Biblio
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Assimilation of sea surface salinities from SMOS in an Arctic coupled ocean and sea ice reanalysis. Ocean Science 19, (2023).
Assimilation of semi-qualitative observations with a stochasticensemble Kalman filter. Quarterly Journal of the Royal Meteorological Society 144, (2018). 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
Asymptotic Forecast Uncertainty and the Unstable Subspace in the Presence of Additive Model Error. SIAM/ASA Journal on Uncertainty Quantification (JUQ) 6, (2018).
Asynchronous data assimilation with the EnKF. Tellus A 62A, (2010). Abstract
Download: Asynchronous data assimilation with the EnKF.pdf (107.25 KB)
Atmospheric boundary layers in storms: advanced theory and modelling applications. Advances in Geosciences 47-49 (2005).
Atmospheric Water Vapor and Cloud Liquid Water Retrieval Over the Arctic Ocean Using Satellite Passive Microwave Sensing. IEEE Transactions on Geoscience and Remote Sensing 48, 283 - 294 (2010).
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization. Foundations of Data Science (FoDS) 2, (2020). Abstract
Benefit of vertical localization for sea surface temperature assimilation in isopycnal coordinate model. Frontiers in Climate 4, (2022).
Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system. The Cryosphere 10, (2016).
Bergen earth system model (BCM-C): model description and regional climate-carbon cycle feedbacks assessment. Geoscientific Model Development 3, (2010). Abstract
Download: gmd-3-123-2010.pdf (3.31 MB)
Bridging observations, theory and numerical simulation of the ocean using machine learning. Environmental Research Letters 16, (2021).
Calculation of the height of stable boundary layers in practical applications. Boundary-layer Meteorology 389-409 (2002).at <http://www.springerlink.com/content/100245/?p=43e84a3972f3443fbe3e13aeb2578da0&pi=0>
Can Environmental Conditions at North Atlantic Deep-Sea Habitats Be Predicted Several Years Ahead? ——Taking Sponge Habitats as an Example. Frontiers in Marine Science 8, (2021).
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
Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error. Nonlinear processes in geophysics 25, (2018).
Characteristics of a convective-scale weather forecasting system for the European Arctic. Monthly Weather Review 145, (2017).
The circlet transform: A robust tool for detecting features with circular shapes. Computers & Geosciences 37, (2011). Abstract
Download: sdarticle-1.pdf (1.86 MB)
Classification of sea ice types in sentinel-1 SAR data using convolutional neural networks. Remote Sensing 12, (2020).
Classification of sea ice types in Sentinel-1 synthetic aperture radar images. The Cryosphere 14, (2020).
CoCoNet: Towards Coast to Coast Networks of Marine Protected Areas (from the shore to the high and deep sea), coupled with Sea-Based Wind Energy Potential. SCIRES-IT SCIentific RESearch and Information Technology 6, (2017).
CoCoNet: Towards Coast to Coast Networks of Marine Protected Areas (from the shore to the high and deep sea), coupled with Sea-Based Wind Energy Potential. SCIRES-IT : SCIentific RESearch and Information Technology 6, (2016).
The cold pool of the Bay of Bengal and its association with the break phase of the Indian summer monsoon. Atmospheric and Oceanic Science Letters 10, (2017). Download: George-AOSL-WarmPool-2017.pdf (1.78 MB)
Collective Damage Growth Controls Fault Orientation in Quasibrittle Compressive Failure. Physical Review Letters 122, (2019).
