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Ageostrophic corrections for power spectra and wave–vortex decomposition

Published online by Cambridge University Press:  08 November 2019

Han Wang*
Affiliation:
Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA
Oliver Bühler
Affiliation:
Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA
*
Email address for correspondence: hannnwangus@gmail.com

Abstract

We present a method to incorporate weakly nonlinear ageostrophic corrections into a previously developed wave–vortex decomposition algorithm for one-dimensional data obtained along horizontal flight, ship or remote-sensing tracks in the atmosphere or ocean. A new statistical omega equation is derived that links the power spectra of a quasi-geostrophic stream function to the power spectra of the ageostrophic correction. This step assumes mutually independent Fourier components for the quasi-geostrophic stream function. Then this equation is used to estimate the ageostrophic correction from one-dimensional track data under the additional assumptions of horizontal isotropy and the dominance of a single vertical wavenumber scale. A robust and accurate numerical method is designed, tested successfully against synthetic data and then applied to atmospheric flight track data near the tropopause. This probes the robustness of the previous linear wave–vortex decomposition method under the ageostrophic corrections. Preliminary findings indicate that the lower stratospheric flight tracks are very robust whilst the upper tropospheric ones showed some sensitivity to the correction.

Type
JFM Papers
Copyright
© 2019 Cambridge University Press 

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References

Balwada, D., LaCasce, J. H. & Speer, K. G. 2016 Scale-dependent distribution of kinetic energy from surface drifters in the gulf of mexico. Geophys. Res. Lett. 43 (20), 10,856–10,863.Google Scholar
Bierdel, L., Snyder, C., Park, S.-H. & Skamarock, W. C. 2016 Accuracy of rotational and divergent kinetic energy spectra diagnosed from flight-track winds. J. Atmos. Sci. 73 (8), 32733286.Google Scholar
Bühler, O., Callies, J. & Ferrari, R. 2014 Wave–vortex decomposition of one-dimensional ship-track data. J. Fluid Mech. 756, 10071026.Google Scholar
Bühler, O. & Holmes-Cerfon, M. 2009 Particle dispersion by random waves in rotating shallow water. J. Fluid Mech. 638, 526.Google Scholar
Bühler, O., Kuang, M. & Tabak, E. G. 2017 Anisotropic Helmholtz and wave–vortex decomposition of one-dimensional spectra. J. Fluid Mech. 815, 361387.Google Scholar
Callies, J., Bühler, O. & Ferrari, R. 2016 The dynamics of mesoscale winds in the upper troposphere and lower stratosphere. J. Atmos. Sci. 73 (12), 48534872.Google Scholar
Callies, J. & Ferrari, R. 2013 Interpreting energy and tracer spectra of upper-ocean turbulence in the submesoscale range (1–200 km). J. Phys. Oceanogr. 43 (11), 24562474.Google Scholar
Callies, J., Ferrari, R. & Bühler, O. 2014 Transition from geostrophic turbulence to inertia–gravity waves in the atmospheric energy spectrum. Proc. Natl Acad. Sci. USA 111 (48), 1703317038.Google Scholar
Callies, J., Ferrari, R., Klymak, J. M. & Gula, J. 2015 Seasonality in submesoscale turbulence. Nature Commun. 6, 6862.Google Scholar
Champeney, D. C. 1987 Power Spectra and Wiener’s Theorems, pp. 102117. Cambridge University Press.Google Scholar
Dasch, C. J. 1992 One-dimensional tomography: a comparison of Abel, onion-peeling, and filtered backprojection methods. Appl. Opt. 31 (8), 11461152.Google Scholar
Davies, H. C. 2015 The quasigeostrophic omega equation: reappraisal, refinements, and relevance. Mon. Weath. Rev. 143 (1), 325.Google Scholar
Holmes-Cerfon, M., Bühler, O. & Ferrari, R. 2011 Particle dispersion by random waves in the rotating Boussinesq system. J. Fluid Mech. 670, 150175.Google Scholar
Holton, J. R. 2004 An Introduction to Dynamic Meteorology, 4th edn. Academic Press.Google Scholar
Kafiabad, H. A. & Bartello, P. 2016 Balance dynamics in rotating stratified turbulence. J. Fluid Mech. 795, 914949.Google Scholar
Kafiabad, H. A., Savva, M. A. C. & Vanneste, J. 2019 Diffusion of inertia-gravity waves by geostrophic turbulence. J. Fluid Mech. 869, R7.Google Scholar
Lindborg, E. 2015 A Helmholtz decomposition of structure functions and spectra calculated from aircraft data. J. Fluid Mech. 762, R4.Google Scholar
Nastrom, G. D. & Gage, K. S. 1985 A climatology of atmospheric wavenumber spectra of wind and temperature observed by commercial aircraft. J. Atmos. Sci. 42 (9), 950960.Google Scholar
Natterer, F. 2001 The Mathematics of Computerized Tomography. SIAM.Google Scholar
Qiu, B., Nakano, T., Chen, S. & Klein, P. 2017 Submesoscale transition from geostrophic flows to internal waves in the northwestern Pacific upper ocean. Nature Commun. 8, 14055.Google Scholar
Rocha, C. B., Chereskin, T. K., Gille, S. T. & Menemenlis, D. 2016a Mesoscale to submesoscale wavenumber spectra in drake passage. J. Phys. Oceanogr. 46 (2), 601620.Google Scholar
Rocha, C. B., Gille, S. T., Chereskin, T. K. & Menemenlis, D. 2016b Seasonality of submesoscale dynamics in the Kuroshio extension. Geophys. Res. Lett. 43 (21), 11304.Google Scholar
Salmon, R. 1998 Lectures on Geophysical Fluid Dynamics. Oxford University Press.Google Scholar
Torres, H. S., Klein, P., Menemenlis, D., Qiu, B., Su, Z., Wang, J., Chen, S. & Fu, L.-L. 2018 Partitioning ocean motions into balanced motions and internal gravity waves: a modeling study in anticipation of future space missions. J. Geophys. Res.: Oceans 123 (11), 80848105.Google Scholar
Yaglom, A. M. 1952 Introduction to the theory of stationary random functions. Usp. Mat. Nauk 7, 3168.Google Scholar
Zhang, F., Wei, J., Zhang, M., Bowman, K. P., Pan, L. L., Atlas, E. & Wofsy, S. C. 2015 Aircraft measurements of gravity waves in the upper troposphere and lower stratosphere during the START08 field experiment. Atmos. Chem. Phys. 15 (13), 7667.Google Scholar