On the effect of moisture on the detection of tropospheric turbulence from in situ measurements

Wilson, Richard; Luce, Hubert; Hashiguchi, Hiroyuki; Shiotani, Masato; Dalaudier, Francis

doi:10.5194/amt-6-697-2013

Cited by 40 publications

(34 citation statements)

References 29 publications

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“…Then, the Thorpe analysis is performed using the reconstructed θ (hereafter θ * ). Air has three states, dry, subsaturated, and saturated, but the effect of water vapor is negligible in the subsaturated air; thus, we only considered dry and saturated air as in Wilson et al (). When the vertical displacement of an air parcel occurs in the saturated layer, θ is not a conservative quantity due to latent heat release, and the stability is lower than in dry air.…”

Section: Methodsmentioning

confidence: 99%

Characteristics of Atmospheric Turbulence Retrieved From High Vertical‐Resolution Radiosonde Data in the United States

Chun

Wilson

et al. 2019

JGR Atmospheres

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In this study, we estimate atmospheric turbulence in the free atmosphere in terms of the Thorpe scale (LT) and eddy dissipation rate (ε) using U.S. high vertical‐resolution radiosonde data over 4 years (September 2012 to August 2016) at 68 operational stations. In addition, same calculations are conducted for 12 years (October 2005 to September 2017) at four stations among the 68 stations. These high vertical‐resolution radiosonde data have a vertical resolution of approximately 5 m and extend to an altitude of approximately 33 km, and thus, turbulence can be retrieved in the entire troposphere and lower stratosphere. There are thicker and stronger turbulent layers in the troposphere than in the stratosphere, with mean ε values of 1.84 × 10−4 and 1.37 × 10−4 m2/s3 in the troposphere and stratosphere, respectively. The vertical structure of ε exhibits strong seasonal variations, especially in the upper troposphere and lower stratosphere, with the largest ε values in summer and the smallest in winter. In the horizontal distribution of ε, large ε is seen mainly above the mountainous region in the troposphere, but this pattern is not seen in the stratosphere. Although ε is estimated by the square of LT multiplied by the cube of the Brunt‐Väisälä frequency (N), the regions of large ε are matched with large LT regions where N is relatively small. For the time series of ε near the tropopause for 12 years at four stations, an annual variation is prominent at all stations without significant interannual variations. There is, however, a slightly increasing trend of ε at two stations.

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Section: Methodsmentioning

confidence: 99%

Characteristics of Atmospheric Turbulence Retrieved From High Vertical‐Resolution Radiosonde Data in the United States

Chun

Wilson

et al. 2019

JGR Atmospheres

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“…4e we show the potential temperature calculated by taking into account the effects of air moisture. As shown by Wilson et al (2013) such effects on turbulence detection can be dramatic for a cloudy or moist atmosphere (saturated with water vapour). Hence, the moist potential temperature is used for further calculation in this study.…”

Section: Derivation Of ε From Radiosonde Datamentioning

confidence: 99%

“…First, pressure is smoothed with a spline fit to be monotonically decreasing, and all data are resampled to have a constant altitude increment. Moisture is handled as suggested by Wilson et al (2013); i.e. moist regions are detected, and within these the moist potential temperature is used instead of the dry one.…”

Section: Derivation Of ε From Radiosonde Datamentioning

confidence: 99%

Derivation of turbulent energy dissipation rate with the Middle Atmosphere Alomar Radar System (MAARSY) and radiosondes at Andøya, Norway

Rapp

Schrön

et al. 2016

Ann. Geophys.

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Abstract. We present the derivation of turbulent energy dissipation rate ε from a total of 522 days of observations with the Middle Atmosphere Alomar Radar SYstem (MAARSY) mesosphere-stratosphere-troposphere (MST) radar running tropospheric experiments during the period of 2010-2013 as well as with balloon-borne radiosondes based on a campaign in the summer 2013. Spectral widths are converted to ε after the removal of the broadening effects due to the finite beam width of the radar. With the simultaneous in situ measurements of ε with balloon-borne radiosondes at the MAARSY radar site, we compare the ε values derived from both techniques and reach an encouraging agreement between them. Using all the radar data available, we present a preliminary climatology of atmospheric turbulence in the UTLS (upper troposphere and lower stratosphere) region above the MAARSY site showing a variability of more than 5 orders of magnitude inherent in turbulent energy dissipation rates. The derived ε values reveal a log-normal distribution with a negative skewness, and the ε profiles show an increase with height which is also the case for each individual month. Atmospheric turbulence based on our radar measurements reveals a seasonal variation but no clear diurnal variation in the UTLS region. Comparison of ε with the gradient Richardson number Ri shows that only 1.7 % of all the data with turbulence occur under the condition of Ri < 1 and that the values of ε under the condition of Ri < 1 are significantly larger than those under Ri > 1. Further, there is a roughly negative correlation between ε and Ri that is independent of the scale dependence of Ri. Turbulence under active dynamical conditions (velocity of horizontal wind U > 10 m s −1 ) is significantly stronger than under quiet conditions (U < 10 m s −1 ). Last but not least, the derived ε values are compared with the corresponding vertical shears of background wind velocity showing a linear relation with a corresponding correlation coefficient r = 58 % well above the 99.9 % significance level. This implies that wind shears play an important role in the turbulence generation in the troposphere and lower stratosphere (through the Kelvin-Helmholtz instability).

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“…Moisture is handled using the routine given by Wilson et al (2013). To this end, saturated regions are detected, and a composite potential temperature profile * is computed by integration of ∂ /∂z using the moist buoyancy frequency within those saturated regions and the dry buoyancy frequency otherwise.…”

Section: Instrumentation and Methodsmentioning

confidence: 99%

Comparing turbulent parameters obtained from LITOS and radiosonde measurements

2015

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Abstract. Stratospheric turbulence is important for the mixing of trace species and the energy balance, but direct measurements are sparse due to the required resolution and accuracy. Recently, turbulence parameters such as the energy dissipation rate ε were inferred from standard radiosonde data by means of a Thorpe analysis. To this end, layers with vertically decreasing potential temperature are analysed, which is expected to indicate turbulence. Such an application assumes a proportionality between the Thorpe length L T and the Ozmidov scale L O . While this relation is accepted for the ocean, experimental evidence for such proportionality in the stratosphere is sparse. We have developed a high-resolution (8 kHz) turbulence measurement system called LITOS (Leibniz Institute Turbulence Observations in the Stratosphere), which for the first time resolves the inner scale of turbulence in the stratosphere. Therewith the energy dissipation rate ε can be determined by spectral analysis. This independent value for ε enables us to check the relation L O ∝ L T . In our measurements no such proportionality can be seen, although the mean of the ratio L O /L T is close to what is assumed in radiosonde analyses. Dissipation rates for individual layers obtained from radiosondes deviate up to a factor of ∼ 3000 from those obtained by spectral analysis. Some turbulent layers measured by LITOS are not observed by the radiosonde at all, and vice versa. However, statements about the statistical mean seem to be possible by Thorpe analysis.

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On the effect of moisture on the detection of tropospheric turbulence from in situ measurements

Cited by 40 publications

References 29 publications

Characteristics of Atmospheric Turbulence Retrieved From High Vertical‐Resolution Radiosonde Data in the United States

Characteristics of Atmospheric Turbulence Retrieved From High Vertical‐Resolution Radiosonde Data in the United States

Derivation of turbulent energy dissipation rate with the Middle Atmosphere Alomar Radar System (MAARSY) and radiosondes at Andøya, Norway

Comparing turbulent parameters obtained from LITOS and radiosonde measurements

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