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  • View in gallery

    Map showing the placement of the towers on the Cape WINDS as of July 2015. [Adapted from Computer Sciences Raytheon (2015).] The yellow circles indicate space launch complexes (SLCs) and the Morrell Operations Center (MOC). The red star marks the approximate location of the 45WS-WSR. It should be noted that the KXMR sounding site is not displayed on this map but is located at approximately 28.47°N, 80.55°W.

  • View in gallery

    Histograms of gridded correlation coefficient values within a region of light stratiform rain as observed from the (top) 45WS-WSR and (bottom) KMLB WSR-88D. Both histograms were taken over the same approximate region in space and time.

  • View in gallery

    East–west vertical cross sections (i.e., view facing north) of the (top) raw radar reflectivity factor and (bottom) gridded radar reflectivity factor from the 45WS-WSR volume scan that ended at 2043 UTC 21 May 2015. Both cross sections were taken approximately 49.5 km north of the 45WS-WSR. The raw data were visualized using the Gibson Ridge GR2Analyst software package (http://www.grlevelx.com/gr2analyst_2/). The black horizontal line in the gridded image marks the height of the 0°C level calculated using KXMR data at 0000 UTC 22 May 2015. The white circles indicate a region of gates containing missing raw data in the top panel and the impact these missing gates have on the gridded data in the bottom panel.

  • View in gallery

    East–west vertical cross sections (i.e., x–z plane shown, view facing north) taken 36.5 km north of the 45WS-WSR during the volume scan that ended at 1934 UTC 20 Jul 2015. The variables shown are the (top) radar reflectivity factor, (middle) differential reflectivity, and (bottom) correlation coefficient. The black horizontal line marks the height of the 0°C level calculated using KXMR data at 1500 UTC 20 Jul 2015.

  • View in gallery

    East–west vertical cross sections (i.e., x–z plane shown, view facing north) taken 11.5 km north of the 45WS-WSR during the volume scan that ended at 2225 UTC 19 Jun 2015. The variables shown are the (top) radar reflectivity factor, (middle) differential reflectivity, and (bottom) correlation coefficient. The black horizontal line marks the height of the 0°C level calculated using KXMR data at 0000 UTC 20 Jun 2015.

  • View in gallery

    An image of composite reflectivity taken from the 45WS-WSR volume scan that ended at 1847 UTC 21 Jul 2015. The black diamond at grid point (0, 0) marks the location of the 45WS-WSR, and the black plus signs (+) mark the locations of the 29 Cape WINDS towers. The black circle denotes an approximate 67-km radius around the 45WS-WSR.

  • View in gallery

    East–west vertical cross sections (i.e., x–z plane shown, view facing north) taken 47.0 km north of the 45WS-WSR during the volume scan that ended at 1847 UTC 21 Jul 2015. The variables shown are the (top) radar reflectivity factor, (middle) differential reflectivity, and (bottom) correlation coefficient. The black horizontal line marks the height of the 0°C level calculated using KXMR data at 1500 UTC 21 Jul 2015.

  • View in gallery

    North–south vertical cross sections (i.e., y–z plane shown, view facing west) taken 10.0 km west of the 45WS-WSR during the volume scan that ended at 2025 UTC 21 May 2015. The variables shown are the (top) radar reflectivity factor, (middle) differential reflectivity, and (bottom) correlation coefficient. The black horizontal line marks the height of the 0°C level calculated using KXMR data at 0000 UTC 22 May 2015.

  • View in gallery

    East–west vertical cross sections (i.e., x–z plane shown, view facing north) taken 29.0 km north of the 45WS-WSR during the volume scan that ended at 2204 UTC 20 May 2015. The variables shown are the (top) radar reflectivity factor, (middle) differential reflectivity, and (bottom) correlation coefficient. The black horizontal line marks the height of the 0°C level calculated using KXMR data at 0000 UTC 21 May 2015.

  • View in gallery

    Line plots showing the values of POD (blue line), POFA (red line), TSS (green line), and CSI (orange line) resulting from use of the direct method in the Threshold-1 analysis for each of the test values for (top left) Signature 1, (top right) Signature 2, (middle left) Signature 3, (middle right) Signature 4, and (bottom left) Signature 5.

  • View in gallery

    As in Fig. 10, but for the indirect method.

  • View in gallery

    Line plots showing the values of POD (blue line), POFA (red line), TSS (green line), and CSI (orange line) resulting from use of the direct method in the Threshold-2 analysis for each of the test values for (top left) Signature 1, (top right) Signature 2, (middle left) Signature 3, (middle right) Signature 4, and (bottom left) Signature 5.

  • View in gallery

    As in Fig. A1, but for the indirect method.

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C-band Dual-Polarization Radar Signatures of Wet Downbursts around Cape Canaveral, Florida

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  • 1 Department of Atmospheric Science, The University of Alabama in Huntsville, Huntsville, Alabama
  • | 2 45th Weather Squadron, Patrick Air Force Base, Florida
  • | 3 NASA Marshall Space Flight Center, Huntsville, Alabama
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Abstract

Wind warnings are the second-most-frequent advisory issued by the U.S. Air Force’s 45th Weather Squadron (45WS) at Cape Canaveral, Florida. Given the challenges associated with nowcasting convection in Florida during the warm season, improvements in 45WS warnings for convective wind events are desired. This study aims to explore the physical bases of dual-polarization radar signatures within wet downbursts around Cape Canaveral and identify signatures that may assist the 45WS during real-time convective wind nowcasting. Data from the 45WS’s C-band dual-polarization radar were subjectively analyzed within an environmental context, with quantitative wind measurements recorded by weather tower sensors for 32 threshold-level downbursts with near-surface winds ≥ 35 kt (1 kt ≈ 0.51 m s−1) and 32 null downbursts. Five radar signatures were identified in threshold-level downburst-producing storms: peak height of 1-dB differential reflectivity ZDR column, peak height of precipitation ice signature, peak reflectivity, height below 0°C level where ZDR increases to 3 dB within a descending reflectivity core (DRC), and vertical ZDR gradient within DRC. Examining these signatures directly in updraft–downdraft cycles that produced threshold-level winds yielded mean lead times of 20.0–28.2 min for cumulus and mature stage signatures and 12.8–14.9 min for dissipating stage signatures, with higher signature test values generally yielding higher skill scores. A conceptual test of utilizing signatures within earlier cells in multicell storms to indirectly predict the potential for intense downbursts in later cells was performed, which offered increased lead times and skill scores for an Eulerian forecast region downstream from the storm initiation location.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Corey G. Amiot, ca0019@uah.edu

Abstract

Wind warnings are the second-most-frequent advisory issued by the U.S. Air Force’s 45th Weather Squadron (45WS) at Cape Canaveral, Florida. Given the challenges associated with nowcasting convection in Florida during the warm season, improvements in 45WS warnings for convective wind events are desired. This study aims to explore the physical bases of dual-polarization radar signatures within wet downbursts around Cape Canaveral and identify signatures that may assist the 45WS during real-time convective wind nowcasting. Data from the 45WS’s C-band dual-polarization radar were subjectively analyzed within an environmental context, with quantitative wind measurements recorded by weather tower sensors for 32 threshold-level downbursts with near-surface winds ≥ 35 kt (1 kt ≈ 0.51 m s−1) and 32 null downbursts. Five radar signatures were identified in threshold-level downburst-producing storms: peak height of 1-dB differential reflectivity ZDR column, peak height of precipitation ice signature, peak reflectivity, height below 0°C level where ZDR increases to 3 dB within a descending reflectivity core (DRC), and vertical ZDR gradient within DRC. Examining these signatures directly in updraft–downdraft cycles that produced threshold-level winds yielded mean lead times of 20.0–28.2 min for cumulus and mature stage signatures and 12.8–14.9 min for dissipating stage signatures, with higher signature test values generally yielding higher skill scores. A conceptual test of utilizing signatures within earlier cells in multicell storms to indirectly predict the potential for intense downbursts in later cells was performed, which offered increased lead times and skill scores for an Eulerian forecast region downstream from the storm initiation location.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Corey G. Amiot, ca0019@uah.edu

1. Introduction

Wind warnings are the second-most-frequent advisory product issued by the U.S. Air Force’s 45th Weather Squadron (45WS), the organization responsible for providing weather support to America’s space program at Cape Canaveral Air Force Station and the National Aeronautics and Space Administration’s Kennedy Space Center (CCAFS/KSC) in Florida (Roeder et al. 2014). The 45WS issues two warning levels for wind events: winds ≥ 35 kt (Threshold-1 or “threshold level”), with 30 min of lead time desired, and winds ≥ 50 kt (Threshold-2), with 60 min of lead time desired (1 kt ≈ 0.51 m s−1; Roeder et al. 2014). Most wind events at CCAFS/KSC are convective winds, specifically wet downbursts that occur during the warm season (i.e., May–September). Downbursts are divergent outflows from a convective downdraft impacting the surface (Fujita and Wakimoto 1983) and may be termed wet downbursts if accompanied by significant precipitation at the surface (Wakimoto 1985). Convective winds have sources other than wet downbursts (e.g., dry downbursts, quasi-linear convective systems, etc.). This study focuses exclusively on wet downbursts at CCAFS/KSC during the warm season.

Past studies (e.g., Loconto 2006; Rennie 2010; Harris 2011; Scholten 2013) have focused on improving 45WS wind warnings. For example, Rennie (2010) identified combinations of single-polarization radar products that yielded probability of detection (POD) values of 0.30–0.75 and probability of false alarm [POFA, equivalent to the false alarm ratio (Barnes et al. 2009)] values of 0.22–0.43 with up to 20 min of lead time for downbursts. However, given the rapid development of convection in Florida during the warm season coupled with 31.8% of all downbursts at CCAFS/KSC meeting Threshold-1 warning criteria (McCue et al. 2010), further improvements in lead time and reductions in POFA are desired by the 45WS while maintaining high POD and high statistical skill scores (Roeder et al. 2014). The 45WS utilizes a wealth of instrumentation, including a C-band dual-polarization radar (herein “45WS-WSR”) (Roeder et al. 2009), the Cape Weather Information Network Display System (Cape WINDS), comprising 29 weather observation towers around CCAFS/KSC (Fig. 1) (Computer Sciences Raytheon 2015), and KSC atmospheric soundings (KXMR).

Fig. 1.
Fig. 1.

Map showing the placement of the towers on the Cape WINDS as of July 2015. [Adapted from Computer Sciences Raytheon (2015).] The yellow circles indicate space launch complexes (SLCs) and the Morrell Operations Center (MOC). The red star marks the approximate location of the 45WS-WSR. It should be noted that the KXMR sounding site is not displayed on this map but is located at approximately 28.47°N, 80.55°W.

Citation: Weather and Forecasting 34, 1; 10.1175/WAF-D-18-0081.1

Ingredients needed to form a wet downburst begin in the cumulus stage of a thunderstorm (i.e., deep convection, herein simply “storm”), with the updraft forcing liquid hydrometeors and condensate above the environmental 0°C level (e.g., Tuttle et al. 1989; Kingsmill and Wakimoto 1991). Lofted liquid hydrometeors are denoted by a region of positive differential reflectivity ZDR extending above the 0°C level, or a “ZDR column” (e.g., Illingworth et al. 1987; Wakimoto and Bringi 1988; Tuttle et al. 1989; Herzegh and Jameson 1992; Kumjian et al. 2014; Snyder et al. 2015; Kuster et al. 2016; Mahale et al. 2016). The height and location of a ZDR column indicate a storm’s updraft magnitude and approximate location, respectively (e.g., Tuttle et al. 1989; Herzegh and Jameson 1992; Kumjian et al. 2014). Lofted liquid hydrometeors freeze atop a ZDR column, decreasing the correlation coefficient ρhv and forming a “ρhv hole” (Kumjian and Ryzhkov 2008). The resulting ice hydrometeors may serve as hailstone embryos (Hubbert et al. 1998) that, when combined with additional ice formation and growth via mixed-phase precipitation processes (e.g., Jameson et al. 1996; Bringi et al. 1997), can produce precipitation ice (i.e., ice hydrometeors, mainly graupel and hail, that descend toward the surface) with radar reflectivity factor ZH values of approximately 30 dBZ or greater (e.g., Straka et al. 2000; Deierling et al. 2008; Dolan et al. 2013) and ZDR values around 0 dB (e.g., Straka et al. 2000; Herzegh and Jameson 1992; Dolan et al. 2013). Although downbursts at CCAFS/KSC are sometimes produced by strong but shallow convection (e.g., cloud tops not reaching the 0°C level), they are relatively infrequent compared to wet downbursts formed by deep convection and will not be discussed further herein.

As a storm progresses into its mature stage, a peak ZH region (i.e., reflectivity core) will develop, which may be directly related to downburst intensity due to increased hydrometeor loading (e.g., Loconto 2006). Precipitation ice will melt below the 0°C level, leading to a ZDR increase and a ρhv decrease (e.g., Rasmussen and Heymsfield 1987; Anderson et al. 2011; Mahale et al. 2016). Under warm humid conditions, melting ice hydrometeors may provide greater negative buoyancy than evaporating liquid hydrometeors despite latent heat differences, given the inverse (direct) relation between evaporation (melting) rates and relative humidity (Srivastava 1987). Rapid preferential melting of smaller ice hydrometeors over a shallow layer, indicated by rapid increases in ZDR (Meischner et al. 1991), can lead to greater downward acceleration within a storm (Atlas et al. 2004), with ZDR reaching 3 dB at C band within strong downbursts (White 2015). During the dissipating stage of a storm, the peak ZH region descends to the surface. The time when this descending reflectivity core (DRC) impacts the surface typically coincides with the downburst (Wakimoto and Bringi 1988). The resulting surface outflow may form a gust front, with ZH around 0–20 dBZ (Rinehart 2010). Lifting along the leading edge of the gust front, due to the density gradient and horizontal vorticity, may force parcels to their level of free convection (e.g., Rotunno et al. 1988; Markowski and Richardson 2010), potentially resulting in the development of new storm cells and the formation of a multicellular (multicell) storm (Browning et al. 1976).

The primary purpose of this study is to explore the physical bases of dual-polarization radar signatures within a relatively large sample of wet downbursts in relation to quantitative reports of their peak winds. Particular emphasis was placed on identifying C-band dual-polarization radar signatures that may be used by 45WS forecasters to differentiate between storms that will produce Threshold-1 winds and those that will remain below Threshold-1 status due to the greater frequency of Threshold-1 winds at CCAFS/KSC compared to Threshold-2 and the greater potential for Threshold-1 false alarms (Roeder et al. 2014). As such, this study has immediate operational relevance to 45WS forecasters in their use of the C-band 45WS-WSR for nowcasting Threshold-1 winds at CCAFS/KSC. Given the increased use of C-band dual-polarization radars by worldwide governmental operational weather services, the commercial weather enterprise in the United States, and research institutions worldwide (e.g., Baldini et al. 2005; Chandrasekar et al. 2008; Vulpiani et al. 2012; Wang et al. 2016; Tsukamoto et al. 2016), the results of this study have broad and immediate implications for convective wind nowcasting and applied research worldwide. The use of Cape WINDS data in this study provided a rare opportunity to associate observed radar signatures with quantitative downburst peak wind values. This allowed for more robust results to be obtained compared to relying on convective wind reports from human observers, which, as described in a recent study by Edwards et al. (2018), tend to overestimate peak wind speeds. To our knowledge, this study is the first of its kind to validate dual-polarization radar signatures of convective downburst winds with a large sample of quantitatively verified wind reports. It was hypothesized that the following features, which are physically related to the formation or melting of precipitation ice, are directly related to peak downburst velocity in the storm cell within which they are observed:

  1. higher peak ZDR column height above the environmental 0°C level;
  2. greater vertical extent of collocated values of ZH ≥ 30 dBZ and near-0-dB ZDR (i.e., from −2 to +1 dB ZDR), hereafter “precipitation ice signature”;
  3. greater peak ZH value in a cell;
  4. ZDR increase to 3 dB more rapidly below the 0°C level within a DRC; and
  5. larger vertical ZDR gradient around the 1-dB contour within a DRC.

During this study, it was noted that many threshold-level (i.e., Threshold-1) downbursts were produced by multicell storms [i.e., storms consisting of new cells forming along outflows from dissipating cells (Browning et al. 1976)] that, in Florida, are strongly affected by sea breezes (e.g., Byers and Rodebush 1948; Pielke 1974; López et al. 1984) in addition to gust fronts (e.g., Kingsmill 1995; Wilson and Megenhardt 1997). Given the 30-min lead time desired by the 45WS for Threshold-1 downbursts, coupled with the forecasting challenge posed by the 20–40-min average lifetime of isolated convective cells in the southeastern United States (Smith et al. 2004), it was speculated that radar signatures observed in earlier cells within multicell storms may indicate that later cells within the same multicell storm will be capable of producing intense downbursts. It has been noted (e.g., Knupp 1996; Vasiloff and Howard 2009; Kuster et al. 2016) that multicell storms may produce multiple downbursts during their relatively long lifetimes. While this idea is limited by storm mode (e.g., not possible for isolated convective cells), the relatively high frequency of multicell storms associated with threshold-level downbursts at CCAFS/KSC suggests this may be a plausible method for increasing the lead times for 45WS convective wind warnings. Though the primary hypothesis was that the identified radar signatures would be directly related to peak downburst velocity during dissipation of the cell within which they are observed, it was speculated that enhanced convection may occur along the leading edge of a stronger gust front due to increased horizontal vorticity (e.g., Rotunno et al. 1988; Markowski and Richardson 2010). If the environment were suitable for the continued formation of downbursts, this could lead to lofting of a greater quantity of liquid hydrometeors and condensate above the 0°C level, forming a greater quantity of precipitation ice, which could lead to the formation of another intense downburst, and the cycle may repeat. In this sense, the secondary hypothesis was that radar signatures observed in the earlier cell would be indirectly related to threshold-level downburst production in later cells. Additionally, storms tend to intensify as they approach CCAFS/KSC from the west, possibly due to sea-breeze front interactions (Roeder et al. 2014). Thus, radar signatures indicative of strong storms within these earlier cells may further suggest the potential for later cells to produce threshold-level winds at CCAFS/KSC. It should be noted that most environmental conditions (e.g., wind shear) are not analyzed in this study but should be considered for future work.

Since multicell storms under these conditions could be producing (potentially threshold level) downbursts during dissipation of each cell, lead times would not increase for the storm system itself (i.e., within a Lagrangian framework). Instead, increased lead times would be offered for locations downstream from the multicell storm, where multiple updraft–downdraft cycles can be analyzed before its arrival at a given location (i.e., within an Eulerian framework), which applies to relatively small warning regions like CCAFS/KSC. This method of analyzing radar signatures in earlier cells to predict downburst intensity in later cells within the same multicell storm (hereafter the “indirect method”) would apply for downburst nowcasting at any local venue (e.g., airports, fairgrounds, individual cities, etc.). For cases where a multicell storm would be contained within the forecast domain for an extended time period [e.g., region covered by a single National Weather Service (NWS) office], this indirect method may help determine areas to be included within severe thunderstorm warnings. It should be noted that the NWS definition of severe winds differs from the 45WS definition of Threshold-1 winds, but 35-kt winds may be of interest to other applications [e.g., airport weather warnings, which are issued by the NWS for winds greater than 30 kt (National Weather Service 2016)]. Results from a limited analysis of Threshold-2 winds, while not the primary focus of this study but which meet the same criteria as the NWS definition of severe winds, are provided in the appendix. It is important to note that these proposed methods for nowcasting downbursts using dual-polarization radar products are physically tied to downburst production and can therefore be tested and potentially utilized for a variety of wind thresholds, depending on the wind speeds that are considered significant to each individual user. For this study, we chose to test these methods for peak winds of 35 kt (primarily) and 50 kt (secondarily), though this is not to suggest that these are the only wind speeds that can be predicted using the signatures and methods discussed herein.

A secondary purpose of this study is to conduct an initial feasibility test for the indirect method of downburst nowcasting. As will be described in section 2, radar signatures were analyzed within earlier cells in multicell storms to explore potential uses of the indirect method to increase lead times for downbursts in later cells at CCAFS/KSC, though not all analyzed storms were multicell. Statistics were used to better understand the potential of utilizing signatures in earlier cells, and lead times for threshold-level downbursts at CCAFS/KSC were calculated based on the first occurrence of a given signature within a storm. It should be noted that lead time for a warning region downstream from a given storm would decrease with decreasing distance from the storm. Since it was necessary for downbursts to occur over the Cape WINDS to get quantitative peak wind measurements, a caveat of this study is prior knowledge that the storms ultimately reached CCAFS/KSC. As a result, increases in POFA were strictly due to a lack of recorded 35+-kt winds, when in reality a storm that misses CCAFS/KSC entirely would also constitute a false alarm if a warning was issued. Because of this, the results in sections 35 represent “best case” scenarios if a forecaster has knowledge of storm propagation and longevity and is confident that the storm will impact the warning region. Thus, this is a conceptual test rather than a forecasting test. Future work, discussed in section 6, will consider all cells within the forecast domain, likely using automated cell identification and tracking, as a forecaster would have no prior confirmation of which cells will reach their warning region.

2. Data and methods

a. Cape WINDS

Threshold-level downbursts were identified using Cape WINDS data. Reported data from multiple sensors on each Cape WINDS tower include 5-min maximum wind velocity, 5-min mean wind direction, sensor height above ground level (AGL), sensor compass direction, and date and time at the start of the 5-min reporting period. Most Cape WINDS towers have sensors at 3.6 and/or 16.4 m AGL, with some towers having additional sensors at higher levels (Computer Sciences Raytheon 2015). These data were extracted for all 5-min reporting periods during the 2015 warm season where the maximum wind speed was ≥35.0 kt. A median time of 2.5 min after the start of the reporting period was assumed to be when the threshold-level winds were first recorded in each case. It was also assumed that the 5-min mean wind direction corresponded to the threshold-level wind direction. Since 45WS wind warnings extend from the surface to 200 ft (300 ft) AGL at CCAFS (KSC) (Loconto 2006), wind reports were considered from all Cape WINDS sensor heights. Threshold-level wind reports were discarded if there was no convective activity near CCAFS/KSC or if 45WS-WSR data were missing around the report time.

b. KXMR soundings

To analyze radar signatures in an environmental context, the height of the environmental 0°C level was calculated from KXMR soundings. Under typical conditions, KXMR soundings are launched daily at 0000, 1000, and 1500 UTC. The 0°C height level was the only environmental parameter considered in this study, but other environmental analyses (e.g., relative humidity, equivalent potential temperature, low-level lapse rates) can be found in Amiot (2017). The KXMR sounding nearest the majority of the storm tracking period (discussed below) was used in each case. Quality control (QC) was performed on all KXMR data before being acquired for this study, which included eliminating anomalous data. QC did not include removing soundings that may have been modified by convection; however, the average difference in the 0°C height level observed between any sounding used in this study and the soundings launched immediately before and after the selected sounding was 150 m and the maximum difference was 476 m, so the impact on the radar analysis discussed below would be very minimal.

c. 45WS-WSR: Gridding, tracking, and analyses

All radar analyses were performed using 45WS-WSR data. A few basic properties of the 45WS-WSR are provided in Table 1, and the typical 45WS-WSR scan strategy can be found in Table 2. A limited QC procedure was applied to the 45WS-WSR data before being acquired, including differential attenuation correction since attenuation effects tend to be larger at C band compared to S band in rain and melting hail (e.g., Carey et al. 2000; Bringi et al. 2001; Borowska et al. 2011). Additional details about the 45WS-WSR can be found in Roeder et al. (2009). One additional point to note is the relatively large amount of 45WS-WSR system noise. Comparing histograms of ρhv for the 45WS-WSR and the Melbourne, Florida, WSR-88D (KMLB; Fig. 2) within the same region of light stratiform rain, the mean ρhv is approximately 0.1 lower for the 45WS-WSR than KMLB (Table 3). This is well below the intrinsic ρhv of 0.98 in light rain (Bringi et al. 1991) and may be compounded by the 24 pulses per radial causing a decrease in data quality (Bringi and Chandrasekar 2001). This reduction often causes 45WS-WSR ρhv within meteorological targets to be approximately 0.1 lower than expected, with values in very heterogeneous hydrometeors decreasing below the typical ρhv minimum of 0.80 in meteorological targets (Kumjian and Ryzhkov 2008). The mean ρhv of 0.88 for the 45WS-WSR also indicates a ZDR standard deviation of at least 0.50 dB, or at least twice as large as typical weather radars (Bringi and Chandrasekar 2001). These factors must be considered when interpreting the results herein, but steps, described below and in Amiot (2017), were taken to mitigate their impact.

Table 1.

Some key components of the 45WS-WSR. [Adapted from Roeder et al. (2009).]

Table 1.
Table 2.

A list of elevation angles used during a typical 45WS-WSR volume scan. Beam number indicates the number of the elevation angle, beam order indicates the order in which the 13 elevation angles are scanned, and beam angle is the elevation angle. [Adapted from Roeder et al. (2009).]

Table 2.
Fig. 2.
Fig. 2.

Histograms of gridded correlation coefficient values within a region of light stratiform rain as observed from the (top) 45WS-WSR and (bottom) KMLB WSR-88D. Both histograms were taken over the same approximate region in space and time.

Citation: Weather and Forecasting 34, 1; 10.1175/WAF-D-18-0081.1

Table 3.

Values of the mean, median, mode, and standard deviation (std dev) of the histograms of the correlation coefficient within light stratiform rain for the 45WS-WSR and KMLB WSR-88D. The histograms used to compute these values can be found in Fig. 2.

Table 3.

Raw 45WS-WSR data were gridded onto a Cartesian coordinate system to smooth noise and gates containing missing data and to increase the flexibility in selecting cross-section locations. Gridding was performed using the Python ARM Radar Toolkit (Helmus and Collis 2016), with a 500-m grid spacing, 1-km constant radius of influence, and Cressman (1959) weighting scheme. Raw ZH and ZDR data were converted from logarithmic units to linear units prior to gridding. These gridding conditions were specifically chosen to maximize the resolution of the gridded data over the Cape WINDS, where the distance across the 45WS-WSR beam is roughly 600 m and the maximum vertical beam distance is roughly 700 m (Roeder et al. 2009), while also providing consistent smoothing of noise within the radar domain and minimizing smoothing while still eliminating missing data gaps in key regions for this study. The gridding methods did result in the appearance of vertical gaps in the data where vertical beam spacing was greater than approximately 2 km. However, this effect was mainly present at higher elevation angles and outside the range from the radar considered in this study as discussed below and was not a major factor within the height range AGL considered for any radar signature in this study. Missing data may also appear in several raw 45WS-WSR gates due to high levels of filtering applied prior to fall 2016, which slightly affects gridded data in some (e.g., circled regions in Fig. 3) but not most cases. The gridded data were visualized using the Interactive Data Language (IDL). To identify the storm cell that likely contributed most strongly to the threshold-level wind report in each case, 5-min mean wind direction at the report time was compared to composite ZH and horizontal cross sections of ZH. The cell closest to the Cape WINDS tower along the mean wind direction was assumed to have contributed most strongly to the threshold-level report unless good reason existed to suspect a different cell (e.g., the only cell near a Cape WINDS tower around the report time not coinciding with the mean wind direction).

Fig. 3.
Fig. 3.

East–west vertical cross sections (i.e., view facing north) of the (top) raw radar reflectivity factor and (bottom) gridded radar reflectivity factor from the 45WS-WSR volume scan that ended at 2043 UTC 21 May 2015. Both cross sections were taken approximately 49.5 km north of the 45WS-WSR. The raw data were visualized using the Gibson Ridge GR2Analyst software package (http://www.grlevelx.com/gr2analyst_2/). The black horizontal line in the gridded image marks the height of the 0°C level calculated using KXMR data at 0000 UTC 22 May 2015. The white circles indicate a region of gates containing missing raw data in the top panel and the impact these missing gates have on the gridded data in the bottom panel.

Citation: Weather and Forecasting 34, 1; 10.1175/WAF-D-18-0081.1

The cell deemed most responsible for the threshold-level winds was tracked backward in time manually (i.e., subjectively) using composite ZH and horizontal cross sections of ZH. All cells that merged with this tracked cell before the threshold-level report were also tracked. A cell merger was defined herein as the joining of the 30-dBZ ZH contours between initially separate cells, based on the cell definition in Johnson et al. (1998) and the definition of a merger in Hastings and Richardson (2016) (albeit without considering the height at which the 30-dBZ contours merged). These cells were tracked until their formation or until reaching a distance of 67 km from the 45WS-WSR, which is where vertical gaps in the gridded data began to occur around the 0°C level (i.e., became significant in regions that were important for this study).

Multiple vertical cross sections of ZH, ZDR, and ρhv were taken in the north–south (N–S) and east–west (E–W) directions through the tracked cell(s) beginning with the earliest volume scan within the tracking period before advancing to subsequent scans. Radial velocity Vr data were not considered because the primary goal of this study was to explore dual-polarization radar signatures in relation to downburst nowcasting, but future work could attempt to combine Doppler and dual-polarization radar signatures. A preliminary analysis also indicated that Vr signatures were not clear above the level of noise present within the 45WS-WSR data, so it was decided that expanding the scope of this research effort to include Vr would distract from the primary purpose of the current study. Vertical cross sections were generated starting at the region of peak ZH before advancing every 500 m north, south, east, and/or west until the edge of the cell was reached. The calculated environmental 0°C height was marked on all vertical cross sections. Common ZH, ZDR, and ρhv trends in the cross sections were noted in 32 threshold-level downburst-producing storms (Table 4), which were selected randomly with the constraint that no more than two cases were chosen on a given day. Multiple heights or magnitudes were selected as “test values” to perform a sensitivity test for each identified signature, and the presence of each signature at each test value in the 32 storms was noted. These test values were selected by first considering values identified in past studies to understand typical values that may be observed for each signature. This information was then supplemented with observations from the subjective analysis herein to produce a range of values to consider for each signature. When identifying signatures in vertical cross sections, care was taken to avoid regions of potential sidelobe error (i.e., ZH gradient > 20 dB km−1) and to confirm horizontal continuity (i.e., presence in at least two adjacent vertical cross sections).

Table 4.

List of the 32 threshold-level downbursts considered in this study. Information provided for each threshold-level downburst includes the date, total period of time (UTC) over which radar data were examined on that date, approximate time of the downburst (UTC), and the identification number of the Cape WINDS tower(s) that recorded the threshold-level winds associated with that downburst at the listed time.

Table 4.

Lead time offered by each signature at each of its test values was calculated as
eq1
where LT is lead time, estimated time of downburst is 2.5 min after the Cape WINDS report time, and end time of volume scan is the end time of the volume scan during which the signature was first observed within the storm-tracking period. In cases where multiple threshold-level winds were recorded on the Cape WINDS data from the same storm, only the first Threshold-1 report was used to calculate lead time unless a Threshold-2 wind occurred after a Threshold-1 wind in the same system, in which case lead times were calculated for both reports since they are two different warning thresholds. It should be mentioned that three Threshold-2 downbursts were included in the Threshold-1 sample in a preliminary effort to begin examining signatures unique to Threshold-2 winds. This Threshold-2 sample from the 2015 warm season proved too small to develop robust results, but the three Threshold-2 downbursts were kept in the Threshold-1 sample since they also meet Threshold-1 by definition.

In an attempt to expand the Threshold-2 sample, six additional Threshold-2 downbursts from the 2016 warm season were combined with the three Threshold-2 downbursts from 2015 to produce a sample of nine Threshold-2 downbursts. These cases were analyzed using the same methods and signatures as the Threshold-1 sample to evaluate the signatures’ performances in predicting Threshold-2 winds, the results of which can be found in the appendix. However, the Threshold-2 sample still proved too small and, as discussed in the appendix, should be expanded further for future work.

The identified radar signatures and their test values were then examined in a sample of null downbursts. Any downburst that occurs over the Cape WINDS and does not produce peak winds ≥ 35 kt would constitute a “null downburst” in general, but additional criteria were used to develop a more meaningful sample of null downbursts for the purposes of comparing and contrasting with the threshold-level downbursts. A downburst had to meet all of the following criteria to be included in the null sample for this study:

  • downdraft could be identified by a DRC (or its remnants) in vertical cross sections;
  • downdraft reached the surface within a 5-km radius of any Cape WINDS tower;
  • downdraft did not produce a 35+-kt wind report on any Cape WINDS tower, nor did the associated storm produce any 35+-kt winds on the Cape WINDS at any time;
  • cells associated with the null downburst did not split from any cells deemed responsible for any threshold-level winds; and
  • peak ZH in the storm cell associated with the null downburst was at least 40 dBZ.
The latter condition was included to avoid “stacking the deck” with a large number of correctly forecasted null downbursts that would be easily identified by forecasters. All downbursts included in the null sample also occurred within 90 min of a threshold-level wind report to examine why certain cells produced null downbursts under conditions where threshold-level downbursts also occurred. In cases where multiple null downbursts occurred in a single storm, only one null updraft–downdraft cycle was analyzed (either the first observed or the one most easily identified by its DRC) to examine why that particular updraft–downdraft cycle did not produce threshold-level winds. Thirty-two null downbursts (Table 5) were selected to complement the 32 threshold-level downbursts, with no more than two null downbursts being chosen from any given date.
Table 5.

List of the 32 null downbursts considered in this study. Information provided for each null downburst includes the date, total period of time (UTC) over which radar data were examined on that date, approximate estimated time of the null downburst (UTC), and the identification number of the Cape WINDS tower(s) nearest the location of the null downburst.

Table 5.

Once all radar signatures were examined in the threshold-level storms and null downbursts, statistics were calculated for each signature’s test values, including POD, POFA, true skill statistic (TSS), and critical success index (CSI) using a 2 × 2 contingency table (Wilks 2011). Mean and median lead times for each signature’s test values were also calculated. Comments were made about which signatures had the strongest statistical performance, but users should decide optimal trade-offs between statistics and lead time for their applications of the results. For this study, when assessing the operational utility of each test value, emphasis was placed primarily on a fair to high TSS [i.e., ≥0.30, which is considered operationally useful by the 45WS (Rennie 2010)], secondarily on a high POD (i.e., 70%–100%), and last on a moderately low to low POFA (i.e., 10%–40%), moderate to high CSI (i.e., ≥0.50), and highest mean lead time; however, other users may wish to emphasize statistical results and lead times differently, which is why we have analyzed and presented a variety of test values for each signature.

3. The radar signatures

Within this section, examples and physical interpretations of the five identified dual-polarization radar signatures are provided. Details regarding statistical performance and lead times for each signature can be found in sections 4 and 5. The radar signatures, their names, and their test values are summarized in Table 6.

Table 6.

A list of the five dual-polarization radar signatures that were tested in this study along with the test values that were considered in the sensitivity test for each signature. The signature number (left column) corresponds to the name used for each signature throughout sections 36 and the appendix.

Table 6.

a. Signature 1: Peak height of 1-dB ZDR column

The first radar signature identified is peak height above the environmental 0°C level of the 1-dB contour within a ZDR column. A value of 1 dB was used to balance mitigating the effects of noise within raw data and potential limitations in identifying liquid hydrometers by only considering ZDR > 1 dB. In Fig. 4, a 1-dB ZDR column is present 2–4 km east of the 45WS-WSR and reaches a peak height of approximately 3 km above the environmental 0°C level. Based on past works (e.g., Tuttle et al. 1989; Herzegh and Jameson 1992; Kumjian et al. 2014), the storm’s updraft was located in this same approximate region and was relatively intense given the vertical extent of the ZDR column. Within the ZDR column, significant amounts of liquid hydrometeors and condensate were lofted above the 0°C level and provided ingredients to produce precipitation ice, which aided in eventual downdraft formation. The freezing of lofted liquid hydrometeors can be inferred from the ρhv hole atop the ZDR column. All five test values for Signature 1 were met in Fig. 4.

Fig. 4.
Fig. 4.

East–west vertical cross sections (i.e., x–z plane shown, view facing north) taken 36.5 km north of the 45WS-WSR during the volume scan that ended at 1934 UTC 20 Jul 2015. The variables shown are the (top) radar reflectivity factor, (middle) differential reflectivity, and (bottom) correlation coefficient. The black horizontal line marks the height of the 0°C level calculated using KXMR data at 1500 UTC 20 Jul 2015.

Citation: Weather and Forecasting 34, 1; 10.1175/WAF-D-18-0081.1

The storm in Fig. 4 was multicellular, with a collapsing cell located 1 km west of the 45WS-WSR and a developing cell collocated with the ZDR column. The updraft–downdraft cycle in Fig. 4 did not produce recorded threshold-level winds. However, the same storm produced a Threshold-1 wind report on the Cape WINDS 33.5 min later at 2007:30 UTC. While all heights tested for Signature 1 were met in Fig. 4, this was not the earliest volume scan wherein these test values were observed in this multicell storm: the 1.0- and 1.5-km heights were first observed at 1834 UTC, the 2.0-km height was first observed at 1837 UTC, and the 2.5- and 3.0-km heights were first observed at 1840 UTC (not shown). In other words, the 1.0- and 1.5-km values were first observed 93.5 min before the first threshold-level winds were recorded by the Cape WINDS for the same storm, while the 2.0-km value was first observed 90.5 min beforehand and the 2.5- and 3.0-km values were first observed 87.5 min beforehand. Based on the indirect method, these would be the lead times offered for CCAFS/KSC by the heights tested for Signature 1. It is possible that threshold-level winds were produced by this multicell storm prior to 2007:30 UTC given the physical implications of Signature 1; however, this could not be verified using the Cape WINDS data. Therefore, these lead times are strictly for CCAFS/KSC and would decrease as the distance from the multicell storm’s initiation location decreased. This holds true for all signatures analyzed via the indirect method in this study.

b. Signature 2: Peak height of precipitation ice signature

The second radar signature identified is the peak height above the environmental 0°C level of collocated ZH ≥ 30 dBZ and near-0-dB ZDR (i.e., precipitation ice signature). Since 45WS forecasters will have faster access to the uncorrected data in real time, ZH was not corrected within precipitation ice to account for dielectric effects (Smith 1984). In Fig. 5, a precipitation ice signature can be found starting 21 km west of the 45WS-WSR and extending westward off the figure, with a peak height of nearly 12 km above the environmental 0°C level 27 km west of the 45WS-WSR. Given the importance of precipitation ice in the formation of intense downbursts in warm humid climate regions (Srivastava 1987), the deep vertical extent of precipitation ice implied in Fig. 5 may indicate a storm that is more likely to produce threshold-level winds at CCAFS/KSC due to latent heat effects and hydrometeor loading.

Fig. 5.
Fig. 5.

East–west vertical cross sections (i.e., x–z plane shown, view facing north) taken 11.5 km north of the 45WS-WSR during the volume scan that ended at 2225 UTC 19 Jun 2015. The variables shown are the (top) radar reflectivity factor, (middle) differential reflectivity, and (bottom) correlation coefficient. The black horizontal line marks the height of the 0°C level calculated using KXMR data at 0000 UTC 20 Jun 2015.

Citation: Weather and Forecasting 34, 1; 10.1175/WAF-D-18-0081.1

The cell in Fig. 5 was part of a long-lived multicell storm. All five heights tested for Signature 2 were met in Fig. 5, or 62.5 min before the first Threshold-1 winds were recorded by the Cape WINDS for the same storm around 2327:30 UTC. However, all five test values were first observed in the same storm at 2043 UTC (not shown), or 164.5 min before the threshold-level report. It is probable that the storm produced threshold-level winds before 2327:30 UTC; however, this could not be confirmed using the Cape WINDS. Therefore, in this case, 164.5 min of lead time was offered for CCAFS/KSC by all Signature 2 test values.

c. Signature 3: Peak ZH value in storm cell

The third radar signature tested is peak ZH value in the storm cell. This signature can be observed using composite ZH, making it relatively simple to identify in real time. An example of Signature 3 in composite ZH is shown in Fig. 6 with a complementary vertical cross section in Fig. 7. In Fig. 6, peak ZH of 55–60 dBZ can be found approximately 5 km east and 47 km north of the 45WS-WSR, which was the storm that produced Threshold-1 winds on the Cape WINDS around 1907:30 UTC. In Fig. 7, the same peak ZH can be seen 4–5 km east of the 45WS-WSR and 3–4 km AGL. Given the increase in hydrometeor loading implied by higher ZH (e.g., Loconto 2006), it was speculated that conditions in Fig. 7 may indicate a storm that is more capable of producing a threshold-level downburst.

Fig. 6.
Fig. 6.

An image of composite reflectivity taken from the 45WS-WSR volume scan that ended at 1847 UTC 21 Jul 2015. The black diamond at grid point (0, 0) marks the location of the 45WS-WSR, and the black plus signs (+) mark the locations of the 29 Cape WINDS towers. The black circle denotes an approximate 67-km radius around the 45WS-WSR.

Citation: Weather and Forecasting 34, 1; 10.1175/WAF-D-18-0081.1

Fig. 7.
Fig. 7.

East–west vertical cross sections (i.e., x–z plane shown, view facing north) taken 47.0 km north of the 45WS-WSR during the volume scan that ended at 1847 UTC 21 Jul 2015. The variables shown are the (top) radar reflectivity factor, (middle) differential reflectivity, and (bottom) correlation coefficient. The black horizontal line marks the height of the 0°C level calculated using KXMR data at 1500 UTC 21 Jul 2015.

Citation: Weather and Forecasting 34, 1; 10.1175/WAF-D-18-0081.1

The storm in Figs. 6 and 7 was multicellular. Based on mean wind direction at the Threshold-1 report 20.5 min later and the eventual descent of the peak ZH region in Figs. 6 and 7 to the surface (not shown), it was determined that the ZH core of 55–60 dBZ likely contributed most strongly to the recorded Threshold-1 winds out of all the outflow boundaries present near that Cape WINDS tower at that time. That is, the peak ZH of 55–60 dBZ was directly related to the threshold-level winds. The volume scan in Figs. 6 and 7 was the first wherein peak ZH was 55 dBZ or greater in this multicell storm. Therefore, the 55-dBZ test value for Signature 3 provided 20.5 min of lead time for CCAFS/KSC. However, the 45- and 50-dBZ test values were first observed in the same storm at 1727 and 1742 UTC, respectively. Thus, the 45-dBZ (50 dBZ) value offered 100.5 min (85.5 min) of lead time for CCAFS/KSC via the indirect method. However, in addition to this indirect relation, 45- and 50-dBZ ZH values are present around the ZH core in Figs. 6 and 7. This suggests that the ZH values of 45 and 50 dBZ were also directly related to threshold-level downburst formation in this later cell.

d. Signature 4: Height at which ZDR increases to 3 dB within DRC

The fourth radar signature identified is the height below the environmental 0°C level where ZDR increases to 3 dB within a DRC. An example is shown in Fig. 8, where a region of 50–55-dBZ ZH was descending toward the surface 56–58 km north of the 45WS-WSR. Within this DRC, ZDR increased to 3 dB roughly 1.2 km below the environmental 0°C level 56 km north of the 45WS-WSR. This rapid increase in ZDR below the 0°C level has been attributed to rapid melting of small ice hydrometeors (e.g., Meischner et al. 1991; Atlas et al. 2004; Mahale et al. 2016), which is implied by the collocated decrease in ρhv. Given the ZDR increase to 3 dB observed in past downburst studies (e.g., White 2015), it was speculated that conditions where ZDR increases more rapidly to 3 dB below the 0°C level indicate greater negative buoyancy through more concentrated melting of small precipitation ice hydrometeors (Atlas et al. 2004) and conditions conducive to greater melting of all ice hydrometeors sizes. This may not hold true under climate conditions where other physical processes (e.g., dynamical forcing) are dominant in downdraft formation, so this signature (as well as Signature 5 discussed below) would need to be reevaluated under these conditions.

Fig. 8.
Fig. 8.

North–south vertical cross sections (i.e., y–z plane shown, view facing west) taken 10.0 km west of the 45WS-WSR during the volume scan that ended at 2025 UTC 21 May 2015. The variables shown are the (top) radar reflectivity factor, (middle) differential reflectivity, and (bottom) correlation coefficient. The black horizontal line marks the height of the 0°C level calculated using KXMR data at 0000 UTC 22 May 2015.

Citation: Weather and Forecasting 34, 1; 10.1175/WAF-D-18-0081.1

The cell in Fig. 8 was in its dissipating stage and part of a multicell storm. All four heights tested for Signature 4 were met in Fig. 8, or 52.5 min before its first Threshold-1 wind report on the Cape WINDS around 2117:30 UTC. This storm also produced a Threshold-2 downburst at 2137:30 UTC. However, all test values were first observed in an earlier cell within the same storm. The 1.5-km height was first observed at 1959 UTC, while the 2.0-, 2.5-, and 3.0-km heights were first observed at 1956 UTC. Therefore, via the indirect method, the 1.5-km value provided 78.5 min (98.5 min) of lead time for the first Threshold-1 (Threshold 2) wind report, while the 2.0-, 2.5-, and 3.0-km values provided 81.5 min (101.5 min) of lead time for the first Threshold-1 (Threshold-2) report. This is especially noteworthy as Signature 4 is associated with the dissipating stage of a storm, where typical lead times may only be a few minutes (e.g., Wakimoto and Bringi 1988; Tuttle et al. 1989). However, as with the previous signatures, these long lead times are highly dependent on the cell’s initiation location and propagation relative to the Eulerian forecast region.

e. Signature 5: Vertical ZDR gradient within DRC

The final radar signature identified is the quantified vertical ZDR gradient over a height range of ±500 m from the 1-dB ZDR contour within a DRC. The 1-dB contour was selected for the same reasons as described in section 3a. An example is provided in Fig. 9, where the peak ZH region (55–65 dBZ) was descending 18–23 km west of the 45WS-WSR. The vertical ZDR gradient of interest is located 19–20 km west of the 45WS-WSR and 2 km AGL. In this region, the vertical ZDR gradient within ±500 m of the 1-dB contour is greater than 6 dB km−1, which was one of the largest ZDR gradients observed in this study. The ZDR values from −2 to −3 dB suggest the presence of large, possibly wet or spongy, hailstones that are in Mie resonance with the 45WS-WSR, which is further indicated by the collocated ZH > 50 dBZ and ρhv < 0.70 (Balakrishnan and Zrnić 1990). While system noise may have had an effect, all three test values for Signature 5 were met in Fig. 9. It was speculated that high ZDR gradients within DRCs, as in Fig. 9, may indicate storms that are producing an intense downburst given the rapid melting of precipitation ice implied by the rapid ZDR increase (e.g., Meischner et al. 1991; Atlas et al. 2004; Mahale et al. 2016). Melting ice hydrometers can be inferred by decreased ρhv (i.e., below 0.75) collocated with the ZDR gradient in Fig. 9.

Fig. 9.
Fig. 9.

East–west vertical cross sections (i.e., x–z plane shown, view facing north) taken 29.0 km north of the 45WS-WSR during the volume scan that ended at 2204 UTC 20 May 2015. The variables shown are the (top) radar reflectivity factor, (middle) differential reflectivity, and (bottom) correlation coefficient. The black horizontal line marks the height of the 0°C level calculated using KXMR data at 0000 UTC 21 May 2015.

Citation: Weather and Forecasting 34, 1; 10.1175/WAF-D-18-0081.1

Figure 9 was taken 48.5 min before the first Threshold-1 winds were recorded by the Cape WINDS from the same multicell storm around 2252:30 UTC. Because of this long lifespan, all three values tested for Signature 5 were first observed within the same storm prior to Fig. 9. In this case, all three values were first observed at 2032 UTC, or about 140.5 min before the first threshold-level report. Using the indirect method, all test values for Signature 5 provided 140.5 min of lead time for CCAFS/KSC, which is remarkable given Signature 5’s association with a storm’s dissipating stage. Given the storm structure in Fig. 9, it is likely that this storm produced threshold-level winds prior to reaching CCAFS/KSC, and thus 140.5 min of lead time would not apply to all locations along its path.

4. Statistical performances and lead times: Direct method

This section details statistics and lead times obtained from analyzing the five radar signatures within the updraft–downdraft cycle identified as having contributed most strongly to the recorded winds in each of the 32 threshold-level reports (i.e., the “direct method”). In multicell storms, earlier cells were not considered for these results but will be discussed in section 5. Statistics calculated using the direct method can be found in Fig. 10, while lead times for each signature via the direct method are presented in Table 7. For reference, a physical description of each signature can be found in Table 6.

Fig. 10.
Fig. 10.

Line plots showing the values of POD (blue line), POFA (red line), TSS (green line), and CSI (orange line) resulting from use of the direct method in the Threshold-1 analysis for each of the test values for (top left) Signature 1, (top right) Signature 2, (middle left) Signature 3, (middle right) Signature 4, and (bottom left) Signature 5.

Citation: Weather and Forecasting 34, 1; 10.1175/WAF-D-18-0081.1

Table 7.

Mean and median lead time (LT; min) provided for the 32 threshold-level downbursts by each test value for each of the five radar signatures described in Table 6. These lead times were calculated using the direct method (i.e., only considering the updraft–downdraft cycle deemed responsible for contributing most strongly to the 35+-kt winds recorded on the Cape WINDS) in each case.

Table 7.

From Table 7, mean lead times for Signature 1 ranged from roughly 23 to 28 min, which are consistent with the 20–30 min between increases in the ZDR column volume aloft and the increases in ZH at the surface observed by Picca et al. (2010) and are similar to the 15–20-min ZDR column lifetimes observed by Kumjian et al. (2014). From Fig. 10, POD, POFA, and CSI generally decreased with increasing height, while TSS did not follow a noticeable trend. The 1.5-km height—given the moderate POD and POFA of 0.75 and 0.33, respectively; the fair TSS and CSI of 0.38 and 0.55, respectively; and a mean lead time of 26.5 min via the direct method—may arguably be the strongest-performing stand-alone test value for Signature 1. However, it is up to the user to select the optimal value based on their desired trade-off between statistics and lead time.

Similar trends in POD and POFA can be seen for Signature 2, while skill scores tended to increase with increasing height for Signature 2. Mean lead times were lower than those for Signature 1 (20.0–23.3 min), which makes sense as many updrafts would likely form a ZDR column prior to forming precipitation ice aloft. These lead times are also consistent with the 20–30 min observed between the formation of a hail–rain mixture above the 0°C level and the downburst time in Mahale et al. (2016). The 4.5-km test value for Signature 2 is arguably the strongest, given its moderate 0.69 POD; low 0.12 POFA; strong TSS and CSI of 0.59 and 0.63, respectively; and mean lead time of 20.5 min. However, as with Signature 1 and all other signatures presented in this section and the next, users may benefit from using other test values depending on their applications of the results.

POD and POFA for Signature 3 tended to decrease with increasing magnitude, while TSS and CSI were maximized at 50 dBZ. Mean lead times were fair, ranging from roughly 20 to 24 min, which, as with Signature 2, are similar to the time observed between hail formation aloft and the downburst in Mahale et al. (2016). However, skill scores were generally lower for Signature 3 compared to Signatures 1 and 2 due to many Signature 3 test values being common in null downbursts. The maximized TSS and CSI of 0.34 and 0.55, respectively, for 50 dBZ with a relatively high 0.81 POD, moderate 0.37 POFA, and mean lead time of 23.5 min arguably make 50 dBZ the strongest test value for Signature 3 using the direct method.

Lead times were considerably lower for Signature 4, which was expected given Signature 4’s association with a storm’s dissipating stage. Mean lead times were about 14–15 min for all four test values, which are similar to the ~10 min observed between the onset of the DRC and the downburst in Wakimoto and Bringi (1988) and the maximum time of 13.6 min observed between the onset of the ZH core descent and the downburst in Kuster et al. (2016), and are a few minutes longer than the 6–9 min of lead time in Tuttle et al. (1989) and the 7 min of lead time in Heinselman et al. (2008). Signature 4’s POD generally improved with increasing height below the 0°C level, while there was no considerable trend in POFA, TSS, or CSI. The 2.5-km test value was arguably the strongest, given its high 0.94 POD, strong 0.57 CSI, and mean lead time of 14.6 min, despite the relatively high 0.41 POFA and fairly low 0.28 TSS (though this was the highest TSS observed for Signature 4 via the direct method).

Finally, for Signature 5, it can be seen that all statistical parameters tended to decrease with increasing magnitude. Skill scores were generally lower for Signature 5 than other signatures, with 0.13–0.25 TSS and 0.24–0.57 CSI. However, it is worth noting that the >2 dB km−1 test value was the only parameter for any signature that had a 100% POD using the direct method. Lead times ranged from approximately 13 to 15 min, similar to Signature 4, which makes sense given both signatures’ association with a storm’s dissipating stage. The >2 dB km−1 magnitude, having the highest POD, TSS, and CSI values combined with a mean lead time of 14.3 min, is arguably the strongest for Signature 5. However, the 0.43 POFA may be undesirably high for some applications.

5. Statistical performances and lead times: Indirect method

This section presents statistics and lead times calculated by analyzing the five radar signatures within all storms that were identified as having contributed (e.g., through gust front enhancement) to the formation or maintenance of the storm that was eventually deemed most responsible for each of the 32 threshold-level wind reports (i.e., the “indirect method”). It should be noted again that lead times presented in this section would only apply to CCAFS/KSC using this Eulerian nowcasting approach, with shorter lead times expected nearer the storm’s initiation location. Given the results in section 4, it is likely that some storms analyzed in this section produced threshold-level winds prior to reaching CCAFS/KSC; however, this could not be verified using the Cape WINDS. All null downbursts and threshold-level winds are the same between sections 4 and 5 (Tables 4 and 5). Statistics for the indirect method are presented in Fig. 11, while lead times for each signature via the indirect method can be seen in Table 8. As before, a physical description of each signature can be found in Table 6.

Fig. 11.
Fig. 11.

As in Fig. 10, but for the indirect method.

Citation: Weather and Forecasting 34, 1; 10.1175/WAF-D-18-0081.1

Table 8.

Mean and median lead time (LT; min) provided for the 32 threshold-level downbursts by each test value for each of the five radar signatures described in Table 6. These lead times were calculated using the indirect method (i.e., considering all updraft–downdraft cycles identified as having contributed to the storm eventually deemed responsible for contributing most strongly to the 35+-kt winds recorded on the Cape WINDS) in each case.

Table 8.

From Table 8, it can be seen that the indirect method provided considerably longer lead times for the Eulerian forecast region (i.e., CCAFS/KSC) for all five signatures compared to the direct method, which makes sense as the indirect method allows for signatures to be analyzed in cells located farther from CCAFS/KSC. Mean lead times for Signature 1 ranged from roughly 79 to 96 min, which was a factor of the lifespan of the multicell storms and their time spent in the usable radar domain (which is related to their propagation direction and velocity). It can be seen in Fig. 11 that POD and POFA for Signature 1 generally decreased with increasing test height, while TSS and CSI reached peak values of 0.72 and 0.74, respectively, at 2.5 km. The increases in skill scores compared to the direct method are likely due to the indirect method allowing more opportunities for Signature 1 (and all other signatures) to be identified via analysis of multiple updraft–downdraft cycles. These strong skill scores, combined with a moderate 0.78 POD, very low 0.07 POFA, and mean lead time of 86.5 min (for CCAFS/KSC), arguably make 2.5 km the strongest stand-alone test value for Signature 1 using the indirect method.

Signature 2 offered similar mean lead times of 79.5–86.1 min using the indirect method, which makes sense given its association with a storm’s updraft like Signature 1. POD and POFA generally decreased while skill scores increased with increasing height. Most notable are the 4.5- and 5.0-km heights, with TSS and CSI of 0.75 and 0.77, respectively, which were the highest skill scores in this study. Due to 4.5 km having a higher POD than 5.0 km (0.84 vs 0.81) and a longer mean lead time (81.6 vs 79.5 min), the 4.5-km test value for Signature 2 is arguably the strongest stand-alone signature in this study.

Signature 3 saw POD and POFA decrease with increasing magnitude while skill scores peaked at 50 dBZ, similar to the direct method. It is worth noting that 45 dBZ had a 100% POD; however, the high 0.46 POFA may be undesirable. Mean lead times ranged from 72.3 to 90.7 min. The 50-dBZ test value is arguably the strongest for Signature 3, given the peak TSS and CSI of 0.47 and 0.64, respectively; 88 min of mean lead time; high 0.94 POD; and moderate 0.33 POFA.

Signature 4 had similar statistical performances between the direct and indirect methods. POD and POFA generally increased with increasing height below the 0°C level, while there was no discernable trend in skill scores. It is noteworthy that mean lead times ranged from 87.0 to 94.1 min, which were longer than those for Signature 2. Given the association of Signature 2 with the cumulus stage and Signature 4 with the dissipating stage of a storm, this was unexpected. However, there were several cases where the initial cell in a multicell storm produced Signatures 4 and 5 without producing Signature 1 or 2, thus yielding longer lead times for Signatures 4 and 5. It is also worth noting that the 2.5- and 3.0-km heights had a 100% POD. Additionally, the 2.5-km value had the lowest POFA (0.40) of all signature test values that had a 100% POD. Based on this, along with the relatively high 0.60 CSI and mean lead time of 87 min, the 2.5-km test value is arguably the strongest for Signature 4 despite the fairly high POFA and relatively low 0.34 TSS.

Finally, mean lead times for Signature 5 varied from 84.9 to 91.7 min, similar to Signature 4 and unexpectedly longer than most for Signature 2. POD and POFA generally decreased while TSS and CSI increased with increasing magnitude. It is worth noting that the >2 dB km−1 value had a 100% POD. Despite this, the >4 dB km−1 test value may arguably be the strongest for Signature 5 through the indirect method given its moderate 0.78 POD; low 0.17 POFA; relatively high TSS and CSI of 0.63 and 0.68, respectively; and mean lead time of 84.9 min for CCAFS/KSC.

6. Summary and future work

The primary purpose of this study was to explore the physical bases of dual-polarization radar signatures within wet downbursts around CCAFS/KSC and identify common trends within threshold-level downburst-producing storms that may be used by 45WS forecasters to improve their 35-kt convective wind warning. Through subjective (i.e., visual) analysis of gridded data from the 45WS-WSR combined with quantitative wind reports from the Cape WINDS, which to our knowledge is the first study to do so using a relatively large sample, five dual-polarization radar signatures were identified herein or tested from past studies:

  1. peak height above environmental 0°C level of the 1-dB contour within a ZDR column;
  2. peak height above environmental 0°C level of precipitation ice signature;
  3. peak ZH value in cell;
  4. height below environmental 0°C level where ZDR increases to 3 dB within DRC; and
  5. vertical ZDR gradient within ±500 m of the 1-dB contour within DRC.
Multiple signature test values were first examined within the updraft–downdraft cycle that contributed most strongly to Threshold-1 winds in 32 cases (the “direct method”). Through the direct method, cumulus stage signatures (Signatures 1 and 2) and the mature stage signature (Signature 3) provided mean lead times of 20.0–28.2 min, while dissipating stage signatures (Signatures 4 and 5) yielded mean lead times of 12.8–14.9 min. In general, lower test values were associated with higher POD and POFA, while moderate or high test values produced higher TSS and CSI. The 4.5-km precipitation ice signature height was noteworthy for offering the highest CSI and TSS scores of all signatures and test values while also offering a moderate 0.69 POD, low 0.12 POFA, and a mean lead time of 20.5 min.

A secondary purpose of this study was to conduct an initial feasibility test for considering the five aforementioned radar signatures within earlier cells in multicell storms to predict the potential for intense downbursts to occur in later cells within the same multicell storm (the “indirect method”), due to intense gust fronts produced by earlier cells enhancing convection. Cell mergers were also considered in the indirect method. The indirect method offered improved statistics and considerably longer lead times for an Eulerian forecast region (i.e., CCAFS/KSC herein). Mean lead times for CCAFS/KSC ranged from 72.3 to 96.1 min, which was a result of the relatively long life cycles and slow propagation velocities of many multicell storms within the 45WS-WSR domain. Statistics for the indirect method followed similar trends as in the direct method, where POD and POFA generally decreased with increasing test values while skill scores were maximized at moderate to high test values. The 4.5- and 5.0-km heights of the precipitation ice signature were especially noteworthy for having the highest TSS and CSI scores of all signatures and test values, with the 4.5-km height arguably being the strongest stand-alone signature in this study with a 0.84 POD, 0.10 POFA, 0.75 TSS, 0.77 CSI, and a mean lead time of 81.6 min for CCAFS/KSC via the indirect method.

It is not expected that the lead times presented for the indirect method would be offered for all locations along a storm’s path (i.e., within a typical Lagrangian framework); lead times would decrease with decreasing distance from the location of the initial storm cell’s formation. Furthermore, for a warning to be verified, the storm in question must reach the forecast region. Thus, a forecaster must have knowledge of a storm’s propagation direction and estimate of storm longevity for the indirect method to be effective for their Eulerian forecast domain. Therefore, the results presented herein are for a best-case nowcasting scenario.

While the results of this study are promising, there is much potential for future work. One necessary caveat for this study centered on prior knowledge of which storms eventually produced Threshold-1 winds at CCAFS/KSC, while forecasters will not have this knowledge in real time. Future work should consider all active cells in the 45WS-WSR domain and develop a probabilistic outlook for each cell’s ability to produce threshold-level winds at CCAFS/KSC. This study focused on the earliest occurrence of each signature in the indirect method. It would be beneficial to consider the number of times each signature also appears in subsequent cells in the same multicell storm to assess the probability that the storm will produce threshold-level winds in the forecast region. This persistence forecasting would likely decrease lead times, but may increase confidence of a warning being verified. This method would be especially powerful if a signature with very low POFA was observed (e.g., 3.0-km height for Signature 1). While these results were designed for use with the 45WS-WSR, future work could examine the same signatures in other climate regions, using other weather radars with differing characteristics such as data quality and frequency, and investigating other wind thresholds. Expanding the null downburst definition to include multiple updraft–downdraft cycles within a storm could be considered. Using multiple radar signatures in combination rather than individually could also be explored. Finally, it would be beneficial to consider other environmental conditions [e.g., wind shear, microburst-day potential index (Wheeler and Roeder 1996), etc.] when analyzing these radar signatures.

To begin exploring the use of these signatures at other wind thresholds, a preliminary analysis was performed using the 50-kt threshold (Threshold-2), which can be found in the appendix. Preliminary results from the Threshold-2 analysis indicate that the 4.5- and 5.0-km precipitation ice signature heights offered the strongest performances in nowcasting winds ≥ 50 kt at CCAFS/KSC, which is similar to the conclusions developed for the Threshold-1 analysis. Skill scores were generally lower for Threshold-2 compared to Threshold-1, primarily due to POFA being significantly higher at Threshold-2, though some signatures yielded fair results. The direct method was unable to meet the 60 min of lead time desired for 45WS Threshold-2 wind warnings due to the relatively short lifetime of individual updraft–downdraft cycles, but all signatures and test values exceeded the desired lead time through the indirect method. These results may have the potential to assist with Threshold-2 nowcasting and to be used elsewhere (e.g., NWS severe thunderstorm warnings for straight-line convective winds ≥ 50 kt), but a considerable amount of future work must be done to produce more robust results, including expanding the number of Threshold-2 cases, verifying the signatures at other weather radar frequencies such as S band, and all other future work suggested above for the Threshold-1 analysis.

Acknowledgments

This work has been supported by the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) and the 45WS through NASA MSFC Grant NNX15AR78G. We thank Mr. Jeffrey Zautner for providing the Cape WINDS and KXMR data, as well as the three anonymous reviewers for their constructive feedback that helped improve the manuscript. The first author would also like to thank Drs. Walter Petersen and Kevin Knupp for their helpful suggestions.

APPENDIX

Threshold-2: Results and Discussion

As mentioned in section 2c, nine Threshold-2 downbursts, three from the 2015 warm season and six from the 2016 warm season, were analyzed to evaluate the performance of the signatures identified in the Threshold-1 analysis when applied to a larger sample of Threshold-2 downbursts. Only about 5.9% of all downbursts at CCAFS/KSC meet the Threshold-2 warning criteria (McCue et al. 2010), which has resulted in a relatively small number of Threshold-2 downbursts occurring since the 45WS-WSR installation. Ultimately, the Threshold-2 results herein are limited and should be expanded for future work once a greater number of Threshold-2 downbursts with supplementary 45WS-WSR data become available.

The Threshold-2 downbursts were analyzed in the same manner as outlined in section 2c, including use of the direct and indirect methods. For the Threshold-2 analysis, all downbursts with peak winds less than 50 kt were classified as null downbursts. Thus, all 32 null downbursts from the Threshold-1 analysis along with 29 of the 32 threshold-level downbursts from the Threshold-1 analysis were considered null downbursts (since 3 of the 32 threshold-level downbursts in the Threshold-1 analysis also met Threshold-2 criteria, as discussed in section 2c). Statistical performances and lead times for each signature and test value via the direct method are presented in Fig. A1 and Table A1, respectively.

Fig. A1.
Fig. A1.

Line plots showing the values of POD (blue line), POFA (red line), TSS (green line), and CSI (orange line) resulting from use of the direct method in the Threshold-2 analysis for each of the test values for (top left) Signature 1, (top right) Signature 2, (middle left) Signature 3, (middle right) Signature 4, and (bottom left) Signature 5.

Citation: Weather and Forecasting 34, 1; 10.1175/WAF-D-18-0081.1

Table A1.

Mean and median lead time (LT; min) provided for the nine Threshold-2 downbursts by each test value for each of the five radar signatures described in Table 6. These lead times were calculated using the direct method (i.e., only considering the updraft–downdraft cycle deemed responsible for contributing most strongly to the 50+-kt winds recorded on the Cape WINDS) in each case.

Table A1.

Comparing Table A1 to Table 7, the mean lead times offered for Threshold-2 downbursts via the direct method were similar to those offered for Threshold-1, which makes sense as a typical updraft–downdraft lifetime should be similar regardless of peak wind. The highest mean lead time was 31.5 min for the 1.0- and 1.5-km test values of Signature 1 and the lowest mean lead time was 16.8 min for the 55-dBZ test value of Signature 3. However, these are less than the 60 min desired by the 45WS for Threshold-2 wind warnings.

When comparing Fig. A1 to Fig. 10, POD trends were similar between the Threshold-2 and Threshold-1 downbursts. However, POD was slightly higher for most signatures and test values in the Threshold-2 analysis, except for Signature 1. During the Threshold-2 analysis, it was noted that ZDR was less than −3 dB above the 0°C level around the Threshold-2 report in many cases, which was attributed to a high number of Threshold-2 downbursts occurring in multicell storms that had a large spatial extent, causing considerable differential attenuation of the radar signal. This is especially noteworthy when comparing these results to observations from S-band radar, since C-band radars are generally more prone to attenuation effects in rain and melting hail (e.g., Carey et al. 2000; Bringi et al. 2001; Borowska et al. 2011). Despite differential attenuation correction applied to 45WS-WSR data, the correction may not work perfectly in every case, especially in melting hail aloft (e.g., Bringi et al. 2001; Borowska et al. 2011). This ZDR decrease may have affected 45WS-WSR observations of ZDR columns in these cases. The highest POD was 100% for the 45-dBZ Signature 3 test value, the 2.5- and 3.0-km values for Signature 4, and the >2 dB km−1 value for Signature 5. The lowest POD was 0.33 for the 55-dBZ test value for Signature 3 and the >4 dB km−1 value for Signature 5. Furthermore, while the POFA trend was similar between Threshold-1 and Threshold-2, the magnitude of Threshold-2 POFA was much higher. This is due to the few Threshold-2 cases available compared to the relatively high number of null downbursts and the relatively strong presence of several signatures in Threshold-1 downbursts. The lowest POFA was 0.60 for the 3.0-km value of Signature 1 and the highest POFA was 0.89 for the 1.0-km value of Signature 1.

TSS and CSI were generally lower for the Threshold-2 direct method results compared to Threshold-1, especially CSI, which had a minimum of 0.10 at the 1.0-km test value for Signature 1 and a maximum of 0.27 at the 3.0-km Signature 1 value. Some signatures still had fair TSS values; the 4.5-km test value for Signature 2 had a TSS of 0.51, which was the highest TSS observed via the direct method for Threshold-2. Conversely, the lowest TSS observed was −0.13 for the 1.0-km Signature 1 test value, which is worse than random forecasting. However, as discussed above, this poor performance was likely caused in part by differential attenuation. As with the Threshold-1 analysis, it seems that the 4.5-km value for Signature 2 is arguably the strongest for nowcasting Threshold-2 downbursts at CCAFS/KSC via the direct method, based on this limited analysis, due to it having the highest TSS, the second-highest CSI, a high 0.89 POD, and 20.9 min of mean lead time, despite the high 0.74 POFA.

Lead times and statistical performances provided by the indirect method for Threshold-2 can be found in Table A2 and Fig. A2, respectively. From Table A2, the indirect method yielded much longer mean lead times for Threshold-2 compared to the direct method, as was the case in the Threshold-1 analysis. This was due to a majority of Threshold-2 downbursts being produced by very long-lived multicell storms that spent a considerable amount of time in the usable radar domain. The highest mean lead time was 118.6 min for the 1.5-km test value of Signature 4 and the lowest mean lead time was 73.5 min for the 55-dBZ value of Signature 3. It is noteworthy that all mean lead times via the indirect method were greater than the 60 min desired for 45WS Threshold-2 wind warnings. However, not every case saw lead times greater than 60 min for all signatures, and further analysis is needed using a larger sample.

Fig. A2.
Fig. A2.

As in Fig. A1, but for the indirect method.

Citation: Weather and Forecasting 34, 1; 10.1175/WAF-D-18-0081.1

Table A2.

Mean and median lead time (LT; min) provided for the nine Threshold-2 downbursts by each test value for each of the five radar signatures described in Table 6. These lead times were calculated using the indirect method (i.e., considering all updraft–downdraft cycles identified as having contributed to the storm eventually deemed responsible for contributing most strongly to the 50+-kt winds recorded on the Cape WINDS) in each case.

Table A2.

POD was higher or similar for all signatures and test values in the indirect method compared to the direct method for Threshold-2 while POFA was very similar between the two methods for most signatures. The highest POD was 100%, which was found in many signatures and test values, while the lowest POD was 0.67 for the 55-dBZ Signature 3 test value. The highest POFA was 0.86 for the 45-dBZ Signature 3 test value, the 3.0-km Signature 4 value, and the >2 dB km−1 Signature 5 value, while the lowest POFA was 0.70 for the 55-dBZ Signature 3 value. Similarly, CSI was similar or slightly improved in the indirect method. The highest CSI was 0.27 for the 5.0-km value for Signature 2, which is the same peak CSI observed in the direct method, while the lowest CSI was 0.14 for the 45-dBZ Signature 3 value and the 3.0-km Signature 4 value. TSS was considerably higher overall for the indirect method, with multiple signatures having a TSS ≥ 0.50. The highest TSS was 0.59 for the 5.0-km value for Signature 2, while the lowest TSS was 0.08 for the 45-dBZ Signature 3 value. It is worth noting that the improved performance of all Signature 1 test values in the indirect method compared to the direct method was likely due to the analysis of multicell storms over a wider variety of locations relative to the radar, which may have increased the chances for a ZDR column to be observed prior to differential attenuation becoming significant. Based on these results, the 5.0-km value for Signature 2 is arguably the strongest for Threshold-2 nowcasting via the indirect method due to it having the highest skill scores, a 100% POD, and a mean lead time of 98.1 min, despite the high 0.74 POFA.

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