Variance methods to estimate regional heat fluxes with aircraft measurements in the convective boundary layer
Introduction
Knowledge of the fluxes of energy, mass and momentum between the land surfaces and the atmosphere is required in many situations encountered in water resource management and atmospheric circulation studies. Since the physical state of the convective boundary layer (CBL) probably reflects the surface fluxes with horizontal scales of the order of 102–105 m (e.g., Raupach and Finnigan, 1995), several approaches to derive surface fluxes with CBL observations have been developed and tested in the past. Examples of such approaches include the eddy correlation method (e.g., Lenschow et al., 1980), the profile or a bulk method of the CBL (e.g., Brutsaert and Sugita, 1991) and the CBL budget approach (e.g., Kustas and Brutsaert, 1987a, Kustas and Brutsaert, 1987b, Betts and Ball, 1994) with data obtained by sensors on a tower (e.g., Berger et al., 2001), on radiosondes (e.g., Sugita et al., 1999), aboard an aircraft (e.g., Lenschow et al., 1980), or by means of ground-based remote sensing devices such as Radar (e.g., Eng et al., 2003). Among them, aircraft measurements have the advantage in both detecting the spatial variability and in deriving area-averaged values depending on the methods applied to the measured variables, but they are not without disadvantages. The most notable feature is the random movement of an aircraft as a platform of observations. It continuously moves in all directions, and thus it requires simultaneous measurements of its precise position and also sophisticated and cumbersome treatment of the data afterward in order to allow vector data analysis, in particular for the application of the eddy correlation technique.
Methods to estimate surface fluxes from the associated variance measurements, on the other hand, are appealing particularly for the aircraft observation because they allow the derivation of surface fluxes only from measurements of a scalar variable without the need for extra measurements of aircraft position and data processing needed for the eddy correlation method as mentioned above. The variance methods are based on flux-variance relationships derived on the basis of similarity arguments and established through the determination of the constant parameters in the derived relationship. Such relations have been established and verified extensively through experiments in the surface layer and it now appears possible to derive surface fluxes with sufficient accuracy (e.g., Wesely, 1988, Katul et al., 1996). In contrast, for the CBL, the relevant flux-variance relationships are still not fully understood and far from established. So far the proposed functional relationships between the variances in the CBL and the corresponding surface fluxes are still limited in number and they have been insufficiently validated (see below). Also, they were used mainly for the purpose of organizing derived variances in terms of similarity functions, and only a few studies have tried to apply such relations for the flux estimation. Sugita and Kawakubo (2003) used the variances of temperature in the lower half of the CBL obtained from tower observations to derive the surface sensible heat fluxes. There is only one study using aircraft data, namely the one by Asanuma, 1996, Asanuma and Brutsaert, 1999, who used mixed and surface layer variance relations with temperature and humidity data obtained during HAPEX-Mobilhy (Hydrologic-Atmospheric Pilot Experiment and Modélisation du Bilan Hydrique) in southwestern France (André et al., 1986) to derive the corresponding surface fluxes.
In view of the lack of studies of the CBL variance relationships in general and their application for the surface flux estimation with aircraft data in particular, the present study was initiated with data sets obtained from an aircraft above an extensive steppe region in Mongolia with simultaneous surface flux observations, in order to investigate the CBL variance relationships and the feasibility to use them for the purpose of surface flux estimations of a region.
Section snippets
Experimental site
The temperature turbulence data in the CBL were obtained by aircraft observations that were carried out from June through October of 2003 as part of the field campaigns of the rangelands atmosphere–hydrosphere–biosphere interaction study experiment in Northeastern Asia (RAISE, Sugita et al., this issue). The RAISE study area covers the Kherlen river basin in the northeastern part of Mongolia, where arid to semi-arid climate is dominant with a boreal forest in the northern and upper reaches of
Variance profiles in the CBL
In order to assess the nature of the temperature variances obtained in the CBL over the experimental area, the observed variables were analyzed based on a similarity theory. In the CBL, the main governing variables are the covariance of w and θ at the surface , the buoyancy parameter g/θ with the gravity acceleration g, and the CBL height hi, from which the convective scales can be organized. The first such proposal was made by Deardorff (1970), and the velocity scale w∗ and the
Conclusions
Turbulence data obtained by aircraft observations in the CBL over an extensive steppe region in Mongolia were analyzed to estimate the surface fluxes by means of CBL variance methods. Observed temperature variances were found to follow, in general, the functional forms proposed in the past for in the CBL, i.e., (2), (3), (7). The same functions in a different form namely (10), (11), (12) were then used for the estimation of from measured values. With the functional forms and
Acknowledgement
The authors would like to thank M. Saandar of Monmap Engineering Services Co. and the AN-2 pilot and engineer of MIAT Mongolian Airline for their assistance to achieve efficient aircraft observation. The data of regional climate model was prepared by T. Sato of Japan Science and Technology Agency. The success of the field experiment in Mongolia is due to the strong support by G. Davaa and D. Oyunbaatar of the Institute of Meteorology and Hydrology of Mongolia headed by D. Azzaya. We are also
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