A Hybrid Profile-Gradient Approach for the Estimation of Surface Fluxes

TL;DR

This paper introduces hybrid methods to estimate surface fluxes from wind speed or temperature data alone, simplifying calculations and providing stability indices, with performance comparable to traditional methods under ideal conditions.

Contribution

The paper develops hybrid approaches that decouple wind and temperature profiles for easier flux estimation, improving upon existing methods.

Findings

Hybrid methods perform nearly as well as profile methods with error-free data.

Hybrid approaches are less effective than traditional methods when data contain random errors.

Proposed indices effectively measure atmospheric stability.

Abstract

The Monin--Obukhov similarity theory-based wind speed and potential temperature profiles are inherently coupled to each other. We have developed hybrid approaches to disentangle them, and as a direct consequence, the estimation of Obukhov length (and associated turbulent fluxes) from either wind-speed or temperature measurements becomes an effortless task. Additionally, our approaches give rise to two easily measurable indices of atmospheric stability. We compare these approaches with the traditional gradient and profile methods that require both wind-speed and temperature profile data. Using Monte-Carlo-type numerical experiments we demonstrate that, if the input profiles are free of any random errors, the performance of the proposed hybrid approaches is almost equivalent to the profile method and better than the gradient method. However, the proposed hybrid approaches are less competitive in comparison to their traditional counterparts in the presence of random errors.