Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Sanita Vetra-Carvalho"'
Autor:
Sanita Vetra-Carvalho, Sarah L. Dance, David C. Mason, Joanne A. Waller, Elizabeth S. Cooper, Polly J. Smith, Jemima M. Tabeart
Publikováno v:
Data in Brief, Vol 33, Iss , Pp 106338- (2020)
We present a new water level dataset extracted from images taken by four Farson Digital Ltd river cameras for a Tewkesbury, UK flood event (21st November – 5th December 2012). This data article presents the new water level data together with a desc
Externí odkaz:
https://doaj.org/article/fd454d9fa06543269ed16b1d459d0fe2
Autor:
Sanita Vetra-Carvalho, Peter Jan van Leeuwen, Lars Nerger, Alexander Barth, M. Umer Altaf, Pierre Brasseur, Paul Kirchgessner, Jean-Marie Beckers
Publikováno v:
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 70, Iss 1, Pp 1-43 (2018)
This paper compares several commonly used state-of-the-art ensemble-based data assimilation methods in a coherent mathematical notation. The study encompasses different methods that are applicable to high-dimensional geophysical systems, like ocean a
Externí odkaz:
https://doaj.org/article/5465d29af7f642e5b91553c94ed17128
Autor:
Dallas Masters, Shradha Mohanty, Matthew Angling, Karl Nordstrom, Sanita Vetra-Carvalho, Oleguer Nogués-Correig, Francois-Xavier Bocquet, Stacey E. Melville, Giorgio Savastano, Vu Nguyen
Publikováno v:
Journal of Space Weather and Space Climate, Vol 11, p 56 (2021)
Radio occultation (RO) provides a cost-effective component of the overall sensor mix required to characterise the ionosphere over wide areas and in areas where it is not possible to deploy ground sensors. The paper describes the RO constellation that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c24d3899c9a2a4ec69a6039a4eed6778
https://www.swsc-journal.org/articles/swsc/full_html/2021/01/swsc210051/swsc210051.html
https://www.swsc-journal.org/articles/swsc/full_html/2021/01/swsc210051/swsc210051.html
Autor:
Polly J. Smith, Elizabeth Cooper, Joanne A. Waller, Sarah L. Dance, Sanita Vetra-Carvalho, Jemima M. Tabeart, David C. Mason
Publikováno v:
Data in Brief
Data in Brief, Vol 33, Iss, Pp 106338-(2020)
Data in Brief, Vol 33, Iss, Pp 106338-(2020)
We present a new water level dataset extracted from images taken by four Farson Digital Ltd river cameras for a Tewkesbury, UK flood event (21st November – 5th December 2012). This data article presents the new water level data together with a desc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5896cddecc4b219d409f8312d057c472
http://europepmc.org/articles/PMC7560713
http://europepmc.org/articles/PMC7560713
Autor:
Matthew Angling, Stacey E. Melville, Karl Nordstrom, German Olivares-Pulido, Sanita Vetra-Carvalho, Francois-Xavier Bocquet, Giorgio Savastano
The ionosphere can affect a wide range of radio frequency (RF) systems operating below 2 GHz. One option for mitigating these effects is to produce assimilative models of the ionospheric density from which products can be derived for specific systems
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b9b0b044679b3ad0d926517e582f4c90
https://doi.org/10.5194/egusphere-egu2020-19447
https://doi.org/10.5194/egusphere-egu2020-19447
Autor:
M. Umer Altaf, Pierre Brasseur, Jean-Marie Beckers, Peter Jan van Leeuwen, Sanita Vetra-Carvalho, Alexander Barth, Lars Nerger, Paul Kirchgessner
Publikováno v:
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 70, Iss 1, Pp 1-43 (2018)
Tellus A: Dynamic Meteorology and Oceanography
EPIC3Tellus Series A-Dynamic Meteorology and Oceanography, 70(1), pp. 1445364
Tellus A: Dynamic Meteorology and Oceanography
EPIC3Tellus Series A-Dynamic Meteorology and Oceanography, 70(1), pp. 1445364
This paper compares several commonly used state-of-the-art ensemble-based data assimilation methods in a coherent mathematical notation. The study encompasses different methods that are applicable to high-dimensional geophysical systems, like ocean a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::494689b785d835da71db30f36cbb42f2
https://doaj.org/article/5465d29af7f642e5b91553c94ed17128
https://doaj.org/article/5465d29af7f642e5b91553c94ed17128
Autor:
Sarah L. Dance, John Bevington, Beatriz Revilla-Romero, Sanita Vetra-Carvalho, Richard Smith, Hannah Cloke, David C. Mason
Publikováno v:
Water
Volume 13
Issue 11
Water, Vol 13, Iss 1577, p 1577 (2021)
Volume 13
Issue 11
Water, Vol 13, Iss 1577, p 1577 (2021)
Remotely sensed flood extents obtained in near real-time can be used for emergency flood incident management and as observations for assimilation into flood forecasting models. High-resolution synthetic aperture radar (SAR) sensors have the potential
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07d80f750135c92db671b1e1e695efe4
https://doi.org/10.3390/w13111577
https://doi.org/10.3390/w13111577
Flooding is a major hazard in both rural and urban areas worldwide, but it is in urban areas that the impacts are most severe. High-resolution synthetic aperture radar (SAR) sensors are able to detect flood extents in urban areas during both day- and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad74b7a8820d099a8a1016ef926a7d18
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-369395
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-369395
Publikováno v:
Quarterly Journal of the Royal Meteorological Society. 138:1709-1720
For data assimilation in numerical weather prediction, the initial forecast-error covariance matrix Pf is required. For variational assimilation it is particularly important to prescribe an accurate initial matrix Pf, since Pf is either static (in th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aadb1bf955b9ee9301a7649c4db5636b
https://doi.org/10.1002/qj.1925
https://doi.org/10.1002/qj.1925
Publikováno v:
Computers & Fluids. 46:493-497
In numerical weather prediction (NWP) data assimilation (DA) methods are used to combine available observations with numerical model estimates. This is done by minimising measures of error on both observations and model estimates with more weight giv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::32effbe9915b84f67bad12472544eb45
https://doi.org/10.1016/j.compfluid.2011.01.033
https://doi.org/10.1016/j.compfluid.2011.01.033