scholarly journals Comparison of Different Techniques to Calculate Properties of Atmospheric Turbulence from Low-Resolution Data

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 199
Author(s):  
Marta Wacławczyk ◽  
Amoussou S. Gozingan ◽  
Jackson Nzotungishaka ◽  
Moein Mohammadi ◽  
Szymon P. Malinowski
Keyword(s):  
Measurement Data ◽  
Order Structure ◽  
Low Resolution ◽  
Zero Crossings ◽  
Depth Analysis ◽  
Atmospheric Measurement ◽  
Velocity Derivative ◽  
Velocity Spectra ◽  
Function Number ◽  
Resolution Data

In this work we study different techniques to estimate basic properties of turbulence, that is its characteristic velocity and length scale from low-resolution data. The methods are based on statistics of the signals like the velocity spectra, second-order structure function, number of signal’s zero-crossings and the variance of velocity derivative. First, in depth analysis of estimates from artificial velocity time series is performed. Errors due to finite averaging window, finite cut-off frequencies and different fitting ranges are discussed. Next, real atmospheric measurement data are studied. It is demonstrated that differences between results of the methods can indicate deviations from the Kolmogorov’s theory or the presence of external intermittency, that is the existence of alternating laminar/turbulent flow patches.

Proteins and direct methods

1991 ◽  
Vol 197 (3-4) ◽  
Author(s):  
Fan Hai-fu ◽  
Hao Quan ◽  
M. M. Woolfson
Keyword(s):  
First Principles ◽  
Figure Of Merit ◽  
Direct Methods ◽  
Large Structure ◽  
Low Resolution ◽  
Figures Of Merit ◽  
Large Structures ◽  
Resolution Data

AbstractConventional direct methods, which work so well for small structures, are less successful for macromolecules. Where it has been demonstrated that a solution might be found using direct methods it is then found that the usual figures of merit are unable to distinguish the few good sets of phases from the large number of sets generated. The reasons for the difficulties with very large structures are considered from a first-principles approach taking into account both the factors of having a large number of atoms and low resolution data. A proposal is made for trying to recognize good phase sets by taking a large structure as a sum of a number of smaller structures for each of which a conventional figure of merit can be applied.

scholarly journals Long Slit Echelle Spectroscopy of Supernova Remnants in M33

1988 ◽  
Vol 101 ◽  
pp. 193-196
Author(s):  
William P. Blair ◽  
You-Hua Chu ◽  
Robert C. Kennicutt
Keyword(s):  
Supernova Remnants ◽  
Low Resolution ◽  
Low Velocity ◽  
Line Intensities ◽  
Echelle Spectroscopy ◽  
Resolution Data

AbstractWe have obtained long slit echelle spectroscopy for 10 of the brightest supernova remnants in M33 using the KPNO 4 m telescope. The profiles at Hα indicate bulk motions in the range 100–350 km s−1 in these remnants. Nearly all of the objects show signs of contamination by low velocity H II emission at some level. This affects the line intensities measured from low resolution data and may affect diameter measurements of these remnants.

scholarly journals Better turbulence spectra from velocity–azimuth display scanning wind lidar

2019 ◽  
Vol 12 (3) ◽  
pp. 1871-1888 ◽  
Author(s):  
Felix Kelberlau ◽  
Jakob Mann
Keyword(s):  
Data Processing ◽  
Resonance Effect ◽  
Measurement Data ◽  
Cross Contamination ◽  
Raw Data ◽  
Combined Use ◽  
Beam Method ◽  
Wind Lidar ◽  
Velocity Spectra ◽  
Methods Numerical

Abstract. Turbulent velocity spectra derived from velocity–azimuth display (VAD) scanning wind lidars deviate from spectra derived from one-point measurements due to averaging effects and cross-contamination among the velocity components. This work presents two novel methods for minimizing these effects through advanced raw data processing. The squeezing method is based on the assumption of frozen turbulence and introduces a time delay into the raw data processing in order to reduce cross-contamination. The two-beam method uses only certain laser beams in the reconstruction of wind vector components to overcome averaging along the measurement circle. Models are developed for conventional VAD scanning and for both new data processing methods to predict the spectra and identify systematic differences between the methods. Numerical modeling and comparison with measurement data were both used to assess the performance of the methods. We found that the squeezing method reduces cross-contamination by eliminating the resonance effect caused by the longitudinal separation of measurement points and also considerably reduces the averaging along the measurement circle. The two-beam method eliminates this averaging effect completely. The combined use of the squeezing and two-beam methods substantially improves the ability of VAD scanning wind lidars to measure in-wind (u) and vertical (w) fluctuations.

scholarly journals An overview of heavy-atom derivatization of protein crystals

2016 ◽  
Vol 72 (3) ◽  
pp. 303-318 ◽  
Author(s):  
Ashley C. W. Pike ◽  
Elspeth F. Garman ◽  
Tobias Krojer ◽  
Frank von Delft ◽  
Elisabeth P. Carpenter
Keyword(s):  
Heavy Atom ◽  
Protein Crystals ◽  
Data Sets ◽  
Low Resolution ◽  
Heavy Atoms ◽  
Density Maps ◽  
Resolution Electron ◽  
First Choice ◽  
First Time ◽  
Resolution Data

Heavy-atom derivatization is one of the oldest techniques for obtaining phase information for protein crystals and, although it is no longer the first choice, it remains a useful technique for obtaining phases for unknown structures and for low-resolution data sets. It is also valuable for confirming the chain trace in low-resolution electron-density maps. This overview provides a summary of the technique and is aimed at first-time users of the method. It includes guidelines on when to use it, which heavy atoms are most likely to work, how to prepare heavy-atom solutions, how to derivatize crystals and how to determine whether a crystal is in fact a derivative.

On the Use of low-resolution Data to Improve Structure Prediction of Proteins and Protein Complexes

2009 ◽  
Vol 5 (11) ◽  
pp. 3129-3137 ◽  
Author(s):  
Marco D’Abramo ◽  
Tim Meyer ◽  
Pau Bernadó ◽  
Carles Pons ◽  
Juan Fernández Recio ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Protein Complexes ◽  
Low Resolution ◽  
Resolution Data
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