Sensitivity tests for an ensemble Kalman filter for aerosol assimilation

• Main Authors: N. A. J. Schutgens, T. Miyoshi, T. Takemura, T. Nakajima
• Format: Article
• Language: English
• Published: Copernicus Publications 2010-07-01
• Series: Atmospheric Chemistry and Physics
• Online Access: http://www.atmos-chem-phys.net/10/6583/2010/acp-10-6583-2010.pdf

We present sensitivity tests for a global aerosol assimilation system utilizing AERONET observations of AOT (aerosol optical thickness) and AAE (aerosol Ångström exponent). The assimilation system employs an ensemble Kalman filter which requires tuning of three numerical parameters: ensemble size <i>n</i><sub>ens</sub>, local patch size <i>n</i><sub>patch</sub> and inflation factor ρ. In addition, experiments are performed to test the impact of various implementations of the system. For instance, we use a different prescription of the emission ensemble or a different combination of observations. <br><br> The various experiments are compared against one-another and against independent AERONET and MODIS/Aqua observations. The assimilation leads to significant improvements in modelled AOT and AAE fields. Moreover remaining errors are mostly random while they are mostly systematic for an experiment without assimilation. In addition, these results do not depend much on our parameter or design choices. <br><br> It appears that the value of the local patch size has by far the biggest impact on the assimilation, which has sufficiently converged for an ensemble size of <i>n</i><sub>ens</sub>=20. Assimilating AOT and AAE is clearly preferential to assimilating AOT at two different wavelengths. In contrast, initial conditions or a description of aerosol beyond two modes (coarse and fine) have only little effect. <br><br> We also discuss the use of the ensemble spread as an error estimate of the analysed AOT and AAE fields. We show that a very common prescription of the emission ensemble (independent random modification in each grid cell) can have trouble generating sufficient spread in the forecast ensemble.