scispace - formally typeset
Search or ask a question
Journal ArticleDOI

An approximate analytical model for footprint estimation of scalar fluxes in thermally stratified atmospheric flows

TL;DR: In this article, an approximate analytical model was developed to estimate scalar flux footprint in thermally stratified atmospheric surface layer flows based on a combination of Lagrangian stochastic dispersion model results and dimensional analysis.
About: This article is published in Advances in Water Resources.The article was published on 2000-06-01. It has received 578 citations till now. The article focuses on the topics: Flux footprint & Stratified flow.
Citations
More filters
Journal ArticleDOI
TL;DR: In this article, the authors present a two-part series of recommendations for documentation to be associated with published evapotranspiration (ET) data and provide guidelines for reducing error in ET retrievals.

743 citations


Cites background or methods from "An approximate analytical model for..."

  • ...More modern ‘footprint’ models are routinely employed (Horst and Weil, 1992; Hsieh et al., 1997, 2000; Leclerc et al., 1997; Schmid, 2002) to define the two-dimensional spatial extent of the source areas for heat and vapor that contribute to the particular H or E measurement....

    [...]

  • ...Horst and Weil (1992), Hsieh et al. (1997, 2000), Leclerc et al. (1997), and Schmid (2002) applied Lagrangian stochastic and large eddy simulation (LES) strategies along with Gaussian or non-Gaussian diffusion assumptions to estimate three-dimensional distribution of point source or line source…...

    [...]

  • ...The ‘footprint’ of a flux or scalar measurement represents the upwind surface area that is statistically responsible for the conditioning of the measurement (Hsieh et al., 2000; Foken and Leclerc, 2004)....

    [...]

Journal ArticleDOI
TL;DR: In this paper, a scaling procedure is introduced for flux footprint functions over a range of stratifications from convective to stable, and receptor heights ranging from near the surface to the middle of the boundary layer.
Abstract: Flux footprint functions estimate the location and relative importance of passive scalar sources influencing flux measurements at a given receptor height. These footprint estimates strongly vary in size, depending on receptor height, atmospheric stability, and surface roughness. Reliable footprint calculations from, e.g., Lagrangian stochastic models or large-eddy simulations are computationally expensive and cannot readily be computed for long-term observational programs. To facilitate more accessible footprint estimates, a scaling procedure is introduced for flux footprint functions over a range of stratifications from convective to stable, and receptor heights ranging from near the surface to the middle of the boundary layer. It is shown that, when applying this scaling procedure, footprint estimates collapse to an ensemble of similar curves. A simple parameterisation for the scaled footprint estimates is presented. This parameterisation accounts for the influence of the roughness length on the footprint and allows for a quick but precise algebraic footprint estimation.

674 citations


Cites background or methods or result from "An approximate analytical model for..."

  • ...Footprint estimates of the present parameterisation were compared with corresponding estimates of Hsieh et al. (2000), who also used results of a Lagrangian stochastic dispersion model to derive a footprint parameterisation. Their model differs from LPDM-B in that it is one-dimensional and based on Gaussian turbulence. Unlike in LPDM-B, the skewness of the vertical velocity, the covariance u′w′, as well as the longitudinal turbulence, are not considered in their model. Hsieh et al. (2000) derived a scaling procedure for the streamwise extent of the footprint and, using their model results, obtained a parameterisation for footprint estimates....

    [...]

  • ...Hsieh et al. (2000) derived a scaling procedure for the streamwise extent of the footprint and, using their model results, obtained a parameterisation for footprint estimates....

    [...]

  • ...Footprint estimates of the present parameterisation were compared with corresponding estimates of Hsieh et al. (2000), who also used results of a Lagrangian stochastic dispersion model to derive a footprint parameterisation....

    [...]

  • ...We also thank an anonymous reviewer for drawing our attention to similarities in the approach of Hsieh et al. (2000)....

    [...]

  • ...Existing footprint modelling studies offer the potential for simple parameterisations as, for example, proposed by Horst and Weil (1992, 1994), Weil and Horst (1992), Schmid (1994) or Hsieh et al. (2000)....

    [...]

Journal ArticleDOI
TL;DR: In this article, a two-dimensional footprint model for flux footprint prediction is proposed. But it is not suitable for application to long time series, due to their high computational demands.
Abstract: . Flux footprint models are often used for interpretation of flux tower measurements, to estimate position and size of surface source areas, and the relative contribution of passive scalar sources to measured fluxes. Accurate knowledge of footprints is of crucial importance for any upscaling exercises from single site flux measurements to local or regional scale. Hence, footprint models are ultimately also of considerable importance for improved greenhouse gas budgeting. With increasing numbers of flux towers within large monitoring networks such as FluxNet, ICOS (Integrated Carbon Observation System), NEON (National Ecological Observatory Network), or AmeriFlux, and with increasing temporal range of observations from such towers (of the order of decades) and availability of airborne flux measurements, there has been an increasing demand for reliable footprint estimation. Even though several sophisticated footprint models have been developed in recent years, most are still not suitable for application to long time series, due to their high computational demands. Existing fast footprint models, on the other hand, are based on surface layer theory and hence are of restricted validity for real-case applications. To remedy such shortcomings, we present the two-dimensional parameterisation for Flux Footprint Prediction (FFP), based on a novel scaling approach for the crosswind distribution of the flux footprint and on an improved version of the footprint parameterisation of Kljun et al. (2004b). Compared to the latter, FFP now provides not only the extent but also the width and shape of footprint estimates, and explicit consideration of the effects of the surface roughness length. The footprint parameterisation has been developed and evaluated using simulations of the backward Lagrangian stochastic particle dispersion model LPDM-B (Kljun et al., 2002). Like LPDM-B, the parameterisation is valid for a broad range of boundary layer conditions and measurement heights over the entire planetary boundary layer. Thus, it can provide footprint estimates for a wide range of real-case applications. The new footprint parameterisation requires input that can be easily determined from, for example, flux tower measurements or airborne flux data. FFP can be applied to data of long-term monitoring programmes as well as be used for quick footprint estimates in the field, or for designing new sites.

524 citations

Journal ArticleDOI
TL;DR: The footprint of a turbulent flux measurement defines its spatial context as mentioned in this paper, and the footprint is used to define the spatial context of a flux measurement, as well as its utility and power where warranted.

497 citations


Cites background or methods from "An approximate analytical model for..."

  • ...LS footprint models for flow above short vegetation A footprint model that follows an analogous approach to (18) and (23) was first presented by Leclerc and Thurtell (1990) ....

    [...]

  • ...Hsieh et al. (2000)presented a hybrid approach, combining elements fromCalder’s (1952)analytical solution with results from an LS model....

    [...]

  • ...LS footprint models for flow above short vegetation A footprint model that follows an analogous approach to (18) and (23) was first presented by Leclerc and Thurtell (1990) . Their two-dimensional model considered Lagrangian dispersion in vertical direction by Gaussian turbulence, and advection with the mean flow in streamwise direction. The flow and turbulence profiles were described by standard M–O similarity relations. They presented model results of the crosswind integrated footprint over a range of conditions and demonstrate a strong dependence on measurements height, roughness and stability (in decreasing order of magnitude). The results for neutral stability were compared to the simple analytical model by Schuepp et al. (1990) , who found good qualitative agreement between the two approaches, and quantitative agreement if theSchuepp et al....

    [...]

  • ...LS footprint models for flow above short vegetation A footprint model that follows an analogous approach to (18) and (23) was first presented by Leclerc and Thurtell (1990) . Their two-dimensional model considered Lagrangian dispersion in vertical direction by Gaussian turbulence, and advection with the mean flow in streamwise direction. The flow and turbulence profiles were described by standard M–O similarity relations. They presented model results of the crosswind integrated footprint over a range of conditions and demonstrate a strong dependence on measurements height, roughness and stability (in decreasing order of magnitude). The results for neutral stability were compared to the simple analytical model by Schuepp et al. (1990) , who found good qualitative agreement between the two approaches, and quantitative agreement if theSchuepp et al. (1990) model uses the local wind velocity at measurement height, rather than the layer average velocity (see (7)). Leclerc and Thurtell (1990)also presented various curves of the cumulative footprint as a function of fetch, in analogy to Gash (1986) ....

    [...]

  • ...LS footprint models for flow above short vegetation A footprint model that follows an analogous approach to (18) and (23) was first presented by Leclerc and Thurtell (1990) . Their two-dimensional model considered Lagrangian dispersion in vertical direction by Gaussian turbulence, and advection with the mean flow in streamwise direction. The flow and turbulence profiles were described by standard M–O similarity relations. They presented model results of the crosswind integrated footprint over a range of conditions and demonstrate a strong dependence on measurements height, roughness and stability (in decreasing order of magnitude). The results for neutral stability were compared to the simple analytical model by Schuepp et al. (1990) , who found good qualitative agreement between the two approaches, and quantitative agreement if theSchuepp et al. (1990) model uses the local wind velocity at measurement height, rather than the layer average velocity (see (7))....

    [...]

Journal ArticleDOI
TL;DR: In this paper, an analytical model using the long-range Lagrangian and the Eulerian transport methods is used to approximate the three-dimensional infinite footprint model, which shows that carbon emissions across stages in a supply chain can constitute a significant threat that warrants careful attention in the design phase of supply chains.

463 citations

References
More filters
Book
01 Oct 2007
TL;DR: In this paper, conversion factors and mathematical symbols are used to describe conversion factors in physical and chemical data and Mathematical Symbols are used for converting, converting, and utilising conversion factors.
Abstract: Section 1: Conversion Factors and Mathematical Symbols Section 2: Physical and Chemical Data Section 3: Mathematics Section 4: Thermodynamics Section 5: Heat and Mass Transfer Section 6: Fluid and Plastic Dynamics Section 7: Reaction Kinetics Section 8: Process Control Section 9: Process Economics Section 10: Transport and Storage of Fluids Section 11: Heat-Transfer Equipment Section 12: Psychrometry, Evaporative Cooling, and Solids Drying Section 13: Distillation Section 14: Equipment for Distillation, Gas Absorption, Phase Dispersion, and Phase Separation Section 15: Liquid-Liquid Extraction and Other Liquid-Liquid Operations and Equipment Section 16: Adsorption and Ion Exchange Section 17: Gas-Solid Operations and Equipment Section 18: Liquid-Solid Operations and Equipment Section 19: Reactors Section 20: Alternative Separation Processes Section 21: Solid-Solid Operations and Processing Section 22: Waste Management Section 23: Process Safety Section 24: Energy Resources, Conversion, and Utilization Section 25: Materials of Construction Index

10,028 citations

Book
31 Jul 1988
TL;DR: In this article, the boundary layer is defined as the boundary of a boundary layer, and the spectral gap is used to measure the spectral properties of the boundary layers of a turbulent flow.
Abstract: 1 Mean Boundary Layer Characteristics.- 1.1 A boundary-layer definition.- 1.2 Wind and flow.- 1.3 Turbulent transport.- 1.4 Taylor's hypothesis.- 1.5 Virtual potential temperature.- 1.6 Boundaiy layer depth and structure.- 1.7 Micrometeorology.- 1.8 Significance of the boundary layer.- 1.9 General references.- 1.10 References for this chapter.- 1.11 Exercises.- 2 Some Mathematical and Conceptual Tools: Part 1. Statistics.- 2.1 The significance of turbulence and its spectrum.- 2.2 The spectral gap.- 2.3 Mean and turbulent parts.- 2.4 Some basic statistical methods.- 2.5 Turbulence kinetic energy.- 2.6 Kinematic flux.- 2.7 Eddy flux.- 2.8 Summation notation.- 2.9 Stress.- 2.10 Friction velocity.- 2.11 References.- 2.12 Exercises.- 3 Application of the Governing Equations to Turbulent Flow.- 3.1 Methodology.- 3.2 Basic governing equations.- 3.3 Simplifications, approximations, and scaling arguments.- 3.4 Equations for mean variables in a turbulent flow.- 3.5 Summary of equations, with simplifications.- 3.6 Case studies.- 3.7 References.- 3.8 Exercises.- 4 Prognostic Equations for Turbulent Fluxes and Variances.- 4.1 Prognostic equations for the turbulent departures.- 4.2 Free convection scaling variables.- 4.3 Prognostic equations for variances.- 4.4 Prognostic equations for turbulent fluxes.- 4.5 References.- 4.6 Exercises.- 5 Turbulence Kinetic Energy, Stability, and Scaling.- 5.1 The TKE budget derivation.- 5.2 Contributions to the TKE budget.- 5.3 TKE budget contributions as a function of eddy size.- 5.4 Mean kinetic energy and its interaction with turbulence.- 5.5 Stability concepts.- 5.6 The Richardson number.- 5.7 The Obukhov length.- 5.8 Dimensionless gradients.- 5.9 Miscellaneous scaling parameters.- 5.10 Combined stability tables.- 5.11 References.- 5.12 Exercises.- 6 Turbulence Closure Techniques.- 6.1 The closure problem.- 6.2 Parameterization rules.- 6.3 Local closure - zero and half order.- 6.4 Local closure - first order.- 6.5 Local closure - one-and-a-half order.- 6.6 Local closure - second order.- 6.7 Local closure - third order.- 6.8 Nonlocal closure - transilient turbulence theory.- 6.9 Nonlocal closure - spectral diffusivity theory.- 6.10 References.- 6.11 Exercises.- 7 Boundary Conditions and External Forcings.- 7.1 Effective surface turbulent flux.- 7.2 Heat budget at the surface.- 7.3 Radiation budget.- 7.4 Fluxes at interfaces.- 7.5 Partitioning of flux into sensible and latent portions.- 7.6 Flux to and from the ground.- 7.7 References.- 7.8 Exercises.- 8 Some Mathematical and Conceptual Tools: Part 2. Time Series.- 8.1 Time and space series.- 8.2 Autocorrelation.- 8.3 Structure function.- 8.4 Discrete Fourier transform.- 8.5 Fast Fourier Transform.- 8.6 Energy spectrum.- 8.7 Spectral characteristics.- 8.8 Spectra of two variables.- 8.9 Periodogram.- 8.10 Nonlocal spectra.- 8.11 Spectral decomposition of the TKE equation.- 8.12 References.- 8.13 Exercises.- 9 Similarity Theory.- 9.1 An overview.- 9.2 Buckingham Pi dimensional analysis methods.- 9.3 Scaling variables.- 9.4 Stable boundary layer similarity relationship lists.- 9.5 Neutral boundary layer similarity relationship lists.- 9.6 Convective boundary layer similarity relationship lists.- 9.7 The log wind profile.- 9.8 Rossby-number similarity and profile matching.- 9.9 Spectral similarity.- 9.10 Similarity scaling domains.- 9.11 References.- 9.12 Exercises.- 10 Measurement and Simulation Techniques.- 10.1 Sensor and measurement categories.- 10.2 Sensor lists.- 10.3 Active remote sensor observations of morphology.- 10.4 Instrument platforms.- 10.5 Field experiments.- 10.6 Simulation methods.- 10.7 Analysis methods.- 10.8 References.- 10.9 Exercises.- 11 Convective Mixed Layer.- 11.1 The unstable surface layer.- 11.2 The mixed layer.- 11.3 Entrainment zone.- 11.4 Entrainment velocity and its parameterization.- 11.5 Subsidence and advection.- 11.6 References.- 11.7 Exercises.- 12 Stable Boundary Layer.- 12.1 Mean Characteristics.- 12.2 Processes.- 12.3 Evolution.- 12.4 Other Depth Models.- 12.5 Low-level (nocturnal) jet.- 12.6 Buoyancy (gravity) waves.- 12.7 Terrain slope and drainage winds.- 12.8 References.- 12.9 Exercises.- 13 Boundary Layer Clouds.- 13.1 Thermodynamics.- 13.2 Radiation.- 13.3 Cloud entrainment mechanisms.- 13.4 Fair-weather cumulus.- 13.5 Stratocumulus.- 13.6 Fog.- 13.7 References.- 13.8 Exercises.- 14 Geographic Effects.- 14.1 Geographically generated local winds.- 14.2 Geographically modified flow.- 14.3 Urban heat island.- 14.4 References.- 14.5 Exercises.- Appendices.- A. Scaling variables and dimensionless groups.- B. Notation.- C. Useful constants parameters and conversion factors.- D. Derivation of virtual potential temperature.- Errata section.

9,111 citations


"An approximate analytical model for..." refers methods in this paper

  • ...With L as the key variable, we propose the following two dimensionless groups (Pi groups [20,24]) and write...

    [...]

  • ...where U(z) is the mean wind velocity under neutral condition and is estimated from Monin±Obukov similarity theory (MOST [15,24]), and z denotes height....

    [...]

Journal ArticleDOI
David J. Thomson1
TL;DR: In this paper, the relationships between the various criteria are examined for a very general class of models and it is shown that most of the criteria are equivalent and also how a model can be designed to satisfy these criteria exactly and to be consistent with inertial-subrange theory.
Abstract: Many different random-walk models of dispersion in inhomogeneous or unsteady turbulence have been proposed and several criteria have emerged to distinguish good models from bad. In this paper the relationships between the various criteria are examined for a very general class of models and it is shown that most of the criteria are equivalent. It is also shown how a model can be designed to satisfy these criteria exactly and to be consistent with inertial-subrange theory. Some examples of models that obey the criteria are described. As an illustration some calculations of dispersion in free-convective conditions are presented.

1,223 citations


"An approximate analytical model for..." refers background or methods in this paper

  • ...For this purpose, similarity theory (dimensional analysis) in conjunction with the Lagrangian stochastic dispersion model [25] simulation outputs are used to construct the relationships among parameters (i....

    [...]

  • ...Using Thomson's [25] Lagrangian model mentioned above, we calculated the 90% ̄ux fetch requirements (i....

    [...]

  • ...In this section, a brief review of the Eulerian analytical models proposed by Gash [6] and Horst and Weil [8] and the Lagrangian particle trajectory model of Thomson [25] is provided....

    [...]

  • ...(a) Comparison between ̄ux estimated by the models of Horst and Weil [8] (closed circles), Thomson [25] Lagrangian stochastic (open squares), and the proposed (open circles) as a function of fetch under unstable condition, where zm ˆ 4 m, z0 ˆ 0:04 m, L ˆ ÿ50 m (top pane1); (b) same as (a) but for footprint (bottom panel)....

    [...]

  • ...With L as the key variable, we propose the following two dimensionless groups (Pi groups [20,24]) and write x L f zu L : 14 Using Thomson's [25] Lagrangian model mentioned above, we calculated the 90% ¯ux fetch requirements (i.e., the x values for reaching F =S0 0:9) for a wide range of zm, z0, and L values....

    [...]

01 Jan 1994
TL;DR: In this paper, the authors present a method to find a reference record for a given reference record created on 2004-09-07, modified on 2016-08-08, created on 2008-07-08
Abstract: Note: Bibliogr : p 279-280 Index Reference Record created on 2004-09-07, modified on 2016-08-08

1,153 citations


"An approximate analytical model for..." refers background or methods in this paper

  • ...where U(z) is the mean wind velocity under neutral condition and is estimated from Monin±Obukov similarity theory (MOST [15,24]), and z denotes height....

    [...]

  • ...By MOST [15], the horizontal mean wind pro®le, U(z), is calculated as U z u k ln z=z0 ÿ wm z=L ; B:1 where Wm is taken as (A.5) for z=L P 0 and (A.8) for z=L < 0, and the vertical velocity standard deviation pro®le, rw , is expressed as rw 1:25u 1ÿ 3z=L 1...

    [...]

  • ...With zp calculated by the Lagrangian similarity equation [26] dzp dx k 2 ln pzp=z0 ÿ wm pzp=L ÿ /h pzp=L A:1 and Businger±Dyer [2,15] formulas for the stability correction function for velocity (wm) and heat (/h) pro®les, Horst and Weil [8] derived x z0 W zp ÿW z0 : A:2 In (A1), L is the Obukhov length de®ned as L ÿu 3 T kghwhi ; A...

    [...]

  • ...In (3), Uu is de®ned as Uu zm R zm z0 U z dzR zm z0 dz u k ln zm=z0 ÿ 1 z0=zm ; 4 where U(z) is the mean wind velocity under neutral condition and is estimated from Monin±Obukov similarity theory (MOST [15,24]), and z denotes height....

    [...]

  • ...and Businger±Dyer [2,15] formulas for the stability correction function for velocity (wm) and heat (/h) pro®les, Horst and Weil [8] derived...

    [...]

Journal ArticleDOI
TL;DR: The use of analytical solutions of the diffusion equation for "footprint prediction" is explored in this paper, where the upwind area most likely to affect a downwind flux measurement at a given height is compared.
Abstract: The use of analytical solutions of the diffusion equation for ‘footprint prediction’ is explored. Quantitative information about the ‘footprint’, i.e., the upwind area most likely to affect a downwind flux measurement at a given height z, is essential when flux measurements from different platforms, particularly airborne ones, are compared. Analytical predictions are evaluated against numerical Lagrangian trajectory simulations which are detailed in a companion paper (Leclerc and Thurtell, 1990). For neutral stability, the structurally simple solutions proposed by Gash (1986) are shown to be capable of satisfactory approximation to numerical simulations over a wide range of heights, zero displacements and roughness lengths. Until more sophisticated practical solutions become available, it is suggested that apparent limitations in the validity of some assumptions underlying the Gash solutions for the case of very large surface roughness (forests) and tentative application of the solutions to cases of small thermal instability be dealt with by semi-empirical adjustment of the ratio of horizontal wind to friction velocity. An upper limit of validity of these solutions for z has yet to be established.

825 citations


"An approximate analytical model for..." refers methods in this paper

  • ...From previous model results [8,12,18,22], the fetch (x) is a function of F, zm, z0, and the atmospheric stability parameter (zm/L), where L is the Obukhov length (see Appendix A for de®nition)....

    [...]

Related Papers (5)