scholarly journals Assimilation of surface NO<sub>2</sub> and O<sub>3</sub> observations into the SILAM chemistry transport model

2014 ◽  
Vol 7 (4) ◽  
pp. 5589-5621 ◽  
Author(s):  
J. Vira ◽  
M. Sofiev
Keyword(s):  
Transport Model ◽  
Model Simulation ◽  
Time Of Day ◽  
Covariance Matrices ◽  
Daily Maximum ◽  
Observation Error ◽  
Chemistry Transport Model ◽  
Error Covariance ◽  
Seasonal And Diurnal Variation ◽  
Free Running

Abstract. This paper describes assimilation of trace gas observations into the chemistry transport model SILAM using the 3D-Var method. Assimilation results for year 2012 are presented for the prominent photochemical pollutants ozone (O3) and nitrogen dioxide (NO2). Both species are covered by the Airbase observation database, which provides the observational dataset used in this study. Attention is paid to the background and observation error covariance matrices, which are obtained primarily by iterative application of a posteriori diagnostics. The diagnostics are computed separately for two months representing summer and winter conditions, and further disaggregated by time of day. This allows deriving background and observation error covariance definitions which include both seasonal and diurnal variation. The consistency of the obtained covariance matrices is verified using χ2 diagnostics. The analysis scores are computed for a control set of observation stations withheld from assimilation. Compared to a free-running model simulation, the correlation coefficient for daily maximum values is improved from 0.8 to 0.9 for O3 and from 0.53 to 0.63 for NO2.

scholarly journals Assimilation of surface NO<sub>2</sub> and O<sub>3</sub> observations into the SILAM chemistry transport model

2015 ◽  
Vol 8 (2) ◽  
pp. 191-203 ◽  
Author(s):  
J. Vira ◽  
M. Sofiev
Keyword(s):  
Transport Model ◽  
Time Of Day ◽  
Covariance Matrices ◽  
Atmospheric Composition ◽  
Integrated Modelling ◽  
Daily Maximum ◽  
Observation Error ◽  
Data Set ◽  
Chemistry Transport Model ◽  
Error Covariance

Abstract. This paper describes the assimilation of trace gas observations into the chemistry transport model SILAM (System for Integrated modeLling of Atmospheric coMposition) using the 3D-Var method. Assimilation results for the year 2012 are presented for the prominent photochemical pollutants ozone (O3) and nitrogen dioxide (NO2). Both species are covered by the AirBase observation database, which provides the observational data set used in this study. Attention was paid to the background and observation error covariance matrices, which were obtained primarily by the iterative application of a posteriori diagnostics. The diagnostics were computed separately for 2 months representing summer and winter conditions, and further disaggregated by time of day. This enabled the derivation of background and observation error covariance definitions, which included both seasonal and diurnal variation. The consistency of the obtained covariance matrices was verified using χ2 diagnostics. The analysis scores were computed for a control set of observation stations withheld from assimilation. Compared to a free-running model simulation, the correlation coefficient for daily maximum values was improved from 0.8 to 0.9 for O3 and from 0.53 to 0.63 for NO2.

scholarly journals Estimation of the error covariance matrix for IASI radiances and its impact on the assimilation of ozone in a chemistry transport model

2021 ◽  
Vol 14 (4) ◽  
pp. 2841-2856
Author(s):  
Mohammad El Aabaribaoune ◽  
Emanuele Emili ◽  
Vincent Guidard
Keyword(s):  
Covariance Matrix ◽  
Spectral Range ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Weather Prediction ◽  
Computational Cost ◽  
Observation Error ◽  
Chemistry Transport Model ◽  
Error Covariance Matrix ◽  
Error Covariance

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 chemistry transport model MOCAGE (Modèle de Chimie Atmosphérique à Grande Echelle). The method used to calculate observation errors is a diagnostic based on the observation and analysis residual statistics already adopted in many numerical weather prediction centres. We used a subset of 280 channels covering the spectral range between 980 and 1100 cm−1 to estimate the observation-error covariance matrix. This spectral range includes ozone-sensitive and atmospheric window channels. We computed hourly 3D-Var analyses and compared the resulting O3 fields against ozonesondes and the measurements provided by the Microwave Limb Sounder (MLS) and by the Ozone Monitoring Instrument (OMI). 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 matrix is employed in the assimilation system, by reducing the number of iterations needed for the minimizer to converge.

scholarly journals First steps towards the assimilation of IASI ozone data into the MOCAGE-PALM system

2009 ◽  
Vol 9 (2) ◽  
pp. 6691-6737 ◽  
Author(s):  
S. Massart ◽  
C. Clerbaux ◽  
D. Cariolle ◽  
A. Piacentini ◽  
S. Turquety ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Total Ozone ◽  
Transport Model ◽  
Polar Regions ◽  
Observation Error ◽  
Random Errors ◽  
Chemistry Transport Model ◽  
Ozone Data ◽  
The One ◽  
New Generation

Abstract. The Infrared Atmospheric Sounding Interferometer (IASI) is one of the five European new generation instruments carried by the polar-orbiting MetOp-A satellite. Data assimilation is a powerful tool to combine these data with a numerical model. This paper presents the first steps made towards the assimilation of the total ozone columns from the IASI measurements into a chemistry transport model. The IASI ozone data used are provided by an inversion of radiances performed at the LATMOS (Laboratoire Atmosphères, Milieux, Observations Spatiales). As a contribution to the validation of this dataset, the LATMOS-IASI data are compared to a four dimensional ozone field, with low systematic and random errors compared to ozonesondes and OMI-DOAS data. This field results from the combined assimilation of ozone profiles from the MLS instrument and of total ozone columns from the SCIAMACHY instrument. It is found that on average, the LATMOS-IASI data tends to overestimate the total ozone columns by 2% to 8%. The random observation error of the LATMOS-IASI data is estimated to about 6%, except over polar regions and deserts where it is higher. Using this information, the LATMOS-IASI data are then assimilated, combined with the MLS data. This first LATMOS-IASI data assimilation experiment shows that the resulting analysis is quite similar to the one obtained from the combined MLS and SCIAMACHY data assimilation.

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 Multiscale Representation of Observation Error Statistics in Data Assimilation

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1460
Author(s):  
Vincent Chabot ◽  
Maëlle Nodet ◽  
Arthur Vidard
Keyword(s):  
Data Assimilation ◽  
Real Life ◽  
Covariance Matrices ◽  
Multiscale Modelling ◽  
Observation Error ◽  
Error Statistics ◽  
Life Data ◽  
Promising Tool ◽  
Error Covariance ◽  
Real Life Data

Accounting for realistic observation errors is a known bottleneck in data assimilation, because dealing with error correlations is complex. Following a previous study on this subject, we propose to use multiscale modelling, more precisely wavelet transform, to address this question. This study aims to investigate the problem further by addressing two issues arising in real-life data assimilation: how to deal with partially missing data (e.g., concealed by an obstacle between the sensor and the observed system), and how to solve convergence issues associated with complex observation error covariance matrices? Two adjustments relying on wavelets modelling are proposed to deal with those, and offer significant improvements. The first one consists of adjusting the variance coefficients in the frequency domain to account for masked information. The second one consists of a gradual assimilation of frequencies. Both of these fully rely on the multiscale properties associated with wavelet covariance modelling. Numerical results on twin experiments show that multiscale modelling is a promising tool to account for correlations in observation errors in realistic applications.

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 CHIMERE-2017: from urban to hemispheric chemistry-transport modeling

2017 ◽  
Vol 10 (6) ◽  
pp. 2397-2423 ◽  
Author(s):  
Sylvain Mailler ◽  
Laurent Menut ◽  
Dmitry Khvorostyanov ◽  
Myrto Valari ◽  
Florian Couvidat ◽  
...  
Keyword(s):  
Transport Model ◽  
Model Simulation ◽  
Point Of View ◽  
Atmospheric Composition ◽  
Test Case ◽  
Polar Regions ◽  
Chemistry Transport Model ◽  
Computational Point ◽  
Improve Model ◽  
Code Complexity

Abstract. CHIMERE is a chemistry-transport model designed for regional atmospheric composition. It can be used at a variety of scales from local to continental domains. However, due to the model design and its historical use as a regional model, major limitations had remained, hampering its use at hemispheric scale, due to the coordinate system used for transport as well as to missing processes that are important in regions outside Europe. Most of these limitations have been removed in the CHIMERE-2017 version, allowing its use in any region of the world and at any scale, from the scale of a single urban area up to hemispheric scale, with or without polar regions included. Other important improvements have been made in the treatment of the physical processes affecting aerosols and the emissions of mineral dust. From a computational point of view, the parallelization strategy of the model has also been updated in order to improve model numerical performance and reduce the code complexity. The present article describes all these changes. Statistical scores for a model simulation over continental Europe are presented, and a simulation of the circumpolar transport of volcanic ash plume from the Puyehue volcanic eruption in June 2011 in Chile provides a test case for the new model version at hemispheric scale.

scholarly journals Evaluation of tropospheric ozone columns derived from assimilated GOME ozone profile observations

2009 ◽  
Vol 9 (20) ◽  
pp. 8105-8120 ◽  
Author(s):  
A. T. J. de Laat ◽  
R. J. van der A ◽  
M. van Weele
Keyword(s):  
Tropospheric Ozone ◽  
Transport Model ◽  
Added Value ◽  
Chemistry Transport Model ◽  
Ozone Profile ◽  
Free Running ◽  
Tropospheric O3 ◽  
Total Column ◽  
Good Agreement

Abstract. Tropospheric O3 column estimates are produced and evaluated from spaceborne O3 observations by the subtraction of assimilated O3 profile observations from total column observations, the so-called Tropospheric O3 ReAnalysis or TORA method. Here we apply the TORA method to six years (1996–2001) of ERS-2 GOME/TOMS total O3 and ERS-2 GOME O3 profile observations using the TM5 global chemistry-transport model with a linearized O3 photochemistry parameterization scheme. Free running TM5 simulations show good agreement with O3 sonde observations in the upper-tropospheric and lower stratospheric region (UTLS), both for short day-to-day variability as well as for monthly means. The assimilation of GOME O3 profile observations counteracts the mid-latitude stratospheric O3 drift caused by the overstrong stratospheric meridional circulation in TM5. Assimilation of GOME O3 profile observations also improves the bias and correlations in the tropical UTLS region but slightly degrades the model-to-sonde correlations and bias of extra-tropical UTLS. We suggest that this degradation is related to the large ground pixel size of the GOME O3 measurements (960×100 km) in combination with retrieval and calibration errors. The added value of the assimilation of GOME O3 profiles compared to stand-alone model simulations lays in the long term variations of stratospheric O3, not in short term synoptic variations. The evaluation of daily and monthly tropospheric O3 columns obtained from total column observations and using the TORA methodology shows that the use of GOME UV-VIS nadir O3 profiles in combination with the spatial resolution of the model does not result in satisfactory residual tropospheric ozone columns.

scholarly journals A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation

2020 ◽  
Vol 148 (10) ◽  
pp. 3973-3994 ◽  
Author(s):  
Pierre Tandeo ◽  
Pierre Ailliot ◽  
Marc Bocquet ◽  
Alberto Carrassi ◽  
Takemasa Miyoshi ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Current Data ◽  
Covariance Matrices ◽  
Estimation Methods ◽  
Observation Error ◽  
Correlated Noise ◽  
Unified Framework ◽  
Error Covariance ◽  
Error Terms ◽  
Observation Space

AbstractData assimilation combines forecasts from a numerical model with observations. Most of the current data assimilation algorithms consider the model and observation error terms as additive Gaussian noise, specified by their covariance matrices and , respectively. These error covariances, and specifically their respective amplitudes, determine the weights given to the background (i.e., the model forecasts) and to the observations in the solution of data assimilation algorithms (i.e., the analysis). Consequently, and matrices significantly impact the accuracy of the analysis. This review aims to present and to discuss, with a unified framework, different methods to jointly estimate the and matrices using ensemble-based data assimilation techniques. Most of the methods developed to date use the innovations, defined as differences between the observations and the projection of the forecasts onto the observation space. These methods are based on two main statistical criteria: 1) the method of moments, in which the theoretical and empirical moments of the innovations are assumed to be equal, and 2) methods that use the likelihood of the observations, themselves contained in the innovations. The reviewed methods assume that innovations are Gaussian random variables, although extension to other distributions is possible for likelihood-based methods. The methods also show some differences in terms of levels of complexity and applicability to high-dimensional systems. The conclusion of the review discusses the key challenges to further develop estimation methods for and . These challenges include taking into account time-varying error covariances, using limited observational coverage, estimating additional deterministic error terms, or accounting for correlated noise.

scholarly journals Variational assimilation of IASI SO<sub>2</sub> plume height and total-column retrievals in the 2010 eruption of Eyjafjallajökull using the SILAM v5.3 chemistry transport model

2016 ◽  
Author(s):  
Julius Vira ◽  
Elisa Carboni ◽  
Roy G. Grainger ◽  
Mikhail Sofiev
Keyword(s):  
Source Term ◽  
Transport Model ◽  
Model Simulation ◽  
Observation Error ◽  
A Posteriori ◽  
So2 Emissions ◽  
Plume Height ◽  
Observation Operator ◽  
Truncated Iteration ◽  
Total Column

Abstract. This study focuses on two new aspects on inverse modelling of volcanic emissions. First, we derive an observation operator for satellite retrievals of plume height, and second, we solve the inverse problem using the 4D-Var method. The approach is demonstrated by assimilating IASI SO2 plume height and total column retrievals in a source term inversion for the 2010 eruption of Eyjafjallajökull. The inversion resulted in temporal and vertical reconstruction of the SO2 emissions during the 1–20 May, 2010 with formal vertical and temporal resolutions of 500 m and 12 hours. The plume height observation operator is based on simultaneous assimilation of the plume height and total column retrievals. The plume height is taken to represent the vertical centre of mass, which is transformed into the first moment of mass. This makes the observation operator linear and simple to implement. The necessary modifications to the observation error covariance matrix are derived. Regularisation by truncated iteration is investigated as a simple and efficient regularisation method for the 4D-Var based inversion. In an experiment with synthetic observations, the truncated iteration was found to perform similarly to the commonly used Tikhonov regularisation. However, the truncated iteration allows the amount of regularisation to be varied a posteriori, without repeating the inversion. For inverting the Eyjafjallajökull SO2 emission at the temporal and vertical resolution used in this study, the 4D-Var method required about 70 % less computational effort than commonly used methods based on performing a separate model simulation for each degree of freedom in the estimated source term. Compared to the inversion using only total column retrievals, assimilating the plume height resulted in a vertical emission profile more closely matching the ash plume heights observed by radar. The a posteriori source term gave an estimate of 0.29 Tg erupted SO2 of which 95 % was injected below 11 km.

Sign in / Sign up

Export Citation Format

Share Document