{"id":313,"date":"2023-10-06T20:41:16","date_gmt":"2023-10-06T20:41:16","guid":{"rendered":"https:\/\/atmos.ucla.edu\/dkondras\/?page_id=313"},"modified":"2025-03-07T21:43:43","modified_gmt":"2025-03-07T21:43:43","slug":"publications","status":"publish","type":"page","link":"https:\/\/atmos.ucla.edu\/dkondras\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n
<\/a><\/form>
65 entries<\/span> «<\/a> ‹<\/a> 1 of 2 ›<\/a> »<\/a> <\/div><\/div>

2026<\/h3>

Kondrashov, Dmitri; Sudakow, Ivan; Livina, Valerie; Yang, Qingping<\/p>

Accurate and robust real-time prediction of September Arctic sea ice<\/a> Journal Article<\/span> <\/p>

In: <\/span>Chaos: An Interdisciplinary Journal of Nonlinear Science, <\/span>vol. 36, <\/span>no. 2, <\/span>pp. 023110, <\/span>2026<\/span>, ISSN: 1054-1500<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@article{10.1063\/5.0295634,
\r\ntitle = {Accurate and robust real-time prediction of September Arctic sea ice},
\r\nauthor = {Dmitri Kondrashov and Ivan Sudakow and Valerie Livina and Qingping Yang},
\r\nurl = {https:\/\/doi.org\/10.1063\/5.0295634},
\r\ndoi = {10.1063\/5.0295634},
\r\nissn = {1054-1500},
\r\nyear  = {2026},
\r\ndate = {2026-01-01},
\r\njournal = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
\r\nvolume = {36},
\r\nnumber = {2},
\r\npages = {023110},
\r\nabstract = {We describe the real-time forecasting of September 2024 Arctic sea ice extent using a theory-guided machine learning method based on data-adaptive harmonic decomposition and frequency-based nonlinear stochastic modeling, as part of the Sea Ice Outlook. Compared to standard statistical and machine learning models, this method adeptly accounts for non-linear behavior, effectively incorporates memory effects, and handles a wide range of time scale variations, from synoptic (stochastic-like) weather effects to low-frequency (red-noise like) variability, significantly enhancing the accuracy and reliability of sea ice prediction.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
We describe the real-time forecasting of September 2024 Arctic sea ice extent using a theory-guided machine learning method based on data-adaptive harmonic decomposition and frequency-based nonlinear stochastic modeling, as part of the Sea Ice Outlook. Compared to standard statistical and machine learning models, this method adeptly accounts for non-linear behavior, effectively incorporates memory effects, and handles a wide range of time scale variations, from synoptic (stochastic-like) weather effects to low-frequency (red-noise like) variability, significantly enhancing the accuracy and reliability of sea ice prediction.<\/div>
Close<\/a><\/p><\/div>