Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Alexandria Grimes"'
Autor:
Alexandria Grimes, Andrew E. Mercer
Publikováno v:
Advances in Meteorology, Vol 2015 (2015)
Forecasting rapid intensification (hereafter referred to as RI) of tropical cyclones in the Atlantic Basin is still a challenge due to a limited understanding of the meteorological processes that are necessary for predicting RI. To address this chall
Externí odkaz:
https://doaj.org/article/485fa7841d9342d9a3a4cd54e3d4cad2
Publikováno v:
Journal of Applied Meteorology and Climatology. 60:119-138
Tropical cyclone (TC) track forecasts have improved in recent decades while intensity forecasts, particularly predictions of rapid intensification (RI), continue to show low skill. Many statistical methods have shown promise in predicting RI using en
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3c42f261c5b6b1b8d6a22a245df041e0
https://doi.org/10.1175/jamc-d-20-0105.1
https://doi.org/10.1175/jamc-d-20-0105.1
Publikováno v:
Procedia Computer Science. 140:359-366
Previous work investigating rapid intensification (RI) predictability within Atlantic tropical cyclones (TCs) has revealed numerous challenges in identifying parameters useful in predicting RI. In particular, false alarm RI forecasts are notably high
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::62059fab91bc78d13c9ee5b1d09178cb
https://doi.org/10.1016/j.procs.2018.10.303
https://doi.org/10.1016/j.procs.2018.10.303
Autor:
Alexandria Grimes, Andrew E. Mercer
Publikováno v:
Procedia Computer Science. 114:333-340
Atlantic tropical cyclone (TC) rapid intensification (RI) continues to be a major forecasting challenge, with forecast skill scores only about 15% better than climatology. To date, RI forecasts have been completed using linear discriminant analysis (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::42f7cbb28a1cb0756d04e35c546674c5
https://doi.org/10.1016/j.procs.2017.09.036
https://doi.org/10.1016/j.procs.2017.09.036
Autor:
Alexandria Grimes, Andrew E. Mercer
Publikováno v:
Procedia Computer Science. 95:223-228
The ability to discriminate rapidly intensifying tropical cyclones (TCs) from their non-rapidly intensifying counterparts remains a major forecasting challenge in operational meteorology. Primarily, approaches to this forecast problem utilize dynamic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e1ca38e9418bb2e5eba8ce220663a1be
https://doi.org/10.1016/j.procs.2016.09.318
https://doi.org/10.1016/j.procs.2016.09.318
Publikováno v:
Journal of Hydrology. 531:360-369
Summary Water resources in the lower Mississippi River alluvial valley play a critical role in agricultural productivity due to the widespread use of irrigation during the growing season. However, the unknown specifics of surface–atmosphere feedbac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::963693df9f94d21dc2f0029b6824287e
https://doi.org/10.1016/j.jhydrol.2015.07.016
https://doi.org/10.1016/j.jhydrol.2015.07.016
Autor:
Andrew E. Mercer, Alexandria Grimes
Publikováno v:
Complex Adaptive Systems
Tropical cyclone rapid intensification (RI) continues to be a problem that eludes operational forecasters. Recent work in this area has revealed the value of applying machine learning techniques to classifying storms as RI or non-RI at 24-hours lead
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c3e276e1ff1e867eb87d840d4b4dc22
https://doi.org/10.1016/j.procs.2015.09.179
https://doi.org/10.1016/j.procs.2015.09.179
Autor:
Andrew E. Mercer, Alexandria Grimes
Publikováno v:
Advances in Meteorology, Vol 2015 (2015)
Forecasting rapid intensification (hereafter referred to as RI) of tropical cyclones in the Atlantic Basin is still a challenge due to a limited understanding of the meteorological processes that are necessary for predicting RI. To address this chall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::21d5894cbb21ce90d0ca349426130fc1
https://doi.org/10.1155/2015/814043
https://doi.org/10.1155/2015/814043