Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Jonathan Poterjoy"'
Autor:
Kenta Kurosawa, Jonathan Poterjoy
Publikováno v:
Monthly Weather Review. 151:105-125
Particle filters avoid parametric estimates for Bayesian posterior densities, which alleviates Gaussian assumptions in nonlinear regimes. These methods, however, are more sensitive to sampling errors than Gaussian-based techniques such as ensemble Ka
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::552ea1cf722150087209e2cd41aaf652
https://doi.org/10.1175/mwr-d-22-0108.1
https://doi.org/10.1175/mwr-d-22-0108.1
Publikováno v:
Monthly Weather Review.
Estimating and predicting the state of the atmosphere is a probabilistic problem, and often employs an ensemble modeling approach to represent uncertainty in the system. Common methods for examining uncertainty and assessing performance for ensembles
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d7811adc0146e558c67f61dec6feb364
https://doi.org/10.1175/mwr-d-22-0281.1
https://doi.org/10.1175/mwr-d-22-0281.1
Autor:
Craig S. Schwartz, Jonathan Poterjoy, Glen S. Romine, David C. Dowell, Jacob R. Carley, Jamie Bresch
Publikováno v:
Weather and Forecasting. 37:1259-1286
Nine sets of 36-h, 10-member, convection-allowing ensemble (CAE) forecasts with 3-km horizontal grid spacing were produced over the conterminous United States for a 4-week period. These CAEs had identical configurations except for their initial condi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::07a274f013d56cde0e2fce2d0253fd06
https://doi.org/10.1175/waf-d-21-0165.1
https://doi.org/10.1175/waf-d-21-0165.1
Autor:
Jonathan Poterjoy
Publikováno v:
Quarterly Journal of the Royal Meteorological Society. 148:2631-2651
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a468758a75d7316b4254a275e24820b8
https://doi.org/10.1002/qj.4328
https://doi.org/10.1002/qj.4328
Publikováno v:
Monthly Weather Review.
Obtaining a faithful probabilistic depiction of moist convection is complicated by unknown errors in subgrid-scale physical parameterization schemes, invalid assumptions made by data assimilation (DA) techniques, and high system dimensionality. As an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02a3aae6ce5084787c23e2f769badad5
https://doi.org/10.1175/mwr-d-22-0260.1
https://doi.org/10.1175/mwr-d-22-0260.1
Publikováno v:
Weather and Forecasting. 36:661-677
Limited-area numerical weather prediction models currently run operationally in the United States and follow a “partially cycled” schedule, where sequential data assimilation is periodically interrupted by replacing model states with solutions in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::96b7a9c9db6251a4919e27e43a3ae15e
https://doi.org/10.1175/waf-d-20-0204.1
https://doi.org/10.1175/waf-d-20-0204.1
Publikováno v:
Monthly Weather Review. 148:4377-4395
The local particle filter (LPF) and the local nonlinear ensemble transform filter (LNETF) are two moment-matching nonlinear filters to approximate the classical particle filter (PF). They adopt different strategies to alleviate filter degeneracy. LPF
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a8e4d6231084e25b787d4004cc69e584
https://doi.org/10.1175/mwr-d-19-0368.1
https://doi.org/10.1175/mwr-d-19-0368.1
Autor:
Jonathan Poterjoy
Publikováno v:
Monthly Weather Review.
Weather prediction models currently operate within a probabilistic framework for generating forecasts conditioned on recent measurements of Earth’s atmosphere. This framework can be conceptualized as one that approximates parts of a Bayesian poster
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::245815b8d2db28c73cf22141cd485c38
https://doi.org/10.1175/mwr-d-21-0228.1
https://doi.org/10.1175/mwr-d-21-0228.1
Publikováno v:
Monthly Weather Review. 147:1107-1126
A series of papers published recently by the first author introduce a nonlinear filter that operates effectively as a data assimilation method for large-scale geophysical applications. The method uses sequential Monte Carlo techniques adopted by part
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6b86e1107fb74431f7dd1bc37ba50055
https://doi.org/10.1175/mwr-d-17-0344.1
https://doi.org/10.1175/mwr-d-17-0344.1
Autor:
Jonathan Poterjoy, Kenta Kurosawa
Publikováno v:
Monthly Weather Review.
The ensemble Kalman Filter (EnKF) and the 4D variational method (4DVar) are the most commonly used filters and smoothers in atmospheric science. These methods typically approximate prior densities using a Gaussian and solve a linear system of equatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::76892d4ba397e8ac1d3ff68a8bc85347
https://doi.org/10.1175/mwr-d-20-0368.1
https://doi.org/10.1175/mwr-d-20-0368.1