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

Abstract

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...

JRAero: the Japanese Reanalysis for Aerosol v1.0

“…Various studies of AOP retrieval and aerosol simulation and assimilation have used the AERONET product for validation (Kinne et al, 2006;Levy et al, 2013;Sessions et al, 2015;Rubin et al, 2016). Because the AERONET product does not include AODs measured at 550 nm, we used the Ångström law to derive AODs at 550 nm from the AODs and Ångström exponents measured at multiple wavelengths (340-870 nm).…”

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

“…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.…”

Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter

“…The traditional EnKF with perturbed observations was initially applied to severe dust storm forecasts over China by assimilating the surface particulate matter (PM) 10 observations to correct the dust ICs once a day, and the results proved that the EnKF could calculate the flow‐dependent B , which generally was not expected in other traditional assimilation techniques (Lin et al, ). Compared with the traditional EnKF, the ensemble adjustment Kalman filter (Anderson, ; Whitaker & Hamill, ) was applied to assimilate bias‐corrected MODIS AOTs every 6 hr in Navy ensemble‐based aerosol forecasts and data assimilation systems, and the ensemble system was better able to capture sharp gradients in aerosol features than the 2DVAR system (Rubin et al, ; Rubin et al, ). The local ensemble transform Kalman filter (LETKF) assimilates observations within a spatially physical local volume at each model grid point simultaneously and does not require an orthogonal basis (Miyoshi et al, ), which significantly enhances the computational efficiency with parallel implementation (Hunt et al, ).…”