Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model

Schutgens, Nick; Miyoshi, Takemasa; Takemura, Toshihiko; Nakajima, Teruyuki

doi:10.5194/acp-10-2561-2010

Cited by 122 publications

(100 citation statements)

References 33 publications

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“…The impact of the AOD assimilation on the model found in our study is coherent with findings of other studies (Zhang et al, 2008;Schutgens et al, 2010;Liu et al, 2011). Our approach is the most similar to the approach used by Benedetti et al (2009).…”

Section: Discussionsupporting

confidence: 82%

“…Benedetti et al (2009) described the assimilation of AOD in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) using the 4-D-VAR method, while Yumimoto et al (2008) used the same approach to assimilate satellite lidar profiles in the RAMS/CFORS-4-D-VAR (RC4) model. Sequential assimilation approaches are also documented: optimal interpolation used by, for example, Collins et al (2001) and Rasch et al (2001) in the Model of Atmospheric Transport and Chemist (MATCH) and (Tombette et al, 2009) in the Polyphemus system; or the ensemble Kalman filter used by, for example, Sekiyama et al (2010) in the Model of Aerosol Species in the Global Atmosphere (MASINGAR), Schutgens et al (2010) in the SPRINTARS model, Pagowski and Grell (2012) in the WRF-Chem model, Dai et al (2014) in the Non-hydrostatic ICosahedral Atmospheric Model (NICAM) and Rubin et al (2016) in the Ensemble Navy Aerosol Analysis Prediction System/Data Assimilation Research Testbed (ENAAPS-DART) system.…”

Section: Introductionmentioning

confidence: 99%

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Aerosol data assimilation in the chemical transport model MOCAGE during the TRAQA/ChArMEx campaign: aerosol optical depth

et al. 2016

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Abstract. In this study, we describe the development of the aerosol optical depth (AOD) assimilation module in the chemistry transport model (CTM) MOCAGE (Modèle de Chimie Atmosphérique à Grande Echelle). Our goal is to assimilate the spatially averaged 2-D column AOD data from the National Aeronautics and Space Administration (NASA) Moderate-resolution Imaging Spectroradiometer (MODIS) instrument, and to estimate improvements in a 3-D CTM assimilation run compared to a direct model run. Our assimilation system uses 3-D-FGAT (first guess at appropriate time) as an assimilation method and the total 3-D aerosol concentration as a control variable. In order to have an extensive validation dataset, we carried out our experiment in the northern summer of 2012 when the pre-ChArMEx (CHemistry and AeRosol MEditerranean EXperiment) field campaign TRAQA (TRAnsport à longue distance et Qualité de l'Air dans le bassin méditerranéen) took place in the western Mediterranean basin. The assimilated model run is evaluated independently against a range of aerosol properties (2-D and 3-D) measured by in situ instruments (the TRAQA size-resolved balloon and aircraft measurements), the satellite Spinning Enhanced Visible and InfraRed Imager (SE-VIRI) instrument and ground-based instruments from the Aerosol Robotic Network (AERONET) network. The evaluation demonstrates that the AOD assimilation greatly improves aerosol representation in the model. For example, the comparison of the direct and the assimilated model run with AERONET data shows that the assimilation increased the correlation (from 0.74 to 0.88), and reduced the bias (from 0.050 to 0.006) and the root mean square error in the AOD (from 0.12 to 0.07). When compared to the 3-D concentration data obtained by the in situ aircraft and balloon measurements, the assimilation consistently improves the model output. The best results as expected occur when the shape of the vertical profile is correctly simulated by the direct model. We also examine how the assimilation can influence the modelled aerosol vertical distribution. The results show that a 2-D continuous AOD assimilation can improve the 3-D vertical profile, as a result of differential horizontal transport of aerosols in the model.

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Section: Discussionsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Aerosol data assimilation in the chemical transport model MOCAGE during the TRAQA/ChArMEx campaign: aerosol optical depth

et al. 2016

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“…In particular, studies have shown notable improvements in aerosol forecasting through the assimilation of satellite aerosol products, mostly from daytime observations (e.g., Zhang et al, 2008aZhang et al, , 2011Zhang et al, , 2014Yumimoto et al, 2008;Uno et al, 2008;Benedetti et al, 2009;Schutgens et al, 2010;Sekiyama et al, 2010). To capture the diurnal cycle, the aerosol modeling community requires nighttime satellite aerosol data hav-ing broad spatial coverage and high temporal resolution to further advance aerosol, visibility, and air quality forecasts (e.g., Zhang et al, 2011Zhang et al, , 2014.…”

Section: Introductionmentioning

confidence: 99%

An improved method for retrieving nighttime aerosol optical thickness from the VIIRS Day/Night Band

McHardy

Zhang

Reid³

et al. 2015

Atmos. Meas. Tech.

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Abstract. Using Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data, a method, dubbed the "variance method", is developed for retrieving nighttime aerosol optical thickness (τ ) values through the examination of the dispersion of radiance values above an artificial light source. Based on the improvement of a previous algorithm, this updated method derives a semi-quantitative indicator of nighttime τ using artificial light sources. Nighttime τ retrievals from the newly developed method are inter-compared with an interpolated value from late afternoon and early morning ground observations from four AErosol RObotic NETwork (AERONET) sites as well as column-integrated τ from one High Spectral Resolution Lidar (HSRL) site at Huntsville, AL, during the NASA Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC 4 RS) campaign, providing full diel coverage. Sensitivity studies are performed to examine the effects of lunar illumination on VI-IRS τ retrievals made via the variance method, revealing that lunar contamination may have a smaller impact than previously thought; however, the small sample size of this study limits the conclusiveness thus far. VIIRS τ retrievals yield a coefficient of determination (r 2 ) of 0.60 and a root-meansquared error (RMSE) of 0.18 when compared against straddling daytime-averaged AERONET τ values. Preliminary results suggest that artificial light sources can be used for estimating regional and global nighttime aerosol distributions in the future.

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“…Rank histograms are generated by repeatedly tallying the rank of the observation relative to values from the ensemble sorted from lowest to highest and can be used for diagnosing errors in the mean and spread of the ensemble forecast (Hamill, 2001). In order to account for the effect of observation error in the rank histograms, the forecast values are randomly perturbed for each ensemble member by the observation error (Anderson, 1996,;Hamill, 2001;Saetra et al, 2004). The focus of this observation-space evaluation relative to MODIS AOT is on the prior since this is a stronger indicator of how the assimilation is impacting the model forecast.…”

Section: Dart Eakf Implementation and Optimizationmentioning

confidence: 99%

Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting

Rubin

Reid²,

Hansen³

et al. 2016

Atmos. Chem. Phys.

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Abstract. An ensemble-based forecast and data assimilation system has been developed for use in Navy aerosol forecasting. The system makes use of an ensemble of the Navy Aerosol Analysis Prediction System (ENAAPS) at 1 × 1 • , combined with an ensemble adjustment Kalman filter from NCAR's Data Assimilation Research Testbed (DART). The base ENAAPS-DART system discussed in this work utilizes the Navy Operational Global Analysis Prediction System (NOGAPS) meteorological ensemble to drive offline NAAPS simulations coupled with the DART ensemble Kalman filter architecture to assimilate bias-corrected MODIS aerosol optical thickness (AOT) retrievals. This work outlines the optimization of the 20-member ensemble system, including consideration of meteorology and sourceperturbed ensemble members as well as covariance inflation. Additional tests with 80 meteorological and source members were also performed. An important finding of this work is that an adaptive covariance inflation method, which has not been previously tested for aerosol applications, was found to perform better than a temporally and spatially constant covariance inflation. Problems were identified with the constant inflation in regions with limited observational coverage. The second major finding of this work is that combined meteorology and aerosol source ensembles are superior to either in isolation and that both are necessary to produce a robust system with sufficient spread in the ensemble members as well as realistic correlation fields for spreading observational information. The inclusion of aerosol source ensembles improves correlation fields for large aerosol source regions, such as smoke and dust in Africa, by statistically separating freshly emitted from transported aerosol species. However, the source ensembles have limited efficacy during long-range transport. Conversely, the meteorological ensemble generates sufficient spread at the synoptic scale to enable observational impact through the ensemble data assimilation. The optimized ensemble system was compared to the Navy's current operational aerosol forecasting system, which makes use of NAVDAS-AOD (NRL Atmospheric Variational Data Assimilation System for aerosol optical depth), a 2-D variational data assimilation system. Overall, the two systems had statistically insignificant differences in root-mean-squared error (RMSE), bias, and correlation relative to AERONETobserved AOT. However, the ensemble system is able to better capture sharp gradients in aerosol features compared to the 2DVar system, which has a tendency to smooth out aerosol events. Such skill is not easily observable in bulk metrics. Further, the ENAAPS-DART system will allow for new avenues of model development, such as more efficient lidar and surface station assimilation as well as adaptive sourcePublished by Copernicus Publications on behalf of the European Geosciences Union. 3928 J. I. Rubin et al.: Development of the ENAAPS and its application of the DART functions. At this early stage of development, the parity with th...

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Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model

Cited by 122 publications

References 33 publications

Aerosol data assimilation in the chemical transport model MOCAGE during the TRAQA/ChArMEx campaign: aerosol optical depth

Aerosol data assimilation in the chemical transport model MOCAGE during the TRAQA/ChArMEx campaign: aerosol optical depth

An improved method for retrieving nighttime aerosol optical thickness from the VIIRS Day/Night Band

Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting

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