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- Author or Editor: Dúsan S. Zrnić x
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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.
Abstract
Demonstration of a method for improved Doppler spectral moment estimation is made on NOAA's research and development Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma. Time series data have been recorded using a commercial processor and digital receiver whereby the sampling frequency is several times larger than the reciprocal of the transmitted pulse width. The in-phase and quadrature-phase components of oversampled weather signals are used to estimate the first three spectral moments by suitably combining weighted averages in range with usual processing at fixed range locations. The weights are chosen in such a manner that the resulting signals become uncorrelated. Consequently, the variance of estimates decreases significantly as is verified by this experiment.
Abstract
Demonstration of a method for improved Doppler spectral moment estimation is made on NOAA's research and development Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma. Time series data have been recorded using a commercial processor and digital receiver whereby the sampling frequency is several times larger than the reciprocal of the transmitted pulse width. The in-phase and quadrature-phase components of oversampled weather signals are used to estimate the first three spectral moments by suitably combining weighted averages in range with usual processing at fixed range locations. The weights are chosen in such a manner that the resulting signals become uncorrelated. Consequently, the variance of estimates decreases significantly as is verified by this experiment.
Abstract
Specific differential propagation phase (K DP) is examined for estimating convective rainfall in Colorado and Kansas. Estimates are made at S band with K DP alone and in combination with radar reflectivity (Z H). Results are compared to gauge observations by computing bias factors, defined as the sum of gauge-measured rainfalls divided by the sum of radar estimates at gauges reporting rainfall, and the correlation coefficient between the gauge and radar-estimated amounts. Rainfall accumulations computed from positive-only values of K DP (provided Z H ≥ 25 dBZ) yield bias factors that vary from 0.76 to 2.42 for 3 storms in Colorado and from 0.78 to 1.46 for 10 storms in Kansas. Correlation coefficients between gauge-observed and radar-estimated rainfalls are 0.76 to 0.95. When negative K DP’s are included as negative rainfall rates, bias factors range from 0.81 to 3.00 in Colorado and from 0.84 to 2.31 in Kansas. In most storms, the correlation between gauge and radar rainfalls decreases slightly.
In an experiment with the K DP/Z H combination, rainfall rates are computed from K DP when K DP is ≥0.4° km−1 and from Z H for K DP < 0.4° km−1 and Z H ≥ 25 dBZ. Neglect of the negative K DP’s and substitution of the always positive Z H rainfall rates result in a tendency to overestimate rainfall. Bias factors are 0.63–1.46 for Colorado storms and 0.68–0.97 for Kansas storms, and correlation coefficients between gauge and radar amounts are 0.80–0.95. In yet another test with the K DP/Z H pair, rainfall estimates are computed from K DP when Z H ≥ 40 dBZ and from Z H when 25 ⩽ Z H < 40 dBZ. For this experiment, bias factors range from 0.90 to 1.91 in Colorado and from 0.88 to 1.46 in Kansas. Correlation coefficients are 0.80–0.96.
Since bias factors and correlation coefficients between estimated rainfalls and gauge observations for K DP are similar to those for radar reflectivity, there was no obvious benefit with K DP rainfalls for a well-calibrated radar. Large underestimates with K DP in two storms were attributed to rainfalls dominated by small drops. In one storm, the problem was aggravated by widespread negative K DP’s thought related to vertical gradients of precipitation. An advantage of K DP-derived rainfall estimates confirmed here is an insensitivity to anomalous propagation.
Abstract
Specific differential propagation phase (K DP) is examined for estimating convective rainfall in Colorado and Kansas. Estimates are made at S band with K DP alone and in combination with radar reflectivity (Z H). Results are compared to gauge observations by computing bias factors, defined as the sum of gauge-measured rainfalls divided by the sum of radar estimates at gauges reporting rainfall, and the correlation coefficient between the gauge and radar-estimated amounts. Rainfall accumulations computed from positive-only values of K DP (provided Z H ≥ 25 dBZ) yield bias factors that vary from 0.76 to 2.42 for 3 storms in Colorado and from 0.78 to 1.46 for 10 storms in Kansas. Correlation coefficients between gauge-observed and radar-estimated rainfalls are 0.76 to 0.95. When negative K DP’s are included as negative rainfall rates, bias factors range from 0.81 to 3.00 in Colorado and from 0.84 to 2.31 in Kansas. In most storms, the correlation between gauge and radar rainfalls decreases slightly.
In an experiment with the K DP/Z H combination, rainfall rates are computed from K DP when K DP is ≥0.4° km−1 and from Z H for K DP < 0.4° km−1 and Z H ≥ 25 dBZ. Neglect of the negative K DP’s and substitution of the always positive Z H rainfall rates result in a tendency to overestimate rainfall. Bias factors are 0.63–1.46 for Colorado storms and 0.68–0.97 for Kansas storms, and correlation coefficients between gauge and radar amounts are 0.80–0.95. In yet another test with the K DP/Z H pair, rainfall estimates are computed from K DP when Z H ≥ 40 dBZ and from Z H when 25 ⩽ Z H < 40 dBZ. For this experiment, bias factors range from 0.90 to 1.91 in Colorado and from 0.88 to 1.46 in Kansas. Correlation coefficients are 0.80–0.96.
Since bias factors and correlation coefficients between estimated rainfalls and gauge observations for K DP are similar to those for radar reflectivity, there was no obvious benefit with K DP rainfalls for a well-calibrated radar. Large underestimates with K DP in two storms were attributed to rainfalls dominated by small drops. In one storm, the problem was aggravated by widespread negative K DP’s thought related to vertical gradients of precipitation. An advantage of K DP-derived rainfall estimates confirmed here is an insensitivity to anomalous propagation.
Abstract
A calibration procedure of differential reflectivity on the Weather Surveillance Radar-1988 Doppler (WSR-88D) is described. It has been tested on NOAA's modified WSR-88D research and development polarimetric radar and is directly applicable to radars that simultaneously transmit and receive waves having horizontal and vertical polarization.
Abstract
A calibration procedure of differential reflectivity on the Weather Surveillance Radar-1988 Doppler (WSR-88D) is described. It has been tested on NOAA's modified WSR-88D research and development polarimetric radar and is directly applicable to radars that simultaneously transmit and receive waves having horizontal and vertical polarization.
Abstract
Characteristics of the magnitude and phase of correlation coefficients between horizontally and vertically polarized returns from ground clutter echoes are quantified by analyzing histograms obtained with an 11-cm wavelength weather surveillance radar in Norman, Oklahoma. The radar receives simultaneously horizontal and vertical (SHV) electric fields and can transmit either horizontal fields or both vertical and horizontal fields. The differences between correlations obtained in this SHV mode and correlations measured in alternate H, V mode are reviewed; a histogram of differential phase obtained in Florida using alternate H, V mode is also presented. Data indicate that the backscatter differential phase of clutter has a broad histogram that completely overlaps the narrow histogram of precipitation echoes. This is important as it implies that a potent discriminator for separating clutter from meteorological echoes is the texture of the differential phase. Values of the copolar cross-correlation coefficient from clutter overlap completely those from precipitation, and effective discrimination is possible only if averages in range are taken. It is demonstrated that the total differential phase (system and backscatter) depends on the polarimetric measurement technique and the type of scatterers. In special circumstances, such as calibrating or monitoring the radar, clutter signal can be beneficial. Specifically, system differential phase can be estimated from histograms of ground clutter, receiver differential phase can be estimated from precipitation returns, and from these two, the differential phase of transmitted waves is easily computed.
Abstract
Characteristics of the magnitude and phase of correlation coefficients between horizontally and vertically polarized returns from ground clutter echoes are quantified by analyzing histograms obtained with an 11-cm wavelength weather surveillance radar in Norman, Oklahoma. The radar receives simultaneously horizontal and vertical (SHV) electric fields and can transmit either horizontal fields or both vertical and horizontal fields. The differences between correlations obtained in this SHV mode and correlations measured in alternate H, V mode are reviewed; a histogram of differential phase obtained in Florida using alternate H, V mode is also presented. Data indicate that the backscatter differential phase of clutter has a broad histogram that completely overlaps the narrow histogram of precipitation echoes. This is important as it implies that a potent discriminator for separating clutter from meteorological echoes is the texture of the differential phase. Values of the copolar cross-correlation coefficient from clutter overlap completely those from precipitation, and effective discrimination is possible only if averages in range are taken. It is demonstrated that the total differential phase (system and backscatter) depends on the polarimetric measurement technique and the type of scatterers. In special circumstances, such as calibrating or monitoring the radar, clutter signal can be beneficial. Specifically, system differential phase can be estimated from histograms of ground clutter, receiver differential phase can be estimated from precipitation returns, and from these two, the differential phase of transmitted waves is easily computed.
A detailed and unique multisensor observation of an undular bore is presented. The data include those from rawinsonde, satellite, two Doppler radars, and a tall instrumented tower. Noteworthy are Doppler radar images that resolve the wave's characteristics and capture a good part of its spatial extent. The basic parameters of the wave train are established from the observations.
A detailed and unique multisensor observation of an undular bore is presented. The data include those from rawinsonde, satellite, two Doppler radars, and a tall instrumented tower. Noteworthy are Doppler radar images that resolve the wave's characteristics and capture a good part of its spatial extent. The basic parameters of the wave train are established from the observations.
Abstract
An analysis of drop size distributions (DSDs) measured in four very different precipitation regimes is presented and is compared with polarimetric radar measurements. The DSDs are measured by a 2D video disdrometer, which is designed to measure drop size, shape, and fall speed with unprecedented accuracy. The observations indicate that significant DSD variability exists not only from one event to the next, but also within each system. Also, despite having vastly different storm structures and maximum rain rates, large raindrops with diameters greater than 5 mm occurred with each system. By comparing the occurrence of large drops with rainfall intensity, the authors find that the largest median diameters are not always associated with the heaviest rainfall, but are sometimes located either in advance of convective cores or, occasionally, in stratiform regions where rainfall rates are relatively low. Disdrometer and polarimetric radar measurements of radar reflectivity Z, differential reflectivity Z DR, specific differential phase K DP, and R(Z) and R(K DP) rain-rate estimators are compared in detail. Overall agreement is good, but it is found that both R(Z) and R(K DP) underestimate rain rate when the DSD is dominated by small drops and overestimate rain rate when the DSD is dominated by large drops. The results indicate that a classification of different rain types (associated with different DSDs) should be an essential part of polarimetric rainfall estimation. Furthermore, observations suggest that Z DR is a key parameter for making such a distinction. Last, the authors compute and compare maximum and average of gamma shape, slope, and intercept parameters for all four precipitation events. Potential measurement errors with the 2D video disdrometer are also discussed.
Abstract
An analysis of drop size distributions (DSDs) measured in four very different precipitation regimes is presented and is compared with polarimetric radar measurements. The DSDs are measured by a 2D video disdrometer, which is designed to measure drop size, shape, and fall speed with unprecedented accuracy. The observations indicate that significant DSD variability exists not only from one event to the next, but also within each system. Also, despite having vastly different storm structures and maximum rain rates, large raindrops with diameters greater than 5 mm occurred with each system. By comparing the occurrence of large drops with rainfall intensity, the authors find that the largest median diameters are not always associated with the heaviest rainfall, but are sometimes located either in advance of convective cores or, occasionally, in stratiform regions where rainfall rates are relatively low. Disdrometer and polarimetric radar measurements of radar reflectivity Z, differential reflectivity Z DR, specific differential phase K DP, and R(Z) and R(K DP) rain-rate estimators are compared in detail. Overall agreement is good, but it is found that both R(Z) and R(K DP) underestimate rain rate when the DSD is dominated by small drops and overestimate rain rate when the DSD is dominated by large drops. The results indicate that a classification of different rain types (associated with different DSDs) should be an essential part of polarimetric rainfall estimation. Furthermore, observations suggest that Z DR is a key parameter for making such a distinction. Last, the authors compute and compare maximum and average of gamma shape, slope, and intercept parameters for all four precipitation events. Potential measurement errors with the 2D video disdrometer are also discussed.
