scholarly journals Reduction of Nonuniform Beamfilling Effects by Multiple Constraints: A Simulation Study

2015 ◽  
Vol 32 (11) ◽  
pp. 2114-2124 ◽  
Author(s):  
David A. Short ◽  
Robert Meneghini ◽  
Amber E. Emory ◽  
Mathew R. Schwaller
Keyword(s):  
Attenuation Correction ◽  
Vertical Structure ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Radar Signal ◽  
Precipitation Radar ◽  
Sensor Field ◽  
Spaceborne Radar ◽  
Ku Band ◽  
Reflectivity Factor

AbstractA spaceborne precipitation radar samples the vertical structure of precipitating hydrometeors from the top down. The viewing geometry and operating frequency result in certain limitations and opportunities. Among the limitations is attenuation of the radar signal that can cause the measured radar reflectivity factor to be substantially less than the desired quantity, the true radar reflectivity factor. Another error source is the spatial variability in precipitation rates that occurs at scales smaller than the sensor field of view (FOV), giving rise to the nonuniform beamfilling (NUBF) effect. The opportunities arise when the radar return from the surface can be used to obtain constraints on the path-integrated attenuation (PIA) for use in hybrid attenuation correction algorithms. The surface return can also provide some information on the degree of NUBF at off-nadir viewing angles. In this paper ground-based radar data are used to simulate spaceborne radar data at nadir and off-nadir viewing angles at Ku band and Ka band and to test attenuation correction algorithms in the presence of nonuniform beamfilling. The cross-FOV gradient in PIA is found to be an important characteristic for describing the performance of attenuation correction algorithms.

scholarly journals An Initial Assessment of the Surface Reference Technique Applied to Data from the Dual-Frequency Precipitation Radar (DPR) on the GPM Satellite

2015 ◽  
Vol 32 (12) ◽  
pp. 2281-2296 ◽  
Author(s):  
Robert Meneghini ◽  
Hyokyung Kim ◽  
Liang Liao ◽  
Jeffrey A. Jones ◽  
John M. Kwiatkowski
Keyword(s):  
Cross Section ◽  
Radar Data ◽  
Single Frequency ◽  
Initial Assessment ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Frequency Method ◽  
Ka Band ◽  
Spaceborne Radar ◽  
Ku Band

AbstractIt has long been recognized that path-integrated attenuation (PIA) can be used to improve precipitation estimates from high-frequency weather radar data. One approach that provides an estimate of this quantity from airborne or spaceborne radar data is the surface reference technique (SRT), which uses measurements of the surface cross section in the presence and absence of precipitation. Measurements from the dual-frequency precipitation radar (DPR) on the Global Precipitation Measurement (GPM) satellite afford the first opportunity to test the method for spaceborne radar data at Ka band as well as for the Ku-band–Ka-band combination.The study begins by reviewing the basis of the single- and dual-frequency SRT. As the performance of the method is closely tied to the behavior of the normalized radar cross section (NRCS or σ0) of the surface, the statistics of σ0 derived from DPR measurements are given as a function of incidence angle and frequency for ocean and land backgrounds over a 1-month period. Several independent estimates of the PIA, formed by means of different surface reference datasets, can be used to test the consistency of the method since, in the absence of error, the estimates should be identical. Along with theoretical considerations, the comparisons provide an initial assessment of the performance of the single- and dual-frequency SRT for the DPR. The study finds that the dual-frequency SRT can provide improvement in the accuracy of path attenuation estimates relative to the single-frequency method, particularly at Ku band.

scholarly journals Regional Attenuation Correction of Weather Radar Using a Distributed Microwave-Links Network

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yang Xue ◽  
Xi-chuan Liu ◽  
Tai-chang Gao ◽  
Chang-ye Yang ◽  
Kun Song
Keyword(s):  
Attenuation Correction ◽  
Tomographic Reconstruction ◽  
Weather Radar ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Correction Coefficient ◽  
Average Deviation ◽  
Initial Field ◽  
Attenuation Coefficients ◽  
Reflectivity Factor

The complex temporal-spatial variation of raindrop size distribution will affect the precision of precipitation quantitative estimates (QPE) produced from radar data, making it difficult to correct echo attenuation. Given the fact that microwave links can obtain the total path attenuation accurately, we introduce the concept of regional attenuation correction using a multiple-microwave-links network based on the tomographic reconstruction of attenuation coefficients. Derived from the radar-based equation, the effect of rainfall distribution on the propagation of radar and microwave link signals was analyzed. This article focuses on modeling of the tomographic reconstruction of attenuation coefficients and regional attenuation correction algorithms. Finally, a numerical simulation of regional attenuation correction was performed to verify the algorithms employed here. The results demonstrate that the correction coefficient (0.9175) falls between the corrected and initial field of radar reflectivity factor (root mean square error, 2.3476 dBz; average deviation, 0.0113 dBz). Compared with uncorrected data, the accuracy of the corrected radar reflectivity factor was improved by 26.12%, and the corrected rainfall intensity distribution was improved by 51.85% validating the region attenuation correction algorithm. This method can correct the regional attenuation of weather radar echo effectively and efficiently; it can be widely used for the radar attenuation correction and the promotion of quantitative precipitation estimation by weather radar.

Fuzzy Logic Classification of S-Band Polarimetric Radar Echoes to Identify Three-Body Scattering and Improve Data Quality

2014 ◽  
Vol 53 (8) ◽  
pp. 2017-2033 ◽  
Author(s):  
Vivek N. Mahale ◽  
Guifu Zhang ◽  
Ming Xue
Keyword(s):  
Fuzzy Logic ◽  
Standard Deviation ◽  
Radar Data ◽  
Classification Algorithm ◽  
Radar Reflectivity ◽  
Membership Functions ◽  
Polarimetric Radar ◽  
Radar Echo ◽  
Three Body ◽  
Reflectivity Factor

AbstractThe three-body scatter signature (TBSS) is a radar artifact that appears downrange from a high-radar-reflectivity core in a thunderstorm as a result of the presence of hailstones. It is useful to identify the TBSS artifact for quality control of radar data used in numerical weather prediction and quantitative precipitation estimation. Therefore, it is advantageous to develop a method to automatically identify TBSS in radar data for the above applications and to help identify hailstones within thunderstorms. In this study, a fuzzy logic classification algorithm for TBSS identification is developed. Polarimetric radar data collected by the experimental S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), are used to develop trapezoidal membership functions for the TBSS class of radar echo within a hydrometeor classification algorithm (HCA). Nearly 3000 radar gates are removed from 50 TBSSs to develop the membership functions from the data statistics. Five variables are investigated for the discrimination of the radar echo: 1) horizontal radar reflectivity factor ZH, 2) differential reflectivity ZDR, 3) copolar cross-correlation coefficient ρhv, 4) along-beam standard deviation of horizontal radar reflectivity factor SD(ZH), and 5) along-beam standard deviation of differential phase SD(ΦDP). These membership functions are added to an HCA to identify TBSSs. Testing is conducted on radar data collected by dual-polarization-upgraded operational WSR-88Ds from multiple severe-weather events, and results show that automatic identification of the TBSS through the enhanced HCA is feasible for operational use.

scholarly journals Mobile Radar Observations of the Evolving Debris Field Compared with a Damage Survey of the Shawnee, Oklahoma, Tornado of 19 May 2013

2020 ◽  
Vol 148 (5) ◽  
pp. 1779-1803 ◽  
Author(s):  
Roger M. Wakimoto ◽  
Zachary Wienhoff ◽  
Howard B. Bluestein ◽  
David J. Bodine ◽  
James M. Kurdzo
Keyword(s):  
Cross Correlation ◽  
Vertical Structure ◽  
Leading Edge ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Vertical Profiles ◽  
Damage Survey ◽  
X Band ◽  
First Time ◽  
Rapid Scanning

Abstract A detailed damage survey is combined with high-resolution mobile, rapid-scanning X-band polarimetric radar data collected on the Shawnee, Oklahoma, tornado of 19 May 2013. The focus of this study is the radar data collected during a period when the tornado was producing damage rated EF3. Vertical profiles of mobile radar data, centered on the tornado, revealed that the radar reflectivity was approximately uniform with height and increased in magnitude as more debris was lofted. There was a large decrease in both the cross-correlation coefficient (ρhv) and differential radar reflectivity (ZDR) immediately after the tornado exited the damaged area rated EF3. Low ρhv and ZDR occurred near the surface where debris loading was the greatest. The 10th percentile of ρhv decreased markedly after large amounts of debris were lofted after the tornado leveled a number of structures. Subsequently, ρhv quickly recovered to higher values. This recovery suggests that the largest debris had been centrifuged or fallen out whereas light debris remained or continued to be lofted. Range–height profiles of the dual-Doppler analyses that were azimuthally averaged around the tornado revealed a zone of maximum radial convergence at a smaller radius relative to the leading edge of lofted debris. Low-level inflow into the tornado encountering a positive bias in the tornado-relative radial velocities could explain the existence of the zone. The vertical structure of the convergence zone was shown for the first time.

scholarly journals A Ground Validation Network for the Global Precipitation Measurement Mission

2011 ◽  
Vol 28 (3) ◽  
pp. 301-319 ◽  
Author(s):  
Mathew R. Schwaller ◽  
K. Robert Morris
Keyword(s):  
Attenuation Correction ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Retrieval Algorithm ◽  
Precipitation Data ◽  
Precipitation Measurement ◽  
Trmm Pr ◽  
Ground Validation ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Abstract A prototype Validation Network (VN) is currently operating as part of the Ground Validation System for NASA’s Global Precipitation Measurement (GPM) mission. The VN supports precipitation retrieval algorithm development in the GPM prelaunch era. Postlaunch, the VN will be used to validate GPM spacecraft instrument measurements and retrieved precipitation data products. The period of record for the VN prototype starts on 8 August 2006 and runs to the present day. The VN database includes spacecraft data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and coincident ground radar (GR) data from operational meteorological networks in the United States, Australia, Korea, and the Kwajalein Atoll in the Marshall Islands. Satellite and ground radar data products are collected whenever the PR satellite track crosses within 200 km of a VN ground radar, and these data are stored permanently in the VN database. VN products are generated from coincident PR and GR observations when a significant rain event occurs. The VN algorithm matches PR and GR radar data (including retrieved precipitation data in the case of the PR) by calculating averages of PR reflectivity (both raw and attenuation corrected) and rain rate, and GR reflectivity at the geometric intersection of the PR rays with the individual GR elevation sweeps. The algorithm thus averages the minimum PR and GR sample volumes needed to “matchup” the spatially coincident PR and GR data types. The result of this technique is a set of vertical profiles for a given rainfall event, with coincident PR and GR samples matched at specified heights throughout the profile. VN data can be used to validate satellite measurements and to track ground radar calibration over time. A comparison of matched TRMM PR and GR radar reflectivity factor data found a remarkably small difference between the PR and GR radar reflectivity factor averaged over this period of record in stratiform and convective rain cases when samples were taken from high in the atmosphere. A significant difference in PR and GR reflectivity was found in convective cases, particularly in convective samples from the lower part of the atmosphere. In this case, the mean difference between PR and corrected GR reflectivity was −1.88 dBZ. The PR–GR bias was found to increase with the amount of PR attenuation correction applied, with the PR–GR bias reaching −3.07 dBZ in cases where the attenuation correction applied is >6 dBZ. Additional analysis indicated that the version 6 TRMM PR retrieval algorithm underestimates rainfall in case of convective rain in the lower part of the atmosphere by 30%–40%.

Comparison of Disdrometer and X-Band Mobile Radar Observations in Convective Precipitation

2014 ◽  
Vol 142 (7) ◽  
pp. 2414-2435 ◽  
Author(s):  
Evan A. Kalina ◽  
Katja Friedrich ◽  
Scott M. Ellis ◽  
Donald W. Burgess
Keyword(s):  
Attenuation Correction ◽  
Numerical Models ◽  
Radar Data ◽  
Convective Precipitation ◽  
Radar Signal ◽  
Squall Line ◽  
Drop Shape ◽  
X Band ◽  
Signal Quality Index ◽  
Differential Reflectivity

Abstract Microphysical data from thunderstorms are sparse, yet they are essential to validate microphysical schemes in numerical models. Mobile, dual-polarization, X-band radars are capable of providing a wealth of data that include radar reflectivity, drop shape, and hydrometeor type. However, X-band radars suffer from beam attenuation in heavy rainfall and hail, which can be partially corrected with attenuation correction schemes. In this research, the authors compare surface disdrometer observations to results from a differential phase-based attenuation correction scheme. This scheme is applied to data recorded by the National Oceanic and Atmospheric Administration (NOAA) X-band dual-polarized (NOXP) mobile radar, which was deployed during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2). Results are presented from five supercell thunderstorms and one squall line (183 min of data). The median disagreement (radar–disdrometer) in attenuation-corrected reflectivity Z and differential reflectivity ZDR is just 1.0 and 0.19 dB, respectively. However, two data subsets reveal much larger discrepancies in Z (ZDR): 5.8 (1.6) dB in a hailstorm and −13 (−0.61) dB when the radar signal quality index (SQI) is less than 0.8. The discrepancies are much smaller when disdrometer and S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) Z are compared, with differences of −1.5 dB (hailstorm) and −0.66 dB (NOXP SQI < 0.8). A comparison of the hydrometeor type retrieved from disdrometer and NOXP radar data is also presented, in which the same class is assigned 63% of the time.

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