Pith. sign in

REVIEW 2 major objections 8 minor 29 references

An extreme value method to study decadal hurricane wind trends

T0 review · 2 major / 8 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper develops a percentile-smoothing method, combining extreme value theory with block-bootstrap resampling, that makes basin-level hurricane wind extremes measurable down to the 99.999th percentile without strong tail assumptions.

desk verdict A solid method paper with an honest internal-consistency argument; the block-bootstrap uncertainty is the main soft spot, but the 99.999th percentile robustness holds without heavy reliance on those variances. read the letter →

arxiv 2505.08591 v1 pith:KAM22CMZ submitted 2025-05-13 physics.ao-ph

Alexandre Payez , Ad Stoffelen , Cees de Valk , Rianne Giesen This is my paper · ORCID
classification physics.ao-ph MSC 62G3262F4062P1286A10
keywords extremevaluetheorywind-speedpercentileshurricanewindstropicalcyclonesASCATscatterometerblockbootstrapdecadaltrendsERA5
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper develops a practical way to estimate extremely rare wind-speed percentiles from satellite scatterometer data, so that hurricane-force winds can be studied statistically without being drowned out by the noise of scarce tail samples. The method smooths the upper tail with extreme value theory: above a high threshold, the 99.99th percentile at basin level, the tail is fitted with an exponential or generalised Pareto distribution, while below the threshold the data speak for themselves. Uncertainty is estimated by block-bootstrap resampling that keeps whole days, one-week clusters, and the seasonal cycle intact, preserving the spatial and temporal dependence of storm winds. Applied to ASCAT-A winds in the Caribbean and both tropical Atlantic basins over 2007-2021, the fitted and empirical-bootstrap percentiles agree down to a probability of exceedance of $10^{-5}$ (the 99.999th percentile) and remain consistent within uncertainties down to $10^{-6}$. This matters because a stable extreme-wind percentile estimator is the prerequisite for asking whether hurricane winds are changing over decades.

What carries the argument

The engine is the extreme-value tail approximation: for a wind-speed distribution $F_X$, the displacement between the quantiles at exceedance probabilities $p$ and $p/\lambda$, divided by a scale factor $a(p)$, converges as $p\to 0$ to the functional form $(\lambda^\gamma-1)/\gamma$ (the generalised Pareto/exponential family). The paper uses this to replace the noisy empirical tail above the 99.99th percentile with a smooth fitted tail, producing percentiles down to exceedance probabilities of $10^{-5}$ and $10^{-6}$. The companion mechanism is a block-bootstrap resampling scheme: whole days are resampled in approximately one-week blocks, with the number of days in each calendar month held fixed, so each synthetic dataset reproduces the spatial and temporal dependence of the original storm winds; fitting each resample separately and averaging over resamples yields both the percentile estimates and their uncertainties. This combination is what lets the paper claim consistency across fitted and non-fitted estimates.

What would settle it

Repeat the entire basin-level analysis with block lengths of two, four, and eight weeks, and with block lengths informed by the autocorrelation of daily basin-maximum winds; if the spread of the resampled percentiles widens substantially and the exponential, generalised Pareto, and raw percentiles no longer agree within those wider uncertainties at the $10^{-5}$ exceedance probability, the central consistency claim fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that basin-level extreme wind percentiles can be smoothed so effectively that they remain trustworthy far beyond the range where empirical percentiles are useful: down to the 99.999th percentile, corresponding to an exceedance probability of $10^{-5}$, and still consistent within uncertainties at $10^{-6}$. The evidence is that in three tropical basins, over ASCAT-A's 2007-2021 record, exponential tail fits, generalised Pareto fits, and raw block-bootstrap empirical percentiles all give nearly the same wind-speed values at these extreme levels, with the 99.99th percentile serving as the threshold. The same agreement holds for two ASCAT spatial resolutions, and the method is insensitive to the choice between fitting the tail or not fitting it at all. The authors frame this as a method paper: they are not claiming to detect a decadal trend, only to provide the reliable extreme-percentile estimates that trend detection will require over a longer, multi-instrument record.

Load-bearing premise

The whole method hangs on the block-bootstrap resampling of whole days in one-week blocks with monthly counts fixed capturing the real dependence structure of extreme winds, so the reported uncertainties are honest.

Editorial extensions

If this is right

  • Basin-level extreme-wind percentiles can be quoted reliably at the 99.999th percentile from a single instrument and a 15-year record, without masking or a strong distributional tail assumption.
  • The same protocol can be run separately on QuikSCAT and ERS scatterometer records and each compared against ERA5, instead of stitching instruments into one time series, which is the paper's proposed route toward decadal trend studies.
  • ERA5 model winds are systematically lower than ASCAT winds at these extreme percentiles, so any model-based trend comparison will need quantile mapping or CDF matching; the paper flags this as the planned next step.
  • The 99.99th percentile emerges as the practical threshold choice: moving it by a factor of ten in exceedance probability leaves the 99.999th percentile estimate stable provided the analysis stays conservative.
  • With a longer overlapping record of ERS, QuikSCAT, ASCAT, and the future SCA instrument, the method should be able to separate decadal trend signals from ENSO-scale variability.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension would pool several years of data and apply the method at sub-basin or pixel scales to see where the robustness breaks down as the tail sample shrinks; the paper deliberately works at basin level for exactly this reason.
  • The agreement between exponential, generalised Pareto, and raw bootstrap percentiles suggests the smoother is a general tool for sparse tail estimation, so it could transfer to other geophysical extremes such as extreme precipitation or ocean wave heights.
  • If the one-week block length is right, the reported uncertainties imply a minimum detectable trend over the 15-year ASCAT record; the paper does not compute it, but it could be estimated directly from the yearly percentile variances.
  • A quick way to test whether reanalysis input changes contaminate trend signals would be to run the method on a long single-model reanalysis alone: any trend that disappears when only the model is used would point to changing observations rather than changing winds.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 8 minor

Summary. Payez et al. develop a percentile-smoothing method, based on extreme value theory, for estimating extreme marine wind-speed percentiles as a first step toward studying decadal trends in tropical-cyclone winds. Using ASCAT-A Level-3 scatterometer winds (0.125° and 0.25° products) and collocated ERA5 winds for 2007-2021 over the Caribbean and the North and South Atlantic (0-30°), the authors pool all pixels within a basin and year, choose the 99.99th percentile of the pooled sample as the threshold, and estimate higher percentiles from exponential or generalised-Pareto fits of the tail. Uncertainty is obtained from a month-stratified block bootstrap that resamples whole days in blocks of about one week, preserving spatial correlation and seasonality. The fitted percentiles are compared with 'raw' empirical percentiles of the resampled data; the authors report close agreement down to an exceedance probability of 10^-5 (their main result, the 99.999th percentile) and agreement 'within uncertainties' down to 10^-6 (the 99.9999th percentile). The results behave as expected with resolution (012 exceeds 025 exceeds ERA5), and the South Atlantic tail is essentially exponential. No trend statistics are computed; the authors stress that longer multi-instrument records are needed before decadal trend conclusions can be drawn.

Significance. If the robustness claim holds, the paper delivers a practical, well-tested recipe for estimating basin-scale extreme wind percentiles at levels relevant to tropical cyclones, with a dependence-aware uncertainty scheme that can be applied across scatterometer missions. The systematic three-way comparison (exponential, generalised Pareto, raw) over two products, three basins, and fifteen years is a genuine strength, as are the physical sanity checks (resolution ordering, ERA5 lower than observations, exponential tail where no tropical cyclones occur) and the authors' explicit refusal to overclaim trend results. The main risk is the bootstrap uncertainty estimation: the one-week block length may not capture intraseasonal clustering, which would make the reported error bars too small and would directly affect the 10^-6 'within uncertainties' claim. The validation is entirely internal, and no external comparison against independent extreme-wind observations is provided; no code release is mentioned. These points are addressable with targeted additional analyses and rephrasing.

major comments (2)
  1. [Section 6a, Section 8] The one-week bootstrap block length is motivated in Section 6a by the time for a tropical cyclone to cross a basin, which is the timescale of an individual storm rather than the timescale over which extreme-wind activity clusters (successive cyclones in an active phase, MJO or other 10-60-day modulation). A moving block bootstrap preserves autocorrelation only up to the block length; if intraseasonal clustering extends beyond one week, the bootstrap variances of the pooled-basin extreme percentiles will be systematically too small. This is load-bearing because the 'quite consistent within the uncertainties' claim for the 99.9999th percentile in Section 8 (Figure 15), and the error bars in Figures 10-12, rest on these variances. The reported sensitivity of the 99.9999th percentile to the number of resamplings (25-150, about 1 m/s) concerns only the point estimate, not the variance estimate. Please add a sensitivity analysis varying the block length (e.g., 3, 7, 14, and 30 days) or adopt a complementary dependence-robust scheme (stationary bootstrap, subsampling), and report whether the conclusions at 10^-5 and 10^-6 survive.
  2. [Section 7, Section 8] The agreement between the 'fit' and 'raw' estimates is computed from the same block-bootstrap resamples, so it demonstrates internal consistency between the parametric tail models and the empirical distribution of the resampled data, not agreement with an independent extreme-wind reference. The statement that 'the method seems trustworthy at least down to the 99.9999th percentile' (Section 8) and the significance statement therefore go a step beyond the evidence presented. I recommend either adding a targeted external check (e.g., comparing fitted basin-level percentiles with the dropsonde/SFMR-adjusted winds discussed in Section 2c and Table 1) or explicitly stating that the validation is internal and that absolute calibration against an external reference is deferred to follow-on work.
minor comments (8)
  1. [Section 6a] The block-bootstrap procedure is described qualitatively; please specify the exact resampling algorithm (block length in days, number of blocks drawn per resample, overlap handling, and how the month-stratified day counts are reconciled with blocks crossing month boundaries) so that the uncertainty estimates are reproducible.
  2. [Section 8] The assertion that the results are 'quite robust when changing the threshold by a factor 10 up or down in probability of exceedance' is not supported by any figure, table, or quantitative result, in contrast to the other robustness claims in Figures 13-15; please add the supporting results or clearly label this as a preliminary check.
  3. [Section 6b] With 50 bootstrap resamples, the variance estimates themselves carry roughly 20% relative Monte Carlo error for a variance; the reported 25-150 resample check covers only the mean percentile (about 1 m/s), so please also report the stability of the variance estimates or increase the number of resamples used for the error bars in Figures 10-12 and 15.
  4. [Sections 5, 7] The fitted tail parameters (exponential rate; GP scale and shape) and their bootstrap uncertainties are not reported; since the extrapolation from the 10^-4 threshold to 10^-6 is governed by the GP shape parameter, a table or figure of shape-parameter estimates by basin and year would make the extrapolation claim transparent.
  5. [Title] The title advertises 'decadal hurricane wind trends', but no trend statistics are computed or claimed in the paper; a title such as 'An extreme value method for estimating hurricane-force wind percentiles from scatterometer data' would match the content better.
  6. [Section 3] For 2021 the analysis uses MetOp-B ASCAT data for the whole year, following MetOp-A's retirement in November 2021; please clarify what overlap checks (if any) were performed between MetOp-A and MetOp-B to confirm that the change of instrument does not affect the basin-level percentile comparisons.
  7. [Sections 5, 7] The estimation method for the exponential and generalised-Pareto tail fits is not stated; please specify the fitting procedure (e.g., maximum likelihood) and the numerical routine used.
  8. [References] The reference list entry 'Haan, L., and A. Ferreira' should read 'de Haan, L., and A. Ferreira', and the umlauts in 'Künsch' and 'Rootzén' are garbled in the current rendering.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: robustness claims rest on independent estimator agreement and standard EVT mathematics, not on self-citations or fits defined as predictions.

full rationale

The derivation chain is standard extreme-value-theory percentile smoothing. The tail assumption (Eqs. 1-2) is taken from Haan and Ferreira (2006) and Coles (2001), and the exponential/generalised-Pareto fits are applied to exceedances above the chosen 99.99th-percentile threshold. The high percentiles are then read off the fitted tail. The 'raw' comparison values are empirical percentiles of block-bootstrap resamples of the same basin data; they are not transformations of the fitted parameters, so agreement between the fit-based and raw estimates is a substantive consistency check rather than an identity forced by construction. The block-bootstrap uncertainty scheme is an explicit modeling assumption about temporal and spatial dependence; if the one-week block length understates clustering, the error bars would be too small, but that is a correctness risk, not a circular dependency. Self-citations (Giesen and Stoffelen 2022; Marseille et al. 2019) are used only to motivate the percentile-based approach and to contrast with previous masking choices, not to justify the main robustness claim. The mathematical load is carried by independent textbook references and by the data itself. No equation defines a result in terms of the quantity it is claimed to predict, and no fitted parameter is renamed as a prediction. The paper's central claim is self-consistency of three estimators on the same data, which is limited in scope but not circular.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The method relies on standard EVT, the representativeness of basin-level aggregation, and the validity of a block-bootstrap design. Free parameters are the threshold percentile, block length, number of resamples, and fitted tail-distribution parameters, none of which are derived from first principles.

free parameters (4)
  • Tail threshold (N_threshold percentile) = 99.99th percentile
    Chosen as the balance between high enough to select TC winds and low enough to retain data; paper states no automatic way to determine it (Sec. 8).
  • Bootstrap block length = about one week
    Set to the time for a TC to cross a basin (Sec. 6a); used to preserve temporal dependence.
  • Number of bootstrap resamplings = 50
    Used for all results; robustness checked over 25-150 (Sec. 8).
  • Tail distribution parameters (exponential rate or GP scale and shape) = not reported in text, fitted per resample
    Parameters of the fitted tails used to compute extreme percentiles (Fig. 9); the central robustness claim is insensitive to their exact values, but they are fitted quantities.
assumptions (4)
  • standard math The tail of the basin-level wind speed distribution satisfies the EVT regularity condition: normalized high-quantile differences converge to h(lambda) = (lambda^gamma - 1)/gamma (Eqs. 1-2).
    Invoked in Section 4 from Haan and Ferreira (2006).
  • domain assumption Block-bootstrap resampling of whole days and one-week blocks, with monthly counts fixed, produces valid uncertainty estimates for dependent spatial-temporal extremes.
    Assumed in Section 6; not validated against an external standard.
  • domain assumption Bundling all pixels in a basin over a year yields a sample representative of the basin's wind-speed population.
    Stated in Section 5.
  • domain assumption ASCAT-A Level-3 winds are stable and accurate enough for extreme-wind analysis without applying quality masks.
    The authors deliberately include all data, including flagged cells, trusting the dataset (Section 3).

how reviews work

0 comments
Cite this review

Pith. "Pith review of An extreme value method to study decadal hurricane wind trends." pith.science (2026). https://pith.science/paper/KAM22CMZ

@misc{pith2026250508591,
  author       = {Pith},
  title        = {Pith review of: An extreme value method to study decadal hurricane wind trends},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KAM22CMZ}},
  note         = {Machine review of arXiv:2505.08591}
}
read the original abstract

This paper presents a method developed using techniques from extreme value theory to estimate smooth wind-speed percentiles, allowing us to consider more extreme wind speeds while being less sensitive to the noise that stems from the scarcity of extreme data. A reliable characterisation of wind extremes is the first required step for studying decadal trends in tropical-cyclone and extra-tropical-cyclone winds. We develop a percentile-smoothing method using ASCAT-A Level-3 products, focusing on a number of tropical basins (Caribbean and Atlantic), estimate the uncertainty with the block-bootstrap technique to address the issue of dependency, and apply our method to both scatterometer winds (ASCAT-A at two different resolutions) and collocated ERA5 model data. The results obtained are very robust at basin level, without having to rely on a strong assumption for the distribution tail: they are very consistent whether we use exponential fits, generalised-Pareto fits, or even no fit at all, down to at least truly extreme wind percentiles such as 99.999th (main result), and remain quite consistent within uncertainties down to 99.9999th. As ensuring scientifically sound decadal-trend conclusions would require going back sufficiently in time, spanning the lifetimes of different instruments with different characteristics and extreme-wind statistics, a natural follow-on study would be to apply this method not only to ASCAT, but also to its predecessors on QuikSCAT and ERS - comparing each scatterometer individually against ERA5, and also to each other as partial overlaps exist between instruments.

Figures

Figures reproduced from arXiv: 2505.08591 by the authors.

Figure 2
Figure 2. time [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

29 extracted references · 13 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archive author booktitle chapter doi edition editor eid eprint howpublished department institution journal key month note number organization pages publisher school series title type url volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 '...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION if.digit duplicate "0" = swap duplicate "1" = swap duplicate "2" = swap duplicate "3" = swap duplicate "4" = swap duplicate "5" = swap duplicate "6" = swap duplicate "7" = swap duplicate "8" = swap "9" = or or or or or or or or or FUNCTION n.separate 't := "" #0 'numnames := t empty not t #-1 #1 subs...

  3. [3]

    A., and L

    Balkema, A. A., and L. De Haan, 1974: Residual life time at great age. The Annals of probability, 2 (5), 792--804

    work page 1974

  4. [4]

    Springer Series in Statistics, Springer-Verlag, London , doi:10.1007/978-1-4471-3675-0

    Coles, S., 2001: An Introduction to Statistical Modeling of Extreme Values. Springer Series in Statistics, Springer-Verlag, London , doi:10.1007/978-1-4471-3675-0

    doi:10.1007/978-1-4471-3675-0 2001

  5. [5]

    Rahmstorf, 2012: A decade of weather extremes

    Coumou, D., and S. Rahmstorf, 2012: A decade of weather extremes. Nature Climate Change, 2, 491--496, doi:10.1038/nclimate1452

    doi:10.1038/nclimate1452 2012

  6. [6]

    The Annals of Statistics, 7 (1), 1--26, doi:10.1214/aos/1176344552

    Efron, B., 1979: Bootstrap Methods: Another Look at the Jackknife . The Annals of Statistics, 7 (1), 1--26, doi:10.1214/aos/1176344552

    arXiv 1979

  7. [7]

    ESA Strategy Document , European Space Agency

    ESA, 2024: Earth Science in Action for Tomorrow’s World – Earth Observation Science Strategy . ESA Strategy Document , European Space Agency. doi:10.5281/zenodo.13819557

    work page doi:10.5281/zenodo.13819557 2024

  8. [8]

    Stoffelen, 2022: Changes in extreme wind speeds over the global ocean

    Giesen, R., and A. Stoffelen, 2022: Changes in extreme wind speeds over the global ocean. Copernicus Ocean State Report , issue 6 -- Journal of Operational Oceanography , Taylor & Francis, Vol. 15, s8--s15, doi:10.1080/1755876X.2022.2095169

    work page arXiv 2022

Show all 29 references
  1. [9]

    Ferreira, 2006: Extreme value theory: an introduction, Vol

    Haan, L., and A. Ferreira, 2006: Extreme value theory: an introduction, Vol. 3. Springer

    2006

  2. [10]

    Quarterly Journal of the Royal Meteorological Society, 146 (730), 1999--2049, doi:10.1002/qj.3803

    Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146 (730), 1999--2049, doi:10.1002/qj.3803

    2020 doi

  3. [11]

    11: Weather and Climate Extreme Events in a Changing Climate, 1513–1766

    IPCC, 2023: Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change , chap. 11: Weather and Climate Extreme Events in a Changing Climate, 1513–1766. Cambridge University Pres...

    2023 doi

  4. [12]

    D., and Coauthors, 2007: An Introduction to the EUMETSAT Polar system

    Klaes, K. D., and Coauthors, 2007: An Introduction to the EUMETSAT Polar system . Bulletin of the American Meteorological Society, 88 (7), 1085--1096, doi:10.1175/BAMS-88-7-1085

    2007 doi

  5. [13]

    Bulletin of the American Meteorological Society, 100 (10), 1987--2007, doi:10.1175/BAMS-D-18-0189.1

    Knutson, T., and Coauthors, 2019: Tropical cyclones and climate change assessment: Part I : Detection and attribution. Bulletin of the American Meteorological Society, 100 (10), 1987--2007, doi:10.1175/BAMS-D-18-0189.1

    2019 doi

  6. [14]

    Bulletin of the American Meteorological Society, 101 (3), E303--E322, doi:10.1175/BAMS-D-18-0194.1

    Knutson, T., and Coauthors, 2020: Tropical cyclones and climate change assessment: Part II : Projected response to anthropogenic warming. Bulletin of the American Meteorological Society, 101 (3), E303--E322, doi:10.1175/BAMS-D-18-0194.1

    2020 doi

  7. [15]

    R., 1989: The jackknife and the bootstrap for general stationary observations

    K \"u nsch, H. R., 1989: The jackknife and the bootstrap for general stationary observations. The annals of Statistics, 1217--1241

    1989

  8. [16]

    Statistics & Probability Letters, 12 (4), 357--362, doi:https://doi.org/10.1016/0167-7152(91)90107-3

    Leadbetter, M., 1991: On a basis for ‘peaks over threshold’ modeling. Statistics & Probability Letters, 12 (4), 357--362, doi:https://doi.org/10.1016/0167-7152(91)90107-3

    1991 doi

  9. [17]

    R., and H

    Leadbetter, M. R., and H. Rootzen, 1988: Extremal Theory for Stochastic Processes . The Annals of Probability, 16 (2), 431--478, doi:10.1214/aop/1176991767

    1988

  10. [18]

    Lee, T.-C., T. R. Knutson, T. Nakaegawa, M. Ying, and E. J. Cha, 2020: Third assessment on impacts of climate change on tropical cyclones in the typhoon committee region – Part I : Observed changes, detection and attribution. Tropical Cyclone Research and Review, 9 (1), 1--22,...

    2020 doi

  11. [19]

    Stoffelen, H

    Marseille, G.-J., A. Stoffelen, H. van den Brink, and A. Stepek, 2019: WISC bias derivation and uncertainty assessment. C3S Windstorm Information Service -- Copernicus ( WISC ), Final Report C3S\_441\_Lot3\_WISC\_SC2-D3.3-CGI-RP-17-0071 , KNMI, De Bilt

    2019

  12. [20]

    Ruf, 2020: Estimating the true maximum sustained wind speed of a tropical cyclone from spatially averaged observations

    Mayers, D., and C. Ruf, 2020: Estimating the true maximum sustained wind speed of a tropical cyclone from spatially averaged observations. Journal of Applied Meteorology and Climatology, 59 (2), 251--262, doi:10.1175/JAMC-D-19-0177.1

    2020 doi

  13. [21]

    Stoffelen, K

    Ni, W., A. Stoffelen, K. Ren, and X. Yang, 2022: Tropical cyclone intensity estimation from spaceborne microwave scatterometry and parametric wind models. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 4719--4729, doi:10.1109/JSTARS.2022.3180281

    2022

  14. [22]

    Portabella, W

    Polverari, F., M. Portabella, W. Lin, J. W. Sapp, A. Stoffelen, Z. Jelenak, and P. S. Chang, 2022: On high and extreme wind calibration using ASCAT . IEEE Transactions on Geoscience and Remote Sensing , 60, 1--10, doi:10.1109/TGRS.2021.3079898

    2022

  15. [23]

    Rabaneda, A. S., M. Portabella, G. Grieco, F. Polverari, A. Stoffelen, J. Sapp, Z. Jelenak, and P. S. Chang, 2025: Inter-calibration of scatterometers and radiometers under extreme wind conditions using NOAA hurricane hunter flight data, doi:10.2139/ssrn.5118483, preprint

    2025 doi

  16. [24]

    Seneviratne, S. I., and Coauthors, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change, chap. 3: Changes in Climate Extremes and their Impacts on the Natural Physical Env...

    2012 doi

  17. [25]

    Stoffelen, A., M. B. Rivas, and J. Verspeek, 2021: Cone Metrics for C and Ku-Band Scatterometers . 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 1627--1629, doi:10.1109/IGARSS47720.2021.9554778

    2021

  18. [26]

    Final report, EUMETSAT

    Stoffelen, A., and Coauthors, 2020: C-band high and extreme-force speeds ( CHEFS ). Final report, EUMETSAT. ://www.eumetsat.int/CHEFS

    2020

  19. [27]

    Journal of the European Meteorological Society, 2, 100\,011, doi:https://doi.org/10.1016/j.jemets.2025.100011

    Velden, C., and Coauthors, 2025: Recent innovations in satellite-based applications and their impacts on tropical cyclone analyses and forecasts. Journal of the European Meteorological Society, 2, 100\,011, doi:https://doi.org/10.1016/j.jemets.2025.100011

    2025

  20. [28]

    Stoffelen, 2017: Algorithm Theoretical Basis Document for the OSI SAF wind products, version 1.4

    Verhoef, A., and A. Stoffelen, 2017: Algorithm Theoretical Basis Document for the OSI SAF wind products, version 1.4 . SAF/OSI/CDOP2/KNMI/SCI/MA/197 , EUMETSAT OSI SAF. Document available on https://scatterometer.knmi.nl/publications/

    2017

  21. [29]

    Vogelzang, J

    Verhoef, A., J. Vogelzang, J. Verspeek, and A. Stoffelen, 2017: Long-term scatterometer wind climate data records. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10 (5), 2186--2194, doi:10.1109/JSTARS.2016.2615873

    2017

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.