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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 15 Oct 2019

Submitted as: research article | 15 Oct 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Application of linear minimum variance estimation to the multi-model ensemble of atmospheric radioactive Cs-137 with observations

Daisuke Goto1, Yu Morino1, Toshimasa Ohara1, Tsuyoshi Thomas Sekiyama2, Junya Uchida3, and Teruyuki Nakajima4 Daisuke Goto et al.
  • 1National Institute for Environmental Studies, Tsukuba, 305-8506, Japan
  • 2Meteorological Research Institute, Tsukuba, 305-0052, Japan
  • 3Atmosphere and Ocean Research Institute, University of the Tokyo, Kashiwa, 277-8568, Japan
  • 4Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba, 305-8505, Japan

Abstract. Great efforts have been made to simulate atmospheric pollutants, but their spatial and temporal distributions are still highly uncertain. Observations can measure their concentrations with high accuracy but cannot estimate their spatial distributions due to the sporadic locations of sites. Here, we propose an ensemble method by applying a linear minimum variance estimation (LMVE) between multi-model ensemble (MME) simulations and measurements to derive a more realistic distribution of atmospheric pollutants. The LMVE is a classical and basic version of data assimilation, although the estimation itself is still useful for obtaining the best estimates by combining simulations and observations without a large amount of computer resources, even for high-resolution models. In this study, we adopt the proposed methodology for atmospheric radioactive caesium (Cs-137) in atmospheric particles emitted from the Fukushima Daiichi Nuclear Power Station (FDNPS) accident in March 2011. The uniqueness of this approach includes (1) the availability of observed Cs-137 concentrations near the surface at approximately 100 sites, providing dense coverage over eastern Japan; (2) the simplicity of identifying the emission source of Cs-137 due to the point source of FDNPS; (3) the novelty of MME with the high-resolution model (3-km horizontal grid) over complex terrain in eastern Japan; and (4) the strong need to better estimate the Cs-137 distribution due to its inhalation exposure among residents in Japan. The ensemble size is six, including two atmospheric transport models (the Weather Research and Forecasting-Community Multi-scale Air Quality (WRF-CMAQ) model and non-hydrostatic icosahedral atmospheric model (NICAM)). The results showed that the MME-that estimated Cs-137 concentrations using all available sites had the lowest geometric mean bias (GMB) against the observations (GMB = 1.53), the lowest uncertainties based on the root-mean-square error (RMSE) against the observations (RMSE = 9.12 Bq m−3), the highest Pearson correlation coefficient (PCC) with the observations (PCC = 0.59) and the highest fraction of data within a factor of 2 (FAC2) with the observations (FAC2 = 54 %) compared to the single-model members, which provided higher biases (GMB = 1.20–4.29), higher uncertainties (RMSE = 19.2–51.2 Bq m−3), lower correlation coefficients (PCC = 0.29–0.45) and lower precision (FAC2 = 10–29 %). At the model grid, excluding the measurements, the MME-estimated Cs-137 concentration was estimated by a spatial interpolation of the variance used in the LMVE equation using the inverse distance weights between the nearest two sites. To test this assumption, the available measurements were divided into two categories, i.e., learning and validation data; thus, the assumption for the spatial interpolation was found to guarantee a moderate PCC value (> 0.4) within an approximate distance of 50 km. Extra sensitivity tests for several parameters, i.e., the site number and the weighting coefficients in the spatial interpolation, the time window in the LMVE and the ensemble size, were performed. The most important assumption was that the ensemble size generated remarkably better results than the single-member model as it increased. Therefore, the proposed ensemble method, with a maximum ensemble size (six in this study), can be applicable for the best estimation of the Cs-137 distribution.

Daisuke Goto et al.
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Short summary
To obtain reliable distribution of atmospheric Cs-137 emitted from the Fukushima accident, we proposed a multi-model ensemble (MME) method using observations. We found the MME-estimated Cs-137 concentrations using all available observations were lower bias, lower uncertainty, higher correlation and higher precision against the observations compared to single-model results. It can be applied not only to the Cs-137 distribution but also any atmospheric materials such as PM2.5 distribution.
To obtain reliable distribution of atmospheric Cs-137 emitted from the Fukushima accident, we...