scholarly journals A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 2: Optimization scheme and identical twin experiment of atmospheric CO<sub>2</sub> inversion

2017 ◽  
Vol 10 (6) ◽  
pp. 2201-2219 ◽  
Author(s):  
Yosuke Niwa ◽  
Yosuke Fujii ◽  
Yousuke Sawa ◽  
Yosuke Iida ◽  
Akihiko Ito ◽  
...  
Keyword(s):  
Transport Model ◽  
Surface Fluxes ◽  
Identical Twin ◽  
Inversion Problem ◽  
Model Errors ◽  
Optimization Scheme ◽  
Error Covariance ◽  
Optimal Setting ◽  
Source Sink ◽  
Quasi Newton

Abstract. A four-dimensional variational method (4D-Var) is a popular technique for source/sink inversions of atmospheric constituents, but it is not without problems. Using an icosahedral grid transport model and the 4D-Var method, a new atmospheric greenhouse gas (GHG) inversion system has been developed. The system combines offline forward and adjoint models with a quasi-Newton optimization scheme. The new approach is then used to conduct identical twin experiments to investigate optimal system settings for an atmospheric CO2 inversion problem, and to demonstrate the validity of the new inversion system. In this paper, the inversion problem is simplified by assuming the prior flux errors to be reasonably well known and by designing the prior error correlations with a simple function as a first step. It is found that a system of forward and adjoint models with smaller model errors but with nonlinearity has comparable optimization performance to that of another system that conserves linearity with an exact adjoint relationship. Furthermore, the effectiveness of the prior error correlations is demonstrated, as the global error is reduced by about 15 % by adding prior error correlations that are simply designed when 65 weekly flask sampling observations at ground-based stations are used. With the optimal setting, the new inversion system successfully reproduces the spatiotemporal variations of the surface fluxes, from regional (such as biomass burning) to global scales. The optimization algorithm introduced in the new system does not require decomposition of a matrix that establishes the correlation among the prior flux errors. This enables us to design the prior error covariance matrix more freely.

scholarly journals A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0): 2. Optimization scheme and identical twin experiment of atmospheric CO<sub>2</sub> inversion

2016 ◽  
Author(s):  
Yosuke Niwa ◽  
Yosuke Fujii ◽  
Yousuke Sawa ◽  
Yosuke Iida ◽  
Akihiko Ito ◽  
...  
Keyword(s):  
Transport Model ◽  
Global Scale ◽  
Surface Fluxes ◽  
Identical Twin ◽  
Model Errors ◽  
Optimization Scheme ◽  
New Approach ◽  
Error Covariance ◽  
Optimal Setting ◽  
Quasi Newton

Abstract. A 4-dimensional variational method (4D-Var) is a popular technique for inverse modeling of atmospheric constituents, but it is not without problems. Using an icosahedral grid transport model and the 4D-Var method, a new atmospheric greenhouse gas (GHG) inversion system has been developed. The system combines off-line forward and adjoint models with a quasi-Newton optimization scheme. The new approach is then used to conduct identical twin experiments to investigate optimal system settings for an atmospheric CO2 inversion problem, and to demonstrate the validity of the new inversion system. It is found that a system of forward and adjoint models that has less model errors but with non-linearity performs better than another system that conserves linearity with exact adjoint relationship. Furthermore, the effectiveness of the prior error correlations is confirmed; the global error is reduced by about 15 % by adding prior error correlations that are simply designed. With the optimal setting, the new inversion system successfully reproduces the spatiotemporal variations of the surface fluxes, from regional (such as biomass burning) to a global scale. The optimization algorithm introduced in the new system does not require difficult decomposition of a matrix that establishes the correlation among the prior flux errors. This enables us to design the prior error covariance matrix more freely.

scholarly journals Towards better error statistics for atmospheric inversions of methane surface fluxes

2013 ◽  
Vol 13 (14) ◽  
pp. 7115-7132 ◽  
Author(s):  
A. Berchet ◽  
I. Pison ◽  
F. Chevallier ◽  
P. Bousquet ◽  
S. Conil ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Transport Model ◽  
Vertical Mixing ◽  
Real Data ◽  
Surface Fluxes ◽  
Error Statistics ◽  
The North ◽  
Error Covariance ◽  
Methane Fluxes ◽  
General Statistical

Abstract. We adapt general statistical methods to estimate the optimal error covariance matrices in a regional inversion system inferring methane surface emissions from atmospheric concentrations. Using a minimal set of physical hypotheses on the patterns of errors, we compute a guess of the error statistics that is optimal in regard to objective statistical criteria for the specific inversion system. With this very general approach applied to a real-data case, we recover sources of errors in the observations and in the prior state of the system that are consistent with expert knowledge while inferred from objective criteria and with affordable computation costs. By not assuming any specific error patterns, our results depict the variability and the inter-dependency of errors induced by complex factors such as the misrepresentation of the observations in the transport model or the inability of the model to reproduce well the situations of steep gradients of concentrations. Situations with probable significant biases (e.g., during the night when vertical mixing is ill-represented by the transport model) can also be diagnosed by our methods in order to point at necessary improvement in a model. By additionally analysing the sensitivity of the inversion to each observation, guidelines to enhance data selection in regional inversions are also proposed. We applied our method to a recent significant accidental methane release from an offshore platform in the North Sea and found methane fluxes of the same magnitude than what was officially declared.

scholarly journals Towards better error statistics for atmospheric inversions of methane surface fluxes

2013 ◽  
Vol 13 (2) ◽  
pp. 3735-3782
Author(s):  
A. Berchet ◽  
I. Pison ◽  
F. Chevallier ◽  
P. Bousquet ◽  
S. Conil ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Transport Model ◽  
Real Data ◽  
Surface Fluxes ◽  
Mass Composition ◽  
Error Statistics ◽  
The North ◽  
Error Covariance ◽  
Data Framework ◽  
General Statistical

Abstract. In this study, we adapt general statistical methods to compute the optimal error covariance matrices in a regional inversion system inferring methane surface emissions from atmospheric concentrations. We optimally estimate the error statistics with a minimal set of physical hypotheses on the patterns of errors. With this very general approach applied within a real-data framework, we recover sources of errors in the observations and in the prior state of the system that are consistent with expert knowledge. By not assuming any specific error patterns, our results show the variability and the inter-dependency of errors induced by complex factors such as the mis-representation of the observations in the transport model or the inability of the model to reproduce well the situations of steep gradients of air mass composition in the atmosphere. By analyzing the sensitivity of the inversion to each observation, ways to improve data selection in regional inversions are also proposed. We applied our method to a recent significant accidental methane release from an offshore platform in the North Sea.

scholarly journals Error characterization of CO<sub>2</sub> vertical mixing in the atmospheric transport model WRF-VPRM

2012 ◽  
Vol 12 (5) ◽  
pp. 2441-2458 ◽  
Author(s):  
R. Kretschmer ◽  
C. Gerbig ◽  
U. Karstens ◽  
F.-T. Koch
Keyword(s):  
Transport Model ◽  
Vertical Mixing ◽  
Atmospheric Model ◽  
Surface Fluxes ◽  
Model Errors ◽  
Simple Method ◽  
European Continent ◽  
Mixing Ratios ◽  
Error Characterization ◽  
The Impact

Abstract. One of the dominant uncertainties in inverse estimates of regional CO2 surface-atmosphere fluxes is related to model errors in vertical transport within the planetary boundary layer (PBL). In this study we present the results from a synthetic experiment using the atmospheric model WRF-VPRM to realistically simulate transport of CO2 for large parts of the European continent at 10 km spatial resolution. To elucidate the impact of vertical mixing error on modeled CO2 mixing ratios we simulated a month during the growing season (August 2006) with different commonly used parameterizations of the PBL (Mellor-Yamada-Janjić (MYJ) and Yonsei-University (YSU) scheme). To isolate the effect of transport errors we prescribed the same CO2 surface fluxes for both simulations. Differences in simulated CO2 mixing ratios (model bias) were on the order of 3 ppm during daytime with larger values at night. We present a simple method to reduce this bias by 70–80% when the true height of the mixed layer is known.

scholarly journals Error characterization of CO2 vertical mixing in the atmospheric transport model WRF-VPRM

2011 ◽  
Vol 11 (10) ◽  
pp. 28169-28217
Author(s):  
R. Kretschmer ◽  
C. Gerbig ◽  
U. Karstens ◽  
F.-T. Koch
Keyword(s):  
Transport Model ◽  
Vertical Mixing ◽  
Atmospheric Model ◽  
Surface Fluxes ◽  
Model Errors ◽  
Simple Method ◽  
European Continent ◽  
Mixing Ratios ◽  
Error Characterization ◽  
The Impact

Abstract. One of the dominant uncertainties in inverse estimates of regional CO2 surface-atmosphere fluxes is related to model errors in vertical transport within the planetary boundary layer (PBL). In this study we present the results from a synthetic experiment using the atmospheric model WRF-VPRM to realistically simulate transport of CO2 for large parts of the European continent at 10 km spatial resolution. To elucidate the impact of vertical mixing error on modeled CO2 mixing ratios we simulated a month during the growing season (August 2006) with different commonly used parameterizations of the PBL (Mellor-Yamada-Janjic (MYJ) and Yonsei-University (YSU) scheme). To isolate the effect of transport errors we prescribed the same CO2 surface fluxes for both simulations. Differences in simulated CO2 mixing ratios (model bias) were on the order of 3 ppm during daytime with larger values during night. We present a simple method to reduce this bias by 70–80% when the true height of the mixed layer is known.

scholarly journals Estimating surface CO<sub>2</sub> fluxes from space-borne CO<sub>2</sub> dry air mole fraction observations using an ensemble Kalman Filter

2009 ◽  
Vol 9 (8) ◽  
pp. 2619-2633 ◽  
Author(s):  
L. Feng ◽  
P. I. Palmer ◽  
H. Bösch ◽  
S. Dance
Keyword(s):  
Kalman Filter ◽  
Mole Fraction ◽  
Ensemble Kalman Filter ◽  
Transport Model ◽  
Co2 Flux ◽  
Surface Flux ◽  
Surface Fluxes ◽  
Co2 Fluxes ◽  
Dry Air ◽  
Geographical Regions

Abstract. We have developed an ensemble Kalman Filter (EnKF) to estimate 8-day regional surface fluxes of CO2 from space-borne CO2 dry-air mole fraction observations (XCO2) and evaluate the approach using a series of synthetic experiments, in preparation for data from the NASA Orbiting Carbon Observatory (OCO). The 32-day duty cycle of OCO alternates every 16 days between nadir and glint measurements of backscattered solar radiation at short-wave infrared wavelengths. The EnKF uses an ensemble of states to represent the error covariances to estimate 8-day CO2 surface fluxes over 144 geographical regions. We use a 12×8-day lag window, recognising that XCO2 measurements include surface flux information from prior time windows. The observation operator that relates surface CO2 fluxes to atmospheric distributions of XCO2 includes: a) the GEOS-Chem transport model that relates surface fluxes to global 3-D distributions of CO2 concentrations, which are sampled at the time and location of OCO measurements that are cloud-free and have aerosol optical depths <0.3; and b) scene-dependent averaging kernels that relate the CO2 profiles to XCO2, accounting for differences between nadir and glint measurements, and the associated scene-dependent observation errors. We show that OCO XCO2 measurements significantly reduce the uncertainties of surface CO2 flux estimates. Glint measurements are generally better at constraining ocean CO2 flux estimates. Nadir XCO2 measurements over the terrestrial tropics are sparse throughout the year because of either clouds or smoke. Glint measurements provide the most effective constraint for estimating tropical terrestrial CO2 fluxes by accurately sampling fresh continental outflow over neighbouring oceans. We also present results from sensitivity experiments that investigate how flux estimates change with 1) bias and unbiased errors, 2) alternative duty cycles, 3) measurement density and correlations, 4) the spatial resolution of estimated flux estimates, and 5) reducing the length of the lag window and the size of the ensemble. At the revision stage of this manuscript, the OCO instrument failed to reach its orbit after it was launched on 24 February 2009. The EnKF formulation presented here is also applicable to GOSAT measurements of CO2 and CH4.

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 Error correlation between CO<sub>2</sub> and CO as constraint for CO<sub>2</sub> flux inversions using satellite data

2009 ◽  
Vol 9 (19) ◽  
pp. 7313-7323 ◽  
Author(s):  
H. Wang ◽  
D. J. Jacob ◽  
M. Kopacz ◽  
D. B. A. Jones ◽  
P. Suntharalingam ◽  
...  
Keyword(s):  
Inverse Modeling ◽  
Large Scale ◽  
Transport Model ◽  
Meteorological Data ◽  
Correlation Coefficients ◽  
Surface Fluxes ◽  
Satellite Observations ◽  
Carbon Surface ◽  
Chemical Transport Model ◽  
Error Correlation

Abstract. Inverse modeling of CO2 satellite observations to better quantify carbon surface fluxes requires a chemical transport model (CTM) to relate the fluxes to the observed column concentrations. CTM transport error is a major source of uncertainty. We show that its effect can be reduced by using CO satellite observations as additional constraint in a joint CO2-CO inversion. CO is measured from space with high precision, is strongly correlated with CO2, and is more sensitive than CO2 to CTM transport errors on synoptic and smaller scales. Exploiting this constraint requires statistics for the CTM transport error correlation between CO2 and CO, which is significantly different from the correlation between the concentrations themselves. We estimate the error correlation globally and for different seasons by a paired-model method (comparing GEOS-Chem CTM simulations of CO2 and CO columns using different assimilated meteorological data sets for the same meteorological year) and a paired-forecast method (comparing 48- vs. 24-h GEOS-5 CTM forecasts of CO2 and CO columns for the same forecast time). We find strong error correlations (r2>0.5) between CO2 and CO columns over much of the extra-tropical Northern Hemisphere throughout the year, and strong consistency between different methods to estimate the error correlation. Application of the averaging kernels used in the retrieval for thermal IR CO measurements weakens the correlation coefficients by 15% on average (mostly due to variability in the averaging kernels) but preserves the large-scale correlation structure. We present a simple inverse modeling application to demonstrate that CO2-CO error correlations can indeed significantly reduce uncertainty on surface carbon fluxes in a joint CO2-CO inversion vs. a CO2-only inversion.

scholarly journals The importance of transport model uncertainties for the estimation of CO<sub>2</sub> sources and sinks using satellite measurements

2010 ◽  
Vol 10 (20) ◽  
pp. 9981-9992 ◽  
Author(s):  
S. Houweling ◽  
I. Aben ◽  
F.-M. Breon ◽  
F. Chevallier ◽  
N. Deutscher ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
A Priori ◽  
Model Errors ◽  
Tracer Transport ◽  
Satellite Measurements ◽  
Transport Models ◽  
Sources And Sinks ◽  
The Impact ◽  
Ppm Level

Abstract. This study presents a synthetic model intercomparison to investigate the importance of transport model errors for estimating the sources and sinks of CO2 using satellite measurements. The experiments were designed for testing the potential performance of the proposed CO2 lidar A-SCOPE, but also apply to other space borne missions that monitor total column CO2. The participating transport models IFS, LMDZ, TM3, and TM5 were run in forward and inverse mode using common a priori CO2 fluxes and initial concentrations. Forward simulations of column averaged CO2 (xCO2) mixing ratios vary between the models by σ=0.5 ppm over the continents and σ=0.27 ppm over the oceans. Despite the fact that the models agree on average on the sub-ppm level, these modest differences nevertheless lead to significant discrepancies in the inverted fluxes of 0.1 PgC/yr per 106 km2 over land and 0.03 PgC/yr per 106 km2 over the ocean. These transport model induced flux uncertainties exceed the target requirement that was formulated for the A-SCOPE mission of 0.02 PgC/yr per 106 km2, and could also limit the overall performance of other CO2 missions such as GOSAT. A variable, but overall encouraging agreement is found in comparison with FTS measurements at Park Falls, Darwin, Spitsbergen, and Bremen, although systematic differences are found exceeding the 0.5 ppm level. Because of this, our estimate of the impact of transport model uncerainty is likely to be conservative. It is concluded that to make use of the remote sensing technique for quantifying the sources and sinks of CO2 not only requires highly accurate satellite instruments, but also puts stringent requirements on the performance of atmospheric transport models. Improving the accuracy of these models should receive high priority, which calls for a closer collaboration between experts in atmospheric dynamics and tracer transport.

Sign in / Sign up

Export Citation Format

Share Document