3,563 research outputs found
Aggressive shadowing of a low-dimensional model of atmospheric dynamics
Predictions of the future state of the Earth's atmosphere suffer from the
consequences of chaos: numerical weather forecast models quickly diverge from
observations as uncertainty in the initial state is amplified by nonlinearity.
One measure of the utility of a forecast is its shadowing time, informally
given by the period of time for which the forecast is a reasonable description
of reality. The present work uses the Lorenz 096 coupled system, a simplified
nonlinear model of atmospheric dynamics, to extend a recently developed
technique for lengthening the shadowing time of a dynamical system. Ensemble
forecasting is used to make forecasts with and without inflation, a method
whereby the ensemble is regularly expanded artificially along dimensions whose
uncertainty is contracting. The first goal of this work is to compare model
forecasts, with and without inflation, to a true trajectory created by
integrating a modified version of the same model. The second goal is to
establish whether inflation can increase the maximum shadowing time for a
single optimal member of the ensemble. In the second experiment the true
trajectory is known a priori, and only the closest ensemble members are
retained at each time step, a technique known as stalking. Finally, a targeted
inflation is introduced to both techniques to reduce the number of instances in
which inflation occurs in directions likely to be incommensurate with the true
trajectory. Results varied for inflation, with success dependent upon the
experimental design parameters (e.g. size of state space, inflation amount).
However, a more targeted inflation successfully reduced the number of forecast
degradations without significantly reducing the number of forecast
improvements. Utilized appropriately, inflation has the potential to improve
predictions of the future state of atmospheric phenomena, as well as other
physical systems.Comment: 14 pages, 16 figure
Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model: Conventional Observation
This paper presents an approach for employing artificial neural networks (NN)
to emulate an ensemble Kalman filter (EnKF) as a method of data assimilation.
The assimilation methods are tested in the Simplified Parameterizations
PrimitivE-Equation Dynamics (SPEEDY) model, an atmospheric general circulation
model (AGCM), using synthetic observational data simulating localization of
balloon soundings. For the data assimilation scheme, the supervised NN, the
multilayer perceptrons (MLP-NN), is applied. The MLP-NN are able to emulate the
analysis from the local ensemble transform Kalman filter (LETKF). After the
training process, the method using the MLP-NN is seen as a function of data
assimilation. The NN were trained with data from first three months of 1982,
1983, and 1984. A hind-casting experiment for the 1985 data assimilation cycle
using MLP-NN were performed with synthetic observations for January 1985. The
numerical results demonstrate the effectiveness of the NN technique for
atmospheric data assimilation. The results of the NN analyses are very close to
the results from the LETKF analyses, the differences of the monthly average of
absolute temperature analyses is of order 0.02. The simulations show that the
major advantage of using the MLP-NN is better computational performance, since
the analyses have similar quality. The CPU-time cycle assimilation with MLP-NN
is 90 times faster than cycle assimilation with LETKF for the numerical
experiment.Comment: 17 pages, 16 figures, monthly weather revie
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