A Combined Wind Profiler and Polarimetric Weather Radar Method for the Investigation of Precipitation and Vertical Velocities

2009 ◽  
Vol 26 (9) ◽  
pp. 1940-1955 ◽  
Author(s):  
Michihiro S. Teshiba ◽  
Phillip B. Chilson ◽  
Alexander V. Ryzhkov ◽  
Terry J. Schuur ◽  
Robert D. Palmer
Keyword(s):  
Weather Radar ◽  
Terminal Velocity ◽  
Doppler Velocity ◽  
Drop Diameter ◽  
Wind Profiler ◽  
Rain Drop ◽  
Air Motion ◽  
Polarimetric Weather Radar ◽  
Uhf Wind Profiler ◽  
Differential Reflectivity

Abstract A method is presented by which combined S-band polarimetric weather radar and UHF wind profiler observations of precipitation can be used to extract the properties of liquid phase hydrometeors and the vertical velocity of the air through which they are falling. Doppler spectra, which contain the air motion and/or fall speed of hydrometeors, are estimated using the vertically pointing wind profiler. Complementary to these observations, spectra of rain drop size distribution (DSD) are simulated by several parameters as related to the DSD, which are estimated through the two polarimetric parameters of radar reflectivity (ZH) and differential reflectivity (ZDR) from the scanning weather radar. These DSDs are then mapped into equivalent Doppler spectra (fall speeds) using an assumed relationship between the equivolume drop diameter and the drop’s terminal velocity. The method is applied to a set of observations collected on 11 March 2007 in central Oklahoma. In areas of stratiform precipitation, where the vertical wind motion is expected to be small, it was found that the fall speeds obtained from the spectra of the rain DSD agree well with those of the Doppler velocity estimated with the profiler. For those cases when the shapes of the Doppler spectra are found to be similar in shape but shifted in velocity, the velocity offset is attributed to vertical air motion. In convective rainfall, the Doppler spectra of the rain DSD and the Doppler velocity can exhibit significant differences owing to vertical air motions together with atmospheric turbulence. Overall, it was found that the height dependencies of Doppler spectra measured by the profiler combined with vertical profiles of Z, ZDR, and the cross correlation (ρHV) as well as the estimated spectra of raindrop physical terminal fall speeds from the polarimetric radar provide unique insight into the microphysics of precipitation. Vertical air motions (updrafts/downdrafts) can be estimated using such combined measurements.

scholarly journals Microphysical retrievals from simultaneous polarimetric and profiling radar observations

2009 ◽  
Vol 27 (12) ◽  
pp. 4435-4448 ◽  
Author(s):  
M. P. Morris ◽  
P. B. Chilson ◽  
T. J. Schuur ◽  
A. Ryzhkov
Keyword(s):  
Remote Sensing Data ◽  
Weather Radar ◽  
Ground Truth ◽  
Ambient Air ◽  
Exponential Model ◽  
Squall Line ◽  
Doppler Velocity ◽  
Wind Profiler ◽  
Polarimetric Weather Radar ◽  
Velocity Spectra

Abstract. The character of precipitation detected at the surface is the final product of many microphysical interactions in the cloud above, the combined effects of which may be characterized by the observed drop size distribution (DSD). This necessitates accurate retrieval of the DSD from remote sensing data, especially radar as it offers large areal coverage, high spatial resolution, and rigorous quality control and testing. Combined instrument observations with a UHF wind profiler, an S-band polarimetric weather radar, and a video disdrometer are analyzed for two squall line events occuring during the calendar year 2007. UHF profiler Doppler velocity spectra are used to estimate the DSD aloft, and are complemented by DSDs retrieved from an exponential model applied to polarimetric data. Ground truth is provided by the disdrometer. A complicating factor in the retrieval from UHF profiler spectra is the presence of ambient air motion, which can be corrected using the method proposed by Teshiba et al. (2009), in which a comparison between idealized Doppler spectra calculated from the DSDs retrieved from KOUN and those retrieved from contaminated wind profiler spectra is performed. It is found that DSDs measured using the distrometer at the surface and estimated using the wind profiler and polarimetric weather radar generally showed good agreement. The DSD retrievals using the wind profiler were improved when the estimates of the vertical wind were included into the analysis, thus supporting the method of Teshiba et al. (2009). Furthermore, the the study presents a method of investigating the time and height structure of DSDs.

Rain drop size distributions estimated from NOAA Snow-Level Radar data

2021 ◽  
Keyword(s):  
Drop Size ◽  
Radar Data ◽  
Limited Range ◽  
Doppler Velocity ◽  
Velocity Spectrum ◽  
A Value ◽  
Rain Drop ◽  
Prior Literature ◽  
Air Motion ◽  
Drop Size Distributions

Abstract Using NOAA’s S-band High Power Snow-Level Radar, HPSLR, a technique for estimating the rain drop size distribution (DSD) above the radar is presented. This technique assumes the DSD can be described by a four parameter, generalized Gamma distribution (GGD). Using the radar’s measured average Doppler velocity spectrum and a value (assumed, measured, or estimated) of the vertical air motion, w, an estimate of the GGD is obtained. Four different methods can be used to obtain w. One method that estimates a mean mass-weighted raindrop diameter, Dm, from the measured reflectivity, Z, produces realistic DSDs compared to prior literature examples. These estimated DSDs provide evidence that the radar can retrieve the smaller drop sizes constituting the “drizzle” mode part of the DSD. This estimation technique was applied to 19 h of observations from Hankins, NC. Results support the concept that DSDs can be modeled using GGDs with a limited range of parameters. Further work is needed to validate the described technique for estimating DSDs in more varied precipitation types and to verify the vertical air motion estimates.

scholarly journals Combining cloud radar and radar wind profiler for a value added estimate of vertical air motion and particle terminal velocity within clouds

2018 ◽  
Author(s):  
Martin Radenz ◽  
Johannes Bühl ◽  
Volker Lehmann ◽  
Ulrich Görsdorf ◽  
Ronny Leinweber
Keyword(s):  
Value Added ◽  
Terminal Velocity ◽  
Wind Profiler ◽  
Air Velocity ◽  
Fall Velocity ◽  
Cloud Radar ◽  
A Value ◽  
Direct Measurements ◽  
Air Motion ◽  
Better Than

Abstract. Vertical-stare observations from a 482 MHz radar wind profiler and a 35 GHz cloud radar are combined on the level of individual Doppler spectra to measure vertical air motions in clear air, clouds and precipitation. For this purpose, a separation algorithm is proposed to remove the influence of falling particles from the wind profiler Doppler spectra and to calculate the terminal fall velocity of hydrometeors. The remaining error of both vertical air motion and terminal fall velocity is estimated to be better than 0.1 m s−1 using numerical simulations. This combination of both instruments allows direct measurements of in-cloud vertical air velocity and particle terminal fall velocity by means of ground-based remote sensing. The possibility of providing a profile every 10 s with a height resolution of

An Analysis of Errors in Drop Size Distribution Retrievals and Rain Bulk Parameters with a UHF Wind Profiling Radar and a Two-Dimensional Video Disdrometer

2008 ◽  
Vol 25 (12) ◽  
pp. 2282-2292 ◽  
Author(s):  
Laura Kanofsky ◽  
Phillip Chilson
Keyword(s):  
Error Analysis ◽  
Size Distribution ◽  
Drop Size ◽  
Ambient Air ◽  
Drop Size Distribution ◽  
Rainfall Rate ◽  
Doppler Velocity ◽  
Drop Diameter ◽  
Fall Speed ◽  
Air Motion

Abstract Vertically pointed wind profiling radars can be used to obtain measurements of the underlying drop size distribution (DSD) for a rain event by means of the Doppler velocity spectrum. Precipitation parameters such as rainfall rate, radar reflectivity factor, liquid water content, mass-weighted mean drop diameter, and median volume drop diameter can then be calculated from the retrieved DSD. The DSD retrieval process is complicated by the presence of atmospheric turbulence, vertical ambient air motion, selection of fall speed relationships, and velocity thresholding. In this note, error analysis is presented to quantify the effect of each of those factors on rainfall rate. The error analysis results are then applied to two precipitation events to better interpret the rainfall-rate retrievals. It was found that a large source of error in rain rate is due to unaccounted-for vertical air motion. For example, in stratiform rain with a rainfall rate of R = 10 mm h−1, a mesoscale downdraft of 0.6 m s−1 can result in a 34% underestimation of the estimated value of R. The fall speed relationship selection and source of air density information both caused negligible errors. Errors due to velocity thresholding become more important in the presence of significant contamination near 0 m s−1, such as ground clutter. If particles having an equivalent volume diameter of 0.8 mm and smaller are rejected, rainfall rate errors from −4% to −10% are possible, although these estimates depend on DSD and rainfall rate.

scholarly journals Combining cloud radar and radar wind profiler for a value added estimate of vertical air motion and particle terminal velocity within clouds

2018 ◽  
Vol 11 (10) ◽  
pp. 5925-5940 ◽  
Author(s):  
Martin Radenz ◽  
Johannes Bühl ◽  
Volker Lehmann ◽  
Ulrich Görsdorf ◽  
Ronny Leinweber
Keyword(s):  
Value Added ◽  
Terminal Velocity ◽  
Wind Profiler ◽  
Separation Algorithm ◽  
Fall Velocity ◽  
Cloud Radar ◽  
A Value ◽  
Cloud Dynamics ◽  
Direct Measurements ◽  
Air Motion

Abstract. Vertical-stare observations from a 482 MHz radar wind profiler and a 35 GHz cloud radar are combined on the level of individual Doppler spectra to measure vertical air motions in clear air, clouds and precipitation. For this purpose, a separation algorithm is proposed to remove the influence of falling particles from the wind profiler Doppler spectra and to calculate the terminal fall velocity of hydrometeors. The remaining error of both vertical air motion and terminal fall velocity is estimated to be better than 0.1 m s−1 using numerical simulations. This combination of instruments allows direct measurements of in-cloud vertical air velocity and particle terminal fall velocity by means of ground-based remote sensing. The possibility of providing a profile every 10 s with a height resolution of <100 m allows further insight into the process scale of in-cloud dynamics. The results of the separation algorithm are illustrated by two case studies, the first covering a deep frontal cloud and the second featuring a shallow mixed-phase cloud.

Characterization of Vertical Velocity and Drop Size Distribution Parameters in Widespread Precipitation at ARM Facilities

2012 ◽  
Vol 51 (2) ◽  
pp. 380-391 ◽  
Author(s):  
Scott E. Giangrande ◽  
Edward P. Luke ◽  
Pavlos Kollias
Keyword(s):  
Standard Deviation ◽  
Size Distribution ◽  
Great Plains ◽  
Rainfall Rate ◽  
Doppler Velocity ◽  
Small Scale ◽  
Drop Diameter ◽  
Dual Frequency ◽  
Raindrop Size Distribution ◽  
Air Motion

AbstractExtended, high-resolution measurements of vertical air motion and median volume drop diameter D0 in widespread precipitation from three diverse Atmospheric Radiation Measurement Program (ARM) locations [Lamont, Oklahoma, Southern Great Plains site (SGP); Niamey, Niger; and Black Forest, Germany] are presented. The analysis indicates a weak (0–10 cm−1) downward air motion beneath the melting layer for all three regions, a magnitude that is to within the typical uncertainty of the retrieval methods. On average, the hourly estimated standard deviation of the vertical air motion is 0.25 m s−1 with no pronounced vertical structure. Profiles of D0 vary according to region and rainfall rate. The standard deviation of 1-min-averaged D0 profiles for isolated rainfall rate intervals is 0.3–0.4 mm. Additional insights into the form of the raindrop size distribution are provided using available dual-frequency Doppler velocity observations at SGP. The analysis suggests that gamma functions better explain paired velocity observations and radar retrievals for the Oklahoma dataset. This study will be useful in assessing uncertainties introduced in the measurement of precipitation parameters from ground-based and spaceborne remote sensors that are due to small-scale variability.

scholarly journals Improving the rainfall rate estimation in the midstream of the Heihe River Basin using rain drop size distribution

2009 ◽  
Vol 6 (5) ◽  
pp. 6107-6134 ◽  
Author(s):  
G. Zhao ◽  
R. Chu ◽  
X. Li ◽  
T. Zhang ◽  
J. Shen ◽  
...  
Keyword(s):  
Size Distribution ◽  
Rain Rate ◽  
Terminal Velocity ◽  
Tibet Plateau ◽  
Heihe River ◽  
Drop Diameter ◽  
Observation Data ◽  
Raindrop Size Distribution ◽  
Rain Drop ◽  
Raindrop Size

Abstract. During the intensive observation period of the Watershed Allied Telemetry Experimental Research (WATER), a total of 1074 raindrop size distribution were measured by the Parsivel disdrometer, a latest state of the art optical laser instrument. Because of the limited observation data in Qinghai-Tibet Plateau, the modeling behavior was not well-done. We used raindrop size distributions to improve the rain rate estimator of meteorological radar, in order to obtain many accurate rain rate data in this area. We got the relationship between the terminal velocity of the rain drop and the diameter (mm) of a rain drop: v(D)=4.67 D0.53. Then four types of estimators for X-band polarimetric radar are examined. The simulation results show that the classical estimator R(Z) is most sensitive to variations in DSD and the estimator R (KDP, Z, ZDR) is the best estimator for estimating the rain rate. The lowest sensitivity of the rain rate estimator R (KDP, Z, ZDP) to variations in DSD can be explained by the following facts. The difference in the forward-scattering amplitudes at horizontal and vertical polarizations, which contributes KDP, is proportional to the 3rd power of the drop diameter. On the other hand, the exponent of the backscatter cross section, which contributes to Z, is proportional to the 6th power of the drop diameter. Because the rain rate R is proportional to the 3.57th power of the drop diameter, KDP is less sensitive to DSD variations than Z.

scholarly journals Differential Doppler Velocity: A Radar Parameter for Characterizing Hydrometeor Size Distributions

1997 ◽  
Vol 36 (6) ◽  
pp. 649-663 ◽  
Author(s):  
Damian R. Wilson ◽  
Anthony J. Illingworth ◽  
T. Mark Blackman
Keyword(s):  
Elevation Angle ◽  
Terminal Velocity ◽  
Mean Value ◽  
Rainfall Rate ◽  
Doppler Velocity ◽  
Size Spectrum ◽  
Drop Diameter ◽  
Mean Values ◽  
Differential Doppler ◽  
The Difference

Abstract Observations of Doppler-resolved spectra of differential radar reflectivity provide estimates of particle shapes as a function of their terminal velocity, and they can be derived by having the antenna at a significant elevation angle. Turbulence tends to smear out the details of the actual spectra observed, but the difference in the mean values of velocity using horizontal and vertical polarizations, which the authors call the “differential Doppler velocity” (DDV), is unaffected. Larger raindrops fall faster and are oblate, so values of DDV are positive. If a gamma function is used for the raindrop size spectrum, then the observed DDV and ZDR correspond to particular values of median drop diameter D0 and the dispersion index m. The scaling parameter N0 is derived from Z. Estimates of m have a mean value of 5 but vary substantially. An error in rainfall rate of up to ±15% results if the rainfall rate is computed from Z and ZDR alone, and m is assumed constant at 5. An overestimation of more than 30% occurs if m is assumed to be 0. DDV values in stratiform ice are slightly negative. The values in ice are explicable in terms of a mixture of slowly falling oblate crystals and faster-falling spherical aggregates. In the bright band, DDV is consistent with the coexistence of oblate snowflakes and faster-falling raindrops.

Doppler-Polarimetric Weather Radar: Returns from Wide Spread Precipitation

2007 ◽  
Vol 66 (8) ◽  
pp. 715-727 ◽  
Author(s):  
F. J. Yanovsky ◽  
C. M. H. Unal ◽  
H. W. J. Russchenberg ◽  
L. P. Ligthart
Keyword(s):  
Weather Radar ◽  
Wide Spread ◽  
Polarimetric Weather Radar
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