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arxiv: 2605.18477 · v1 · pith:2RIP3JABnew · submitted 2026-05-18 · ⚛️ physics.ao-ph

Global kilometre-scale tropical cyclone inner-core vector winds from sparse scalar CYGNSS observations

Xinhai Han , Xiaohui Li , Jingsong Yang , Zeyi Niu , Guoqi Han , Jiuke Wang , Wei Huang , Yunxia Zheng
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Hanyue Ni Yiqi Wang Wei Tao Lotfi Aouf Shaoliang Peng Dake Chen
This is my paper

Pith reviewed 2026-05-20 02:18 UTC · model grok-4.3

classification ⚛️ physics.ao-ph
keywords tropical cycloneCYGNSSvector winddata assimilationdiffusion modelinner coresatellite observationwind field reconstruction
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The pith

Sparse scalar wind speeds from CYGNSS satellites yield full 1.5 km vector fields inside tropical cyclone cores worldwide.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that the complete 10 m vector wind field in tropical cyclone inner cores can be reconstructed at 1.5 km resolution from sparse CYGNSS scalar observations alone. It achieves this by generalizing score-based diffusion assimilation to a nonlinear observation operator and adding three tropical cyclone boundary-layer constraints, while introducing an Observation Coverage Sufficiency criterion to identify reliable cases without external references. Applied across thousands of snapshots in all active basins, the approach reduces maximum wind biases relative to reanalyses and provides an observation-anchored global description. This matters because routine direct vector observations remain limited to a few basins and infrequent aircraft flights, leaving large gaps in intensity forecasting and surge prediction.

Core claim

By generalising score-based diffusion assimilation to a nonlinear observation operator and injecting three TC boundary-layer constraints, the full 10 m vector wind field inside the TC inner core can be reconstructed globally at 1.5 km resolution from sparse CYGNSS scalar observations alone. The authors further propose a CYGNSS-intrinsic Observation Coverage Sufficiency (OCS) criterion that flags reliable reconstructions without external references. When tested on 4,955 snapshots of 249 tropical cyclones from 2020 to 2022, the method reduces systematic Vmax bias against best-track data by roughly 79 percent relative to ERA5 and 75 percent relative to CCMP, with independent radar validation on

What carries the argument

Generalized score-based diffusion assimilation using a nonlinear observation operator together with three tropical cyclone boundary-layer constraints and an Observation Coverage Sufficiency criterion.

If this is right

  • Systematic Vmax bias is reduced by approximately 79 percent against IBTrACS best-track data relative to ERA5.
  • Independent Tail Doppler Radar validation yields 6.9 m/s wind speed RMSE on coverage-sufficient cases.
  • The three physical constraints reduce wind-direction RMSE by 60 percent while preserving speed accuracy.
  • Adding only 11 dropsonde vector observations to CYGNSS data for one storm reduces cross-eye profile RMSE by 42 percent.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method could supply consistent inner-core vector winds in basins that lack routine aircraft reconnaissance.
  • The OCS criterion might be generalized to assess sufficiency for other sparse scalar wind datasets.
  • Joint assimilation with additional sensors such as SAR or scatterometers could produce even higher-resolution fields.

Load-bearing premise

The three TC boundary-layer constraints are accurate and sufficient to resolve wind-direction ambiguity from scalar speed observations without introducing large systematic errors.

What would settle it

Independent high-resolution aircraft or radar wind measurements that reveal wind-direction RMSE substantially above 7.5 m/s or systematic speed biases larger than those reported against IBTrACS would show the reconstructions do not deliver accurate vectors.

Figures

Figures reproduced from arXiv: 2605.18477 by Dake Chen, Guoqi Han, Hanyue Ni, Jingsong Yang, Jiuke Wang, Lotfi Aouf, Shaoliang Peng, Wei Huang, Wei Tao, Xiaohui Li, Xinhai Han, Yiqi Wang, Yunxia Zheng, Zeyi Niu.

Figure 2
Figure 2. Figure 2: Spatial comparison of baseline methods for TC LEE (2023-09-11 12UTC) under OSSE Scenario B (no-eye [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison between QiFeng reconstruction and SAR wind field for TC IAN (2022-09-28 00UTC, [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison between QiFeng reconstruction and SAR wind field for TC HINNAMNOR (2022-08-31 00UTC, [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Threshold sensitivity analysis of the three OCS criteria (January 2020 to September 2022, 4,450 valid cases). [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Vmax scatter plot comparison against IBTrACS best-track Vmax. (a) QiFeng, all cases; (b) QiFeng, OCS￾qualified (coverage adequate) subset; (c) ERA5 reanalysis, all cases; (d) CCMP satellite wind field, all cases. Colors indicate point density; gray dashed lines are 1:1 reference lines; solid lines are linear regression fits. QiFeng exhibits better consistency in high wind speed regimes after OCS screening,… view at source ↗
Figure 7
Figure 7. Figure 7: Scatter plot comparison between QiFeng reconstruction and dropsonde observations (2020–2022). (a) [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Scatter density plot of QiFeng reconstruction vs. TDR corrected 10 m wind field (2020–2022). (a) [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Comparison between QiFeng reconstruction and TDR-corrected 10 m wind field for TC IDA (2021-08-29 [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Multi-source observation fusion case study for TC FIONA (2022-09-18 12UTC, IBTrACS [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
read the original abstract

Tropical cyclone (TC) inner-core surface wind vectors underpin intensity forecasting and storm-surge prediction, yet direct observations remain scarce: routine aircraft reconnaissance is confined to the North Atlantic and Eastern Pacific and, even there, samples each storm only episodically. CYGNSS is the only satellite that penetrates heavy precipitation to measure inner-core surface winds, but delivers directionless scalar wind speeds and is assimilated by no operational analysis system. Here we show that the full 10 m vector wind field inside the TC inner core can be reconstructed globally at 1.5 km resolution from sparse CYGNSS scalar observations alone, by generalising score-based diffusion assimilation to a nonlinear observation operator and injecting three TC boundary-layer constraints; we further propose a CYGNSS-intrinsic Observation Coverage Sufficiency (OCS) criterion that flags reliable reconstructions without external references. Applied to 4,955 snapshots of 249 TCs across all six active basins (2020-2022), the reconstructions reduce systematic Vmax bias against IBTrACS best-track by ~79% and ~75% relative to ERA5 and CCMP. Independent Tail Doppler Radar validation (47 storms) yields a wind speed RMSE of 6.9 m/s on the 23 coverage-sufficient cases (7.5 m/s overall); ablation across the full sample shows that the physical constraints cut wind-direction RMSE by 60% without degrading speed accuracy. The framework further supports joint assimilation of heterogeneous observations: adding only 11 dropsonde vectors to CYGNSS for TC FIONA (2022) reduces the cross-eye profile RMSE by 42%, outlining a practical pathway for fusing CYGNSS with SFMR, SAR and scatterometer data. The result is a globally consistent, observation-anchored kilometre-scale description of TC inner-core vector winds across all six active basins, including those without routine aircraft reconnaissance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript claims that the full 10 m vector wind field inside the TC inner core can be reconstructed globally at 1.5 km resolution from sparse CYGNSS scalar observations alone. This is achieved by generalising score-based diffusion assimilation to a nonlinear observation operator and injecting three TC boundary-layer constraints to resolve direction ambiguity; an Observation Coverage Sufficiency (OCS) criterion is also proposed to flag reliable cases without external references. The method is demonstrated on 4,955 snapshots from 249 TCs (2020-2022) across all basins, yielding ~79% and ~75% reductions in Vmax systematic bias relative to ERA5 and CCMP against IBTrACS, plus radar-validated speed RMSE of 6.9 m/s on coverage-sufficient cases (7.5 m/s overall) and a 60% direction-RMSE reduction in ablation when constraints are included.

Significance. If the central claim is supported, the work would deliver a globally consistent, observation-anchored kilometre-scale vector-wind product for TC inner cores in all six active basins, including those lacking routine aircraft reconnaissance. The reported bias reductions, independent radar validation, and the OCS flagging mechanism represent practical advances for intensity forecasting and storm-surge applications. The framework's extensibility to joint assimilation with dropsondes, SFMR, SAR or scatterometer data is a further strength that could influence operational analysis systems.

major comments (3)
  1. [Methods / assimilation framework] The three TC boundary-layer constraints that resolve wind-direction ambiguity from scalar CYGNSS speeds are load-bearing for the central claim yet receive only high-level description. Their precise functional form, derivation, and regime-specific validity (e.g., in sheared or rapidly intensifying storms) must be stated explicitly, together with quantitative tests showing that they do not introduce coherent directional biases when the underlying TC structure deviates from the training sample.
  2. [Validation section] Independent Tail Doppler Radar validation is reported for 47 storms but only the 23 coverage-sufficient cases are emphasised (RMSE 6.9 m/s versus 7.5 m/s overall). A fuller error breakdown by storm intensity, basin, radial distance, and coverage fraction is required to substantiate the global applicability claim.
  3. [Ablation / results] The ablation study demonstrates a 60% direction-RMSE reduction from the physical constraints, but does not test whether those constraints remain unbiased outside the derivation sample. A targeted experiment on sheared or asymmetric TCs would directly address the risk that direction fields acquire systematic errors while speed RMSE stays low.
minor comments (2)
  1. [Methods] Notation for the nonlinear observation operator and the score-based diffusion update should be introduced with a short equation or pseudocode block to improve readability for readers unfamiliar with the prior diffusion-assimilation literature.
  2. [Figures] Figure captions for reconstructed wind fields should explicitly note the OCS flag value and the reference dataset used for each panel.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive review of our manuscript. We address each of the major comments in detail below and have revised the manuscript to incorporate the suggested improvements where feasible.

read point-by-point responses

  1. Referee: The three TC boundary-layer constraints that resolve wind-direction ambiguity from scalar CYGNSS speeds are load-bearing for the central claim yet receive only high-level description. Their precise functional form, derivation, and regime-specific validity (e.g., in sheared or rapidly intensifying storms) must be stated explicitly, together with quantitative tests showing that they do not introduce coherent directional biases when the underlying TC structure deviates from the training sample.

    Authors: We agree that a more detailed description is necessary. In the revised manuscript, we will expand the Methods section to include the precise mathematical expressions for the three TC boundary-layer constraints, their derivation based on established TC dynamics, and an assessment of their validity across different regimes including sheared and rapidly intensifying storms. Additionally, we will provide quantitative tests on a subset of cases where the TC structure deviates from the main sample to show that no coherent directional biases are introduced. revision: yes

  2. Referee: Independent Tail Doppler Radar validation is reported for 47 storms but only the 23 coverage-sufficient cases are emphasised (RMSE 6.9 m/s versus 7.5 m/s overall). A fuller error breakdown by storm intensity, basin, radial distance, and coverage fraction is required to substantiate the global applicability claim.

    Authors: We acknowledge this point and will enhance the validation section in the revised manuscript. We will include a comprehensive error breakdown, presenting RMSE values stratified by storm intensity (e.g., TS, Cat 1-5), by ocean basin, by radial distance from the TC center, and by CYGNSS coverage fraction. This additional analysis will better support the claims of global applicability. revision: yes

  3. Referee: The ablation study demonstrates a 60% direction-RMSE reduction from the physical constraints, but does not test whether those constraints remain unbiased outside the derivation sample. A targeted experiment on sheared or asymmetric TCs would directly address the risk that direction fields acquire systematic errors while speed RMSE stays low.

    Authors: We concur that further testing on sheared or asymmetric TCs is valuable. We will conduct a targeted ablation experiment on identified sheared and asymmetric cases within our dataset and report the results in the revised paper. This will include checks for systematic directional errors in addition to the speed RMSE to confirm the constraints' robustness. revision: yes

Circularity Check

0 steps flagged

No circularity: derivation uses external constraints and independent validation

full rationale

The paper reconstructs TC inner-core vector winds by generalizing score-based diffusion assimilation to a nonlinear observation operator and adding three boundary-layer constraints, then validates the outputs against external IBTrACS best-track records (showing ~79% and ~75% bias reduction vs ERA5/CCMP) and independent Tail Doppler Radar data (RMSE 6.9 m/s on coverage-sufficient cases). Ablation quantifies the constraints' contribution to direction accuracy but does not redefine outputs in terms of fitted parameters. The proposed OCS criterion is intrinsic to CYGNSS coverage and does not rely on self-citation chains or rename prior results. No load-bearing step reduces by the paper's own equations to a tautological input or self-referential fit; the central claim remains falsifiable against held-out observations.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the validity of generalizing score-based diffusion assimilation to nonlinear operators and on the sufficiency of three TC boundary-layer constraints to resolve direction from scalar speeds. No explicit free parameters or new physical entities are described in the abstract.

axioms (2)
  • domain assumption Score-based diffusion assimilation can be generalized to a nonlinear observation operator for wind vector reconstruction from scalar speeds
    Core of the proposed method as stated in the abstract.
  • domain assumption Three TC boundary-layer constraints are accurate and sufficient to determine wind directions
    Injected into the assimilation to resolve the direction ambiguity.

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Reference graph

Works this paper leans on

71 extracted references · 71 canonical work pages

  1. [1]

    Statistical tropical cyclone wind radii prediction using climatology and persistence.Weather and Forecasting, 22 (4):781–791, 2007

    John A Knaff, Charles R Sampson, Mark DeMaria, Timothy P Marchok, James M Gross, and Colin J McAdie. Statistical tropical cyclone wind radii prediction using climatology and persistence.Weather and Forecasting, 22 (4):781–791, 2007

    work page 2007

  2. [2]

    Further improvements to the Statistical Hurricane Intensity Prediction Scheme (SHIPS).Weather and Forecasting, 20(4):531–543, 2005

    Mark DeMaria, Michelle Mainelli, Lynn K Shay, John A Knaff, and John Kaplan. Further improvements to the Statistical Hurricane Intensity Prediction Scheme (SHIPS).Weather and Forecasting, 20(4):531–543, 2005

    work page 2005

  3. [3]

    A hurricane boundary layer and wind field model for use in engineering applications.Journal of Applied Meteorology and Climatology, 48(2):381–405, 2009

    Peter J Vickery, Dhiraj Wadhera, Mark D Powell, and Yingzhao Chen. A hurricane boundary layer and wind field model for use in engineering applications.Journal of Applied Meteorology and Climatology, 48(2):381–405, 2009

    work page 2009

  4. [4]

    The dynamics of boundary layer jets within the tropical cyclone core

    Jeffrey D Kepert. The dynamics of boundary layer jets within the tropical cyclone core. Part I: Linear theory. Journal of the Atmospheric Sciences, 58(17):2469–2484, 2001

    work page 2001

  5. [5]

    Hurricane sea surface inflow angle and an observation-based parametric model

    Jun A Zhang and Eric W Uhlhorn. Hurricane sea surface inflow angle and an observation-based parametric model. Monthly Weather Review, 140(11):3587–3605, 2012

    work page 2012

  6. [6]

    Multi-station water level forecasting using advanced graph convolutional networks with adversarial learning

    Xinhai Han, Xiaohui Li, Jingsong Yang, Jiuke Wang, Guoqi Han, Jun Ding, Hui Shen, Jun Yan, and Dake Chen. Multi-station water level forecasting using advanced graph convolutional networks with adversarial learning. Geo-spatial Information Science, 28(6):3258–3276, 2025

    work page 2025

  7. [7]

    Enhanced offshore wind speed forecasts along the US East Coast: A deep learning framework leveraging NDBC buoy data.Ocean-Land-Atmosphere Research, 2:0031, 2023

    Xinhai Han, Xiaohui Li, Jingsong Yang, Jiuke Wang, Jun Ding, Hui Shen, Jun Yan, He Fang, and Qingmei Xiao. Enhanced offshore wind speed forecasts along the US East Coast: A deep learning framework leveraging NDBC buoy data.Ocean-Land-Atmosphere Research, 2:0031, 2023. 29 A MANUSCRIPT

    work page 2023

  8. [8]

    Weak self-induced cooling of tropical cyclones amid fast sea surface warming.Nature Geoscience, 19:153–158, 2026

    Shoude Guan, Mengya Huang, Wenju Cai, Zhengguang Zhang, I-I Lin, Hyun-Sook Kim, Lei Zhou, Xiaopei Lin, Zhao Xu, Fei-Fei Jin, Wei Mei, Qian Wang, Chun Zhou, Ze Meng, Jiwei Tian, and Wei Zhao. Weak self-induced cooling of tropical cyclones amid fast sea surface warming.Nature Geoscience, 19:153–158, 2026. doi:10.1038/s41561-025-01879-x

    work page doi:10.1038/s41561-025-01879-x 2026

  9. [9]

    The ERA5 global reanalysis.Quarterly Journal of the Royal Meteorological Society, 146(730):1999–2049, 2020

    Hans Hersbach, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, Carole Peubey, Raluca Radu, Dinand Schepers, et al. The ERA5 global reanalysis.Quarterly Journal of the Royal Meteorological Society, 146(730):1999–2049, 2020

    work page 1999

  10. [10]

    Robert Atlas, Ross N Hoffman, Joseph Ardizzone, S Mark Leidner, Juan Carlos Jusem, Deborah K Smith, and Daniel Gombos. A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications.Bulletin of the American Meteorological Society, 92(2):157–174, 2011

    work page 2011

  11. [11]

    A near-real-time version of the cross-calibrated multiplatform (CCMP) ocean surface wind velocity data set.Journal of Geophysical Research: Oceans, 124(10):6997–7010, 2019

    Carl A Mears, Joel Scott, Frank J Wentz, Lucrezia Ricciardulli, S Mark Leidner, Ross Hoffman, and Robert Atlas. A near-real-time version of the cross-calibrated multiplatform (CCMP) ocean surface wind velocity data set.Journal of Geophysical Research: Oceans, 124(10):6997–7010, 2019

    work page 2019

  12. [12]

    Assessing the representation of tropical cyclones in ERA5 with the CNRM tracker.Climate Dynamics, 62:223–238, 2024

    William Dulac, Julien Cattiaux, Fabrice Chauvin, Stella Bourdin, and Sébastien Fromang. Assessing the representation of tropical cyclones in ERA5 with the CNRM tracker.Climate Dynamics, 62:223–238, 2024. doi:10.1007/s00382-023-06902-8

    work page doi:10.1007/s00382-023-06902-8 2024

  13. [13]

    A global ERA5-based tropical cyclone wind field dataset enhanced by integrated parametric correction methods.Scientific Data, 12: 1429, 2025

    Gengbin Liu, Shuailong Jiang, Minglin Zheng, Sen Lin, Yang Kong, and Peng Zhan. A global ERA5-based tropical cyclone wind field dataset enhanced by integrated parametric correction methods.Scientific Data, 12: 1429, 2025. doi:10.1038/s41597-025-05789-w

    work page doi:10.1038/s41597-025-05789-w 2025

  14. [14]

    Assessment of CCMP in capturing high winds with respect to individual satellite datasets.Remote Sensing, 16(22):4215, 2024

    Pingping Rong and Hui Su. Assessment of CCMP in capturing high winds with respect to individual satellite datasets.Remote Sensing, 16(22):4215, 2024. doi:10.3390/rs16224215

    work page doi:10.3390/rs16224215 2024

  15. [15]

    GPS dropwindsonde wind profiles in hurricanes and their operational implications.Weather and Forecasting, 18(1):32–44, 2003

    James L Franklin, Michael L Black, and Krystal Valde. GPS dropwindsonde wind profiles in hurricanes and their operational implications.Weather and Forecasting, 18(1):32–44, 2003

    work page 2003

  16. [16]

    The NCAR GPS dropwindsonde.Bulletin of the American Meteorological Society, 80(3):407–420, 1999

    Terry F Hock and James L Franklin. The NCAR GPS dropwindsonde.Bulletin of the American Meteorological Society, 80(3):407–420, 1999. doi:10.1175/1520-0477(1999)080<0407:TNGD>2.0.CO;2

    work page doi:10.1175/1520-0477(1999)080 1999

  17. [17]

    Thirty years of tropical cyclone research with the NOAA P-3 aircraft.Bulletin of the American Meteorological Society, 87(8):1039–1056, 2006

    Sim D Aberson, Michael L Black, Robert A Black, Joseph J Cione, Christopher W Landsea, Frank D Marks, and Robert W Burpee. Thirty years of tropical cyclone research with the NOAA P-3 aircraft.Bulletin of the American Meteorological Society, 87(8):1039–1056, 2006

    work page 2006

  18. [18]

    Dual-aircraft investigation of the inner core of Hurricane Norbert

    Frank D Marks, Robert A Houze, and John F Gamache. Dual-aircraft investigation of the inner core of Hurricane Norbert. Part I: Kinematic structure.Journal of the Atmospheric Sciences, 49(11):919–942, 1992. doi:10.1175/1520-0469(1992)049<0919:DAIOTI>2.0.CO;2

    work page doi:10.1175/1520-0469(1992)049 1992

  19. [19]

    Environmental flow impacts on tropical cyclone struc- ture diagnosed from airborne Doppler radar composites.Monthly Weather Review, 141(9):2949–2969, 2013

    Paul D Reasor, Robert F Rogers, and Sylvie Lorsolo. Environmental flow impacts on tropical cyclone struc- ture diagnosed from airborne Doppler radar composites.Monthly Weather Review, 141(9):2949–2969, 2013. doi:10.1175/MWR-D-12-00334.1

    work page doi:10.1175/mwr-d-12-00334.1 2013

  20. [20]

    Hurricane surface wind measurements from an operational stepped frequency microwave radiometer.Monthly Weather Review, 135(9):3070–3085, 2007

    Eric W Uhlhorn, Peter G Black, James L Franklin, Mark Goodberlet, Jim Carswell, and Albin S Goldstein. Hurricane surface wind measurements from an operational stepped frequency microwave radiometer.Monthly Weather Review, 135(9):3070–3085, 2007

    work page 2007

  21. [21]

    Julio Figa-Saldaña, John J W Wilson, Evert Attema, Richard Gelsthorpe, Mark R Drinkwater, and Ad Stoffelen. The advanced scatterometer (ASCAT) on the meteorological operational (MetOp) platform: A follow on for European wind scatterometers.Canadian Journal of Remote Sensing, 28(3):404–412, 2002

    work page 2002

  22. [22]

    An introduction to the near-real-time QuikSCAT data.Weather and Forecasting, 20(4):476–493, 2005

    Ross N Hoffman and S Mark Leidner. An introduction to the near-real-time QuikSCAT data.Weather and Forecasting, 20(4):476–493, 2005. doi:10.1175/W AF841.1

    work page doi:10.1175/w 2005

  23. [23]

    Impact of rain on spaceborne Ku-band wind scatterometer data.IEEE Transactions on Geoscience and Remote Sensing, 40(9):1973–1983, 2002

    Bryan W Stiles and Simon H Yueh. Impact of rain on spaceborne Ku-band wind scatterometer data.IEEE Transactions on Geoscience and Remote Sensing, 40(9):1973–1983, 2002. doi:10.1109/TGRS.2002.803846

    work page doi:10.1109/tgrs.2002.803846 1973

  24. [24]

    Combined co- and cross-polarized SAR measurements under extreme wind conditions.IEEE Transactions on Geoscience and Remote Sensing, 55(12): 6746–6755, 2017

    Alexis A Mouche, Bertrand Chapron, Biao Zhang, and Romain Husson. Combined co- and cross-polarized SAR measurements under extreme wind conditions.IEEE Transactions on Geoscience and Remote Sensing, 55(12): 6746–6755, 2017

    work page 2017

  25. [25]

    Tropical cyclone morphology from spaceborne synthetic aperture radar.Bulletin of the American Meteorological Society, 94(2):215–230, 2013

    Xiaofeng Li, Jun A Zhang, Xiaofeng Yang, William G Pichel, Mark DeMaria, David Long, and Ziwei Li. Tropical cyclone morphology from spaceborne synthetic aperture radar.Bulletin of the American Meteorological Society, 94(2):215–230, 2013. doi:10.1175/BAMS-D-11-00211.1

    work page doi:10.1175/bams-d-11-00211.1 2013

  26. [26]

    V ortex initialization in the NCEP operational hurricane models.Atmosphere, 11(9):968, 2020

    Xiaolin Xu, Weiguo Wang, Lin Zhu, Qingfu Liu, Mingjing Zhang, Ligia R Bernardet, Banglin Zhang, Zhan Zhang, Bin Liu, Vijay Tallapragada, Avichal Mehra, and Samuel Trahan. V ortex initialization in the NCEP operational hurricane models.Atmosphere, 11(9):968, 2020. doi:10.3390/atmos11090968. 30 A MANUSCRIPT

    work page doi:10.3390/atmos11090968 2020

  27. [27]

    Xu Lu, Xuguang Wang, Mingjing Tong, and Vijay Tallapragada. GSI-based, continuously cycled, dual-resolution hybrid ensemble–variational data assimilation system for HWRF: System description and experiments with Edouard (2014).Monthly Weather Review, 145(12):4877–4898, 2017. doi:10.1175/MWR-D-17-0068.1

    work page doi:10.1175/mwr-d-17-0068.1 2014

  28. [28]

    New ocean winds satellite mission to probe hurricanes and tropical convection.Bulletin of the American Meteorological Society, 97(3):385–395, 2016

    Christopher S Ruf, Robert Atlas, Paul S Chang, M Patrick Clarizia, James L Garrison, Scott Gleason, Stephen J Katzberg, Zorana Jelenak, Joel T Johnson, Sharanya J Majumdar, et al. New ocean winds satellite mission to probe hurricanes and tropical convection.Bulletin of the American Meteorological Society, 97(3):385–395, 2016

    work page 2016

  29. [29]

    In-orbit performance of the constellation of CYGNSS hurricane satellites

    Christopher Ruf, Shakeel Asharaf, Rajeswari Balasubramaniam, Scott Gleason, Timothy Lang, Darren McKague, Dorina Twigg, and Duane Waliser. In-orbit performance of the constellation of CYGNSS hurricane satellites. Bulletin of the American Meteorological Society, 100(10):2009–2023, 2019. doi:10.1175/BAMS-D-18-0337.1

    work page doi:10.1175/bams-d-18-0337.1 2009

  30. [30]

    Spaceborne GNSS-R minimum variance wind speed estimator.IEEE Transactions on Geoscience and Remote Sensing, 52(11): 6829–6843, 2014

    Maria Paola Clarizia, Christopher S Ruf, Philip Jales, and Christine Gommenginger. Spaceborne GNSS-R minimum variance wind speed estimator.IEEE Transactions on Geoscience and Remote Sensing, 52(11): 6829–6843, 2014

    work page 2014

  31. [31]

    Mary Morris and Christopher S Ruf. Determining tropical cyclone surface wind speed structure and intensity with the CYGNSS satellite constellation.Journal of Applied Meteorology and Climatology, 56(7):1847–1865, 2017

    work page 2017

  32. [32]

    Assessment of FY-3E GNOS-II GNSS-R global wind product

    Feixiong Huang, Junming Xia, Cong Yin, Xiaochun Zhai, Na Xu, Guanglin Yang, Weihua Bai, Yueqiang Sun, Qifei Du, Xianyi Wang, and Lichang Duan. Assessment of FY-3E GNOS-II GNSS-R global wind product. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15:7899–7912, 2022. doi:10.1109/JSTARS.2022.3205507

    work page doi:10.1109/jstars.2022.3205507 2022

  33. [33]

    Evaluation and deep learning-based calibration of nearshore sea surface wind speeds from FY-3E GNOS-II and Tianmu-1 missions.Geo-spatial Information Science, 28(6):2709–2720, 2025

    Xinhai Han, Xiaohui Li, Jingsong Yang, Wei Tao, Guoqi Han, Jiuke Wang, Yiqi Wang, Qinghua Bao, Lin Chen, and Weiqiang Li. Evaluation and deep learning-based calibration of nearshore sea surface wind speeds from FY-3E GNOS-II and Tianmu-1 missions.Geo-spatial Information Science, 28(6):2709–2720, 2025

    work page 2025

  34. [34]

    Evaluating FY-3E GNOS-II global wind product for nearshore and open ocean: A study utilizing NDBC and TAO/TRITON buoy data

    Xinhai Han, Xiaohui Li, Jingsong Yang, Wei Tao, and Yiqi Wang. Evaluating FY-3E GNOS-II global wind product for nearshore and open ocean: A study utilizing NDBC and TAO/TRITON buoy data. InProceedings of IGARSS 2024: IEEE International Geoscience and Remote Sensing Symposium, pages 6372–6375. IEEE, 2024

    work page 2024

  35. [35]

    Feixiong Huang, Cong Yin, Yan Liu, Yueqiang Sun, Junming Xia, Weihua Bai, Qi Tang, Xianyi Wang, Qifei Du, Yuerong Cai, Zhuoyan Wang, Cheng Liu, Hao Zhang, Peng Hu, Ruhan Wu, Guangyuan Tan, Hao Qiao, Fu Li, Congliang Liu, Xiangguang Meng, and Xiuqing Hu. Tianmu-1 constellation GNSS-R in-orbit performance: Spatiotemporal characteristics, product application...

    work page doi:10.1109/tgrs.2025.3581343 2025

  36. [36]

    Gap- free GNSS-R wind field reconstruction: A neural mapping scheme and initial validation.Remote Sensing of Environment, 334:115218, 2026

    Hao Du, Ronan Fablet, Thi Thuy Nga Nguyen, Weiqiang Li, Estel Cardellach, and Bertrand Chapron. Gap- free GNSS-R wind field reconstruction: A neural mapping scheme and initial validation.Remote Sensing of Environment, 334:115218, 2026

    work page 2026

  37. [37]

    Weichen Sun, Dongkai Yang, Feng Wang, Xiangchao Ma, and Chuanrui Tan. A physics-constrained network for daily gap-free gridded wind speed product considering high wind speeds from CYGNSS.IEEE Transactions on Geoscience and Remote Sensing, 64:1–22, 2026

    work page 2026

  38. [38]

    Daniel Pascual, Maria Paola Clarizia, and Christopher S. Ruf. Spaceborne demonstration of GNSS-R scattering cross section sensitivity to wind direction.IEEE Geoscience and Remote Sensing Letters, 18(12):2120–2124,

    work page

  39. [39]

    doi:10.1109/LGRS.2021.3066106

    work page doi:10.1109/lgrs.2021.3066106 2021

  40. [40]

    CYGNSS storm-centric tropical cyclone gridded wind speed product.Journal of Applied Meteorology and Climatology, 62(3):329–339, 2023

    David R Mayers, Christopher S Ruf, and April M Warnock. CYGNSS storm-centric tropical cyclone gridded wind speed product.Journal of Applied Meteorology and Climatology, 62(3):329–339, 2023

    work page 2023

  41. [41]

    Characterization of CYGNSS ocean surface wind speed products.Remote Sensing, 16(22):4341, 2024

    Christopher Ruf, Mohammad Al-Khaldi, Shakeel Asharaf, Rajeswari Balasubramaniam, Darren McKague, Daniel Pascual, Anthony Russel, Dorina Twigg, and April Warnock. Characterization of CYGNSS ocean surface wind speed products.Remote Sensing, 16(22):4341, 2024. doi:10.3390/rs16224341

    work page doi:10.3390/rs16224341 2024

  42. [42]

    Impact of CYGNSS-derived winds on tropical cyclone forecasts in a global and regional model.Monthly Weather Review, 149(10):3433–3447, 2021

    Michael J Mueller, Bachir Annane, S Mark Leidner, and Lidia Cucurull. Impact of CYGNSS-derived winds on tropical cyclone forecasts in a global and regional model.Monthly Weather Review, 149(10):3433–3447, 2021. doi:10.1175/MWR-D-21-0094.1

    work page doi:10.1175/mwr-d-21-0094.1 2021

  43. [43]

    Influence of CYGNSS L2 wind data on tropical cyclone analy- sis and forecasts in the coupled HAFS/HYCOM system.Frontiers in Earth Science, 12:1418158, 2024

    Bachir Annane and Lewis J Gramer. Influence of CYGNSS L2 wind data on tropical cyclone analy- sis and forecasts in the coupled HAFS/HYCOM system.Frontiers in Earth Science, 12:1418158, 2024. doi:10.3389/feart.2024.1418158

    work page doi:10.3389/feart.2024.1418158 2024

  44. [44]

    An analytic model of the wind and pressure profiles in hurricanes.Monthly Weather Review, 108 (8):1212–1218, 1980

    Greg J Holland. An analytic model of the wind and pressure profiles in hurricanes.Monthly Weather Review, 108 (8):1212–1218, 1980

    work page 1980

  45. [45]

    Dual-level contextual attention generative adversarial network for reconstructing SAR wind speeds in tropical cyclones.Remote Sensing, 15(9):2454, 2023

    Xinhai Han, Xiaohui Li, Jingsong Yang, Jiuke Wang, Gang Zheng, Lin Ren, Peng Chen, He Fang, and Qingmei Xiao. Dual-level contextual attention generative adversarial network for reconstructing SAR wind speeds in tropical cyclones.Remote Sensing, 15(9):2454, 2023. 31 A MANUSCRIPT

    work page 2023

  46. [46]

    Xiaohui Li, Xinhai Han, Jingsong Yang, Jiuke Wang, and Guoqi Han. Transfer learning-based generative adversarial network model for tropical cyclone wind speed reconstruction from SAR images.IEEE Transactions on Geoscience and Remote Sensing, 62:1–16, 2024

    work page 2024

  47. [47]

    Tropical cyclone multi-level wind-speed structure reconstruction from sparse dropsonde data via adversarial learning.Geophysical Research Letters, 52(16):e2024GL110661, 2025

    Xinhai Han, Xiaohui Li, Jingsong Yang, Jiuke Wang, Guoqi Han, Wei Tao, and Lotfi Aouf. Tropical cyclone multi-level wind-speed structure reconstruction from sparse dropsonde data via adversarial learning.Geophysical Research Letters, 52(16):e2024GL110661, 2025

    work page 2025

  48. [48]

    Realistic tropical cyclone wind and pressure fields can be reconstructed from sparse data using deep learning.Communications Earth & Environment, 5(1):8, 2024

    Ryan Eusebi, Gabriel A Vecchi, Ching-Yao Lai, and Mingjing Tong. Realistic tropical cyclone wind and pressure fields can be reconstructed from sparse data using deep learning.Communications Earth & Environment, 5(1):8, 2024

    work page 2024

  49. [49]

    Physics-guided score-based diffusion for 3d reconstruction of tropical cyclones from sparse observations.npj Climate and Atmospheric Science, 2026

    Xinhai Han, Xiaohui Li, Zeyi Niu, Jingsong Yang, Guoqi Han, Jiuke Wang, Wei Tao, Lotfi Aouf, Shaoliang Peng, and Dake Chen. Physics-guided score-based diffusion for 3d reconstruction of tropical cyclones from sparse observations.npj Climate and Atmospheric Science, 2026

    work page 2026

  50. [50]

    Denoising diffusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. InAdvances in Neural Information Processing Systems, volume 33, pages 6840–6851, 2020

    work page 2020

  51. [51]

    Score- based generative modeling through stochastic differential equations

    Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score- based generative modeling through stochastic differential equations. InInternational Conference on Learning Representations, 2021

    work page 2021

  52. [52]

    Elucidating the design space of diffusion-based generative models

    Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. InAdvances in Neural Information Processing Systems, volume 35, 2022

    work page 2022

  53. [53]

    Score-based data assimilation

    François Rozet and Gilles Louppe. Score-based data assimilation. InAdvances in Neural Information Processing Systems, volume 36, 2023

    work page 2023

  54. [54]

    DiffDA: a diffusion model for weather-scale data assimilation

    Langwen Huang, Lukas Gianinazzi, Yuejiang Yu, Peter D Dueben, and Torsten Hoefler. DiffDA: a diffusion model for weather-scale data assimilation. InProceedings of the 41st International Conference on Machine Learning, volume 235, pages 19798–19815, 2024

    work page 2024

  55. [55]

    A score-based filter for nonlinear data assimilation.Journal of Computational Physics, 514:113207, 2024

    Feng Bao, Zezhong Zhang, and Guannan Zhang. A score-based filter for nonlinear data assimilation.Journal of Computational Physics, 514:113207, 2024

    work page 2024

  56. [56]

    Probabilistic weather forecasting with machine learning.Nature, 637(8044):84–90, 2025

    Ilan Price, Alvaro Sanchez-Gonzalez, Ferran Alet, Tom R Andersson, Andrew El-Kadi, Dominic Masters, Timo Ewalds, Jacklynn Stott, Shakir Mohamed, Peter Battaglia, Remi Lam, and Matthew Willson. Probabilistic weather forecasting with machine learning.Nature, 637(8044):84–90, 2025

    work page 2025

  57. [57]

    Vijay Tallapragada, Chanh Kieu, Samuel Trahan, Qingfu Liu, Weiguo Wang, Zhan Zhang, Mingjing Tong, Banglin Zhang, Ligia Zhu, and Brian Strahl. Forecasting tropical cyclones in the Western North Pacific basin using the NCEP operational HWRF model: Model upgrades and evaluation of real-time performance in 2013.Weather and Forecasting, 31(3):877–894, 2016

    work page 2013

  58. [58]

    Academic Press, Cambridge, 5 edition, 2013

    James R Holton and Gregory J Hakim.An Introduction to Dynamic Meteorology. Academic Press, Cambridge, 5 edition, 2013

    work page 2013

  59. [59]

    The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone data.Bulletin of the American Meteorological Society, 91(3):363–376, 2010

    Kenneth R Knapp, Michael C Kruk, David H Levinson, Howard J Diamond, and Charles J Neumann. The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone data.Bulletin of the American Meteorological Society, 91(3):363–376, 2010

    work page 2010

  60. [60]

    Consistency models

    Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. Consistency models. InProceedings of the 40th International Conference on Machine Learning, pages 32211–32252. PMLR, 2023

    work page 2023

  61. [61]

    Progressive distillation for fast sampling of diffusion models

    Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. InInternational Conference on Learning Representations, 2022

    work page 2022

  62. [62]

    Tropical cyclone dataset for a high- resolution global nonhydrostatic atmospheric simulation.Data in Brief, 48:109135, 2023

    Daisuke Matsuoka, Chihiro Kodama, Yohei Yamada, and Masuo Nakano. Tropical cyclone dataset for a high- resolution global nonhydrostatic atmospheric simulation.Data in Brief, 48:109135, 2023

    work page 2023

  63. [63]

    The asymmetric boundary layer flow under a translating hurricane.Journal of the Atmospheric Sciences, 40(8):1984–1998, 1983

    Lloyd J Shapiro. The asymmetric boundary layer flow under a translating hurricane.Journal of the Atmospheric Sciences, 40(8):1984–1998, 1983. doi:10.1175/1520-0469(1983)040<1984:TABLFU>2.0.CO;2

    work page doi:10.1175/1520-0469(1983)040 1984

  64. [64]

    Asymmetric hurricane boundary layer structure from dropsonde composites in relation to the environmental vertical wind shear.Monthly Weather Review, 141(11):3968–3984, 2013

    Jun A Zhang, Robert F Rogers, Paul D Reasor, Eric W Uhlhorn, and Frank D Marks. Asymmetric hurricane boundary layer structure from dropsonde composites in relation to the environmental vertical wind shear.Monthly Weather Review, 141(11):3968–3984, 2013. doi:10.1175/MWR-D-12-00335.1

    work page doi:10.1175/mwr-d-12-00335.1 2013

  65. [65]

    Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolving simulations.Journal of Computational Physics, 227(7):3486–3514, 2008

    Masaki Satoh, Taroh Matsuno, Hirofumi Tomita, Hiroaki Miura, Tomoe Nasuno, and Shin-ichi Iga. Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolving simulations.Journal of Computational Physics, 227(7):3486–3514, 2008. 32 A MANUSCRIPT

    work page 2008

  66. [66]

    The non-hydrostatic icosahedral atmospheric model: Description and development.Progress in Earth and Planetary Science, 1:18, 2014

    Masaki Satoh, Hirofumi Tomita, Hisashi Yashiro, Hiroaki Miura, Chihiro Kodama, Tatsuya Seiki, Akira T Noda, Yohei Yamada, Daisuke Goto, Masahiro Sawada, et al. The non-hydrostatic icosahedral atmospheric model: Description and development.Progress in Earth and Planetary Science, 1:18, 2014

    work page 2014

  67. [67]

    CYGNSS level 2 wind speed retrieval algorithm theoretical basis document (ATBD), revision 6

    Maria Paola Clarizia, Valery Zavorotny, and Christopher Ruf. CYGNSS level 2 wind speed retrieval algorithm theoretical basis document (ATBD), revision 6. Technical report, University of Michigan, CYGNSS Project Document 148-0138, 2020. Available athttps://podaac.jpl.nasa.gov/dataset/CYGNSS_L2_CDR_V3.2

    work page 2020

  68. [68]

    Observed boundary layer wind structure and balance in the hurricane core

    Jeffrey D Kepert. Observed boundary layer wind structure and balance in the hurricane core. Part I: Hurricane Georges.Journal of the Atmospheric Sciences, 63(9):2169–2193, 2006

    work page 2006

  69. [69]

    Estimating maximum surface winds from hurricane reconnaissance measurements.Weather and Forecasting, 24(3):868–883, 2009

    Mark D Powell, Eric W Uhlhorn, and Jeffrey D Kepert. Estimating maximum surface winds from hurricane reconnaissance measurements.Weather and Forecasting, 24(3):868–883, 2009

    work page 2009

  70. [70]

    A regional artificial intelligence model for skillful typhoon prediction.npj Natural Hazards,

    Zeyi Niu, Wei Huang, Sirong Huang, Zhuo Wang, Mu Mu, Mengqi Yang, Xinhai Han, Haofei Sun, Zhaoyang Huo, and Bo Qin. A regional artificial intelligence model for skillful typhoon prediction.npj Natural Hazards,

    work page

  71. [71]

    doi:10.1038/s44304-026-00219-2. Acknowledgements This work was supported by the National Natural Science Foundation of China (42530402, 42192561), the Special Project–Original Exploration (Grant 42450254), the NSFC–FDCT Grants 62361166662, the National Key R&D Program of China (2023YFC3503400, 2022YFC3400400), the Project of State Key Laboratory of Satell...

    work page doi:10.1038/s44304-026-00219-2