A local ensemble Kalman filter for atmospheric data assimilation
Edward Ott,Brian R. Hunt,Istvan Szunyogh,Aleksey V. Zimin,Eric J. Kostelich,M. Corazza,Eugenia Kalnay,D. J. Patil,James A. Yorke +8 more
TL;DR: A new, local formulation of the ensemble Kalman filter approach for atmospheric data assimilation based on the hypothesis that, when the Earth’s surface is divided up into local regions of moderate size, vectors of the forecast uncertainties in such regions tend to lie in a subspace of much lower dimension than that of the full atmospheric state vector of such a region.
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Abstract: In this paper, we introduce a new, local formulation of the ensemble Kalman filter approach for atmospheric data assimilation. Our scheme is based on the hypothesis that, when the Earth’s surface is divided up into local regions of moderate size, vectors of the forecast uncertainties in such regions tend to lie in a subspace of much lower dimension than that of the full atmospheric state vector of such a region. Ensemble Kalman filters, in general, take the analysis resulting from the data assimilation to lie in the same subspace as the expected forecast error. Under our hypothesis the dimension of the subspace corresponding to local regions is low. This is used in our scheme to allow operations only on relatively low-dimensional matrices. The data assimilation analysis is performed locally in a manner allowing massively parallel computation to be exploited. The local analyses are then used to construct global states for advancement to the next forecast time. One advantage, which may take on more importance as ever-increasing amounts of remotely-sensed satellite data become available, is the favorable scaling of the computational cost of our method with increasing data size, as compared to other methods that assimilate data sequentially. The method, its potential advantages, properties, and implementation requirements are illustrated by numerical experiments on the Lorenz-96 model. It is found that accurate analysis can be achieved at a cost which is very modest compared to that of a full global ensemble Kalman filter.
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Citations
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TL;DR: The role played by chaotic wave dynamics in the propagation of information and the use of localization in ensemble Kalman filtering and the resulting impact on forecasts is elucidated and elucidated.
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A framework for indoor air quality sensor placement accounting for uncertainties and performing risk assessments
Himanshu Sharma
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Abstract: Monitoring and maintaining air quality in a built environment is essential for occupants health and safety. An indoor environment is subjected to various particulate, gaseous matter etc. Exposure to these contaminants can result in various health problems such as asthma, skin diseases and in some case cancer. Therefore indoor air quality monitoring sensors are important for early detection of these contaminants. An indoor contaminant is transported via the airflow. Various building uncertainties affect the airflow. Therefore it is important to account these uncertainties for designing optimal sensor network. Further, in case of an accidental or intentional release of hazardous contaminants, the network should also assist for risk assessments such as after release contaminant source distribution and identifying source location. The purpose of this research is to develop a unified framework for designing an optimal contaminant monitoring sensor network accounting building uncertainties and develop a methodology for carrying risk assessment under hazardous contaminant release. The framework uses the discrete form of Perron-Frobenius (PF) transfer operator to carry fast, accurate contaminant transport analysis. The work develops a methodology for accounting occupancy and weather uncertainties to designing the sensor network. Once constructed the P-F operator is also used with an Ensemble Kalman Filter (EnKF) estimator to estimate contaminant distribution using sensor measurement. Further, for identifying the release location a Bayesian inference method is developed using the constructed P-F operator. The developed framework can be used in developing strategies for people evacuation during toxic contaminant release containment of airborne infectious disease. It can also be integrated with it with the buildings to make smart HVAC systems.
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