Derivation of the relationship between the Obukhov stability parameter and the bulk Richardson number for flux-profile studies

Launiainen, Jouko

doi:10.1007/bf00710895

Cited by 101 publications

(87 citation statements)

References 31 publications

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“…Tests in conditions of z 0 from 10 5 to 10 1 m and z 0 /z m from 0Ð5 to 7Ð3 were shown to generally yield estimates within a few percent of those found using iteration (Launiainen, 1995).…”

Section: Parameterizations and Evaluationsmentioning

confidence: 79%

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Evaluation of MOD16 algorithm using MODIS and ground observational data in winter wheat field in North China Plain

Sun

Wang

Ouyang

et al. 2007

Hydrological Processes

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Abstract:Evapotranspiration (ET ) is an important factor for understanding hydrological processes and climate dynamics. Remote sensing is considered as the most promising tool for estimation of ET over a large spatial scale. The purpose of this study is to analyse and evaluate the MOD16 algorithm, developed by Nishida et al. (2003a,b), in winter wheat fields by using MODIS land products, MOD11-land surface temperature and MOD13-standard normalized difference vegetation index (NDVI), as well as observations at the Yucheng Experimental Station, China in 2002. The original MOD16 is a dual-source ET model in which the evaporation fraction (EF ) for bare soil surface is estimated using the VI-Ts diagram method, and EF for vegetation is determined by a function of canopy resistance, aerodynamic resistance and air temperature. We analysed a radiation budget sub-model and found that the estimate for solar radiation is acceptable only on cloud-free days. We also found that, in the original MOD16, seasonal variations of minimum canopy resistance and physiological temperatures are not considered, which results in overestimation of canopy resistance when leaf area index (LAI) is less than 2Ð5. Because the strong unstable thermal stratification over the dry bare soil surface is ignored in the original algorithm, wind speed is overestimated and aerodynamic resistance above the vegetation surface is underestimated. We then modified the original MOD16 algorithm. The result shows that both EF and ET for vegetation estimated with the modified algorithm are in consistent with both the observations of an eddy covariance system and the calculations using the Penman-Monteith method.

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Section: Parameterizations and Evaluationsmentioning

confidence: 79%

“…For practical applications, this is inconvenient. Byun (1990) and Launiainen (1995) gave a series of empirical expressions for z/L as a function of Ri b (bulk Richardson number). In this study, we used Launiainen's solution to parameterize L for an unstable region,…”

Section: Parameterizations and Evaluationsmentioning

confidence: 99%

Evaluation of MOD16 algorithm using MODIS and ground observational data in winter wheat field in North China Plain

Sun

Wang

Ouyang

et al. 2007

Hydrological Processes

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“…This is avoided in the heat flux parameterization proposed by Launiainen and Cheng (1995) and Launiainen (1995), abbreviated LC95. It is based on a semi-analytical relationship between the bulk Richardson-number and the Obukhov stability parameter ζ = z/L, which is the dimensionless fraction of the respective measurement height z (2.4 m) and the Obukhov-length L. Universal Functions for momentum M and sensible heat H from Businger et al (1971), Dyer (1974), and Högström (1988) for unstable conditions, and for stable conditions the function suggested by Holtslag and De Bruin (1988), are used to derive the bulk transfer coefficients instead.…”

Section: Methodsmentioning

confidence: 99%

“…Most problems result from uncertainties in the appropriate determination of the bulk aerodynamic transfer coefficients for sensible and latent heat over snow and ice (Louis, 1979;Banke et al, 1980;Overland, 1985;Andreas and Murphy, 1986;Andreas, 2002), the aerodynamic roughness expressed by drag coefficients (Guest and Davidson, 1991) as well as the estimation of the surface roughness lengths (Andreas, 1987) and stability correction terms for the universal functions (e.g. Handorf et al, 1999;Holtslag and DeBruin, 1988;Launiainen, 1995;Grachev et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

The effect of misleading surface temperature estimations on the sensible heat fluxes at a high Arctic site – the Arctic Turbulence Experiment 2006 on Svalbard (ARCTEX-2006)

Lüers

Bareiss

2010

Atmos. Chem. Phys.

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Abstract. The observed rapid climate warming in the Arctic requires improvements in permafrost and carbon cycle monitoring, accomplished by setting up long-term observation sites with high-quality in-situ measurements of turbulent heat, water and carbon fluxes as well as soil physical parameters in Arctic landscapes. But accurate quantification and well adapted parameterizations of turbulent fluxes in polar environments presents fundamental problems in soilsnow-ice-vegetation-atmosphere interaction studies. One of these problems is the accurate estimation of the surface or aerodynamic temperature T (0) required to force most of the bulk aerodynamic formulae currently used. Results from the Arctic-Turbulence-Experiment (ARCTEX-2006) performed on Svalbard during the winter/spring transition 2006 helped to better understand the physical exchange and transport processes of energy. The existence of an atypical temperature profile close to the surface in the Arctic spring at Svalbard could be proven to be one of the major issues hindering estimation of the appropriate surface temperature. Thus, it is essential to adjust the set-up of measurement systems carefully when applying flux-gradient methods that are commonly used to force atmosphere-ocean/land-ice models. The results of a comparison of different sensible heat-flux parameterizations with direct measurements indicate that the use of a hydrodynamic three-layer temperature-profile model achieves the best fit and reproduces the temporal variability of the surface temperature better than other approaches.

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“…The partially corrected transfer coefficient is then recalculated and so the cycle goes. Strub and Powell (1987) and Launiainen (1995), presented an alternative based on estimation of the bulk Richardson number, Ri B , defined as: where it is specified that C HN = C WN = C HWN . Figure A2 illustrates the relationship between the degree of atmospheric stratification (as described by both the bulk Richardson number and the Monin-Obukhov stability parameter) and the transfer coefficients scaled by their neutral value.…”

Section: Ammentioning

confidence: 99%

A General Lake Model (GLM 2.4) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON)

Hipsey

Bruce

Boon

et al. 2017

Preprint

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Abstract. The General Lake Model (GLM) is a one-dimensional open-source model code designed to simulate the hydrodynamics of lakes, reservoirs and wetlands. GLM was developed to support the science needs of the Global Lake Ecological Observatory Network (GLEON), a network of lake sensors and researchers attempting to understand lake functioning and address questions about how lakes around the world vary in response to climate and land-use change. The scale and diversity of lake types, locations and sizes, as well as the observational data within GLEON, created the need for a 25 robust community model of lake dynamics with sufficient flexibility to accommodate a range of scientific and management needs of the GLEON community. This paper summarises the scientific basis and numerical implementation of the model algorithms, including details of sub-models that simulate surface heat exchange and ice-cover dynamics, vertical mixing and inflow/outflow dynamics. A summary of typical parameter values for lakes and reservoirs collated from a range of sources is included. GLM supports a dynamic coupling with biogeochemical and ecological modelling libraries for integrated 30 simulations of water quality and ecosystem health. An overview of approaches for integration with other models, and utilities for the analysis of model outputs and for undertaking sensitivity and uncertainty assessments is also provided.Finally, we discuss application of the model within a distributed cloud-computing environment, and as a tool to support learning of network participants.Geosci. Model Dev. Discuss., https://doi

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Derivation of the relationship between the Obukhov stability parameter and the bulk Richardson number for flux-profile studies

Cited by 101 publications

References 31 publications

Evaluation of MOD16 algorithm using MODIS and ground observational data in winter wheat field in North China Plain

Evaluation of MOD16 algorithm using MODIS and ground observational data in winter wheat field in North China Plain

The effect of misleading surface temperature estimations on the sensible heat fluxes at a high Arctic site – the Arctic Turbulence Experiment 2006 on Svalbard (ARCTEX-2006)

A General Lake Model (GLM 2.4) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON)

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