1. Introduction
The WSR-88D network is undergoing a life extension program to improve its capability and performance until at least 2030. Parallel to this effort, the National Oceanic and Atmospheric Administration is looking ahead for candidate replacements. One potent nascent technology is the agile-beam phased array radar (PAR), which could augment the weather-observing mission (Zrnić at al. 2007) and serve other purposes, such as air traffic control and monitoring, protection of terminals, and homeland security (Weber et al. 2007).
To be a viable replacement, the PAR should meet all WSR-88D performance standards. One of these is the superresolution mode. The term means that spectral moments are produced at an azimuthal spacing of half a beamwidth while the antenna is rotating at a rate at which statistical errors produced a beamwidth apart are acceptable. In the superresolution mode, the effective beamwidth of the antenna pattern is about 25% narrower than in the standard mode (Torres and Curtis 2007). This increase in azimuthal resolution comes at a price of larger estimate errors.
Simulations indicate (Brown et al. 2002) the superresolution (at 0.5° on the WSR-88D) extends by at least 50% the detection range of mesocyclones and tornadoes. Moreover, the estimates of the Doppler velocity difference between the extreme positive and negative values also increase, better quantifying the strengths of the circulation. Thus, the superresolution data enable forecasters to more easily recognize small hazardous features and to forewarn the public of eminent dangers. Because of increased statistical errors in estimates, the superresolution data are only directly processed by the mesocyclone detection algorithm. For other algorithms, the superresolution data are recombined to form radials of spectral moments spaced 1° apart.
In principle, the PAR could scan as a conventional radar. However, that would degrade the inherent advantages of no beam smearing, such as better filtering of ground clutter (its spectrum width is not broadened) and smaller statistical errors in Doppler variables. This paper introduces a way to obtain superresolution data on the PAR at the same volume coverage speeds as on the WSR-88D with the same azimuthal resolution but lower statistical errors.
The next section discusses the proposed superresolution method, including step scans, window functions, and effective beamwidth. Predicted errors in spectral moment estimates from the PAR in the superresolution mode are quantified in section 3. Fields of spectral moments obtained with the superresolution PAR mode are presented in section 4. Section 5 summarizes the paper.
2. Superresolution mode on PAR
We start with a brief depiction of the superresolution on the WSR-88D and then contrast it to how data are collected on the PAR. Figure 1 is a schematic of a continuous scan with overlapping samples. Here 2L is the number of samples within the continuous scan. The two convex curves represent the weighting functions applied to these samples. The horizontal ribbon with two arrows indicates the beginning and end positions of the beam center. The horizontal axis corresponds to angles that are related to time via the antenna rotation rate. Because the beamwidth of the WSR-88D antenna is 1°, it follows that the superresolution spectral moments are spaced half a degree apart.
Depiction of (top) continuous scan and (bottom) step scan. (top) The horizontal wide double-arrow bands depict the span of data used in computation of spectral moments. The number of returns (pulses) within the beamwidth θ1 is 2L. On the abscissa is time or angle for a continuously scanning beam. The curves above the ribbons represent window functions. (bottom) The single-arrow bands represent locations where the beam is pointing while L pulses are transmitted by the PAR. The size of the arrows insinuates the relative weight given to the samples from the three consecutive beam positions (spaced at θ1/2) for synthesizing a better estimate.
Citation: Journal of Atmospheric and Oceanic Technology 32, 9; 10.1175/JTECH-D-15-0018.1
The PARs scan by stepping the beam from position to position and pausing long enough to obtain a sufficient number of independent samples (bottom of Fig. 1), and thus constrain the estimate errors. The vertically oriented ribbons with arrows indicate the positions of the beam (Fig. 1). To maintain the same speed as in a continuous scan, the number of samples per position must be L. This raises the issue of how to combine the data from adjacent azimuths and preserve similar or better angular resolution compared to the conventional radar while maintaining lower estimate errors. Combining data reduces estimate errors. Moreover, the Doppler spectral resolution of the combined sequence (3L) is 3 times finer than the resolution of the L sample sequence. Consequently, filtering ground clutter from the longer sequence in the spectral domain (as on the WSR-88D; Siggia and Passarelli 2004) is more efficient than that from the shorter one. Spectral processing is also crucial for separating overlaid echoes on the WSR-88D using systematic phase codes (Sachidananda and Zrnić 1999); hence, finer spectral resolution improves this procedure as well. The spectral aspects of the PAR processing and challenges due to discontinuities in position and/or time are not considered herein.
a. Combining data and window functions
Two related ways of combining data from three overlapping azimuths are contrasted. In one, the power or autocovariance samples are weighted by a power data window. This is dubbed a continuous window combination. In the other, all data at a single azimuth have the same weight, but the composite estimate is a weighted sum of estimates from the adjacent azimuths. We call this “step window” and choose the window coefficients, so that the relative contributions from the three adjacent radials are the same as in the continuous window case. The ribbons with vertical arrows in Fig. 1 (bottom) depict this concept. The length of the ribbon is proportional to the weight and the position indicates azimuthal location. As illustrated in Fig. 1, the total number of samples in the “step scan” mode is 3L (50% larger than in the continuous scan mode), while the angular span from beam center to beam center over which data are collected is the same. This is possible because the side beam of one central position and the main beam of the adjacent central position share the same samples. What matters is the effective beamwidth (Doviak and Zrnić 2006), which in the stepped scan can be equal to or smaller than in the continuous scan.
b. Effective beam pattern
Window functions determine the effective beam pattern as well as the variance reduction achievable by averaging. Herein we choose the Taylor window (Tay) for comparative studies because its spectral sidelobes are uniform. The ratio of the main lobe to sidelobe levels can be easily changed, making it convenient for analysis and drawing conclusions. Sidelobe suppressions of 30, 54, and 100 dB are considered, and the corresponding window functions are designated with Tay30, Tay54, and Tay100, respectively. The reasons for these choices are explained in this section.
Effective main lobe of the antenna pattern. The relative position in azimuth is normalized by the one-way 3-dB width of the antenna pattern. Step Tay100 and Step Tay30 are applied to the three positions (−0.5, 0, and 0.5) of the stepped scan. The Scan Tay54 is applied to the continuous scan between −0.5 and 0.5 of the normalized position.
Citation: Journal of Atmospheric and Oceanic Technology 32, 9; 10.1175/JTECH-D-15-0018.1
The window types we consider are listed in Table 1, where the number of samples is as on the National Weather Radar Testbed (NWRT); the values are very close to the ones on the WSR-88D. Of all the Taylor windows with length 40, the Tay54 is the best fit in the least squares sense to the von Hann window of length 40. The von Hann is applied in the superresolution mode to the WSR-88D data. The Step Tay100 applied to 60 samples (20 from each adjacent azimuth) produces the same width (defined by the 6-dB points) as the Scan (continuous) Tay54. It is about 20% broader than the intrinsic one (i.e., stationary antenna). Thus, as far as effective beamwidth is concerned, the Step Tay100 applied to 60 samples and the Scan Tay54 applied to 40 samples can be fairly compared in terms of estimate errors.
3. Errors in estimates
Comparisons of errors in reflectivity, velocity, and spectrum width are made between the stepped scan (such as on the PAR) and the continuous scan (such as on the WSR-88D) assuming the times to cover one scan in azimuth are equal. We consider a scenario applicable to the NWRT because data from that radar are presented in section 4.
The NWRT has a phased array antenna mounted on a rotating pedestal. For data collection, the antenna is positioned in the azimuth to cover the weather of interest, and electronic scanning is applied to capture the event. In any single direction, two consecutive sequences of pulses are transmitted. The first contains 12 pulses at the pulse repetition time (PRT) of 3 ms for reflectivity estimation, whereas the second contains 20 pulses at the PRT of 0.8 ms for velocity estimation (Table 2). Thus, the reflectivity estimates are free of range ambiguities and are used to determine potential overlaid and/or range ambiguous returns in the short PRT sequence. This long–short sequence of pulses on the NWRT serves the same purpose as the long PRT in the surveillance scan followed by the short PRT in the Doppler scan on the WSD-88D (Zrnić 2011). Moreover, on the two radars the transmitters are the same and the PRTs are very similar (the other parameters are in Table 2). Pertinent to our analysis are the accuracy requirements of estimates for the WSR-88D (Table 3), which the future PAR should satisfy. Thus, these requirements determine the parameters used in the comparisons.
Parameters of the NWRT and WSR-88D.
Accuracy requirements for spectral moments on the WSR-88D.
a. Reflectivity
The effects of Taylor window functions with 30, 54, and 100 dB of sidelobe suppression on the estimate errors are examined. The 30-dB window is included for reference to show the tendencies in effective beamwidth and errors. Furthermore, this window type would be an efficient choice for some radar systems in which spectral spurious components are up to 30 dB below signal components; hence, a more aggressive window is not beneficial. The effect of these windows on the equivalent number of independent samples in the case of white noise power is presented in Fig. 3 [from Eq. (A9)]. It is evident (Fig. 3) that given the same window type, the step window produces about 8%–10% more independent samples and a proportional decrease in variance. The step window has a similar slight advantage if only the signal is present. The window with less sidelobe suppression produces a larger number of independent samples and is preferred. However, applications to spectral processing may favor windows with lower sidelobes.
Equivalent number of independent samples for power estimation of pure noise. The numbers in the legend are the sidelobe levels (spectral) of the Taylor window. Dashed lines represent results for continuous window, and full lines are for step-equivalent window.
Citation: Journal of Atmospheric and Oceanic Technology 32, 9; 10.1175/JTECH-D-15-0018.1
To quantify the advantage of the step scan we present the standard error of reflectivity estimates in Fig. 4. The results are obtained using theoretical expressions for the variance of estimates [Eq. (A8)]. On the abscissa is the total number of available samples M = 3L over the three beam positions. Here and throughout the paper M indicates the sum of the number of samples from three adjacent positions. Thus, for reflectivity M = 3 × 12 = 36 and for velocity it is 60 (see Table 1). The total M is actually used only in the Step Tay100 case; in all other cases, the appropriate numbers of available (and used) samples is smaller, as indicated in the legend. For example, the top curve, labeled Fixed Rec M/3, is for a single-beam position on a PAR with M/3 power samples. This is the worst among the examined possibilities, as the dwell time is shortest. The Scan Tay54 with 2M/3 samples is for the continuous window applied to power samples obtained from contiguous azimuthal locations within one beamwidth while the beam is scanning. It illustrates what is achievable on the WSR-88D in the superresolution mode and in the Step Tay100 scan of the NWRT, which has the same “effective beamwidth” (see Fig. 2). The latter achieves significantly lower estimate errors (dashed red curve in Fig. 4). The Scan Rec is included for reference. It produces the smallest errors but applies to radial spacing of one beamwidth in regular scans if clutter is not filtered and there is no phase coding for mitigation of range ambiguities.
Standard deviation of reflectivity estimates vs the total number of samples available from the three-beam position M = 3L. The curves are for the following conditions. The Rec M/3 means a single stationary beam collecting M/3 power samples. The Scan Tay54 2M/3 is for a scanning beam over a 1° interval and collecting 2M/3 power samples. The beam displacement has been accounted according to Eqs. (A6), (A7), and (A8). Step Tay100 M is for a combined estimate from the three pointing directions (L = M/3 estimates from each) and the decorrelation due to the three beam positions is accounted for via Eqs. (A10), (A11), and (A8). The Scan Rec 2M/3 is for a scanning beam over a 1° interval collecting 2M/3 power estimates, and the beam displacement is accounted for via Eqs. (A5) and (A6). The assumed SNR = 10 dB and the spectrum width συ = 1 m s−1.
Citation: Journal of Atmospheric and Oceanic Technology 32, 9; 10.1175/JTECH-D-15-0018.1
The values in Fig. 4 are for the SNR = 10 dB and συ of 1 m s−1. At this συ, the errors are larger than those at the συ = 4 m s−1 specified by the WSR-88D requirement (Table 2). At συ = 4 m s−1, any one of the four windows produces estimates that exceed the requirement. The requirement applies to the average over four samples in range and because our plot is valid for one sample, the curves should be divided by two for comparison with the value applicable to the WSR-88D. Thus, with 24 samples (two-thirds of 36) the four-sample average of the Scan Rec sequence produces a standard deviation SD(Z) smaller than 1 dB; whereas, for the other modes, the errors are larger. But in the superresolution processing on the WSR-88D, the von Hann equivalent to Scan Tay54 mode is used (full curve in Fig. 4) and it has noticeably poorer performance than the Step Tay100 at all spectrum widths. The reasons for using the von Hann equivalent are better clutter filtering and finer lateral resolution than possible with the rectangular window.
In conclusion the PAR with a step window can have the same effective beamwidth as the WSR-88D and can produce better estimates of reflectivity in the same volume update time. This is because it utilizes more independent samples.
b. Velocity
We next compare the standard deviations of velocity estimates as functions of the number of samples and radar parameters corresponding to the NWRT (Table 2). The fixed total number of samples M = 60 implies 20 returns from a discrete position of the PAR beam and 40 returns in the case of a scanning beam (like WSR-88D) over a beamwidth interval. We make relative comparisons between the proposed superresolution mode for the PAR and the superresolution mode as on the WSR-88D, but we assume the PAR parameters for both. The signal-to-noise ratio is set to 8 dB to match the WSR-88D requirement. The results are obtained using theoretical expressions for the variance of estimates [Eq. (A13)].
The estimates of velocity in the case of Fixed Rec and 20 returns have the largest errors (Fig. 5). Continuously sampled data weighted with the Tay54 window over 40 returns yield almost the same errors. Continuous sampling over one beamwidth and uniform weight (Scan Rec 2M/3) results in the smallest SD, whereas processing on the PAR (Step Tay100 M) produces errors that are between those of the two continuously scanning cases.
SD of velocity estimates vs the spectrum width and for the same conditions as in Fig. 4. The beam displacement in the Scan Tay54 and Scan Rec has been accounted for by inserting Eqs. (A6) and (A7) into Eq. (A13). For the Step Tay100 M curve, Eqs. (A10) and (A4) are combined into Eq. (A6), and then Eqs. (A6), (A7), and (A11) are inserted into Eq. (A13). The SNR = 8 dB.
Citation: Journal of Atmospheric and Oceanic Technology 32, 9; 10.1175/JTECH-D-15-0018.1
Considering the requirement for velocity estimate errors (Table 3), the implications of the result in Fig. 5 are as follows. The requirement is almost satisfied (it is off by 5%) for the scanning radar and processing of uniformly weighted 40 samples spread over a beamwidth (Scan Rec 2M/3 = 40, in Fig. 5); the scan has been designed to satisfy the requirement for the actual dwell time on the WSR-88D. The Step Tay100 is off by 23% (error of about 1.23 m s−1). This compares to the 45% larger error (1.45 m s−1) of Scan Tay54 applicable to the WSR-88D.
The conclusion from this analysis is that, the superresolution on the PAR achieved by application of the Step Tay100 window on data from three consecutive positions spaced half a beamwidth apart produces about 20% smaller errors than in the superresolution mode on a conventional radar. This is a definite advantage of the agile beam with step scan over the mechanically steered antenna.
c. Spectrum width
The recently accepted “hybrid” spectrum width συ estimator on the WSR-88D uses one of the autocorrelation ratios: R(0)/R(1), R(1)/R(2), or R(1)/R(3) (Meymaris 2012). At low to moderate spectrum widths, the R(1)/R(2) is chosen [Eq. (A15)], while at larger widths the choice is the R(0)/R(1) estimator [Eq. (A14)]. At very narrow spectrum widths, the R(1)/R(3) estimator is used. At the συ and SNR specified in the accuracy requirement (Table 3), theory predicts that the R(1)/R(2) estimator is better than the R(0)/R(1) estimator (see Doviak and Zrnić 2006, Figs. 6.6 and 6.7). At somewhat larger widths starting at the crossover point, συx = 5 m s−1 [after Eq. (A15)], the R(0)/R(1) estimator produces smaller errors. Because συx is close to 4 m s−1 for which the requirement is stipulated, we have evaluated both estimators for the same window types, as in the cases of the reflectivity and velocity estimators. The errors (Figs. 6, 7) are obtained from simulations (appendix, section c).
SD of spectrum width estimates obtained from R(0)/R(1) vs the spectrum width. The parameters are as in Fig. 5, but the SNR = 10 dB. The curves depict the results from simulations.
Citation: Journal of Atmospheric and Oceanic Technology 32, 9; 10.1175/JTECH-D-15-0018.1
SD of spectrum width estimates obtained from R(1)/R(2) vs the spectrum width. The parameters are as in Fig. 6. The curves depict the results from simulations.
Citation: Journal of Atmospheric and Oceanic Technology 32, 9; 10.1175/JTECH-D-15-0018.1
Examination of Fig. 6 reveals that the Step Tay100, R(0)/R(1) estimator outperforms the Scan Tay54 at συ larger than about 6 m s−1. None of the estimators except the Scan Rec meets the requirement (at the συ = 4 m s−1). This changes if the R(1)/R(2) estimator is applied (Fig. 7). Then the Step Tay100 (applicable only to PAR) also meets, albeit barely, the requirement, demonstrating again the advantage of the agile beam. Note, that at συ less than about 3 m s−1, the Step Tay100 performs a bit better than the Scan Tay54.
The fact stands that in the superresolution mode, the hybrid spectrum width estimator satisfies the requirement if the Step Tay100 window is applied to the PAR data. If the Scan Tay54 window is applied to the WSR-88D data, then the hybrid spectrum width estimator does not satisfy the requirement.
4. Examples of data from a supercell
Select procedures described in section 3 are applied to the data collected with the NWRT. We compare the application of the Fixed Rec window on a single radial (M/3 samples) with applications of the Step Tay30, Step Tay54, Step Tay100, and continuous Tay100 each on three consecutive radials and for reflectivity estimation. The continuous Tay100 is a sequence of power coefficients applied to the sequence of power samples from the three consecutive beam positions. Comparison among the three step windows is meant to show the influence on the spectral moments as the windows’ width changes (windows with lower spectral sidelobes are narrower in the time domain). The Step Tay54 is included because it is similar to Scan Tay54, whose proxy (von Hann) is applied in the WSR-88D scanning mode, but on two-thirds of the number of samples used here. Inclusion of the continuous Taylor is to show its marginal inferiority to the Step Taylor window. Comparison of the Fixed Rec on the single radial with the Step Tay100 shows PAR’s improvement if data from its three radials are combined.
The effects of three window types (Fixed Rec, Step Tay54, and Step Tay100) on the velocity and spectrum width fields are also examined. The relative outcomes of the Step Tay30 and Tay100 windows on the Z field are similar to the outcomes on the υ and συ fields and are hence not considered in sections 4b and 4c.
The mode of operation summarized in Table 2 was used to collect data from a supercell storm that occurred on 31 May 2013 and produced a devastating tornado in El Reno, Oklahoma (Atkins et al. 2014; Burgess et al. 2014; Wurman et al. 2014). The spacing of the radials in the azimuth was at half a beamwidth. The large leading storm (maximum Z of close to 70 dBZ) is depicted in Fig. 8a. It produced the tornado, but it dissipated 3 min before this field was recorded. The reflectivity (Fig. 8a) exhibits a double hook echo (x = −60 km, y = 20 km; and x = −70 km, y = 20 km) where trailing storms formed. Remnants of circulation and strong convergence are centered at x = −22 km, y = 22 km, as can be discerned in the velocity field (Figs. 10a,b). At ranges within 20 km of the radar, ground clutter is evident as the clutter filter is not applied.
(a) Reflectivity field of the storm at 2346:46 UTC 31 May 2013. Azimuthal spacing of radials is half a beamwidth, and elevation angle is 0.51°. The number of samples for reflectivity estimates is 12, and all samples are uniformly weighted (i.e., Fixed Rec window). Data with SNR ≥ 10 dB is displayed. (b) As in (a), but the value Zw in each radial is obtained by combining powers from three adjacent radials and weighting these with the Step Tay100 coefficients. Thus, the total number of samples per estimate is 36.
Citation: Journal of Atmospheric and Oceanic Technology 32, 9; 10.1175/JTECH-D-15-0018.1
a. Effects on the reflectivity field
We use Eq. (5) to compute the power in the central direction and compute the coefficients α and b in Eq. (4) to be equivalent to Taylor windows. We compare the averaged with the original Z field (from single radials with no window), quantify the difference between the two, and examine the texture of these fields. In Fig. 8b is the Zw field (w indicates windowed) obtained after application of the Step Tay100 window. It has a visibly smoother appearance, yet the fine structures (the encircled reflectivity core and few pockets of 60-dBZ values) are well preserved. The histogram of the difference between the original Z field (Fig. 8a) and the Zw field (Fig. 8b) is shown in Fig. 9a. It has a relatively narrow but skewed distribution. This asymmetry is primarily caused by the nonlinear transformation of powers into logarithmic units (dBZ) and conforms to theoretical prediction. The median is −0.08 dB and about 90% of points are within ±1 dB. Similar comparisons can be made if the other two step windows are applied.
(a) Histogram of the difference field, Z (Fixed Rec window) in Fig. 8a minus Zw (Step Taylor100) in Fig. 8b. (b) Histogram of the texture (SD) of Zw from Fig. 8b.
Citation: Journal of Atmospheric and Oceanic Technology 32, 9; 10.1175/JTECH-D-15-0018.1
We define texture as a 21-point running SD along range of the reflectivity field. This corresponds to a range interval of 1.26 km because the spacing of samples is 60 m (Table 2). The median textures after application of the four windows are listed in Table 4. SNR thresholds of 10 and 20 dB illustrate the SNR effects. Fixed Rec is applied to the 12 available samples from a single-beam position (for Z; Table 2). The other windows are applied to power samples from three consecutive positions in the azimuth.
Median texture (SD) of the Z field.
The results in Table 4 depict a progressive increase in SDs starting from the Step Tay30 case and moving to the aggressive Step Tay100 case. Note that the ratio of median SD(Z)s between the Fixed Rec and Step Tay100 case in Table 4 (SNR ≥ 10 dB) is 2.02/1.74 = 1.16. The ratio of SD(Z) in Fig. 4 (M = 36 points total) is 2.57/2.17 = 1.17. This agreement confirms the relation between these two processing options, suggesting that the other two window-processing applications are correctly quantified in Fig. 4. The graph is valid at SNR = 10 dB and the spectrum width of 1 m s−1, whereas the Z field is for SNR ≥ 10 dB with no restrictions on the spectrum width. The median spectrum width for these data is 2.7 m s−1 (section 4c), which is considerably larger than 1 m s−1 for which the graph in Fig. 4 is valid. At the 2.7 m s−1 spectrum width, the statistical fluctuation of estimates for the Fixed Rec and Step Tay100 windows [from Eq. (A8)] are 1.67 and 1.42 dB. These are smaller than the 2.02 and 1.74 dB estimated from the data (Table 4 at SNR ≥ 10 dB) for these two windows. The excess in measured values is attributed to small-scale fluctuations in the Z field. The theoretical ratio of SD(Z) at the spectrum width of 2.7 m s−1 is 1.67/1.42 = 1.18, again very close to the measured value and the value predicted at the smaller spectrum width of 1 m s−1. Thus, the use of the Step Tay100 window on the reflectivity data produces the expected decrease in the standard deviation of estimates but also smooths small-scale reflectivity features. The median values of SD(Z) at the SNR ≥ 20 dB in Table 4 are smaller than at the SNR ≥ 10 dB because the effect of noise is smaller. The trend of the SDs versus the window type is the same for the two SNR thresholds.
The histogram of the texture (Fig. 9b) is bimodal. The well-pronounced peak at small values of texture is primarily due to the weather signal, whereas the secondary wide mode is caused by ground clutter and possibly other nonmeteorological scatterers. This is because the clutter returns along range are almost uncorrelated. The spread is similar to the one in the running difference squared of reflectivity, which has been one criterion for identification of ground clutter (Hubbert et al. 2009).
b. Effects on the Doppler velocity field
We next examine the Doppler velocity field (Fig. 10a) estimated after application of the Fixed Rec (no window, single radial with 20 samples; Table 2) and the Step Tay100 window (Fig. 10b). It is evident that the latter field is smoother (see textures, Table 5). The S-like zero Doppler curve (within the ellipse in Fig. 10) indicates strong convergence close to the radar (bottom of the S curve), then some rotation (middle of the curve), and again tight convergence with a miniscule convergence couplet at the top of the S curve. This S curve is delineated equally well in both images (Figs. 10a,b), confirming that the reduction in azimuthal resolution by the step averaging is not detrimental to the recognition of the feature.
(a) Doppler velocity field of the storm at 2346:46 UTC 31 May 2013 (as in Fig. 8). The number of autocovariance samples for velocity estimates is 19 (one less than the sequence length of 20), and all samples are uniformly weighted (i.e., Fixed Rec window). Data with SNR ≥ 10 dB is displayed. (b) As in (a), but the value υw in each radial is obtained by combining autocovariance samples from three adjacent radials and weighting these with the Step Tay100 power coefficients. Thus, the total number of autocovariance samples per velocity estimate is 57.
Citation: Journal of Atmospheric and Oceanic Technology 32, 9; 10.1175/JTECH-D-15-0018.1
Median texture (SD) of the Doppler velocity field.
The histogram of υ − υw (the subscript w stands for the field after application of the Step Tay100) in Fig. 11 indicates very good agreement between the two fields. The median difference is smaller than 1 m s−1 and most values are within −1 to 1 m s−1.
Histogram of the difference field, υ (Fixed Rec window) in Fig. 10a minus υw (Step Tay100) in Fig. 10b.
Citation: Journal of Atmospheric and Oceanic Technology 32, 9; 10.1175/JTECH-D-15-0018.1
Next we compare the error of estimates at the spectrum width of 2.7 m s−1 (median of the field; see section 4c) and SNR ≥ 10 dB with the textures (Table 5) produced after application of the Fixed Rec and Step Tay100 windows. The values of errors in velocity estimates [obtained from Eq. (A13)] are 1.07 m s−1 (Fixed Rec) and 0.91 m s−1 (Step Tay100). These values are about 50% smaller than the corresponding textures (1.51 and 1.24 m s−1, respectively; Table 5). The excess texture over statistical errors is likely due to the spatial variations of the velocity field, which include effects of clutter and occasional aliasing. These variations are comparable contributors (in the rms sense) to the texture. Hence, the reduction of fluctuation (1.51/1.24 = 1.22) is larger than what it would be (1.07/0.91 = 1.18) if an intrinsically flat field were subjected to the same procedures. Clearly, the reduction in errors is appreciable, while the gradients in the azimuth (such as across the rotation signature) are faithfully preserved.
c. Effects on the spectrum width field
We present the συ field obtained from the R1/R2 estimator because it outperforms the other lag estimators in the range from about 2 to 5 m s−1 (Meymaris et al. 2009) and the median here is 2.7 m s−1. The median is larger than reported in three isolated severe storms (1.4–1.9 m s−1) by Fang et al. (2004), but is in line with the 2.9 m s−1 measured by these authors in a severe storm cluster.
Notable in Figs. 12a,b (encircled) is the S-like feature of increased συ coincident with the same feature of near-zero Doppler velocities in Figs. 10a,b. Because the feature delineates the maximum in radial and azimuthal shears of Doppler velocities, we conclude that the increased συ is mostly due to shear. Close examination of Figs. 12a,b suggests that the Step Tay100 better depicts this shear zone as well as the rest of the image. This attests to the effectiveness of the PAR with the proposed processing (Step Tay100) to achieve superresolution.
(a) Doppler spectrum width field of the 31 May 2013 storm corresponding to the velocity field in Fig. 10a. The field is computed by applying the autocovariance estimator R(2)/R(1) to the same sequences as in Fig. 10a (i.e., Fixed Rec window is used). (b) As in (a), except the value συw in each radial is obtained by combining autocovariance samples as done in Fig. 10b (Step Tay100 window is applied).
Citation: Journal of Atmospheric and Oceanic Technology 32, 9; 10.1175/JTECH-D-15-0018.1
The median difference of the fields συ − συw is 0.05 m s−1 and the histogram is in Fig. 13. The conclusion is consistent with the differences in the Z and υ fields (Figs. 9a, 11). The smoothed fields have no perceptible bias, and variations with respect to the original (i.e., Fixed Rec) fields are insignificant.
Histogram of the difference field, συ (Fixed Rec window) in Fig. 12a minus συw (Step Tay100) in Fig. 12b.
Citation: Journal of Atmospheric and Oceanic Technology 32, 9; 10.1175/JTECH-D-15-0018.1
The textures of the spectrum width fields are listed in Table 6. We compare the values for SNR ≥ 10 dB with statistical errors of estimates plotted in Fig. 7, which are 1.25 m s−1 (Fixed Rec) and 0.93 m s−1 (Step Tay100). These are very close to the corresponding textures (Table 6) of 1.18 and 0.97 m s−1, respectively, suggesting the statistical errors are the principal contributors to the texture.
Median texture (SD) of the spectrum width field.
5. Summary and conclusions
This paper presents a way to obtain superresolution data with a PAR at volume coverage speeds and the same azimuthal resolution as on the WSR-88D but with lower statistical errors. On the WSR-88D the superresolution is achieved by overlapping 50% of time series data and applying a window function to these data before computing spectral moments. Therefore, radial arrays of moments are produced at half-beamwidth spacing from data collected over beamwidth-wide azimuthal sections. The window function serves to narrow the effective beamwidth and where needed to be part of ground clutter filter.
For the PAR with step scans, we have proposed a weighted average of powers and autocorrelations from three consecutive radials as follows. We have defined the Step Taylor weighting by uniform weights at each radial. The middle radial has the strongest weights and the two lateral radials have equal weights. The weights are chosen so that the average power in a specific radial equals the average power that would be obtained with the part of the true Taylor window sequence applied to that radial.
We have assumed that the total number of samples M from the three consecutive radials is constant. Then we made comparisons of estimate errors that result if 1) PAR estimates are from a radial with M/3 uniformly weighted samples; 2) PAR estimates are from three radials, and the weighting of powers and autocorrelations is with the three Steps Tay100 coefficients, one per radial; 3) WSR-88D estimates are from 2M/3 samples uniformly weighted over one beamwidth-wide azimuthal sector; and 4) as in point 3 but the powers and autocorrelations are weighted with the Scan Tay54 window. This window is very close to the von Hann window used on the WSR-88D. The Step Tay100 window applied to the three radials of the PAR (M samples total) produces the same effective beamwidth as the Scan Tay54 applied to the WSR-88D (2M/3 samples total).
The errors of the three spectral moments are smaller on the PAR with the Step Tay100 than the corresponding errors on a conventional radar with the Scan Tay54 window. For example, the superresolution on the PAR has about 20% smaller errors of velocity estimates than the superresolution mode on conventional radar. Furthermore, in the superresolution mode on the PAR with the Tay100 window, the spectrum width estimator satisfies the WSR-88D requirements. At the same effective azimuthal resolution as on the PAR, the WSR-88D does not satisfy this requirement. This is a definite advantage of the agile beam with step scan over the mechanically steerable beam.
Our conclusion comes from theoretical computations whereby the pertinent radar parameters are those of the NWRT. Except for the beamwidth, these parameters are very similar to the parameters on the WSR-88D.
We have demonstrated the proposed superresolution processing on the spectral moment fields obtained by the NWRT. Visual inspection of the three spectral moment fields before and after application of the superresolution processing revealed no significant degradation of meteorological features. The histograms of the differences between the original and processed fields were mostly contained within ~1 dB and ~1 m s−1 for reflectivity and velocity/spectrum width, respectively. The textures of the superresolution fields indicate the decrease with respect to the texture of original fields as expected from theoretical formulations.
In summary the proposed superresolution processing on the PAR is superior to similar processing on conventional radar because it produces smaller errors of estimates and avoids decorrelation due to changes in beam position. The step scan is very flexible and adaptable to adjust weights for optimal performance (smallest errors) as a function of SNR, the number of samples, and/or spectrum width. This adjustment remains to be explored.
The challenging issue of spectral processing and clutter filtering is postponed to the future. As the phased array steps its beam to different positions, there will likely be time gaps between contiguous chunks of samples from each position. Thus, the spectral processing of the time series data across multiple beam positions would be done over an effectively uneven PRT sequence. We are testing a few procedures to do it.
The authors acknowledge discussions with Drs. C. Curtis, R. Doviak, I. Ivic, S. Torres, and R. Palmer; Mr. D. Prignitz; Mr. E. Forren; and Ms. H. Guifu. The NSSL’s engineering team and the NWRT Software Upgrades Project (MPARSUP) implemented controls and processing on the NWRT. Anonymous reviewers provided extremely useful suggestions. The two lead authors were supported by NOAA Grant NA11OAR4320072.
APPENDIX
Variances of Spectral Moments
a. Reflectivity estimates
If the L returns at one beam position are uncorrelated with the L returns at the adjacent position (as on the NWRT with two sequential PRTs; Table 1), the variance can be computed by inserting M/3 into Eq. (A8) and multiplying the result with (2a4 + b4). This, at small M (<20), produced negligibly lower variance than in the correlated case (uniform long PRT). These two cases are indistinguishable at M > 20.
b. Velocity estimates
We consider the pulse-pair estimator. Its variance is proportional to the variance of the autocorrelation function (Zrnić 1977). Hence, it suffices to compute this variance and inject it into the formula for the velocity variance.
For comparison we considered the following.
1) PAR averaging from three beams spaced half a beamwidth apart
- The Taylor window sequence dm is applied to the M = 3L (60) time samples of (I, Q) voltages and then M−3 (57) autocorrelation samples at lag one are computed (omitted are the terms straddling beam transitions). The velocity is computed from the argument of the samples’ sum. The decrease in the terms of the autocorrelation matrix
- The Taylor power window sequence
- The Step Taylor power equivalent window is applied to the autocorrelation sample sequence. The number of coefficients per left, central, and right radials is as in (i)—that is, 20 for power and 19 for autocorrelation at each beam position. Comparison between this approach (Step Tay100 with M = 60 samples) and the continuous Tay100 sequence applied across the three beam positions [as in (i)] revealed that the standard error of velocity estimates is smaller in the Step Tay100 case. The improvement is between 0.05 m s−1 (at συ = 1 m s−1) and 0.1 m s−1 (at συ= 10 m s−1), and supports the preference for the Step Taylor on the combined sequence.
In Fig. 5, the graph labeled Step Tay100 M, PAR has been obtained following the outlined procedure and Eq. (A12) in which 
2) Continuously scanning beam as on the WSR-88D
- Rectangular window is applied to 2M/3 autocorrelation samples assuming these are obtained in a continuous scan over one beamwidth. Accounting for beam smearing is accomplished by multiplying each term of
- Tay54 window over the 2M/3 samples [other details as in (i)].
c. Spectrum width estimates
REFERENCES
Atkins, N. R., , Butler K. M. , , Flynn K. R. , , and Wakimoto R. , 2014: An integrated damage, visual, and radar analysis of the 2013 Moore, Oklahoma, EF5 tornado. Bull. Amer. Meteor. Soc., 95, 1549–1561, doi:10.1175/BAMS-D-14-00033.1.
Brown, R. A., , Wood V. T. , , and Sirmans D. , 2002: Improved tornado detection using simulated and actual WSR-88D data with enhanced resolution. J. Atmos. Oceanic Technol., 19, 1759–1771, doi:10.1175/1520-0426(2002)019<1759:ITDUSA>2.0.CO;2.
Burgess, D., and Coauthors, 2014: 20 May 2013 Moore, Oklahoma, tornado: Damage survey and analysis. Wea. Forecasting, 29, 1229–1237, doi:10.1175/WAF-D-14-00039.1.
Doviak, R. J., , and Zrnić D. S. , 2006: Doppler Radar and Weather Observations. Dover Publications, 562 pp.
Doviak, R. J., , and Zrnić D. S. , 2015: Errata and supplements. NOAA, 53 pp. [Available online at http://www.nssl.noaa.gov/publications/books/Errata_2nd_edition_3rd_and_4th%20prt_wo_comments.pdf?v=15jun2015.]
Fang, M., , Doviak R. J. , , and Melnikov V. , 2004: Spectrum width measured by WSR-88D: Error sources and statistics of various weather phenomena. J. Atmos. Oceanic Technol., 21, 888–904, doi:10.1175/1520-0426(2004)021<0888:SWMBWE>2.0.CO;2.
Hubbert, J. C., , Dixon M. , , and Ellis S. M. , 2009: Weather radar ground clutter. Part II: Real-time identification and filtering. J. Atmos. Oceanic Technol., 26, 1181–1197, doi:10.1175/2009JTECHA1160.1.
Melnikov, V. M., 2004: Simultaneous transmission mode for the polarimetric WSR-88D: Statistical biases and standard deviations of polarimetric variables. NOAA/NSSL Rep., 84 pp. [Available online at www.nssl.noaa.gov/publications/wsr88d_reports/SHV_statistics.pdf.]
Meymaris, G., 2012: Updates to the hybrid spectrum width estimator. 28th Conf. on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, New Orleans, LA, Amer. Meteor. Soc., 9A.6. [Available online at https://ams.confex.com/ams/92Annual/webprogram/Paper202232.html.]
Meymaris, G., , Williams J. K. , , and Hubbert J. C. , 2009: Performance of a proposed hybrid spectrum width estimator for the NEXRAD ORDA. 25th Conf. on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Phoenix, AZ, Amer. Meteor. Soc., 11B.1. [Available online at https://ams.confex.com/ams/89annual/techprogram/paper_145958.htm.]
Sachidananda, M., , and Zrnić D. S. , 1999: Systematic phase codes for resolving range overlaid signals in a Doppler weather radar. J. Atmos. Oceanic Technol., 16, 1351–1363, doi:10.1175/1520-0426(1999)016<1351:SPCFRR>2.0.CO;2.
Siggia, A. D., , and Passarelli R. E. Jr., 2004: Gaussian model adaptive processing (GMAP) for improved ground clutter cancellation and moment calculation. Third European Conference on Radar in Meteorology and Hydrology (ERAD) together with the COST 717 Final Seminar, ERAD Publication Series, Copernicus, 67–73.
Torres, M. S., , and Curtis C. , 2007: Initial implementation of super-resolution data on the NEXRAD network. 23rd Conf. on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, San Antonio, TX, Amer. Meteor. Soc, 5B.10. [Available online at https://ams.confex.com/ams/87ANNUAL/techprogram/paper_116240.htm.]
Weber, M. E., , Cho J. Y. N. , , Herd J. S. , , Flavin J. M. , , Benner W. E. , , and Torok G. S. , 2007: The next-generation multimission U.S. surveillance radar network. Bull. Amer. Meteor. Soc., 88, 1739–1751, doi:10.1175/BAMS-88-11-1739.
Wurman, J., , Kosiba K. , , Robinson P. , , and Marshall T. , 2014: The role of multiple-vortex tornado structure in causing storm researcher fatalities. Bull. Amer. Meteor. Soc., 95, 31–45, doi:10.1175/BAMS-D-13-00221.1.
Zrnić, D. S., 1975: Simulation of weatherlike Doppler spectra and signals. J. Appl. Meteor., 14, 619–620, doi:10.1175/1520-0450(1975)014<0619:SOWDSA>2.0.CO;2.
Zrnić, D. S., 1977: Spectral moment estimates from correlated pulse pairs. IEEE Trans. Aerosp. Electron. Syst., AES-13, 344–354, doi:10.1109/TAES.1977.308467.
Zrnić, D. S., 2011: Doppler radar for USA weather surveillance. Doppler Radar Observations—Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications, J. Bech and J. L. Chau, Eds., InTech, 3–32.
Zrnić, D. S., , and Doviak R. J. , 1976: Effective antenna pattern of scanning radars. IEEE Trans. Aerosp. Electron. Syst., AES-12, 551–555, doi:10.1109/TAES.1976.308254.
Zrnić, D. S., and Coauthors, 2007: Agile-beam phased array radar for weather observations. Bull. Amer. Meteor. Soc., 88, 1753–1766, doi:10.1175/BAMS-88-11-1753.
