Yiguo Wang
Job Position:
Researcher Research Group:
E-mail:
Phone:
+47 466 36 012 Academic Degree:
Doctor in Assimilation of lidar observations, Ecole Polytechnique de Paris, 2013
Research interests
Data assimilation, seasonal-to-decadal climate predictions, air quality forecast.
My main interest is to apply advanced data assimialtion methods into prediction systems (e.g. Norwegian Earth System Model) to enhance prediction skill of the systems.
Project leader of the following projects
Peer Review Publications and Books
18 total publications.
2021
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Relating model bias and prediction skill in the equatorial Atlantic. Climate Dynamics. 2021;56(7-8).
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NorCPM1 and its contribution to CMIP6 DCPP. Geoscientific Model Development. 2021;14(11).
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Propagation of Thermohaline Anomalies and their predictive potential along the Atlantic water pathway. Journal of Climate. 2021;
2020
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North Atlantic climate far more predictable than models imply. Nature. 2020;583.
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Basin Interactions and predictability. In: Interacting Climates of Ocean Basins. 2020.
2019
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Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF. Climate Dynamics. 2019;19(9-10).
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Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic. Journal of Advances in Modeling Earth Systems. 2019;11.
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. A downscaling-merging method for high-resolution daily precipitation estimation. Journal of Hydrology. 2019;581.
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The mean state and variability of the North Atlantic circulation: A perspective from ocean reanalyses. Journal of Geophysical Research (JGR): Oceans. 2019;124(12).
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. Impact of snow initialization in subseasonal-to-seasonal winter forecasts with the Norwegian Climate Prediction Model. Journal of Geophysical Research (JGR): Atmospheres. 2019;124(17-18).
2018
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Global sea-level budget 1993–present. Earth System Science Data. 2018;10(3).
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. Downscaling satellite-derived daily precipitation products with an integrated framework. International Journal of Climatology. 2018;
2017
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. Optimising assimilation of hydrographic profiles into isopycnal ocean models with ensemble data assimilation. Ocean Modelling. 2017;114.
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. Seasonal predictability of Kiremt rainfall in coupled general circulation models. Environmental Research Letters. 2017;12(11).
2016
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. Alleviating the bias induced by the linear analysis update with an isopycnal ocean model. Quarterly Journal of the Royal Meteorological Society. 2016;142(695).
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Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian climate prediction model. Tellus. Series A, Dynamic meteorology and oceanography. 2016;68:32437.
2014
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Assimilation of lidar signals: Application to aerosol forecasting in the western Mediterranean basin. Atmospheric Chemistry and Physics (ACP). 2014;14(22).
Other Publications
2014
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. Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign. Atmospheric Chemistry and Physics [Internet]. 2014;14:3511–3532. Available from: https://www.atmos-chem-phys.net/14/3511/2014/
2013
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. Assimilation of ground versus lidar observations for PM$_{10}$ forecasting. Atmospheric Chemistry and Physics [Internet]. 2013;13:269–283. Available from: https://www.atmos-chem-phys.net/13/269/2013/
