Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Isabelle Mirouze"'
Autor:
Jennifer Waters, Matthew J. Martin, Isabelle Mirouze, Elisabeth Rémy, Robert R. King, Lucile Gaultier, Clement Ubelmann, Craig Donlon, Simon Van Gennip
Publikováno v:
Frontiers in Marine Science, Vol 11 (2024)
Operational forecasts rely on accurate and timely observations and it is important that the ocean forecasting community demonstrates the impact of those observations to the observing community and its funders while providing feedback on requirements
Externí odkaz:
https://doaj.org/article/1552ecc5a5c94e309e7964eea841be95
Publikováno v:
Frontiers in Marine Science, Vol 11 (2024)
Representing and forecasting global ocean velocities is challenging. Velocity observations are scarce and sparse, and are rarely assimilated in a global ocean configuration. Recently, different satellite mission candidates have been proposed to provi
Externí odkaz:
https://doaj.org/article/e2c5ab10fdaf49e382c66e7a9311e7ea
Autor:
Isabelle Mirouze, Sophie Ricci
Publikováno v:
Journal of Open Research Software, Vol 9, Iss 1 (2021)
Smurf is an open source modular system developed in Python for running and cycling data assimilation (DA) systems. It is organised around three super classes for numerical model management, assimilation schemes and observation instruments. Any new it
Externí odkaz:
https://doaj.org/article/db463c27556c49abb14753b531fa590f
Autor:
Florent Gasparin, Stephanie Guinehut, Chongyuan Mao, Isabelle Mirouze, Elisabeth Rémy, Robert R. King, Mathieu Hamon, Rebecca Reid, Andrea Storto, Pierre-Yves Le Traon, Matthew J. Martin, Simona Masina
Publikováno v:
Frontiers in Marine Science, Vol 6 (2019)
A coordinated effort, based on observing system simulation experiments (OSSEs), has been carried out by four European ocean forecasting centers for the first time, in order to provide insights on the present and future design of the in situ Atlantic
Externí odkaz:
https://doaj.org/article/ce696ee9a8874c8b82a7e0b2cd8f3fdb
Autor:
Isabelle Mirouze, Andrea Storto
Publikováno v:
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 71, Iss 1 (2019)
Running ensemble of reanalyses or forecasts has proved successful at improving their performances, despite the cost. Generating ensemble simulations requires generating perturbations within the models, and for the assimilated observations and subsidi
Externí odkaz:
https://doaj.org/article/8fde4f1ba92c49df8c1c052dfbb2f3ca
Publikováno v:
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 68, Iss 0, Pp 1-13 (2016)
Ocean data assimilation systems can take into account time and space scale variations by representing background error covariance functions with more complex shapes than the classical Gaussian function. In particular, the construction of the correlat
Externí odkaz:
https://doaj.org/article/32efb8ecc7444910badf2afbfd3da6c5
Publikováno v:
Quarterly Journal of the Royal Meteorological Society. 146:401-414
Autor:
Eric Jansen, Wang-Hung Tse, Gerasimos Korres, Isabelle Mirouze, Dimitra Denaxa, Sam Pimentel, Andrea Storto
Publikováno v:
Ocean science
15 (2019): 1023–1032. doi:10.5194/os-15-1023-2019
info:cnr-pdr/source/autori:Jansen, Eric; Pimentel, Sam; Tse, Wang-Hung; Denaxa, Dimitra; Korres, Gerasimos; Mirouze, Isabelle; Storto, Andrea/titolo:Using canonical correlation analysis to produce dynamically based and highly efficient statistical observation operators/doi:10.5194%2Fos-15-1023-2019/rivista:Ocean science (Print)/anno:2019/pagina_da:1023/pagina_a:1032/intervallo_pagine:1023–1032/volume:15
Ocean Science, Vol 15, Pp 1023-1032 (2019)
15 (2019): 1023–1032. doi:10.5194/os-15-1023-2019
info:cnr-pdr/source/autori:Jansen, Eric; Pimentel, Sam; Tse, Wang-Hung; Denaxa, Dimitra; Korres, Gerasimos; Mirouze, Isabelle; Storto, Andrea/titolo:Using canonical correlation analysis to produce dynamically based and highly efficient statistical observation operators/doi:10.5194%2Fos-15-1023-2019/rivista:Ocean science (Print)/anno:2019/pagina_da:1023/pagina_a:1032/intervallo_pagine:1023–1032/volume:15
Ocean Science, Vol 15, Pp 1023-1032 (2019)
Observation operators (OOs) are a central component of any data assimilation system. As they project the state variables of a numerical model into the space of the observations, they also provide an ideal opportunity to correct for effects that are n
Autor:
Sophie Ricci, Isabelle Mirouze
Publikováno v:
Journal of Open Research Software, Vol 9, Iss 1 (2021)
Journal of Open Research Software; Vol 9, No 1 (2021); 2
Journal of Open Research Software; Vol 9, No 1 (2021); 2
Smurf is an open source modular system developed in Python for running and cycling data assimilation (DA) systems. It is organised around three super classes for numerical model management, assimilation schemes and observation instruments. Any new it
Publikováno v:
Ocean modelling (Oxf., Print) 128 (2018): 67–86. doi:10.1016/j.ocemod.2018.06.005
info:cnr-pdr/source/autori:Storto, Andrea; Oddo, Paolo; Cipollone, Andrea; Mirouze, Isabelle; Lemieux-Dudon, Benedicte/titolo:Extending an oceanographic variational scheme to allow for affordable hybrid and four-dimensional data assimilation/doi:10.1016%2Fj.ocemod.2018.06.005/rivista:Ocean modelling (Oxf., Print)/anno:2018/pagina_da:67/pagina_a:86/intervallo_pagine:67–86/volume:128
info:cnr-pdr/source/autori:Storto, Andrea; Oddo, Paolo; Cipollone, Andrea; Mirouze, Isabelle; Lemieux-Dudon, Benedicte/titolo:Extending an oceanographic variational scheme to allow for affordable hybrid and four-dimensional data assimilation/doi:10.1016%2Fj.ocemod.2018.06.005/rivista:Ocean modelling (Oxf., Print)/anno:2018/pagina_da:67/pagina_a:86/intervallo_pagine:67–86/volume:128
The traditional formulation of three-dimensional variational (3DVAR) data assimilation schemes for oceanographic applications neglects the temporal evolution of background errors within and across assimilation temporal windows. Such a simplification