Parameter‐field estimation for atmospheric dispersion: application to the Chernobyl accident using 4D‐Var

Abstract

Atmospheric chemistry and air-quality numerical models are driven by uncertain forcing fields: emissions, boundary conditions, wind fields, vertical turbulent diffusivity, kinetic chemical rates, etc. Data assimilation can help to assess these parameters or fields of parameters. Because such parameters are often much more uncertain than the fields diagnosed in meteorology and oceanography, data assimilation is much more of an inverse modelling challenge in this context. In this article these ideas are experimented with by revisiting the Chernobyl accident dispersion event over Europe. A fast four-dimensional variational scheme (4D-Var) is developed, which seems appropriate for the retrieval of large parameter fields from large observation sets and the retrieval of parameters that are nonlinearly related to concentrations. The 4D-Var, and especially an approximate adjoint of the transport model, is tested and validated using several advection schemes that are influential on the forward simulation as well as on the data-assimilation results. Firstly, the inverse modelling system is applied to the assessment of the dry and wet deposition parameters. It is then applied to the retrieval of the emission field alone, the joint optimization of removal-process parameters and source parameters and the optimization of larger parameter fields such as horizontal and vertical diffusivities or the dry-deposition velocity field. The physical parameters used so far in the literature for the Chernobyl dispersion simulation are partly supported by this study. The crucial question of deciding whether such an inversion is merely a tuning of parameters or a retrieval of physically meaningful quantities is discussed. Even though inversion of parameter fields may fail to determine physical values for the parameters, it achieves statistical adaptation that partially corrects for model errors and, using the inverted parameter fields, leads to considerable improvement in the simulation scores.

Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review

“…Navon 1997, Gong et al 1998, Zhu and Navon 1999, Pulido and Thuburn 2005, and Bocquet 2011, among many others, have used the 4D-Var technique to estimate model parameters. The adjoint model has to include the model sensitivity to the parameters.…”

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

“…The (ensemble) filtering approach requires the augmentation of the state variables with the parameters (Ruiz et al, 2013). 4D-Var easily lends itself to data assimilation since the parameter variables can often be accounted for in the cost function (Penenko et al, 2002;Elbern et al, 2007;Bocquet, 2012;Penenko et al, 2012). However, it is often required to derive new adjoint operators corresponding to the gradient of the cost function with respect to these parameters if the driving mechanisms are not external forcings.…”

“…However, it is often required to derive new adjoint operators corresponding to the gradient of the cost function with respect to these parameters if the driving mechanisms are not external forcings. Often, adjoint models and operators can nonetheless be obtained through a simplifying approximation (Issartel and Baverel, 2003;Krysta and Bocquet, 2007;Bocquet, 2012;Singh and Sandu, 2012).…”

“…The use of data assimilation in atmospheric chemistry is more recent, because numerical deterministic models of atmospheric chemistry have been used routinely for air quality forecasting only since the mid 1990s; previously, most air quality forecasts were conducted with statistical approaches (Zhang et al, 2012a). Data assimilation has also been used in air quality since the 1990s for re-analysis to produce air pollutant concentration maps (e.g., Elbern and Schmidt, 2001), inverse modeling to improve (or identify errors in) emission rates (e.g., Elbern et al, 2007;Vira and Sofiev, 2012;Yumimoto et al, 2012), boundary conditions (e.g., Roustan and Bocquet, 2006) and model parameters (e.g., Barbu et al, 2009;Bocquet, 2012). Regarding air quality re-analyses, the 2008/50 European Union (EU) Air Quality Directive (AQD) suggests the use of modeling in combination with fixed measurements "to provide adequate information on the spatial distribution of the ambient air quality" (Borrego et al, 2015;OJEU, 2008).…”

Investigation of Uncertainty in the Release Rates of 131I and 137Cs from Fukushima Dai-ichi NPS Estimated from Environmental Data

“…In the absence of extensive environmental monitoring data, evaluation of the uncertainty of the source term is a difficult task. In these cases, the uncertainty of the source term can typically be evaluated by a sensitivity analysis of parameters that might affect the source term [6]. The purpose of this study is to estimate the release rates of 131 I and 137 Cs and their uncertainties.…”