Biblio
Filters: Author is Counillon, Francois [Clear All Filters]
Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model. Tellus A: Dynamic Meteorology and Oceanography 70:1435945, (2018).
Monitoring the spreading of the Amazon freshwater plume by MODIS, SMOS, Aquarius, and TOPAZ. Journal of Geophysical Research (JGR): Oceans 120, (2015).
Causes of the large warm bias in the Angola–Benguela Frontal Zone in the Norwegian Earth System Model. Climate Dynamics (2017).doi:10.1007/s00382-017-3896-2
Impact of snow initialization in subseasonal-to-seasonal winter forecasts with the Norwegian Climate Prediction Model. Journal of Geophysical Research (JGR): Atmospheres 124, (2019). Abstract
An assessment of the added value from data assimilation on modelled Nordic Seas hydrography and ocean transports. Ocean Modelling 99, (2016).
Sensitivity of the Arctic and North Atlantic Oceans to the Bering Strait inflow: A modeling study. NERSC Technical report no. 317 (2010). Download: BS.pdf (5.05 MB)
Observational needs for improving ocean and coupled reanalysis, S2S Prediction, and decadal prediction. Frontiers in Marine Science 6:391, (2019).
Spaceborne investigation of the long-term variability of primary productivity in the Arctic Basin. The international conference ”Climate Changes in Polar and Subpolar Regions" (2011).
Validation of the 20-year TOPAZ4 Reanalysis. NERSC Technical Report (2014). Download: Renkl_TOPAZ4_reanalysis_validation_final.pdf (39.84 MB)
TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic. Ocean Science 8, (2012). Abstract
Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment. Tellus A: Dynamic Meteorology and Oceanography 72, (2019). Abstract
A comparison of sequential assimilation schemes for ocean prediction with the HYbrid Coordinate Ocean Model (HYCOM): Twin experiments with static forecast error covariances. Ocean Modelling 37, (2011). Abstract
Download: sdarticle-4.pdf (3.72 MB)
Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF. Climate Dynamics 19, (2019).
Optimising assimilation of hydrographic profiles into isopycnal ocean models with ensemble data assimilation. Ocean Modelling 114, (2017).
Alleviating the bias induced by the linear analysis update with an isopycnal ocean model. Quarterly Journal of the Royal Meteorological Society 142, (2016). Abstract
Analysis of the northern South China Sea counter-wind current in winter using a data assimilation model. Ocean Dynamics (2015).doi:10.1007/s10236-015-0817-y
Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system. The Cryosphere 10, (2016).
Impact of assimilating altimeter data on eddy characteristics in the South China Sea. Ocean Modelling 155, (2020).
An eddy resolving tidal-driven model of the South China Sea assimilating along-track SLA data using the EnOI. Ocean Science 7, 609 - 627 (2011).
Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis. The Cryosphere 12, (2018). Abstract
Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991-2013. Ocean Science 13, (2017).
The Climate Model: An ARCPATH Tool to Understand and Predict Climate Change. Nordic Perspectives on the Responsible Development of the Arctic: Pathways to Action (2020).doi:10.1007/978-3-030-52324-4_8
