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...

A four-dimensional ensemble variational (4D-EnVar) data assimilation has been developed for a limited area model. The integration of tangent linear and adjoint models, as applied in standard 4D-Var, is replaced with the use of an ensemble of non-linear model states to estimate fourdimensional background error covariances over the assimilation time window. The computational costs for 4D-En-Var a...

1. Introduction The Advanced TIROS Operational Vertical Sounder (ATOVS) radiances have been operationally assimilated in the Japan Meteorological Agency (JMA) global 3D-Var data assimilation system since 28 May 2003. It replaced TOVS/ATOVS retrievals, which had been assimilated in the JMA global data assimilation system. The direct assimilation of ATOVS radiances is superior to its retrieval as...

The literature on ensemble-based data assimilation techniques has been growing rapidly in past decade. These techniques are being explored as possible alternatives to current operational analysis techniques. Ensemble-based assimilation techniques are typically comprised of an ensemble of parallel data assimilation and forecast cycles. The background-error covariances used in the data assimilati...

Ensemble-based data assimilation techniques are being explored as possible alternatives to current operational analysis techniques such as threeor four-dimensional variational assimilation. Ensemble-based assimilation techniques utilise an ensemble of parallel data assimilation and forecast cycles. The background-error covariances are estimated using the forecast ensemble and are used to produc...

Rates of dissolved inorganic carbon (DIC), formate, and acetate mineralization and/or assimilation were determined in 13 high-temperature (>73 °C) hot springs in Yellowstone National Park (YNP), Wyoming, in order to evaluate the relative importance of these substrates in supporting microbial metabolism. While 9 of the hot spring communities exhibited rates of DIC assimilation that were greater ...