1. Introduction
Through several decades of studies, researchers worldwide have demonstrated the ability of polarimetric radars to provide improved rainfall estimates (e.g., Seliga and Bringi 1976, 1978; Ulbrich and Atlas 1984; Sachidananda and Zrnić 1987; Brandes et al. 2002; Ryzhkov et al. 2005a), classification of bulk hydrometeor characteristics (e.g., Hall et al. 1980, 1984; Höller et al. 1994; Vivekanandan et al. 1999; Liu and Chandrasekar 2000; Straka et al. 2000; Zrnić et al. 2001; Schuur et al. 2003a), and better data quality through the recognition and elimination of nonmeteorological echoes (e.g., Ryzhkov et al. 2002, 2005c; Zrnić and Ryzhkov 1999). These results, however, were mostly obtained using much different scanning strategies than required by Weather Surveillance Radar-1988 Doppler (WSR-88D) guidelines.
In the spring of 2003, the National Severe Storms Laboratory (NSSL) in Norman, Oklahoma, conducted the Joint Polarization Experiment (JPOLE), which was designed in part to test in an operational environment the utility and added value of real-time polarimetric data and products collected by a prototype polarimetric WSR-88D radar (hereafter referred to as KOUN). This paper reports on the results of the JPOLE operational demonstration, during which real-time polarimetric data and products were delivered to forecasters at the Norman, Oklahoma, National Weather Service Weather Forecast Office (WFO). An overview of JPOLE as a whole, including a discussion of polarimetric rainfall estimation and hydrometeor discrimination techniques, can be found in Ryzhkov et al. (2005c).
In preparation for JPOLE, polarimetric radar data and products from the NSSL Cimarron research polarimetric radar were delivered to the WFO beginning in the spring of 2001. The data feed was switched to the KOUN radar in the spring of 2002, upon completion of the polarimetric upgrade (Melnikov et al. 2003). After approximately 3 months of evaluation and testing, the first high quality KOUN dataset was delivered to the WFO on 16 June 2002. Fairly regular real-time data delivery began that fall. Data were delivered on an event-driven basis through the winter of 2002/03, as work continued to enhance algorithm performance and streamline the real-time data processing and delivery system. All data delivered during this early JPOLE period were collected with volume coverage patterns (VCPs) that included only a few low-altitude elevation angles as the radar was still in test and evaluation mode. Much of the early data analysis focused on developing techniques to assure high quality radar calibration.
The JPOLE intense observation period (IOP) was conducted from 15 March through 15 June 2003 in an effort to provide data in real time to forecasters on a regular basis, and to gather their feedback and evaluation. VCPs used during the IOP were designed to emulate the elevation angles, scanning rates, and volume coverage times of the standard WSR-88D (up to 14 elevation angles every 5–6 min). In addition to the qualitative and quantitative analyses of polarimetric base data and algorithms, the data were examined to assure no degradation to conventional reflectivity and velocity information occurred.
In this paper, the operational benefits provided by polarimetric radar data and products are demonstrated. These benefits include immunity of polarimetric rainfall estimators to partial beam blockage, mitigation of brightband contamination and drop size distribution variability in rainfall estimation, and the ability to remotely identify bulk particle information, improving detection of hail, tornadoes, winter storms, and nonmeteorological echoes. Examples of enhancements to WFO warnings and forecasts will show the operational benefits radar polarimetry can provide. Finally, other possible applications in operational meteorology are discussed.
2. Overview of JPOLE data collection and operational delivery
a. KOUN data
Several “base” polarimetric products from KOUN were delivered to WFO forecasters during JPOLE. Differential reflectivity (ZDR) is the reflectivity-weighted mean axis ratio of scatterers in a sample volume. Negative values of ZDR can denote vertically oriented scatterers or ground clutter returns, while positive values signify oblate shapes with horizontal orientation. The correlation coefficient (ρhv) describes the similarities in the backscatter characteristics of the horizontally and vertically polarized echoes. Progressively smaller ρhv values indicate a progressively greater mixture of scatterer shapes, sizes, orientations, and eccentricities. Finally, specific differential phase shift (KDP) describes the difference between propagation constants for horizontally and vertically polarized radar echoes over a given range interval. The KDP values for isotropic bulk scatterers, such as falling hail, are typically near 0° km−1, but can become quite large (to over 4° km−1) in heavy rain. A summary of polarimetric variables and their relationships to bulk hydrometeor properties is provided by Zrnić and Ryzhkov (1999).
As noted in Ryzhkov et al. (2005c), the KOUN data archive contains a collection of 98 cases, including both warm and cold season weather events, as well as meteorological and nonmeteorological echoes. Among the warm season events are tornadic events on two consecutive days in the Oklahoma City area, several nontornadic supercells, a severe storm that produced hail over 13 cm in diameter, numerous other damaging hail events, and convective cells producing high rainfall rates. Among the cold season events are a major winter storm with mixed precipitation types, two significant snowfall events on consecutive days, and several systems producing stratiform precipitation and “brightband” reflectivity signatures. The database provides a unique opportunity to examine the accuracy and skill of the quantitative precipitation estimation algorithms (QPEAs) and hydrometeor classification algorithm (HCA) on storms having significant economic and societal impacts. Furthermore, since several tornadoes occurred near the KOUN radar, these data set the stage for studying the potential use of polarimetric signatures to improve tornado warning lead time.
A variety of nonmeteorological echoes were also observed by KOUN, including those due to anomalous propagation, birds, insects, and chaff. Many of these nonmeteorological echoes were observed by KOUN in clear air conditions (15 events), whereas others were concurrent with precipitation (31 events). Some of the nonmeteorological echoes were embedded within precipitation while others were not. This archive provides ample data to establish the efficacy of the dual-polarimetric-based HCA in identifying and mitigating the effects of ground clutter, anomalous propagation, and biological scatterers. The HCA uses “fuzzy logic” to determine the bulk scatterer characteristics in a radar volume using polarimetric base data, as described by Schuur et al. (2003a) and Ryzhkov et al. (2005c).
Verification of observed polarimetric signatures was a major focus of JPOLE. Two hail-intercept vehicles were available from 28 April through 13 June 2003. During the course of the project, cars were staffed by University of Oklahoma students and two members of the operational demonstration team. The purpose of the hail-chase effort was to intercept thunderstorm cores that had the potential to produce hail at the surface. Observations from the chase teams were compared with KOUN HCA output at low levels to verify the algorithm’s ability to discriminate between rain and hail. The vehicles collected more than 28 h of data on five separate days, and these data continue to aid in the analysis of JPOLE datasets and will lead to improvements in the HCA (Heinselman and Ryzhkov 2004).
b. Operational data delivery
Since the insight of operational forecasters was vital to the evaluation of polarimetric WSR-88D products and display concepts, great emphasis was placed on forecaster interactions during the IOP. NSSL observers were scheduled to assist WFO forecasters in the analysis and interpretation of the polarimetric radar data and products for each significant weather event. Feedback and comments from WFO forecasters were compiled from evaluation forms that were designed to determine the usefulness and performance of each polarimetric measurement and product (Schuur et al. 2003b). This information was used to improve polarimetric system performance.
Table 1 describes the KOUN data and products delivered to WFO forecasters. These data were successfully delivered to the WFO for about 480 h during the IOP. The data were viewed at the WFO on two Linux workstations that ran the NSSL Warning Decision Support System-Integrated Information (WDSS-II) software package (Hondl 2002). An NSSL representative was at the WFO for at least a portion of 22 separate significant weather events.
During severe weather events, the NSSL representative would simultaneously observe the data and update forecast staff on polarimetric signatures and products potentially useful in the warning decision process. The WFO and NSSL meteorologists were asked to record instances of KOUN data integration into WFO operations, note polarimetric characteristics in hazardous weather (including winter storms, flooding rains, squall lines, supercells, tornadoes, and hail), and evaluate the performance of the polarimetric QPEAs and HCA. Additionally, NSSL meteorologists were asked to coordinate KOUN usage and calibration issues with radar operators, assist WFO staff in collecting verification information, and give “on the fly” training to forecasters on polarimetric theory, signatures, and the WDSS-II software.
3. Rainfall estimation
In their written feedback, WFO forecasters agreed that polarimetric radar provided significantly improved rainfall accumulation estimates during JPOLE (Schuur et al. 2003b), primarily through real-time comparison with Oklahoma Mesonet reports. Several forecasters remarked that this capability will likely be a significant benefit to operational meteorology. Forecasters used and evaluated the traditional R(Z) QPEA, which estimates rain rate R using only reflectivity Z, and three QPEAs using polarimetric variables: one using reflectivity and differential reflectivity ZDR, R(Z, ZDR); a second using only specific differential phase shift KDP, R(KDP); and a third using specific differential phase shift and differential reflectivity, R(KDP, ZDR). After the JPOLE IOP, a “synthetic” R(Z, KDP, ZDR) algorithm was developed, as described by Ryzhkov et al. (2005a), which uses R(Z, ZDR) in areas of light rain (Z < 36 dBZ), R(KDP, ZDR) in moderate rain (36 dBZ ≥ Z ≥ 49 dBZ), and R(KDP) in areas of heavy rain (Z > 49 dBZ).
Experience during JPOLE suggests polarimetric radar QPEAs outperform the traditional R(Z) algorithm in several regimes. Later statistical study of the JPOLE data confirmed these observations (Ryzhkov et al. 2005c). First, brightband echoes, or regions of enhanced radar reflectivity associated with the melting of snow and graupel aloft, typically cause an overestimate of rain amounts by the traditional R(Z) algorithm. Second, R(Z) typically underestimates precipitation due to attenuation, which is frequently observed when the radar signal propagates through heavy precipitation or when the radome is wet. Finally, the R(Z) relation is often variable in both time and space due to variability in the drop size distribution (DSD). Since polarimetric variables help mitigate uncertainties due to DSD variability and hail contamination, polarimetric QPEAs showed much improved rainfall estimation after verification with Oklahoma Mesonet information (Ryzhkov et al. 2005c). This is a major advantage in regions of complex precipitation regimes (e.g., in mixtures of stratiform and convective precipitation or in regions with hail).
The flexibility and superior performance of polarimetric QPEAs might have major implications for operational meteorologists and users of their products. In warning for flash floods, forecasters will potentially be able to exhibit better confidence in the provided radar rainfall estimates and reduce false alarms. The polarimetric QPEA output from a rainfall event might be used more effectively in water resource management and remote estimations of soil moisture content. These improvements may help hydrological models produce better flash-flood guidance and river-forecast information, aiding forecasts for flooding in the 1–3-day period. Three cases during JPOLE highlight the advantages of polarimetric rainfall estimation.
a. 18–20 October 2002: Heavy rain and brightbanding
A heavy rain event in the Red River valley of southern Oklahoma, a sensitive area for water resource management and hydrologic modeling, began late on 18 October 2002 and ended early on 20 October, in a region centered about 180 km south and southeast of KOUN. The bulk of the heavy rain, with reported amounts up to 80 mm (3.15 in.), fell during the daylight hours of 19 October. Conventional R(Z) estimates were considerably higher than the polarimetric R(KDP) estimates in this case (Fig. 1); R(Z) values near 150 mm and R(KDP) values near 80 mm were observed in the vicinity of the highest reported rainfall total. Further, the R(Z) algorithm suffered from regions of partial beam blockage, revealed by azimuths with anomalously low R compared to adjacent azimuths. Note that KDP is immune to partial beam blockage, so the R(KDP) algorithm did not show such abnormalities.
As a result of the enhanced reflectivity in the brightband, the R(Z) estimates were significantly inflated above the reported totals. Although the R(Z) estimated rain accumulations were near or slightly above the “flash-flood guidance” available to forecasters (rainfall rates estimated to be sufficient to exceed threshold runoff values and cause local flash flooding, as determined by National Weather Service River Forecast Centers), forecasters reported they were able to use the polarimetric QPEA output to be confident flash flooding was not a major concern; forecasters could instead focus staffing resources on other forecast issues. With the polarimetric data, users could be more confident in rainfall accumulations than they could be with only R(Z) data and avoid overforecasting the resultant river and lake levels.
b. 8 September 2002: Rain in a “tropical” environment
The air mass over southern Oklahoma on 8 September 2002 was diagnosed by forecasters as maritime tropical, originating over the Gulf of Mexico. Observed regional soundings depicted a layer characterized by high humidity, warm temperatures, and marginal lapse rates, with observed precipitable water values above 50 mm (far above the climatological average for the date). Scattered thunderstorms and larger areas of light, stratiform precipitation were observed. The R(Z, KDP, ZDR) algorithm showed important differences from the traditional R(Z) algorithm (Fig. 2). In areas affected by convection, R(Z, KDP, ZDR) showed up to 40% higher rainfall accumulation than R(Z). On the other hand, during the same period, the R(Z, KDP, ZDR) algorithm showed up to 25% less accumulation than R(Z) in regions characterized by lighter, stratiform precipitation. Available gauge measurements showed better agreement with R(Z, KDP, ZDR) algorithm output than R(Z) output.
The different behaviors of the algorithms in this case are inferred by the DSDs typical of precipitation in tropical air masses (Bringi et al. 2003). The R(Z) algorithm underestimated the rainfall accumulations in the convective regions due to a DSD characterized by a relatively high concentration of small drops compared to typical continental convection. The R(Z) algorithm overestimated accumulations in the stratiform region where the DSD was estimated to be dominated by a relatively sparse concentration of larger drops. In cases of light ambient winds and a tropical air mass, heavy precipitation may persist in some areas for many hours, and these R(Z) errors can accumulate to become quite large. These improvements can give forecasters and other data users better confidence in remote rainfall accumulation estimation in tropical environments than the R(Z) estimator alone can provide.
c. 14 May 2003: Heavy rain and hail in supercellular convection
A northwest–southeast-oriented line of supercell thunderstorms was observed by KOUN on the morning of 14 May 2003. These thunderstorms produced wide swaths of damaging hail over a 6-h period. The supercells moved from northwest to southeast, so some locations were affected by several different supercells in a short amount of time, and flash flooding became a major warning concern at the WFO. The conventional R(Z) algorithm showed accumulations 50%–75% higher than R(Z, KDP, ZDR) in the heavy rain areas (Fig. 3). The large hail in this case caused the R(Z) algorithm to severely overestimate the rain rate (despite the 53-dBZ “cap” on reflectivity in the R(Z) relation). As discussed in Ryzhkov et al. (2005b), in heavy rain rates the rainfall estimator R(KDP) is most reliable, and was the output used by forecasters during the 14 May 2003 event.
The amounts depicted by R(Z) in the 14 May case reached or slightly exceeded the flash-flood guidance values available to WFO forecasters. Forecasters were able to use the superior polarimetric QPEA output to be assured the flash-flooding threat did not require the issuance of warnings, and no flash flooding was reported.
4. Signatures related to deep moist convection
a. The “ZDR column” signature
Another unique polarimetric radar signature often observed is a vertical area of enhanced differential reflectivity above the ambient melting level, often called a ZDR column (e.g., Bringi et al. 1991; Brandes et al. 1995). The ZDR column is a region of enhanced ZDR values extending above the freezing level, associated with rising motion. Enhanced ZDR (frequently 2–5 dB) and low Z (usually 35–50 dBZ) relative to surrounding areas imply the presence of oblate hydrometeors (liquid drops), and in situ data confirm the presence of low concentrations of 1–3-mm-diameter liquid drops or drops with ice cores and rising motion within ZDR columns, partially from drops recirculated from melted graupel and hail (Loney et al. 2002; Schlatter 2003, among others).
The precise placement of ZDR columns relative to the updraft differs from storm to storm. For example, Bringi et al. (1991) studied a Florida multicell storm and found the ZDR columns and main updrafts collocated, while studies from supercells in Oklahoma and Colorado have found a ZDR column on a flank of the main updraft (e.g., Conway and Zrnić 1993; Askelson et al. 1998; Hubbert et al. 1998). The drops within any ZDR column are either advected into an updraft from elsewhere below the 0°C level, or may grow in situ. The ZDR columns are therefore good indicators of regions of updraft in any particular storm, and the farther above the 0°C level the column extends, the more vigorous the updraft.
The case of the afternoon of 10 May 2003 is an excellent example of the operational utility of the “ZDR column” signature (Fig. 4). Available model guidance and observational data indicated that severe storms were likely to develop along a dryline in central Oklahoma during the afternoon hours. If storms developed, very favorable low-level wind shear and thermodynamic profiles were expected to sustain significant tornadic supercells in the region for a third consecutive day. Thus, before and during the beginning stages of the 10 May event, WFO forecasts reflected a substantial threat of “high end” severe weather, including the possibility of long-track and violent tornadoes. Forecasters were also more likely to issue tornado warnings earlier and at lower radar-based thresholds than on most other typical severe weather days.
Although thunderstorms did develop and propagate off the dryline into the warm sector as expected, forecasters using KOUN data noted that ZDR columns associated with strong convective updrafts weakened as they propagated east and away from the dryline circulation (Fig. 4a), and did not resemble the well-defined ZDR columns typically associated with intense supercell updrafts (e.g., Fig. 4b). This strongly suggested the vigorous updrafts that were being forced by the dryline circulation were unable to be maintained in the warm sector, despite the continuing high-reflectivity values observed in some storm cells. Within 2 h after initial storm development, WFO forecasters had deduced something was wrong with the initial forecast of a high-end event, and enhanced situation awareness aided by KOUN data initiated a rapid and significant change in the forecast philosophy. Forecasts from WFO Norman were changed from ones representative of a high-end severe weather day to ones more representative of a typical severe weather day. Accomplishing such a successful change in overall office forecast philosophy is often a very challenging goal to achieve in midevent in a timely manner (Quoetone et al. 2001).
b. Polarimetric hail detection
Researchers have long demonstrated an enhanced ability to discriminate hail using polarimetric radar data (e.g., Aydin et al. 1986; Balakrishnan and Zrnić 1990; Bringi and Chandrasekar 2001). Hail is usually recognized by low ZDR combined with high Z, due to the bulk isotropic profile of hailstones in freefall. In many cases storms with high Z values exhibit a local minimum in ZDR, indicating the presence of hail, while storms with similar Z characteristics do not show a minimum in ZDR and likely do not contain significant hail. In the case of Fig. 5, at 2300 UTC on 10 June 2003, the thunderstorm’s core is marked by reflectivity to 60 dBZ, corresponding with a local minimum in ZDR. HCA output alerted forecasters to the presence of hail, and hail larger than 2 cm in diameter was observed by one of the JPOLE hail-intercept vehicles in the vicinity of the signature from 2327 to 2339 UTC.
These signatures allow forecasters and the HCA to determine with higher confidence whether hail is present. Typically, ρhv is also reduced in rain–hail mixtures due to diverse hydrometeor shapes and sizes, and in areas of large hail due to resonance scattering. One storm observed during JPOLE, responsible for producing hail at the surface over 13 cm (5.25 in.) in diameter, exhibited Z values near 70 dBZ, ZDR near 0.5 dB, and ρhv as low as 0.7. This combination of values strongly suggested the presence of giant, water-coated hail, and forecasters issued strongly worded warnings, alerting users to the likelihood of destructive hail.
While traditional WSR-88D hail detection algorithms, by design, use empirical information to determine hail probability, polarimetric variables can yield a three-dimensional explicit mapping of likely hail location, allowing more specific hail threat information to be passed from forecasters to users. Figure 6 illustrates how the legacy and polarimetric algorithms compared during one JPOLE event. While the legacy WSR-88D hail detection algorithm determined a 100% probability of hail associated with a large severe thunderstorm, the HCA output correctly suggested the near-surface hail was confined to a region away from the JPOLE intercept vehicle.
During JPOLE, the HCA offered forecasters algorithm guidance with explicit mapping of hail location, and scored a higher probability of detection and lower false alarm rate than the legacy hail algorithm. Quantitative analyses of HCA skill can be found in Ryzhkov et al. (2005c) and Heinselman and Ryzhkov (2004). Using the JPOLE dataset, investigators are also attempting to employ polarimetric variables in the improvement of hail size estimation and forecasting.
c. Tornado debris signature
Polarimetric radar information can help alert forecasters to the presence of tornadic debris. Significant tornadoes struck the Oklahoma City area twice in a 30-h period, on 8 and 9 May 2003 (10 May UTC). Both of these tornadoes were relatively close to the radar and produced well-defined debris signatures in the polarimetric and conventional reflectivity fields, as discussed in detail by Ryzhkov et al. (2005b). It is presently unclear to what distance and tornado intensity this signature can be expected to be identifiable.
The 8 May 2003 tornado lofted substantial amounts of debris within 15 km of the radar, allowing very easy identification. The second tornado occurred after local sunset in an area with numerous trees, thus storm spotters faced difficulty confirming the presence of a tornado. Although a tornado warning was already in effect when the signature was observed in both cases, the debris detection increased confidence at the WFO that a damaging tornado was in progress, and forecasters were able to use enhanced wording in follow-up statements.
5. Winter storms
Operational advantages of polarimetric radar are not limited to warm season convective events. The altitude of the melting layer or freezing level can be critical to operational meteorologists needing to forecast precipitation type (Scharfenberg and Maxwell 2003), and can be well observed by polarimetric radar (Ikeda and Brandes 2003). Such capability may also prove critical in determining the altitude of the snow level in mountainous terrain.
Polarimetric classification capability proved to be particularly beneficial during a winter storm on 24–25 February 2003 in southern Oklahoma. Snow was the primary precipitation type produced by this storm in the affected areas of southern Oklahoma, with a 65-km-wide area where storm total accumulations were in excess of 10 cm (4 in.), and maximum amounts were near 25 cm (10 in.). Despite some sampling limitations due to range, real-time evaluation of KOUN data was quite helpful to WFO forecasters during this event (Miller and Scharfenberg 2003).
The synoptic situation was typical for a heavy snow event in Oklahoma (Branick 1985): a strong baroclinic zone extended from northwest Texas into Arkansas, with a midtropospheric short-wave trough moving eastward into the region. Although only a weak surface reflection of this feature was noted, lower- to midtropospheric deformation within the baroclinic zone resulted in strong frontogenetic forcing and ascent. However, up until the onset of precipitation, forecast confidence in heavy snow accumulation was low, due to forecast thermodynamic profiles that were considered borderline regarding precipitation type. Forecasters initially issued a winter weather advisory at 2036 UTC on 24 February for ∼2.5–5 cm (1–2 in.) of total snow accumulation in southern Oklahoma, with a significant amount of the precipitation expected to fall as freezing rain and/or ice pellets, greatly limiting snow accumulations. Forecasters did acknowledge, however, that sufficient atmospheric cooling due to strong deep-layer ascent, and/or diabatic cooling from melting snow (Kain et al. 2000), could change the dominant precipitation type to snow.
Precipitation developed over southern Oklahoma during the mid- to late-afternoon hours on 24 February, and rapidly increased in areal coverage and intensity between 2030 and 2130 UTC. During this time, forecasters monitored both traditional WSR-88D imagery from Oklahoma City, Oklahoma (KTLX), and Z, ZDR, and ρhv data from KOUN (Fig. 7a). Several calls were made to spotters to ascertain precipitation type. During the early stages of the event, one spotter report confirmed a mixture of freezing rain, ice pellets, and snow (marked by the arrow in Fig. 7a).
Over the next 2 h (Fig. 7b), forecasters observed a decrease in ZDR and increase in ρhv, signaling a change in the precipitation regime from mixed phase to a single precipitation type. The changes in KOUN polarimetric radar data prompted additional calls to spotters, one of whom confirmed most of the precipitation had changed to snow by 2345 UTC (arrows in Fig. 7b). WFO forecasters successfully upgraded to a heavy snow warning based on this new information. Up to 25 cm (10 in.) of snow was reported, a climatologically rare event in the southern part of Oklahoma (Branick 2002), underscoring the importance of an early warning. KOUN’s depiction of the rapidly evolving precipitation type in this case led to greatly increased situation awareness and likely resulted in several hours additional lead time in upgrading to a heavy snow warning.
Polarimetric radar data also may be used to differentiate between regions of snow dominated by dry crystals and regions dominated by wet aggregates. These different types of crystals have very different liquid equivalents (Ryzhkov et al. 1998) for a given radar reflectivity factor, and the ability to discriminate between them using polarimetric variables will improve the performance of radar-based snow accumulation algorithms and potentially aid in detecting conditions favorable for hazardous aircraft icing. Such improvements may have major benefits in water resource management, particularly in areas dependent on seasonal snowmelt.
6. Benefits and other applications
Polarimetric radar data provided numerous benefits to Norman, Oklahoma, WFO forecasters during JPOLE. The explicit depiction of features such as hail cores, tornadic debris, and heavy rain cores, which are much smaller than the overall storm scale, frequently aided in enhanced forecaster situation awareness. Perhaps most importantly, identification of these very small-scale signatures associated with dangerous weather enabled forecasters to issue warnings, statements, and graphical guidance for these events with greater spatial and temporal precision and with a higher level of confidence than ever before.
Other areas of research were too nascent during JPOLE to be tested operationally, but also show potential benefit to the operational community. A small number of observations suggest that the melting hail associated with wet microbursts has an identifiable polarimetric signature (Scharfenberg 2003). The water-coated, melting hail aloft in wet microbursts has a clear signature of enhanced Z, lowered ZDR and ρhv, and very high KDP. Microbursts produced by severe thunderstorms are a difficult warning problem, and such a signature may provide additional warning lead time. In addition, the microphysical evolution near the upshear flank of supercell thunderstorms is being carefully scrutinized to determine if these data may be used to delineate between tornadic and nontornadic supercells. Further, polarimetric data input into storm-scale models may prove helpful in modeling the behavior of supercell convection (Weygandt et al. 2002).
Improvements to radar data quality, including the discrimination of nonmeteorological echoes, are expected to have a wide-ranging impact. For example, many users (aviation and others) want a radar display showing only meteorological echoes. The HCA shows great skill in discriminating the presence of nonmeteorological echoes such as anomalous propagation, birds, and insects (Schuur et al. 2003a); ground and sea clutter (Ryzhkov et al. 2002); and chaff (Zrnić and Ryzhkov 2004). Successfully identifying the presence of birds would allow these scatterers to be removed from velocity azimuth display (VAD) wind profiles, thus eliminating a frequent source of contamination. Partial beam blockage in heavy precipitation often has unwanted effects on radar reflectivity. Differential phase measurements from polarimetric radar may be used to quantify these effects and correct the displayed reflectivity field.
The ability to discriminate nonmeteorological scatterers is well illustrated by the case of 6 February 2003. On that date, a band of snow was moving across northern Oklahoma. A region of echoes that had similar Z characteristics appeared in southwestern Oklahoma (Fig. 8). The polarimetric data revealed these echoes were not meteorological; ρhv values were considerably lower and ZDR considerably higher than in the snow echoes. These echoes were found to be the result of chaff released upstream.
7. Concluding remarks
Data delivered to the National Weather Service Weather Forecast Office (WFO) in Norman, Oklahoma, during the Joint Polarization Experiment (JPOLE) demonstrated the ability of a polarimetric WSR-88D radar to 1) improve radar rainfall estimation, 2) accurately discriminate hydrometeor type and identify nonmeteorological scatterers, and 3) improve data quality. Polarimetric radar can be a major asset to operational meteorologists on a national scale and benefit users in a variety of other fields.
A final goal of JPOLE was to provide research and analysis that could be used to conduct a cost–benefit study and to demonstrate the economic benefits provided by a potential national network of polarimetric WSR-88D radars. The possible economic benefits of such a network are substantial, and a number of industries can benefit from polarimetric radar. For example, improved detection of nonmeteorological echoes has the potential to greatly enhance data quality and improve rainfall accumulation estimates, aiding hydrologic interests. Improved detection of precipitation type may benefit transportation administration capabilities (through, e.g., airspace recovery times and highway closures).
Feedback from operational users will be an integral part in developing this rapidly emerging technology. On evaluation forms, WFO forecasters strongly agreed polarimetric data were a valuable addition to operations (Schuur et al. 2003b). The polarimetric QPEAs received particularly high marks, and in several cases, forecasters said these algorithms were used specifically in the decision not to issue flash-flood warnings in regions where the traditional R(Z) rainfall accumulations were incorrectly inflated. Several forecasters used ZDR to identify regions of enhanced hail threat and areas of supercooled water above the melting level in convective updrafts. Forecasters showed a great deal of confidence in the HCA output, particularly regarding the discrimination between rain and hail, and between precipitating and nonprecipitating scatterers.
Acknowledgments
This work would not have been possible without the dedicated support from the NSSL and CIMMS/University of Oklahoma staff who maintain and operate the KOUN polarimetric WSR-88D, and the entire group from NSSL, CIMMS, and WFO Norman who participated in JPOLE. Several University of Oklahoma student volunteers collected hail verification data in the field in support of JPOLE. Drs. Alexander Ryzhkov, David Schultz, Kim Elmore, and three anonymous reviewers provided helpful feedback during preparation of this manuscript.
The authors would like to acknowledge funding support for this work from the U.S. National Weather Service, the Federal Aviation Administration, and the Air Force Weather Agency through the NEXRAD Products Improvement Program. This research is partially in response to requirements and funding provided by the Federal Aviation Administration (FAA). The views expressed are those of the authors and do not necessarily represent the official policy or position of the FAA. The CIMMS authors are funded under NOAA–OU Cooperative Agreement NA17RJ1277.
Oklahoma Mesonet data are provided courtesy of the Oklahoma Mesonet, a cooperative venture between Oklahoma State University and the University of Oklahoma. Continued funding for maintenance of the network is provided by the taxpayers of Oklahoma.
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(a) KOUN radar rainfall accumulation estimation using the standard WSR-88D R(Z) relation from 1400 UTC 18 Oct to 1400 UTC 20 Oct 2002, in a region of reflectivity “brightband” echoes. Overlaid numbers indicate rainfall accumulation reports (in.) from rain gauge data courtesy of the Oklahoma Climatological Survey’s Oklahoma Mesonet. (b) As in (a) but for the KOUN polarimetric radar rainfall accumulation estimation using specific differential phase shift, R(KDP). Rainfall estimates based on data collected at approximately 3.5-km-altitude above radar level (ARL) (180-km range at 0.5° elevation angle).
Citation: Weather and Forecasting 20, 5; 10.1175/WAF881.1
(a) KOUN radar rainfall accumulation estimation using the standard WSR-88D R(Z) relation from 1720 to 1820 UTC 8 Sep 2002, depicting precipitation areas in a tropical air mass. Circles represent areas of convection and arrows represent regions of stratiform precipitation. (b) As in (a) but for the KOUN polarimetric R(Z, KDP, ZDR) algorithm. Rainfall estimates based on data collected at approximately 3.5-km-altitude ARL (180-km range at 0.5° elevation angle).
Citation: Weather and Forecasting 20, 5; 10.1175/WAF881.1
(a) KOUN radar rainfall accumulation using the standard WSR-88D R(Z) relation, from 0653 to 0953 UTC 14 May 2003, in a region of supercell convection. Overlaid numbers indicate rainfall accumulation reports (in.) from rain gauge data courtesy of the Oklahoma Climatological Survey’s Oklahoma Mesonet. (b) As in (a) but for the KOUN polarimetric R(Z, KDP, ZDR) algorithm. Rainfall estimates based on data collected at approximately 0.4-km-altitude ARL (40-km range at 0.5° elevation angle).
Citation: Weather and Forecasting 20, 5; 10.1175/WAF881.1
(a) PPI of reflectivity (Z, left) at 3.5° elevation (92-km range, about 6.0 km ARL) and associated cross section of differential reflectivity (ZDR, right) for a severe nonsupercell thunderstorm at 1831 UTC 10 May 2003. According to a sounding launched from KOUN at 1700 UTC, the temperature at 6.0-km altitude [1.5 km above the 0°C level (dashed white line)] was −11°C. (b) As in (a) but for a tornadic supercell at 2151 UTC 8 May 2003, PPI at altitude 6.1-km ARL (24-km range at 14° elevation angle). According to a sounding launched at 1700 UTC 8 May, the temperature at this altitude [1.7 km above the 0°C level (dashed white line)] was −12°C.
Citation: Weather and Forecasting 20, 5; 10.1175/WAF881.1
(a) Reflectivity (Z, left), differential reflectivity (ZDR, center), and hydrometeor classification algorithm output (HCA, right) images from the KOUN polarimetric WSR-88D, at 2300 UTC 10 Jun 2003. Data height approximately 0.9-km ARL (0.0° elevation angle at 124-km range). (b) As in (a) but for a different thunderstorm at a similar range from the radar.
Citation: Weather and Forecasting 20, 5; 10.1175/WAF881.1
(a) Reflectivity (top) and storm cell attributes table (bottom) from KTLX WSR-88D, at 2301 UTC 10 Jun 2003. The attributes table indicates the legacy hail detection algorithm has a calculated probability of hail (POH) for cell 68 (circled) of 100%. The location of a JPOLE hail-intercept vehicle is marked by the star. (b) Hydrometeor classification algorithm output from the KOUN polarimetric WSR-88D at approximately the same time as the image in (a). Data height about 0.9-km ARL (0.0° at 124-km range).
Citation: Weather and Forecasting 20, 5; 10.1175/WAF881.1
(a) Reflectivity (Z, left), differential reflectivity (ZDR, center), and correlation coefficient (ρhv, right) images from the KOUN polarimetric WSR-88D, at 2141 UTC 24 Feb 2003. An area of enhanced Z is associated with high ZDR and low ρhv. A weather spotter is located at the location marked with the arrows. Data height is approximately 2-km ARL (125 km at 0.5° elevation angle). (b) As in (a) but at 2333 UTC.
Citation: Weather and Forecasting 20, 5; 10.1175/WAF881.1
Reflectivity (Z, left), differential reflectivity (ZDR, center), and correlation coefficient (ρhv, right) from the KOUN polarimetric WSR-88D, at 2343 UTC 6 Feb 2003. Echoes over the northern half of the radar range are the result of light snow, while echoes over southern OK are due to a cloud of chaff. No ground clutter filtering was applied to these radar data; the echoes near the center of the image are ground clutter. Data height at the chaff cloud is approximately 3-km ARL (165 km at 0.5° elevation angle).
Citation: Weather and Forecasting 20, 5; 10.1175/WAF881.1
Partial list of products made available to WFO Norman forecasters from the KOUN polarimetric WSR-88D, and their abbreviations. Doviak and Zrnić (1993) and Bringi and Chandrasekar (2001) provide detailed descriptions of Z, ZDR, KDP, and ρhv. Schuur et al. (2003a) details the HCA, and the QPEAs are described by Ryzhkov et al. (2005a).
