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

2009 ◽  
Vol 9 (14) ◽  
pp. 5073-5091 ◽  
Author(s):  
S. Massart ◽  
C. Clerbaux ◽  
D. Cariolle ◽  
A. Piacentini ◽  
S. Turquety ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Total Ozone ◽  
Three Dimensional ◽  
Polar Regions ◽  
Observation Error ◽  
Random Errors ◽  
Ozone Monitoring Instrument ◽  
Data Set ◽  
Scanning Imaging ◽  
The One

Abstract. With the use of data assimilation, we study the quality of the Infrared Atmospheric Sounding Interferometer (IASI) total ozone column measurements. The IASI data are provided by the inversion of IASI radiances performed at the Laboratoire ATmosphères, Milieux, Observations Spatiales (LATMOS). This data set is initially compared on a five-month period to a three-dimensional time varying ozone field that we take as a reference. This reference field results from the combined assimilation of ozone profiles from the Microwave Limb Sounder (MLS) instrument and of total ozone columns from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument. It has low systematic and random errors when compared to ozonesondes and Ozone Monitoring Instrument (OMI) data. The comparison shows 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 7%, except over polar regions and deserts where it is higher. The daytime data have generally lower biases but higher random error than the nighttime data. 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. The differences are mainly due to the lack of SCIAMACHY measurements during polar night, and to the higher LATMOS-IASI random errors especially over the southern polar region.

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 Diagnosing the transition layer at extratropical latitudes using MLS O<sub>3</sub> and MOPITT CO analyses

2013 ◽  
Vol 13 (14) ◽  
pp. 7225-7240 ◽  
Author(s):  
J. Barré ◽  
L. El Amraoui ◽  
P. Ricaud ◽  
W. A. Lahoz ◽  
J.-L. Attié ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Transition Region ◽  
Transition Layer ◽  
Transport Model ◽  
Global Scale ◽  
British Isles ◽  
Chemistry Transport Model ◽  
Free Model ◽  
The Relationship

Abstract. The behavior of the extratropical transition layer (ExTL) is investigated using a chemistry transport model (CTM) and analyses derived from assimilation of MLS (Microwave Limb Sounder) O3 and MOPITT (Measurements Of Pollution In The Troposphere) CO data. We firstly focus on a stratosphere–troposphere exchange (STE) case study that occurred on 15 August 2007 over the British Isles (50° N, 10° W). We evaluate the effect of data assimilation on the O3–CO correlations. It is shown that data assimilation disrupts the relationship in the transition region. When MLS O3 is assimilated, CO and O3 values are not consistent between each other, leading to unphysical correlations at the STE location. When MLS O3 and MOPITT CO assimilated fields are taken into account in the diagnostics the relationship happens to be more physical. We then use O3–CO correlations to quantify the effect of data assimilation on the height and depth of the ExTL. When the free-model run O3 and CO fields are used in the diagnostics, the ExTL distribution is found 1.1 km above the thermal tropopause and is 2.6 km wide (2σ). MOPITT CO analyses only slightly sharpen (by −0.02 km) and lower (by −0.2 km) the ExTL distribution. MLS O3 analyses provide an expansion (by +0.9 km) of the ExTL distribution, suggesting a more intense O3 mixing. However, the MLS O3 analyses ExTL distribution shows a maximum close to the thermal tropopause and a mean location closer to the thermal tropopause (+0.45 km). When MLS O3 and MOPITT CO analyses are used together, the ExTL shows a mean location that is the closest to the thermal tropopause (+0.16 km). We also extend the study at the global scale on 15 August 2007 and for the month of August 2007. MOPITT CO analyses still show a narrower chemical transition between stratosphere and troposphere than the free-model run. MLS O3 analyses move the ExTL toward the troposphere and broaden it. When MLS O3 analyses and MOPITT CO analyses are used together, the ExTL matches the thermal tropopause poleward of 50°.

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 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 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 Extended and refined multi sensor reanalysis of total ozone for the period 1970–2012

2015 ◽  
Vol 8 (3) ◽  
pp. 3283-3319 ◽  
Author(s):  
R. J. van der A ◽  
M. A. F. Allaart ◽  
H. J. Eskes
Keyword(s):  
Data Assimilation ◽  
Total Ozone ◽  
Transport Model ◽  
Forecast Error ◽  
Satellite Observations ◽  
Weather Forecasts ◽  
Stratospheric Temperature ◽  
The Individual ◽  
Data Assimilation Technique ◽  
Ozone Column

Abstract. The ozone multi-sensor reanalysis (MSR) is a multi-decadal ozone column data record constructed using all available ozone column satellite datasets, surface Brewer and Dobson observations and a data assimilation technique with detailed error modelling. The result is a high-resolution time series of 6 hourly global ozone column fields and forecast error fields that may be used for ozone trend analyses as well as detailed case studies. The ozone MSR is produced in two steps. First, the latest reprocessed versions of all available ozone column satellite datasets are collected, and are corrected for biases as function of solar zenith angle, viewing angle, time (trend), and stratospheric temperature using Brewer/Dobson ground measurements from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC; http://www.woudc.org/). Subsequently the debiased satellite observations are assimilated within the ozone chemistry and data assimilation model TMDAM driven by meteorological analyses of the European Centre for Medium-Range Weather Forecasts (ECMWF). The MSR2 (MSR version 2) reanalysis upgrade described in this paper consists of an ozone record for the 43 year period 1970–2012. The chemistry-transport model and data assimilation system have been adapted to improve the resolution, error modelling and processing speed. BUV satellite observations have been included for the period 1970–1977. The total record is extended with 13 years compared to the first version of the ozone multi sensor reanalysis, the MSR1. The latest total ozone retrievals of 15 satellite instruments are used: BUV-Nimbus4, TOMS-Nimbus7, TOMS-EP, SBUV-7, -9, -11, -14, -16, -17, -18, -19, GOME, SCIAMACHY, OMI and GOME-2. The resolution of the model runs, assimilation and output is increased from 2° x 3° to 1° x 1°. The analysis is driven by three-hourly meteorology from the ERA-interim reanalysis of ECMWF starting from 1979, and ERA-40 before that date. The chemistry parameterization has been updated. The performance of the MSR2 analysis is studied with the help of observation-minus-forecast (OmF) departures from the data assimilation, by comparisons with the individual station observations and with ozone sondes. The OmF statistics show that the mean bias of the MSR2 analyses is less than 1% with respect to debiased satellite observations after 1979.

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.

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