On stability correction functions over the Indian region under stable conditions

Srivastava, Piyush; Sharan, Maithili; Kumar, Manoj; Dhuria, Aditya Kumar

doi:10.1002/met.1880

Cited by 9 publications

(16 citation statements)

References 41 publications

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“…(2007) and Srivastava et al . (2020) in stable conditions, and Fairall et al . (1996) in convective conditions, are incorporated in the model.…”

Section: Integrated Similarity Functionsmentioning

confidence: 91%

“…Srivastava et al . (2020) (GM) have evaluated the empirical forms of stability functions proposed by Grachev et al . (2007) with the turbulent observations over the Indian region.…”

Section: Integrated Similarity Functionsmentioning

confidence: 99%

“…Further, Gryanik et al (2020) have also proposed a set of new similarity functions based on previous functions derived from SHEBA data (described in Section 3.2.1) by Grachev et al (2007) for stable conditions. In particular, Gryanik et al (2020) used Ranchi data over the Indian region (Srivastava et al, 2020). Moreover, they also developed a non-iterative scheme to calculate turbulent fluxes and corresponding bulk transfer coefficients for momentum and heat for a package of five pairs of stability functions (Gryanik et al, 2021).…”

Section: Impact Of Gm (Srivastava Et Al 2020) Functionsmentioning

confidence: 99%

“…bySrivastava et al (2020) Srivastava et al (2020) (GM) have evaluated the empirical forms of stability functions proposed byGrachev et al (2007) with the turbulent observations over the Indian region. The similarity function for momentum 𝜙 m was demonstrated to increase slowly in comparison to the classical linear form in the range 0.5 < 𝜁 < 1.…”

mentioning

confidence: 99%

“…On the other hand, the functional form of 𝜙 h is taken to be the same as proposed byGrachev et al (2007). The functional forms for integrated similarity functions suggested bySrivastava et al (2020) are essentially similar (Equations 27 and 28) to the Grachev et al with values of the coefficients as a m = 5.4 and b m = 0.89.…”

mentioning

confidence: 99%

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Impact of the similarity functions of surface layer parametrization in a climate model over the Indian region

Namdev

Sharan

Mishra

2022

Quart J Royal Meteoro Soc

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Impact of the similarity functions of surface layer parametrization in National Centre for Atmospheric Research Community Atmosphere Model version 5 (NCAR-CAM5) is examined over the Indian region. The surface layer parametrization is based on the Monin-Obukhov similarity theory (MOST),which uses momentum and heat similarity functions to parametrize surface turbulent fluxes. In the present study, some of the well-established similarity functions under stable as well as unstable stratification have been incorporated in the surface layer scheme of NCAR-CAM5. Eight simulations have been conducted using different similarity functions with and without a limit on stability parameter along with the simulation using the default surface layer scheme. The impact of the incorporated functions on surface fluxes, near-surface variables, and precipitation has been assessed in this climate model over different climatic zones for the boreal winter and summer seasons. The model represents the timing of diurnal variation of 2 m temperature and turbulent fluxes accurately with higher correlation in all simulations, but the performance of the schemes is found to be sensitive to the climatic zones and seasons. During the winter season, most of the schemes overestimate the night-time surface air temperature over different climatic zones, but the simulation having a model performance index (MPI) of about 0.91 alleviates the bias. Under unstable stratification, the default scheme performs better than incorporated schemes in the winter season, whereas in boreal summer under unstable (stable) stratification, the incorporated functional form having an MPI of about 0.94 (0.97) does better than default and other schemes. As the performance of the functions is spatially and temporally dependent, based on the region and season of interest, the schemes may judiciously be chosen for the climate simulations.

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“…(2007) and Srivastava et al . (2020) in stable conditions, and Fairall et al . (1996) in convective conditions, are incorporated in the model.…”

Section: Integrated Similarity Functionsmentioning

confidence: 91%

“…Srivastava et al . (2020) (GM) have evaluated the empirical forms of stability functions proposed by Grachev et al . (2007) with the turbulent observations over the Indian region.…”

Section: Integrated Similarity Functionsmentioning

confidence: 99%

Section: Impact Of Gm (Srivastava Et Al 2020) Functionsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Impact of the similarity functions of surface layer parametrization in a climate model over the Indian region

Namdev

Sharan

Mishra

2022

Quart J Royal Meteoro Soc

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A Package of Momentum and Heat Transfer Coefficients for the Stable Surface Layer Extended by New Coefficients over Sea Ice

Gryanik

Lüpkes

2022

Boundary-Layer Meteorol

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The bulk transfer coefficients of momentum, heat, and humidity belong to the main ingredients of numerical weather prediction and climate models. They are needed for the calculation of turbulent fluxes in the surface layer and often rely on the Monin–Obukhov similarity theory requiring universal stability functions. The problem of a derivation of transfer coefficients based on different stability functions has been considered by many researchers over the years but it remains to this day. In this work, dedicated to the memory of S.S. Zilitinkevich, we also address this task, and obtain transfer coefficients from three pairs of theoretically derived stability functions suggested by Zilitinkevich and co-authors for stable conditios. Additionally, we construct non-iterative parametrizations of these transfer coefficients based on earlier work. Results are compared with state-of-the-art coefficients for land, ocean, and sea ice. The combined parametrizations form a package in a universal framework relying on a semi-analytical solution of the Monin-Obukhov similarity theory equations. A comparison with data of the Surface Heat Budget of the Arctic Ocean campaign (SHEBA) over sea ice reveals large differences between the coefficients for land conditions and the measurements over sea ice. However, two schemes of Zilitinkevich and co-authors show, after slight modification, good agreement with SHEBA although they had not been especially developed for sea ice. One pair of the modified transfer coefficients is superior and is compatible to earlier SHEBA-based parametrizations. Finally, an algorithm for practical use of all transfer coefficients in climate models is given.

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Using the Sensible Heat Flux Eddy Covariance-Based Exchange Coefficient to Calculate Latent Heat Flux from Moisture Mean Gradients Over Snow

González-Herrero,

Sigmund,

Haugeneder

et al. 2024

Boundary-Layer Meteorol

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In absence of the high-frequency measurements of wind components, sonic temperature and water vapour required by the eddy covariance (EC) method, Monin–Obukhov similarity theory (MOST) is often used to calculate heat fluxes. However, MOST requires assumptions of stability corrections and roughness lengths. In most environments and weather situations, roughness length and stability corrections have high uncertainty. Here, we revisit the modified Bowen-ratio method, which we call C-method, to calculate the latent heat flux over snow. In the absence of high-frequency water vapour measurements, we use sonic anemometer data, which have become much more standard. This method uses the exchange coefficient for sensible heat flux to estimate latent-heat flux. Theory predicts the two exchange coefficients to be equal and the method avoids assuming roughness lengths and stability corrections. We apply this method to two datasets from high mountain (Alps) and polar (Antarctica) environments and compare it with MOST and the three-layer model (3LM). We show that roughness length has a great impact on heat fluxes calculated using MOST and that different calculation methods over snow lead to very different results. Instead, the 3LM leads to good results, in part due to the fact that it avoids roughness length assumptions to calculate heat fluxes. The C-method presented performs overall better or comparable to established MOST with different stability corrections and provides results comparable to the direct EC method. An application of this method is provided for a new station installed in the Pamir mountains.

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On stability correction functions over the Indian region under stable conditions

Cited by 9 publications

References 41 publications

Impact of the similarity functions of surface layer parametrization in a climate model over the Indian region

Impact of the similarity functions of surface layer parametrization in a climate model over the Indian region

A Package of Momentum and Heat Transfer Coefficients for the Stable Surface Layer Extended by New Coefficients over Sea Ice

Using the Sensible Heat Flux Eddy Covariance-Based Exchange Coefficient to Calculate Latent Heat Flux from Moisture Mean Gradients Over Snow

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