1. Introduction
The accuracy of radar-based rain rates is limited by the calibration of radar reflectivity ZH, which must be measured within 1 dB for rainfall estimates to have an accuracy of 15%. Several approaches to radar calibration have been undertaken and are summarized in Atlas (2002). The receive component of the radar can be calibrated using a transmitter with a known signal strength. Transmit and receive components can be calibrated jointly by positioning a reflective target with a known radar cross section into the radar beam using aircraft, a balloon, etc. Another approach is to compare radar reflectivity to disdrometer measurements, as in Joss et al. (1968). The relative calibrations of the U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) have also been explored by comparisons with spaceborne radar (Bolen and Chandrasekar 2000) and neighboring WSR-88D radars (Gourley et al. 2003). None of these approaches has emerged as the standard procedure for calibrating radars.
Gorgucci et al. (1992) first noted the self-consistency of ZH, differential reflectivity, ZDR, and the range derivative of the differential propagation phase ΦDP (or specific differential phase KDP) in rain and suggested a calibration method based on adjusting ZH so that rainfall R derived from ZH and ZDR agreed with R derived from KDP. Following this pioneering work, Goddard et al. (1994) and Scarchilli et al. (1996) showed that in theory KDP can be estimated from observations of ZH and ZDR, integrated to yield ΦDP, and then compared to observed ΦDP values; differences are attributed to miscalibration of ZH. Methods to calibrate radar reflectivity using the consistency principle have been demonstrated by Gorgucci et al. (1992), Goddard et al. (1994), Illingworth and Blackman (2002), Vivekanandan et al. (2003), and Ryzhkov et al. (2005).
Goddard et al. (1994) and Illingworth and Blackman (2002) formulated the consistency relation as the ratio of KDP to ZH as a function of ZDR. The consistency relation is derived from a normalized gamma drop size distribution (DSD) and makes use of scaling properties between ZH, KDP, and the normalized concentration parameter of the DSD. To formulate a closed relationship between KDP, ZH, and ZDR from a three-parameter gamma DSD, one must either fix the shape parameter μ or randomly cycle through a discrete set of variations in parameter ranges (Ulbrich 1983). Illingworth and Blackman (2002) demonstrated that the consistency relation was well behaved and, more importantly, virtually independent of variations in μ. Vivekanandan et al. (2003) simplified the three-parameter DSD representation by relating the shape and slope parameters (μ–Λ) of the gamma distribution from disdrometer observations to yield a closed form of the consistency relation. Although the equations to estimate KDP from observations of ZH and ZDR are slightly different from Illingworth and Blackman (2002), their calibration procedure—that is, estimating KDP, integrating it in the radial direction, and comparing to observed ΦDP values—is essentially the same.
Ryzhkov et al. (2005) used multiple linear regression to relate ZH, ZDR, and KDP. The coefficients of the regression equation were found empirically using a large sample of DSDs from a disdrometer. The empirical approach differs from the approach that relies on a fixed consistency relationship deduced from a normalized gamma DSD, but it was found to be necessary in Oklahoma where drastically different DSDs were discovered for convective rainfall events versus stratiform events. The use of a fixed consistency relation does not account for variations in DSD but rather assumes that the normalized gamma function adequately describes the DSD in natural rain. Although no physical explanations were provided for the discrepancy in the large and small DSDs in Ryzhkov et al. (2005), it is feasible that they are specific to the intense convective storms unique to the region. This method also differs from earlier studies by using area–time integrals of KDP instead of radial profiles of ΦDP. Integrating KDP to yield the total phase change along the path reduces the noise in the ΦDP signal, whereas differentiating ΦDP gives an even noisier KDP estimate. Estimating KDP requires one to choose an adequate rain path over which ΦDP increases linearly. Longer (shorter) path lengths yield less (more) noisy values of KDP. If ΦDP increases nonlinearly in the path, then, as Gorgucci et al. (1999) showed, KDP can be biased either negatively or positively.
The study presented here formalizes the ΦDP-based calibration method originally proposed by Goddard et al. (1994) so that agencies that are upgrading to dual-polarization capabilities can readily calibrate their radars. Careful data quality procedures were developed and are presented here to fully automate the method; manual selection of candidate rays with a large differential phase is no longer required, as was the case in Illingworth and Blackman (2002) and Vivekanandan et al. (2003). Vivekanandan et al. (2003) also examined the sensitivity of their results to two different models describing raindrop oblateness (represented as a drop aspect ratio) to equal-volume spherical diameter. The differing raindrop shape models yielded an average difference in reflectivity biases of 1.2 dB. Auxiliary information from traditional calibration methods was introduced to infer the correctness of the raindrop shape models. In this study, a sensitivity test that relies on the calibration being independent of rainfall rate and ZDR was carried out on various models relating raindrop aspect ratio to diameter, which ultimately reveals their suitability without the need for auxiliary information. These results suggest that there is a unique model that relates drop oblateness to diameter in natural rain, at least in midlatitudes.
Section 2 outlines the methodology of our approach using the total phase shift in rain to assess the calibration of ZH. Error sources that can either offset or enhance the apparent miscalibration in ZH due to biases in the raw, polarimetric variables and influences from nonraining pixels (e.g., ground clutter, insects, hail, partially melted hydrometeors, ice, etc.) are discussed and correction procedures are presented. The French national weather service, Météo-France, has been operating a C-band polarimetric radar in simultaneous transmission and reception mode since the summer of 2004. Details of the radar’s operating characteristics are provided in section 3. Moreover, this section demonstrates application of the ZH calibration method using polarimetric observations from six precipitation episodes.
Currently, there is some doubt in the community regarding the correct model to relate drop oblateness to equal-volume spherical diameter, especially for small drops with diameters of 0.5–1.5 mm (Thurai et al. 2007). Section 4 examines the sensitivity of calibration results to several raindrop shape models used in the literature. As it turns out, the sensitivity test provides an additional constraint on the various drop shape models that have been proposed. Section 4 also supplies the equations for calibration curves valid at the X, C, and S bands. The implications of employing a simple linear slope parameter linking drop oblateness to diameter on rainfall rate estimation and attenuation correction schemes are discussed. Conclusions and a summary of results are provided in section 5.
2. Description of polarimetric method to calibrate ZH
a. Consistency theory
The polarimetric variables ZH, ZDR, and KDP can be modeled at C band using the transition (𝗧) matrix formulation of Barber and Yeh (1975). Both ZH and KDP scale with Nw, so their ratio is independent of Nw, as is ZDR. Figure 1 shows that consistency curves of the ratio KDP/ZH are well-defined functions of ZDR. The different curves correspond to different raindrop temperatures ranging from 0° to 20°C. The curves begin to diverge as ZDR values exceed 1 dB. Theoretical values of KDP (
Observations of ZH (in mm6 m−3) and ZDR (in dB) and the bottom consistency curve in Fig. 1 were used to provide a value of
b. Correction of biases and spurious signals in polarimetric variables
Prior to implementing the consistency-based approach to calibrating ZH, it is very important to examine the quality of the raw polarimetric quantities. Otherwise, differences between
Polarimetric measurements at vertical incidence were used for calibrating ZDR. The Trappes antenna points vertically every 15 min and is rotated 360° while at zenith. An azimuthal average of ZDR should be 0 dB even if the raindrops are canted in the mean or the antenna is wobbling. An analysis performed on measurements collected in the antenna’s far field for a 6-h stratiform rainfall event indicated Trappes’ ZDR was biased 0.08 dB too low. A correction factor of 0.08 dB has thus been added to all measurements of ZDR. Measurements of ZDR were also found to be biased as a function of azimuth by as much as 0.4 dB because of metallic structures within close proximity to the radome. This near-radome interference effect was observed to be repeatable from case to case. An empirical mask was developed and implemented hereafter to offset the biases in ZDR measurements. After ZDR was calibrated and corrected due to near-radome interference effects, Gourley et al. (2006) found the expected precision in ZDR to be 0.2 dB in rain. The uncertainty in ZDR calibration is explored further in section 4.
Inspection of a movie loop of ZH when convective echoes passed directly over the radar site revealed a sudden, unrealistic reduction over the entire domain. It is hypothesized that a water-coated radome resulted in the observed power losses. Reductions in ZH and ZDR due to attenuation were also observed behind intense convective cells. These attenuated measurements were readily recognizable and potentially correctable because of an associated increase in 
Attenuation and differential attenuation of the signal at C band are known to reduce measurements of ZH and ZDR below their intrinsic values. Several correction methods have been proposed and are summarized in Bringi and Chandrasekar (2001). A simple approach linearly relates losses in ZH and ZDR with increases in
Mie scattering effects occur with equivolumetric median diameter drops >2.5 mm or ZDR > 2.5–3 dB at C band. These large drops can produce differential phase shift on backscatter, leading to transient maxima in
Because the goal of the calibration experiment is to compare
When comparing
c. Rejection of rays containing nonrain echoes
The presence of nonprecipitating targets, predominantly from anomalous propagation and insects, impacted measurements of ZH, ZDR, ρHV(0), and
Precipitating echoes from the perspective of the fuzzy logic algorithm include pixels containing hail, partially melted hydrometeors, and frozen hydrometeors. Consistency theory, however, is only valid for hydrometeors in liquid phase. Rays that contained a single pixel with ZH > 50 dBZ were discarded to mitigate the impacts of hail. Measurements within and above the melting layer were avoided by setting a maximum range for the rain path’s end point to 65 km. This range was found manually by observing a decrease in ρHV(0) with range, an increase and greater fluctuation of ZDR, and an increase in ZH. Determining the maximum range at which rain measurements are possible can be easily automated by detecting the bright band, as demonstrated in Gourley and Calvert (2003), Brandes and Ikeda (2004), Giangrande et al. (2005), Tabary et al. (2006), and Matrosov et al. (2007). In this study, the criteria for rejecting rays with potentially frozen hydrometeors were set stringently so that questionable pixels were simply discarded. Each ray meeting the aforementioned criteria was considered a candidate for computing the difference between
3. Calibration of ZH for the Trappes C-band polarimetric radar
Data collected during June through September of 2005 by Météo-France’s operational radar, situated approximately 30 km southwest of Paris, are used to evaluate its calibration. The transmitted pulses have a width of 2 μs, a frequency of 5.64 GHz, a peak power of 250 kW, and pulse repetition frequencies of 379, 321, and 305 Hz. The 3-dB beamwidth of the 3.7-m diameter antenna is less than 1.1°. Further details of the radar are provided in Table 2. The radar uses simultaneous transmission and reception of horizontally and vertically polarized waves, so cross-coupling between the orthogonally polarized waves could in theory bias ZDR, but, as pointed out by Ryzhkov and Zrnic (2007), this should be negligible in rain because the net mean canting angle of raindrops is close to zero. Beam blocking was common at the lowest elevation angle of 0.4°, so 484 scans of unblocked data at an elevation angle of 1.5° on 23, 26, 28, and 30 June, 4 July, and 10 September 2005 were used in the calibration experiment; the resolution of the polar data files was slightly oversampled at 0.5° in azimuth by 240 m in range, so a total of 348 480 rays were examined. Because most of the rays did not contain rain, 5280 rays met all criteria discussed in section 2. In practice, we found the calibration method activated for most rays containing rain within 50 km of the radar.
Figure 3 shows range profiles of ZH, ZDR, raw
4. Sensitivity of calibration technique to raindrop shape model
a. Various drop shape models
Small drizzle drops (D < 0.5 mm) are known to be spherical, whereas the shapes of raindrops become more oblate with increasing diameter. Polarimetric radar measurements serve as the basis for improved rainfall rate estimates, but they rely on the relationship between raindrop aspect ratio and diameter, for which there is no consensus in the polarimetric community. Relatively small errors in the assumed raindrop shape model lead to significant errors in rainfall rate retrievals (Bringi and Chandrasekar 2001, chapter 7).
Despite the prevalence of nonlinear drop shape models, Gorgucci et al. (2000) used polarimetric radar to infer raindrop size–shape relationships by treating the 0.062 slope parameter in (6) as a variable, called β. The so-called β-retrieval method assumes a variable, linear relationship between drop aspect ratio and diameter. It is assumed that there is no unique drop shape model, and the variability is attributed to asymmetric oscillations excited by collisions and vortex shedding. This method was later incorporated in polarimetric rainfall estimation techniques for S- and X-band radar (Gorgucci et al. 2001; Matrosov et al. 2002) as well as in DSD parameter retrievals (Moisseev et al. 2006). The implications of assuming a linear raindrop shape model are explored in the next section.
BZV proposed the polynomial shown in (1), which is a synthesis of the measurements of Pruppacher and Pitter (1971), Chandrasekar et al. (1988), Beard and Kubesh (1991), and ABL. More recently, Thurai and Bringi (2005) showed excellent agreement of their observations of drop aspect ratios measured by a 2D video disdrometer of drops falling 80 m from a railway bridge with the BZV formula in (1). However, the smallest drop size for which they could derive drop aspect ratios was 1.5 mm. There is still some uncertainty of the precise character of drop shapes in range of 0.5–1.5 mm. Section 4b evaluates the sensitivity and behavior of calibration results for the proposed raindrop shape models shown in Fig. 5.
b. Calibration performance for various drop shapes
The T-matrix formulation at C band was used to compute relationships among ZH, ZDR, and KDP assuming drop spectra are adequately represented by a normalized gamma function with μ = 5 and a drop temperature of 0°C. Figure 6 shows the resulting consistency curves for the proposed raindrop shape models discussed in section 4a and illustrated in Fig. 5. Note that a hybrid model was considered (ABL/GCB), which assumes ABL shapes from 0–1.3 mm and then GCB for larger drops. The ABL/GCB hybrid model avoids the unrealistic kink in the GCB model at 1.1 mm.
Analysis of drop aspect ratios (Fig. 5) and their resulting consistency curves (Fig. 6) shows that the two linear models of PB and MKMR yield much higher differential phase shifts (per ZH in mm6 m−3) for an observed ZDR (in dB) because the drops are much more oblate, especially for D < 2.5 mm. The sudden jog in drop shapes to spherical at D = 1.1 mm in the GCB model results in much less differential phase shift with small drops for D < 1.55 mm and is responsible for the fall in the consistency curve for ZDR < 0.7 dB in Fig. 6. Drops described by the ABL model are slightly less oblate than with the other models for D of 1.7–3.7 mm. This less oblate shape results in less differential phase shift for ZDR observations in the range of 0.8–2.6 dB.
Tests were then carried out to evaluate the sensitivity of apparent radar calibration [C; see (5)] to the drop shape models shown in Fig. 5. The polarimetric calibration method described in section 2 was applied to the same dataset described in section 3, but using the various consistency curves shown in Fig. 6. The calibration of ZH for a radar system should be general such that it is not a function of D. Information regarding the suitability of a given drop shape model is revealed upon examination of C as a function of D. If C varies with D for a given drop shape model, then there is evidence suggesting the model is inappropriate. It is recognized that ZDR is related to D for single drops, and as such could be used to evaluate C as a function of D. However, monodispersed raindrop spectra do not occur naturally within a ray, so as a proxy to D we computed
Figure 7 shows histograms of C (in dB and %) using the various drop shape models as a function of the ratio of
The relatively large negative slopes of both linear models (PB, −1.22%; MKMR, −3.57%) indicate a strong dependence of C on the ratio. This result suggests one or a combination of the following: 1) the linear models yield drops that are too oblate for small drops, 2) the linear models yield drops that are too spherical for large drops, or 3) observed ZDR is miscalibrated despite the bias correction steps that were taken in section 2b. Further analysis of Fig. 5 shows a general convergence of the linear models to the nonlinear ones with increasing ratio, or D. Significant differences in drop aspect ratios between linear and nonlinear models are seen at D < 2.5 mm, which indicates that the linear PB and MKMR drop shape models are too oblate for small drops. Lastly, error bars representing the standard error of the mean are larger for the two linear models than for the nonlinear ones, which is indicative of more ray-to-ray variability of C.
The ABL model also has a negative slope of −0.67% (see Table 3). In this case, Fig. 5 shows that drop aspect ratios with this model are less oblate than the other models for D of 1.7–3.7 mm. Because drop shapes are similar to the other nonlinear models for D < 1.7 mm, we can conclude that the ABL model yields drops that are not oblate enough for medium-sized drops in the range of 1.7–3.7 mm. The GCB model, on the other hand, has a positive slope of 0.81%. Drop aspect ratios from this model are rather more spherical than other nonlinear models for D < 1.5 mm and have an unrealistic kink at 1.1 mm (Fig. 5). This oversimplified model yields less
To address the third assertion that miscalibration in ZDR dictates the slopes, or dependence on D, we added positive and negative perturbations of 0.2 dB to ZDR observations and then recomputed the curves resulting from each of the drop shape models. This has the effect of nudging the calibration curves in Fig. 6 to the right and left by 0.2 dB. Figure 8 shows curves of the histograms as in Fig. 7 for the drop shape models. The most notable feature in Fig. 8 is the large negative excursions by all drop shape models at low ratios for the +0.2-dB ZDR perturbations. The slopes of the calibration curves in Fig. 6 indicate that lower
The analysis of the dependence of ZH calibration for the various drop shape models on D indicates that the BZV and ABL/GCB models are the most suitable and are virtually indistinguishable. Figure 7 indicates their difference in oblateness for D in the range of 1.7–3.3 mm (Fig. 5) results in C near 8% (<0.35 dB). The use of slightly attenuated ZH and ZDR data adds additional uncertainty of 0.2–0.3 dB. The uncertainty due to drop shape model selection combined with attenuation at C band yields a ZH calibration accuracy using our proposed method within 0.6 dB.
c. Calibration curves at X-, C-, and S-band frequencies
d. Implications on methods that retrieve the β slope parameter
Polarimetric radar studies such as Gorgucci et al. (2000), Matrosov et al. (2002), Anagnostou et al. (2004), and Moisseev et al. (2006) have adopted variable raindrop aspect ratio to diameter relationships through the retrieval of the β slope parameter. Gorgucci et al. (2006) plot radar observed values of KDP/ZH against ZDR as in Fig. 6 and find many data points much closer to the PB drop shape line than to the curves produced by the ABL, BZV, and GCB drop shape models. It has been hypothesized that raindrop shapes become less oblate because of collisions and vortex shedding, necessitating a variable relationship between drop aspect ratio and drop diameter. These collisions and subsequent asymmetric oscillations should be evident in heavy rainfall where there is increased turbulence. To test this hypothesis, we evaluated C as a function of the maximum ZH found within the rain path. Turbulence should increase with increasing maximum ZH, resulting in less oblate drops than predicted from the valid nonlinear models of ABL, ABL/GCB, BZV, and GCB. Note that the models of PB and MKMR have been eliminated from this analysis because they were shown to be invalid from the analysis in section 4b. Less oblate drops would have the effect of producing less differential phase shift than predicted from consistency theory and would cause C in (5) to decrease with increasing maximum ZH.
Figure 9 shows C plotted as a function of maximum ZH at 1-dB increments in the range of 41–50 dBZ. Sample sizes, which are plotted against the right ordinate in gray bars, became too small for maximum ZH bins smaller than 41 dBZ. Rays with maximum ZH > 50 dBZ have been eliminated because of potential contamination from hail (see discussion in section 2c and Table 1). Figure 9 shows little variability of C with maximum ZH in this analysis. We conclude that there is no evidence to suggest that drop shapes fundamentally deviate from the nonlinear models with increasing rain rates where collision frequencies increase.
5. Discussion and summary
This study formalizes the method originally proposed by Goddard et al. (1994) to calibrate ZH using the relationship among ZH, ZDR, and the total differential phase shift ΦDP along individual radar rays in rain. Development of the method illuminated several data quality issues with the raw variables, which correction procedures were developed to address. The method was then employed to radar observations collected by Météo-France’s C-band polarimetric radar located in Trappes. Daily averages of ZH calibration prior to the waveguide replacement were found to be biased 22% (0.8 dB) too high and varied by less than 5% (0.2 dB). The method detected a sudden jump following the hardware replacement up to 55% (1.7 dB).
A sensitivity test was carried out to determine the impact of different drop shape models on calibration results. The data sample was subdivided into classes based on the amount of theoretical differential phase shift caused by observations with ZDR > 1 dB to ZDR < 1 dB. This enabled us to examine the stability of ZH calibration as a function of drop size for commonly used drop shape models. This sensitivity test revealed information regarding the suitability of the models themselves, thus providing a constraint on drop shapes. The linear models of PB and MKMR were not supported because they yielded calibration results that depend on drop size. This assertion was confirmed by simulating the impacts of ZDR bias on the results, which also showed a dependence of the linear drop shape models on drop size. This finding raises concerns for polarimetric attenuation correction and DSD and rainfall retrieval algorithms that rely on a fitted slope parameter relating drop axis ratio to diameter, at least for data collected in midlatitudes. The model proposed by BZV and a hybrid model composed of ABL shapes from 0–1.3 mm and then GCB thereafter both led to stable calibration results, with much less variability from ray to ray, that were independent of drop size. This consistency over many different rays supports our contention that the natural variability of raindrop spectra is well captured by the use of a normalized gamma function. The difference in calibration of ZH using these two models was 8% which, when considering the slight attenuation effects on ZH and ZDR, suggests that ZH can be calibrated within 0.6 dB using the proposed method. The inferred calibration for these drop shapes was unchanged over the range of ZH from 40 to 50 dBZ, leading us to question recent suggestions that drop shapes become more spherical in heavy rainfall because of increased turbulence and/or collisions. Results presented in this study support the notion that there is a unique model that relates drop oblateness to diameter for rain in midlatitudes, and the linear drop shape models are not suitable.
The ZH calibration method developed here differs from that proposed by Ryzhkov et al. (2005) in its use of the differential phase shift ΦDP along individual, nonattenuated rays instead of KDP and a fixed, normalized gamma distribution representing drop spectra instead of large statistics of DSDs collected with a disdrometer. The normalized gamma distribution was found to be representative of raindrop spectra observed near Paris and is thus applicable to regions including but not limited to Europe, Japan, and Canada. The proposed calibration method is readily applicable at X and S band provided considerations are made for attenuation effects. Polarimetric data collected at S band are less susceptible to attenuation effects; thus, rain paths with larger values of
We believe the proposed ZH calibration method and associated consistency relationships will be useful to agencies that are upgrading their radars with polarimetric capabilities.
Acknowledgments
This work was done in the frame of the PANTHERE Project (Programme ARAMIS Nouvelles Technologie en Hydrometeorologie Extension et Renouvellement) supported by Météo-France, the Ministere de L’Ecologie et du Developpement Durable, the European Regional Development Fund (ERDF) of the European Union, and CEMAGREF.
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Consistency curve showing interdependence of ZH (mm6 m−3), ZDR (dB), and KDP (one-way, deg km−1) at C band assuming BZV raindrop shapes; their spectra are represented by a normalized gamma distribution with a shape parameter of five. Varying μ from 0 to 10 altered the consistency curves by less than 5%. Curves are also shown to be insensitive to modeled drop temperature.
Citation: Journal of Atmospheric and Oceanic Technology 26, 4; 10.1175/2008JTECHA1152.1
Scatterplot of initial mean
Citation: Journal of Atmospheric and Oceanic Technology 26, 4; 10.1175/2008JTECHA1152.1
Radial profiles of observed differential phase shift (dotted gray line), observed differential phase shift smoothed along 25 gates (thick black line), theoretical differential phase shift smoothed along 25 gates (thick gray line), and reflectivity (lines connecting the “x” symbols) plotted against the left ordinate for the 228° azimuth valid at 1015 UTC 26 Jun 2005. Theoretical differential phase shifts with +1- and −1-dB perturbations in reflectivity are shown as thin gray lines. Differential reflectivity is plotted against the right ordinate and is shown as lines connecting open circles. Horizontal dotted lines correspond to an initial differential phase value of 0° and final threshold value of 12°.
Citation: Journal of Atmospheric and Oceanic Technology 26, 4; 10.1175/2008JTECHA1152.1
Calibration of reflectivity (% on left ordinate; dB on right ordinate) for six widespread rain episodes during the summer months of 2005. Mean values are computed for each scan, or plan-position indicator (PPI), and are shown as filled black circles. Error bars correspond to standard error in estimating the mean. Thick black line of daily averaged calibration shows little variability until after the waveguide was replaced on 15 Aug 2005.
Citation: Journal of Atmospheric and Oceanic Technology 26, 4; 10.1175/2008JTECHA1152.1
Various models relating raindrop aspect ratio to equal-volume spherical diameter. Refer to the discussion in section 4a for details regarding the model descriptions, abbreviations, and formulas.
Citation: Journal of Atmospheric and Oceanic Technology 26, 4; 10.1175/2008JTECHA1152.1
Consistency curves at C band for the raindrop shape models illustrated in Fig. 5. Refer to the discussion in section 4a for descriptions of the models and their abbreviations.
Citation: Journal of Atmospheric and Oceanic Technology 26, 4; 10.1175/2008JTECHA1152.1
Sensitivity of apparent calibration of ZH (dB and % on left ordinate) as a function of proxy variable to drop diameter for the raindrop shape models illustrated in Fig. 5. Vertical error bars correspond to standard error in estimating the mean at each bin. Thin black lines indicate fits using least squares regression. Sample sizes are shown as gray bars and are plotted against the right ordinate.
Citation: Journal of Atmospheric and Oceanic Technology 26, 4; 10.1175/2008JTECHA1152.1
As in Fig. 7, but observed ZDR values have been perturbed by ±0.2 dB to illustrate the sensitivity of curves to potential calibration bias in ZDR. Dashed pattern indicates sample sizes were less than 10. Blue (red) bars correspond to sample sizes of −0.2 (+0.2) dB perturbations and are plotted against the right ordinate.
Citation: Journal of Atmospheric and Oceanic Technology 26, 4; 10.1175/2008JTECHA1152.1
Sensitivity of apparent calibration of ZH (dB and % on left ordinate) as a function of maximum reflectivity found in each radial for the four valid drop shape models indicated in the legend. Refer to the discussion in section 4a for descriptions of the models and their abbreviations. Sample sizes are shown as gray bars and are plotted against the right ordinate.
Citation: Journal of Atmospheric and Oceanic Technology 26, 4; 10.1175/2008JTECHA1152.1
List of error sources in polarimetric radar observations requiring correction prior to application of radar calibration method.
Slopes of linear fits and rms error of fits to curves in Fig. 7 representing calibration of ZH as a function of proxy variable to drop diameter for the different raindrop shape models illustrated in Fig. 5. Refer to the discussion in section 4a for descriptions of the models and their abbreviations. Slopes closest to 0.0 indicate the least sensitivity of apparent radar calibration to drop diameter.
Coefficients for a third-degree polynomial fit to the calibration curves for X-, C-, and S-band frequency radars. Refer to (10) in the text for the form of the equation and associated units. The calibration curves assume raindrop shapes are modeled as in BZV and raindrop spectra follow a normalized gamma distribution (μ = 5, drop temperature = 0°C).
