Laurent Bertino

Laurent Bertino
Nansen Environmental and Remote Sensing Center | NERSC · Data Assimilation Group

PhD

About

212
Publications
47,408
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5,983
Citations
Citations since 2016
91 Research Items
4371 Citations
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20162017201820192020202120220200400600800
20162017201820192020202120220200400600800

Publications

Publications (212)
Article
Full-text available
The Copernicus Ocean State Report is an annual publication of the Copernicus Marine Service, established in 2014 by the European Commission for Copernicus 1 and renewed in 2021 for Copernicus 2. The report provides a comprehensive, state-of-the-art, scientific overview on the current conditions, natural variations, and ongoing changes in the global...
Preprint
Full-text available
Advanced data assimilation (DA) methods, widely used in geophysical and climate studies to merge observations with numerical models, can improve the state estimates and consequent forecasts. We interface the deterministic Ensemble Kalman filter (DEnKF) to the Lagrangian sea ice model, neXtSIM. The ensemble is generated by perturbing the atmospheric...
Article
Full-text available
Increasing model resolution can improve the performance of a data assimilation system because it reduces model error, the system can more optimally use high-resolution observations, and with an ensemble data assimilation method the forecast error covariances are improved. However, increasing the resolution scales with a cubical increase of the comp...
Preprint
Full-text available
In the Arctic, the sea surface salinity (SSS) plays a key role in processes related to water mixing and sea ice. However, the lack of salinity observations causes large uncertainties in Arctic Ocean forecasts and reanalysis. Recently the Soil Moisture and Ocean Salinity (SMOS) satellite mission was used by the Barcelona Expert Centre to propose an...
Article
Full-text available
In response to declining sea ice cover, human activity in the Arctic is increasing, with access to the Arctic Ocean becoming more important for socio-economic reasons. Accurate knowledge of sea ice conditions is therefore becoming increasingly important for reducing the risk and operational cost of human activities in the Arctic. Satellite-based se...
Article
Full-text available
During submission of the paper http://dx.doi.org/10.5194/essd-14-307-2022 , an error was introduced in Fig. 6. The plots a and b were swapped.
Article
Full-text available
Measuring salinity from space is challenging since the sensitivity of the brightness temperature (T B) to sea surface salinity (SSS) is low (about 0.5 K/psu), while the SSS range in the open ocean is narrow (about 5 psu, if river discharge areas are not considered). This translates into a high accuracy requirement of the radiometer (about 2-3 K). M...
Article
Precise information on sea ice thickness (SIT) and its prediction at medium-range (2-week) timescale is crucial for the safe maritime navigation in the Arctic Ocean. In this study, we investigate the sensitivity of medium-range prediction skill of summertime SIT distribution in the Arctic marginal seas to atmospheric forecast data, using the 51-mem...
Preprint
Full-text available
Measuring salinity from space is challenging since the sensitivity of the brightness temperature (TB) to sea surface salinity (SSS) is low (about 0.5 K / psu), while the SSS range in the open ocean is narrow (about 5 psu, if river discharge areas are not considered). This translates into a high accuracy requirement of the radiometer (about 2–3 K)....
Preprint
Increasing the resolution of a model can improve the performance of a data assimilation system: first because model field are in better agreement with high resolution observations, then the corrections are better sustained and, with ensemble data assimilation, the forecast error covariances are improved. However, resolution increase is associated w...
Article
Full-text available
The Arctic climate system is rapidly transitioning into a new regime with a reduction in the extent of sea ice, enhanced mixing in the ocean and atmosphere, and thus enhanced coupling within the ocean–ice–atmosphere system; these physical changes are leading to ecosystem changes in the Arctic Ocean. In this review paper, we assess one of the critic...
Preprint
Full-text available
Sea-level variations in coastal areas can differ significantly from those in the nearby open ocean. Monitoring coastal sea-level variations is therefore crucial to understand how climate variability can affect the densely populated coastal regions of the globe. In this paper, we study the sea-level variability along the coast of Norway by means of...
Article
Full-text available
In recent years, machine learning (ML) has been proposed to devise data-driven parametrizations of unresolved processes in dynamical numerical models. In most cases, the ML training leverages high-resolution simulations to provide a dense, noiseless target state. Our goal is to go beyond the use of high-resolution simulations and train ML-based par...
Article
Full-text available
The amount and spatial extent of Greenland Sea (GS) ice are primarily controlled by the sea ice export across the Fram Strait (FS) and by local seasonal sea ice formation, melting, and sea ice dynamics. In this study, using satellite passive microwave sea ice observations, atmospheric and a coupled ocean-sea ice reanalysis system, TOPAZ4, we show t...
Article
Full-text available
Wintertime sea level variability over the northern European continental shelf is largely wind-driven. Using daily gridded sea level anomaly from altimetry, we examine both the spatial and the temporal relationship between northern European sea level variability and large-scale atmospheric circulation patterns as represented by the jet cluster parad...
Article
Full-text available
We study the response of the Lagrangian sea ice model neXtSIM to the uncertainty in sea surface wind and sea ice cohesion. The ice mechanics in neXtSIM are based on a brittle-like rheological framework. The study considers short-term ensemble forecasts of Arctic sea ice from January to April 2008. Ensembles are generated by perturbing the wind inpu...
Preprint
Full-text available
The Arctic climate system is rapidly transitioning into a new regime with a reduction in the extent of sea ice, enhanced mixing in the ocean and atmosphere, and thus enhanced coupling within the ocean-ice-atmosphere system; these physical changes are leading to ecosystem changes in the Arctic Ocean. In this review paper, we assess one of the critic...
Article
From satellite altimetry, it is known the benefit of assimilating sea level anomalies (SLA) has been shown in the context of operational ocean forecast systems. However, how much data assimilation (DA) of altimetry data improves the representation of mesoscale eddies has still not been investigated in previous studies. Especially in the South China...
Preprint
We study the response of the Lagrangian sea ice model neXtSIM to the uncertainty in the sea surface wind and sea ice cohesion. The ice mechanics in neXtSIM is based on a brittle-like rheological framework. The study considers short-term ensemble forecasts of the Arctic sea ice from January to April 2008. Ensembles are generated by perturbing the wi...
Preprint
In recent years, machine learning (ML) has been proposed to devise data-driven parametrisations of unresolved processes in dynamical numerical models. In most cases, the ML training leverages high-resolution simulations to provide a dense, noiseless target state. Our goal is to go beyond the use of high-resolution simulations and train ML-based par...
Article
Full-text available
Sea level change is an important indicator of climate change. Our study focuses on the sea level budget assessment of the Arctic Ocean using: (1) the newly reprocessed satellite altimeter data with major changes in the processing techniques; (2) ocean mass change data derived from GRACE satellite gravimetry; (3) and steric height estimated from gri...
Preprint
Full-text available
The amount and spatial extent of Greenland Sea (GS) sea ice are primarily driven by the sea ice export across the Fram Strait (FS) and by local seasonal sea ice formation, melting and sea ice dynamics. Maximum sea ice concentration (SIC) variability is found in the marginal ice zone and ‘Odden’ region in the central GS. In this study, using satelli...
Article
A novel method, based on the combination of data assimilation and machine learning is introduced. The new hybrid approach is designed for a two-fold scope: (i) emulating hidden, possibly chaotic, dynamics and (ii) predicting their future states. The method consists in applying iteratively a data assimilation step, here an ensemble Kalman filter, an...
Conference Paper
Full-text available
The warming of the Arctic air during summer is an increasingly frequent phenomenon. With surface air temperature exceeding more than 10ºC the mean of the last 40 years, and weekly average temperature values close to this peak value, the melt runoff in Greenland has dramatically increased. This phenomenon has reached its maximum expression in 2012 a...
Preprint
Full-text available
The reconstruction from observations of high-dimensional chaotic dynamics such as geophysical flows is hampered by (i) the partial and noisy observations that can realistically be obtained, (ii) the need to learn from long time series of data, and (iii) the unstable nature of the dynamics. To achieve such inference from the observations over long t...
Article
Full-text available
The seasonal snow-cover is one of the most rapidly varying natural surface features on Earth. It strongly modulates the terrestrial water, energy, and carbon balance. Fractional snow-covered area (fSCA) is an essential snow variable that can be retrieved from multispectral satellite imagery. In this study, we evaluate fSCA retrievals from multiple...
Preprint
A novel method, based on the combination of data assimilation and machine learning is introduced. The new hybrid approach is designed for a two-fold scope: (i) emulating hidden, possibly chaotic, dynamics and (ii) predicting their future states. The method consists in applying iteratively a data assimilation step, here an ensemble Kalman filter, an...
Article
Full-text available
The Arctic Front (AF) in the Norwegian Sea is an important biologically productive region which is well-known for its large feeding schools of pelagic fish. A suite of satellite data, a regional coupled ocean–sea ice data assimilation system (the TOPAZ reanalysis) and atmospheric reanalysis data are used to investigate the variability in the latera...
Poster
Full-text available
The Arctic Ocean contains only a 1% of the world's ocean water, but the rivers that flow out into it account for the 10% of the volume world's rivers freshwater. The upper layer of fresher water facilitates the creation of sea ice and plays an important role in the position of the jet stream and storms over the northern hemisphere [ISBN, 978-82-797...
Article
Full-text available
Recently two gridded sea surface salinity (SSS) products that cover the Arctic Ocean have been derived from the European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) mission: one developed by the Barcelona Expert Centre (BEC) and the other developed by the Ocean Salinity Expertise Center of the Centre Aval de Traitement des Données...
Article
Full-text available
There is a growing need for operational oceanographic predictions in both the Arctic and Antarctic polar regions. In the former, this is driven by a declining ice cover accompanied by an increase in maritime traffic and exploitation of marine resources. Oceanographic predictions in the Antarctic are also important, both to support Antarctic operati...
Data
SPRINT presentation was a 4 minutes presentation previous to poster exhibition. IGARSS 2019
Poster
Full-text available
Despite representing only the 1% of the total ocean’s water, the discharge by Arctic rivers accounts for about the 11% of the freshwater flow into the ocean. This huge volume of low density freshwater restricts the mixing between surface and deep ocean layers, because of the induced strong stratification. The accumulation of low salinity water on t...
Article
Full-text available
This paper summarizes recent efforts on Observing System Evaluation (OS-Eval) by the Ocean Data Assimilation and Prediction (ODAP) communities such as GODAE OceanView and CLIVAR-GSOP. It provides some examples of existing OS-Eval methodologies, and attempts to discuss the potential and limitation of the existing approaches. Observing System Experim...
Article
Full-text available
Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques. We will show how the same goal can be directly achieved using data assimilation techniques without leveraging on machine learning software librarie...
Article
Full-text available
The Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic reference information on the physical and biogeochemical ocean and sea-ice state for the global ocean and the European regional seas. CMEMS serves a wide range of users (more than 15,000 users are now registered to the service) and applications. Observation...
Article
Full-text available
The Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic reference information on the physical and biogeochemical ocean and sea-ice state for the global ocean and the European regional seas. CMEMS serves a wide range of users (more than 15,000 users are now registered to the service) and applications. Observation...
Article
Full-text available
A novel method, based on the combination of data assimilation and machine learning is introduced. The new hybrid approach is designed for a two-fold scope: (i) emulating a hidden, possibly chaotic, dynamics and (ii) predicting its future states. The method applies alternatively a data assimilation step, here an ensemble Kalman filter, and a neural...
Preprint
Full-text available
A newly introduced stochastic data assimilation method, the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) is applied to a realistic coupled ice-ocean model of the Arctic, the TOPAZ4 configuration, in a twin experiment framework. The method is shown to add value to range-limited thin ice thickness measurements, as obtained from passive microwave...
Article
Full-text available
Ocean data assimilation is increasingly recognized as crucial for the accuracy of real-time ocean prediction systems and historical re-analyses. The current status of ocean data assimilation in support of the operational demands of analysis, forecasting and reanalysis is reviewed, focusing on methods currently adopted in operational and real-time p...
Article
Full-text available
Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques. We will show how the same goal can be directly achieved using data assimilation techniques without leveraging on machine learning software librarie...
Article
Full-text available
A number of studies have shown that assimilation of satellite derived soil moisture using the ensemble Kalman Filter (EnKF) can improve soil moisture estimates, particularly for the surface zone. However, the EnKF is computationally expensive since an ensemble of model integrations have to be propagated forward in time. Here, assimilating satellite...
Article
Full-text available
Five different parameterizations of Langmuir turbulence (LT) effect are investigated in a realistic model of the North Atlantic and Arctic using realistic wave forcing from a global wave hindcast. The parameterizations mainly apply an enhancement to the turbulence velocity scale, and/or to the entrainment buoyancy flux in the surface boundary layer...
Article
Full-text available
The Arctic Front (AF) in the Norwegian Sea is an important biologically productive region which is well-known for its large feeding schools of pelagic fish. A suite of satellite data, a regional coupled ocean-sea ice data assimilation system (the TOPAZ reanalysis) and atmospheric reanalysis data is used to investigate the variability in the lateral...
Preprint
Full-text available
A number of studies have shown that assimilation of satellite derived soil moisture using the ensemble Kalman Filter (EnKF) can improve soil moisture estimates, particularly for the surface zone. However, the EnKF is computationally expensive since an ensemble of model integrations have to be propagated forward in time. Here, assimilating satellite...
Article
Full-text available
Although the stratification of the upper Arctic Ocean is mostly salinity-driven, the sea surface salinity (SSS) is still poorly known in the Arctic, due to its strong variability and the sparseness of in-situ observations. Recently, two gridded SSS products have been derived from the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (S...
Article
Full-text available
The goal of this paper is to study transport, from a Lagrangian perspective, across selected circulation patterns in the upper Arctic Ocean waters. To this end, we apply the methodology of Lagrangian descriptors, using the function M, to the velocity field dataset provided by the Copernicus Marine Environment Monitoring Service. We focus our analys...
Article
Full-text available
Accurately forecasting the sea-ice thickness (SIT) in the Arctic is a major challenge. The new SIT product (referred to as CS2SMOS) merges measurements from the CryoSat-2 and SMOS satellites on a weekly basis during the winter. The impact of assimilating CS2SMOS data is tested for the TOPAZ4 system – the Arctic component of the Copernicus Marine En...
Article
Atlantic Water (AW) transported from the Nordic Seas is the major source of oceanic heat to the Arctic Ocean. Based on results from the TOPAZ reanalysis, a regional coupled ice-ocean data assimilation system, we show that interannual variability of AW temperature in the Fram Strait (FS) is associated with the strength of the Greenland Sea gyre (GSG...