Assimilation of Stratospheric Chemical Tracer Observations Using a Kalman Filter. Part II:χ2-Validated Results and Analysis of Variance and Correlation Dynamics

Ménard, Richard; Chang, Li-Yen

doi:10.1175/1520-0493(2000)128<2672:aoscto>2.0.co;2

Cited by 92 publications

(79 citation statements)

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“…The performance of the tropospheric NO 2 column data assimilation with the optimized settings was evaluated from the χ 2 test (e.g. Ménard and Chang, 2000;Zupanski and Zupanski, 2006). The χ 2 is estimated from the ratio of the actual OmF to the estimated background covariance.…”

Section: Sensitivity To Assimilation Parametersmentioning

confidence: 99%

Global NOx emission estimates derived from an assimilation of OMI tropospheric NO2 columns

2012

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Abstract.A data assimilation system has been developed to estimate global nitrogen oxides (NO x ) emissions using OMI tropospheric NO 2 columns (DOMINO product) and a global chemical transport model (CTM), the Chemical Atmospheric GCM for Study of Atmospheric Environment and Radiative Forcing (CHASER). The data assimilation system, based on an ensemble Kalman filter approach, was applied to optimize daily NO x 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 NO 2 columns and vertical NO 2 profiles obtained from aircraft and lidar measurements. The emission correction greatly improved the agreement between the simulated and observed NO 2 fields; this implies that the data assimilation system efficiently derives NO x 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 NO x sources from top-down approaches.

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Section: Sensitivity To Assimilation Parametersmentioning

confidence: 99%

Global NOx emission estimates derived from an assimilation of OMI tropospheric NO2 columns

2012

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“…A quantitative criterion for the choice of the background error is a chi-square (χ 2 ) test, the χ 2 diagnostics (e.g. Ménard and Chang, 2000). χ 2 should approach 1 if the background error covariances are properly specified, while a value higher (lower) than 1 indicates an underestimation (overestimation) of the background error covariance matrices.…”

Section: Self-consistency Testsmentioning

confidence: 99%

Simultaneous assimilation of satellite NO2, O3, CO, and HNO3 data for the analysis of tropospheric chemical composition and emissions

et al. 2012

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Comparisons against independent satellite, aircraft, and ozonesonde data show that the data assimilation results in substantial improvements for various chemical compounds. These improvements include a reduced negative tropospheric NO 2 column bias (by 40-85 %), a reduced negative CO bias in the Northern Hemisphere (by 40-90 %), and a reduced positive O 3 bias in the middle and upper troposphere (from 30-40 % to within 10 %). These changes are related to increased tropospheric OH concentrations by 5-15 % in the tropics and the Southern Hemisphere in July. Observing System Experiments (OSEs) have been conducted to quantify the relative importance of each data set on constraining the emissions and concentrations. The OSEs confirm that the assimilation of individual data sets results in a strong influence on both assimilated and non-assimilated species through the inter-species error correlation and the chemical coupling described by the model. The simultaneous adjustment of the emissions and concentrations is a powerful approach to correcting the tropospheric ozone budget and profile analyses.

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“…the observations are uncorrelated). In the background error covariance matrix, the time evolution of the diagonal elements (variances) is computed explicitly following the approach of Ménard and Chang (2000); The off-diagonal elements are the product of the square root of these variances and a correlation function that is flow-dependent and specified in terms of distance and PV (El Amraouli et al, 2004). PV is a quasi-conserved tracer on short time scales (i.e.…”

Section: Mimosa-assimentioning

confidence: 99%

The ASSET intercomparison of stratosphere and lower mesosphere humidity analyses

et al. 2009

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Abstract. This paper presents results from the first detailed intercomparison of stratosphere-lower mesosphere water vapour analyses; it builds on earlier results from the EU funded framework V "Assimilation of ENVISAT Data" (AS-SET) project. Stratospheric water vapour plays an important role in many key atmospheric processes and therefore an improved understanding of its daily variability is desirable. With the availability of high resolution, good quality Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) water vapour profiles, the ability of four different atmospheric models to assimilate these data is tested. MI-PAS data have been assimilated over September 2003 into the models of the European Centre for Medium Range Weather Forecasts (ECMWF), the Belgian Institute for Space and Aeronomy (BIRA-IASB), the French Service d'Aéronomie (SA-IPSL) and the UK Met Office. The resultant middle atmosphere humidity analyses are compared against independent satellite data from the Halogen Occultation Experiment (HALOE), the Polar Ozone and Aerosol Measurement (POAM III) and the Stratospheric Aerosol and Gas Experiment (SAGE II). The MIPAS water vapour profiles are generally well assimilated in the ECMWF, BIRA-IASB and SA systems, producing stratosphere-mesosphere water vapour fields where the main features compare favourably with the independent observations. However, the models are less capable of assimilating the MIPAS data where water vapour values are locally extreme or in regions of strong humidity gradients, such as the southern hemisphere lower stratosphere polar vortex. Differences in the analyses can be attributed to the choice of humidity control variable, how the background error covariance matrix is generated, the model Correspondence to: H. E. Thornton (hazel.thornton@metoffice.gov.uk) resolution and its complexity, the degree of quality control of the observations and the use of observations near the model boundaries. Due to the poor performance of the Met Office analyses the results are not included in the intercomparison, but are discussed separately. The Met Office results highlight the pitfalls in humidity assimilation, and provide lessons that should be learnt by developers of stratospheric humidity assimilation systems. In particular, they underline the importance of the background error covariances in generating a realistic troposphere to mesosphere water vapour analysis.

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Assimilation of Stratospheric Chemical Tracer Observations Using a Kalman Filter. Part II:χ²-Validated Results and Analysis of Variance and Correlation Dynamics

Cited by 92 publications

References 0 publications

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

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

Simultaneous assimilation of satellite NO<sub>2</sub>, O<sub>3</sub>, CO, and HNO<sub>3</sub> data for the analysis of tropospheric chemical composition and emissions

The ASSET intercomparison of stratosphere and lower mesosphere humidity analyses

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Assimilation of Stratospheric Chemical Tracer Observations Using a Kalman Filter. Part II:χ2-Validated Results and Analysis of Variance and Correlation Dynamics

Cited by 92 publications

References 0 publications

Global NO&lt;sub&gt;x&lt;/sub&gt; emission estimates derived from an assimilation of OMI tropospheric NO&lt;sub&gt;2&lt;/sub&gt; columns

Global NO&lt;sub&gt;x&lt;/sub&gt; emission estimates derived from an assimilation of OMI tropospheric NO&lt;sub&gt;2&lt;/sub&gt; columns

Simultaneous assimilation of satellite NO&lt;sub&gt;2&lt;/sub&gt;, O&lt;sub&gt;3&lt;/sub&gt;, CO, and HNO&lt;sub&gt;3&lt;/sub&gt; data for the analysis of tropospheric chemical composition and emissions

The ASSET intercomparison of stratosphere and lower mesosphere humidity analyses

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Product

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Assimilation of Stratospheric Chemical Tracer Observations Using a Kalman Filter. Part II:χ²-Validated Results and Analysis of Variance and Correlation Dynamics

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

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

Simultaneous assimilation of satellite NO<sub>2</sub>, O<sub>3</sub>, CO, and HNO<sub>3</sub> data for the analysis of tropospheric chemical composition and emissions