scholarly journals Evaluation of turbulent dissipation rate retrievals from Doppler Cloud Radar

2012 ◽  
Vol 5 (6) ◽  
pp. 1375-1385 ◽  
Author(s):  
M. D. Shupe ◽  
I. M. Brooks ◽  
G. Canut
Keyword(s):  
Dissipation Rate ◽  
Sonic Anemometer ◽  
Doppler Velocity ◽  
Data Sets ◽  
Tethered Balloon ◽  
Turbulent Dissipation ◽  
Turbulent Dissipation Rate ◽  
Cloud Radar ◽  
Stratocumulus Clouds

Abstract. Turbulent dissipation rate retrievals from cloud radar Doppler velocity measurements are evaluated using independent, in situ observations in Arctic stratocumulus clouds. In situ validation data sets of dissipation rate are derived using sonic anemometer measurements from a tethered balloon and high frequency pressure variation observations from a research aircraft, both flown in proximity to stationary, ground-based radars. Modest biases are found among the data sets in particularly low- or high-turbulence regimes, but in general the radar-retrieved values correspond well with the in situ measurements. Root mean square differences are typically a factor of 4–6 relative to any given magnitude of dissipation rate. These differences are no larger than those found when comparing dissipation rates computed from tethered-balloon and meteorological tower-mounted sonic anemometer measurements made at spatial distances of a few hundred meters. Temporal lag analyses suggest that approximately half of the observed differences are due to spatial sampling considerations, such that the anticipated radar-based retrieval uncertainty is on the order of a factor of 2–3. Moreover, radar retrievals are clearly able to capture the vertical dissipation rate structure observed by the in situ sensors, while offering substantially more information on the time variability of turbulence profiles. Together these evaluations indicate that radar-based retrievals can, at a minimum, be used to determine the vertical structure of turbulence in Arctic stratocumulus clouds.

scholarly journals Evaluation of turbulent dissipation rate retrievals from Doppler cloud radar

2012 ◽  
Vol 5 (1) ◽  
pp. 747-774 ◽  
Author(s):  
M. D. Shupe ◽  
I. M. Brooks ◽  
G. Canut
Keyword(s):  
Dissipation Rate ◽  
Doppler Velocity ◽  
Data Sets ◽  
Tethered Balloon ◽  
Validation Data ◽  
Turbulent Dissipation ◽  
Turbulent Dissipation Rate ◽  
Cloud Radar ◽  
Stratocumulus Clouds

Abstract. Turbulent dissipation rate retrievals from cloud radar Doppler velocity measurements are evaluated using independent, in situ observations in Arctic stratocumulus clouds. In situ validation data sets of dissipation rate are derived using sonic anemometer measurements from a tethered balloon and high frequency pressure variation observations from a research aircraft, both flown in proximity to stationary, ground-based radars. Modest biases are found among the data sets in particularly low- or high-turbulence regimes, but in general the radar-retrieved values correspond well with the in situ measurements. Root mean square differences are typically a factor of 4–6 relative to any given magnitude of dissipation rate. These differences are no larger than those found when comparing dissipation rates computed from tethered-balloon and 15-m tower sonic measurements made at spatial distances of a few hundred meters. Moreover, radar retrievals are able to capture the vertical dissipation rate structure observed by the in situ sensors, while offering substantially more information on the time variability of turbulence profiles. Together these evaluations indicate that radar-based retrievals can, at a minimum, be used to determine the vertical structure of turbulence in Arctic stratocumulus clouds.

On Deriving Vertical Air Motions from Cloud Radar Doppler Spectra

2008 ◽  
Vol 25 (4) ◽  
pp. 547-557 ◽  
Author(s):  
Matthew D. Shupe ◽  
Pavlos Kollias ◽  
Michael Poellot ◽  
Edwin Eloranta
Keyword(s):  
Vertical Velocity ◽  
Dissipation Rate ◽  
Department Of Energy ◽  
Doppler Spectrum ◽  
Liquid Droplets ◽  
Turbulent Dissipation ◽  
Turbulent Dissipation Rate ◽  
Cloud Radar ◽  
Research Aircraft ◽  
Atmospheric Radiation Measurement

Abstract A method for deriving vertical air motions from cloud radar Doppler spectrum measurements is introduced. The method is applicable to cloud volumes containing small particles, in this case liquid droplets, which are assumed to trace vertical air motions because of their limited size. The presence of liquid droplets is confirmed using multiple ground-based remote sensors. Corrections for Doppler spectrum broadening due to turbulence, wind shear, and radar beamwidth are applied. As a result of the turbulence broadening correction, the turbulent dissipation rate can also be estimated. This retrieval is demonstrated using measurements from the Department of Energy (DOE) Atmospheric Radiation Measurement Program’s (ARM) site in Barrow, Alaska, during the Mixed-Phase Arctic Cloud Experiment (MPACE) of autumn 2004. Comparisons of the retrievals with measurements by research aircraft near Barrow indicate that, on the whole, the retrievals perform well. A small bias in vertical velocity between the retrievals and aircraft measurements is found, based on a statistical comparison of four cases comprising nearly 6 h of data. Turbulent dissipation rate comparisons suggest that the radar-retrieved vertical velocity might be slightly underestimated because of an underestimate of the turbulence broadening correction. However, large uncertainties in aircraft vertical velocity measurements likely impact the comparison.

scholarly journals Using artificial neural networks to predict riming from Doppler cloud radar observations

2021 ◽  
Author(s):  
Teresa Vogl ◽  
Maximilian Maahn ◽  
Stefan Kneifel ◽  
Willi Schimmel ◽  
Dmitri Moisseev ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Doppler Velocity ◽  
Data Sets ◽  
Radar Observations ◽  
Cloud Radar ◽  
Target Values ◽  
In Situ Observations ◽  
Artificial Neural

Abstract. Riming, i.e. the accretion and freezing of SLW on ice particles in mixed-phase clouds, is an important pathway for precipitation formation. Detecting and quantifying riming using ground-based cloud radar observations is of great interest, however, approaches based on measurements of the mean Doppler velocity (MDV) are unfeasible in convective and orographically influenced cloud systems. Here, we show how artificial neural networks (ANNs) can be used to predict riming using ground-based zenith-pointing cloud radar variables as input features. ANNs are a versatile means to extract relations from labeled data sets, which contain input features along with the expected target values. Training data are extracted from a data set acquired during winter 2014 in Finland, containing both Ka-band cloud radar and in-situ observations of snowfall. We focus on two configurations of input variables: ANN #1 uses the equivalent radar reflectivity factor (Ze), MDV, the width from left to right edge of the spectrum above the noise floor (spectrum edge width; SEW), and the skewness as input features. ANN #2 only uses Ze, SEW and skewness. The application of these two ANN configurations to case studies from different data sets demonstrates that both are able to predict strong riming (riming index = 1) and yield low values (riming index ≤ 0.4) for unrimed snow. In general, the predictions of ANN #1 and ANN #2 are very similar, advocating the capability to predict riming without the use of MDV. It is demonstrated that both ANN setups are able to generalize to W-band radar data. The predictions of both ANNs for a wintertime convective cloud fit coinciding in-situ observations extremely well, suggesting the possibility to predict riming even within convective systems. Application of ANN #2 to an orographic case yields high riming index values coinciding with observations of solid graupel particles at the ground.

scholarly journals A Dynamic Flight Model for Slocum Gliders and Implications for Turbulence Microstructure Measurements

2019 ◽  
Vol 36 (2) ◽  
pp. 281-296 ◽  
Author(s):  
Lucas Merckelbach ◽  
Anja Berger ◽  
Gerd Krahmann ◽  
Marcus Dengler ◽  
Jeffrey R. Carpenter
Keyword(s):  
Dissipation Rate ◽  
Input Parameter ◽  
Weather Conditions ◽  
Water Velocity ◽  
Doppler Velocity ◽  
Electromagnetic Current ◽  
Model Errors ◽  
Turbulent Dissipation ◽  
Adverse Weather ◽  
Flight Model

Abstract The turbulent dissipation rate ε is a key parameter to many oceanographic processes. Recently, gliders have been increasingly used as a carrier for microstructure sensors. Compared to conventional ship-based methods, glider-based microstructure observations allow for long-duration measurements under adverse weather conditions and at lower costs. The incident water velocity U is an input parameter for the calculation of the dissipation rate. Since U cannot be measured using the standard glider sensor setup, the parameter is normally computed from a steady-state glider flight model. As ε scales with U2 or U4, depending on whether it is computed from temperature or shear microstructure, respectively, flight model errors can introduce a significant bias. This study is the first to use measurements of in situ glider flight, obtained with a profiling Doppler velocity log and an electromagnetic current meter, to test and calibrate a flight model, extended to include inertial terms. Compared to a previously suggested flight model, the calibrated model removes a bias of approximately 1 cm s−1 in the incident water velocity, which translates to roughly a factor of 1.2 in estimates of the dissipation rate. The results further indicate that 90% of the estimates of the dissipation rate from the calibrated model are within a factor of 1.1 and 1.2 for measurements derived from microstructure temperature sensors and shear probes, respectively. We further outline the range of applicability of the flight model.

scholarly journals Observations of Convective Thermals with Weather Radar

2017 ◽  
Vol 34 (7) ◽  
pp. 1585-1590 ◽  
Author(s):  
Valery Melnikov ◽  
Dusan S. Zrnić
Keyword(s):  
Dissipation Rate ◽  
Weather Radar ◽  
Radar Observations ◽  
Turbulent Dissipation ◽  
Turbulent Dissipation Rate ◽  
Rise Rate ◽  
Differential Reflectivity

AbstractIt is shown that the NEXRAD weather radar with enhanced detectability is capable of observing the evolution of convective thermals. The fields of radar differential reflectivity show that the upper parts of the thermals are observable due to Bragg scatter, whereas scattering from insects dominates in the lower parts. The thermal-top rise rate is between 1.5 and 3.7 m s−1 in the analyzed case. Radar observations of thermals also enable estimations of their maximum heights, horizontal sizes, and the turbulent dissipation rate within each thermal. These attributes characterize the intensity of convection.

Turbulent Dissipation Rate In The Boundary Layer Via UHF Wind Profiler Doppler Spectral Width Measurements

2002 ◽  
Vol 103 (3) ◽  
pp. 361-389 ◽  
Author(s):  
Sandra Jacoby-Koaly ◽  
B. Campistron ◽  
S. Bernard ◽  
B. Bénech ◽  
F. Ardhuin-Girard ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Dissipation Rate ◽  
Spectral Width ◽  
Wind Profiler ◽  
Turbulent Dissipation ◽  
Turbulent Dissipation Rate ◽  
Width Measurements ◽  
Uhf Wind Profiler

scholarly journals A Method for Estimating the Turbulent Kinetic Energy Dissipation Rate from a Vertically Pointing Doppler Lidar, and Independent Evaluation from Balloon-Borne In Situ Measurements

2010 ◽  
Vol 27 (10) ◽  
pp. 1652-1664 ◽  
Author(s):  
Ewan J. O’Connor ◽  
Anthony J. Illingworth ◽  
Ian M. Brooks ◽  
Christopher D. Westbrook ◽  
Robin J. Hogan ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Convective Boundary Layer ◽  
Sonic Anemometer ◽  
Horizontal Wind ◽  
Inertial Subrange ◽  
Doppler Velocity ◽  
Doppler Lidar ◽  
Convective Boundary ◽  
Dissipation Rates

Abstract A method of estimating dissipation rates from a vertically pointing Doppler lidar with high temporal and spatial resolution has been evaluated by comparison with independent measurements derived from a balloon-borne sonic anemometer. This method utilizes the variance of the mean Doppler velocity from a number of sequential samples and requires an estimate of the horizontal wind speed. The noise contribution to the variance can be estimated from the observed signal-to-noise ratio and removed where appropriate. The relative size of the noise variance to the observed variance provides a measure of the confidence in the retrieval. Comparison with in situ dissipation rates derived from the balloon-borne sonic anemometer reveal that this particular Doppler lidar is capable of retrieving dissipation rates over a range of at least three orders of magnitude. This method is most suitable for retrieval of dissipation rates within the convective well-mixed boundary layer where the scales of motion that the Doppler lidar probes remain well within the inertial subrange. Caution must be applied when estimating dissipation rates in more quiescent conditions. For the particular Doppler lidar described here, the selection of suitably short integration times will permit this method to be applicable in such situations but at the expense of accuracy in the Doppler velocity estimates. The two case studies presented here suggest that, with profiles every 4 s, reliable estimates of ε can be derived to within at least an order of magnitude throughout almost all of the lowest 2 km and, in the convective boundary layer, to within 50%. Increasing the integration time for individual profiles to 30 s can improve the accuracy substantially but potentially confines retrievals to within the convective boundary layer. Therefore, optimization of certain instrument parameters may be required for specific implementations.

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