203 research outputs found
Design and performance evaluation of 1-by-64 multimode interference power splitter for optical communications
Facile synthesis of <em>o</em>-Nitrobenzyl carbamate and 1-(2-Nitrophenylethyl) carbamate Protected <em>α,w</em>-Diamines
Data assimilation in integrated hydrological modeling using ensemble Kalman filtering:evaluating the effect of ensemble size and localization on filter performance
Groundwater head and stream discharge is assimilated using the ensemble
transform Kalman filter in an integrated hydrological model with the aim of
studying the relationship between the filter performance and the ensemble
size. In an attempt to reduce the required number of ensemble members, an
adaptive localization method is used. The performance of the adaptive
localization method is compared to the more common distance-based
localization. The relationship between filter performance in terms of
hydraulic head and discharge error and the number of ensemble members is
investigated for varying numbers and spatial distributions of groundwater
head observations and with or without discharge assimilation and parameter
estimation. The study shows that (1) more ensemble members are needed when
fewer groundwater head observations are assimilated, and (2) assimilating
discharge observations and estimating parameters requires a much larger
ensemble size than just assimilating groundwater head observations. However,
the required ensemble size can be greatly reduced with the use of adaptive
localization, which by far outperforms distance-based localization. The
study is conducted using synthetic data only
Data assimilation in integrated hydrological modelling in the presence of observation bias
The use of bias-aware Kalman filters for estimating and correcting
observation bias in groundwater head observations is evaluated using both
synthetic and real observations. In the synthetic test, groundwater head
observations with a constant bias and unbiased stream discharge observations
are assimilated in a catchment-scale integrated hydrological model with the
aim of updating stream discharge and groundwater head, as well as several
model parameters relating to both streamflow and groundwater modelling. The
coloured noise Kalman filter (ColKF) and the separate-bias Kalman
filter (SepKF) are tested and evaluated for correcting the observation
biases. The study found that both methods were able to estimate most of the
biases and that using any of the two bias estimation methods resulted in
significant improvements over using a bias-unaware Kalman filter. While the
convergence of the ColKF was significantly faster than the convergence of the
SepKF, a much larger ensemble size was required as the estimation of biases
would otherwise fail. Real observations of groundwater head and stream
discharge were also assimilated, resulting in improved streamflow modelling
in terms of an increased Nash–Sutcliffe coefficient while no clear
improvement in groundwater head modelling was observed. Both the ColKF and
the SepKF tended to underestimate the biases, which resulted in drifting
model behaviour and sub-optimal parameter estimation, but both methods
provided better state updating and parameter estimation than using a
bias-unaware filter
Fundamental design of a distributed erbium-doped fiber amplifier for long-distance transmission
Detailed comparison of two approximate methods for the solution of the scalar wave equation for a rectangular optical waveguide
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