1. Introduction
Quality characterization of observational data is one of the most important steps before applying processing algorithms. At the same time, as data quantity and number of various types of applications increase, the demand for automated and flexible quality characterization and correction rises. Since the number of operational weather radar systems has increased over the last few years, radar-based precipitation forecast and severe weather warning systems have become a fundamental part in everyday life, for instance, the Integrated Terminal Weather System (ITWS; Evans and Ducot 1994), the Generating Advanced Nowcasts for Development in Operational Landbased Flood Forecast (GANDOLF; Pierce et al. 2000), or Convection in Radar (CONRAD; Lang 2001). Ongoing research focuses intensively on the use of radar data for assimilation in numerical weather prediction and hydrological models to improve quantitative precipitation forecasts. As part of this goal, the European cooperation in the field of scientific and technical research (COST) Action 717 investigated how to make the best use of radar information (Rossa et al. 2005). This paper presents the work of one part of this COST Action 717 dealing with the quantification of radar data errors. It is based on a survey on user and application requirements conducted by several European weather services.
Weather radars sample reflectivity factor (hereinafter referred to as reflectivity), in some cases Doppler velocity and polarimetric parameters, over a wide horizontal range (∼250 km) with a spatial resolution of several hundred meters and a temporal resolution within minutes. Radar data are often biased by various factors. Echo returns from non-weather-related objects, for example, ground and sea clutter, birds, and attenuation of the transmitted electromagnetic wave by hydrometeors, are the main factors contributing to uncertainties in the measurement. Figure 1 shows a reflectivity field during the passage of a squall line measured by the polarimetric diversity C-band Doppler radar (POLDIRAD). The radar is operated by the Deutsche Zentrum für Luft- und Raumfahrt at Oberpfaffenhofen, which is located 30 km southwest of Munich in Germany. The reflectivity data are contaminated by ground clutter from the Alps south of the radar and attenuation by hydrometeors behind the squall line west of the radar. Those biases affect the data and yield to mismatches in determining radar products like rain-rate estimation. A variety of error sources and their impact on radar measurements have been studied intensively and are summarized, for instance, in Battan (1973), Zawadzki (1984), Hannesen (2001), Alberoni et al. (2002), and Meischner (2003), among other studies.
Over the years algorithms have been developed to either detect or correct contaminations. Grecu and Krajewski (2000) and Krajewski and Vignal (2001) for instance developed a methodology for detecting anomalous propagation echoes using neural networks. Data quality of the Next-Generation Weather Radar (NEXRAD) has been constantly optimized using reflectivity and Doppler velocity in a fuzzy logic–based anomalous propagation clutter mitigation schemes (Kessinger et al. 2003). Steiner and Smith (2002) used the three-dimensional reflectivity structures to detect automatically sea clutter and anomalous propagation either separated from or embedded within precipitation echoes. Sempere-Torres et al. (2003) developed an algorithm to detect signal instabilities of radar measurements by analyzing temporal variations of mountain returns. A correction for precipitation attenuation based on the minimization of a cost function is suggested by Berenguer et al. (2002). Attenuation correction using dual-polarization radar measurements has been discussed by Aydin et al. (1989), Bringi et al. (1990), and Gorgucci et al. (1996). This listing presents only a short extraction of the large number of different radar correction and error detecting algorithms. Processing and scanning techniques have also been improved to overcome certain shortages like Doppler spectrum aliasing, ground clutter contamination, and second-trip echo return. Unal and Moisseev (2004) introduced a simple processing technique for the Doppler analysis combining simultaneous measures required for polarimetry analysis and maximum unambiguous Doppler velocity. Many operational Doppler weather radars operate with a dual-pulse-repetition frequency (dual-PRF) technique in order to extend the unambiguous Doppler velocity interval (Dazhang et al. 1982; Holleman and Beekhuis 2003). Alternatively, a staggered-pulse-repetition frequency allows the increase of the maximum unambiguous velocity while maintaining an adequate unambiguous range (Zrnic and Mahapatra 1985). Phase coding techniques have been employed in order to isolate radar echoes returning from a transmitted pulse at times subsequent to when previous pulses have been transmitted (Sachidananda 1997).
For broader usage in terms of different applications, increasing numbers of measured quantities, and an increasing number of radar systems, most of the aforementioned correction algorithms and processing techniques are lacking in a few key respects. First, they only focused on specific kinds of contamination for specific applications, which complicates the combination of several algorithms. Most algorithms focus on data correction rather than quality characterization. Also, no information about the corrections applied and the quality of the data is provided to the end user or for product generation. Finally, data correction is not standardized at present, and therefore, these procedures may produce different results, even when applying the same basic method.
To properly address these issues, a consistent quality control concept needs to be developed. This is accomplished in this paper by characterizing the quality of radar reflectivity, polarimetric parameters, and Doppler velocity to be used for any application. This concept is unique for the following reasons.
A consistent strategy is applied on a pixel-by-pixel basis to all three quantities.
This is the first quality control concept for polarimetric parameters.
The concept focuses primarily on the quality characterization without applying data modification.
End users have access to values quantifying the amount of contamination. Therewith, they will be able to choose the amount of data and the level of data quality required for their specific application.
2. Concept for the quality control scheme
The quality control scheme for radar reflectivity, polarimetric parameters, and Doppler velocity should consist of 1) gross error filtering and recovery of dealiased Doppler velocities or second-trip echoes in order to avoid artificial gradients leading to misinterpretation from automated algorithms and 2) quality control procedures based on a pixel-by-pixel basis. Each individual step is illustrated in the data flow diagram shown in Fig. 2. The quality control scheme is assembled in a modular way, which allows the extension, modification, and omission of algorithms in order to meet user and application requirements (Einfalt et al. 2004). In this paper, the focus is solely on the determination of quality-index fields generated by quality-index algorithms. The following paragraph describes briefly the main parts of the quality control concept.
The new generation of signal processors already includes a large number of gross error filtering and recovery techniques such as clutter and speckle filtering, filtering and recovery of second-trip echoes, Doppler velocity dealiasing, and a simple quality control [for more information, refer to Sigmet's RVP8 user's manual (Sigmet 2004)]. Since the majority of weather radar systems operate with older signal processors that include only parts of the aforementioned algorithms, filtering and recovery are applied afterward. Large numbers of filtering algorithms have been developed and successfully applied for ground clutter (e.g., Joss and Lee 1995; Lee et al. 1995; Sanchez-Diezma et al. 2001), Doppler velocity dealiasing [see, e.g., James and Houze (2001) and Tabary et al. (2001) for an overview], and removal of isolated pixels and radial anomalies (Gabella and Notarpietro 2002). Information about whether the data pixel was eliminated or how it was modified is essential in order to detect algorithm failure and retrace certain pixels. Although it is included in the processing chain in Fig. 2, further research on this topic needs to be addressed.
The quality of the measurement is evaluated by quality-index algorithms that identify and estimate the amount of contamination. The information is encoded on a pixel-by-pixel basis into quality-index fields. The quality-index schemes for reflectivity, polarimetric parameters, and Doppler velocity are discussed in more detail in the following sections.
The second part in the quality control includes the final check for spatial and temporal consistency (denoted as “cross-check” in Fig. 2). Multiple sensor information, for example, from independent radars, are used to cross-check the radar measurements. An example of a cross-check procedure is given by Friedrich and Hagen (2004) using multiple-Doppler information to detect irregularities in the Doppler velocity measurement. Additional data sources can also include satellite data, surface synoptic stations, radiosoundings, or numerical model output. After the data passed the quality control scheme, radar products can be generated. Missing single rays or small wholes in the radar pictures can be filled applying simple interpolation algorithms (Golz et al. 2004). Data can be smoothed, filtered, or extrapolated. The end user has access to the quality-controlled observational data or the generated products, the respective averaged quality-index field, and possibly the modification monitoring field.
3. Quality-index scheme for reflectivity
a. Methodology
beam broadening and height of the first radar echo (denoted as Frange),
partial or complete beam shielding due to ground clutter (denoted as Fshield),
attenuation of electromagnetic energy by hydrometeors (denoted as Fatt),
inhomogeneous vertical profile of reflectivity (denoted as Fvpr).
Each quality-index field and the average index field range between zero and one. When data are contaminated by ground clutter, indicated as Fshield = 0, or strongly attenuated by hydrometeors, indicated as Fatt = 0, these pixels will not be used for further data processing, and
b. Utilizing beam broadening and height of the first radar echo
Figure 5 shows the increase in both resolution volume size and height of the first radar echo with increasing distance. On 21 July 1992 rmax was set to 300 km yielding to a maximum volume size of 6.4 km3 and a height of the first radar echo of 10.5 km (ϕ = 1°, Θ = Φ = 1°, τ = 2 μs).
As a result, the maximum range depends on the spatial resolution and coverage required. Table 3 lists minimum and maximum ranges for different applications. For nowcasting requiring a large spatial coverage, rmax should be chosen to be as large as possible. For rain-rate estimations requiring high-resolution measurements close to the ground, the maximum range should not exceed 130 km yielding to a maximum resolution volume of 0.61 km3 and the first radar echo at 3.3-km height. For data assimilation, however, rmax should be adjusted to the spatial resolution of the numerical model.
c. Utilizing ground clutter contamination
Ground clutter can totally or partially shield the transmitted radar beam and can contaminate directly the received signal due to a strong backscattering signal. Over the last few years great effort has been achieved by detecting and removing ground clutter using Doppler velocity information in signal processing (see, e.g., Lee et al. 1995; Hagen 1997; Joss et al. 1998; Seltmann 2000). Nevertheless, even an efficiently working Doppler velocity filter can incorrectly remove precipitation information especially for slowly moving or stationary precipitation and does not consider beam-shielding effects.
In case of beam shielding, the reduced peak intensity propagates further, leading to a reduced backscattering signal as illustrated schematically in Fig. 6a. Furthermore, behind the target the received signal is assigned to a lower height because the main beam is shielded and the backscattering signal comes from the pulse edges (Fig. 6b).
Figure 7 presents the horizontal distribution of Fshield for a radial sampling range of 300 km around POLDIRAD. The calculation is based on the topography dataset measured by the European Remote Sensing Satellite-2 (ERS-2) satellite with a horizontal resolution of about 250 m and a vertical one of 1 m. The data are averaged and interpolated onto a spherical coordinate system with an angular resolution of 1° and a radial resolution of 250 m. Figure 7 illustrates beam shielding for an elevation limit ranging between 0.2° and 1.2° (POLDIRAD configuration for 21 July 1992). Based on the ground clutter map, strong or complete shielding of the radar beam is expected within the first 50-km range southeast, south, and southwest of the radar. Partial shielding occurs mainly east, southeast, south-southwest, and west of the radar. The ground clutter contamination is also visible as high-reflectivity returns in optical clear air shown in Fig. 1. While ground clutter contamination south and southeast of POLDIRAD is clearly visible in the reflectivity field (Fig. 1), ground clutter returns southwest of the radar are embedded in precipitation.
d. Utilizing attenuation by hydrometeors
Attenuation of electromagnetic energy by hydrometeors results from both absorption and scattering. The amount of absorption by water or ice, however, depends on the transmitted wavelength (λ). It mainly occurs at radars operating at 4 GHz (C band) or higher frequencies. Attenuation accumulates behind large reflectivity values in radial direction such as behind convective storms or the bright band. An example of strong attenuation of electromagnetic energy by precipitation at λ = 5.5 cm (C band) is given in Fig. 1. Behind the strong reflectivity values (∼40 dBZ) located between 25 and 50 km west of POLDIRAD oriented in north–south direction, the reflectivity factor is reduced to 10–20 dB. The objective of this quality-index field is to quantify the amount of contamination due to attenuation.
Figure 8a illustrates two-way pathlength attenuation by rain and snow based on the reflectivity field measured at 1953 UTC on 21 July 1992 shown in Fig. 1. For that purpose Eqs. (7) and (8) are integrated along the radial path. Based on a radiosounding launched at 1200 UTC, the height of the freezing level was located at about 3 km. The freezing levels can be determined alternatively from the location of the bright band in the radar reflectivity or a sounding derived from numerical weather prediction model output. For an elevation angle of 0.7°, measurements beyond a range larger than 123.3 km from POLDIRAD are located above the freezing level. In this area, the electromagnetic energy is attenuated by snow [Eq. (8)]. Within 123.3-km range, pathlength attenuation by raindrops is assumed [Eq. (7)]. A discrepancy between the relatively low attenuation in Fig. 8a (∼5 dB) and the relatively high decrease in reflectivity in Fig. 1 (∼20 dBZ) occurs behind the main reflectivity core west of the radar. This area consists probably of a mixture of ice particles, melting hail, and rain, while solely attenuation by rain is assumed [Eq. (7)]. Attenuation caused by the bright band within stratiform precipitation that also contains a mixture of rain and melting particles has been investigated by Bellon et al. (1997) using X-band radar and a UHF vertically pointing radar. Brightband attenuation 3–5 times larger than the rain equivalent was observed. Also, in Fig. 1 reflectivity is displayed as plan position indicator; that is, the radar beam at 150 km is located about 2.4 km higher compared to the 50-km range. Areas of lower reflectivity are intersected at farther ranges.
Attenuation effects are very critical for rain-rate estimations. Figure 9 portrays the differences in rain-rate estimation when using a reflectivity value that is 3–5 dBZ lower than the true value. When a reflectivity value of 35 dBZ instead of 40 dBZ is measured, which is equivalent to an attenuation of 5 dB, the error in rain-rate estimation is about 7 mm h−1. This value increases to 25 mm h−1 when a reflectivity value of 47 dBZ instead of 50 dBZ (3-dB attenuation) is used. The threshold for the maximum attenuation Kmax is set to 5 dBZ for stratiform precipitation, which consists mainly of moderate rain and snow particles with typical reflectivity values less than 40 dBZ. Within convective precipitation, Kmax is reduced to 3 dBZ since the precipitation consists of a mixture of snow, graupel, hail, and larger raindrops (>5 mm). These thresholds can be applied to all applications as listed in Table 3.
e. Utilizing vertical reflectivity profile
The vertical profile of reflectivity is highly variable in time and space owing to different growth effects (e.g., coagulation, distribution, condensation, evaporation), change of phase of water (e.g., ice, liquid, melting snow), fall speed, and the dependence of Ze on the sixth power of the hydrometeor size. To overcome this variability, a simple approach is suggested for a quality-index field. Reflectivity values within the layer close to the 0° isotherm (referred to as melting layer or bright band) are overdetermined since it consists mainly of water-coated non-Rayleigh scatterers. This area is flagged with a zero quality index. The melting layer is usually located between 200 m above the FL to 500 m below it (Doviak and Zrnic 1992).
Figure 11 illustrates the distribution of Fvpr at 1953 UTC on 21 July 1992. According to the radiosounding at 1200 UTC, the melting layer ranges between a height of 2.5 and 3.2 km MSL corresponding to a radial distance of 103.7 and 130.6 km from POLDIRAD.
f. Average quality-index field for reflectivity
All separately calculated quality-index fields are now averaged according to Eq. (1). The reflectivity observed on 21 July 1992 and the average quality-index field 
Radar reflectivities with high quality are observed close to the radar at a range of 50 km and northwest of the radar where no obvious ground clutter and attenuation contamination was discovered. High uncertainties of the reflectivity measurements (
Weighting factors need to be chosen according to weather situation and application as demonstrated in Table 1. Influence of range resolution and beam shielding on
4. Quality-index scheme for polarimetric radar products
a. Methodology
Although polarimetric radar products have not been derived operationally yet, future trends toward operationally working radars with polarimetric diversity capabilities can be seen (Zrnic 1996; Parent du Châtelet et al. 2005). Polarimetric radar measurements have improved quantitative precipitation estimation especially in terms of reducing the uncertainty caused by the unknown Z–R relation. Nevertheless, as shown by Seliga and Bringi (1976) and Richter and Hagen (1997), polarimetric quantities are required with high accuracy, otherwise a large error in rainfall-rate retrieval will occur.
The quality-index field for polarimetric radar products can be used either for calculating rain rates or for using directly polarimetric quantities like the linear depolarization ratio (LDR), differential reflectivity ratio (ZDR), and differential propagation phase (KDP). The following parameters have to be considered for calculating a quality-index field for polarimetric radar products:
beam broadening and height of the first radar echo (denoted as Frange),
partial or complete beam shielding due to ground clutter (denoted as Fshield),
attenuation of electromagnetic energy by hydrometeors (denoted as Fatt),
amount of homogeneous beam filling (denoted as Fbea),
rain or no-rain discrimination (denoted as Frain),
consistency check between Ze, ZDR, and KDP (denoted as Fcon).
In case of strong attenuation (Fatt = 0) or ground clutter contamination (Fshield = 0),
The determination of the quality-index field for polarimetric parameters will be exemplified using radar measurement achieved by POLDIRAD at 1520 UTC on 26 June 1997. The reflectivity field using horizontally transmitted and received polarization at 1° elevation is presented in Fig. 14. With stratiform precipitation and embedded convection, this case is considered as having hybrid character.
b. Utilizing beam broadening and first radar echo, ground clutter contamination, and attenuation
The quality-index field identifying beam broadening, contamination due to ground clutter, and attenuation by hydrometeors can be applied for the same reasons and in the same way to polarimetric data as for reflectivity demonstrated in sections 3b–d.
Linear depolarization ratio (LDR) is an excellent measure for ground clutter contamination and supplement clutter filters as shown by Hagen (1997). Unfortunately, most operational weather radars will derive polarimetric quantities without expensive polarization switches using simultaneous transmission and reception of horizontal and vertical polarization. As a consequence, LDR can only be retrieved by running a separate scan where only horizontal polarization will be transmitted. As an alternative to those time-consuming measurements, ZDR in combination with Doppler velocity can be used to identify ground clutter contamination (Giuli et al. 1991). However, if Ze or ZDR measurements are contaminated by ground clutter returns, the rain rate will be overestimated. Furthermore, Illingworth (2003) investigated that KDP can be heavily contaminated by ground clutter even when only the sidelobes hit the ground. Beam shielding affects mainly reflectivity measurements like ZDR and has less impact on the phase measurements. Nevertheless, signals are assigned to a lower height when shielding occurs as illustrated in Fig. 6b.
Attenuation of electromagnetic energy by hydrometeors affecting measurements is mainly observed at radars operating at 4 GHz (C band) or higher frequencies. In the presence of nonspherical particles, horizontally and vertically polarized waves are attenuated differently, resulting in a differential attenuation of ZDR (e.g., Gorgucci et al. 1998; Torlaschi and Zawadzki 2003). In rain, for instance, ZDR normally ranges between 0 and 3 dB, but reduces due to differential attenuation and even becomes negative with intense rain. Attenuation affects only reflectivity measurements like Ze and ZDR; the differential phase measurements (KDP) are not affected by attenuation and therefore can be used to correct reflectivity [for more details see Gorgucci et al. (1998) or section 4e].
c. Utilizing cross-beam gradients
Figure 15 illustrates the quality-index field Fbea for the polarimetric radar measurements taken at 1520 UTC on 26 June 1997. Low values of Fbea are observed along the edges of the precipitation cells and when reflectivity is contaminated by ground clutter, for instance, south of the radar; Fbea also decreases when precipitation evolves and is advected with time between the elevation scans. This was observed on 26 June 1997 at an azimuth of 70° and a range of 25 km. The threshold gmin and gmax were set to 0 dB deg−1 and 20 dB deg−1, respectively.
Not much experience has been collected with the effects of cross-beam gradients. It is unknown how far this effect will influence operational polarimetric measurements. Table 3 lists thresholds of gmin and gmax for different applications and weather situations. Generally, the maximum threshold should be higher than gradients observed along the edge of the bright band or convective cells. For convective weather situation gmax should be set to about 20 dB deg−1, while for stratiform precipitation gmax is set to 15 dB deg−1. At the same time, it should be smaller than the gradient of the antenna's power pattern between the main beam and the secondary lobe.
d. Utilizing rain or no-rain discrimination
Polarization diversity allows for the discrimination of the various types of precipitation particles (Höller et al. 1994; Vivekanandan et al. 1999). A quality-index field Frain is introduced in order to estimate polarimetric rainfall rate solely within regions dominated by raindrops.
Based on the classification introduced by Höller et al. (1994), the presence of rain (Frain = 1) can be assumed when both the air temperature is above 0°C and LDR is below −35 dB, or when LDR ranges between −35 and −25 dB and ZDR is above 1 dB. Measurements indicating hydrometeors other than rain are denoted as Frain = 0 as schematically indicated in Fig. 13b; Frain can be defined in a similar way using other classification schemes like the fuzzy logic scheme introduced by Vivekanandan et al. (1999).
Figure 16 shows the quality-index field Frain for the measurements obtained at 1520 UTC on 26 June 1997. Based on the radiosounding launched at 1200 UTC at Munich-Oberschleissheim located about 27 km northeast of the radar, the 0° isotherm was located at about 1.8 km above the radar. As a result, hydrometeors observed beyond the 80-km range are considered to include an ice phase. Ground clutter close to the radar and south of the radar are identified correctly as no rain by the algorithm. The areas with Frain = 0 to the west and east of the radar were identified as melting graupel or snow by the classification scheme (Höller et al. 1994).
e. Consistency check using Ze, ZDR, and KDP
The consistency check between Ze, ZDR, and KDP was originally proposed by Goddard et al. (1994) and in a different description by Scarchilli et al. (1996) in order to check the performance of radar systems within rain. Here Ze and ZDR are used to first derive the raindrop size distribution and then estimate KDP from the raindrop size distribution. The latter is denoted as K̂DP. If K̂DP and the measured KDP agree, the data are consistent.
An alternative approach was proposed by Gorgucci et al. (1998). They used Ze and ZDR in order to derive the attenuation in rain (denoted as αH). On the other hand, KDP can also be used to estimate the attenuation in rain (referred to as α*H). If αH and α*H agree, the measurements of Ze, ZDR, and KDP are considered to be consistent. Differences between K̂DP − KDP and αH − α*H result from calibration errors or wrong assumptions concerning scattering properties and size distribution of raindrops (Gorgucci et al. 1998). The consistency check, however, will fail for hydrometeor types other than rain since up to now it is only defined for rain. It also will fail for strong attenuation that is not considered by the procedure proposed by Gorgucci et al. (1998).
The quality-index field Fcon for the polarimetric parameters measured on 26 June 1997 is illustrated in Fig. 17. The thresholds emin and emax were set to 0° km−1 and 1° km−1, respectively. Low values of Fcon are observed in regions with ground clutter contamination and in regions where Frain = 0. Within stratiform precipitation emax is set to about 0.5° km−1, which is equivalent to an error of about 10 mm h−1 for rainfall rate. For convective or hybrid cases, emax is set to 1° km−1 (Table 3).
Even though the consistency check is based on the assumption that rain is present, it will not necessarily fail if other particles than rain are dominant. This can be seen when comparing Figs. 16 and 17 beyond the 80-km range where temperatures are below 0°C. Here Fcon is above 0.8 north-northwest of the radar where ice is present as indicated by low Ze, ZDR, and KDP values. For rain-rate estimation
f. Average quality-index field for polarimetric radar products
In Fig. 18a the average quality-index field is shown for polarimetric radar products observed on 26 June 1997. This case is assumed to have hybrid character with stratiform precipitation and embedded convection. Weights and thresholds were set according to the values for rain-rate estimation within hybrid cases (Tables 1 and 3). Observations were taken up to a range of 120 km on that day. Since
Figure 18b shows the weighing factor combination when polarimetric products like hydrometeor classification are used for nowcasting purposes or when water and ice content are assimilated into NWP models In those cases, Wbea is set to 0.5, while Wrain and Wcon remain zero (Table 1). In this case,
5. Quality-index scheme for Doppler velocity
a. Methodology
The quality of Doppler velocity is more related to the quality of the phase measurement and contaminations by moving non-weather-related objects. Nevertheless, beam broadening and ground clutter contamination, as listed as field (1) and (2) for reflectivity, can indeed affect the quality of Doppler velocity measurements and are also applied for the quality-index field for Doppler velocity measurements. The following quality-index fields are monitored and encoded:
beam broadening (denoted as Frange),
partial or complete beam shielding due to ground clutter (denoted as Fshield),
standard deviation of the Doppler velocity (denoted as Fσυ),
nonprecipitating clutter contamination due to birds, chaff, airplanes (denoted as Fnp).
When the radar beam is shielded completely by ground clutter (Fshield = 0), the quality-index field for Doppler velocity data
b. Utilizing beam broadening and ground clutter contamination
The broadening of the radar beam causes an increase in spatial resolution resulting in smoothing small-scale wind features such as vertical wind shear zones. Ground clutter contamination influences significantly the quality of the Doppler velocity measurement. A complete shielding of the main beam, that is, zero Doppler velocity return, can be detected and removed usually during signal processing. When the beam is shielded completely by ground clutter, the main power is blocked and the backscattering signal comes from the pulse volume edges that are located at a higher elevation than the main axis of the radar beam (Fig. 6b). In the presence of vertical wind shear, the Doppler velocity measured at the pulse volume edge differs from that measured at the beam axis. When, for instance, 80% of the transmitted beam is shielded measurements are related only to the upper pulses' volume edge creating a height error for instance of about 0.35 km (0.4°) at a distance of 50 km for a 1° beamwidth. Assuming a vertical wind shear of 10 m s−1 km−1, the resulting velocity error is about 3.5 m s−1.
c. Utilizing standard deviation of the Doppler velocity
d. Utilizing non-weather-related objects
Generally, hydrometeors or insects follow the airflow, so that backscattering echoes from those targets represent the wind velocity. On the other hand, Doppler radar measurements are often contaminated by other targets such as birds, ships, chaff, ground clutter, or airplanes. Based on the idea of the velocity–azimuth display (VAD) analysis (Lhermitte and Atlas 1961; Browning and Wexler 1968), Doppler velocities related to nonweather objects and failures of Doppler velocity dealiasing algorithms can be detected. With this quality-index scheme those objects that do not represent the current airflow are detected. In contrast to the VAD analysis, limitations due to the effect of inhomogeneities in precipitation fall speed, reflectivity distribution, or variation of the elevation angle can be neglected since this quality control focuses on gross errors that lie far outside the Doppler velocity standard deviation.
e. Average quality-index field for Doppler velocity
To determine the quality of Doppler velocity measurements, the four separately calculated quality-index fields are weighted and averaged according to Eq. (13). Both Frange and Fshield are calculated according to Eqs. (2) and (6). On that day, data were only available up to a range of 50 km, which is used as the maximum range rmax. Doppler velocity and the average quality-index field for the 25 October 1999 case are presented in Fig. 23. All weights are set to one; that is, the impact of each error source on the overall quality is treated equally. Most of the measurements with
Generally, the influence of each quality-index field to the average can be treated similar as listed in Table 1. Solely during weather situations having turbulent winds, the detection of non-weather-related objects can become difficult since the wind field can have similar discontinuity structures as they were observed during bird migration. In this case, we suggest reducing the influence of Fnp on the average to range between about 0.6 and 0.8. Consequently, Wnp should also be lowered slightly in weather situations consisting of both convective and stratiform precipitation (Table 1).
6. Conclusions
A concept for a quality control scheme for radar reflectivity, polarimetric parameters, and Doppler velocity has been presented in this paper. It consists of two main parts: 1) filtering of gross irregularities and recovery of dealiased Doppler velocities or second-trip echoes and 2) quality control procedures based on a pixel-by-pixel basis. The latter part is the focus of this paper. The concept of quantifying factors contributing to uncertainties in radar measurements and then transforming these into quality-index fields is demonstrated using very simple detection and quantification algorithms. Since the quality of the indices strongly depends on the quality of the detection algorithm, for example, for ground clutter or attenuation, we suggest that more sophisticated approaches or algorithms are used that are already tested or implemented in the particular radar system. The scheme is set up in a modular way allowing the extension, modification, and omission of algorithms. The average three-dimensional quality-index fields for reflectivity, polarimetric parameters, and Doppler velocity can be easily transferred with the measurements and can be easily interpreted either by a nontrained end user or an automated scheme that generates radar products.
For reflectivity measurements main factors leading to uncertainties are the increase in resolution with range, beam shielding from ground clutter, attenuation of the electromagnetic wave by hydrometeors, and inhomogeneous vertical profile of reflectivity. The quality of polarimetric parameters is quantified by determining range resolution, beam shielding, the amount of attenuation, homogeneous beam filling, discriminating rain from other hydrometeors, and applying a consistency check between Ze, ZDR, and KDP. Main factors quantifying the quality of Doppler velocity measurements are the spatial resolution, beam shielding, contamination from nonmeteorological targets, and utilizing the standard deviation.
The bias quantification depends mainly on thresholds marking lower and upper contamination limits. The influence of each quality index on the averaged field is quantified by weighting factors. Both thresholds and weighting factors rely on the application, the location of the radar and its relation to regional geography, and the weather situation. Most of the threshold limits and weights are either based on empirical findings or physical reasoning. This paper gives an example for a polarimetric C-band Doppler radar located in the Alpine foreland. Thresholds and weighting factors for this radar site are listed in Tables 1 and 3 for different applications and weather situations. Figure 24 shows the reflectivity field of the convective precipitation case measured on 21 July 1992 and the quality-index fields for rain-rate estimation, assimilation, and nowcasting application.
The proposed method can also be applied to radars operating at different transmitting frequencies and beamwidths, and located at different orographic or climatic regions. The impact of attenuation as intermittent bias increases with increasing transmitted frequency and increasing reflectivity. Attenuation should not be underestimated as shown by Bellon et al. (1997) for the X band within the melting layer and Zrnic and Ryzhkov (1999) for S-band frequencies along a squall line. As a consequence the impact of attenuation can be reduced by lower values of Watt. Uncertainties due to increasing resolution, beam shielding, and inhomogeneous vertical reflectivity profile will decrease for decreasing beamwidth; that is, Wrange, Wshield, and Wvpr can be reduced. Although the impact of sea clutter was not discussed in this paper, it certainly occurs along coastal regions. The distinction between prevailing precipitation types (e.g., convective, stratiform, or hybrid), the performance of the quality index quantifying the inhomogeneous vertical reflectivity profile with varying height of the bright band, and the impact of the discrimination between rain and other precipitation types have to be adjusted according to the climatic conditions. Quality-index fields can also be combined to a composite map for radar networks in the same way as reflectivity maps.
Up to now this quality control has been operated only in a research mode and for a C-band radar. A precise evaluation of different weighting factors for different applications and weather situations will show what improvements can be achieved using this quality control scheme. In the next step, the propagation of the quality information through forecasting networks such as atmospheric and hydrological numerical models will be evaluated in the subsequent European COST Action 731 and the Mesoscale Alpine Program Forecast Demonstration Project (MAP D-PHASE) under the aegis of the World Meteorological Organization (WMO) World Weather Research Program (WWRP).
Acknowledgments
First we thank all the members of the COST 717 Working Group 3, especially Daniel Michaelson, Iwan Holleman, and Andrea Rossa. Without the encouraging discussions about error sources, user requirements, and realistic realization within an operational requirement, we would not be able to assemble this quality control concept. We extend special thanks to the three anonymous reviewers for providing comments and suggestions that enhanced the quality of the paper.
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Reflectivity factor field (Ze) displayed as PPI at 0.7°-elevation angle. Measurements were achieved by POLDIRAD (located in the center) at 1953 UTC on 21 Jul 1992. Grayscale for reflectivity is shown at top. Radar measurements are contaminated by ground clutter from the Alps, which are located south of POLDIRAD. Data are also contaminated by attenuation of electromagnetic energy caused by hydrometeors that occur west of the squall line located about 40 km west of POLDIRAD.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Data-flow diagram depicting the processing chain for the quality control scheme. Observational data flow is depicted by thick, solid lines; modification monitoring is depicted by thick, dashed lines; the transfer of the quality-index (QI) fields is depicted as thin, solid lines; and raw data transfer is depicted by a solid line. More explanations are found in the text.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Computation of the quality-index field (a) Frange based on the influence of range resolution, (b) Fshield based on the amount of beam shielding due to ground clutter, (c) Fatt based on the amount of pathlength attenuation, and (d) Fvpr based on the variability of the vertical reflectivity profile. (a) Maximum and minimum sample range are denoted as rmin, rmax. (b) The 3-dB beamwidth is margined by θ−3dB and θ+3dB. The elevation angle of the mainlobe axis is denoted as θ0. (c) The thresholds for maximum and minimum attenuation are Kmax and Kmin, respectively. (d) The thickness of the melting layer is defined as freezing level plus 200 m, hFL+200, and freezing level minus 500 m is denoted as hFL−500.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Quality-index field Frange representing the dependency of the size of the resolution volume and the height of the first radar echo on the distance from the radar. All observational data within a range between 0.75 and 300 km are included into the quality-index field in order to achieve a large spatial coverage.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Diagram illustrating the range–height (solid lines) and range-resolution volume dependency (dashed lines) for different elevation angles and pulse duration times, respectively. The horizontal and vertical beamwidth is assumed to be 1°.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Principle for estimating the loss of transmitted power with respect to the unobscured beam distinguishing between (a) partial shielding (0 < Fshield < 1), (b) complete shielding (Fshield = 0), and (c) no shielding (Fshield = 1) of the 3-dB radar beam. The 3-dB beamwidth is indicated by θ−3dB and θ+3dB. The elevation angle of the mainlobe axis is denoted as θ0. The angle spanning between ground level and clutter height is denoted as θGC. The elevation angles are related to the radar site. The radiation pattern of the transmitted pulse is pictured having a Gaussian distribution.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Quality-index field Fshield presenting the amount of beam shielding for the POLDIRAD antenna at an elevation angle of 0.7°. No shielding is indicated by Fshield = 1, while total beam blockage is assigned by Fshield = 0. Calculations were based on the topography dataset measured by the ERS-2 satellite.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
(a) Two-way pathlength attenuation by rain (Kr) and snow (Ks) and (b) the respective quality-index field Fatt for the reflectivity field measured by POLDIRAD on 21 Jul 1992 (Fig. 1). (a) Attenuation by rain is assumed below the freezing level, while electromagnetic energy is expected to be attenuated by snow above the freezing level, which is located at 3 km MSL corresponding to a range of 123.3 km from the radar.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Diagram illustrating the bias in rain-rate estimation when the reflectivity shown along the abscissa is reduced by 3, 4, or 5 dB due to attenuation effects.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Principle for estimating the variation of the vertical reflectivity field. Range resolution volumes located entirely below the melting layer (single-dashed area) are classified as Fvpr = 1. Range resolution volumes located within the melting layer (double-dashed area) are encoded as Fvpr = 0. All measurements above the melting layer are encoded as Fvpr = 0.5 (double-dashed area). Range volumes that encounter the lower and upper boundary of the melting layer (cross-hatched areas) are classified according to Eq. (10). Melting layer is located between 500 m below the 0° isotherm (denoted as hFL) and 200 m above it.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Quality-index field illustrating the representativeness of the vertical reflectivity profile Fvpr for the 21 Jul 1992 case.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
(a) Reflectivity factor field (Ze) as presented in Fig. 1, but gross errors (e.g., ground clutter) have been filtered by basic quality algorithms. (b) Average quality control field for reflectivity (
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Computation of the quality-index fields for polarimetric radar products based on (a) the influence of homogeneous beam filling Fbea, (b) the discrimination between rain and no rain Frain, and (c) the consistency check Fcon. The minimum and maximum threshold for the reflectivity gradient and the relative error for attenuation are denoted as gmin, gmax and emin, emax, respectively. The thresholds for the rain and no rain discrimination, indicated as TR, are T > 0°C and LDR < −35 dB.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
PPI of the reflectivity factor field at 1° elevation measured by POLDIRAD (located in the center) with transmitting and receiving horizontal polarization at 1520 UTC on 26 Jun 1997. Grayscale for reflectivity is shown at top. Range rings are plotted every 50 km.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Quality-index field Fbea illustrating the amount of homogeneous beam filling by calculating the vertical and horizontal cross-beam reflectivity gradients. Calculations are based on the reflectivity field illustrated in Fig. 14.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Quality-index field Frain identifying areas consisting mainly of rain (Frain = 1) and areas including other hydrometeor types (Frain = 0). Calculations are based on the polarimetric parameters measured at 1520 UTC on 26 Jun 1997.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Quality-index field Fcon illustrating the results of the consistency check between Ze, ZDR, and KDP. Calculations are based on polarimetric parameters measured at 1520 UTC on 26 Jun 1997.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Average quality-index field
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Computation of the quality-index fields for Doppler velocity based on (a) the standard deviation of the Doppler velocity expressed by the NCP or σv values (Fσυ) and (b) contamination due to nonprecipitating echoes (Fnp). Contamination is detected when the Doppler velocity lies outside the interval of [υ*r − ɛ; υ*r + ɛ].
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
PPI of (a) Doppler velocity (υr), (b) spectrum width (σv), and (c) reflectivity (Ze) at 1°-elevation angle. Measured were achieved by POLDIRAD (located in the center) at 1536 UTC on 25 Oct 1999. (a) Negative Doppler velocity indicates a movement toward the radar and positive away from the radar.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Quality-index field illustrating the standard deviation of the Doppler velocity (Fσυ). Calculations are based on the spectrum width measurements taken at 1536 UTC on 25 Oct 1999 (Fig. 20b).
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
(a) VAD of the Doppler velocity along the 360° azimuth angle (thin, solid lines) together with the fitted sine curve (thick, dashed lines). Illustrated are measurements between a range of 46.5 and 49.8 km plotted with 10 m s−1 offsets with the 46.8-km range centered around υr = 0 m s−1. The threshold ɛ ± 4 m s−1 is displayed as thick, solid lines. (b) Quality-index field Fnp detecting returns related to nonprecipitating echoes for the 25 Oct 1999 case.
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
(a) Doppler velocity (υr) as presented in Fig. 20a and (b) the resulting average quality control field (
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
(a) Reflectivity field (Ze) measured on 21 Jul 1992 as illustrated in Fig. 1 and (b), (c), (d) average quality-index fields
Citation: Journal of Atmospheric and Oceanic Technology 23, 7; 10.1175/JTECH1920.1
Weighting factor combinations for nowcasting, assimilation, and rain-rate estimation to be applied for different weather situations such as stratiform precipitation, convective precipitation, and a combination of both (denoted as hybrid) for POLDIRAD.
Factors leading to uncertainties in the reflectivity and Doppler velocity measurements. Trends with increasing range and height of the beam elevations are outlined [adapted and modified from Yuter (2003)].
Threshold limits for creating the quality-index fields according to the application and weather situation. Range limits indicated by the maximum and minimum distance from the radar, rmin and rmax, are required for reflectivity, polarimetric parameters, and Doppler velocity. Lower and upper thresholds for attenuation by hydrometeors are indicated by Kmin and Kmax, respectively. For polarimetric parameters cross-beam gradients limit ranging between gmin and gmax, and limits for the differences between measured and estimated KDP (emin and emax) are required. To detect non-weather-related objects in the Doppler velocity field the deviation of the Doppler velocity measurement from applied fitting coefficient is denoted as ɛ. Note that some thresholds are based on empirical findings using a polarimetric C-band Doppler radar located in the Alpine foreland. Here Δx and Δy denote the horizontal resolution of a numerical weather prediction model.
