scholarly journals A mechanism for catastrophic filter divergence in data assimilation for sparse observation networks

2013 ◽  
Vol 20 (5) ◽  
pp. 705-712 ◽  
Author(s):  
G. A. Gottwald ◽  
A. J. Majda
Keyword(s):  
Data Assimilation ◽  
Blow Up ◽  
Forecast Model ◽  
Numerical Instability ◽  
Dimensional Model ◽  
Numerical Instabilities ◽  
Five Dimensional Model ◽  
Observational Noise ◽  
Filter Divergence ◽  
Filtering Procedure

Abstract. We study catastrophic filter divergence in data assimilation procedures whereby the forecast model develops severe numerical instabilities leading to a blow-up of the solution. Catastrophic filter divergence can occur in sparse observational grids with small observational noise for intermediate observation intervals and finite ensemble sizes. Using a minimal five-dimensional model, we establish that catastrophic filter divergence is a numerical instability of the underlying forecast model caused by the filtering procedure producing analyses which are not consistent with the true dynamics, and stiffness caused by the fast attraction of the inconsistent analyses towards the attractor during the forecast step.

scholarly journals A Localized Adaptive Particle Filter within an Operational NWP Framework

2019 ◽  
Vol 147 (1) ◽  
pp. 345-362 ◽  
Author(s):  
Roland Potthast ◽  
Anne Walter ◽  
Andreas Rhodin
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Degrees Of Freedom ◽  
Grid Point ◽  
Forecast Model ◽  
Horizontal Resolution ◽  
Classical Particle ◽  
Mcmc Methods ◽  
Ensemble Transform Kalman Filter ◽  
Filter Divergence

Particle filters are well known in statistics. They have a long tradition in the framework of ensemble data assimilation (EDA) as well as Markov chain Monte Carlo (MCMC) methods. A key challenge today is to employ such methods in a high-dimensional environment, since the naïve application of the classical particle filter usually leads to filter divergence or filter collapse when applied within the very high dimension of many practical assimilation problems (known as the curse of dimensionality). The goal of this work is to develop a localized adaptive particle filter (LAPF), which follows closely the idea of the classical MCMC or bootstrap-type particle filter, but overcomes the problems of collapse and divergence based on localization in the spirit of the local ensemble transform Kalman filter (LETKF) and adaptivity with an adaptive Gaussian resampling or rejuvenation scheme in ensemble space. The particle filter has been implemented in the data assimilation system for the global forecast model ICON at Deutscher Wetterdienst (DWD). We carry out simulations over a period of 1 month with a global horizontal resolution of 52 km and 90 layers. With four variables analyzed per grid point, this leads to 6.6 × 106 degrees of freedom. The LAPF can be run stably and shows a reasonable performance. We compare its scores to the operational setup of the ICON LETKF.

scholarly journals Forest and Society’s Welfare: Impact Assessment in Lithuania

2021 ◽  
Vol 13 (10) ◽  
pp. 5598
Author(s):  
Stasys Mizaras ◽  
Diana Lukmine
Keyword(s):  
Impact Assessment ◽  
Forest Policy ◽  
Assessment Model ◽  
Dimensional Model ◽  
Important Goal ◽  
Welfare Impact ◽  
Global Problem ◽  
Five Dimensional Model ◽  
Welfare Index ◽  
The Impact

Effective formation and implementation of forest policy can only be achieved with orientation to the most important goal—increasing society’s welfare. The global problem is, at present, that the impact of forests on society welfare indexes have not been identified. The aim of the study is to design an assessment model and assess the impact of Lithuanian forests on the society welfare index. The impact of forests was determined by multiplying the country’s welfare of society index by the forest contribution coefficient. In this study, to assess the index of the welfare of Lithuanian society, a five-dimensional model with 16 indicators was applied. The study is based on the Eurostat database and on Lithuanian forestry statistics. The Lithuanian welfare of society index calculated according to the model was 51.4% and the contribution of forests in this index was 3.9%. It represented 7.6% of the index of the welfare of society. Forests have the greatest impact in the environmental dimension, according to the assessment results.

A New Approach for Estimating the Observation Impact in Ensemble–Variational Data Assimilation

2018 ◽  
Vol 146 (2) ◽  
pp. 447-465 ◽  
Author(s):  
Mark Buehner ◽  
Ping Du ◽  
Joël Bédard
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Observation Error ◽  
New Approach ◽  
Regional Prediction ◽  
Observation Impact ◽  
Adjoint Approach ◽  
The Impact

Abstract Two types of approaches are commonly used for estimating the impact of arbitrary subsets of observations on short-range forecast error. The first was developed for variational data assimilation systems and requires the adjoint of the forecast model. Comparable approaches were developed for use with the ensemble Kalman filter and rely on ensembles of forecasts. In this study, a new approach for computing observation impact is proposed for ensemble–variational data assimilation (EnVar). Like standard adjoint approaches, the adjoint of the data assimilation procedure is implemented through the iterative minimization of a modified cost function. However, like ensemble approaches, the adjoint of the forecast step is obtained by using an ensemble of forecasts. Numerical experiments were performed to compare the new approach with the standard adjoint approach in the context of operational deterministic NWP. Generally similar results are obtained with both approaches, especially when the new approach uses covariance localization that is horizontally advected between analysis and forecast times. However, large differences in estimated impacts are obtained for some surface observations. Vertical propagation of the observation impact is noticeably restricted with the new approach because of vertical covariance localization. The new approach is used to evaluate changes in observation impact as a result of the use of interchannel observation error correlations for radiance observations. The estimated observation impact in similarly configured global and regional prediction systems is also compared. Overall, the new approach should provide useful estimates of observation impact for data assimilation systems based on EnVar when an adjoint model is not available.

scholarly journals Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming

2012 ◽  
Vol 12 (12) ◽  
pp. 3719-3732 ◽  
Author(s):  
L. Mediero ◽  
L. Garrote ◽  
A. Chavez-Jimenez
Keyword(s):  
Data Assimilation ◽  
Genetic Programming ◽  
Real Time ◽  
High Performance ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Distribution Functions ◽  
Model Parameters ◽  
Hydrological Process ◽  
Model Based

Abstract. Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.

scholarly journals Synchronicity in predictive modelling: a new view of data assimilation

2006 ◽  
Vol 13 (6) ◽  
pp. 601-612 ◽  
Author(s):  
G. S. Duane ◽  
J. J. Tribbia ◽  
J. B. Weiss
Keyword(s):  
Dynamical Systems ◽  
Data Assimilation ◽  
Ad Hoc ◽  
Predictive Modelling ◽  
Dimensional System ◽  
General View ◽  
Machine Perception ◽  
Loosely Coupled ◽  
Unidirectional Flow ◽  
Observational Noise

Abstract. The problem of data assimilation can be viewed as one of synchronizing two dynamical systems, one representing "truth" and the other representing "model", with a unidirectional flow of information between the two. Synchronization of truth and model defines a general view of data assimilation, as machine perception, that is reminiscent of the Jung-Pauli notion of synchronicity between matter and mind. The dynamical systems paradigm of the synchronization of a pair of loosely coupled chaotic systems is expected to be useful because quasi-2D geophysical fluid models have been shown to synchronize when only medium-scale modes are coupled. The synchronization approach is equivalent to standard approaches based on least-squares optimization, including Kalman filtering, except in highly non-linear regions of state space where observational noise links regimes with qualitatively different dynamics. The synchronization approach is used to calculate covariance inflation factors from parameters describing the bimodality of a one-dimensional system. The factors agree in overall magnitude with those used in operational practice on an ad hoc basis. The calculation is robust against the introduction of stochastic model error arising from unresolved scales.

scholarly journals SOFT SUPERSYMMETRY BREAKING MASSES AND μ PARAMETER FROM DYNAMICAL REARRANGEMENT OF EXOTIC U(1) SYMMETRIES

2011 ◽  
Vol 26 (25) ◽  
pp. 4405-4418 ◽  
Author(s):  
YOSHIHARU KAWAMURA ◽  
TAKASHI MIURA
Keyword(s):  
Supersymmetry Breaking ◽  
Gauge Boson ◽  
Dimensional Model ◽  
Five Dimensional Model ◽  
Soft Supersymmetry Breaking ◽  
Large Hierarchy

We propose a mechanism that the soft supersymmetry breaking masses and μ parameter can be induced from the dynamical rearrangement of local U(1) symmetries in a five-dimensional model. It offers to a solution to μ problem if there is a large hierarchy among the relevant U(1) charge of Higgsinos and that of hidden fields which stabilize the extra-dimensional component of U(1) gauge boson.

scholarly journals Forecasting of Surface Currents via Correcting Wind Stress with Assimilation of High-Frequency Radar Data in a Three-Dimensional Model

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Lei Ren ◽  
Stephen Nash ◽  
Michael Hartnett
Keyword(s):  
Data Assimilation ◽  
High Frequency ◽  
Three Dimensional ◽  
Hf Radar ◽  
Surface Velocity ◽  
Shear Stresses ◽  
Surface Currents ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
The Impact

This paper details work in assessing the capability of a hydrodynamic model to forecast surface currents and in applying data assimilation techniques to improve model forecasts. A three-dimensional model Environment Fluid Dynamics Code (EFDC) was forced with tidal boundary data and onshore wind data, and so forth. Surface current data from a high-frequency (HF) radar system in Galway Bay were used for model intercomparisons and as a source for data assimilation. The impact of bottom roughness was also investigated. Having developed a “good” water circulation model the authors sought to improve its forecasting ability through correcting wind shear stress boundary conditions. The differences in surface velocity components between HF radar measurements and model output were calculated and used to correct surface shear stresses. Moreover, data assimilation cycle lengths were examined to extend the improvements of surface current’s patterns during forecasting period, especially for north-south velocity component. The influence of data assimilation in model forecasting was assessed using a Data Assimilation Skill Score (DASS). Positive magnitude of DASS indicated that both velocity components were considerably improved during forecasting period. Additionally, the improvements of RMSE for vector direction over domain were significant compared with the “free run.”

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