Dynamic processes contributing to the mesoscale spectrum of atmospheric motions
Kenneth S. Gage
Abstract
Kenneth S. Gage
Abstract
Introduction This chapter is a review of recent developments in our understanding of the mesoscale spectrum of atmospheric motions. This topic has received considerable attention in the two decades since Doug Lilly's (1983) seminal paper on stratified turbulence. The subject has not been without controversy as atmospheric scientists and fluid dynamicists have debated the relative contributions of turbulent processes and internal waves to the spectrum of atmospheric motions. In this review we focus attention on the lower atmosphere, which is of primary interest to meteorologists. Several papers that preceded Lilly's work are worth noting. Gage and Jasperson (1979) noted the variability in high-resolution sequential wind observations taken with a novel balloon sounding system. Gage (1979) placed these observations in a turbulence context and attributed much of the variability in these observations to two-dimensional turbulence arguing that the scales were too large to be associated with three-dimensional turbulence. Dewan (1979) examined stratospheric spectra and concluded that while the spectra had many of the features generally associated with turbulence they could also be explained by a spectrum of internal waves. Similar arguments were made by VanZandt (1982) who argued for a universal spectrum of internal waves analogous to the Garrette-Munk spectrum of internal waves in the ocean (Garrett and Munk, 1972). The importance of an improved understanding of mesoscale variability has recently become evident as increasing importance is attached to the assimilation of diverse atmospheric data into numerical models. The current situation is summarized by Daley (1997) who argues that model forecasts depend critically on the assimilation of data with a specified error covariance.
OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Introduction This chapter is a review of recent developments in our understanding of the mesoscale spectrum of atmospheric motions. This topic has received considerable attention in the two decades since Doug Lilly's (1983) seminal paper on stratified turbulence. The subject has not been without controversy as atmospheric scientists and fluid dynamicists have debated the relative contributions of turbulent processes and internal waves to the spectrum of atmospheric motions. In this review we focus attention on the lower atmosphere, which is of primary interest to meteorologists. Several papers that preceded Lilly's work are worth noting. Gage and Jasperson (1979) noted the variability in high-resolution sequential wind observations taken with a novel balloon sounding system. Gage (1979) placed these observations in a turbulence context and attributed much of the variability in these observations to two-dimensional turbulence arguing that the scales were too large to be associated with three-dimensional turbulence. Dewan (1979) examined stratospheric spectra and concluded that while the spectra had many of the features generally associated with turbulence they could also be explained by a spectrum of internal waves. Similar arguments were made by VanZandt (1982) who argued for a universal spectrum of internal waves analogous to the Garrette-Munk spectrum of internal waves in the ocean (Garrett and Munk, 1972). The importance of an improved understanding of mesoscale variability has recently become evident as increasing importance is attached to the assimilation of diverse atmospheric data into numerical models. The current situation is summarized by Daley (1997) who argues that model forecasts depend critically on the assimilation of data with a specified error covariance.
Key concepts: Mesoscale meteorology, Environmental science, Spectrum (functional analysis), Meteorology, Geology, Geography, Physics, Quantum mechanics