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Abstract Here we present a machine learning–based wind reconstruction model. The model reconstructs hurricane surface winds with XGBoost, which is a decision-tree-based ensemble predictive algorithm. The model treats the symmetric and asymmetric wind fields separately. The symmetric wind field is approximated by a parametric wind profile model and two Bessel function series. The asymmetric field, accounting for asymmetries induced by the storm and its ambient environment, is represented using a small number of Laplacian eigenfunctions. The coefficients associated with Bessel functions and eigenfunctions are predicted by XGBoost based on storm and environmental features taken from NHC best-track and ERA-Interim data, respectively. We use HWIND for the observed wind fields. Three parametric wind profile models are tested in the symmetric wind model. The wind reconstruction model’s performance is insensitive to the choice of the profile model because the Bessel function series correct biases of the parametric profiles. The mean square error of the reconstructed surface winds is smaller than the climatological variance, indicating skillful reconstruction. Storm center location, eyewall size, and translation speed play important roles in controlling the magnitude of the leading asymmetries, while the phase of the asymmetries is mainly affected by storm translation direction. Vertical wind shear impacts the asymmetry phase to a lesser degree. Intended applications of this model include assessing hurricane risk using synthetic storm event sets generated by statistical–dynamical downscaling hurricane models.
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Size-refocusing fitting of small-angle X-ray scattering from polydisperse nanoparticles for shape determination
Small-angle X-ray scattering (SAXS) which records reciprocal-space signals with characteristic Bessel-type oscillations is a powerful technique for studying nanoparticles. However, the size polydispersity (or size distribution) of nanoparticles in an ensemble sample smears the oscillational peaks and valleys in the SAXS profile, making it difficult to extract accurate real-space information (e.g.three-dimensional geometry) on the nanoparticles. In this work, a method capable of eliminating the size-distribution-induced smearing effect from SAXS profiles by taking the known size-distribution function into consideration has been developed. The method employs a penalized iterative regression to fit the pair distance distribution function (PDDF) derived from a SAXS profile, recovering the representative PDDF of the nanoparticles. The method has been evaluated with a series of nanoparticle systems of various shapes and size distributions, showing their PDDF profiles to have high fidelity to the reference ideal PDDF profiles. Inverse Fourier transformation of the recovered PDDF profiles gives SAXS profiles presenting the characteristic Bessel-type oscillations, enabling reconstruction of the representative three-dimensional geometry of the nanoparticles. This method will help in the use of SAXS to image synthesized colloidal nanoparticles where size polydispersity is inevitable.
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- Award ID(s):
- 2002960
- PAR ID:
- 10523226
- Publisher / Repository:
- International Union of Crystallography
- Date Published:
- Journal Name:
- Journal of Applied Crystallography
- Volume:
- 56
- Issue:
- 6
- ISSN:
- 1600-5767
- Page Range / eLocation ID:
- 1739 to 1750
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1107/S160057672300910X
