions of long-lived trace gases from a sparse global network of high-frequency observatories, using both a global Eulerian chemical transport model and Lagrangian particle dispersion model. Emissions are derived in a single step after determining sensitivities of the observations to initial conditions, the high-resolution emissions field close to observation points, and larger regions further from the measurements. This method has the several advantages over inversions using one type of model alone, in that: high-resolution simulations can be carried out in limited domains close to the measurement sites, with lower resolution being used further from them; the influence of errors due to aggregation of emissions close to the measurement sites can be minimized; assumptions about boundary conditions to the Lagrangian model do not need to be made, since the entire emissions field is estimated; any combination of appropriate models can be used, with no code modification. Because the sensitivity to the entire emissions field is derived, the estimation can be carried out using traditional statistical methods without the need for multiple steps in the inversion. We demonstrate the utility of this approach by determining global SF6 emissions using measurements from the Advanced Global Atmospheric Gases Experiment (AGAGE) between 2007 and 2009. The global total and large-scale patterns of the derived emissions agree well with previous studies, whilst allowing emissions to be determined at higher resolution than has previously been possible, and improving the agreement between the modeled and observed mole fractions at some sites.

scholarly journals Global NO<sub>x</sub> emission estimates derived from an assimilation of OMI tropospheric NO<sub>2</sub> columns

2011 ◽  
Vol 11 (12) ◽  
pp. 31523-31583 ◽  
Author(s):  
K. Miyazaki ◽  
H. J. Eskes ◽  
K. Sudo
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
A Priori ◽  
Eastern China ◽  
Nox Emissions ◽  
Chemical Transport Model ◽  
Background Error ◽  
Tropospheric No2 ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A data assimilation system has been developed to estimate global nitrogen oxides (NOx) emissions using OMI tropospheric NO2 columns (DOMINO product) and a global chemical transport model (CTM), CHASER. The data assimilation system, based on an ensemble Kalman filter approach, was applied to optimize daily NOx emissions with a horizontal resolution of 2.8° during the years 2005 and 2006. The background error covariance estimated from the ensemble CTM forecasts explicitly represents non-direct relationships between the emissions and tropospheric columns caused by atmospheric transport and chemical processes. In comparison to the a priori emissions based on bottom-up inventories, the optimized emissions were higher over Eastern China, the Eastern United States, Southern Africa, and Central-Western Europe, suggesting that the anthropogenic emissions are mostly underestimated in the inventories. In addition, the seasonality of the estimated emissions differed from that of the a priori emission over several biomass burning regions, with a large increase over Southeast Asia in April and over South America in October. The data assimilation results were validated against independent data: SCIAMACHY tropospheric NO2 columns and vertical NO2 profiles obtained from aircraft and lidar measurements. The emission correction greatly improved the agreement between the simulated and observed NO2 fields; this implies that the data assimilation system efficiently derives NOx emissions from concentration observations. We also demonstrated that biases in the satellite retrieval and model settings used in the data assimilation largely affect the magnitude of estimated emissions. These dependences should be carefully considered for better understanding NOx sources from top-down approaches.

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 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 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.

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