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Eksporter 154 resultater:

Filtre: Forfatter er Laurent Bertino [Slett Alle Filtre]

2020

Raj, R.P., mfl. Arctic Sea level Budget Assessment During the GRACE/Argo Time Period. Remote Sensing 125, (2020).

Bocquet, M., Brajard, J., Carrassi, A. & Bertino, L. Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization. Foundations of Data Science 2, (2020). Abstract

Brajard, J., Carrassi, A., Bocquet, M. & Bertino, L. Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model. Journal of Computational Science 44, (2020).

Aalstad, K., Westermann, S. & Bertino, L. Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography. Remote Sensing of Environment 239, (2020).

Chatterjee, S., mfl. The impact of atmospheric and oceanic circulations on the Greenland Sea iceconcentration. The Cryosphere Discussions (2020).doi:https://doi.org/10.5194/tc-2020-127

Bertino, L. & Xie, J. Operational Forecasting of Sea Ice in the Arctic Using TOPAZ system. (2020).

2019

Shah, A. Stochastic data assimilation of observations with a detection limit. 97 (2019).

Raj, R.P., Chatterjee, S., Bertino, L., Turiel, A. & Portabella, M. The Arctic Front and its variability in the Norwegian Sea. Ocean Science 15, (2019).

Shah, A., Bertino, L., Counillon, F., Gharamti, M.E. & Xie, J. Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment. Tellus. Series A, Dynamic meteorology and oceanography 72, (2019). Abstract

Ali, A., mfl. A comparison of Langmuir turbulence parameterizations and key wave effects in a numerical model of the North Atlantic and Arctic Oceans. Ocean Modelling 137, (2019).

Brajard, J., Bocquet, M., Carrassi, A. & Bertino, L. Connections between data assimilation and machine learning to emulate a numerical model. (2019).doi:10.5065/y82j-f154

Bocquet, M., Brajard, J., Carrassi, A. & Bertino, L. Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models. Nonlinear processes in geophysics 26, (2019).

Xie, J., Raj, R.P., Bertino, L., Samuelsen, A. & Wakamatsu, T. Evaluation of Arctic Ocean surface salinities from the Soil Moisture and Ocean Salinity (SMOS) mission against a regional reanalysis and in situ data. Ocean Science 15, (2019).

Blyverket, J., mfl. An Evaluation of the EnKF vs. EnOI and the Assimilation of SMAP, SMOS and ESA CCI Soil Moisture Data over the Contiguous US. Remote Sensing 11, (2019). Abstract

Bertino, L. & Traon, P.-Y.L. From Observation to Information and Users: The Copernicus Marine Service Perspective. Frontiers in Marine Science (2019).doi:10.3389/fmars.2019.00234

Traon, P.-Y.L., mfl. From observation to information and users: The Copernicus Marine Service Perspective. 6:234, (2019).

Fujii, Y., mfl. Observing System Evaluation Based on Ocean Data Assimilation and Prediction Systems: On-Going Challenges and a Future Vision for Designing and Supporting Ocean Observational Networks. Frontiers in Marine Science (2019).doi:10.3389/fmars.2019.00417

Fujii, Y., mfl. Observing system evaluation based on ocean data assimilation and prediction systems: On-going challenges and future vision for designing/supporting ocean observational networks. 6:417, (2019).

Smith, G.C., mfl. Polar ocean observations: A critical gap in the observing system and its effect on environmental predictions from hours to a season. 6:429, (2019).

Smith, G.C., mfl. Polar Ocean Observations: A critical gap in the observing system and its effect on environmental predictions from hours to a season. Frontiers in Marine Science (2019).doi:10.3389/fmars.2019.00429

Moore, A.M., mfl. Synthesis of ocean observations using data assimilation for operational, real-time and reanalysis systems: a more complete picture of the state of the ocean. Frontiers in Marine Science 6, (2019).

2018

Shah, A., Gharamti, M.E. & Bertino, L. Assimilation of semi-qualitative observations with a stochasticensemble Kalman filter. Quarterly Journal of the Royal Meteorological Society 144, (2018). Abstract

Carrassi, A., Bocquet, M., Bertino, L. & Evensen, G. Data assimilation in the geosciences - An overview of methods, issues and perspectives. WIREs Climate Change (2018).doi:doi: 10.1002/wcc.535

Aalstad, K., Westermann, S., Schuler, T., Boike, J. & Bertino, L. Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites. The Cryosphere 12, (2018). Abstract

Xie, J., Counillon, F. & Bertino, L. Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis. The Cryosphere 12, (2018). Abstract

Nansen Environmental and Remote Sensing Center	Nansen-Bangladesh International Centre for Coastal, Ocean and Climate Studies	Nansen International Environmental and Remote Sensing Center
Nansen Environmental Research Center – India	Nansen–Zhu International Research Centre	Nansen-Tutu Centre for Marine Environmental Research