Automatic Determination of Wind Profiles with Generalized SCIDAR

Prieur, Jean‐Louis; Ávila, Raúl; Daigne, G.; Vernin, J.

doi:10.1086/423161

Cited by 22 publications

(22 citation statements)

References 13 publications

(31 reference statements)

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“…The G-SCIDAR instrumental noise at any particular level is equivalent to ϳ10 Ϫ18 m Ϫ2/3 . This leads to an error in the Fried parameter r 0 and therefore in seeing of ϳ5% (Masciadri et al 2002;Prieur et al 2001Prieur et al , 2004. G-SCIDAR measurements during the 2002 Mauna Kea campaign show that half of the turbulence integral is caused by turbulence below 0.7 km and that this contribution is uncorrelated with the free-atmosphere turbulence (Tokovinin et al 2005).…”

Section: Instrumentation and Optical Turbulence Datamentioning

confidence: 99%

Modeling Optical Turbulence and Seeing over Mauna Kea*

Cherubini

Businger

Lyman

et al. 2008

Journal of Applied Meteorology and Climatology

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Atmospheric turbulence is a primary concern for astronomers. Turbulence causes amplitude and phase fluctuations in electromagnetic waves propagating through the atmosphere, constraining the maximum telescope resolution and resulting in telescope image degradation. Astronomical parameters that quantify these effects are generically referred to as seeing. Adaptive optics (AO) is used to reduce image degradation associated with optical turbulence. However, to optimize AO, knowledge of the vertical profile of turbulence and overall (integrated) seeing is needed. In this paper, an optical turbulence algorithm is described that makes use of the information on turbulence kinetic energy provided by a planetary boundary layer scheme available in the fifth-generation Pennsylvania State University-NCAR Mesoscale Model (MM5). Optical turbulence data collected on Mauna Kea during the 2002 site monitoring campaign are used to validate the algorithm, which has been implemented in operational runs of MM5 at the Mauna Kea Weather Center.

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Section: Instrumentation and Optical Turbulence Datamentioning

confidence: 99%

Modeling Optical Turbulence and Seeing over Mauna Kea*

Cherubini

Businger

Lyman

et al. 2008

Journal of Applied Meteorology and Climatology

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“…The refractive index structure constant profiles, C 2 N (h), are derived from SCIDAR observations through automated programs using inversion methods (Vernin 1993; Kluckers et al 1998; Prieur, Daigne & Avila 2001; Johnston et al 2002). The velocities of the turbulent layers are obtained through interactive programs (Kluckers et al 1998; Avila, Vernin & Sánchez 2001; Avila et al 2003; Vernin et al 2000) based on the clean method (Prieur et al 2001) and the first iterative and potentially automated algorithm also based on the clean procedure has been recently published (Prieur et al 2004). We present here an alternative method for deriving the wind velocity of turbulent layers from G‐SCIDAR measurements based on wavelet analysis.…”

Section: Introductionmentioning

confidence: 99%

Processing of turbulent-layer wind speed with Generalized SCIDAR through wavelet analysis

García-Lorenzo

Fuensalida

2006

Monthly Notices of the Royal Astronomical Society

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We describe a new method involving wavelet transforms for deriving the wind velocity associated with atmospheric turbulent layers from Generalized SCIDAR measurements. The algorithm analyses the cross‐correlation of a series of scintillation patterns separated by lapses of Δt, 2Δt, 3Δt, 4Δt and 5Δt using wavelet transforms. Wavelet analysis provides the position, direction and altitude of the different turbulent layers detected in each cross‐correlation. The comparison and consistency of the turbulent‐layer displacements in consecutive cross‐correlations allow the determination of their velocities and avoid misidentifications associated with noise and/or overlapping layers. To validate the algorithm, we have compared the velocity of turbulent layers derived on four nights with the wind vertical profile provided by balloon measurements. The software is fully automated and is able to analyse huge amounts of Generalized SCIDAR measurements.

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“…First results of simultaneous detection of C 2 N (h) and V (h) were published in [1]. Since then, an interactive CLEAN type algorithm for V (h) estimation was developed [7], which led to an automatic V (h) determination method based on the CLEAN algorithm [8]. A more recent V (h) determination approach uses wavelet analysis [3].…”

Section: Scidar (Scintillation Detection and Ranging)mentioning

confidence: 99%

Detection of wind velocities from scintillation images

Mohr

Johnston

Cottrell

2008

2008 23rd International Conference Image and Vision Computing New Zealand

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SCIDAR (SCIntillation Detection And Ranging) has been used at various sites around the world to characterise turbulence strength. However little focus has been given to finding average wind velocity, V (h), from SCIDAR data. Current methods rely on the detection of full spatio-temporal covariance profiles, containing a triplet pattern associated with each turbulent layer present. A new technique for finding V (h) from noisy scintillation covariance images using a modified CLEAN algorithm has been developed to handle partial spatio-temporal covariance profiles. The application of the new algorithm to data collected from Mount John University Observatory is demonstrated.

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Automatic Determination of Wind Profiles with Generalized SCIDAR

Cited by 22 publications

References 13 publications

Modeling Optical Turbulence and Seeing over Mauna Kea*

Modeling Optical Turbulence and Seeing over Mauna Kea*

Processing of turbulent-layer wind speed with Generalized SCIDAR through wavelet analysis

Detection of wind velocities from scintillation images

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