This manuscript is devoted to the attempts on the design of new nonlinear data assimilation schemes. The variational and sequential assimilation methods are reviewed with emphasis on their performances on dealing with nonlinearity and high dimension of the environmental dynamical systems. The nonlinear data assimilation is based on Bayesian formulation and its approximate solutions. Sequential ...

Many of the talks and posters during this year’s conference will discuss how both ensemble forecasting and atmospheric data assimilation can work synergistically together. We detail provide a brief description of the underlying theoretical basis for this research. The unifying idea is that the chaotic nature of the atmosphere can actually be put to use to improve data assimilation. Ensemble for...

We describe a sequential assimilation approach useful for assimilating tracer measurements into a threedimensional chemical transport model (CTM) of the stratosphere. The numerical code, developed largely according to Khattatov et al. (2000), uses parameterizations and simplifications allowing assimilation of sparse observations and the simultaneous evaluation of analysis errors, with reasonabl...

Modelling of terrestrial systems is continuously moving towards more integrated modelling approaches, where different terrestrial compartment models are combined in order to realise a more sophisticated physical description of water, energy and carbon fluxes across compartment boundaries and to provide a more integrated view on terrestrial processes. While such models can effectively reduce cer...

Fast and accurate numerical models are critical for the tracking, prediction, and control of fluid flows. Traditional grid-based modelling techniques, though highly accurate, are often too slow for these purposes. So-called reduced-order models—which track abstract flow structures, rather than the details of fluid velocities at every point—are much faster, but the inherent coarseness of their m...

Data assimilation is a method of combining an imperfect simulation model and a number of incomplete observation data. Sequential data assimilation is a data assimilation in which simulation variables are corrected at every time step of observation. The ensemble Kalman filter is developed for a sequential data assimilation and frequently used in geophysics. On the other hand, the particle filter...

Data assimilation into sea ice models designed for climate studies has started about 15 years ago. In most of the studies conducted so far, it is assumed that the improvement brought by the assimilation is straightforward. However, some studies suggest this might not be true. In order to elucidate this question and to find an appropriate way to further assimilate sea ice concentration and veloc...

[1] To overcome the difficulties in determining the optimal parameters needed for a radiative transfer model (RTM), which acts as the observational operator in a land data assimilation system, we have designed a dual-pass assimilation (DP-En4DVar) framework to optimize the model state (volumetric soil moisture content) and model parameters simultaneously using the gridded Advanced Microwave Sca...

RFID technology is recently an emerging technology that is being used in many echelons of supply chain participants. Most recent IS research on this technology focus on factors which will impact its organizational adoption. However, adoption is just one part of assimilation process which cannot make sure that RFID can be full-scale deployed in an organization. Assimilation theories also suggest...

Contrast and assimilation are two opposite perceptual phenomena deriving from the relationships among perceptual elements in a visual field. In contrast, perceptual differences are enhanced; while, in assimilation, they are decreased. Indeed, if contrast or assimilation occurs depends on various factors. Interestingly, Gestalt scientists explained both phenomena as the result of perceptual belo...