Influence of Ground Clutter Contamination on Polarimetric Radar Parameters

2009 ◽  
Vol 26 (2) ◽  
pp. 251-269 ◽  
Author(s):  
Katja Friedrich ◽  
Urs Germann ◽  
Pierre Tabary
Keyword(s):  
Reference Data ◽  
Critical Level ◽  
Elevation Angle ◽  
Convective Precipitation ◽  
Polarimetric Radar ◽  
Mountain Ranges ◽  
Ground Clutter ◽  
The Mean ◽  
Rain Events ◽  
Differential Reflectivity

Abstract The influence of ground clutter contamination on the estimation of polarimetric radar parameters, horizontal reflectivity (Zh), differential reflectivity (Zdr), correlation coefficient (ρhυ), and differential propagation phase (ϕdp) was examined. This study aims to derive the critical level of ground clutter contamination for Zh, Zdr, ρhυ, and ϕdp at which ground clutter influence exceeds predefined precision thresholds. Reference data with minimal ground clutter contamination consist of eight precipitation fields measured during three rain events characterized by stratiform and convective precipitation. Data were collected at an elevation angle of 0.8° by the Météo-France operational, polarimetric Doppler C-band weather radar located in Trappes, France, ∼30 km southwest of Paris. Nine different ground clutter signatures, ranging from point targets to more complex signatures typical for mountain ranges or urban obstacles, were added to the precipitation fields. This is done at the level of raw in-phase and quadrature component data in the two polarimetric channels. For each ground clutter signature, 30 simulations were conducted in which the mean reflectivity of ground clutter within the resolution volume varied between being 30 dB higher to 30 dB lower than the mean reflectivity of precipitation. Differences in Zh, Zdr, ρυ, and ϕdp between simulation and reference were shown as a function of ratio between ground clutter and precipitation intensities. As a result of this study, horizontal reflectivity showed the lowest sensitivity to ground clutter contamination. Furthermore, a precision of 1.7 dBZ in Zh is achieved on average when the precipitation and ground clutter intensities are equal. Requiring a precision of 0.2 dB in Zdr and 3° in ϕdp, the reflectivity of precipitation needs to be on average ∼5.5 and ∼6 dB, respectively, higher compared to the reflectivity of ground clutter. The analysis also indicates that the highest sensitivity to the nine clutter signatures was derived for ρhυ. To meet a predefined precision threshold of 0.02, reflectivity of precipitation needs to be ∼13.5 dB higher than the reflectivity of ground clutter.

scholarly journals Sensitivities of Quantitative Precipitation Forecasts for Typhoon Soudelor (2015) near Landfall to Polarimetric Radar Data Assimilation

2020 ◽  
Vol 12 (22) ◽  
pp. 3711
Author(s):  
Chih-Chien Tsai ◽  
Kao-Shen Chung
Keyword(s):  
Data Assimilation ◽  
Radar Data ◽  
Convective Precipitation ◽  
Polarimetric Radar ◽  
Differential Phase ◽  
Observation Operator ◽  
Quantitative Precipitation Forecasts ◽  
Series Of Experiments ◽  
Differential Reflectivity ◽  
Radar Data Assimilation

Based on the preciousness and uniqueness of polarimetric radar observations collected near the landfall of Typhoon Soudelor (2015), this study investigates the sensitivities of very short-range quantitative precipitation forecasts (QPFs) for this typhoon to polarimetric radar data assimilation. A series of experiments assimilating various combinations of radar variables are carried out for the purpose of improving a 6 h deterministic forecast for the most intense period. The results of the control simulation expose three sources of the observation operator errors, including the raindrop shape-size relation, the limitations for ice-phase hydrometeors, and the melting ice model. Nevertheless, polarimetric radar data assimilation with the unadjusted observation operator can still improve the analyses, especially rainwater, and consequent QPFs for this typhoon case. The different impacts of assimilating reflectivity, differential reflectivity, and specific differential phase are only distinguishable at the lower levels of convective precipitation areas where specific differential phase is found most helpful. The positive effect of radar data assimilation on QPFs can last three hours in this study, and further improvement can be expected by optimizing the observation operator in the future

scholarly journals Use of Ground Clutter to Monitor Polarimetric Radar Calibration

2012 ◽  
Vol 29 (2) ◽  
pp. 159-176 ◽  
Author(s):  
L. Borowska ◽  
D. Zrnic
Keyword(s):  
Environmental Factors ◽  
Heavy Precipitation ◽  
Cold Season ◽  
Polarimetric Radar ◽  
Operational Monitoring ◽  
Ground Clutter ◽  
X Band ◽  
Hot Days ◽  
Radar Calibration ◽  
Differential Reflectivity

Abstract It is suggested that urban ground clutter can have a role in monitoring calibration of reflectivity factor ZH and differential reflectivity ZDR on polarimetric radars. The median and average values of these variables are considered. Analysis of data from 1 month of cold season in Germany (X-band radar) and 3.5 hot days in Oklahoma (S-band radar) is presented. In the presence of up to moderate rain or snow a reflectivity threshold suffices for separating significant clutter from precipitation observed with an X-band radar. The same threshold was suitable on observations with an S-band radar in Oklahoma because heavy precipitation was not present. The tests suggest the scheme is worthy considering for operational monitoring of ZH as its median values at both locations were within the quantization interval of 0.5 dB. Environmental factors that can influence reflectivities from clutter are examined. The effects on ZDR can be significant. These are quantified in the data and possible uses for calibration and monitoring radar status are indicated.

scholarly journals Continuous Monitoring of Differential Reflectivity Bias for C-Band Polarimetric Radar Using Online Solar Echoes in Volume Scans

2019 ◽  
Vol 11 (22) ◽  
pp. 2714
Author(s):  
Chu ◽  
Liu ◽  
Zhang ◽  
Kou ◽  
Li
Keyword(s):  
High Frequency ◽  
Continuous Monitoring ◽  
Information Science ◽  
Elevation Angle ◽  
Gaussian Model ◽  
Likelihood Estimation ◽  
Polarimetric Radar ◽  
Time Calibration ◽  
Calibration Methods ◽  
Differential Reflectivity

The measurement error of differential reflectivity (ZDR), especially systematic ZDR bias, is a fundamental issue for the application of polarimetric radar data. Several calibration methods have been proposed and applied to correct ZDR bias. However, recent studies have shown that ZDR bias is time-dependent and can be significantly different on two adjacent days. This means that the frequent monitoring of ZDR bias is necessary, which is difficult to achieve with existing methods. As radar sensitivity has gradually been enhanced, large amounts of online solar echoes have begun to be observed in volume-scan data. Online solar echoes have a high frequency, and a known theoretical value of ZDR (0 dB) could thus allow the continuous monitoring of ZDR bias. However, online solar echoes are also affected by low signal-to-noise ratio and precipitation attenuation for short-wavelength radar. In order to understand the variation of ZDR bias in a C-band polarimetric radar at the Nanjing University of Information Science and Technology (NUIST-CDP), we analyzed the characteristics of online solar echoes from this radar, including the daily frequency of occurrence, the distribution along the radial direction, precipitation attenuation, and fluctuation caused by noise. Then, an automatic method based on online solar echoes was proposed to monitor the daily ZDR bias of the NUIST-CDP. In the proposed method, a one-way differential attenuation correction for solar echoes and a maximum likelihood estimation using a Gaussian model were designed to estimate the optimal daily ZDR bias. The analysis of three months of data from the NUIST-CDP showed the following: (1) Online solar echoes occurred very frequently regardless of precipitation. Under the volume-scan mode, the average number of occurrences was 15 per day and the minimum number was seven. This high frequency could meet the requirements of continuous monitoring of the daily ZDR bias under precipitation and no-rain conditions. (2) The result from the proposed online solar method was significantly linearly correlated with that from the vertical pointing method (observation at an elevation angle of 90°), with a correlation coefficient of 0.61, suggesting that the proposed method is feasible. (3) The day-to-day variation in the ZDR bias was relatively large, and 32% of such variations exceeded 0.2 dB, meaning that a one-time calibration was not representative in time. Accordingly, continuous calibration will be necessary. (4) The ZDR bias was found to be largely influenced by the ambient temperature, with a large negative correlation between the ZDR bias and the temperature.

scholarly journals Distinguishing between Warm and Stratiform Rain Using Polarimetric Radar Measurements

2021 ◽  
Vol 13 (2) ◽  
pp. 214
Author(s):  
Sergey Y. Matrosov
Keyword(s):  
Logarithmic Scale ◽  
Polarimetric Radar ◽  
Stratiform Rain ◽  
Bright Band ◽  
Warm Rain ◽  
Independent Information ◽  
Radar Measurements ◽  
The Mean ◽  
Polarimetric Weather Radar ◽  
Differential Reflectivity

Modeled statistical differential reflectivity–reflectivity (i.e., ZDR–Ze) correspondences for no bright-band warm rain and stratiform bright-band rain are evaluated using measurements from an operational polarimetric weather radar and independent information about rain types from a vertically pointing profiler. It is shown that these relations generally fit observational data satisfactorily. Due to a relative abundance of smaller drops, ZDR values for warm rain are, on average, smaller than those for stratiform rain of the same reflectivity by a factor of about two (in the logarithmic scale). A ZDR–Ze relation, representing a mean of such relations for warm and stratiform rains, can be utilized to distinguish between warm and stratiform rain types using polarimetric radar measurements. When a mean offset of observational ZDR data is accounted for and reflectivities are greater than 16 dBZ, about 70% of stratiform rains and approximately similar amounts of warm rains are classified correctly using the mean ZDR–Ze relation when applied to averaged data. Since rain rate estimators for warm rain are quite different from other common rain types, identifying and treating warm rain as a separate precipitation category can lead to better quantitative precipitation estimations.

scholarly journals Polarimetric Radar Observations of Biological Scatterers in Hurricanes Irene (2011) and Sandy (2012)

2013 ◽  
Vol 30 (12) ◽  
pp. 2754-2767 ◽  
Author(s):  
Matthew S. Van Den Broeke
Keyword(s):  
Tropical Cyclones ◽  
Elevation Angle ◽  
Bird Species ◽  
Hurricane Sandy ◽  
Polarimetric Radar ◽  
Biological Transport ◽  
Radar Observations ◽  
Hurricane Irene ◽  
Constant Altitude ◽  
Differential Reflectivity

Abstract Biological scatterers, consisting of birds and insects, may become trapped near the circulation center of tropical cyclones, particularly if a well-developed eyewall is present. These scatterers may be observed using weather radar, where they may appear to the radar operator as areas of light precipitation. Polarimetric radar characteristics of these scatterers, informed by additional observations of known bioscatter, include a combination of very high differential reflectivity (3–7.9 dB) and very low copolar correlation coefficient (0.3–0.8). Polarimetric radar observations of bioscatter are presented for Hurricane Irene (2011) and Hurricane Sandy (2012). In these storms, the bioscatter signature first appeared at the 0.5° elevation angle at a distance of 100–120 km from the radar. The signature appeared on successively higher tilts as the circulation center neared the radar, and its areal coverage in constant altitude plan position indicator (CAPPI) slices was primarily governed by the distribution of convection in the eye and by the timing of landfall. The highest altitude at which the signature appears may represent the inversion level within certain tropical cyclone eyes. For Hurricane Irene, inland observations of oceanic bird species support biological transport. Knowledge of the bioscatter signature has value to meteorologists monitoring tropical cyclones within the range of a polarimetric radar, possible value for estimating inversion height changes within the eyes of well-structured tropical cyclones, and value to biologists who wish to estimate the magnitude of biological transport in tropical cyclones.

scholarly journals Using Dual-Polarized Radar and Dual-Frequency Profiler for DSD Characterization: A Case Study from Darwin, Australia

2009 ◽  
Vol 26 (10) ◽  
pp. 2107-2122 ◽  
Author(s):  
V. N. Bringi ◽  
C. R. Williams ◽  
M. Thurai ◽  
P. T. May
Keyword(s):  
Elevation Angle ◽  
Angle Distribution ◽  
Polarimetric Radar ◽  
Dual Frequency ◽  
Continuous Transition ◽  
Stratiform Rain ◽  
Convective Rain ◽  
Median Volume ◽  
Distribution Parameters ◽  
Rain Events

Abstract Comparisons are made between the reflectivity Z, median volume diameter D0, and rain rate R from a dual-frequency profiler and the C-band polarimetric radar (C-POL), which are both located near Darwin, Australia. Examples from the premonsoon “buildup” regime and the monsoon (oceanic) regime are used to illustrate the excellent agreement between the dual-profiler retrievals and the polarimetric radar-based retrievals. This work builds on similar works that were limited in scope to shallow tropical showers and predominantly stratiform rain events. The dual-frequency profiler retrievals of D0 and R herein are based on ensemble statistics, whereas the polarimetric radar retrievals are based on algorithms derived by using one season of disdrometer data from Darwin along with scattering simulations. The latest drop shape versus D relation is used as well as the canting angle distribution results obtained from the 80-m fall bridge experiment in the scattering simulations. The scatterplot of D0 from dual-frequency profiler versus Zdr measurements from C-POL is shown to be consistent not only with the theoretical simulations and prior data but also within prior predicted error bars for both stratiform rain as well as convective rain. Based on dual-frequency profiler–retrieved gamma drop size distribution parameters, a new smoothly varying “separator” indexing scheme has been developed that classifies between stratiform and convective rain types, including a continuous “transition” region between the two. This indexing technique has been applied on a number of low-elevation-angle plan position indicator (PPI) sweeps with the C-POL from the two regime examples, to construct “unconditioned” histograms of D0 in stratiform and convective rain (to within the sensitivity of the radar). With reference to the latter, it is demonstrated that the distribution of D0 is different in the buildup example than in the monsoon example, because of the differences in both the microphysical and kinematic features between the two regimes. In particular, (i) the mean D0 is significantly larger in the buildup example than in the monsoon example, irrespective of rain type; (ii) the histogram width (or standard deviation) is much larger for the buildup example than the monsoon example, irrespective of rain type; and (iii) the histogram skewness is negative for the monsoon regime example because of a lack of larger D0 values, whereas the buildup histogram is positively skewed irrespective of rain type.

scholarly journals Unsupervised classification of vertical profiles of dual polarization radar variables

2019 ◽  
Author(s):  
Jussi Tiira ◽  
Dmitri N. Moisseev
Keyword(s):  
Input Parameter ◽  
Surface Air Temperature ◽  
Unsupervised Classification ◽  
Vertical Profiles ◽  
Polarimetric Radar ◽  
Finnish Meteorological Institute ◽  
Ice Cloud ◽  
Rain Events ◽  
The University ◽  
Differential Reflectivity

Abstract. Vertical profiles of polarimetric radar variables can be used to identify fingerprints of snow growth processes. In order to systematically study such manifestations of precipitation processes, we have developed an unsupervised classification method. The method is based on k-means clustering of vertical profiles of polarimetric radar variables, namely reflectivity, differential reflectivity and specific differential phase. For rain events, the classification is applied to radar profiles truncated at the melting layer top. For the snowfall cases, the surface air temperature is used as an additional input parameter. The proposed unsupervised classification was applied to 3.5 years of data collected by the Finnish Meteorological Institute Ikaalinen radar. The vertical profiles of radar variables were computed above the University of Helsinki Hyytiälä station, located 64 km east of the radar. Using these data, we show that the profiles of radar variables can be grouped into 10 and 16 classes for rainfall and snowfall events respectively. These classes seem to capture most important snow growth and ice cloud processes. Using this classification, main features of the precipitation formation processes, as observed in Finland, are presented.

scholarly journals Unsupervised classification of vertical profiles of dual polarization radar variables

2020 ◽  
Vol 13 (3) ◽  
pp. 1227-1241 ◽  
Author(s):  
Jussi Tiira ◽  
Dmitri Moisseev
Keyword(s):  
Input Parameter ◽  
Surface Air Temperature ◽  
Unsupervised Classification ◽  
Vertical Profiles ◽  
Polarimetric Radar ◽  
Finnish Meteorological Institute ◽  
Ice Cloud ◽  
Rain Events ◽  
The University ◽  
Differential Reflectivity

Abstract. Vertical profiles of polarimetric radar variables can be used to identify fingerprints of snow growth processes. In order to systematically study such manifestations of precipitation processes, we have developed an unsupervised classification method. The method is based on k-means clustering of vertical profiles of polarimetric radar variables, namely reflectivity, differential reflectivity and specific differential phase. For rain events, the classification is applied to radar profiles truncated at the melting layer top. For the snowfall cases, the surface air temperature is used as an additional input parameter. The proposed unsupervised classification was applied to 3.5 years of data collected by the Finnish Meteorological Institute Ikaalinen radar. The vertical profiles of radar variables were computed above the University of Helsinki Hyytiälä station, located 64 km east of the radar. Using these data, we show that the profiles of radar variables can be grouped into 10 and 16 classes for rainfall and snowfall events, respectively. These classes seem to capture most important snow growth and ice cloud processes. Using this classification, the main features of the precipitation formation processes, as observed in Finland, are presented.

Performance of the Hail Differential Reflectivity (HDR) Polarimetric Radar Hail Indicator

2007 ◽  
Vol 46 (8) ◽  
pp. 1290-1301 ◽  
Author(s):  
Tracy K. Depue ◽  
Patrick C. Kennedy ◽  
Steven A. Rutledge
Keyword(s):  
Elevation Angle ◽  
Maximum Diameter ◽  
State University ◽  
Polarimetric Radar ◽  
Colorado State University ◽  
Surface Wetness ◽  
Critical Success ◽  
State Water ◽  
Linear Depolarization Ratio ◽  
Differential Reflectivity

Abstract A series of poststorm surveys were conducted in the wake of hailstorms observed by the Colorado State University–University of Chicago–Illinois State Water Survey (CSU-CHILL) S-Band polarimetric radar. Information on hail characteristics (maximum diameter, building damage, apparent hailstone density, etc.) was solicited from the general-public storm observers that were contacted during the surveys; the locations of their observations were determined using GPS equipment. Low-elevation angle radar measurements of reflectivity, differential reflectivity ZDR, and linear depolarization ratio (LDR) were interpolated to the ground-observer locations. Relationships between the hail differential reflectivity parameter HDR and the observer-reported hail characteristics were examined. It was found that HDR thresholds of 21 and 30 dB were reasonably successful (critical success index values of ∼0.77) in respectively identifying regions where large (>19 mm in diameter) and structurally damaging hail were observed. The LDR characteristics in the observed hail areas were also examined. Because of sensitivities to variations in the hailstone bulk ice density, degree of surface wetness, and shape irregularities, the basic correlation between LDR magnitude and hail diameter was poor. However, when the reported hail diameters exceeded ∼25 mm, LDR levels below ∼−24 dB were uncommon.

scholarly journals Polarimetric Radar Characteristics of Tornadogenesis Failure in Supercell Thunderstorms

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 581
Author(s):  
Matthew Van Den Broeke
Keyword(s):  
Polarimetric Radar ◽  
Low Level ◽  
Supercell Storms ◽  
Supercell Thunderstorms ◽  
Near Term ◽  
Tornadic Storms ◽  
Differential Reflectivity

Many nontornadic supercell storms have times when they appear to be moving toward tornadogenesis, including the development of a strong low-level vortex, but never end up producing a tornado. These tornadogenesis failure (TGF) episodes can be a substantial challenge to operational meteorologists. In this study, a sample of 32 pre-tornadic and 36 pre-TGF supercells is examined in the 30 min pre-tornadogenesis or pre-TGF period to explore the feasibility of using polarimetric radar metrics to highlight storms with larger tornadogenesis potential in the near-term. Overall the results indicate few strong distinguishers of pre-tornadic storms. Differential reflectivity (ZDR) arc size and intensity were the most promising metrics examined, with ZDR arc size potentially exhibiting large enough differences between the two storm subsets to be operationally useful. Change in the radar metrics leading up to tornadogenesis or TGF did not exhibit large differences, though most findings were consistent with hypotheses based on prior findings in the literature.

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