Use of Three-Dimensional Reflectivity Structure for Automated Detection

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Use of Three-Dimensional Re¯ectivity Structure for Automated Detection and Removal of Nonprecipitating Echoes in Radar Data

MATTHIAS STEINER AND JAMES A. SMITH Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey

(Manuscript received 16 May 2001, in ®nal form 12 September 2001)

ABSTRACT This study aims at assessing the potential of anomalous propagation conditions to occur, reviews past attempts to mitigate ground clutter contamination of radar data resulting from anomalous signal propagation, and presents a new algorithm for radar data quality control. Based on a 16-yr record of operational sounding data, the likelihood of atmospheric conditions to occur across the United States that potentially lead to anomalous propagation of radar signals is estimated. Anomalous signal propagation may lead to a signi®cant contamination of radar data from ground echoes normally not seen by the radar, which could result in serious rainfall overestimates, if not recognized and treated appropriately. Many different approaches have been proposed to eliminate the problem of regular ground clutter close to the radar and temporary clutter resulting from anomalous signal propagation. None of the reported approaches, however, satisfactorily succeeds in the case of anomalous propagation ground returns embedded in precipitation echoes, a problem that remains a challenge today for radar data quality control. Taking strengths and weaknesses of past approaches into consideration, a new automated procedure has been developed that makes use of the three-dimensional re¯ectivity structure. In particular, the vertical extent of radar echoes, their spatial variability, and vertical gradient of intensity are evaluated by means of a decision tree. The new algorithm appears to work equally well in situations where anomalous propagation ground returns are either separated from or embedded within precipitation echoes. Moreover, sea clutter echoes are identi®ed as not raining and successfully removed.

1. Introduction control is combined with terrain-based visibility and vertical precipitation structure, and gauge adjustments Quality control is essential to meaningful radar-based to achieve the most reliable rainfall estimates. Fulton et rainfall estimation. Radar echoes may be contaminated al. (1998) report on the data quality control and rainfall by nonmeteorological echoes that need to be identi®ed estimation procedures of the operational radar network and removed before rainfall estimation. This is partic- in the United States. Despite elaborate and sophisticated ularly true for operational applications such as precip- efforts in data quality assurance, however, evaluations itation nowcasting and (¯ash) ¯ood warning. A well- by Smith et al. (1996) show that anomalously propa- trained person may successfully recognize nonmeteo- gated ground returns remain a serious problem, espe- rological contamination in radar echoes, such as ground cially for situations where AP is embedded in precipi- clutter or anomalously propagated ground returns tation echoes. (called ``AP'' or ``Anaprop'' echoes). For of¯ine case The aim of this paper is threefold: to investigate the studies, manual editing of the data may be feasible and potential of anomalous propagation conditions to occur appropriate; however, for operational applications au- throughout the United States from an atmospheric per- tomated procedures need to be used. spective (section 2), review past efforts in dealing with Harrison et al. (2000) present recent efforts under way ground clutter and AP contamination in radar data (sec- in the United Kingdom that show how extensive quality tion 3), and present and discuss a new approach for control may effectively reduce the root-mean-square automated radar data quality control (sections 4 and 5). (rms) difference between gauge-measured and radar-estimated rainfall amounts. Joss and Lee (1995) discuss elaborate procedures in place for operational radar data 2. Anomalous propagation of radar signals processing in Switzerland, where extensive data quality a. Refractive index and signal propagation At microwave frequencies, the propagation of elec- Corresponding author address: Dr. Matthias Steiner, Department of Civil and Environmental Engineering, Princeton University, tromagnetic signals is in¯uenced by atmospheric con- Princeton, NJ 08540. ditions. A commonly used quantity to describe the prop- E-mail: [email protected] agation behavior of electromagnetic signals is the index

᭧ 2002 American Meteorological Society

Unauthenticated | Downloaded 09/24/21 05:11 PM UTC 674 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 19 of refraction n, or the refractivity N, which can be ap- warm dry air from land over cooler bodies of water proximated by (e.g., ocean), causing a temperature inversion in the boundary layer. At the same time, moisture is added by 77.6p 3.73 ϫ 105e (n Ϫ 1) ϫ 106 ϭ N ϭϩ , (1) evaporation from the water surface, producing a mois- TT2 ture gradient (Skolnik 1980; Puzzo et al. 1989). Evap- where p is the barometric pressure in millibars, e the oration ducts are common just above the surface of the partial pressure of water vapor in millibars, and T the sea, where air may become saturated by evaporation absolute temperature in kelvins (Gossard 1977; Skolnik from the sea surface. Over land, ducting is often caused 1980; Babin 1996; Fabry et al. 1997). by radiational cooling during clear nights, particularly It is the vertical gradient of the refractivity within the in the summer when the ground is moist. Thus, over lowest several hundred meters above the ground that is land, ducting is most noticeable at night and tends to especially important for characterizing radar signal disappear during the warmest part of the day (e.g., propagation (e.g., Pratte et al. 1995). A decrease in at- Moszkowicz et al. 1994). Superrefraction or ground mospheric refractivity with altitude, dN/dh, tends to ducts may also be produced by the diverging downdraft bend the radar rays so as to extend coverage beyond under a thunderstorm and resulting gust fronts. The rel- that expected with a uniform atmosphere. This abnormal atively cool air, which spreads out from the base of a propagation of electromagnetic waves is called anom- thunderstorm, may produce a temperature inversion alous propagation. Four basic modes of propagation are within the lowest, possibly several hundred meters. The distinguished: moisture gradient along the out¯ow boundary is also appropriate for the formation of a duct. The conditions Ϫ1 • subrefraction dN/dh Ͼ 0m , favorable for the formation of a thunderstorm duct are Ϫ1 • normal refraction 0 Ͼ dN/dh ϾϪ0.0787 m , relatively short-lived and have timescales on the order • superrefraction Ϫ0.0787 Ͼ dN/dh ϾϪ0.157 of 30 min to 1 hr, although in extreme cases such con- Ϫ1 m , and ditions may last for hours (Weber et al. 1993). • trapping or ducting dN/dh ϽϪ0.157 mϪ1.

Trapping or ducting is the most severe case of anom- b. Climatological assessment of vertical refractivity alous signal propagation, and results in ground returns gradients (AP echoes) from locations where the radar beam in- tersects the ground or objects at the earth's surface. Using a 16-yr record of operational sounding data In order to propagate energy within the duct, the angle (1973±88), the potential of anomalous propagation con- the radar ray makes with the duct should be small, usually ditions to occur throughout the continental United States less than a degree (e.g., only the lowest elevation scans is assessed climatologically. Similar studies, for ex- of surface-based radar are affected). Only those radar rays ample, have been conducted by Bech et al. (2000) using launched nearly parallel to the duct will be trapped. At- soundings for Mediterranean coastal sites, Babin (1996) mospheric ducts are generally of the order of tens to using helicopter-based refractivity measurements off the hundreds of meters (Gossard 1977; Cook 1991; Babin coast of Wallops Island (Virginia), and Gossard (1977) 1996; Brooks et al. 1999). A simpli®ed approximate mod- using airmass analyses. For each sounding of the da- el of propagation in atmospheric ducts (Skolnik 1980) taset, the average refractivity gradient within the lowest predicts a maximum wavelength ␭max that can be prop- 500 m above ground level (AGL) is determined. (The agated in a surface duct of depth d as given by maximum gradient might be more relevant for the radar signal propagation problem; however, the limited and ␭ ϭ 2.5(Ϫdn/dh)1/2d 3/2, (2) max variable vertical resolution of the operational sounding where ␭max, dh, and d are in the same units (e.g., meters). data may result in questionable maximum gradient val- For an operational Weather Surveillance Radar-1988 ues.) These values are then compiled into a climatology Doppler (WSR-88D) of the Next Generation Weather of average refractivity gradients for each operational Radar (NEXRAD) network (Heiss et al. 1990; Baer 1991; sounding station and used to study the likelihood of Crum et al. 1998) with wavelength of 10 cm (S band), atmospheric conditions across the United States that are the duct must be at least 22 m thick in order for trapping susceptible to anomalous propagation of radar signals. to occur. Often only parts of the radar beam may be The sounding-based climatology will highlight large- trapped. scale temperature inversions and moisture gradients, yet A duct is produced when the index of refraction rap- only by chance capture conditions favorable to anom- idly decreases with height. In order to achieve this, the alous propagation produced by thunderstorm out¯ow temperature must increase and/or the humidity (water boundaries. Moreover, relatively thin layers of strong vapor content) must decrease with height. Temperature vertical gradients may cause anomalous propagation, inversions must be very pronounced in order to produce but the operational sounding data (variable resolution superrefraction, while water vapor gradients are more of one to several hundred meters) do not resolve tens effective than temperature gradients alone (Fabry et al. of meters in the vertical. High-resolution refractivity 1997). A common cause of ducting is the movement of pro®les may be obtained, for example, from detailed

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FIG. 1. Average refractivity gradient within 500 m AGL across the continental United States, based on a 16-yr climatology of operational soundings taken at 1200 UTC and shown by season (winter ϭ DJF, spring ϭ MAM, summer ϭ JJA, fall ϭ SON). The fraction of a box shaded either gray (superrefraction) or black (trapping) indicates the percentage of soundings of that particular station exhibiting the respective atmospheric conditions. atmospheric measurements aboard an airplane [e.g., Ba- 0400 LST at the west coast to 0700 (about sunrise) at bin (1996) used a helicopter] or combined pro®ler and the east coast. Figure 1 shows the distribution of average radio acoustic sounding systems (RASS) (Gossard et al. refractivity gradients within 500 m AGL for each op- 1995). Lidar-based observations of temperature and wa- erational sounding station. The critical categories of ter vapor (e.g., Eichinger et al. 1993, 1999) may provide ``superrefraction'' and ``trapping'' are shaded in gray alternative methods for obtaining high-resolution re- and black, respectively, and the fraction of the box cov- fractivity information. Fabry et al. (1997) also showed ered indicates the percentage of soundings that exhibit that radar-based phase measurements of ground targets such atmospheric conditions. For simplicity, we discuss can be used to reveal the spatial, near-surface structure seasonal trends only, although we noticed signi®cant of the index of refraction. variability from month to month. There are many areas of the continental United States The general nighttime pattern revealed by Fig. 1 is that have conditions at least favorable for anomalous that conditions of superrefraction may occur anywhere propagation conditions to occur on a regular basis, as throughout the United States. During the winter season will be discussed below. For example, the southwestern (Fig. 1a), superrefractive propagation conditions occur part of the United States, particularly southern Califor- most likely south of 40Њ latitude, and particularly along nia, is especially prone to this problem (Gossard 1977; the coastlines. For those areas, on average, superrefrac- Pappert and Goodhart 1977; Babin and Rowland 1992; tive conditions may occur at least once a week, and Burk and Thompson 1997). Signi®cant local, regional, maybe twice or three times that for southern California. diurnal, and seasonal differences are found among the The maximum likelihood of superrefractive conditions different sounding stations. is displayed during the summer (Fig. 1c), when chances for such conditions to occur exceed 20% throughout most of the United States and are larger than 30% for 1) ANALYSES OF 1200 UTC SOUNDINGSÐ southern California and most of the eastern seaboard NIGHTTIME states. The spring (Fig. 1b) and fall seasons (Fig. 1d) The 1200 UTC soundings taken across the contiguous also show widespread conditions of superrefraction at United States re¯ect nighttime conditions, ranging from least once a week, with local maxima in excess of 30%

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FIG. 2. Same as Fig. 1, but for soundings taken at 0000 UTC. generally south of 40Њ latitude and throughout states valleys, there is also an increase in superrefractive con- bordering the ocean. Signi®cant trapping conditions (at ditions throughout the states along the east and west least once a week) occur mainly in southern California coasts of the United States. (e.g., San Diego), essentially throughout the year. The soundings launched from Huron, South Dakota, display an unusually high percentage of superrefractive and trapping conditions during the summer (Fig. 2c) 2) ANALYSES OF 0000 UTC SOUNDINGSÐ and fall (Fig. 2d) seasons compared to their neighboring DAYTIME stations. The operational soundings from this location The 0000 UTC soundings re¯ect conditions in the were discontinued in November 1994 and since moved late afternoon, ranging from 1600 LST at the west coast to Aberdeen, South Dakota. to 1900 (about sunset) at the east coast. The general daytime pattern revealed for the four seasons, as shown 3. Review of approaches to mitigate clutter in Fig. 2 (similar to Fig. 1), is more structured than the problems nighttime conditions depicted in Fig. 1. Conditions of superrefraction at least once a week are seen throughout There are various levels where the problem of ground the year in the coastline states. Again, southern Cali- clutter and AP echo contamination in radar data may fornia displays a maximum likelihood of superrefractive be approached (e.g., Joss and Wessels 1990; Keeler and conditions to occur twice, if not three times a week, and Passarelli 1990; Pratte et al. 1995), namely: trapping conditions approximately 20% of the time (e.g., • the radar installation (site, hardware), San Diego). • the data processing (before and/or after recording), An interesting seasonal feature is an enhanced like- and lihood (Ͼ20%) of superrefractive conditions spreading • through comparison with other data sources. up the Missouri, Mississippi, and Ohio River valleys, reaching its maximum spatial extent in the summer (Fig. The ®rst may be considered a static approach, while the 2c), before retreating again. The corresponding mini- latter two are dynamic and more easily modi®ed. The mum is observed in the winter season (Fig. 2a). In con- main focus of this study is on the processing of archived cert with this seasonal ¯uctuation of superrefractive con- data; however, we review a variety of approaches for ditions in the Missouri, Mississippi, and Ohio River mitigation of ground clutter and AP echoes.

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Visual inspection of radar volume scan re¯ectivity data sarelli 1990; Rinehart 1991; Smith 1993, 1998). How- reveals that ground returns resulting from anomalous sig- ever, to balance application needs versus costs, one may nal propagation have little vertical extent and tend to have to compromise further. For example, use of longer clutter the lowest elevation sweeps only (generally below wavelengths avoids problems caused from attenuation by 2Њ elevation), depending on the vertical gradient of the precipitation; however, the relationship between precip- refractive index (Sekhon and Atlas 1972; Johnson et al. itation and ground clutter worsens with increasing wave- 1975; Sirmans and Dooley 1980; Moszkowicz et al. length. Moreover, for a given size of antenna, a shorter 1994; Lee et al. 1995). Anomalously propagated echoes, wavelength will result in a narrower beam and thus better similar to regular ground clutter close to the radar, de- spatial resolution and reduced ground clutter. Also, the correlate rapidly in space and are spatially heterogeneous cost increases roughly proportionally to the weight of the to the greatest degree (Joss and Wessels 1990; Joss and antenna, that is, with the third power of the wavelength Lee 1993; Pratte et al. 1993). Thus, AP signatures may (Joss and Wessels 1990). be recognized in re¯ectivity data by their larger spatial variability than precipitation echoes. In contrast to regular 2) SIGNAL PROCESSING BEFORE DATA RECORDING (stationary) ground clutter, which exhibits a longer time correlation than weather echoes (Tatehira and Shimizu For noncoherent radar, a check on the temporal vari- 1978, 1980; Sirmans and Dooley 1980), AP ground re- ability (¯uctuation rate) of echoes from pulse to pulse turns can appear much like precipitation, exhibiting (i.e., ``Doppler simulation'') or the (auto)correlation in growth, decay, and motion similar to that of rainstorms time have been suggested by Reid (1970), Johnson et al. (Johnson et al. 1975; Weber et al. 1993). (1975), Geotis and Silver (1976), Aoyagi (1978), Tatehira For ground-based radar, signals returned from radar and Shimizu (1978, 1980), Sirmans and Dooley (1980), beams intersecting the ground should exhibit radial Passarelli (1981), Joss and Wessels (1990), Coveri et al. Doppler velocity values close to zero (not for sea clutter (1993), Michelson and Andersson (1995), and Haddad though), dependent on vegetation cover, wind, and an- et al. (2000). For semicoherent radar, Andersson (1993) tenna rotation (Rinehart 1991). The Doppler spectrum recommends a check on the agreement of velocity esti- width should be small as well (Hamidi and Zrnic 1981; mates based on staggered pulse repetition frequency Joss and Wessels 1990). From visual inspection of radar (PRF) processing (at least two different PRFs are nec- volume scan velocity data, land-based AP returns in the essary). Pulse-pair processing (Anderson 1981; Hamidi lowest elevation sweep can clearly be recognized by and Zrnic 1981), time domain ®ltering (Mann et al. 1986; their near-zero Doppler velocity. Contamination of AP Michelson and Andersson 1995; Pratte et al. 1995), and in the second-lowest elevation sweep, however, may frequency domain ®ltering (Passarelli et al. 1981; Schmid exhibit velocities similar to precipitation echoes, al- et al. 1991; Torres and Zrnic 1999) are choices for a though the re¯ectivity signature indicates AP. The dis- coherent radar. Joss and Lee (1993, 1995), Lee et al. tribution of spectrum width data is signi®cantly broader (1995), and Archibald (2000) advocate ``clutter detec- than the radial Doppler velocity for AP echoes (e.g., tion'' (and rejection) rather than ``clutter suppression'' see Fig. 1 of Steiner et al. 1999b) and thus appears to (which may result from time or frequency domain ®l- be less useful for identi®cation purposes. tering) by means of a sophisticated decision tree, making use of high spatial resolution radar information. a. Radar installation b. Processing of archived data 1) CHOICE OF RADAR SITE AND HARDWARE For many applications, the user may not be able to A clever siting of the radar may be very effective in in¯uence the data recording and, therefore, has to resort minimizing clutter contamination in the desired ®eld of to an intelligent processing of the archived data. Many view (Smith 1972; Mann et al. 1986; Joss and Wessels different approaches have been suggested; for example, 1990; Joss and Lee 1995). However, the placing of the checks on the spatial (horizontal and vertical) and tem- radar antenna involves a compromise between extending poral continuity of re¯ectivity echoes (Hogg 1978; the horizon and minimizing clutter contamination. Pref- Smith 1990), and analysis of the horizontal and vertical erence may be given to an elevated radar site (e.g., high re¯ectivity gradients (Mueller and Sims 1975; Riley and tower or top of a mountain), because modern signal pro- Austin 1976; Collier et al. 1980; Lee et al. 1995), in- cessing techniques are increasingly capable of mitigating cluding echo tops (Johnson et al. 1975; Moszkowicz et many problems caused by clutter contamination, but al. 1994; Rosenfeld et al. 1995). Other approaches focus nothing can be done to detect precipitation blocked from on the texture (spatial variability) of echo patterns based the radar view at a low site. A smart choice of radar on signal-to-noise ratio, re¯ectivity, Doppler velocity characteristics (wavelength, antenna, polarization, Dopp- and spectral width, or differential re¯ectivity ®elds (Hall ler, system stability, scan strategy, etc.) may also help in et al. 1984; Smith 1990; Joss and Wessels 1990; Joe reducing the clutter problem (Skolnik 1980; Pratte and 1991; Giuli et al. 1991; Pratte et al. 1993; Cornelius Keeler 1986; Joss and Wessels 1990; Keeler and Pas- 1994). Probabilistic analyses, using multiple parameters

Unauthenticated | Downloaded 09/24/21 05:11 PM UTC 678 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 19 as input to neural network or fuzzy logic procedures, formation from above. The algorithm is executed in have been explored more recently (Cornelius 1994; Gre- polar space to remain at the level of the data recording cu and Krajewski 1999, 2000; VanAndel 2001; Kessin- (i.e., how the radar sees its environment). Interpolation ger et al. 2001). The advent of multiple polarization of data to a Cartesian grid may simplify subsequent data radar observations, particularly the correlation coef®- management and processing, but would likely introduce cient between horizontally and vertically polarized undesired range-dependent artifacts (e.g., Trapp and Do- backscatter signals and differential phase shift param- swell 2000). eters, brought considerable skills to the detection of AP The ECHOtop parameter indexes the highest eleva- and ground clutter contamination (Blackman and Il- tion sweep (tilt) that contains re¯ectivity echoes in ex- lingworth 1993; Zrnic and Ryzhkov 1996; Ryzhkov and cess of a minimum intensity (REFLthresh ϭ 5dBZ) Zrnic 1998; Collier 2000). Some of these data quality either directly above the base scan (®rst tilt) pixel of tests based on polarimetric parameters could be imple- interest or its surrounding eight neighbors. This check mented also at the signal processing level before the on the pixel's neighbors is included to reduce edge ef- data are recorded, similar to Doppler-based procedures. fects accounting for potentially tilted storm cells. The SPINchange parameter indicates the number of c. Comparison with other data sources re¯ectivity ¯uctuations larger than 2 dBZ within an 11 (azimuth) by 21 (radial) pixel window, expressed as a Some more elaborate approaches embrace additional percentage of all possible ``spin'' changes. A re¯ectivity information from independent sources for assessing qual- increase (decrease) in radial direction from one pixel to ity of the radar data. For example, Mann et al. (1986), the next by more than 2 dBZ would cause the spin to Schmid et al. (1991), Lee et al. (1995), and Joss and Lee point up (down). Re¯ectivity ¯uctuations smaller than (1995) suggest using adaptive clutter or clutter residue 2dBZ are deemed insigni®cant and thus have no effect maps, albeit as a last resort rather than a primary check. on the spin setting. The window is investigated in radial Moores and Harrold (1975), Hogg (1978), Delrieu et al. direction, beam by beam. The SPINchange parameter (1995), and Archibald (2000) propose using digital ele- represents the sum of spin changes found within a win- vation data and code to predict the beam pattern for nor- dow centered on the scrutinized pixel, normalized by mal and anomalous propagation. Johnson et al. (1995), all potentially possible spin changes. Pratte et al. (1995), and Fabry et al. (1997) suggest as- The vertGRAD parameter gauges the vertical re¯ec- sessing the atmospheric conditions through direct or in- tivity difference between pixels directly above each oth- direct measurements of the refractive index gradient. er of the lowest two elevation sweeps, normalized by Atkinson and James (1991) and Klingle-Wilson et al. the elevation angle difference. The dimension of the (1995) evaluate data from multiple radar covering the vertGRAD parameter is dBZ degreeϪ1. same area, incorporating also rain gauge and/or satellite data (Fiore et al. 1986). Pamment and Conway (1998) Figure 3 shows the decision tree of the algorithm. discuss a probabilistic scheme used in the United King- The sequence of tests is applied to each pixel of the dom that combines synoptic reports, satellite infrared polar-spaced base scan to examine whether that pixel data, lightning data, and AP echo climatology for radar should be kept, removed, or potentially replaced. The data quality control. algorithm removes echoes that are weaker than RE- FLthresh as a ®rst step. Then, echoes without a signif- icant vertical extent (i.e., ECHOtop equal base scan) are 4. A new algorithm removed as well. (This might be a problem at far ranges, The new radar data quality control algorithm, de- where the radar potentially overshoots precipitating signed for single volume-scanning radar, makes use of cloud systems and only the lowest elevation sweep con- the three-dimensional re¯ectivity structure. Radial tains signi®cant echoes.) The remaining pixels (i.e., ech- Doppler velocity and spectrum width information, al- oes exhibiting some vertical depth) are subsequently though readily available for many modern radar sys- checked on their SPINchange parameter and kept if that tems, is not used as part of the algorithm, which will value would be less than an intensity-dependent SPIN- keep the amount of data processing to a minimum (par- thresh, de®ned as SPINthresh ϭ 8 Ϫ (Zpixel Ϫ 40)/15, ticularly relevant for operational applications). The al- where Zpixel is the re¯ectivity (in dBZ) of the pixel gorithm builds upon three key parameters, namely, the evaluated. (SPINthresh was ®ne-tuned such that this ®l- vertical extent of radar echoes (ECHOtop), the spatial ter would be more aggressive for higher intensities but variability of the re¯ectivity ®eld (SPINchange), and less so for weaker echoes.) Re¯ectivity pixels that fail the vertical gradient of re¯ectivity (vertGRAD). Steiner the SPINchange test are investigated further before re- et al. (1999b) evaluated several other parameters as well, moval. If the vertGRAD for these pixels is less than or but these three appear to be the most useful ones. In equal to 10 dBZ degreeϪ1, they are kept despite a large addition, gaps in rainfall echo areas that are potentially SPINchange parameter. Such a threshold is very similar created by the algorithm in situations with AP echoes to the 12 dBZ degreeϪ1 suggested by Lee et al. (1995), embedded in precipitation will be ®lled using echo in- and also consistent with the results of Mueller and Sims

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FIG. 3. Decision tree of the new radar data quality control algorithm.

(1975), who found 80% of the vertical gradients ob- Joss and Pittini 1991; Kitchen et al. 1994; Andrieu and served in severe storms to be less than 12 dBZ kmϪ1. Creutin 1995; Andrieu et al. 1995; Joss and Lee 1995; The sequential order of the tests applied in the de- Amitai 1999; Vignal et al. 1999, 2000; Seo et al. 2000), cision tree takes care of the most obvious unwanted or potentially otherwise obtain it sideways (e.g., SaÂn- echoes ®rst and subsequently focuses on the more prob- chez-Diezma et al. 2001), and ®ll the base scan gaps. lematic areas that may require additional computational effort to be tackled. The ®ne-tuning of the parameter 5. Example case studies and sensitivity analysis settings (e.g., for SPINchange and SPINthresh) was done empirically and does not bear any physical basis. Five typical situations were chosen to exemplify the The ECHOtop test will remove most of the AP and performance of the algorithm: sea clutter echoes that are separated from precipitation, • AP echoes near or separated from precipitation (Figs. because of a lack of vertical extent of those echoes. 4 and 5), However, boundary layer features, such as gust fronts, • AP echoes embedded in precipitation (Fig. 6), will be removed as well. The SPINchange parameter • sea clutter (Fig. 7), grasps the spatial variability of AP echoes well, which • strong clear air and boundary layer echoes (Figs. 7 is particularly useful for AP echoes embedded in pre- and 4), and cipitation, where the ECHOtop parameter is clueless. • pure precipitation (Fig. 8). The SPINchange parameter tends to be too erosive at the storm cell boundaries, which is why the vertGRAD For each of these examples, the unedited velocity and test was introduced to limit echo removal that is likely re¯ectivity ®elds of the base scan (®rst tilt) are shown precipitation. together with the re¯ectivity of the second tilt. In ad- In this form, the algorithm successfully identi®es and dition, three different stages of the edited re¯ectivity removes AP echoes. However, AP echoes that were em- ®eld are shown: namely, before ®lling the gaps in rain- bedded in precipitation will be removed, leaving a gap fall echo areas caused by the removal of pixels (stage behind. Therefore, as a ®nal processing step, the al- 4), and after ®lling those gaps by either using re¯ectivity gorithm checks if there are echoes directly above, in the information from the second (stage 5a) or third tilt (stage next higher (second tilt) elevation sweep, that exceed 5b) above. the REFLthresh, and if so, will use those pixel values The examples shown in Figs. 4, 5, and 6 are related to ®ll the gaps in the base scan. There is an option, to to anomalous propagation of radar signals caused by check on the third tilt instead of the second, which works thunderstorm out¯ow boundaries, which are not cap- better, particularly in situations of severe AP contami- tured by the sounding climatology presented in section nation, where even the second tilt may be affected. How- 2. Figure 7 shows a typical example of anomalous signal ever, use of this option may result in loss of precipitation propagation caused by a nighttime temperature inver- echoes at far ranges, where the third tilt might overshoot sion, which is well captured by the sounding climatol- cloud tops. This gap-®lling procedure is a ®rst approx- ogy. Figure 8 shows normal propagation conditions, also imation only, and future improvements will have to con- represented by the climatology discussed in section 2. sider vertical re¯ectivity pro®le information to down- For a quantitative evaluation of the effect of the qual- ward extrapolate re¯ectivity measured aloft (see, e.g., ity control algorithm on radar-based rainfall estimates,

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FIG. 4. Radar data collected by the Amarillo, Texas, WSR-88D (KAMA) on 25 May 1994 at 0034 UTC. Shown are the (a) unedited radial Doppler velocity and (b) re¯ectivity of the base scan, together with (c) the re¯ectivity of the second tilt. In addition, three different stages of the edited base scan re¯ectivity ®eld are shown: (d) before ®lling the gaps in rainfall echo areas caused by the removal of pixels, and after ®lling those gaps by either using re¯ectivity information from the (e) second or (f) third tilt above. re¯ectivity Z (mm6 mϪ3) is converted to rain rate R (mm be affected. Visually, using the third tilt option to ®ll hϪ1) using the relationship Z ϭ 300R1.4 of the NEXRAD the gaps in the base scan would be slightly more suc- precipitation processing scheme (Fulton et al. 1998). We cessful in this particular situation. From a quantitative do not consider potential biases compared to rain gauge perspective (see Table 1), the removal of the AP echoes accumulations or problems with hail contamination for reduced the unconditional, area-average rainfall rate this study. These factors are extensively discussed in from 1.0 and 1.1 mm hϪ1 for the unedited data to 0.4 Steiner et al. (1999a, and references therein). Rainfall mm hϪ1 and essentially zero for the edited data shown estimates are based on the base scan re¯ectivity ®eld in Figs. 4 and 5, respectively. The effect of the gap- without consideration of the vertical pro®le for extrap- ®lling procedure was not signi®cant. olating re¯ectivity measured aloft down to the surface. Vertical pro®le corrections are extensively discussed in b. AP echoes embedded in precipitation Vignal et al. (2000, and references therein). A major thunderstorm passing over the WSR-88D site at St. Louis, Missouri (KLSX), on 7 July 1993 resulted a. AP echoes near or separated from precipitation in severe AP contamination embedded within precipi- A thunderstorm passing over the WSR-88D site at tation echoes (Fig. 6). The AP echoes can be recognized Amarillo, Texas (KAMA), on 25 May 1994 caused by their near-zero velocity and highly variable re¯ec- widespread severe AP echoes to occur behind the storm. tivity pattern, beyond a range of 100 km to the east from Figure 4 shows several storm cells triggered along a the radar. Because they are embedded within rainfall gust front that was moving in a southerly direction. The echoes, the vertical extent of the AP contamination is out¯ow from these storms left atmospheric conditions obscured. The re¯ectivity pattern of the second tilt (Fig. behind that caused the radar signals at the lowest ele- 6c) hints that the AP contamination may extend up to vations to be trapped in the boundary layer, resulting this level, although the velocity signatures are no longer in widespread severe AP echoes (Fig. 5). The AP echoes zero (not shown). This remains the most challenging can easily be recognized by their zero velocity signature, situation to deal with, particularly for operational ap- high spatial variability in re¯ectivity, and essentially no plications such as ¯ash ¯ood forecasting and warning vertical extent of the echoesÐfor example, those seen (Smith et al. 1996). The 7 July 1993 storm was a major to the north of the radar in Fig. 4 and covering most of rain event of the Mississippi River ¯ood episode during the area centered on the radar in Fig. 5. the summer of 1993 (Kunkel et al. 1994; Gumley and The quality control algorithm succeeds in removing King 1995; Giorgi et al. 1996; Arritt et al. 1997). most of the AP echoes but keeps the rainfall echoes. The quality control procedure successfully ¯ags and Some smaller contaminated echo areas remain, owing removes the most severe AP echoes (Fig. 6d). However, to a severe AP situation that caused the second tilt to because the sweeps above the base scan may be affected

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FIG. 5. Same as Fig. 4, but for data collected by the Amarillo, Texas, WSR-88D (KAMA) on 25 May 1994 at 0231 UTC. by AP as well, ®lling in echoes from above will not entirely c. Sea clutter solve the problem. From a quantitative perspective, AP contamination severely affects the rainfall estimates, as A strong nighttime inversion on 9 May 1998 caused shown by the dramatic reduction of area-average rain rate the signals emitted at the lowest elevation angle by the from the unedited to the edited data (Table 1). Moreover, Houston, Texas, WSR-88D (KHGX) to be trapped in ®lling the gaps created by the pixel removal is crucial the boundary layer over the ocean, resulting in severe under these circumstances. The detail of whether the sec- sea clutter to the southeast of the radar at ranges ex- ond or third tilt is used is less signi®cant. ceeding 100 km (Fig. 7). The radial Doppler velocity

TABLE 1. Comparison of radar echo area, and unconditional and conditional area-average rain rate based on the unedited and edited (various stages) radar re¯ectivity ®elds. Site KAMA KAMA KLSX KHGX KMLB Date 25 May 1994 25 May 1994 7 Jul 1993 9 May 1998 7 Jul 1998 Time (UTC) 0034 0231 0404 0902 2201 Echo area, expressed as percentage of domain (230-km radius) covered Stage 0: unedited 50.4 63.8 81.7 70.7 47.4 Stage 1: REFLthresh 39.2 55.4 73.7 58.1 39.2 Stage 2: ϩ ECHOtop 26.8 21.2 71.6 29.3 38.8 Stage 3: ϩ SPINchange 19.0 7.7 59.8 27.0 35.7 Stage 4: ϩ vertGRAD 21.9 11.0 66.6 28.4 38.4 Stage 5a: ϩ ®ll from second tilt 23.1 16.5 71.3 28.6 38.5 Stage 5b: ϩ ®ll from third tilt 22.6 11.1 70.2 28.4 38.5 Rain rate, conditioned on echo area (mm hϪ1) Stage 0: unedited 1.976 1.720 7.458 0.578 1.229 Stage 1: REFLthresh 1.974 1.718 7.456 0.575 1.227 Stage 2: ϩ ECHOtop 1.856 1.175 7.451 0.152 1.227 Stage 3: ϩ SPINchange 1.603 0.067 2.267 0.139 0.985 Stage 4: ϩ vertGRAD 1.692 0.095 3.181 0.142 1.221 Stage 5a: ϩ ®ll from second tilt 1.696 0.130 3.873 0.142 1.222 Stage 5b: ϩ ®ll from third tilt 1.695 0.096 3.856 0.142 1.222 Rain rate, unconditional area-average (mm hϪ1) Stage 0: unedited 0.995 1.098 6.091 0.409 0.583 Stage 1: REFLthresh 0.774 0.952 5.493 0.334 0.482 Stage 2: ϩ ECHOtop 0.498 0.249 5.333 0.045 0.476 Stage 3: ϩ SPINchange 0.305 0.005 1.355 0.038 0.352 Stage 4: ϩ vertGRAD 0.370 0.010 2.119 0.040 0.469 Stage 5a: ϩ ®ll from second tilt 0.392 0.021 2.760 0.041 0.470 Stage 5b: ϩ ®ll from third tilt 0.383 0.011 2.708 0.040 0.470

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FIG. 6. Same as Fig. 4, but for data collected by the Saint Louis, Missouri, WSR-88D (KLSX) on 7 Jul 1993 at 0404 UTC. is not zero, as expected for land-based ground clutter, the second or third tilt option was used. The removal re¯ecting instead the wave motion at the ocean surface. of the sea clutter echoes clearly affected the rainfall The re¯ectivity pattern of sea clutter is much smoother estimates, reducing them to near zero (Table 1). than for ground clutter or AP echoes over land, resem- bling more closely real precipitation echoes. However, d. Strong clear air and boundary layer echoes the second tilt clearly shows that these echoes over water have no depth (Fig. 7c) and are thus not representing The nighttime inversion depicted in Fig. 7 resulted precipitation but rather sea clutter. not only in trapping of radar signals over the ocean (sea The quality control algorithm picked up on the lack clutter), but also caused strong clear air returns over of vertical depth and successfully removed those sea land to the northwest of the Houston, Texas, WSR-88D. clutter echoes. There were essentially no gaps to be These widespread clear air echoes exhibited little var- ®lled and thus it did not make any difference whether iability in re¯ectivity (other than wavelike structures

FIG. 7. Same as Fig. 4, but for data collected by the Houston, Texas, WSR-88D (KHGX) on 9 May 1998 at 0902 UTC.

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FIG. 8. Same as Fig. 4, but for data collected by the Melbourne, Florida, WSR-88D (KMLB) on 7 Jul 1998 at 2201 UTC.

around 100-km range); however, they reached appre- f. Sensitivity analysis ciable intensity that would result in noticeable surface The algorithm builds upon several quality control ®l- rainfall estimates if not identi®ed as clear air echoes and ters that are sequentially applied by means of a decision removed. tree. Table 1 highlights the effect of adding various stag- The quality control algorithm was not able to com- es of data editing to the radar echo area and rain-rate pletely remove all of the clear air echoes, because the estimates. Thresholding of the re¯ectivity ®eld (stage second tilt contained a signi®cant amount of such ech- 1) reduced the echo area (and, consequently, the un- oes. The remaining echoes, however, contribute very conditional rain rate) of the examples discussed by ap- little to the estimated surface rainfall (Table 1). proximately 10%, but left the conditional rain rate es- The high sensitivity of the WSR-88D may reveal sig- sentially untouched. Application of the vertical echo ni®cant structures, such as gust fronts (Fig. 4) or wind- depth ®lter (stage 2) clearly affected the examples with induced rolls (e.g., sea breeze), in the otherwise clear clutter separated from precipitation echoes, removing boundary layer. These features of the boundary layer gen- up to 30% or more of the echo area. The spatial vari- erally have little vertical depth and thus are removed by ability check (stage 3) ¯agged another signi®cant the ECHOtop criterion of this quality control algorithm. amount of echo area; however, some of that was pre- vented from removal by the vertical echo gradient check (stage 4). The SPINchange parameter test (stage 3) e. Precipitation clearly demonstrated skill in tackling the embedded AP contamination for the St. Louis example (Fig. 6), as Figure 8 shows a typical summer mid-afternoon rain- seen from the drastic reduction in average rain rate (Ta- fall pattern, with scattered multicellular thunderstorms, ble 1). The process of ®lling in gaps (stage 5), poten- as observed by the Melbourne, Florida, WSR-88D tially created by the previous stages, had some, albeit (KMLB). The quality control algorithm should leave minor, effect as well. these echoes untouched. From a visual perspective, this And, as a ®nal note, the effect of the different ®lters is the case (except for weak clear air echoes close to applied to the radar data was much more signi®cant than the radar). Close inspection, though, may reveal a few the effect of ®ne-tuning the various parameter settings isolated spots that were retouched by the algorithm. for the ®lters. For example, a wide range of value set- Quantitatively, application of the quality control algo- tings tested for the SPINchange parameter affected the echo area by a few percent only (not shown). rithm caused a slight reduction of the unconditional area-average rain rate but left the conditional rain rate the same (Table 1). This situation with scattered, mul- 6. Summary and conclusions ticellular storms represents a worst-case scenario in This study was aimed at assessing the potential of terms of potential ``edge effects'' for the algorithm. anomalous propagation conditions to occur, reviewing

Unauthenticated | Downloaded 09/24/21 05:11 PM UTC 684 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 19 past attempts to mitigate a ground return contamination The algorithm works well with WSR-88D data, but in radar data resulting from anomalous signal propa- may need ®ne-tuning for data collected by other radars gation, and presenting a new algorithm for radar data or a different scanning strategy. An extensive compar- quality control. ison of several radar data quality control algorithms is A 16-yr record of operational sounding data collected under way (Robinson et al. 2001). across the United States has been analyzed to assess the likelihood of atmospheric conditions that potentially Acknowledgments. The sounding data used in this lead to anomalous propagation of electromagnetic radar study were compiled and quality controlled by A. Allen signals. This evaluation provides a lower bound on the Bradley of the University of Iowa. The comments and susceptibility to anomalous signal propagation, because suggestions provided by Chris Porter of the National the soundings likely represent synoptic-scale tempera- Severe Storms Laboratory, and three anonymous re- ture inversions and moisture gradients, and capture viewers, were greatly appreciated. The manuscript was thunderstorm out¯ow boundary conditions only by carefully proofread by Mary D. Steiner of Princeton chance. The analyses show that there are many locations University. This project was supported by the National across the country that experience atmospheric condi- Aeronautics and Space Administration (NASA) Grant tions leading to anomalous signal propagation on a reg- NAG5-7744 and by the National Weather Service and ular basis. Coastal areas, including southern California, the NEXRAD Operations Support Facility under Co- are particularly prone to this problem. For other areas, operative Agreement NA87WH0518. anomalous propagation conditions may be limited to nighttime or vary by season. REFERENCES Anomalous propagation ground returns may resemble precipitation echoes, exhibiting growth, motion, and decay Amitai, E., 1999: Relationships between radar properties at high el- similar to that of rain storms. However, a contamination evations and surface rain rate: Potential use for spaceborne rain- of radar data by anomalous propagation echoes is mostly fall measurements. J. Appl. Meteor., 38, 321±333. Anderson, J. R., 1981: Evaluating ground clutter ®lters for weather limited to the lowest scan or two (e.g., below 2Њ elevation). radars. Preprints, 20th Conf. on Radar Meteorology, Boston, Moreover, these echoes may easily be recognized on the MA, Amer. Meteor. Soc., 314±318. basis of their great spatial variability of re¯ectivity (in- Andersson, T., 1993: Clutter suppression with Doppler radarÐA case cluding differential re¯ectivity) and near-zero radial Dopp- study. Preprints, 26th Int. Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., 241±243. ler velocity. This is particularly true for anomalous prop- Andrieu, H., and J. D. Creutin, 1995: Identi®cation of vertical pro®les agation echoes over land, but not so over water. of radar re¯ectivity for hydrological applications using an inverse Many different ways have been attempted to clean method. Part I: Formulation. J. Appl. Meteor., 34, 225±239. up radar data from ground clutter and particularly anom- ÐÐ, G. Delrieu, and J. D. Creutin, 1995: Identi®cation of vertical alous propagation echo contamination. Several of these pro®les of radar re¯ectivity for hydrological applications using an inverse method. Part II: Sensitivity analysis and case study. past efforts were able to remove ground clutter and echo J. Appl. Meteor., 34, 240±259. contamination caused by anomalous signal propagation, Aoyagi, J., 1978: Ground clutter rejection by MTI weather radar. as long as those echoes were not embedded within rain- Preprints, 18th Conf. on Radar Meteorology, Atlanta, GA, Amer. fall echoes. This latter, worst-case scenario remains a Meteor. Soc., 358±363. Archibald, E., 2000: Enhanced clutter processing for the UK weather challenge today. radar network. Phys. Chem. Earth, 25B, 823±828. A new algorithm has been presented that makes use Arritt, R. W., T. D. Rink, M. Segal, D. P. Todey, C. A. Clark, M. J. of the three-dimensional radar re¯ectivity structure. The Mitchell, and K. M. Labas, 1997: The Great Plains low-level jet key parameters of this radar data quality control algo- during the warm season of 1993. Mon. Wea. Rev., 125, 2176± rithm are based on the vertical structure and extent of 2192. Atkinson, N. C., and P. K. James, 1991: Quality control of data from the echoes, and their spatial variability. By means of a overlapping weather radars. Preprints, 25th Int. Conf. on Radar decision tree, the various parameters are evaluated for Meteorology, Paris, France, Amer. Meteor. Soc., 206±209. each pixel of the polar space base scan to determine Babin, S. M., 1996: Surface duct height distributions for Wallops whether that pixel would represent real rainfall or not. Island, Virginia, 1985±1994. J. Appl. Meteor., 35, 86±93. ÐÐ, and J. R. Rowland, 1992: Observation of a strong surface radar The new algorithm works equally well in situations duct using helicopter acquired ®ne-scale radio refractivity mea- where anomalous propagation ground returns are either surements. Geophys. Res. Lett., 19, 917±920. separated from or embedded within precipitation ech- Baer, V. E., 1991: The transition from the present radar dissemination oes. Moreover, sea clutter echoes are identi®ed as not system to the NEXRAD Information Dissemination Service raining and removed. However, the algorithm does not (NIDS). Bull. Amer. Meteor. Soc., 72, 29±33. Bech, J., A. Sairouni, B. Codina, J. Lorente, and D. Bebbington, handle strong and widespread clear air echoes well. In 2000: Weather radar anaprop conditions at a Mediterranean addition, boundary layer features, such as gust fronts coastal site. Phys. Chem. Earth, 25B, 829±832. (although not raining), will be removed as well, which Blackman, M., and A. J. Illingworth, 1993: Differential phase mea- may be unfortunate for applications that are geared to- surement of precipitation. Preprints, 26th Int. Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., 745±747. ward forecasting new storm cell formation that may be Brooks, I. M., A. K. Goroch, and D. P. Rogers, 1999: Observations triggered along gust fronts (e.g., Wilson and Schreiber of strong surface radar ducts over the Persian Gulf. J. Appl. 1986; Wilson et al. 1998). Meteor., 38, 1293±1310.

Unauthenticated | Downloaded 09/24/21 05:11 PM UTC MAY 2002 STEINER AND SMITH 685

Burk, S. D., and W. T. Thompson, 1997: Mesoscale modeling of anomalous radar propagation using ®rst-order two-state Markov summertime refractive conditions in the Southern California chains. Atmos. Res., 52, 283±292. bight. J. Appl. Meteor., 36, 22±31. Hall, M. P. M., J. W. F. Goddard, and S. M. Cherry, 1984: Identi®- Collier, C. G., 2000: COST-75 ®nal reportÐA summary. Phys. Chem. cation of hydrometeors and other targets by dual-polarization Earth, 25B, 817±822. radar. Radio Sci., 19, 132±140. ÐÐ, S. Lovejoy, and G. L. Austin, 1980: Analysis of bright bands Hamidi, S., and D. S. Zrnic, 1981: Considerations for the design of ground from 3-D radar data. Preprints, 19th Conf. on Radar Meteorol- clutter cancelers for weather radars. Preprints, 20th Conf. on Radar ogy, Miami Beach, FL, Amer. Meteor. Soc., 44±47. Meteorology, Boston, MA, Amer. Meteor. Soc., 319±326. Cook, J., 1991: A sensitivity study of weather data inaccuracies on Harrison, D. L., S. J. Driscoll, and M. Kitchen, 2000: Improving evaporation duct height algorithms. Radio Sci., 26, 731±746. precipitation estimates from weather radar using quality control Cornelius, R., 1994: Detecting ground clutter contamination of weath- and correction techniques. Meteor. Appl., 7, 135±144. er radar data with neural networks. Preprints, World Congress Heiss, W. H., D. L. McGrew, and D. Sirmans, 1990: NEXRAD: Next on Neural Networks, San Diego, CA, International Neural Net- Generation Weather Radar (WSR-88D). Microwave J., 33, 79± work Society. 98. Coveri, C., P. Caldini, G. F. Vezzani, and R. Lee, 1993: Statistical Hogg, W. D., 1978: Quality control and analysis of an archive of ground clutter ®lter. Preprints, 26th Int. Conf. on Radar Mete- digital radar data. Preprints, 18th Conf. on Radar Meteorology, orology, Norman, OK, Amer. Meteor. Soc., 244±246. Atlanta, GA, Amer. Meteor. Soc., 150±154. Crum, T. D., R. E. Saf¯e, and J. W. Wilson, 1998: An update on the Joe, P., 1991: Classi®cation of radar imagery using texture analyses. NEXRAD program and future WSR-88D support to operations. Preprints, 25th Int. Conf. on Radar Meteorology, Paris, France, Wea. Forecasting, 13, 253±262. Amer. Meteor. Soc., 107±110. Delrieu, G., J. D. Creutin, and H. Andrieu, 1995: Simulation of radar Johnson, G. N., P. L. Smith Jr., F. E. Nathanson, and L. W. Brooks, mountain returns using a digitized terrain model. J. Atmos. Oce- 1975: An analysis of techniques for dealing with anomalous anic Technol., 12, 1038±1049. propagation. Preprints, 16th Radar Meteorology Conf., Houston, Eichinger, W. E., D. I. Cooper, M. Parlange, and G. Katul, 1993: The TX, Amer. Meteor. Soc., 374±377. application of a scanning, water Raman-lidar as a probe of the Joss, J., and H. Wessels, 1990: Ground clutter suppression for weather atmospheric boundary layer. IEEE Trans. Geosci. Remote Sens., radar data. COST Tech. Rep. 73/WD/130, 6 pp. 31, 70±79. ÐÐ, and A. Pittini, 1991: Real-time estimation of the vertical pro®le ÐÐ, ÐÐ, P. R. Forman, J. Griegos, M. A. Osborn, D. Richter, L. of radar re¯ectivity to improve the measurement of precipitation L. Tellier, and R. Thornton, 1999: The development of a scanning in an Alpine region. Meteor. Atmos. Phys., 47, 61±72. Raman water vapor lidar for boundary layer and tropospheric ÐÐ, and R. W. Lee, 1993: Scan strategy, clutter suppression, cali- observations. J. Atmos. Oceanic Technol., 16, 1753±1766. bration and vertical pro®le corrections. Preprints, 26th Int. Conf. Fabry, F., C. Frush, I. Zawadzki, and A. Kilambi, 1997: On the ex- on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., 390± traction of near-surface index of refraction using radar phase 392. measurements from ground targets. J. Atmos. Oceanic Technol., ÐÐ, and ÐÐ, 1995: The application of radar±gauge comparisons 14, 978±987. to operational precipitation pro®le corrections. J. Appl. Meteor., Fiore, J. V., R. K. Farnsworth, and G. Huffman, 1986: Quality control 34, 2612±2630. of radar-rainfall data with VISSR satellite data. Preprints, Joint Keeler, R. J., and R. E. Passarelli, 1990: Signal processing for at- Sessions 23d Conf. on Radar Meteorology and Conf. on Cloud mospheric radars. Radar in Meteorology, D. Atlas, Ed., Amer. Meteor. Soc., 199±230. Physics, Vol. 3, Snowmass, CO, Amer. Meteor. Soc., JP15±JP18. Kessinger, C., S. Ellis, and J. VanAndel, 2001: NEXRAD data quality: Fulton, R. A., J. P. Breidenbach, D. J. Seo, D. A. Miller, and T. The AP clutter mitigation scheme. Preprints, 30th Int. Conf. on O'Bannon, 1998: The WSR-88D rainfall algorithm. Wea. Fore- Radar Meteorology, Munich, Germany, Amer. Meteor. Soc., casting, 13, 377±395. 707±709. Geotis, S. G., and W. M. Silver, 1976: An evaluation of techniques Kitchen, M., R. Brown, and A. G. Davies, 1994: Real-time correction for automatic ground-echo rejection. Preprints, 17th Conf. on of weather radar data for the effects of bright band, range and Radar Meteorology, Seattle, WA, Amer. Meteor. Soc., 448±452. orographic growth in widespread precipitation. Quart. J. Roy. Giorgi, F., L. O. Mearns, C. Shields, and L. Mayer, 1996: A regional Meteor. Soc., 120, 1231±1254. model study of the importance of local versus remote controls Klingle-Wilson, D., E. Mann, and R. Boldi, 1995: An algorithm to of the 1988 drought and the 1993 ¯ood over the central United remove anomalous propagation clutter returns from ASR-9 States. J. Climate, 9, 1150±1162. weather channel data using pencil beam radar data. Preprints, Giuli, D., M. Gherardelli, A. Freni, T. A. Seliga, and K. Aydin, 1991: Sixth Conf. on Aviation Weather Systems, Dallas, TX, Amer. Rainfall and clutter discrimination by means of dual-linear po- Meteor. Soc., 366±371. larization radar measurements. J. Atmos. Oceanic Technol., 8, Kunkel, K. E., S. A. Changnon, and J. R. Angel, 1994: Climatic 777±789. aspects of the 1993 upper Mississippi River basin ¯ood. Bull. Gossard, E. E., 1977: Refractive index variance and its height dis- Amer. Meteor. Soc., 75, 811±822. tribution in different air masses. Radio Sci., 12, 89±105. Lee, R., G. Della Bruna, and J. Joss, 1995: Intensity of ground clutter ÐÐ, R. G. Strauch, B. B. Stankov, and D. E. Wolfe, 1995: Mea- and of echoes of anomalous propagation and its elimination. surement of property gradients and turbulence aloft with ground- Preprints, 27th Conf. on Radar Meteorology, Vail, CO, Amer. based Doppler radars. NOAA Tech. Memo. ERL 453-ETL 67, Meteor. Soc., 651±652. Environmental Technology Laboratory, Boulder, CO, 31 pp. Mann, D. R., J. E. Evans, and M. W. Merritt, 1986: Clutter suppression Grecu, M., and W. F. Krajewski, 1999: Detection of anomalous prop- for low altitude wind shear detection by Doppler weather radars. agation echoes in weather radar data using neural networks. IEEE Preprints, 23d Conf. on Radar Meteorology, Vol. 1, Snowmass, Trans. Geosci. Remote Sens., 37, 287±296. CO, Amer. Meteor. Soc., R9±R13. ÐÐ, and ÐÐ, 2000: An ef®cient methodology for detection of Michelson, D. B., and T. Andersson, 1995: Identi®cation and sup- anomalous propagation echoes in radar re¯ectivity data using pression of anomalous propagation echoes in two-dimensional neural networks. J. Atmos. Oceanic Technol., 17, 121±129. radar images. Preprints, 27th Conf. on Radar Meteorology, Vail, Gumley, L. E., and M. D. King, 1995: Remote sensing of ¯ooding CO, Amer. Meteor. Soc., 656±658. in the U.S. upper Midwest during the summer of 1993. Bull. Moores, W., and T. W. Harrold, 1975: Estimating the distribution and Amer. Meteor. Soc., 76, 933±943. intensity of ground clutter. Preprints, 16th Radar Meteorology Haddad, B., A. Adane, F.Mesnard, and H. Sauvageot, 2000: Modeling Conf., Houston, TX, Amer. Meteor. Soc., 370±373.

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Moszkowicz, S., G. J. Ciach, and W. F. Krajewski, 1994: Statistical Meteorology Conf., Champaign±Urbana, IL, Amer. Meteor. Soc., detection of anomalous propagation in radar re¯ectivity patterns. 222±227. J. Atmos. Oceanic Technol., 11, 1026±1034. Seo, D. J., J. Breidenbach, R. Fulton, D. Miller, and T. O'Bannon, Mueller, E. A., and A. L. Sims, 1975: Statistics of high radar re¯ec- 2000: Real-time adjustment of range-dependent biases in WSR- tivity gradients. Preprints, 16th Radar Meteorology Conf., Hous- 88D rainfall estimates due to nonuniform vertical pro®le of re- ton, TX, Amer. Meteor. Soc., 401±403. ¯ectivity. J. Hydrometeor., 1, 222±240. Pamment, J. A., and B. J. Conway, 1998: Objective identi®cation of Sirmans, D., and J. T. Dooley, 1980: Ground clutter statistics of a 10 echoes due to anomalous propagation in weather radar data. J. cm ground based radar. Preprints, 19th Conf. on Radar Meteo- Atmos. Oceanic Technol., 15, 98±113. rology, Miami Beach, FL, Amer. Meteor. Soc., 184±191. Pappert, R. A., and C. L. Goodhart, 1977: Case studies of beyond- Skolnik, M. I., 1980: Introduction to Radar Systems. 2d ed. McGraw- the-horizon propagation in tropospheric ducting environments. Hill International Editions, 581 pp. Radio Sci., 12, 75±87. Smith, J. A., D. J. Seo, M. L. Baeck, and M. D. Hudlow, 1996: An Passarelli, R. E., Jr., 1981: Autocorrelation techniques for ground intercomparison study of NEXRAD precipitation estimates. Wa- clutter rejection. Preprints, 20th Conf. on Radar Meteorology, ter Resour. Res., 32, 2035±2045. Boston, MA, Amer. Meteor. Soc., 308±313. Smith, P. L., Jr., 1972: Siting considerations for weather radars. Pre- ÐÐ, P. Romanik, S. G. Geotis, and A. D. Siggia, 1981: Ground clutter prints, 15th Radar Meteorology Conf., Champaign±Urbana, IL, rejection in the frequency domain. Preprints, 20th Conf. on Radar Amer. Meteor. Soc., 99±100. Meteorology, Boston, MA, Amer. Meteor. Soc., 295±300. ÐÐ, 1990: Precipitation measurement and hydrology: Panel report. Pratte, J. F., and R. J. Keeler, 1986: Sensitivities of operational weath- Radar in Meteorology, D. Atlas, Ed., Amer. Meteor. Soc., 607± er radars. Preprints, Joint Sessions 23d Conf. on Radar Mete- 618. orology and Conference on Cloud Physics, Vol. 3, Snowmass, ÐÐ, 1993: An update on weather radar system sensitivity. Preprints, CO, Amer. Meteor. Soc., JP333±JP336. 26th Int. Conf. on Radar Meteorology, Norman, OK, Amer. Me- ÐÐ, R. Gagnon, and R. Cornelius, 1993: Ground clutter character- teor. Soc., 384±386. istics and residue mapping. Preprints, 26th Int. Conf. on Radar ÐÐ, 1998: On the minimum useful elevation angle for weather Meteorology, Norman, OK, Amer. Meteor. Soc., 50±52. surveillance radar scans. J. Atmos. Oceanic Technol., 15, 841± ÐÐ, R. J. Keeler, R. Gagnon, and D. Sirmans, 1995: Clutter pro- 843. cessing during anomalous propagation conditions. Preprints, Steiner, M., J. A. Smith, S. J. Burges, C. V. Alonso, and R. W. Darden, 27th Conf. on Radar Meteorology, Vail, CO, Amer. Meteor. Soc., 1999a: Effect of bias adjustment and rain gauge data quality 139±141. control on radar rainfall estimation. Water Resour. Res., 35, Puzzo, D. C., S. W. Troxel, M. A. Meister, M. E. Weber, and J. V. 2487±2503. Pieronek, 1989: Appendix I: Methods for ®ltering clutter as a ÐÐ, ÐÐ, C. Kessinger, and B. S. Ferrier, 1999b: Evaluation of result of anomalous propagation. ASR-9 Weather Channel Test algorithm parameters for radar data quality control. Preprints, Report. Project Rep. ATC-165, Lincoln Laboratory, MIT, Lex- 29th Int. Conf. on Radar Meteorology, Montreal, QC, Canada, ington, MA, 155±170. Amer. Meteor. Soc., 582±585. Reid, J. D., 1970: Decorrelation time in weather radar signals. Pre- Tatehira, R., and T. Shimizu, 1978: Intensity measurement of preprints, 14th Radar Meteorology Conf., Tucson, AZ, Amer. Me- cipitation echo superposed on ground clutterÐA new automatic teor. Soc., 419±420. technique for ground clutter rejection. Preprints, 18th Conf. on Riley, G. F., Jr., and P. M. Austin, 1976: Some statistics of gradients Radar Meteorology, Atlanta, GA, Amer. Meteor. Soc., 364±369. of precipitation intensity. Preprints, 17th Conf. on Radar Me- ÐÐ, and ÐÐ, 1980: Improvements in performance of ground clutter teorology, Seattle, WA, Amer. Meteor. Soc., 492±497. rejection. Preprints, 19th Conf. on Radar Meteorology, Miami Rinehart, R. E., 1991: Antenna rotation-produced moving sidelobe Beach, FL, Amer. Meteor. Soc., 176±179. Torres, S. M., and D. S. Zrnic, 1999: Ground clutter canceling with echoes from ground clutter with a Doppler radar. Preprints, 25th a regression ®lter. J. Atmos. Oceanic Technol., 16, 1364±1372. Int. Conf. on Radar Meteorology, Paris, France, Amer. Meteor. Trapp, R. J., and C. A. Doswell III, 2000: Radar data objective anal- Soc., 339±342. ysis. J. Atmos. Oceanic Technol., 17, 105±120. Robinson, M., M. Steiner, D. B. Wolff, C. Kessinger, and R. A. Fulton, VanAndel, J., 2001: Radar echo classi®er algorithm development us- 2001: Radar data quality control: Evaluation of several algo- ing Python. Preprints, 30th Int. Conf. on Radar Meteorology, rithms based on accumulating rainfall statistics. Preprints, 30th Munich, Germany, Amer. Meteor. Soc., 704±706. Int. Conf. on Radar Meteorology, Munich, Germany, Amer. Me- Vignal, B., H. Andrieu, and J. D. Creutin, 1999: Identi®cation of teor. Soc., 274±276. vertical pro®les of re¯ectivity from volume scan radar data. J. Rosenfeld, D., E. Amitai, and D. B. Wolff, 1995: Classi®cation of Appl. Meteor., 38, 1214±1228. rain regimes by the three-dimensional properties of re¯ectivity ÐÐ, G. Galli, J. Joss, and U. Germann, 2000: Three methods to ®elds. J. Appl. Meteor., 34, 198±211. determine pro®les of re¯ectivity from volumetric radar data to Ryzhkov, A. V., and D. S. Zrnic, 1998: Polarimetric rainfall estimation correct precipitation estimates. J. Appl. Meteor., 39, 1715±1726. in the presence of anomalous propagation. J. Atmos. Oceanic Weber, M. E., M. L. Stone, and J. A. Cullen, 1993: Anomalous prop- Technol., 15, 1320±1330. agation associated with thunderstorm out¯ows. Preprints, 26th SaÂnchez-Diezma, R., D. Sempere-Torres, G. Delrieu, and I. Zawadzki, Int. Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. 2001: An improved methodology for ground clutter substitution Soc., 238±240. based on a pre-classi®cation of precipitation types. Preprints, Wilson, J. W., and W. E. Schreiber, 1986: Initiation of convective 30th Int. Conf. on Radar Meteorology, Munich, Germany, Amer. storms at radar-observed boundary-layer convergence lines. Meteor. Soc., 271±273. Mon. Wea. Rev., 114, 2516±2536. Schmid, W., D. HoÈgl, A. Vckovski, and J. Joss, 1991: Test of clutter ÐÐ, N. A. Crook, C. K. Mueller, J. Z. Sun, and M. Dixon, 1998: suppression techniques in the Swiss Alps. Preprints, 25th Int. Nowcasting thunderstorms: A status report. Bull. Amer. Meteor. Conf. on Radar Meteorology, Paris, France, Amer. Meteor. Soc., Soc., 79, 2079±2099. 875±878. Zrnic, D. S., and R. V. Ryzhkov, 1996: Advantages of rain mea- Sekhon, R. S., and D. Atlas, 1972: Doppler radar structure of the surements using speci®c differential phase. J. Atmos. Oceanic melting zone and ground clutter effects. Preprints, 15th Radar Technol., 13, 454±464.

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