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Section: New Results
Inverse modeling
Many of this year's studies have focused on inverse modeling, including the reconstruction of the Fukushima radionuclide atmospheric and marine source terms. All were targeted to a particular application. However most of them include new methodological developments, in particular non-Gaussian data assimilation schemes.
Estimation of errors in the inverse modeling of accidental release of atmospheric pollutant: Application to the reconstruction of the Fukushima Daiichi source term
Participants : Victor Winiarek, Marc Bocquet, Olivier Saunier [IRSN] , Anne Mathieu [IRSN] .
The aim of this research activity is the implementation of data assimilation methods, particularly inverse modeling methods, in the context of an accidental radiological release from a nuclear power plant and their application in the specific case of the Fukushima Daiichi accident. The particular methodological focus is the a posteriori estimation of the prior errors statistics. In the case of the Fukushima Daiichi accident, the number of available observations is small compared to the number of source parameters to retrieve and the reconstructed source is highly sensitive to the prior errors. That is the why they need to be well established and justified. In this aim, three methods have been proposed: one method relies on a L-curve estimation technique, another one on the Desroziers' iterative scheme and the last method, assumed to be the most robust, relies on the maximum likelihood principle, generalised to a non-Gaussian context. These three methods have been applied to the reconstruction of cesium-137 and iodine-131 source terms from the Fukushima Daiichi accident. Because of the poor observability of the Fukushima Daiichi emissions, these methods provide lower-bounds for cesium-137 and iodine-131 reconstructed activities. Nevertheless, with the new method based on semi-Gaussian statistics for the background errors, the lower-bound estimates for cesium-137, Bq with an estimated standard deviation range of , and for iodine-131, Bq with an estimated standard deviation range of , are of the same order of magnitude as those provided by the Japanese Nuclear and Industrial Safety Agency, and about 5 to 10 times less than the Chernobyl atmospheric releases.
Assessment of the amount of Cesium-137 released into the Pacific Ocean after the Fukushima accident and analysis of its dispersion in Japanese coastal waters
Participants : Claude Estournel [LA] , Emmanuel Bosc [IAEA] , Marc Bocquet, Caroline Ulses [LA] , Patrick Marsailex [LA] , Victor Winiarek, Iolanda Osvath [IAEA] , Cyril Nguyen [LA,LEGOS] , Thomas Duhaut [LA] , Florent Lyard [LEGOS] , Héloïse Michaud [LA] , Francis Auclair [LA] .
Numerical modeling was used to provide a new estimate of the amount of cesium-137 released directly into the ocean from the Fukushima Daiichi nuclear power plant (NPP) after the accident in March 2011 and to gain insights into the physical processes that led to its dispersion in the marine environment during the months following the accident. An inverse method was used to determine the time-dependent cesium-137 input responsible for the observed concentrations. The method was then validated through comparisons of the simulated concentrations with concentrations measured in seawater at different points in the neighborhood of the nuclear power plant. An underestimation was noticed for stations located 30 km offshore. The resulting bias in the release inventory was estimated. Finally, the maximum cesium-137 activity released directly to the ocean was estimated to lie between 5.1 and 5.5 PBq (Peta Becquerel = 1015 Bq) but uncertainties remain on the amount of radionuclides released during the first few days after the accident. This estimate was compared to previous ones and differences were further analysed. The temporal and spatial variations of the cesium-137 concentration present in the coastal waters were shown to be strongly related to the wind intensity and direction. During the first month after the accident, winds blowing toward the south confined the radionuclides directly released into the ocean to a narrow coastal band. Afterwards, frequent northward wind events increased the dispersion over the whole continental shelf, leading to strongly reduced concentrations.
What eddy-covariance measurements tell us about prior land flux errors in CO2-flux inversion schemes?
Participants : Frédéric Chevallier [LSCE] , Tao Wang [LSCE] , Philippe Ciais [LSCE] , Marc Bocquet, Altaf Arain [McMaster University, Canada] , Alessandro Cescatti [Joint Research Centre, Italy] , Jiquan Chen [University of Toledo, USA] , Johannes Dolman [Vrije Universiteit, the Netherlands] , Beverly Law [Oregon State University, USA] , Hank Margolis [Université Laval, Canada] , Leonardo Montagnani [University of Bolzano, Italy] .
To guide the future development of CO2-atmospheric inversion modeling systems, we analysed the errors arising from prior information about terrestrial ecosystem fluxes. We compared the surface fluxes calculated by a process-based terrestrial ecosystem model with daily averages of CO2 flux measurements at 156 sites across the world in the FLUXNET network. At the daily scale, the standard deviation of the model-data fit was 2.5 gC·m−2·d−1; temporal autocorrelations were significant at the weekly scale (> 0.3 for lags less than four weeks), while spatial correlations were confined to within the first few hundred kilometers (< 0.2 after 200 km). Separating out the plant functional types did not increase the spatial correlations, except for the deciduous broad-leaved forests. Using the statistics of the flux measurements as a proxy for the statistics of the prior flux errors was shown not to be a viable approach. A statistical model allowed us to upscale the site-level flux error statistics to the coarser spatial and temporal resolutions used in regional or global models. This approach allowed us to quantify how aggregation reduces error variances, while increasing correlations. As an example, for a typical inversion of grid point (300 km × 300 km) monthly fluxes, we found that the prior flux error follows an approximate e-folding correlation length of 500 km only, with correlations from one month to the next as large as 0.6.