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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 23
Atmos. Chem. Phys., 17, 14811–14819, 2017
https://doi.org/10.5194/acp-17-14811-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 17, 14811–14819, 2017
https://doi.org/10.5194/acp-17-14811-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 12 Dec 2017

Research article | 12 Dec 2017

Retrieving characteristics of inertia gravity wave parameters with least uncertainties using the hodograph method

Gopa Dutta, Palla Vinay Kumar, and Salauddin Mohammad Gopa Dutta et al.
  • Vignana Bharathi Institute of Technology, Hyderabad 501301, India

Abstract. We have analyzed wind velocities measured with high-resolution Global Positioning System (GPS) radiosondes which have been flown continuously from Hyderabad for 120 h with an interval of 6 h. Hodograph method has been used to retrieve inertia gravity waves (IGWs) parameters. Background winds are removed from the time series by detrending, whereas polynomials of different orders are removed to obtain the fluctuations from individual profiles. A Butterworth filter is used to extract the monochromatic IGW component. Another filter finite impulse response (FIR1) is tried in a similar manner to test the effects of filters in estimating IGW characteristics. Results reveal that the fluctuation profiles differ with the change in polynomial orders, but the IGW parameters remain same when a Butterworth filter is chosen to extract the monochromatic wave component. The FIR1 filter produces results with a broader range. The direction of wave propagation can be confirmed with additional temperature information.

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Short summary
Gravity wave stress is crucial for weather prediction purposes. It is found that proper filtering of the data is essential to reduce uncertainties in the popular hodograph method to delineate low-frequency gravity wave parameters. Our research helped in improving the estimates of gravity wave stress by reducing errors.
Gravity wave stress is crucial for weather prediction purposes. It is found that proper...
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