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Deep learning models improve imputation of missing tropical cyclone wind data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed deep learning models, including 1DCNNs and LSTMs, to impute missing Radius of Maximum Winds (Rmax) values in tropical cyclone best-track data. The study found that incorporating the radius of 34-knot winds (R34) significantly improved model performance. Temporal models, despite using fewer samples, showed better preservation of Rmax variability, especially when R34 was unavailable, indicating that temporal information can partially compensate for missing storm-size predictors. Transfer learning did not yield improvements, likely due to distributional differences between synthetic and observational datasets. AI

IMPACT Enhances the accuracy of climate hazard assessments by improving the completeness of tropical cyclone data.

RANK_REASON The cluster contains an academic paper detailing a new research methodology using deep learning for data imputation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning models improve imputation of missing tropical cyclone wind data

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The cluster contains an academic paper detailing a new research methodology using deep learning for data imputation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Swastik Agrawal, Nishkal Hundia, Ziyue Liu, Michelle Bensi ·

    Deep Learning Imputation of Missing Radius of Maximum Winds (Rmax) Values in Tropical Cyclone Best-Track Data

    arXiv:2608.09683v1 Announce Type: new Abstract: Probabilistic coastal hazard assessments require accurate characterization of tropical cyclone (TC) parameters, yet datasets often contain missing records for the radius of maximum winds (Rmax), a key variable in Joint Probability M…

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