Characteristics of the Tropical Easterly Jet: long-term trends and their features during active and break monsoon phases

Abstract

Using high-vertical resolution Indian mesosphere-stratosphere-troposphere (MST) radar and GPS sonde observations from Gadanki (13.5°N, 79.2°E), an attempt has been made for the first time to delineate the characteristics of Indian summer monsoon winds with special emphasis on the Tropical Easterly Jet stream (TEJ), its day-to-day, monthly, and interannual variability using observations available for more than a decade (1996-2008). For comparison, data available during 1996-2008 from routine radiosonde observations from nearby India Meteorological Department (IMD) observatory located at Chennai (13.0°N, 80.1°E) and National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis data sets have been considered. Relatively poor vertical resolution data from IMD lead to underestimation of the jet characteristics like jet width, vertical shear above and below the jet, and the wind reversal height. Although reanalysis data sets represented well the spatial variability, they underestimate the peak zonal winds by about 5 m/s when compared to Indian MST radar and GPS sonde observations. Large day-to-day variability of TEJ characteristics (peak jet speed, jet height, jet width, zonal wind reversal height, and vertical shear of zonal wind) over Gadanki is noticed. No significant trend is observed in peak jet speed using reanalysis data sets either over Gadanki grid or over jet core region in Indian summer monsoon months. However, MST radar and GPS sonde observations show increasing trend (~1 m/s/yr) in peak jet speed particularly from the year 2000. Characteristics of the TEJ during active and break phases showed quite different features. Thus, statistics generated using the long-term database from Indian MST radar and GPS sonde provide valuable information for studying the TEJ characteristics and will be useful to incorporate in the future reanalysis data sets.