## 1. Introduction

[2] Turbulence in the atmosphere can be described as the dissipation of mechanical energy to internal energy occurring by an energy cascade process through a series of Fourier modes of the velocity field, in which large-scale eddies break up, subdividing into smaller eddies until they disappear by means of heat dissipation through molecular viscosity [*Justus,* 1969]. Turbulence can be generated by nonlinear breaking and critical level interactions of upward propagating gravity waves. Small-scale turbulence plays a crucial role in middle atmospheric dynamics, as it is the end product of many dynamical motions in the atmosphere. It not only heats the atmosphere but also causes diffusion of momentum, heat, and matter. It transports energy and momentum extracted from the wave, contributing to the eddy diffusion process. Energy loss is significant in the dissipation region, which is separated from the energy input region by the inertial subrange. Thus, all the energy is transmitted to the viscous subrange through the inertial subrange without any significant loss. Hence, turbulence plays a significant role in the energy budget and thermal structure of the atmosphere.

[3] Atmospheric turbulence depends largely on background atmospheric parameters such as wind, temperature, and humidity [*VanZandt et al.*, 1981]. To quantify the turbulence from VHF radar measurements three methods are mainly used: the Doppler spectral width method, backscatter signal power method, and velocity variance method. With these methods characteristics of turbulence are estimated using mainly the turbulence refractivity structure constant *C*_{n}^{2}, turbulent eddy dissipation rate ɛ, and turbulence or eddy diffusivity *K*. A few studies were carried out over this observational site using 53 MHz mesosphere-stratosphere-troposphere (MST) radar to quantify the turbulence parameters in the troposphere and lower stratosphere [*Rao et al.*, 1997], and later these studies were extended to bring out the height structure of *C*_{n}^{2} for different seasons using 4 years of MST radar observations [*Rao et al.*, 2001a] and the height structure of *K* in the troposphere, lower stratosphere, and mesosphere [*Rao et al.*, 2001b]. *Sasi and Vijayan* [2001] estimated the turbulent kinetic energy dissipation rate and eddy diffusion coefficient using MST radar data. *Satheesan and Krishna Murthy* [2002] adopted the variance method and estimated the turbulence parameter ɛ and the eddy diffusivity for momentum *K*_{m} in the upper troposphere and lower stratosphere. They also made a comparative study using all three techniques. *Nastrom et al.* [2004] measured atmospheric turbulence using a dual-beam-width method.

[4] All these studies were made under the assumption of local homogeneity and stationarity of the refractive index fluctuations on the basis of *Kolmogorov*'s [1941] theory. However, a major difficulty in radar experiments is the assumption of homogeneity and stationarity of turbulence within the illuminated volume, which is hardly satisfied [e.g., see *Dole et al.*, 2001; *Wilson et al.*, 2005]. Atmospheric turbulence is known to occur in thin layers (say, 100 m depth or less) and is highly intermittent in time and space. Radar experiments also have limitation for the height region of 13–15 km owing to the poor signal-to-noise ratio and, further, are limited to an altitude of 20 km. Note that in the tropics, turbulence parameters show large seasonal variations because of extreme weather phenomena such as monsoons and associated tropical easterly jets.

[5] In situ measurements from radiosonde are also used to study the turbulence parameters. Using a small data set from these measurements, an attempt has been made to study *C*_{n}^{2} [*Ghosh et al.,* 2001] over this site. Recent efforts by *Clayson and Kantha* [2008] provided a new approach for retrieving turbulence properties in the free atmosphere from high-resolution soundings which was originally designed for oceanic mixing. In the present paper an attempt is made to characterize the seasonal variation of *C*_{n}^{2}, ɛ, and *K* using 3 years of high-resolution GPS radiosonde measurements following *Clayson and Kantha* [2008]. Although the temporal resolution is poor, GPS radiosonde provides meteorological parameters with very high vertical resolution (5 m) and a height coverage of 30 km. In addition, temperature measurements using radiosonde allow us to derive the profiles of Brunt Väisälä (BV) frequency (*N*) and hence the Richardson number (Ri). Further, a totally different approach has been followed to estimate the profile of *C*_{n}^{2} for the tropical station. The purpose of this paper is threefold: first, to estimate the probability of turbulence, a method to derive *C*_{n}^{2} profiles from the high-resolution radiosonde data; second, to study the role of temperature and humidity gradients in the refractive index gradient; and, finally, to calculate the thickness of the turbulent layer and hence *C*_{n}^{2}, ɛ, and *K*.