Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"

• Main Author: Sukun Cheng
• Format: Other/Unknown Material
• Language: unknown
• Published: Zenodo 2023
• Subjects: Sea ice
• Online Access: https://doi.org/10.5281/zenodo.7847752

The .zip file contains temporal-spatial averagedmetrics for evaluating simulations against observed ice thickness, concentration, volume, and drift. These quantities are presented in the manuscript"Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020" Subfolders are named by the experiment IDs, including metrics obtained from the relevant experimental results and observations. In case information is missing, do not hesitate to contact [e-mail] We thank Pavel Sakov for helpful discussions and improvement regarding the EnKF-C code and Jiping Xie for contributing the TOPAZ interface to sea ice observations. We are grateful for the support from Timothy Williams and Anton Korosov regarding the environments of neXtSIM and its analysis tools. The work is funded by the DASIM-II grant from ONR (grant nos. N00014-18-1-2493 and N00014-18-1-2204). Alberto Carrassi, Christopher K. R. T. Jones, Ali Aydo ̆gdu, and Pierre Rampal acknowledge the support of the project SASIP funded by Schmidt Futures – a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologies. Sukun Cheng and Laurent Bertino were co-funded by the FOCUS project from the Research Council of Norway (grant no. 301450), and Alberto Carrassi and Yumeng Chen are also supported by the UK National Centre for Earth Observation (grant no. NCEO02004). Computations were carried out on the Norwegian Supercomputing InfrastructureSigma2 (grants nn2993k for computing and NS2993K for data storage)