Data Assimilation in Multiscale Chemical Transport Models
نویسندگان
چکیده
In this paper we discuss variational data assimilation using the STEM atmospheric Chemical Transport Model. STEM is a multiscale model and can perform air quality simulations and predictions over spatial and temporal scales of different orders of magnitude. To improve the accuracy of model predictions we construct a dynamic data driven application system (DDDAS) by integrating data assimilation techniques with STEM. We illustrate the improvements in STEM field predictions before and after data assimilation. We also compare three popular optimization methods for data assimilation and conclude that LBFGS method is the best for our model because it requires fewer model runs to recover optimal initial conditions.
منابع مشابه
Stochastic Superparameterization and Multiscale Filtering of Turbulent Tracers
Data assimilation or filtering combines a numerical forecast model and observations to provide accurate statistical estimation of the state of interest. In this paper we are concerned with accurate data assimilation of a sparsely observed passive tracer advected in turbulent flows using a reduced-order forecast model. The turbulent flows which contain anisotropic and inhomogeneous structures su...
متن کاملMultiscale Methods for Data Assimilation in Turbulent Systems
Data assimilation of turbulent signals is an important challenging problem because of the extremely complicated large dimension of the signals and incomplete partial noisy observations which usually mix the large scale mean flow and small scale fluctuations. Due to the limited computing power in the foreseeable future, it is desirable to use multiscale forecast models which are cheap and fast t...
متن کاملConstruction of non-diagonal background error covariance matrices for global chemical data assimilation
Chemical data assimilation attempts to optimally use noisy observations along with imperfect model predictions to produce a better estimate of the chemical state of the atmosphere. It is widely accepted that a key ingredient for successful data assimilation is a realistic estimation of the background error distribution. Particularly important is the specification of the background error covaria...
متن کاملEnsemble Methods for Dynamic Data Assimilation of Chemical Observations in Atmospheric Models*
The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems (DDDAS) that efficiently integrate the observational data and the models. Data assimilation, the dynamic incorporation of additional data into an executing application, is an essential DDDAS concept with wide applicability. In this paper we discuss practical aspects o...
متن کاملEnsemble-Based Data Assimilation for Atmospheric Chemical Transport Models
The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems (das) that efficiently integrate the observational data and the models. In this paper we discuss fundamental aspects of nonlinear ensemble data assimilation applied to atmospheric chemical transport models. We formulate autoregressive models for the background errors ...
متن کامل