Zobrazeno 1 - 10
of 91
pro vyhledávání: '"Jeffrey S. Whitaker"'
Autor:
Tse‐Chun Chen, Stephen G. Penny, Jeffrey S. Whitaker, Sergey Frolov, Robert Pincus, Stefan Tulich
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 14, Iss 11, Pp n/a-n/a (2022)
Abstract Weather forecasts made with imperfect models contain state‐dependent errors. Data assimilation (DA) partially corrects these errors with new information from observations. As such, the corrections, or “analysis increments,” produced by
Externí odkaz:
https://doaj.org/article/6c4951f0460442fbb48290097aebc45b
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 11, Iss 6, Pp 1627-1636 (2019)
Abstract Ensemble‐based data assimilation systems typically use covariance localization to dampen spurious correlations associated with sampling error while increasing the rank of the covariance estimate. Variational methods use model‐space local
Externí odkaz:
https://doaj.org/article/4fbda6017a2943f28d799438778a5c4b
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 12, Iss 10, Pp n/a-n/a (2020)
Abstract The ensemble Kalman filter (EnKF) has been widely used in atmosphere, ocean, and land applications. The observing network has been significantly developed, and thus, observations with highly dense temporal resolutions have become available.
Externí odkaz:
https://doaj.org/article/69fbd9f6d9064ef79a78bc4f7526192d
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 12, Iss 8, Pp n/a-n/a (2020)
Abstract Localization is essential to effectively assimilate satellite radiances in ensemble Kalman filters. However, the vertical location and separation from a model grid point variable for a radiance observation are not well defined, which results
Externí odkaz:
https://doaj.org/article/f9972f6990d14417a8571945b741d187
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 6 (2020)
Externí odkaz:
https://doaj.org/article/177146e93ffa4605821bb2bdc7daac0a
Autor:
Anna Shlyaeva, Jeffrey S. Whitaker
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 10, Iss 7, Pp 1414-1420 (2018)
Abstract Within the National Oceanic and Atmospheric Administration National Weather Service, the hybrid ensemble‐variational system (Gridpoint Statistical Interpolation, GSI) is run together with the 80‐member ensemble square root filter (EnSRF)
Externí odkaz:
https://doaj.org/article/0294085479ad4ac3ab4be43f3943c9bb
Publikováno v:
Quarterly Journal of the Royal Meteorological Society. 148:2086-2101
Autor:
Laura C. Slivinski, Donald E. Lippi, Jeffrey S. Whitaker, Guoqing Ge, Jacob R. Carley, Curtis R. Alexander, Gilbert P. Compo
Publikováno v:
Monthly Weather Review. 150:1317-1334
The U.S. operational global data assimilation system provides updated analysis and forecast fields every 6 h, which is not frequent enough to handle the rapid error growth associated with hurricanes or other storms. This motivates development of an h
Autor:
Sergey Frolov, Anna Shlyaeva, Wei Huang, Travis Sluka, Clara Sophie Draper, Bo Huang, Kriti Bhargava, Jeffrey S. Whitaker
The Joint Effort for Data assimilation Integration (JEDI) is an international collaboration aimed at developing an open software ecosystem for model agnostic data assimilation. This paper considers implementation of the model-agnostic family of the l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9cc74c53018dbff2c11fec8a838cb3e1
https://doi.org/10.22541/essoar.167839978.89014280/v1
https://doi.org/10.22541/essoar.167839978.89014280/v1
Autor:
Hong Guan, Yuejian Zhu, Eric Sinsky, Bing Fu, Wei Li, Xiaqiong Zhou, Xianwu Xue, Dingchen Hou, Jiayi Peng, M. M. Nageswararao, Vijay Tallapragada, Thomas M. Hamill, Jeffrey S. Whitaker, Gary Bates, Philip Pegion, Sherrie Frederick, Matthew Rosencrans, Arun Kumar
Publikováno v:
Monthly Weather Review. 150:647-665
For the newly implemented Global Ensemble Forecast System, version 12 (GEFSv12), a 31-yr (1989–2019) ensemble reforecast dataset has been generated at the National Centers for Environmental Prediction (NCEP). The reforecast system is based on NCEP