Integration of Machine Learning Methods into Lagrangian Particle Models of Atmospheric Transport

Туркменова, Р.Т.

Рақамли технологияларнинг назарий ва амалий масалалари · 2025-yil

Annotatsiya

This review systematizes how machine learning (ML) can be integrated into Lagrangian particle dispersion models (LPDM: FLEXPART, HYSPLIT, STILT, NAME) as a physics-consistent augmentation rather than a replacement of the core dynamics. We highlight four strands: (1) physics-informed surrogates for parameterizations (notably PBL/vertical diffusion and wet removal), (2) variance-reduction and Monte-Carlo acceleration (importance sampling, control variates), (3) bias correction and probabilistic calibration of ensembles (EMOS/quantile mapping) using proper scoring rules (CRPS, reliability), and (4) mass-conserving super-resolution of concentration fields without re-integrating trajectories. Inversions based on footprint matrices (Bayesian, variational, and ensemble approaches) and observation-network requirements are discussed separately. The methodology includes a formalized literature search (2000–2025; WoS/Scopus/Scholar) and reproducibility practices (fixed meteorological drivers, reporting protocols). We argue that robust ML gains depend on two disciplines: preserving physical invariants (mass balance, well-mixed, non-negativity) and methodological rigor (RMSE/correlation/CSI-POFD/CRPS, reliability diagnostics). A “minimal viable” operational pipeline is proposed: fix ERA5/WRF or GDAS/GFS forcings and LPDM configs; add mass-conserving bias correctors and probabilistic calibration; apply a sum-preserving SR module; optionally use PI surrogates for nocturnal PBL regimes, with mandatory validation on real episodes.

Maqola ma’lumotlari
MualliflarТуркменова, Р.Т.
JurnalРақамли технологияларнинг назарий ва амалий масалалари
NashriyotТАТУ Самарқанд филиали
ISSN2181-3086, 2181-3094
Nashr sanasi2025-09-30
Jild8
Son3
Betlar130-141
TilIngliz
Ilmiy sohaTexnika
DOI10.62132/ijdt.v8i3.296
Indeksga qo‘shilgan2026-08-26

DOI: 10.62132/ijdt.v8i3.296 · Maqolaning asl sahifasi

Kalit so‘zlar

лагранжево-частичные модели, FLEXPART, HYSPLIT, STILT, NAME, машинное обучение, супер-разрешение, вероятностная калибровка, CRPS, инверсии источников, воспроизводимость, Lagrangian particle dispersion, FLEXPART, HYSPLIT, STILT, NAME, machine learning, physics-informed, super-resolution, probabilistic calibration, CRPS, source inversions, reproducibility

Ilmiy soha

Texnika

Iqtibos keltirish

APAТуркменова, Р.Т. (2025). Integration of Machine Learning Methods into Lagrangian Particle Models of Atmospheric Transport. Рақамли технологияларнинг назарий ва амалий масалалари, 8(3), 130-141. https://doi.org/10.62132/ijdt.v8i3.296
GOST (OAK)Туркменова, Р.Т. Integration of Machine Learning Methods into Lagrangian Particle Models of Atmospheric Transport // Рақамли технологияларнинг назарий ва амалий масалалари. – 2025. – T. 8, № 3. – P. 130-141. – DOI: 10.62132/ijdt.v8i3.296.
MLAТуркменова, Р.Т. “Integration of Machine Learning Methods into Lagrangian Particle Models of Atmospheric Transport.” Рақамли технологияларнинг назарий ва амалий масалалари, 8(3), 130-141, 2025. https://doi.org/10.62132/ijdt.v8i3.296
ChicagoТуркменова, Р.Т. “Integration of Machine Learning Methods into Lagrangian Particle Models of Atmospheric Transport.” Рақамли технологияларнинг назарий ва амалий масалалари, 8(3), 130-141 (2025). https://doi.org/10.62132/ijdt.v8i3.296
HarvardТуркменова, Р.Т (2025) ‘Integration of Machine Learning Methods into Lagrangian Particle Models of Atmospheric Transport’, Рақамли технологияларнинг назарий ва амалий масалалари, 8(3), 130-141. https://doi.org/10.62132/ijdt.v8i3.296
BibTeX
@article{article2025,
  title = {Integration of Machine Learning Methods into Lagrangian Particle Models of Atmospheric Transport},
  author = {Туркменова, Р.Т.},
  journal = {Рақамли технологияларнинг назарий ва амалий масалалари},
  year = {2025},
  volume = {8},
  number = {3},
  pages = {130-141},
  doi = {10.62132/ijdt.v8i3.296},
  url = {https://doi.org/10.62132/ijdt.v8i3.296}
}

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