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pro vyhledávání: '"Mandel, Jan"'
Autor:
Mandel, Jan
These notes started to educate ourselves and to collect some background for our future work, with the hope that perhaps they will be useful to others also. Many if not all results are more or less elementary or available in the literature, but we nee
Externí odkaz:
http://arxiv.org/abs/2310.15818
Autor:
Shaddy, Bryan, Ray, Deep, Farguell, Angel, Calaza, Valentina, Mandel, Jan, Haley, James, Hilburn, Kyle, Mallia, Derek V., Kochanski, Adam, Oberai, Assad
Increases in wildfire activity and the resulting impacts have prompted the development of high-resolution wildfire behavior models for forecasting fire spread. Recent progress in using satellites to detect fire locations further provides the opportun
Externí odkaz:
http://arxiv.org/abs/2309.02615
Publikováno v:
Advances in Forest Fire Research 2018, edited by D.X. Viegas, Imprensa da Universidade de Coimbra, 1918, pp. 959-968
Data likelihood of fire detection is the probability of the observed detection outcome given the state of the fire spread model. We derive fire detection likelihood of satellite data as a function of the fire arrival time on the model grid. The data
Externí odkaz:
http://arxiv.org/abs/1808.03318
Publikováno v:
Atmosphere 2018, 9, 296
Observational data collected during experiments, such as the planned Fire and Smoke Model Evaluation Experiment (FASMEE), are critical for progressing and transitioning coupled fire-atmosphere models like WRF-SFIRE and WRF-SFIRE-CHEM into operational
Externí odkaz:
http://arxiv.org/abs/1806.06460
Publikováno v:
Lecture Notes in Computer Science 10861, pp. 711-723, 2018
Assimilation of data into a fire-spread model is formulated as an optimization problem. The level set equation, which relates the fire arrival time and the rate of spread, is allowed to be satisfied only approximately, and we minimize a norm of the r
Externí odkaz:
http://arxiv.org/abs/1804.03995
Autor:
Kasanický, Ivan, Mandel, Jan
Bayesian inverse problem on an infinite dimensional separable Hilbert space with the whole state observed is well posed when the prior state distribution is a Gaussian probability measure and the data error covariance is a cylindric Gaussian measure
Externí odkaz:
http://arxiv.org/abs/1701.08298
Publikováno v:
Communications in Statistics - Theory and Methods (2018)
The asymptotic variance of the maximum likelihood estimate is proved to decrease when the maximization is restricted to a subspace that contains the true parameter value. Maximum likelihood estimation allows a systematic fitting of covariance models
Externí odkaz:
http://arxiv.org/abs/1701.08185
Publikováno v:
Nonlinear Processes in Geophysics, 22, 485-497, 2015
A new type of ensemble Kalman filter is developed, which is based on replacing the sample covariance in the analysis step by its diagonal in a spectral basis. It is proved that this technique improves the aproximation of the covariance when the covar
Externí odkaz:
http://arxiv.org/abs/1501.00219
Publikováno v:
Applied Numerical Mathematics 137 (2019) 151-168
Ensemble methods, such as the ensemble Kalman filter (EnKF), the local ensemble transform Kalman filter (LETKF), and the ensemble Kalman smoother (EnKS) are widely used in sequential data assimilation, where state vectors are of huge dimension. Littl
Externí odkaz:
http://arxiv.org/abs/1411.4608