Toward a Monitoring and Forecasting System for Atmospheric Composition

Hollingsworth, A.; Engelen, Richard; Textor, C.; Benedetti, Angela; Boucher, Oliviér; Chevallier, F.; Dethof, A.; Elbern, Hendrik; Eskes, H. J.; Flemming, Johannes; Granier, Claire; Kaiser, Johannes; Morcrette, J. J.; Peuch, Vincent-Henri; Rouïl, Laurence; Schultz, Martin; Simmons, A. J.

doi:10.1175/2008bams2355.1

Cited by 272 publications

(217 citation statements)

References 38 publications

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“…For these reasons, output from a global transport model was used to determine if a DCC developed in a polluted or clean environment. The Global and Regional Earth-System Monitoring Using Satellite and In Situ Data (GEMS) project (http://gems.ecmwf.int) [Hollingsworth et al, 2008] utilizes data assimilation of aerosol emissions inventories and satellite data, in combination with the European Centre for Medium-Range Weather Forecasts (ECMWF) model, in order to provide detailed global coverage of chemical and aerosol species. The use of data assimilation in the analysis provides improved AOD information as compared to the model alone and allows for gaps in satellite data to be filled in [Benedetti et al, 2009].…”

Section: Methodsmentioning

confidence: 99%

Observations of aerosol‐induced convective invigoration in the tropical east Atlantic

2014

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Four years of CloudSat data have been analyzed over a region of the east Atlantic Ocean in order to examine the influence of aerosols on deep convection. The satellite data were combined with information about aerosols taken from the Global and Regional Earth-System Monitoring Using Satellite and In Situ Data model. Only those profiles fitting the definition of deep convective clouds were analyzed. Overall, the cloud center of gravity, cloud top, and rain top were all found to increase with increased aerosol loading. These effects were largely independent of the environment, and the differences between the cleanest and most polluted clouds sampled were found to be statistically significant. When examining an even smaller subset of deep convective clouds likely to be part of the convective core, similar trends were seen. These observations suggest that convective invigoration occurs with increased aerosol loading, leading to deeper, stronger storms in polluted environments.

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Section: Methodsmentioning

confidence: 99%

Observations of aerosol‐induced convective invigoration in the tropical east Atlantic

2014

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“…This data product has been used in several key European projects, e.g. ESA's Climate Change Initiative (CCI, www.esa-ghg-cci.org and Buchwitz et al, 2013b;Hollmann et al, 2013) and the EU's Monitoring of Atmospheric Composition and Climate (MACC, Hollingsworth et al, 2008) project.…”

Section: Introductionmentioning

confidence: 99%

Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

et al. 2015

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Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO 2 ) are required for carbon cycle and climate-related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY on-board ENVISAT (March 2002-April 2012 and TANSO-FTS onboard GOSAT (launched in January 2009), to retrieve XCO 2 , the column-averaged dry-air mole fraction of CO 2 . BESD has been initially developed for SCIAMACHY XCO 2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO 2 product. GOSAT BESD XCO 2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System. We describe the modifications of the BESD algorithm needed in order to retrieve XCO 2 from GOSAT and present detailed comparisons with ground-based observations of XCO 2 from the Total Carbon Column Observing Network (TC-CON). We discuss detailed comparison results between all three XCO 2 data sets (SCIAMACHY, GOSAT and TC-CON). The comparison results demonstrate the good consistency between SCIAMACHY and GOSAT XCO 2 . For example, we found a mean difference for daily averages of −0.60±1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34±1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10±1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (± 2 h, 10• around TCCON sites), i.e. the observed air masses are not exactly identical but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellitederived XCO 2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and Published by Copernicus Publications on behalf of the European Geosciences Union. J. Heymann et al.: SCIAMACHY and GOSAT BESD XCO 2 retrievalsGOSAT data set discussed here by also using data from other missions (e.g. OCO-2, GOSAT-2, CarbonSat) in the future.

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“…Within the Monitoring Atmospheric Composition and Climate (MACC) and the Global and regional Earth-system Monitoring using Satellite and in situ data (GEMS) project (Hollingsworth et al, 2008), the Integrated Forecasting System (IFS) of ECWMF, which had been used to produce the ERA40 and ERA-Intrim meteorological re-analysis, was extended to simulate chemically reactive gases (Flemming et al, 2009), aerosols (Morcrette et al, 2009Benedetti et al, 2008) and greenhouse gases (Engelen et al, 2009), so that ECMWF's 4D-Var system (Courtier et al, 1994;Rabier et al, 2000) could be used to assimilate satellite observations of atmospheric composition together with meteorological observations on the global scale.…”

Section: Case Study From Ecmwf: Macc Re-analysis Of Atmospheric Compomentioning

confidence: 99%

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

et al. 2015

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Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models

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Toward a Monitoring and Forecasting System for Atmospheric Composition

Cited by 272 publications

References 38 publications

Observations of aerosol‐induced convective invigoration in the tropical east Atlantic

Observations of aerosol‐induced convective invigoration in the tropical east Atlantic

Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

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Toward a Monitoring and Forecasting System for Atmospheric Composition

Cited by 272 publications

References 38 publications

Observations of aerosol‐induced convective invigoration in the tropical east Atlantic

Observations of aerosol‐induced convective invigoration in the tropical east Atlantic

Consistent satellite XCO&lt;sub&gt;2&lt;/sub&gt; retrievals from SCIAMACHY and GOSAT using the BESD algorithm

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

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Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm