Identification of inertia gravity wave sources observed in the troposphere and the lower stratosphere over a tropical station Gadanki
Introduction
Atmospheric gravity waves (GWs) have an important role in the structure and dynamics of the atmosphere. In general, GWs are generated in the troposphere mainly by orography, wind shear and convection (Fritts and Alexander, 2003). Convection can generate waves via three mechanisms namely pure thermal forcing, mechanical oscillator effect and obstacle effect (Fritts and Alexander, 2003, Kim et al., 2003). The first mechanism involves pure thermal forcing due to convective clouds radiating GWs into stably stratified atmosphere above the clouds (e.g. Salby and Garcia, 1987, Alexander et al., 1995, Piani et al., 2000, Fritts and Alexander, 2003, Fritts et al., 2006, Kim et al., 2003). The second mechanism is the mechanical oscillator effect that can generate gravity waves above the clouds (e.g. Clark et al., 1986, Fovell et al., 1992, Vincent and Alexander, 2000, Kim et al., 2003). In the third mechanism (obstacle effect) the GW generation is attributed to the flow over convective obstacles, analogous to topographic wave generation (e.g. Clark et al., 1986, Pfister et al., 1993, Kim et al., 2003). These waves carry energy and momentum from the source region to the middle atmosphere (Fritts and Alexander, 2003).
The parameterization of GWs is essential in global circulation models (GCM) (Geller et al., 2013) as well as for the understanding of vertical coupling between the lower and middle atmosphere. For parameterization, identification of the source/sources of the GWs is very important. The sources can be identified directly by ray tracing with Gravity wave Regional or Global Ray Tracer (GROGRAT) model (Marks and Eckermann, 1995). Before applying ray tracing, one has to obtain GW parameters like period, vertical and horizontal wavelengths, vertical and horizontal phase speeds. It is essential to determine one set of parameters which fully characterizes the wave from the measurements, other sets of parameters may be determined via e.g. polarization relations.
In order to obtain GW parameters, different methods like hodograph, Stokes parameter method and techniques using phase and group velocity tracing are generally employed (Serafimovich et al., 2005). Each method has its own advantages as well as disadvantages. Among these methods, the widely used one is the hodograph method but it assumes monochromatic waves which often are not realistic. It is applicable for low frequency GWs (elliptic polarization) (Guest et al., 2000). Hodograph analysis may give variable results due to the superposition of different waves in the wind field (Serafimovich et al., 2005). In order to avoid these issues, one can use Stokes parameter method in which average of the four Stokes parameters over a wave number band can be taken to obtain the wave parameters (Serafimovich et al., 2005). Propagation characteristics can be obtained using rotary spectra analysis.
Using the above mentioned techniques, several studies have been carried out to extract the GW parameters over different locations. There are several studies (Kumar, 2006, Kumar, 2007, Dhaka et al., 2002, Venkat Ratnam et al., 2008, Nath et al., 2009, Dutta et al., 2009, Das et al., 2010, Kaur et al., 2012, Leena et al., 2012) over the Indian region using high resolution MST radar and radiosonde measurements. Very recently, Pramitha et al. (2015) successfully applied reverse ray tracing for locating the sources of the high frequency waves observed at airglow altitudes (97 km) over Gadanki. Some studies (Venkat Ratnam et al., 2008, Leena et al., 2012) also dealt with their source mechanisms, using methods like hodograph. Leena et al. (2012) reported inertia gravity wave (IGW) characteristics and their sources over Gadanki region (inertial period is 51 h) applying the hodograph method for the high-resolution radiosonde measurements. However, note that extraction of wave parameters and sources using hodograph method has limitations as it assumes monochromatic waves which are not valid generally as mentioned earlier.
In the present study we also aim to investigate IGWs, but in order to avoid the above mentioned problems of the hodograph method, we use the Stokes parameter method along with rotary analysis to extract the IGW parameters. The wave parameters thus obtained are used to find the sources of these waves using the GROGRAT model (Marks and Eckermann, 1995). The present paper is organized in the following way: The data base used for the present study is discussed in Section 2 and background atmospheric conditions prevailing over the study region in Section 3. Section 4 describes the methodology adapted for extracting the IGW parameters. Section 5 discusses the GROGRAT model used to identify the IGW sources. Section 6 presents results and discussion. The method for identifying sources is given in Section 7 and finally the summary and conclusions are provided in Section 8.
Section snippets
Data base
GPS radiosondes were launched every day from May 2006 to March 2014 around 17:30 IST (IST = UT + 5.5 h) for selected events at 3 h intervals (Venkat Ratnam et al., 2013a). This data spanning nearly 8 years are used in the present study. There are major data gaps during April 2007 and December 2012 which however, do not affect the present study. The average ascent rate of these sondes is ~ 5 m/s and the sondes attained an altitude of 28 km on the average in all seasons. Data from sondes that didn't reach
Background conditions
Based on background conditions prevailing over the Indian region, India Meteorological Department (IMD) has grouped different months into four seasons namely winter (December, January and February, DJF), pre-monsoon (March, April and May, MAM), monsoon (June, July and August, JJA) and post-monsoon (September, October and November, SON). The meteorological parameters obtained on individual days are averaged over a month to obtain monthly, season, annual and inter-annual variations. Fig. 1 shows
Methodology
In order to extract IGW parameters in the troposphere and the stratosphere, we separated the profiles into altitude regions 2–15 km and 18–27 km for two reasons. First reason is to avoid that the sharp temperature change close to the tropopause, which is situated around 17 km (Mehta et al., 2010) affects the analysis results and Tropical Easterly Jet (TEJ) occurring during monsoon season (Venkat Ratnam et al., 2013b). The second reason is to avoid effects of the change of the Brunt Väisälä
GROGRAT model for GW source identification
GROGRAT is an algorithm for GWs which is developed by Marks and Eckermann (1995). It can trace non hydrostatic waves in a gridded atmosphere. The sources for the GWs which are observed from 2006 to 2014 are investigated using this model. For developing background atmosphere we used 6 hourly ERA-Interim data with resolution of 3° × 3°. Estimated GW parameters like zonal and meridional wave numbers and wave frequency are given as input parameters to the model. Note that this model also assumes
Results and discussion
Fig. 2a (Fig. 2b) shows the intrinsic and ground based periods and the difference between them in a box plot for the troposphere (stratosphere). These wave parameters are obtained for each individual event during 2006 to 2014. Doppler shifting in the troposphere is smaller than in the stratosphere. The average difference between the intrinsic and ground based periods is around 5 h in the troposphere and 10 h in the stratosphere, respectively. The ratio of intrinsic frequency to Coriolis frequency
Source identification
After extracting the IGW parameters for each event, we tried to identify the sources for these waves individually using the GROGRAT model. The input parameters that we have given to the model are zonal and meridional wave numbers, ground based frequency, initial latitude, longitude and altitude. We have used ERA-Interim horizontal wind and temperature data available publicly at 6 hourly intervals to obtain background atmospheric parameters for the model. We have given the initial latitude,
Summary and conclusions
In the present study an attempt has been made to identify the sources for the IGWs observed over a tropical station Gadanki. IGW wave characteristics are extracted from the long-term measurements of high resolution radiosonde observations available from May 2006 to March 2014 by using Stokes method for each day. The ratio of intrinsic frequency to Coriolis frequency is found to be between 1 and 1.7 confirming that the observed waves are IGWs. These waves have intrinsic period ranging from 30 to
Acknowledgments
We thank the NARL staff for providing the data used in the present study. We are deeply grateful to NOAA and ECMWRF for providing OLR and ERA-Interim data, respectively, used in the present study through their ftp sites. This work is carried out as a part of SAFAR campaign fully supported by Indian Space Research Organization. The data used in this paper can be obtained on request.
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