Quantitative evaluation of turbulence reconstruction algorithms for flux estimation based on the characteristics of atmospheric turbulence
Saved in:
| Title: | Quantitative evaluation of turbulence reconstruction algorithms for flux estimation based on the characteristics of atmospheric turbulence |
|---|---|
| Authors: | Zihan Liu, Hongsheng Zhang, Xuhui Cai, Yu Song |
| Source: | Physics of Fluids. 37 |
| Publisher Information: | AIP Publishing, 2025. |
| Publication Year: | 2025 |
| Description: | This study evaluates turbulence reconstruction algorithms based on the characteristics of atmospheric turbulence using large-eddy simulation (LES). Three idealized boundary-layer regimes (convective, stable, and neutral) under homogeneous surface conditions, together with a near-realistic full-day case incorporating land surface model, large-scale forcing, and nudging modules are examined. The LES is configured under four nested regions, combined with high-resolution data from an observational experiment at Horqin Sandy Land in July 2022, which generates a hybrid dataset validated via spectral analysis and contribution test to preserve resolved turbulence structures while compensating subgrid-scale limitations. From the idealized simulations, single-point eddy covariance flux calculations prove to be consistent with horizontal averages in magnitude with high temporal variability and systematic overestimation due to contamination by non-turbulent motions. These disadvantages are mitigated by the reconstruction algorithms derived from three distinct aspects of turbulence characteristics: the properties of transport excel in stable/neutral conditions but risk overcorrection in convective regimes; fractal dimension methods aggressively isolate turbulence in strongly unstable layers; anisotropy-based approaches demonstrate robustness across stratification types except rare neutral conditions. Full-day simulations confirm diurnal patterns of flux overestimation and algorithm efficacy, with context-dependent selection reducing flux overestimation from 40%–90% to around 20%. These results highlight the potential of turbulence characteristics in turbulence reconstruction and turbulent transport estimation, advocating further exploration concerning the efforts in the parameterization schemes of the atmospheric boundary layer. |
| Document Type: | Article |
| Language: | English |
| ISSN: | 1089-7666 1070-6631 |
| DOI: | 10.1063/5.0279166 |
| Rights: | CC BY |
| Accession Number: | edsair.doi...........b32d28fd9af6e2b358f4a4f9e6d0ce27 |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | This study evaluates turbulence reconstruction algorithms based on the characteristics of atmospheric turbulence using large-eddy simulation (LES). Three idealized boundary-layer regimes (convective, stable, and neutral) under homogeneous surface conditions, together with a near-realistic full-day case incorporating land surface model, large-scale forcing, and nudging modules are examined. The LES is configured under four nested regions, combined with high-resolution data from an observational experiment at Horqin Sandy Land in July 2022, which generates a hybrid dataset validated via spectral analysis and contribution test to preserve resolved turbulence structures while compensating subgrid-scale limitations. From the idealized simulations, single-point eddy covariance flux calculations prove to be consistent with horizontal averages in magnitude with high temporal variability and systematic overestimation due to contamination by non-turbulent motions. These disadvantages are mitigated by the reconstruction algorithms derived from three distinct aspects of turbulence characteristics: the properties of transport excel in stable/neutral conditions but risk overcorrection in convective regimes; fractal dimension methods aggressively isolate turbulence in strongly unstable layers; anisotropy-based approaches demonstrate robustness across stratification types except rare neutral conditions. Full-day simulations confirm diurnal patterns of flux overestimation and algorithm efficacy, with context-dependent selection reducing flux overestimation from 40%–90% to around 20%. These results highlight the potential of turbulence characteristics in turbulence reconstruction and turbulent transport estimation, advocating further exploration concerning the efforts in the parameterization schemes of the atmospheric boundary layer. |
|---|---|
| ISSN: | 10897666 10706631 |
| DOI: | 10.1063/5.0279166 |