Articles | Volume 17, issue 23
https://doi.org/10.5194/gmd-17-8799-2024
https://doi.org/10.5194/gmd-17-8799-2024
Methods for assessment of models
 | 
11 Dec 2024
Methods for assessment of models |  | 11 Dec 2024

Assimilation of snow water equivalent from AMSR2 and IMS satellite data utilizing the local ensemble transform Kalman filter

Joonlee Lee, Myong-In Lee, Sunlae Tak, Eunkyo Seo, and Yong-Keun Lee

Related authors

Implementation of multi-layer snow scheme in seasonal forecast system and its impact on model climatological bias
Eunkyo Seo and Paul A. Dirmeyer
EGUsphere, https://doi.org/10.5194/egusphere-2024-1066,https://doi.org/10.5194/egusphere-2024-1066, 2024
Short summary
Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024,https://doi.org/10.5194/gmd-17-1869-2024, 2024
Short summary
Understanding the diurnal cycle of land–atmosphere interactions from flux site observations
Eunkyo Seo and Paul A. Dirmeyer
Hydrol. Earth Syst. Sci., 26, 5411–5429, https://doi.org/10.5194/hess-26-5411-2022,https://doi.org/10.5194/hess-26-5411-2022, 2022
Short summary
Decadal changes in the leading patterns of sea level pressure in the Arctic and their impacts on the sea ice variability in boreal summer
Nakbin Choi, Kyu-Myong Kim, Young-Kwon Lim, and Myong-In Lee
The Cryosphere, 13, 3007–3021, https://doi.org/10.5194/tc-13-3007-2019,https://doi.org/10.5194/tc-13-3007-2019, 2019
Short summary
Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data
Sanggyun Lee, Hyangsun Han, Jungho Im, Eunna Jang, and Myong-In Lee
Atmos. Meas. Tech., 10, 1859–1874, https://doi.org/10.5194/amt-10-1859-2017,https://doi.org/10.5194/amt-10-1859-2017, 2017
Short summary

Related subject area

Numerical methods
Potential-based thermodynamics with consistent conservative cascade transport for implicit large eddy simulation: PTerodaC3TILES version 1.0
John Thuburn
Geosci. Model Dev., 18, 3331–3357, https://doi.org/10.5194/gmd-18-3331-2025,https://doi.org/10.5194/gmd-18-3331-2025, 2025
Short summary
Positive matrix factorization of large real-time atmospheric mass spectrometry datasets using error-weighted randomized hierarchical alternating least squares
Benjamin C. Sapper, Sean Youn, Daven K. Henze, Manjula Canagaratna, Harald Stark, and Jose L. Jimenez
Geosci. Model Dev., 18, 2891–2919, https://doi.org/10.5194/gmd-18-2891-2025,https://doi.org/10.5194/gmd-18-2891-2025, 2025
Short summary
Numerical simulations of ocean surface waves along the Australian coast with a focus on the Great Barrier Reef
Xianghui Dong, Qingxiang Liu, Stefan Zieger, Alberto Alberello, Ali Abdolali, Jian Sun, Kejian Wu, and Alexander V. Babanin
EGUsphere, https://doi.org/10.5194/egusphere-2025-698,https://doi.org/10.5194/egusphere-2025-698, 2025
Short summary
CLAQC v1.0 – Country Level Air Quality Calculator: an empirical modeling approach
Stefania Renna, Francesco Granella, Lara Aleluia Reis, and Paulina Schulz-Antipa
Geosci. Model Dev., 18, 2373–2408, https://doi.org/10.5194/gmd-18-2373-2025,https://doi.org/10.5194/gmd-18-2373-2025, 2025
Short summary
Hydro-geomorphological modelling of leaky wooden dam efficacy from reach to catchment scale with CAESAR-Lisflood 1.9j
Joshua M. Wolstenholme, Christopher J. Skinner, David Milan, Robert E. Thomas, and Daniel R. Parsons
Geosci. Model Dev., 18, 1395–1411, https://doi.org/10.5194/gmd-18-1395-2025,https://doi.org/10.5194/gmd-18-1395-2025, 2025
Short summary

Cited articles

Allen, R. J. and Zender, C. S.: Forcing of the Arctic Oscillation by Eurasian snow cover, J. Climate, 24, 6528–6539, 2011. 
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. 
Brasnett, B.: A global analysis of snow depth for numerical weather prediction, J. Appl. Meteorol., 38, 726–740, 1999. 
Brown, L. C., Howell, S. E., Mortin, J., and Derksen, C.: Evaluation of the Interactive Multisensor Snow and Ice Mapping System (IMS) for monitoring sea ice phenology, Remote Sens. Environ., 147, 65–78, https://doi.org/10.1016/j.rse.2014.02.012, 2014. 
Download
Short summary
We developed an advanced snow water equivalent (SWE) data assimilation framework using satellite data based on a land surface model. The results of this study highlight the beneficial impact of data assimilation by effectively combining land surface model and satellite-derived data according to their relative uncertainty, thereby controlling not only transitional regions but also the regions with heavy snow accumulation that are difficult to detect by satellite.
Share