scholarly journals Linear versus Nonlinear Filtering with Scale-Selective Corrections for Balanced Dynamics in a Simple Atmospheric Model

2012 ◽  
Vol 69 (11) ◽  
pp. 3405-3419 ◽  
Author(s):  
Aneesh C. Subramanian ◽  
Ibrahim Hoteit ◽  
Bruce Cornuelle ◽  
Arthur J. Miller ◽  
Hajoon Song
Keyword(s):  
Nonlinear Model ◽  
Nonlinear Filtering ◽  
Atmospheric Model ◽  
Wave Mode ◽  
Slow Manifold ◽  
Bayesian Filtering ◽  
Ensemble Kalman Filters ◽  
Importance Resampling ◽  
Balanced Dynamics ◽  
Better Than

Abstract This paper investigates the role of the linear analysis step of the ensemble Kalman filters (EnKF) in disrupting the balanced dynamics in a simple atmospheric model and compares it to a fully nonlinear particle-based filter (PF). The filters have a very similar forecast step but the analysis step of the PF solves the full Bayesian filtering problem while the EnKF analysis only applies to Gaussian distributions. The EnKF is compared to two flavors of the particle filter with different sampling strategies, the sequential importance resampling filter (SIRF) and the sequential kernel resampling filter (SKRF). The model admits a chaotic vortical mode coupled to a comparatively fast gravity wave mode. It can also be configured either to evolve on a so-called slow manifold, where the fast motion is suppressed, or such that the fast-varying variables are diagnosed from the slow-varying variables as slaved modes. Identical twin experiments show that EnKF and PF capture the variables on the slow manifold well as the dynamics is very stable. PFs, especially the SKRF, capture slaved modes better than the EnKF, implying that a full Bayesian analysis estimates the nonlinear model variables better. The PFs perform significantly better in the fully coupled nonlinear model where fast and slow variables modulate each other. This suggests that the analysis step in the PFs maintains the balance in both variables much better than the EnKF. It is also shown that increasing the ensemble size generally improves the performance of the PFs but has less impact on the EnKF after a sufficient number of members have been used.

scholarly journals Four-Dimensional Data Assimilation and Balanced Dynamics

2006 ◽  
Vol 63 (7) ◽  
pp. 1840-1858 ◽  
Author(s):  
Lisa J. Neef ◽  
Saroja M. Polavarapu ◽  
Theodore G. Shepherd
Keyword(s):  
Data Assimilation ◽  
Degrees Of Freedom ◽  
Forecast Error ◽  
Wave Mode ◽  
Slow Manifold ◽  
Error Statistics ◽  
Ensemble Kalman Filters ◽  
Error Covariance ◽  
Balanced Dynamics ◽  
Four Dimensional Data Assimilation

Abstract The problem of spurious excitation of gravity waves in the context of four-dimensional data assimilation is investigated using a simple model of balanced dynamics. The model admits a chaotic vortical mode coupled to a comparatively fast gravity wave mode, and can be initialized such that the model evolves on a so-called slow manifold, where the fast motion is suppressed. Identical twin assimilation experiments are performed, comparing the extended and ensemble Kalman filters (EKF and EnKF, respectively). The EKF uses a tangent linear model (TLM) to estimate the evolution of forecast error statistics in time, whereas the EnKF uses the statistics of an ensemble of nonlinear model integrations. Specifically, the case is examined where the true state is balanced, but observation errors project onto all degrees of freedom, including the fast modes. It is shown that the EKF and EnKF will assimilate observations in a balanced way only if certain assumptions hold, and that, outside of ideal cases (i.e., with very frequent observations), dynamical balance can easily be lost in the assimilation. For the EKF, the repeated adjustment of the covariances by the assimilation of observations can easily unbalance the TLM, and destroy the assumptions on which balanced assimilation rests. It is shown that an important factor is the choice of initial forecast error covariance matrix. A balance-constrained EKF is described and compared to the standard EKF, and shown to offer significant improvement for observation frequencies where balance in the standard EKF is lost. The EnKF is advantageous in that balance in the error covariances relies only on a balanced forecast ensemble, and that the analysis step is an ensemble-mean operation. Numerical experiments show that the EnKF may be preferable to the EKF in terms of balance, though its validity is limited by ensemble size. It is also found that overobserving can lead to a more unbalanced forecast ensemble and thus to an unbalanced analysis.

scholarly journals Tracking of pendulum using particle filter with residual resampling

2018 ◽  
Vol 7 (2.7) ◽  
pp. 12
Author(s):  
Penumarty Hiranmayi ◽  
Kola Sai Gowtham ◽  
S Koteswara Rao ◽  
V Gopi Tilak
Keyword(s):  
Monte Carlo ◽  
Kalman Filter ◽  
Particle Filter ◽  
Extended Kalman Filter ◽  
Horizontal Direction ◽  
Bayesian Filtering ◽  
Discrete State ◽  
Harmonic Motion ◽  
Simple Pendulum ◽  
Importance Resampling

The phenomenon of simple harmonic motion is more vigilantly explained using a simple pendulum. The angular motion of a pendulum is linear in nature. But the analysis of the motion along the horizontal direction is non-linear. To estimate this, several algorithms like the Kalman filter, Extended Kalman Filter etc. are adopted. Here in this paper, Particle filter is chosen which is a method to form Monte Carlo approximations to the solutions of Bayesian filtering equations. Sequential importance resampling based Particle filters are used where the filtering distributions are multi-nodal or consist of discrete state components since under these circumstances the Bayesian approximations do not always work well.

scholarly journals ESTUDO AVALIATIVO DA PREVISÃO NUMÉRICA DO TEMPO DE CURTO PRAZO PARA O MUNICÍPIO DE MACEIÓ/AL, UTILIZANDO O MODELO WRF

2020 ◽  
Vol 12 (6) ◽  
pp. 2121
Author(s):  
Rosiberto Salustiano Silva Junior ◽  
Bruno César Teixeira Cardoso ◽  
Hugo Cainã Ferreira Monteiro ◽  
Ewerton Hallan de Lima Silva
Keyword(s):  
Weather Forecast ◽  
Atmospheric Model ◽  
Weather Research And Forecasting ◽  
Atmospheric Models ◽  
Economic Activities ◽  
Land User ◽  
The City ◽  
Model Topography ◽  
Better Than ◽  
Weather Research

Sendo as diferentes atividades econômicas fortemente influenciadas pela condição do tempo, faz-se necessário antever com dias de antecedência a situação meteorológica favorável ou não para o cotidiano da sociedade. E os modelos atmosféricos são ferramentas amplamente utilizados para avaliar o estado futuro da atmosfera, neste contexto, avaliar a precisão das previsões realizadas por estas ferramentas, tem sido cada fez mais recorrente. Neste trabalho foi utilizado o modelo atmosférico WRF (Weather Research and Forecasting) para realizar previsões diárias com duração de 72h, durante o período de 10 a 19 de julho de 2017 para a cidade de Maceió/AL. Para validar as previsões foram utilizados os dados observados da estação meteorológica automática do INMET (Instituto Nacional de Meteorologia). Para este estudo também foi proposto a atualização da topografia e uso do solo da área de estudo em questão, que gerou melhorias nas comparações realizadas para todas as variáveis analisadas, em destaque a previsão da variável pressão atmosférica, quando atualizada a topografia houve sensíveis melhorias nos indicadores estatísticos em comparação aos demais testes que não contaram com mesma atualização. Além disso, as análises estatísticas e os gráficos apresentados comprovam que o modelo previu melhor para 24h do que para 48h e nesta sequência melhor que 72h, ou seja, existiu a depreciação das previsões com o aumento da duração das previsões. Study of the Efficiency of the Short-Term Numerical Forecast for the City of Maceió / Al, Using the WRF ModelA B S T R A C TThe different economic activities are strongly influenced by the condition of the weather, it is necessary to forecast with days in advance the meteorological situation favorable or not for the daily life of the society. The atmospheric models are tools widely used to assess the future state of the atmosphere, in this context, assess the accuracy of the forecasts made by these tools, has been each made more recurrent. In this work the atmospheric model WRF (Weather Research and Forecasting) was used to make daily forecasts with a duration of 72h during the period from July 10 to 19, 2017 for the city of Maceió / AL, to validate the forecasts were used the observed data of the INMET (National Meteorological Institute) automatic weather station. For this study it was also proposed to update the topography and land user of the study area, which generated improvements in the comparisons made for all variables analyzed, in particular the prediction of the variable atmospheric pressure, when updated the topography there were sensible improvements in statistical indicators compared to the other tests that did not have the same update. In addition, the statistical analyzes and the graphs presented show that the model predicted better for 24h than for 48h and in this sequence better than 72h, that is, there was depreciation of the forecasts with the increase of the forecast duration.Keywords: Weather Forecast, Atmospheric Model, Topography, Land User.

scholarly journals Comparison of Soil Hydraulic Parameterizations for Mesoscale Meteorological Models

2005 ◽  
Vol 44 (7) ◽  
pp. 1116-1132 ◽  
Author(s):  
Frank J. Braun ◽  
Gerd Schädler
Keyword(s):  
Soil Water ◽  
Atmospheric Model ◽  
Surface Data ◽  
Rhine Valley ◽  
Soil Hydraulic ◽  
Cover Up ◽  
Meteorological Models ◽  
Water Contents ◽  
Soil Water Contents ◽  
Better Than

Abstract Soil water contents, calculated with seven soil hydraulic parameterizations, that is, soil hydraulic functions together with the corresponding parameter sets, are compared with observational data. The parameterizations include the Campbell/Clapp–Hornberger parameterization that is often used by meteorologists and the van Genuchten/Rawls–Brakensiek parameterization that is widespread among hydrologists. The observations include soil water contents at several soil depths and atmospheric surface data; they were obtained within the Regio Klima Projekt (REKLIP) at three sites in the Rhine Valley in southern Germany and cover up to 3 yr with 10-min temporal resolution. Simulations of 48-h episodes, as well as series of daily simulations initialized anew every 24 h and covering several years, were performed with the “VEG3D” soil–vegetation model in stand-alone mode; furthermore, 48-h episodes were simulated with the model coupled to a one-dimensional atmospheric model. For the cases and soil types considered in this paper, the van Genuchten/Rawls–Brakensiek model gives the best agreement between observed and simulated soil water contents on average. Especially during episodes with medium and high soil water content, the van Genuchten/Rawls–Brakensiek model performs better than the Campbell/Clapp–Hornberger model.

Baroclinic Instability and Loss of Balance

2005 ◽  
Vol 35 (9) ◽  
pp. 1505-1517 ◽  
Author(s):  
M. Jeroen Molemaker ◽  
James C. McWilliams ◽  
Irad Yavneh
Keyword(s):  
Gravity Waves ◽  
Large Scale ◽  
Baroclinic Instability ◽  
Boussinesq Equations ◽  
Approximate Model ◽  
Slow Manifold ◽  
Energy Cascades ◽  
Centrifugal Instability ◽  
Forward Energy ◽  
Balanced Dynamics

Abstract Under the influences of stable density stratification and the earth’s rotation, large-scale flows in the ocean and atmosphere have a mainly balanced dynamics—sometimes called the slow manifold—in the sense that there are diagnostic hydrostatic and gradient-wind momentum balances that constrain the fluid acceleration. The nonlinear balance equations are a widely successful, approximate model for this regime, and mathematically explicit limits of their time integrability have been identified. It is hypothesized that these limits are indicative, at least approximately, of the transition from the larger-scale regime of inverse energy cascades by anisotropic flows to the smaller-scale regime of forward energy cascade to dissipation by more nearly isotropic flows and intermittently breaking inertia–gravity waves. This paper analyzes the particular example of an unbalanced instability of a balanced, horizontally uniform, vertically sheared current, as it occurs within the Boussinesq equations. This ageostrophic, anticyclonic, baroclinic instability is investigated with an emphasis on how it relates to the breakdown of balance in the neighborhood of loss of balanced integrability and on how its properties compare with other examples of ageostrophic anticyclonic instability of rotating, stratified, horizontally sheared currents. It is also compared with the more familiar types of instability for a vertically sheared current: balanced (geostrophic) baroclinic instability, centrifugal instability, and Kelvin–Helmholtz instability.

scholarly journals TESTING FOR REGIME SWITCHING IN SINGAPOREAN BUSINESS CYCLES

2005 ◽  
Vol 50 (01) ◽  
pp. 25-34 ◽  
Author(s):  
ROBERT BREUNIG ◽  
ALISON STEGMAN
Keyword(s):  
Model Selection ◽  
Business Cycles ◽  
Nonlinear Model ◽  
Regime Switching ◽  
Markov Switching ◽  
Markov Switching Model ◽  
Switching Model ◽  
Intended Use ◽  
Better Than ◽  
Model Fits

We examine a Markov-Switching model of Singaporean GDP using a combination of formal moment-based tests and informal graphical tests. The tests confirm that the Markov-Switching model fits the data better than a linear, autoregressive alternative. The methods are extended to allow us to identify precisely which features of the data are better captured by the nonlinear model. The methods described here allow model selection to be related to the intended use of the model.

scholarly journals Nonlinear Filter for a System with Randomly Delayed Measurements and Inputs

2020 ◽  
Vol 2 (1) ◽  
pp. 40
Author(s):  
Kundan Kumar ◽  
Shovan Bhaumik
Keyword(s):  
Nonlinear Filtering ◽  
Communication Channel ◽  
Data Transfer ◽  
Networked Control System ◽  
Random Delay ◽  
Generalized Framework ◽  
Delayed Measurements ◽  
Remote State Estimation ◽  
Simulation Problem ◽  
Better Than

This paper deals with a remote state estimation problem for a nonlinear system. In a typical networked control system (NCS) scenario, the estimator and controller are remotely located, and they are connected with the plant through a common communication network. Traditional Bayesian filters assume that the measurements are always available. However, this may not be the case in reality. As the sensor measurements are transmitted to the remotely located estimator through an unreliable communication channel, delay may arise during data transfer. Similarly, the control signal is also applied remotely, and it reaches to the plant through a similar unreliable communication channel, and due to which here also delay may occur. In this paper, the authors develop a generalized framework of nonlinear filtering where the states can be estimated in the presence of arbitrary random delay in (i) transmission of measurement from sensor to the estimator and (ii) transmission of input from the remotely located controller to the system. The filtering algorithm in such a scenario is realized with deterministic sample points. The performance of the proposed method is tested experimentally on one simulation problem. With the help of the simulation result, it is shown that the developed method performs better than traditional non-delayed nonlinear filters in the presence of arbitrary delay in measurement and input.

scholarly journals Evaluation of Surface Radiative Fluxes over the Tropical Oceans in AMIP Simulations

Atmosphere ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 606 ◽  
Author(s):  
Xin Zhou ◽  
Pallav Ray ◽  
Kristine Boykin ◽  
Bradford S. Barrett ◽  
Pang-Chi Hsu
Keyword(s):  
Longwave Radiation ◽  
Atmospheric Model ◽  
Surface Radiation ◽  
Shortwave Radiation ◽  
Systematic Bias ◽  
Model Ensemble ◽  
Low Level ◽  
Tropical Oceans ◽  
Ensemble Mean ◽  
Better Than

The performance of 20 models from the Atmospheric Model Intercomparison Project (AMIP) was evaluated concerning surface radiation over the tropical oceans (30° S–30° N) from 1979 to 2000. The model ensemble mean of the net surface shortwave radiation (QSW) was underestimated compared to the International Satellite Cloud Climatology Project (ISCCP) data by 4 W m−2. On the other hand, net longwave radiation (QLW) was overestimated by 4 W m−2, leading to an underestimation of the net surface radiation (Qrad) by 8 W m−2. The most prominent bias in the Qrad appears to be over regions of low-level clouds in the off-equatorial eastern Pacific, eastern Atlantic, and the south-eastern Indian Ocean. The root means squared error of QLW was larger than that of QSW in 17 out of 20 AMIP models. Overestimation of the total cloud cover and atmospheric humidity contributed to the underestimation of Qrad. In general, models with higher horizontal resolutions performed slightly better than those with coarser horizontal resolutions, although some systematic bias persists in all models and in all seasons, in particular, in regions of low-level clouds for QLW, and high-level clouds for QSW. The ensemble mean performed better than most models, but two high-resolution models (GFDL-HIRAM-C180 and GFDL-HIRAM-C360) outperform the model ensemble.

Hyperparameter tuning for hidden unit conditional random fields

2017 ◽  
Vol 34 (6) ◽  
pp. 2054-2062 ◽  
Author(s):  
Eun-Suk Yang ◽  
Jong Dae Kim ◽  
Chan-Young Park ◽  
Hye-Jeong Song ◽  
Yu-Seop Kim
Keyword(s):  
Nonlinear Model ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Random Search ◽  
Search Method ◽  
Grid Search ◽  
Content Type ◽  
Manual Search ◽  
Optimization Search ◽  
Better Than

Purpose In this paper, the problem of a nonlinear model – specifically the hidden unit conditional random fields (HUCRFs) model, which has binary stochastic hidden units between the data and the labels – exhibiting unstable performance depending on the hyperparameter under consideration. Design/methodology/approach There are three main optimization search methods for hyperparameter tuning: manual search, grid search and random search. This study shows that HUCRFs’ unstable performance depends on the hyperparameter values used and its performance is based on tuning that draws on grid and random searches. All experiments conducted used the n-gram features – specifically, unigram, bigram, and trigram. Findings Naturally, selecting a list of hyperparameter values based on a researchers’ experience to find a set in which the best performance is exhibited is better than finding it from a probability distribution. Realistically, however, it is impossible to calculate using the parameters in all combinations. The present research indicates that the random search method has a better performance compared with the grid search method while requiring shorter computation time and a reduced cost. Originality/value In this paper, the issues affecting the performance of HUCRF, a nonlinear model with performance that varies depending on the hyperparameters, but performs better than CRF, has been examined.

Ultra High Frequency SINC and Trigonometric Higher Order Neural Networks for Data Classification

2016 ◽  
pp. 113-153 ◽  
Author(s):  
Ming Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nonlinear Model ◽  
High Frequency ◽  
Data Classification ◽  
Higher Order ◽  
Ultra High Frequency ◽  
Higher Order Neural Networks ◽  
Better Than

This chapter develops a new nonlinear model, Ultra high frequency SINC and Trigonometric Higher Order Neural Networks (UNT-HONN), for Data Classification. UNT-HONN includes Ultra high frequency siNc and Sine Higher Order Neural Networks (UNS-HONN) and Ultra high frequency siNc and Cosine Higher Order Neural Networks (UNC-HONN). Data classification using UNS-HONN and UNC-HONN models are tested. Results show that UNS-HONN and UNC-HONN models are better than other Polynomial Higher Order Neural Network (PHONN) and Trigonometric Higher Order Neural Network (THONN) models, since UNS-HONN and UNC-HONN models can classify the data with error approaching 0.0000%.

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