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- Author or Editor: Dusan Zrnic x
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Abstract
Examined is bias in differential reflectivity and its effect on estimates of rain rate due to coupling of the vertically and horizontally polarized fields through the radiation patterns. To that end, a brief review of the effects of the bias on quantitative rainfall measurements is given. Suggestions for tolerable values of this bias are made. Of utmost interest is the bias produced by radars simultaneously transmitting horizontally and vertically polarized fields, as this configuration has been chosen for pending upgrades to the U.S. national network of radars (Weather Surveillance Radar-1988 Doppler; WSR-88D). The bias strongly depends on the cross-polar radiation pattern. Two patterns, documented in the literature, are considered.
Abstract
Examined is bias in differential reflectivity and its effect on estimates of rain rate due to coupling of the vertically and horizontally polarized fields through the radiation patterns. To that end, a brief review of the effects of the bias on quantitative rainfall measurements is given. Suggestions for tolerable values of this bias are made. Of utmost interest is the bias produced by radars simultaneously transmitting horizontally and vertically polarized fields, as this configuration has been chosen for pending upgrades to the U.S. national network of radars (Weather Surveillance Radar-1988 Doppler; WSR-88D). The bias strongly depends on the cross-polar radiation pattern. Two patterns, documented in the literature, are considered.
Abstract
A new synthesis of information forming the foundation for rule-based systems to deduce dominant bulk hydrometeor types and amounts using polarimetric radar data is presented. The information is valid for a 10-cm wavelength and consists of relations that are based on an extensive list of previous and recent observational and modeling studies of polarimetric signatures of hydrometeors. The relations are expressed as boundaries and thresholds in a space of polarimetric radar variables. Thus, the foundation is laid out for identification of hydrometeor types (species), estimation of characteristics of hydrometeor species (size, concentrations, etc.), and quantification of bulk hydrometeor contents (amounts). A fuzzy classification algorithm that builds upon this foundation will be discussed in a forthcoming paper.
Abstract
A new synthesis of information forming the foundation for rule-based systems to deduce dominant bulk hydrometeor types and amounts using polarimetric radar data is presented. The information is valid for a 10-cm wavelength and consists of relations that are based on an extensive list of previous and recent observational and modeling studies of polarimetric signatures of hydrometeors. The relations are expressed as boundaries and thresholds in a space of polarimetric radar variables. Thus, the foundation is laid out for identification of hydrometeor types (species), estimation of characteristics of hydrometeor species (size, concentrations, etc.), and quantification of bulk hydrometeor contents (amounts). A fuzzy classification algorithm that builds upon this foundation will be discussed in a forthcoming paper.
Abstract
Disdrometer data collected during three spring days, with moderate to heavy rain in the Norman, Oklahoma region are used with various polarimetric radar algorithms to simulate rain rates. It is assumed that available measurables are 1) reflectivity at horizontal polarization, Z H , 2) differential reflectivity, Z DR (ratio of horizontal to vertical reflectivity factors in dB), and 3) differential propagation constant, K DP . The accuracies of the simulated rain rates from Z H , Z DR , and K DP are evaluated and compared. A new algorithm that utilizes both reflectivity factor and differential propagation constant is also examined. In comparing the relative accuracies, the disdrometer-derived rain rates are assumed to be the “truth” measurements.
Abstract
Disdrometer data collected during three spring days, with moderate to heavy rain in the Norman, Oklahoma region are used with various polarimetric radar algorithms to simulate rain rates. It is assumed that available measurables are 1) reflectivity at horizontal polarization, Z H , 2) differential reflectivity, Z DR (ratio of horizontal to vertical reflectivity factors in dB), and 3) differential propagation constant, K DP . The accuracies of the simulated rain rates from Z H , Z DR , and K DP are evaluated and compared. A new algorithm that utilizes both reflectivity factor and differential propagation constant is also examined. In comparing the relative accuracies, the disdrometer-derived rain rates are assumed to be the “truth” measurements.
Abstract
This paper describes the implementation of the staggered pulse repetition time (PRT) technique on NOAA's research and development WSR-88D in Norman, Oklahoma. The prototype algorithm incorporates a novel rule for the correct assignment of Doppler mean velocity that is needed to accommodate arbitrary stagger ratios. Description of the rule, consideration of errors, and choice of appropriate stagger ratios are presented. The staggered PRT algorithm is integrated with the standard processing on the WSR-88D, some details of which are included in the paper. A simple ground clutter canceller removes the pure complex time series mean (DC) component from autocovariance estimates; censoring of overlaid echoes and thresholding are equivalent to those used on the WSR-88D. Further, a cursory verification of statistical errors indicates good agreement with theoretical expectations. Although the staggered PRT algorithm operates in real time, it was advantageous to collect several events of staggered PRT time series data for further scrutiny. Results presented from one of the events demonstrate the potency of the staggered PRT to mitigate range and velocity ambiguities.
Abstract
This paper describes the implementation of the staggered pulse repetition time (PRT) technique on NOAA's research and development WSR-88D in Norman, Oklahoma. The prototype algorithm incorporates a novel rule for the correct assignment of Doppler mean velocity that is needed to accommodate arbitrary stagger ratios. Description of the rule, consideration of errors, and choice of appropriate stagger ratios are presented. The staggered PRT algorithm is integrated with the standard processing on the WSR-88D, some details of which are included in the paper. A simple ground clutter canceller removes the pure complex time series mean (DC) component from autocovariance estimates; censoring of overlaid echoes and thresholding are equivalent to those used on the WSR-88D. Further, a cursory verification of statistical errors indicates good agreement with theoretical expectations. Although the staggered PRT algorithm operates in real time, it was advantageous to collect several events of staggered PRT time series data for further scrutiny. Results presented from one of the events demonstrate the potency of the staggered PRT to mitigate range and velocity ambiguities.
Abstract
Currently, signal detection and censoring in operational weather radars is performed by using thresholds of the estimated signal-to-noise ratio (SNR) and/or the magnitude of the autocorrelation coefficient at the first temporal lag. The growing popularity of polarimetric radars prompts the quest for improved detection schemes that take advantage of the signals from the two orthogonally polarized electric fields. A hybrid approach is developed based on the sum of the cross-correlation estimates as well as the powers and autocorrelations from each of the dual-polarization returns. The hypothesis that “signal is present” is accepted if the sum exceeds a predetermined threshold; otherwise, the data are considered to represent noise and are censored. The threshold is determined by the acceptable rate of false detections that is less than or equal to a preset value. The scheme is evaluated both in simulations and through implementation on time series data collected by the research weather surveillance radar (KOUN) in Norman, Oklahoma.
Abstract
Currently, signal detection and censoring in operational weather radars is performed by using thresholds of the estimated signal-to-noise ratio (SNR) and/or the magnitude of the autocorrelation coefficient at the first temporal lag. The growing popularity of polarimetric radars prompts the quest for improved detection schemes that take advantage of the signals from the two orthogonally polarized electric fields. A hybrid approach is developed based on the sum of the cross-correlation estimates as well as the powers and autocorrelations from each of the dual-polarization returns. The hypothesis that “signal is present” is accepted if the sum exceeds a predetermined threshold; otherwise, the data are considered to represent noise and are censored. The threshold is determined by the acceptable rate of false detections that is less than or equal to a preset value. The scheme is evaluated both in simulations and through implementation on time series data collected by the research weather surveillance radar (KOUN) in Norman, Oklahoma.
Abstract
In Doppler weather radars, signals may exhibit coherency in sample time, whereas noise does not. Additionally, in dual-polarized radars, samples of precipitation echo obtained in the two orthogonally polarized channels are substantially more correlated than samples of noise. Therefore, estimates of auto- and cross correlations can be used individually, collectively, and/or with power measurements to enhance detection of precipitation signals, compared to the approach that uses only power estimates from one channel. A possible advantage of using only estimates of coherency for signal detection is that the detector’s performance is less sensitive to errors in noise power measurements. Hence, censoring is more likely to produce desired false alarm rates even if nonnegligible uncertainties are present in the noise power estimates. In this work these aspects are considered using real data from weather radars. Three novel censoring approaches are evaluated and compared to the censoring approach that uses only estimates of signal and noise powers. The first approach uses only cross-correlation measurements, and the second approach combines these with the lag-1 autocorrelation estimates. The third approach utilizes all estimates as in the previous two approaches in combination with power measurements from the horizontal and the vertical channels. Herein, it is shown that, when more accurate measurements of noise powers are available, the third approach produces the highest detection rates followed by the second and the first approaches. Also, it is corroborated that the first and the second approaches exhibit less sensitivity to inaccurate system noise power measurements than the third one.
Abstract
In Doppler weather radars, signals may exhibit coherency in sample time, whereas noise does not. Additionally, in dual-polarized radars, samples of precipitation echo obtained in the two orthogonally polarized channels are substantially more correlated than samples of noise. Therefore, estimates of auto- and cross correlations can be used individually, collectively, and/or with power measurements to enhance detection of precipitation signals, compared to the approach that uses only power estimates from one channel. A possible advantage of using only estimates of coherency for signal detection is that the detector’s performance is less sensitive to errors in noise power measurements. Hence, censoring is more likely to produce desired false alarm rates even if nonnegligible uncertainties are present in the noise power estimates. In this work these aspects are considered using real data from weather radars. Three novel censoring approaches are evaluated and compared to the censoring approach that uses only estimates of signal and noise powers. The first approach uses only cross-correlation measurements, and the second approach combines these with the lag-1 autocorrelation estimates. The third approach utilizes all estimates as in the previous two approaches in combination with power measurements from the horizontal and the vertical channels. Herein, it is shown that, when more accurate measurements of noise powers are available, the third approach produces the highest detection rates followed by the second and the first approaches. Also, it is corroborated that the first and the second approaches exhibit less sensitivity to inaccurate system noise power measurements than the third one.
Abstract
This paper describes the characteristics and evolving nature of a vigorous thunderstorm density current very early in the morning of 9 May 1981 in Oklahoma. Because the ambient lower atmosphere was stratified, interesting interactions between the outflow current and the ambient environment resulted. The leading portion of the current was modulated by at least three gravity wavelike perturbations of horizontal spacing 12 km which initially coexisted with it. However, as the current evolved, it initiated an undular borelike disturbance which propagated ahead into the stable boundary layer, carrying cold outflow air in large amplitude rolls. Eventually the wave family left the decelerating outflow air in its wake. This borelike disturbance resembles the Australian “morning glory” phenomenon and appears to represent an early stage in the development of a solitary wave family.
The observations resemble other reported morning glories and solitary waves as well their laboratory and numerically simulated counterparts. Comparisons are discussed. This case study is unique not only because it combines Doppler radar, tall tower, and surface mesonet observations, but especially because the period of observation captures the disturbance in its formative stage when it is still very near the density current.
Abstract
This paper describes the characteristics and evolving nature of a vigorous thunderstorm density current very early in the morning of 9 May 1981 in Oklahoma. Because the ambient lower atmosphere was stratified, interesting interactions between the outflow current and the ambient environment resulted. The leading portion of the current was modulated by at least three gravity wavelike perturbations of horizontal spacing 12 km which initially coexisted with it. However, as the current evolved, it initiated an undular borelike disturbance which propagated ahead into the stable boundary layer, carrying cold outflow air in large amplitude rolls. Eventually the wave family left the decelerating outflow air in its wake. This borelike disturbance resembles the Australian “morning glory” phenomenon and appears to represent an early stage in the development of a solitary wave family.
The observations resemble other reported morning glories and solitary waves as well their laboratory and numerically simulated counterparts. Comparisons are discussed. This case study is unique not only because it combines Doppler radar, tall tower, and surface mesonet observations, but especially because the period of observation captures the disturbance in its formative stage when it is still very near the density current.
Abstract
Advances in clear air Doppler radar measurement have made practical the monitoring of radial velocities in the troposphere and lower stratosphere and even the vector wind, under some assumptions. Because the objective of wind profiling is to monitor winds representative of larger scale atmospheric motions, an assumption of a time-invariant spatially uniform wind field is commonly used. Then, the accuracy of the wind estimators depends on the error variance of the radial velocity, the departure from uniformity of the wind field and the measurement geometry.
We derive expressions for the variance and bias for some of these estimators when applied to a spatially linear wind field. The techniques we consider are three fixed beams, azimuthal scanning (VAD) and elevation scanning (VED). In addition, we examine a method based on the integration of the continuity equation to estimate the areal-averaged wind. This technique sometimes leads to better estimates than do direct methods.
Abstract
Advances in clear air Doppler radar measurement have made practical the monitoring of radial velocities in the troposphere and lower stratosphere and even the vector wind, under some assumptions. Because the objective of wind profiling is to monitor winds representative of larger scale atmospheric motions, an assumption of a time-invariant spatially uniform wind field is commonly used. Then, the accuracy of the wind estimators depends on the error variance of the radial velocity, the departure from uniformity of the wind field and the measurement geometry.
We derive expressions for the variance and bias for some of these estimators when applied to a spatially linear wind field. The techniques we consider are three fixed beams, azimuthal scanning (VAD) and elevation scanning (VED). In addition, we examine a method based on the integration of the continuity equation to estimate the areal-averaged wind. This technique sometimes leads to better estimates than do direct methods.
Abstract
Examples of automatic interpretation of polarimetric measurements made with an algorithm that classifies precipitation, from an Oklahoma squall line and a Florida airmass storm are presented. Developed in this paper are sensitivity tests of this algorithm to various polarimetric variables. The tests are done subjectively by comparing the fields of hydrometeors obtained using the full set of available polarimetric variables with a diminished set whereby some variables have been left out. An objective way to test the sensitivity of the algorithm to variables and rank their utility is also devised. The test involves definition of a measure, which is the number of data points classified into a category using subsets of available variables. Ratios of various measures (similar to probabilities) define the percentage of occurrence of a class. By comparing these percentages for cases in which some variables are excluded to those whereby all are included, a relative merit can be assigned to the variables. Results of this objective sensitivity study reveal the following: the reflectivity factor and differential reflectivity combined have the strongest discriminating power. Inclusion of the temperature profile helps eliminate a substantial number of spurious errors. Although the absence of temperature information degrades the scheme, it appears that the resultant fields are generally coherent and not far off from the fields obtained by adding temperature to the suite of polarimetric variables.
Abstract
Examples of automatic interpretation of polarimetric measurements made with an algorithm that classifies precipitation, from an Oklahoma squall line and a Florida airmass storm are presented. Developed in this paper are sensitivity tests of this algorithm to various polarimetric variables. The tests are done subjectively by comparing the fields of hydrometeors obtained using the full set of available polarimetric variables with a diminished set whereby some variables have been left out. An objective way to test the sensitivity of the algorithm to variables and rank their utility is also devised. The test involves definition of a measure, which is the number of data points classified into a category using subsets of available variables. Ratios of various measures (similar to probabilities) define the percentage of occurrence of a class. By comparing these percentages for cases in which some variables are excluded to those whereby all are included, a relative merit can be assigned to the variables. Results of this objective sensitivity study reveal the following: the reflectivity factor and differential reflectivity combined have the strongest discriminating power. Inclusion of the temperature profile helps eliminate a substantial number of spurious errors. Although the absence of temperature information degrades the scheme, it appears that the resultant fields are generally coherent and not far off from the fields obtained by adding temperature to the suite of polarimetric variables.
