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.
Peer Review Publications and Books
7 total publications.
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. 2014;14(22).
Other Publications
2018
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Global sea-level budget 1993–present. Earth System Science Data [Internet]. 2018;10:1551–1590. Available from: https://www.earth-syst-sci-data.net/10/1551/2018/
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. Downscaling satellite-derived daily precipitation products with an integrated framework. International Journal of Climatology [Internet]. 2018;Available from: https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.5879
2017
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. Seasonal predictability of Kiremt rainfall in coupled general circulation models. Environmental Research Letters [Internet]. 2017;12:114016. Available from: http://stacks.iop.org/1748-9326/12/i=11/a=114016
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/
