Observation of vertical eddy diffusivity and mixing length during landfalling Super Typhoons

He, Junyi; Chan, P. W.; Li, Q.S.; Li, L.; Zhang, L.; Yang, Haiguang

doi:10.1016/j.jweia.2021.104816

Cited by 3 publications

(2 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is often adopted for air pollution modeling (Tomasi et al, 2019;McNider and Pour-Biazar, 2020), and has served to calculate particle fluxes based on sUAS data (Platis et al, 2016). The method has even been extended to ABL conditions in a tropical cyclone (He et al, 2021) using tower observations and hurricane conditions in the low-level troposphere (Zhang et al, 2010) using aircraft observations.…”

Section: Flux Gradient Methods and Turbulent Exchange Coefficientmentioning

confidence: 99%

Estimating turbulent energy flux vertical profiles from uncrewed aircraft system measurements: Exemplary results for the MOSAiC campaign

Egerer

Cassano

Shupe

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. This study analyzes turbulent energy fluxes in the Arctic atmospheric boundary layer (ABL) using measurements with a small Uncrewed Aircraft System (sUAS). Turbulent fluxes constitute a major part of the atmospheric energy budget and influence the surface heat balance by distributing energy vertically in the atmosphere. However, only few in-situ measurements exist of the vertical profile of turbulent fluxes in the Arctic ABL. The study presents a method to derive turbulent heat fluxes from DataHawk2 sUAS turbulence measurements, based on the flux gradient method with a parameterization of the turbulent exchange coefficient. This parameterization is derived from high-resolution horizontal wind speed measurements in combination with formulations for the turbulent Prandtl number and anisotropy depending on stability. Measurements were taken during the MOSAiC expedition in the Arctic sea ice during the melt season of 2020. For three example cases from this campaign, vertical profiles of turbulence parameters and turbulent heat fluxes are presented and compared to balloon-borne, radar and near-surface measurements. The combination of all measurements draws a consistent picture of ABL conditions and demonstrates the unique potential of the presented method for studying turbulent exchange processes in the vertical ABL profile with sUAS measurements.

show abstract

Section: Flux Gradient Methods and Turbulent Exchange Coefficientmentioning

confidence: 99%

Estimating turbulent energy flux vertical profiles from uncrewed aircraft system measurements: Exemplary results for the MOSAiC campaign

Egerer

Cassano

Shupe

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…It is often adopted for air pollution modeling (Tomasi et al, 2019;McNider and Pour-Biazar, 2020) and has also served to calculate particle fluxes based on sUAS data (Platis et al, 2016). The method has even been extended to ABL conditions in a tropical cyclone (He et al, 2021) using tower observations and to hurricane conditions in the low-level troposphere (Zhang et al, 2010) using aircraft observations. To apply the Hanna (1968) parameterization, some assumptions have to be made.…”

Section: Flux Gradient Methods and Turbulent Exchange Coefficientmentioning

confidence: 99%

Estimating turbulent energy flux vertical profiles from uncrewed aircraft system measurements: exemplary results for the MOSAiC campaign

Egerer¹,

Cassano²,

Shupe³

et al. 2023

Atmos. Meas. Tech.

View full text Add to dashboard Cite

Abstract. This study analyzes turbulent energy fluxes in the Arctic atmospheric boundary layer (ABL) using measurements with a small uncrewed aircraft system (sUAS). Turbulent fluxes constitute a major part of the atmospheric energy budget and influence the surface heat balance by distributing energy vertically in the atmosphere. However, only few in situ measurements of the vertical profile of turbulent fluxes in the Arctic ABL exist. The study presents a method to derive turbulent heat fluxes from DataHawk2 sUAS turbulence measurements, based on the flux gradient method with a parameterization of the turbulent exchange coefficient. This parameterization is derived from high-resolution horizontal wind speed measurements in combination with formulations for the turbulent Prandtl number and anisotropy depending on stability. Measurements were taken during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in the Arctic sea ice during the melt season of 2020. For three example cases from this campaign, vertical profiles of turbulence parameters and turbulent heat fluxes are presented and compared to balloon-borne, radar, and near-surface measurements. The combination of all measurements draws a consistent picture of ABL conditions and demonstrates the unique potential of the presented method for studying turbulent exchange processes in the vertical ABL profile with sUAS measurements.

show abstract

Reconstruction of tropical cyclone boundary layer wind field using physics-informed machine learning

Hu,

2024

Physics of Fluids

View full text Add to dashboard Cite

A physics-informed machine learning model is proposed in this paper to reconstruct the high-fidelity three-dimensional boundary layer wind field of tropical cyclones. The governing equations of the wind field, which incorporate a spatially varying eddy diffusivity coefficient, are derived and embedded within the model's loss function. This integration allows the model to learn the underlying physics of the boundary layer wind field. The model is applied to reconstruct two tropical cyclone events in different oceanic basins. A wide range of observational data from satellite, dropsonde, and Doppler radar records are assimilated into the model. The model's performance is evaluated by comparing its results with observations and a classic linear model. The findings demonstrate that the model's accuracy improves with an increased amount of real data and the introduction of spatially varying eddy diffusivity. Furthermore, the proposed model does not require strict boundary conditions to reconstruct the wind field, offering greater flexibility compared to traditional numerical models. With the assimilation of observational data, the proposed model accurately reconstructs the horizontal, radial, and vertical distributions of the wind field. Compared with the linear model, the proposed model more effectively captures the nonlinearities and asymmetries of the wind field, thus presents more realistic outcomes.

show abstract

Observation of vertical eddy diffusivity and mixing length during landfalling Super Typhoons

Cited by 3 publications

References 73 publications

Estimating turbulent energy flux vertical profiles from uncrewed aircraft system measurements: Exemplary results for the MOSAiC campaign

Estimating turbulent energy flux vertical profiles from uncrewed aircraft system measurements: Exemplary results for the MOSAiC campaign

Estimating turbulent energy flux vertical profiles from uncrewed aircraft system measurements: exemplary results for the MOSAiC campaign

Reconstruction of tropical cyclone boundary layer wind field using physics-informed machine learning

Contact Info

Product

Resources

About