Yiguo Wang
Job Position:
Senior 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
26 total publications.
2023
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Toward Quantifying the Increasing Accessibility of the Arctic Northeast Passage in the Past Four Decades. Advances in Atmospheric Sciences. 2023;
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. Enhancing Seasonal Forecast Skills by Optimally Weighting the Ensemble from Fresh Data. Weather and forecasting. 2023;38(8).
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Better synoptic and subseasonal sea ice thickness predictions are urgently required: a lesson learned from the YOPP data validation. Environmental Research Letters. 2023;18(7).
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. Editorial: Recent advances in climate reanalysis. Frontiers in Climate. 2023;5.
2022
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WMO Global Annual to Decadal Climate Update A Prediction for 2021-25. Bulletin of The American Meteorological Society - (BAMS). 2022;103(4).
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. Benefit of vertical localization for sea surface temperature assimilation in isopycnal coordinate model. Frontiers in Climate. 2022;4.
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. Estimation of Ocean Biogeochemical Parameters in an Earth System Model Using the Dual One Step Ahead Smoother: A Twin Experiment. Frontiers in Marine Science. 2022;9.
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. Reliability of satellite-derived precipitation data in driving hydrological simulations: a case study of the upper Huaihe River basin, China. Journal of Hydrology. 2022;612.
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;35(7).
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. I: 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).
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 [Internett]. 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 [Internett]. 2013;13:269–283. Available from: https://www.atmos-chem-phys.net/13/269/2013/
