Trade-Offs in Flux Disaggregation: A Large-Eddy Simulation Study

Sühring, Matthias; Metzger, Stefan; Xu, Ke; Durden, David; Desai, Ankur R.

doi:10.1007/s10546-018-0387-x

Cited by 23 publications

(26 citation statements)

References 48 publications

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“…Virtual measurements in flow simulations are often used to verify and validate model components by comparing model results with in situ measurement data (e.g., Maronga et al, 2014;Heinze et al, 2017;Resler et al, 2017). Vice versa, virtual measurements can help to uncover deficiencies in measurement strategies by imitation and evaluation of different strategies (e.g., Sühring and Raasch, 2013;Maronga, 2014;Sühring et al, 2019). The advantages of numerical simulations are, e.g., that boundary and background conditions are known, and by means of ensemble runs or idealized simulation setups, more reliable statistics can be achieved.…”

Section: Virtual Measurementsmentioning

confidence: 99%

Overview of the PALM model system 6.0

Maronga

Banzhaf²,

Burmeister³

et al. 2020

Geosci. Model Dev.

Self Cite

227

141

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Abstract. In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue.

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Section: Virtual Measurementsmentioning

confidence: 99%

Overview of the PALM model system 6.0

Maronga

Banzhaf²,

Burmeister³

et al. 2020

Geosci. Model Dev.

Self Cite

227

141

View full text Add to dashboard Cite

show abstract

“…Note that the algorithm described above is more elaborate than the fixed-angle track definitions utilized by Schröter et al (2000) and Sühring et al (2019). To reiterate, each track of length L i is required to be periodic with period L i , and individual realizations of a given track length are distributed so as to both maximize and equalize their separation, subject to the aforementioned minimum separations.…”

Section: Track Definitionsmentioning

confidence: 99%

“…Alternatively, the LES fields may serve as a virtual environment within which turbulence may be sampled in a manner consistent with the platform (Schröter et al, 2000;Sühring and Raasch, 2013;Sühring et al, 2019). An attractive feature of this second approach, which is the focus of this paper, is the unique ability to compare the flux estimate obtained from a limited sample with the known "true" domainaveraged value, in contrast to the case for real-world comparisons between inherently disparate aircraft and tower measurements of the area-averaged flux.…”

Section: Introductionmentioning

confidence: 99%

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Sampling error in aircraft flux measurements based on a high-resolution large eddy simulation of the marine boundary layer

Petty

2021

Atmos. Meas. Tech.

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Abstract. A high-resolution (1.25 m) large eddy simulation (LES) of the nocturnal cloud-topped marine boundary layer is used to evaluate random error as a function of continuous track length L for virtual aircraft measurements of turbulent fluxes of sensible heat, latent heat, and horizontal momentum. Results are compared with the widely used formula of Lenschow and Stankov (1986). In support of these comparisons, the relevant integral length scales and correlations are evaluated and documented. It is shown that for heights up to approximately 100 m (z/zi=0.12), the length scales are accurately predicted by empirical expressions of the form If=Azb. The Lenschow and Stankov expression is found to be remarkably accurate at predicting the random error for shorter (7–10 km) flight tracks, but the empirically determined errors decay more rapidly with L than the L-1/2 relationship predicted from theory. Consistent with earlier findings, required track lengths to obtain useful precision increase sharply with altitude. In addition, an examination is undertaken of the role of uncertainties in empirically determined integral length scales and correlations in flux uncertainties as well as of the flux errors associated with crosswind and along-wind flight tracks. It is found that for 7.2 km flight tracks, flux errors are improved by factor of approximately 1.5 to 2 for most variables by making measurements in the crosswind direction.

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“…Vice versa, virtual measurements can help to uncover deficiencies in measurement strategies by imitation and evaluation of different strategies (e.g. Sühring and Raasch, 2013;Maronga, 2014;Sühring et al, 2019). The advantages of numerical simulations are, e.g., that boundary and background conditions are known, and by means of ensemble runs or idealized simulation setups, more realiable statistics can be achieved.…”

Section: Virtual Measurementsmentioning

confidence: 99%

Overview of the PALM model system 6.0

Maronga¹,

Banzhaf²,

Burmeister³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. In this paper we describe the PALM model system 6.0. PALM is a Fortran based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model by components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue.

show abstract

Trade-Offs in Flux Disaggregation: A Large-Eddy Simulation Study

Cited by 23 publications

References 48 publications

Overview of the PALM model system 6.0

Overview of the PALM model system 6.0

Sampling error in aircraft flux measurements based on a high-resolution large eddy simulation of the marine boundary layer

Overview of the PALM model system 6.0

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