Uncertainty in the Parameterization of Surface Fluxes under Unstable Conditions

Srivastava, Piyush; Sharan, Maithili

doi:10.1175/jas-d-20-0350.1

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…On the other hand,

{C}_H

is found to be increasing continuously for both the functions as instability grows. However, it is noticed that

{C}_H

corresponding to default functional forms exhibits a non‐monotonic nature with respect to atmospheric instability (Srivastava and Sharan, 2021) on rough surfaces. Thus, significant deviations in the computation of the surface fluxes in the model can be expected between functional forms of default and Fairall similarity functions.…”

Section: Integrated Similarity Functionsmentioning

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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“…On the other hand,

{C}_H

is found to be increasing continuously for both the functions as instability grows. However, it is noticed that

{C}_H

Section: Integrated Similarity Functionsmentioning

confidence: 99%

“…These coefficients are used to compute the turbulent fluxes (Srivastava and Sharan, 2021). We would like to highlight the impact of certain forms of similarity functions on transfer coefficients.…”

Section: Ta B L Ementioning

confidence: 99%

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

Self Cite

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“…To date, the inability of climate models to capture the large spatio-temporal variability of land-atmosphere interactions remains a significant source of uncertainty in climate change projections [1,2]. Model validation and calibration are complicated by the scale mismatch between land-atmosphere processes captured by site observations (≤ 1 km), and the grid scales at which climate models are run (1 − 100 km).…”

Section: Introductionmentioning

confidence: 99%

Using reinforcement learning to improve drone-based inference of greenhouse gas fluxes

Van Hove,

Aalstad,

Pirk

2023

NMI

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Accurate mapping of greenhouse gas fluxes at the Earth’s surface is essential for the validation and calibration of climate models. In this study, we present a framework for surface flux estimation with drones. Our approach uses data assimilation (DA) to infer fluxes from drone-based observations, and reinforcement learning (RL) to optimize the drone’s sampling strategy. Herein, we demonstrate that an RL-trained drone can quantify a CO2 hotspot more accurately than a drone sampling along a predefined flight path thattraverses the emission plume. We find that information-based reward functions can match the performance of an error-based reward function that quantifies the difference between the estimated surface flux and the true value. Reward functions based on information gain and information entropy can motivate actions that increase the drone’s confidence in its updated belief, without requiring knowledge of the true surface flux. These findings provide valuable insights for further development of the framework for the mapping of more complex surface flux fields.

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An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system

Namdev,

Sharan,

Srivastava

et al. 2024

Geosci. Model Dev.

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Abstract. The accurate parameterization of atmospheric surface layer processes is crucial for weather forecasts using numerical weather prediction models. Here, an attempt has been made to improve the surface layer parameterization in the Weather Research and Forecasting model version 4.2.2 (WRFv4.2.2) by implementing similarity functions proposed by Kader and Yaglom (1990) to make it consistent in producing the transfer coefficient for momentum observed over the tropical region (Srivastava and Sharan, 2015). The surface layer module in WRFv4.2.2 is modified in such a way that it contains the commonly used similarity functions for momentum (φm) and heat (φh) under convective conditions instead of the existing single functional form. The updated module has various alternatives of φm and φh, which can be controlled by a flag introduced in the input file. The impacts of utilizing different functional forms have been evaluated using the bulk flux algorithm as well as real-case simulations with the WRFv4.2.2 model. The model-simulated variables have been evaluated with observational data from a flux tower at Ranchi (23.412° N, 85.440° E), India, and the ERA5-Land reanalysis dataset. The transfer coefficient for momentum simulated using the implemented scheme is found to agree well with its observed non-monotonic behavior in convective conditions (Srivastava and Sharan, 2021). The study suggests that the updated surface layer scheme performs well in simulating the surface transfer coefficients and could be potentially utilized for the parameterization of surface fluxes under convective conditions in the WRF model.

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Uncertainty in the Parameterization of Surface Fluxes under Unstable Conditions

Cited by 3 publications

References 36 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

Using reinforcement learning to improve drone-based inference of greenhouse gas fluxes

An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system

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