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89
A wildland fire model with data assimilation
, 2006
"... A wildfire model is formulated based on balance equations for energy and fuel, where the fuel loss due to combustion corresponds to the fuel reaction rate. The resulting coupled partial differential equations have coefficients which can be approximated from prior measurements of wildfires. An Ensemb ..."
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Cited by 23 (4 self)
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A wildfire model is formulated based on balance equations for energy and fuel, where the fuel loss due to combustion corresponds to the fuel reaction rate. The resulting coupled partial differential equations have coefficients which can be approximated from prior measurements of wildfires. An Ensemble Kalman Filter technique is then used to assimilate temperatures measured at selected points into running wildfire simulations. The assimilation technique is able to modify the simulations to track the measurements correctly even if the simulations were started with an erroneous ignition location that is quite far away from the correct one.
A.: Demonstrating the validity of a wildfire DDDAS
 In: Computational Science  ICCS 2006: 6th International Conference
"... Abstract. We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for shortrange forecast of weather and wildfire behavior from realtime weather data, images, and sensor streams. The system changes the forecast as new data is received. We encapsulate the model code ..."
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Abstract. We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for shortrange forecast of weather and wildfire behavior from realtime weather data, images, and sensor streams. The system changes the forecast as new data is received. We encapsulate the model code and apply an ensemble Kalman filter in timespace with a highly parallel implementation. In this paper, we discuss how we will demonstrate that our system works using a DDDAS testbed approach and data collected from an earlier fire. 1
A deterministic formulation of the ensemble Kalman filter: An alternative to ensemble square root filters
 Tellus
"... The use of perturbed observations in the traditional ensemble Kalman filter (EnKF) results in a suboptimal filter behaviour, particularly for small ensembles. In this work, we propose a simple modification to the traditional EnKF that results in matching the analysed error covariance given by Kalman ..."
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Cited by 18 (4 self)
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The use of perturbed observations in the traditional ensemble Kalman filter (EnKF) results in a suboptimal filter behaviour, particularly for small ensembles. In this work, we propose a simple modification to the traditional EnKF that results in matching the analysed error covariance given by Kalman filter in cases when the correction is small; without perturbed observations. The proposed filter is based on the recognition that in the case of small corrections to the forecast the traditional EnKF without perturbed observations reduces the forecast error covariance by an amount that is nearly twice as large as that is needed to match Kalman filter. The analysis scheme works as follows: update the ensemble mean and the ensemble anomalies separately; update the mean using the standard analysis equation; update the anomalies with the same equation but half the Kalman gain. The proposed filter is shown to be a linear approximation to the ensemble square root filter (ESRF). Because of its deterministic character and its similarity to the traditional EnKF we call it the ‘deterministic EnKF’, or the DEnKF. A number of numerical experiments to compare the performance of the DEnKF with both the EnKF and an ESRF using three small models are conducted. We show that the DEnKF performs almost as well as the ESRF and is a significant improvement over the EnKF. Therefore, the DEnKF combines the numerical effectiveness, simplicity and versatility of the EnKF with the performance of the ESRFs. Importantly, the DEnKF readily permits the use of the traditional Schur productbased localization schemes. 1.
On the Measurement of
 Preferences in the Analytic Hierarchy Process”, Journal of Multicriteria Decision Analysis
, 1997
"... An enterprise is categorized as an SME if it has employees fewer than 200 and fixed capital less than 200 million baht, excluding land and building. It was reported that there are more than 80,000 SMEs in Thailand. Thus, SMEs are the main blood vessels of the Thai economy. Since the Thai economic co ..."
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Cited by 13 (0 self)
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An enterprise is categorized as an SME if it has employees fewer than 200 and fixed capital less than 200 million baht, excluding land and building. It was reported that there are more than 80,000 SMEs in Thailand. Thus, SMEs are the main blood vessels of the Thai economy. Since the Thai economic collapse in 1997, large numbers of Thai SMEs went bankrupt and wiped out of the industries. This resulted in SMEs which are tolerant to the changing economy and new environment. The objectives of this study are to investigate the status of intellectual capital (IC) of SMEs in Thailand and to enhance awareness of SME entrepreneurs regarding the value of IC in their companies. The findings from this study report recent status of IC in Thai SMEs. It should be helpful for enterprises that want to improve their management and maximize their IC assets.
2006: A note on the particle filter with posterior gaussian resampling
 Tellus A
"... Particle filter (PF) is a fully nonlinear filter with Bayesian conditional probability estimation, compared here with the wellknown ensemble Kalman filter (EnKF). A Gaussian resampling (GR) method is proposed to generate the posterior analysis ensemble in an effective and efficient way. The Lorenz ..."
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Cited by 13 (1 self)
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Particle filter (PF) is a fully nonlinear filter with Bayesian conditional probability estimation, compared here with the wellknown ensemble Kalman filter (EnKF). A Gaussian resampling (GR) method is proposed to generate the posterior analysis ensemble in an effective and efficient way. The Lorenz model is used to test the proposed method. The PF with Gaussian resampling (PFGR) can approximate more accurately the Bayesian analysis. The present work demonstrates that the proposed PFGR possesses good stability and accuracy and is potentially applicable to largescale data assimilation problems. 1.
Assimilation of IASI partial tropospheric columns with an Ensemble Kalman Filter over Europe, Atmos
 Chem. Phys
, 2012
"... HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte p ..."
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Cited by 11 (1 self)
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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et a ̀ la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Atmos. Chem. Phys., 12, 2513–2532, 2012 www.atmoschemphys.net/12/2513/2012/ doi:10.5194/acp1225132012 © Author(s) 2012. CC Attribution 3.0 License.
Data Assimilation for Geophysical Fluids
"... The ultimate purpose of environmental studies is the forecast of its natural evolution. A prerequisite before a prediction is to retrieve at best the state of the environment. Data assimilation is the ensemble of techniques which, starting from heterogeneous information, permit to retrieve the initi ..."
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Cited by 8 (0 self)
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The ultimate purpose of environmental studies is the forecast of its natural evolution. A prerequisite before a prediction is to retrieve at best the state of the environment. Data assimilation is the ensemble of techniques which, starting from heterogeneous information, permit to retrieve the initial state of a flow. In the first part, the mathematical models governing geophysical flows are presented together with the networks of observations of the atmosphere and of the ocean. In variational methods, we seek for the minimum of a functional estimating the discrepancy between the solution of the model and the observation. The derivation of the optimality system, using the adjoint state, permits to compute a gradient which is used in the optimization. The definition of the cost function permits to take into account the available statistical information through the choice of metrics in the space of observation and in the space of the initial condition. Some examples are presented on simplified models, especially an application in oceanography. Among the tools of optimal control, the adjoint model permits to carry out sensitivity studies, but if we look for the sensitivity of the prediction with respect to the observations, then a secondorder analysis should be considered. One of the first methods used for assimilating data in oceanography is the nudging method, adding a forcing term in the equations. A variational variant of nudging method is described and also a socalled Computational Methods for the Atmosphere and the Oceans
Controlling instabilities along a 3DVar analysis cycle by assimilating in the unstable subspace: a comparison with the EnKF
, 2008
"... A hybrid scheme obtained by combining 3DVar with the Assimilation in the Unstable Subspace (3DVarAUS) is tested in a QG model, under perfect model conditions, with a fixed observational network, with and without observational noise. The AUS scheme, originally formulated to assimilate adaptive obser ..."
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Cited by 6 (5 self)
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A hybrid scheme obtained by combining 3DVar with the Assimilation in the Unstable Subspace (3DVarAUS) is tested in a QG model, under perfect model conditions, with a fixed observational network, with and without observational noise. The AUS scheme, originally formulated to assimilate adaptive observations, is used here to assimilate the fixed observations that are found in the region of local maxima of BDAS vectors (Bred vectors subject to assimilation), while the remaining observations are assimilated by 3DVar. The performance of the hybrid scheme is compared with that of 3DVar and of an EnKF. The improvement gained by 3DVarAUS and the EnKF with respect to 3DVar alone is similar in the present model and observational configuration, while 3DVarAUS outperforms the EnKF during the forecast stage. The 3DVarAUS 1 algorithm is easy to implement and the results obtained in the idealized conditions of this study encourage further investigation toward an implementation in more realistic contexts.
Efficient Targeting of Sensor Networks for LargeScale Systems
 SUBMITTED TO IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
, 2009
"... This paper proposes an efficient approach to an observation targeting problem that is complicated by a combinatorial number of targeting choices and the large dimension of the system state, when the goal is to minimize the uncertainty in some quantities of interest. The primary improvements in the e ..."
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Cited by 5 (2 self)
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This paper proposes an efficient approach to an observation targeting problem that is complicated by a combinatorial number of targeting choices and the large dimension of the system state, when the goal is to minimize the uncertainty in some quantities of interest. The primary improvements in the efficiency are obtained by computing the impact of each possible measurement choice on the uncertainty reduction backwards. This backward method provides an equivalent solution to a traditional forward approach under some standard assumptions, while removing the requirement of calculating a combinatorial number of covariance updates. A key contribution of this paper is to prove that the backward approach operates never slower than the forward approach, and that it works significantly faster than the forward one for ensemblebased representations. The primary benefits are shown on a simplified weather problem using the Lorenz95 model.
On numerical properties of the ensemble kalman filter for data assimilation
 Computer Methods in Applied Mechanics and Engineering
, 2008
"... a b s t r a c t Ensemble Kalman filter (EnKF) has been widely used as a sequential data assimilation method, primarily due to its ease of implementation resulting from replacing the covariance evolution in the traditional Kalman filter (KF) by an approximate Monte Carlo ensemble sampling. In this p ..."
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Cited by 3 (0 self)
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a b s t r a c t Ensemble Kalman filter (EnKF) has been widely used as a sequential data assimilation method, primarily due to its ease of implementation resulting from replacing the covariance evolution in the traditional Kalman filter (KF) by an approximate Monte Carlo ensemble sampling. In this paper rigorous analysis on the numerical errors of the EnKF is conducted in a general setting. Error bounds are provided and convergence of the EnKF to the exact Kalman filter is established. The analysis reveals that the ensemble errors induced by the Monte Carlo sampling can be dominant, compared to other errors such as the numerical integration error of the underlying model equations. Methods to reduce sampling errors are discussed. In particular, we present a deterministic sampling strategy based on cubature rules (qEnKF) which offers much improved accuracy. The analysis also suggests a less obvious fact more frequent data assimilation may lead to larger numerical errors of the EnKF. Numerical examples are provided to verify the theoretical findings and to demonstrate the improved performance of the qEnKF.