An Intercomparison of UW Cloud-Top Cooling Rates with WSR-88D Radar Data

Daniel C. Hartung Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

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Justin M. Sieglaff Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

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Lee M. Cronce Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

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Wayne F. Feltz Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

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Abstract

The University of Wisconsin Convective Initiation (UWCI) algorithm utilizes geostationary IR satellite data to compute cloud-top cooling (UW-CTC) rates and assign CI nowcasts to vertically growing clouds. This study is motivated by National Weather Service (NWS) forecaster reviews of the algorithm output, which hypothesized that more intense cloud-top cooling corresponds to more vigorous short-term (0–60 min) convective development. An objective validation of UW-CTC rates using a satellite-based object-tracking methodology is presented, along with a prognostic evaluation of such cloud-top cooling rates for use in forecasting the growth and development of deep convection. In general, both a cloud object’s instantaneous and maximum cooling rate(s) are shown to be useful prognostic tools in predicting future radar intensification. UW-CTC rates are shown to be most skillful in detecting convective clouds that achieved intense radar signatures. The UW-CTC rate lead time ahead of the various radar fields is also shown, along with an illustration of the benefit of UW-CTC rates in operational forecasting. The results of this study suggest that convective clouds with the strongest UW-CTC rates are more likely to achieve significant near-term (0–60 min) radar signatures in such fields as composite reflectivity, vertically integrated liquid (VIL), and maximum estimated size of hail (MESH) compared to clouds that exhibit only weak UW-CTC rates.

Corresponding author address: Daniel Hartung, 1225 W. Dayton St., Madison, WI 53706. E-mail: daniel.hartung@ssec.wisc.edu

Abstract

The University of Wisconsin Convective Initiation (UWCI) algorithm utilizes geostationary IR satellite data to compute cloud-top cooling (UW-CTC) rates and assign CI nowcasts to vertically growing clouds. This study is motivated by National Weather Service (NWS) forecaster reviews of the algorithm output, which hypothesized that more intense cloud-top cooling corresponds to more vigorous short-term (0–60 min) convective development. An objective validation of UW-CTC rates using a satellite-based object-tracking methodology is presented, along with a prognostic evaluation of such cloud-top cooling rates for use in forecasting the growth and development of deep convection. In general, both a cloud object’s instantaneous and maximum cooling rate(s) are shown to be useful prognostic tools in predicting future radar intensification. UW-CTC rates are shown to be most skillful in detecting convective clouds that achieved intense radar signatures. The UW-CTC rate lead time ahead of the various radar fields is also shown, along with an illustration of the benefit of UW-CTC rates in operational forecasting. The results of this study suggest that convective clouds with the strongest UW-CTC rates are more likely to achieve significant near-term (0–60 min) radar signatures in such fields as composite reflectivity, vertically integrated liquid (VIL), and maximum estimated size of hail (MESH) compared to clouds that exhibit only weak UW-CTC rates.

Corresponding author address: Daniel Hartung, 1225 W. Dayton St., Madison, WI 53706. E-mail: daniel.hartung@ssec.wisc.edu

1. Background

For decades, operational weather forecasters have utilized visible (VIS) and infrared (IR) geostationary satellite data [e.g., the Geostationary Operational Environmental Satellite (GOES) series] to more accurately identify, track, and forecast the onset (i.e., initiation) and intensification of deep convection (e.g., Pautz 1971; Purdom 1976; Suchman et al. 1981; Purdom 1986; Moller et al. 1994; Weaver et al. 1999). An atmospheric state in which conditions are favorable for the development of deep convection is one characterized by abundant mid- or lower-tropospheric moisture, steep low- to midlevel lapse rates (conditional instability), and a sufficient lifting mechanism to assure that moist lower-tropospheric parcels will reach their level of free convection (LFC; Doswell 1985, 1987; Johns and Doswell 1992). One of the greatest challenges for a forecaster when predicting the timing and location of thunderstorm development is in extrapolating relatively coarse spatial and temporal observations (surface data and soundings) forward in time to derive the most accurate atmospheric profile for the forecast period of interest (Moller et al. 1994; Moller 2001). Many studies have illustrated how valuable high-resolution (1 km) visible satellite imagery can be to a forecaster during the daytime in identifying the location of likely convective intensification along interacting surface boundaries (marked by the development of a cumulus field on their leading edge) (e.g., Purdom 1971, 1976, 1982, 1986; Weaver and Purdom 1995; Bikos et al. 2002), as well as monitoring changing boundary layer stability and wind profiles resulting from these convective-scale interactions (e.g., Moller et al. 1994; Weaver et al. 1994; Bikos et al. 2006). As spatial resolution in the infrared channels improved (4–8 km), geostationary satellite data became more widely used to monitor storm-scale environments and convective evolution throughout the entire diurnal cycle (Menzel and Purdom 1994; Arnold 2008). Thus, unlike other available observing tools (e.g., rawinsondes, surface observations, etc.), geostationary satellite imagery contributes substantial value to forecasts in that the observed clouds and cloud patterns “represent the integrated effect of ongoing dynamic and thermodynamic processes in the atmosphere (Purdom 1982).”

Convection has long been studied from a satellite perspective using ~11-μm IR window channel radiances to compute values and trends of brightness temperature (TB) between consecutive satellite images (e.g., Adler and Fenn 1979a,b, 1981; Adler et al. 1985; Roberts and Rutledge 2003). Since the troposphere exhibits generally decreasing temperatures with height, saturated air parcels that have surpassed the LFC are positively buoyant with respect to the surrounding environment and grow vertically until reaching their level of neutral buoyancy (Wallace and Hobbs 2006). Thus, cloud tops exhibit a monotonic temperature–height relationship in which the highest point of the cloud is often the coldest (Adler and Mack 1986; Fig. 1a). Figure 1b illustrates the top-down IR perspective of a growing cumulus cloud as viewed from a geostationary satellite. Since satellites only “see” clouds from above, the observed TB of a developing cumulus cloud decreases as the tower ascends and matures (Fig. 1). It should also be noted that since the highest (coldest) point of a cloud is often much smaller than an IR satellite pixel (4 km × 4 km), the actual temperature of the cloud top is likely colder than the brightness temperature sensed by a GOES imager.

Fig. 1.
Fig. 1.

Conceptual model of a developing cumulonimbus cloud (a) illustrating how cloud-top temperature decreases with increased vertical growth (maturation) over time in the XZ plane and (b) as observed in the IR brightness temperature field from a geostationary satellite. Note that the brightness temperatures in (b) are slightly warmer than the actual temperature of the cloud top at each stage in (a).

Citation: Weather and Forecasting 28, 2; 10.1175/WAF-D-12-00021.1

Many prior studies have leveraged cloud-top TB data to both advance the understanding of thunderstorm development and quantify convective intensity by correlating different characteristics of the convective life cycle (e.g., updraft velocity, anvil expansion, minimum cloud-top temperature) with severe storm reports and radar signatures (e.g., Adler and Fenn 1979a,b, 1981; Negri and Adler 1981; Adler et al. 1985; Roberts and Rutledge 2003). During the initial stages of development, cloud-top cooling to subfreezing temperatures (TB < 0°C) has been shown to be a good proxy for the commencement of cloud-top glaciation and the formation of precipitation (Roberts and Rutledge 2003). Studies by Adler and Fenn (1979a,b) found that severe storms with vigorous cloud-top cooling in excess of −4 K min−1 exhibited updraft speeds that were twice those of nonsevere storms. Adler and Fenn (1981) showed that for supercell thunderstorms, a period of rapid vertical cloud growth often precedes or occurs simultaneously with the detection of a mesocyclone by radar. Roberts and Rutledge (2003) further explored the relationship between cloud-top characteristics and maximum storm intensity, ultimately categorizing convective growth based on the presence of subfreezing cloud-top temperatures and rates of change of TB. Cooling rates between −4 and −8 K (15 min)−1 were considered weak, while values < −8 K (15 min)−1 were considered indicative of growth that precedes more vigorous storm intensification (Roberts and Rutledge 2003). Therefore, satellite-observed cloud-top cooling has been shown to serve as a robust indicator of real-time convective growth, providing up to 30 min of lead time prior to the detection of a 35-dBZ precipitation echo by ground-based radar [i.e. convective initiation (CI); Roberts and Rutledge 2003].

To address the operational forecast challenges associated with thunderstorm development, a variety of objective algorithms have been developed to nowcast convective initiation (e.g., Mueller et al. 2003; Mecikalski and Bedka 2006; Zinner et al. 2008; Sieglaff et al. 2011) and intensification (e.g., Carvalho and Jones 2001; Feidas and Cartarlis 2001, 2005; Morel and Senesi 2002; Vila et al. 2008) using geostationary satellite data. The CI nowcasting approach introduced by Mecikalski and Bedka (2006), the Satellite Analysis and Tracking System (SATCAST), combines information from satellite-derived atmospheric motion vectors (AMVs; Bedka and Mecikalski 2005) and a daytime-only cumulus cloud mask (Berendes et al. 2008) with IR TB value trends and CI interest fields computed using multispectral band difference techniques to identify newly developing convective storms. Zinner et al. (2008) describes another daytime-only approach (Cumulonimbus Tracking and Monitoring; Cb-TRAM) that leverages visible, thermal IR, and NWP model information to nowcast CI over central Europe. In an effort to address the need for a diurnally independent satellite-based CI nowcasting capability, the University of Wisconsin-Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) developed the University of Wisconsin Convective Initiation-Cloud-Top Cooling (UWCI-CTC) algorithm, which applies the concept of “box averaging” to compute IR-window channel cloud-top cooling rate and cloud-top-type trends derived from multispectral GOES data to assign CI nowcasts throughout the diurnal cycle (Sieglaff et al. 2011). The UWCI-CTC algorithm was designed to capture the period from initial vertical cloud growth through the time when the cloud top glaciates and the storm is considered mature (Sieglaff et al. 2011). The reader is referred to Sieglaff et al. (2011) for a complete explanation of the UWCI-CTC methodology.

An intercomparison between UWCI cloud-top cooling (henceforth referred to as UW-CTC) rates and various Weather Surveillance Radar-1988 Doppler (WSR-88D) fields is presented. The Warning Decision Support System-Integrated Information (WDSS-II; Lakshmanan et al. 2007b) object-tracking framework is employed with a UW-CIMSS postprocessing methodology (Sieglaff et al. (2013, hereafter S13) to track satellite-based cloud objects in an automated manner. This unique satellite-based object-tracking framework functions as an independent vehicle from which a fused array of meteorological data including satellite-derived UW-CTC rates, radar, and lightning information for each cloud object can be examined simultaneously. The purpose of this study is to objectively validate UW-CTC rates using an independent object-tracking methodology, as well as to demonstrate the prognostic value of such cooling rates in forecasting the growth and development of deep convection. Since National Weather Service (NWS) forecaster feedback and interactions played a key role in motivating this work, results presented herein are discussed primarily with respect to implications for operational forecasting.

2. Motivation

Operational forecasters had the opportunity to evaluate the CI nowcast and UW-CTC information output by the semioperational UWCI-CTC algorithm during their participation in the 2009–12 GOES-R Proving Ground (GOES-RPG) Hazardous Weather Testbed (HWT; Clark et al. 2012) spring experiment at the Storm Prediction Center (SPC) and the 2010–12 GOES-RPG local HWT (information available online at http://cimss.ssec.wisc.edu/goes_r/proving-ground/SPC/UWCI_Feedback.html) at the NWS Milwaukee/Sullivan, Wisconsin (MKX), Weather Forecast Office (WFO). In general, forecasters found the instantaneous UW-CTC rates to be more useful for decision support than the yes–no CI nowcasts produced by the UWCI-CTC algorithm. Real-time feedback, observations, and examples from the experiments can be found online (http://goesrhwt.blogspot.com/?m=1). Since the UWCI-CTC algorithm only considers the presence (and not the magnitude) of a measurable UW-CTC rate [of at least −4 K (15 min)−1 in magnitude] when assigning a CI nowcast (Sieglaff et al. 2011), forecasters are not able to deduce information about the vigor of convective growth from the nowcasts alone. However, many forecasters hypothesized that more intense UW-CTC rates correspond to more rapid and vigorous short-term (0–60 min) convective development.

In response to this feedback, this study tests the above hypothesis to better understand the prognostic relationship between different magnitudes of UW-CTC rates and near-term convective intensification in a variety of diagnostic radar fields including composite reflectivity, reflectivity at the −10°C isotherm, vertically integrated liquid (VIL; Greene and Clark 1972), and maximum estimated size of hail (MESH; Witt et al. 1998a). The importance of utilizing the instantaneous UW-CTC rate of a cloud object from two different perspectives is also discussed. Finally, since forecasters heavily rely on radar data to diagnose hazardous convection, a comprehensive lead-time analysis relating UW-CTC rates to various radar field thresholds is shown in order to highlight the advantage of monitoring convective cloud intensification via geostationary satellite prior to the detection of a significant radar echo.

3. Data and methods

a. WDSS-II satellite-based object tracking framework

This study employs the WDSS-II/UW-CIMSS satellite-based object-tracking framework detailed in S13 to monitor and track individual convective clouds ranging in size from infancy (3 IR pixels) to convective maturity (100+ IR pixels) throughout the diurnal cycle. Cloud objects are generated, assigned unique IDs, and tracked by the S13 framework using remapped parallax-corrected 0.04° latitude–longitude IR-window top-of-the-troposphere cloud emissivity (Pavolonis 2010). Thus, by simply remapping multiple data fields [e.g., satellite, radar, NWP, National Lightning Detection Network (NLDN), etc.] to the S13 cloud-object grid, values and temporal trends of those fields can be evaluated for any cloud object. For example, satellite and radar data are fused using this framework by first building satellite-based cloud objects out of geostationary IR data at t1 using WDSS-II. Higher spatial and temporal resolution composite reflectivity is then remapped to the cloud-object grid at each radar volume scan after t1 and prior to the next satellite scan. The maximum composite reflectivity from all radar volume scans between t1 and the next satellite scan that is located within a t1 cloud object is then stored in a data structure under the t1 cloud-object ID. This process is repeated once a new satellite scan is available from which new cloud objects are created at the new time (t2); subsequent radar data that overlap with the tracked cloud object at t2 are recorded, resulting in a radar time series for the corresponding cloud-object ID. This unique fusion methodology is implemented in this study for the data types described in the following sections to allow for an automated coupled intercomparison of the different datasets.

b. UWCI cloud-top cooling rate data

The UWCI-CTC algorithm uses multispectral GOES imager data to compute instantaneous cloud-top cooling rates. Satellite pixel cooling rates are computed by differencing the 11-μm IR-window TB using a box-averaging technique between consecutive geostationary scan times (Sieglaff et al. 2011). A series of tests are then conducted to ensure that 1) the resultant cooling signal meets the −4 K (15 min)−1 minimum threshold from Roberts and Rutledge (2003) and 2) that the final cooling rate is due to vertical cloud growth (not horizontal cloud advection). This study analyzes derived UW-CTC rates for 23 convective afternoons (1815–2345 UTC) and 11 convective nights (0015–0615 UTC; see Tables 1 and 2 in Sieglaff et al. 2011 for individual dates) during the spring and early summer of 2008 and 2009. The domain of interest shown in Fig. 2 is the same as that used by Sieglaff et al. (2011) and covers a region of the interior plains of the contiguous United States extending from 30° to 46°N and from 94° to 104°W.

Fig. 2.
Fig. 2.

Analysis domain over the interior plains of the United Sates (red contour) within which satellite-based cloud objects are used to investigate the relationship between various UWCI-CTC algorithm cooling rates and WSR-88D and NLDN lightning data. This analysis domain is the same as that used by Sieglaff et al. (2011).

Citation: Weather and Forecasting 28, 2; 10.1175/WAF-D-12-00021.1

c. WSR-88D data

Next Generation Weather Radar (NEXRAD) superresolution 0.25 km × 0.5° WSR-88D data (mapped onto a 0.01° × 0.01° grid taking into account beam blockage and data from multiple radars) were provided and quality controlled by the Cooperative Institute for Mesoscale Meteorological Studies at the University of Oklahoma (OU-CIMMS; Lakshmanan et al. 2007a). Radar fields that were employed in this study include composite reflectivity, reflectivity at the −10°C isotherm, VIL, and MESH.

Many studies have pointed out the limitations of using Storm Data reports to verify radar-derived hail algorithms (e.g., MESH) including a lack of reports for storms that occur in unpopulated rural areas, the discreet nature of reported hail sizes (e.g., 0.75, 0.88, 1 in., etc.), the unlikelihood of storm spotters being able to observe the absolute largest hailstone produced by a given storm, and the tendency for spotters to focus on the more urgent phenomena when two severe events occur simultaneously (i.e., hail being underreported for supercells that also produce tornadoes) (Witt et al. 1998b; Stumpf et al. 2004; Ortega et al. 2006). A recent study by Cintineo et al. (2012) used high-resolution hail reports from the Severe Hazards Analysis and Verification Experiment (SHAVE; Ortega et al. 2009) to show that multiradar MESH has appreciable skill, provides superior coverage and resolution over Storm Data hail reports, and is unbiased by nonmeteorological factors. Therefore, this study utilized quality controlled, blended multiradar MESH data that are available to operational forecasters as a metric for diagnosing storm intensity based on the radar-estimated size of hail in a storm.

Maximum values of the aforementioned radar fields and UW-CTC output were preserved for each cloud object at each time that the object existed. The first time that an object achieved a particular UW-CTC rate, as well as multiple thresholds for each radar field, was retained in order to perform lead-time analyses using a special UW-CIMSS statistical postprocessing framework.

d. Convective cloud-object tracking errors

It is necessary to briefly discuss how errors within the S13 object-tracking framework are manifested in the UW-CTC object validation statistics and the radar–UW-CTC rate intercomparisons. Similar to other object-tracking systems, artifacts such as inconsistencies in identification, merging and splitting of cloud objects, and differing temporal scan times of geostationary imagers inherently contribute to the sampling error of the UW-CTC rate distributions compiled using the S13 automated framework. When evaluating the performance of the cloud-object-tracking methodology, S13 showed that object tracking improved substantially as cloud-object size and satellite temporal resolution increased, with the least error observed when tracking objects larger than 100 IR pixels and scan gaps less than 20 min. Table 1 illustrates how the mean size of a cloud object is moderate (<50 IR pixels) when the maximum cooling signal is detected (i.e., the period of most rapid vertical development). Despite these limitations, results presented in subsequent sections testify to the satisfactory performance of the S13 system for conducting such a study.

Table 1.

Mean and standard deviation (in no. of IR pixels) of cloud-object size at the time when the maximum UW cloud-top cooling rate was observed for weak, moderate, and strong convective growth clouds.

Table 1.

Another source of error in the S13 object-tracking system stems from inconsistencies in satellite temporal resolution. Regardless of the operational geostationary satellite scan mode (i.e., rapid scan or routine), 3-hourly 30-min gaps are unavoidable during scheduled full-disk scans with the current GOES imager. S13 showed that object-tracking performance degrades rapidly with data gaps that exceed 20 min, especially for smaller cloud objects. Therefore, a small convective cloud that encounters a 30-min data gap during its development is more likely to be lost by the object-tracking system. This results in an underrepresentation of the actual maximum radar signature of the cloud object that is not captured by the interrupted tracking of the object. Thus, in order to ensure data quality, cloud objects that are assigned a UW-CTC rate ≥17 min after a radar signature is detected are counted as misses in the probability of detection–false alarm rate (POD–FAR) analysis in Table 2 and are excluded from the analyses in section 4.

Table 2.

POD, FAR, and UW-CTC count statistics for the interior plains region of the United States for the 23 convective days and 11 convective nights within the validation dataset. Hit and miss counts for each radar reflectivity threshold at the −10°C isotherm (Ref-10) include all hits and misses for Ref-10 greater than or equal to the bin value. False alarm counts include cloud objects that had a UW cloud-top cooling rate and no Ref-10 value, as well as those objects that had a cooling rate and achieved a maximum Ref-10 less than the bin value. A “hit” is defined as any cloud object that was assigned a UW-CTC rate and also achieved a Ref-10 value of the corresponding bin magnitude or greater during its lifetime. A “miss” is a cloud object that achieved a Ref-10 magnitude that was greater than or equal to the bin value during its lifetime but was never assigned a UW-CTC rate or any cloud object that achieved a Ref-10 magnitude greater than or equal to the corresponding bin ≥17 min prior to it being assigned a corresponding UW-CTC rate.

Table 2.

Finally, the S13 framework was designed to track individual convective cloud objects from infancy into satellite maturity (development of an anvil), so long as individual convective cloud objects are kept separate from neighboring convective clouds. A consequence of using satellite data with a top-down perspective to build cloud objects is that in many instances, neighboring convective anvils can be merged together into one large cloud object. Due to the large uncertainty of relating corresponding radar-based observations to the appropriate merged thunderstorm anvil, the S13 framework automatically purges cloud objects that become too large (>1000 IR pixels). Thus, it is possible for convective clouds to be tracked only until they are mature on satellite, in which case full radar-based maturity for the respective objects is never achieved, even for isolated convection. The resultant statistics for such objects are consequently an underrepresentation of the actual maximum radar signature, much like cloud objects impacted by large temporal data gaps discussed earlier.

On the other hand, cloud objects with weak cooling signals occasionally merge with nearby objects that did not have a cooling signal but already exhibit an intense radar reflectivity signature. If the ID from the first cloud object is preserved after the merger, the larger radar reflectivity of the previously adjacent cell will erroneously be assigned to the object ID that had a weak UW-CTC rate. Thus, as a result of the cloud-object tracking error, the cloud object with a weak UW-CTC rate is associated with a much higher radar reflectivity than which actually occurred. The corresponding tracking errors are embedded within the analyses of section 4 and act to inflate the standard deviation (1σ) for each UW-CTC bin. Although the S13 framework has been shown to perform well in tracking developing convective cloud objects, it is important to keep in mind these error sources when interpreting the following results.

4. Results

a. UWCI-CTC validation

S13 highlighted two primary utilities for their satellite-based convective cloud-object-tracking methodology: 1) algorithm validation and 2) the investigation of convective cloud growth and development. As a complement to the manual validation of the UWCI-CTC algorithm against NLDN cloud-to-ground (CG) lightning data performed by Sieglaff et al. (2011), this study employs the objective satellite-based object-tracking framework from S13 to validate the UW-CTC rate calculations within cloud objects against composite reflectivity at the −10°C isotherm (Ref-10; Lakshmanan et al. 2006). Sieglaff et al. (2011) considered a CI nowcast to be a hit if it had a corresponding CG lightning strike associated with it at any time in the future. Their approach utilized all instantaneous CI nowcasts for validation, and thus inherently resulted in skewed POD and FAR statistics from validating multiple nowcasts for the same convective storm. According to Wilks (2006), the POD is commonly defined as the number of correct “yes” forecasts (hits) divided by the total number of yes and “no” forecasts (hits + misses), while the FAR is computed by dividing the number of incorrect yes forecasts (false alarms) by the total number of yes forecasts (hits + false alarms).

The UWCI-CTC algorithm assigns CI nowcasts to calculated CTC rates independent of the actual cooling rate magnitude; for comparative purposes, the validation presented here consists of POD and FAR statistics that only consider the presence of a UW-CTC rate within an S13 cloud object, regardless of the total number of instantaneous UW-CTC rates observed throughout an object’s lifetime. It is also important to note that if a UW-CTC region consists of less than five IR pixels or contained greater than 5% thick ice at the previous satellite scan, the conservative UWCI-CTC algorithm does not assign a CI nowcast to these CTC signals for reasons described in Sieglaff et al. (2011). By including the UW-CTC rates that were disqualified for a CI nowcast by the UWCI-CTC algorithm, this study validates a unique larger UW-CTC observational dataset than the original CI nowcast dataset validated by Sieglaff et al. (2011).

POD and FAR statistics were computed as a function of Ref-10 in order to evaluate the skill of the UW-CTC rates in identifying convective clouds that intensify to various Ref-10 thresholds (Table 2). The POD and FAR statistics for cloud objects that achieved 35+ dBZ at Ref-10 are 22.0% and 36.0%, respectively. Given the conservative design of the UWCI-CTC algorithm (i.e., only more significant convective storms are targeted), the POD steadily increases to 34.0% for moderate intensity (45+ dBZ) Ref-10 echoes and 62% for 60+ dBZ Ref-10 radar signatures (Table 2). Due to the relatively infrequent nature of strong convective storms (50+ dBZ at Ref-10) compared to weak and moderate radar echoes, it was expected that the FAR would increase with increasing Ref-10; this notion is confirmed in Table 2. Although the FAR generally increases with increasing Ref-10, an interesting trend emerges when examining the change in FAR with increasing UW-CTC intensity, in that the FAR decreases for cloud objects that achieve more intense cooling rates (in a relative sense) (Table 2). Although the POD only exceeds the FAR for the strongest growing clouds that go on to achieve a large Ref-10, an operational forecaster can be confident that when a strong UW-CTC rate is observed, there is a very good chance that the convective cloud will go on to develop a composite reflectivity of 55+ dBZ in the next 0–60 min (Table 2).

Based on the sampled dataset, the statistics suggest that a forecaster can be confident that approximately half of the convective storms that go on to achieve a 50+ dBZ Ref-10 radar signature will have been identified (assigned a UW-CTC rate) by the UWCI-CTC algorithm (Table 2). In addition, the lower FAR for rapidly growing cloud objects with UW-CTC rates < −20 K (15 min)−1 lends increased confidence to operational forecasters that rapidly growing vertical convection identified by the UWCI-CTC algorithm is more likely to achieve a moderate or greater Ref-10 echo than slower-growing convection with UW-CTC rates > −10 K (15 min)−1 (Table 2).

b. Comparison of UW-CTC and WSR-88D radar fields

1) Composite reflectivity

Operational weather forecasters can infer a substantial amount of information about the near-term convective development of a cloud by monitoring its rate of vertical growth (i.e., cloud-top cooling rate). Figure 3 shows a series of boxplots relating all cloud-object UW-CTC observations to the maximum value achieved by each cloud object during its lifetime for the following radar fields: composite radar reflectivity [Refc, panel (a)], VIL [panel (b)], and MESH [panel (c)] from the time period and geographic region described in section 3. UW-CTC observations in Fig. 3, as well as throughout the remainder of section 4, are binned into three categories based on UW-CTC intensity [K (15 min)−1]: weak (UW-CTC > −10), moderate (−10 ≥ UW-CTC > −20), and strong (UW-CTC ≤ −20) convective growth.

Fig. 3.
Fig. 3.

Comparison of all instantaneous UW cloud-top cooling rates to (a) maximum composite reflectivity (dBZ), (b) VIL, and (c) MESH for cloud objects that had both a UW-CTC rate and associated radar field at some point in their lifetime. UW-CTC rates for cloud objects are binned by intensity [K (15 min)−1] with weak, moderate, and strong convective growth defined as UW-CTC > −10, −10 ≥ UW-CTC > −20, and UW-CTC ≤ −20, respectively. For each boxplot, the median (red line), 25th and 75th percentiles (top and bottom bounds of blue box), and one standard deviation (whiskers) are shown. The medians of different intensity bins are significantly different at the 5% significance level if the widths of the notches centered on the medians do not overlap.

Citation: Weather and Forecasting 28, 2; 10.1175/WAF-D-12-00021.1

When comparing a cloud object’s instantaneous UW-CTC observations to its maximum composite reflectivity, it is difficult to delineate a statistically significant trend between weak, moderate, and strong cooling convective clouds and their highest achieved Refc (Fig. 3a). This particular conclusion is not surprising, given that a convective cloud can have multiple UW-CTC observations during the thunderstorm growth stage, implying that it is likely that the weak and moderate UW-CTC bins in Fig. 3a are composed of observations from both slower-growing convection and the initial growth period of much more vigorous convection.

The following analyses offer two different perspectives for interpreting and utilizing UW-CTC rates in an operational environment. The first approach, shown in Fig. 3, compares all UW-CTC rate observations to radar field maxima achieved by each cloud object throughout its lifetime. Since in a real-time operational environment a forecaster never knows whether or not a cloud object has achieved its maximum CTC rate, he or she can use this approach to understand the full range of possible near-term radar signature developments associated with UW-CTC rates that compose a cloud object’s CTC history. The second approach incorporates the 2-yr dataset from this study to understand which magnitudes of various radar fields that a particular cloud object is more likely to develop in the near term, assuming that the largest UW-CTC rate achieved up to the current time is the maximum cooling rate it will achieve. It may appear that the first perspective is most applicable to operational forecasters, yet 93% (99%) of developing thunderstorms achieve their maximum cooling rate within two (three) diagnosed UW-CTC scans; thus, both methods are presented and the preferred method of interpretation is left up to the reader.

One example of how a forecaster can use the information presented in Fig. 3a is as follows. A cloud object is assigned a −4 K (15 min)−1 UW-CTC rate at the current time. The forecaster sees that this weak cooling rate falls into the first bin in Fig. 3a and concludes that there is a large range of possible radar reflectivity intensities that this cloud may or may not develop. This is obvious in that the earliest stages of convective growth as diagnosed by cooling cloud tops could signify either weaker convection or be the early stages of future vigorous convection. When the next CTC observation is available and has a magnitude of −15 K (15 min)−1, the forecaster can hone in on the moderate and strong growth bins as possibilities for near-term radar development, thus eliminating some of the uncertainty present after the first observation. Finally, if the cloud object achieves a UW-CTC rate of −22 K (15 min)−1 at the next scan time, Fig. 3a suggests that this particular cloud object is likely to develop higher composite reflectivities. Convective cloud objects that at some point exhibit the most intense maximum cooling rates, tend to develop the strongest precipitation cores (median Refc of 55 dBZ), with 75% of cloud objects in that UW-CTC bin achieving a composite reflectivity of at least 40 dBZ in their lifetime and a 1σ value encompassing 70-dBZ cores (Fig. 3a). Therefore, the greatest benefit of the all UW-CTC versus maximum radar field analyses is to assist operational forecasters with interpreting the range of possible radar field magnitudes that have been associated with a given observed magnitude of UW-CTC rate.

In contrast, given the full UW-CTC history of a cloud object, the maximum UW-CTC rate over the course of an object’s lifetime is a good indicator of its future maximum echo intensity (Fig. 4a). When considering only the maximum UW-CTC rate of cloud objects, weakly growing convective clouds achieved a median maximum Refc of 45 dBZ, while moderate growth convection achieved a slightly more intense median of 50 dBZ; half of the cloud objects in the moderate bin reached a maximum Refc of 40–55 dBZ at some point during their lifetime (Fig. 4a). Similar to the all-CTC analysis, convective cloud objects that exhibited the most intense maximum cooling rates developed the strongest precipitation cores (Fig. 4a). Also, for reasons described in section 3d, the upper bound whisker on the weak UW-CTC box is likely an artifact of the erroneous merging of cloud objects with a weak UW-CTC signal with adjacent cloud objects that had a more intense composite reflectivity (Figs. 3a and 4a). Finally, in contrast to the all-CTC analysis, median values of achieved maximum Refc for binned cloud objects that had weak, moderate, or strong UW-CTC at their time of most intense vertical growth are statistically different from one another at the 95% confidence level (Fig. 4a), indicating that more intense vertical growth is a good proxy for the development of a more intense precipitation core.

Fig. 4.
Fig. 4.

As in Fig. 3, but a comparison of maximum UW cloud-top cooling rates to maximum achieved values for the radar fields described in Fig. 3.

Citation: Weather and Forecasting 28, 2; 10.1175/WAF-D-12-00021.1

Of the 881 cloud objects analyzed in this study that were assigned a UW-CTC rate during their lifetime, 70% of the time the maximum UW-CTC rate occurred at the first scan time that the cloud object existed. Moreover, 82% (727) of cloud objects achieved their maximum UW-CTC signal within 15 min of the first geostationary scan time and 90% (791) exhibited a maximum UW-CTC rate within 30 min of development. Even though forecasters do not know when the maximum cooling rate of a cloud object is reached, for reasons motivated previously, the authors have included the maximum UW-CTC versus maximum radar analyses, in turn providing an alternative set of information from which forecasters can make their expectations on convective evolution.

The UWCI-CTC algorithm was shown in section 4a to have skill in detecting cooling convective clouds that went on to produce moderate and strong reflectivity echoes; it is equally important from a forecasting perspective to investigate how much lead time is provided by a cloud object’s UW-CTC rate(s) prior to the development of various radar thresholds. It should be noted that only a small percentage of the total cloud objects with observed UW-CTC rates go on to achieve the operationally significant radar signature thresholds discussed in the following sections. Figure 5a illustrates the normalized distribution of positive (≥0 to 60 min) and negative (<0 to −17 min) lead times for all cloud objects that had a weak, moderate, or strong UW-CTC rate and achieved a Refc ≥ 35 dBZ during their lifetime. The presence of small negative lead times (<0 to −17 min) in which the radar precedes the instantaneous UW-CTC rate for a cloud object is largely a by-product of each dataset having a different temporal resolution [i.e. radar (~5 min) compared to geostationary satellite (~15 min)]. For example, if a cloud object’s UW-CTC rate was diagnosed at 1932 UTC (computed by the UWCI-CTC algorithm using satellite data from 1932 and 1915 UTC), but achieved a Refc of 35 dBZ at 1925 UTC, the lead time of the cloud object’s cooling rate to when it developed a 35-dBZ radar echo is −7 min. All lead times greater than 60 min are assigned to the upper tail bin, while lead times less than −17 min are considered misses and are excluded from the analysis. Finally, lead times of maximum UW-CTC rates to maximum composite reflectivity are shown in Fig. 5b.

Fig. 5.
Fig. 5.

(a) Normalized frequency of lead times (min) between the time that a cloud object’s weak (light gray), moderate (gray), and strong (black) UW cloud-top cooling rate was observed with respect to when the object developed its maximum composite reflectivity signature on radar. All bins contain unique data points; for example, data that fall in the 0 ≤ L < 15 min bin indicate that a UW-CTC rate was observed at least 0, but less than 15, min before the cloud object achieved its maximum composite reflectivity. (b) As in (a), but for maximum UW cloud-top cooling rate to maximum cloud-object composite reflectivity. (c),(d) As in (a),(b), but for maximum VIL. All lead time values greater than 60 min were placed in the respective tail bin, while values less than −17 min were excluded.

Citation: Weather and Forecasting 28, 2; 10.1175/WAF-D-12-00021.1

In general, using all UW-CTC rates or maximum UW-CTC rates for computing lead time versus maximum Refc yields very similar results (Figs. 5a,b). A significant portion of the population (40%–50%) of each UW-CTC bin exhibits lead times of 15+ min ahead of a thunderstorm achieving its maximum Refc. The small negative and small positive lead-time populations are also considerable for Refc. The smaller lead times are largely attributed to storms that achieve only moderate Refc (either because they are simply weak convection or the cloud object merged with a nearby object and the second object’s ID was tracked, giving the appearance of only moderate reflectivity of the merged storm) or due to the comparatively poor temporal resolution of GOES imagers versus radar data.

An examination of the lead times of storms that achieve 60+ dBZ Refc in their lifetimes (a forecaster would have more interest in these storms in a warning operations environment) shows considerably increased lead times (Figs. 6a,b). Again using all UW-CTC rates or maximum UW-CTC rates does not indicate appreciable differences, but the majority of storms (65%–75%) in each UW-CTC bin exhibit lead times of 15+ min, with 30%–35% of each UW-CTC bin showing lead times of 60+ min (Figs. 6a,b). Although not shown, it is important to note that the median lead time of the UW-CTC rate versus maximum Refc steadily increases with increasing maximum Refc, with ~10 min of lead time for 45 dBZ, 25 min for 60 dBZ, and over an hour for the most intense reflectivity cores (65+ dBZ).

Fig. 6.
Fig. 6.

As in Fig. 5, but comparing lead times of (a),(c) instantaneous and (b),(d) maximum UW cloud-top cooling rates to when a cloud object achieved (a),(b) a composite reflectivity of 60 dBZ or (c),(d) a VIL of 40 kg m−2 for those that had both a UW-CTC rate and the shown field threshold at some point in their lifetime.

Citation: Weather and Forecasting 28, 2; 10.1175/WAF-D-12-00021.1

2) VIL and MESH

To assess the utility of UW-CTC rates in operational forecasting, instantaneous cloud-object UW-CTC rates are also compared to radar-derived products frequently entrusted by forecasters to estimate the column loading of precipitation-sized hydrometeors (VIL) and MESH for convective clouds. It is important to note that forecasters often rely on such radar-derived fields to assess the potential intensity of a thunderstorm and whether or not a subsequent public warning is warranted based on the information in order to provide as much lead time as possible to the development of severe conditions at the surface.

As discussed earlier, one significant challenge that is presented when using a satellite-based object-tracking system is to successfully track cloud objects long enough for a corresponding mature signature to develop in various radar fields (e.g., VIL, MESH). This is because satellite-based cloud objects reach “satellite maturity” (i.e., the development and horizontal merging of anvils) prior to the maturation of the precipitation core on radar. Thus, the sample sizes of cloud objects with both instantaneous UW-CTC rate(s) and a VIL or MESH signature in Figs. 3b–d and Figs. 4b–d are substantially smaller than those with a cooling rate and maximum composite reflectivity in Figs. 3a and 4a (Table 3).

Table 3.

Cloud-object counts for Figs. 38 of cloud objects with a UW cloud-top cooling rate and a measured value of the radar-derived fields of interest broken down by UW cloud-top cooling convective growth intensity for (top) all instantaneous rate observations and (bottom) only for maximum UW-CTC rate observations.

Table 3.

The total column-integrated precipitation-sized hydrometeors in a convective cloud are strongly related to the cloud’s updraft intensity and the amount of available low- to midlevel moisture (Doswell et al. 1996). Similar to composite reflectivity, clouds with UW-CTC > −10 K (15 min)−1 have more uncertainty surrounding their future development and often achieved a VIL of less than 30 kg m−2 (Fig. 3b; Table 3) with a median of 15 kg m−2. Cloud objects that attained moderate UW-CTC rates had a median VIL slightly higher than weak growth clouds (20 kg m−2), but the distribution of the most intense 50% of the data encompassed much larger VIL values, some in excess of 45 kg m−2 (Fig. 3b). Figure 3b also demonstrates that convective cloud objects with the strongest observed UW-CTC rates developed the largest median VIL (30 kg m−2), which is statistically higher than both weak and moderate growth clouds. Likely a result of such vigorous updrafts, the most intense UW-CTC signals were observed with cloud objects that developed significantly higher VIL (60+ kg m−2) during their lifetime compared to weak and moderate growth storms (Fig. 3b). Overall, the UWCI-CTC algorithm diagnosed a UW-CTC rate for 41.0% of storms that achieved a VIL of 5+ kg m−2 and the POD steadily increased to 64.0% for cloud objects that attained a maximum VIL of at least 35 kg m−2.

Although the relationship between UW-CTC intensity and maximum VIL achieved by cloud objects is very similar when looking at only maximum cloud object UW-CTC rates (Fig. 4b), the uncertainty that composes the upper whisker for each cooling bin in Fig. 4b is much lower than when including all UW-CTC rates in the analysis. For example, when only considering cloud objects that achieved a weak maximum UW-CTC rate during their lifetime, 75% achieved a VIL of ≤25 kg m−2, whereas when including all instantaneous cooling rates in the analyses, that VIL threshold increases to 40 kg m−2 (Fig. 3b).

Since an intense updraft is one of the key ingredients necessary for a developing convective cloud to achieve a large VIL as its precipitation core matures, it is expected that the cloud’s diagnosed vertical growth (i.e., UW-CTC rate) would provide more lead time to when it achieves its maximum VIL threshold than was shown earlier for composite reflectivity. Figure 5c shows the lead time of a cloud object’s UW-CTC rates to its maximum VIL threshold, displayed in the same manner as composite reflectivity in Fig. 5a. In contrast to composite reflectivity, UW-CTC rates of all intensities provided substantially more lead time to a cloud object’s maximum VIL signature on radar, with ~60% of those cloud objects having a strong UW-CTC rate ≥15 min ahead of the maximum VIL signature and ~70% of cloud objects having a weak or moderate UW-CTC rate that proceeded the maximum VIL signature by ≥15 min (Fig. 5c). When only using a cloud object’s maximum UW-CTC rate, 55% had a maximum cooling rate that occurred ≥15 min ahead of the maximum VIL signature, regardless of intensity.

When only considering more operationally significant storms (cloud objects that achieved a VIL of 40+ kg m−2), ~75% of weak (55% of moderate or greater), UW-CTC rates provided a lead time of ≥60 min between the UW-CTC signal and when the VIL threshold was reached on radar (Figs. 6c,d). In addition, the median lead time of the maximum UW-CTC rate steadily increased from 20 min for cloud objects that achieved a VIL of 20 kg m−2 to 60+ min for those that developed a VIL of 45+ kg m−2 (not shown). Thus, monitoring a convective cloud object’s satellite-based vertical growth signal cannot only detect the development of, but also provide ≥15 min of lead time prior to, a radar-detectable VIL signature and 50+ min of lead time to more significant VIL values (40+ kg m−2; Figs. 6c,d).

While VIL is largely a measure of column-integrated precipitation-sized hydrometeors, additional atmospheric conditions are required for hail formation within a thunderstorm [i.e., a robust updraft, low freezing level, and high column liquid water content; Wallace and Hobbs (2006)]. Thus, observed sample sizes of cloud objects with a UW-CTC rate and a MESH signature decreased by 40%–70% compared to those objects that had both a UW-CTC rate and an observed VIL (Table 3). The greatest decrease in the number of cloud objects was for those that had only a weak or moderate UW-CTC signal (60% and 56%, respectively), while a smaller decrease was observed for objects with an intense UW-CTC rate (36%; Table 3).

Figure 3c shows that 75% of cloud objects that possessed a UW-CTC rate [CTC ≤ −4 K (15 min)−1] and also a radar-derived MESH, achieved a radar-estimated hail size of at least 0.25-in. diameter at some point in their lifetime. The majority (75%) of cloud objects with a detectable MESH and moderate to strong convective growth achieved a radar-estimated hail diameter of 0.5+ in., while 50% of the strongest cooling clouds achieved at least a severe (1.00+ in. diameter defined by the SPC) MESH during their lifetime (Fig. 3c). Of the cloud objects that developed a 0.25+ in. MESH, 57% were diagnosed with a UW-CTC rate. The UWCI-CTC algorithm was most skillful in detecting convective clouds that developed more intense MESH (1.00+ in.; POD = 71.0%; 2.00+ in.; POD = 72.0%).

Once again, examining only maximum cloud object UW-CTC rates yields a median maximum MESH that is statistically different between the three intensity bins (Fig. 4c). Cloud objects that only achieve weak cooling tend to develop the smallest MESH on radar, while clouds that have a moderate or strong maximum UW-CTC rate are more likely to achieve a 0.75+ in. MESH with 25% of strong growth convection developing a MESH of 1.5+ in. on radar (Fig. 4c). The UW-CTC MESH analysis gives a forecaster confidence that more intense UW-CTC rates are often associated with larger radar-estimated hail sizes, and that severe hail may occur ~50% of the time for storms falling into the strong UW-CTC bin. It should be noted that severe hail is a rarely occurring event, especially when considering convection as a whole (as indicated by the sample size of 1.00+ in. MESH over the 34 days studied). Thus, these findings should be used in conjunction with knowledge of the environment’s thermodynamic conditions in order to determine the likelihood of large hail development.

Since large MESH usually develops at a later stage in the convective life cycle (than previously shown Refc and VIL), it is not surprising that 60%–85% of storms in each UW-CTC intensity bin provided ≥15 min of lead time to a cloud object developing a maximum MESH on radar (Figs. 7a,b). In addition, 30%–45% of storms exhibited a UW-CTC rate lead time of 60+ min before they developed their maximum MESH.

Fig. 7.
Fig. 7.

As in Fig. 5, but showing lead times of (a) all UW-CTC and (b) maximum cloud object UW-CTC rates to MESH signatures on radar.

Citation: Weather and Forecasting 28, 2; 10.1175/WAF-D-12-00021.1

For the most operationally significant convective storms (MESH of 1.00+ in. on radar), 70%–95% of cloud objects in each cooling rate bin had a UW-CTC rate that provided ≥30 min of lead time (Fig. 8a), while 65%–85% provided a lead time of ≥30 min when only maximum UW-CTC rates are considered (Fig. 8b). Of the 128 cloud objects with a maximum UW-CTC rate that achieved a MESH of 1.00 in. as seen in Fig. 8b, 82% had a moderate or strong maximum UW-CTC rate (Table 4). Thus, with a POD ≥ 70.0% for large MESH, an operational forecaster can be confident that of the convective storms that attain a 1.00+ in. radar-estimated hail signature, the majority will have a preceding moderate or strong UW-CTC rate by a median of ~45+ min (Fig. 8b). It should be noted that many cloud objects with observed moderate and strong UW-CTC rates do not actually develop large VIL and MESH thresholds.

Fig. 8.
Fig. 8.

As in Fig. 6, but showing lead times of a cloud object’s (a) instantaneous and (b) maximum UW cloud-top cooling rates to when a 1.00-in. MESH was achieved at some point in their lifetime.

Citation: Weather and Forecasting 28, 2; 10.1175/WAF-D-12-00021.1

Table 4.

Counts for Figs. 7 and 8 of cloud objects with both a UW cloud-top cooling rate and a measured value of the radar-derived fields of interest.

Table 4.

5. Application of UW-CTC rates in operational forecasting

Figures 9 and 10 highlight one example from 13 May 2009 in which UW-CTC rates provide prognostic value to an operational forecaster in identifying the region of most likely development of significant convection over far south-central Kansas and central Oklahoma. A line of shallow cumulus clouds along a surface frontal boundary extending from northeast to southwest across the domain is visible in the 11-μm IR brightness temperature field at 2215 UTC with the vast majority of cloud tops being warmer than 250 K (Fig. 9a). At the same time, the small convective cell within the line just to the northwest of Oklahoma City, Oklahoma (OKC), has a UW-CTC rate of −16 K (15 min)−1 and thus appears to be undergoing intense vertical growth, yet does not have a corresponding radar reflectivity signature at the 2216 UTC volume scan time (Figs. 9b,c). At this stage, a forecaster can use the cooling cloud’s UW-CTC rate to deduce its likelihood of future convective development. Since no prior cooling rates were observed for this cloud, the only cooling rate information available to a forecaster is the −16 K (15 min)−1 cooling rate at the present time. Analyses shown in Fig. 3 indicate that a cloud growing vertically at this particular rate is much more likely to achieve a median maximum composite reflectivity of 50+ dBZ, a VIL of 20+ kg m−2, and a 1.00+ in. radar-derived MESH within the following 60-min period than a cloud with a weaker or undetectable UW-CTC cooling signal.

Fig. 9.
Fig. 9.

GOES-12 11-μm IR-window (a),(d) brightness temperature (K) and (b),(e) UW-CTC rate [K (15 min)−1] valid at 2215 and 2225 UTC, respectively, on 13 May 2009. (c),(f) Quality controlled NEXRAD composite reflectivity (dBZ) valid at 2216 and 2226 UTC, respectively, on 13 May 2009. The developing convective cloud of interest is located just to the northwest of OKC.

Citation: Weather and Forecasting 28, 2; 10.1175/WAF-D-12-00021.1

Fig. 10.
Fig. 10.

GOES-12 11-μm IR-window on 13 May 2009 for (a),(c) brightness temperature (K) valid at 2240 and 2255 UTC, respectively, and (b),(d) quality controlled NEXRAD composite reflectivity (dBZ) valid at 2241 and 2301 UTC, respectively. The developing convective cloud of interest is located just to the northwest of OKC.

Citation: Weather and Forecasting 28, 2; 10.1175/WAF-D-12-00021.1

By 2225 UTC, the convective cloud to the northwest of OKC has cooled to 240 K, while continuing to exhibit intense vertical growth (cooling) with a UW-CTC rate in excess of −20 K (15 min)−1 (Figs. 9d,e). A radar scan at 2226 UTC shows that the cloud’s precipitation core continues to mature and now has a maximum composite reflectivity of 35 dBZ (Fig. 9f). Thus, the original −16 K (15 min)−1 UW-CTC signal provides 10 min of lead time to the development of a 35-dBZ reflectivity signature. With the additional strong cooling signal at 2225 UTC, a forecaster can refocus his or her attention to the strong cooling bins in Fig. 3 to determine the range of most likely future development in the various radar fields. The analyses presented in Figs. 38 indicate that this particular convective tower is most likely to develop a composite reflectivity of 55+ dBZ, a VIL of 25+ kg m−2, and a 1.00+ in. radar-derived MESH in the next 0–60 min. Moreover, Figs. 10b and 10c clearly show that the convective cloud went on to achieve a composite reflectivity of 40 dBZ at 2241 UTC and 55 dBZ at 2301 UTC, 36 min after the strongest UW-CTC signal was observed. In addition, the storm attained a radar-derived MESH of 1.00 in. at 2316 UTC (not shown) and the first associated surface report of severe 1.25-in. hail was received at 2337 UTC, 72 min after the strong UW-CTC rate was detected and 29 min after the Norman, Oklahoma, WFO issued a severe thunderstorm warning for the storm. This is just one example of how UW-CTC rates can be used in operational forecasting to better understand the potential near-term development of growing convective clouds prior to the development of various thresholds of radar-derived fields often depended on to nowcast convective intensity.

6. Discussion and concluding remarks

An objective validation of the University of Wisconsin Convective Initiation-Cloud-Top Cooling (UWCI-CTC) algorithm cloud-top cooling rates using the satellite-based object-tracking methodology described by S13 was presented, along with a prognostic evaluation of UW-CTC rates for use in forecasting the growth and development of deep convection. This study was motivated by NWS forecasters evaluating the UW-CTC output and hypothesized that more intense cloud-top cooling corresponds to more vigorous short-term (0–60 min) convective development. For the analyses, UW-CTC rates were binned into three categories based on intensity [K (15 min)−1]: weak (UW-CTC > −10), moderate (−10 ≥ UW-CTC > −20), and strong (UW-CTC ≤ −20) convective growth. Overall, a cloud object’s instantaneous UW-CTC rate was found to be a useful prognostic tool in predicting future radar intensification.

In general, UW-CTC rates were shown to be most skillful in detecting convective clouds that achieved intense radar signatures [e.g. POD of 62.0% for cloud objects that developed a 60+ dBZ reflectivity echo at the −10°C isotherm (Ref-10), 64% for those that developed a VIL of 35+ kg m−2, and 71% for storms that attained a severe radar-estimated hail size (MESH) of 1.00+ in.]. However, the reader is reminded that FAR values for the above thresholds are of similar magnitude to their POD counterparts (e.g., FAR of 82.0% for cloud objects that developed a 60+ dBZ Ref-10). A decrease of FAR with increasing UW-CTC rate magnitude was demonstrated for moderate and strong cooling clouds. For example, only 16% of the false alarms that composed the UWCI-CTC algorithm’s 35-dBZ Ref-10 FAR of 36.0% belonged to the strong UW-CTC bin (39% and 44% of the false alarms belonged to weak and moderate UW-CTC bins, respectively). These results suggest that a forecaster can be increasingly confident that the more intense the cloud-top cooling rate detected by the UWCI-CTC algorithm, the higher the likelihood that the storm will achieve a moderate or greater Ref-10 echo compared to weaker growing convection. It is important to remember that the above performance statistics must be considered hand in hand with a developing cloud’s thermodynamic environment. Thus, when a UW-CTC rate is detected in an environment that is favorable for more intense convective development, the POD statistics described above become more applicable, as the FAR steadily decreases with increasing UW-CTC intensity for those that go on to develop stronger radar signatures.

The median values of composite reflectivity and MESH for the weak, moderate, and strong UW-CTC rate bins at their time of most intense vertical growth were found to be statistically different from one another at the 95% confidence level, indicating that more intense vertical growth is a good proxy for the development of a more intense precipitation core. Likely a consequence of more vigorous updraft speeds, cloud objects that achieved the highest VIL (50+ kg m−2; POD = 64.0%) and severe radar-derived MESH (1.00+ in.; POD = 71.0%) had the most intense UW-CTC signals. In addition to increased forecaster confidence, 50% of moderate and strong UW-CTC rates were shown to provide a lead time of 30+ min prior to a convective cloud achieving a composite reflectivity of 60 dBZ or a VIL of 40 kg m−2. Longer lead times were also observed for development of a severe MESH of 1.00 in. It is also important to keep in mind that only a small percentage of cloud objects with moderate and strong UW-CTC rates went on to achieve these intense radar signature thresholds.

In general, instantaneous UW-CTC rates were demonstrated to be a useful tool for operational forecasters when diagnosing the development and intensification of deep convection. The example provided in section 5 illustrates the value of using UW-CTC rates to determine the likelihood of near-term convective development of a growing convective cloud. By using observed cooling rate information that a convective cloud achieved during its growth, an operational forecaster is able to make an informed decision about the likelihood of the convective cloud achieving a certain level of intensity in a variety of radar-measured and derived fields prior to the actual development of such radar signatures. Thus, if a forecaster observes three convective towers developing along a dryline and each has a detected UW-CTC rate of −5, −15, and −30 K (15 min)−1, respectively, the results of this study suggest that the convective cloud with the most intense UW-CTC rate is more likely to achieve significant short-term radar signatures in such fields as composite reflectivity, VIL, and MESH, so long as the developing towers are located in an environment favorable for strong convective development. Finally, it is important to remember that the results presented in this paper are only applicable to the Great Plains region during the spring and early summer months; a similar verification study to be performed over the remainder of the continental United States is a subject of future work.

Acknowledgments

The authors thank Valliappa Lakshmanan and John Cintineo for their assistance in acquiring and quality controlling the large volume of NEXRAD radar data, Ralph Petersen for his insightful comments on the analyses, as well as the forecasters that participated in the Hazardous Weather Testbed spring experiment at the Storm Prediction Center (SPC) and the forecasting team at the Milwaukee/Sullivan National Weather Service Forecast Office for their thorough evaluation and critique of the UWCI-CTC algorithm cooling rate output. Comments and thoughtful insight from Philip Schumacher and two anonymous reviewers also greatly enhanced the quality and clarity of this manuscript. This research was supported by NOAA GOES Product Assurance Plan (GIMPAP) Award NA10NES4400013.

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  • Feidas, H., and Cartarlis C. , 2005: Application of an automated cloud-tracking algorithm on satellite imagery for tracking and monitoring small mesoscale convective cloud systems. Int. J. Remote Sens., 26, 16771698.

    • Search Google Scholar
    • Export Citation
  • Greene, D. R., and Clark R. A. , 1972: Vertically integrated liquid water—A new analysis tool. Mon. Wea. Rev., 100, 548552.

  • Johns, R. H., and Doswell C. A. III, 1992: Severe local storms forecasting. Wea. Forecasting, 7, 588612.

  • Lakshmanan, V., Smith T. , Hondl K. , Stumpf G. J. , and Witt A. , 2006: A real-time, three-dimensional, rapidly updating, heterogeneous radar merger technique for reflectivity, velocity and derived products. Wea. Forecasting, 21, 802823.

    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., Fritz A. , Smith T. , Hondl K. , and Stumpf G. J. , 2007a: An automated technique to quality control radar reflectivity data. J. Appl. Meteor., 46, 288305.

    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., Smith T. , Stumpf G. J. , and Hondl K. , 2007b: The Warning Decision Support System-Integrated Information. Wea. Forecasting, 22, 596612.

    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., and Bedka K. M. , 2006: Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery. Mon. Wea. Rev., 134, 4978.

    • Search Google Scholar
    • Export Citation
  • Menzel, W. P., and Purdom J. F. W. , 1994: Introducing GOES-I: The first of a new generation of Geostationary Observational Environmental Satellites. Bull. Amer. Meteor. Soc., 75, 757781.

    • Search Google Scholar
    • Export Citation
  • Moller, A. R., 2001: Severe local storms forecasting. Severe Convective Storms, Meteor. Monogr., No. 50, Amer. Meteor. Soc., 433–480.

  • Moller, A. R., Doswell C. A. III, Foster M. P. , and Woodall G. R. , 1994: The operational recognition of supercell thunderstorm environments and storm structures. Wea. Forecasting, 9, 327347.

    • Search Google Scholar
    • Export Citation
  • Morel, C., and Senesi S. , 2002: A climatology of mesoscale convective systems over Europe using satellite infrared imagery. I: Methodology. Quart. J. Roy. Meteor. Soc., 128, 19531971.

    • Search Google Scholar
    • Export Citation
  • Mueller, C., Saxen T. , Roberts R. , Wilson J. , Betancourt T. , Dettling S. , Oien N. , and Yee J. , 2003: NCAR Auto-Nowcast System. Wea. Forecasting, 18, 545561.

    • Search Google Scholar
    • Export Citation
  • Negri, A. J., and Adler R. F. , 1981: Relation of satellite-based thunderstorm intensity to radar-estimated rainfall. J. Appl. Meteor., 20, 288300.

    • Search Google Scholar
    • Export Citation
  • Ortega, K. L., Smith T. M. , and Stumpf G. J. , 2006: Verification of multi-sensor, multi-radar hail diagnosis techniques. Preprints, Symp. on the Challenges of Severe Convective Storms, Atlanta, GA, Amer. Meteor. Soc., P1.1. [Available online at https://ams.confex.com/ams/pdfpapers/104885.pdf.]

  • Ortega, K. L., Smith T. M. , Manross K. L. , Scharfenberg K. A. , Witt A. , Kolodziej A. G. , and Gourley J. J. , 2009: The Severe Hazards Analysis and Verification Experiment. Bull. Amer. Meteor. Soc., 90, 15191530.

    • Search Google Scholar
    • Export Citation
  • Pautz, M., 1971: Meso-scale analysis and forecasting. Technical Procedures Branch, Weather Analysis and Forecasting Division, National Weather Service/NOAA, 39 pp.

  • Pavolonis, M. J., 2010: Advances in extracting cloud composition information from spaceborne infrared radiances—A robust alternative to brightness temperatures. Part I: Theory. J. Appl. Meteor. Climatol., 49, 19922012.

    • Search Google Scholar
    • Export Citation
  • Purdom, J. F. W., 1971: Satellite imagery and severe weather warnings. Preprints, Seventh Conf. on Severe Local Storms, Kansas City, MO, Amer. Meteor. Soc., 120–137.

  • Purdom, J. F. W., 1976: Some uses of high-resolution GOES imagery in the mesoscale forecasting of convection and its behavior. Mon. Wea. Rev., 104, 14741483.

    • Search Google Scholar
    • Export Citation
  • Purdom, J. F. W., 1982: Subjective interpretations of geostationary satellite data for nowcasting. Nowcasting, K. Browning, Ed., Academic Press, 149–166.

  • Purdom, J. F. W., 1986: The development and evolution of deep convection. Precipitation Connection, Vol. 2, Satellite Imagery Interpretation for Forecasters, National Weather Association, 4-a-1–4-a-8. [Available from NWA, 4400 Stamp Rd., No. 404, Temple Hills, MD 20748.]

  • Roberts, R. D., and Rutledge S. , 2003: Nowcasting storm initiation and growth using GOES-8 and WSR-88D data. Wea. Forecasting, 18, 562584.

    • Search Google Scholar
    • Export Citation
  • Sieglaff, J. M., Cronce L. M. , Feltz W. F. , Bedka K. M. , Pavolonis M. J. , and Heidinger A. K. , 2011: Nowcasting convective storm initiation using satellite-based box-averaged cloud-top cooling and cloud-type trends. J. Appl. Meteor. Climatol., 50, 110126.

    • Search Google Scholar
    • Export Citation
  • Sieglaff, J. M., Hartung D. C. , Feltz W. F. , Cronce L. M. , and Lakshmanan V. , 2013: A satellite-based convective cloud object tracking and multipurpose data fusion tool with application to developing convection. J. Atmos. Oceanic Technol., 30, 510525.

    • Search Google Scholar
    • Export Citation
  • Stumpf, G. J., Smith T. M. , and Hocker J. , 2004: New hail diagnostic parameters derived by integrating multiple radars and multiple sensors. Preprints, 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., P7.8. [Available online at https://ams.confex.com/ams/pdfpapers/88063.pdf.]

  • Suchman, D., Auvine B. , and Hinton B. , 1981: Determining economic benefits of satellite data in short-range forecasting. Bull. Amer. Meteor. Soc., 62, 14581465.

    • Search Google Scholar
    • Export Citation
  • Vila, D. A., Machado L. A. T. , Laurent H. , and Velasco I. , 2008: Forecasting and Tracking the Evolution of Cloud Clusters (ForTraCC) using satellite infrared imagery: Methodology and validation. Wea. Forecasting, 23, 233245.

    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., and Hobbs P. V. , 2006: Atmospheric Science: An Introductory Survey. 2nd ed. Academic Press, 483 pp.

  • Weaver, J. F., and Purdom J. F. W. , 1995: An interesting mesoscale storm–environment interaction observed just prior to changes in severe storm behavior. Wea. Forecasting, 10, 449453.

    • Search Google Scholar
    • Export Citation
  • Weaver, J. F., Purdom J. F. W. , and Smith S. B. , 1994: Comments on “Nowcasts of thunderstorm initiation and evolution.” Wea. Forecasting, 9, 658662.

    • Search Google Scholar
    • Export Citation
  • Weaver, J. F., Dostalek J. F. , Motta B. C. , and Purdom J. F. W. , 1999: Severe thunderstorms on 31 May 1996: A satellite training case. Natl. Wea. Dig., 23 (4), 319.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 627 pp.

  • Witt, A., Eilts M. D. , Stumpf G. J. , Johnson J. T. , Mitchell E. D. W. , and Thomas K. W. , 1998a: An enhanced hail detection algorithm for the WSR-88D. Wea. Forecasting, 13, 286303.

    • Search Google Scholar
    • Export Citation
  • Witt, A., Eilts M. D. , Stumpf G. J. , Mitchell J. E. D. W. , Johnson J. T. , and Thomas K. W. , 1998b: Evaluating the performance of WSR-88D severe storm detection algorithms. Wea. Forecasting, 13, 513518.

    • Search Google Scholar
    • Export Citation
  • Zinner, T., Mannstein H. , and Tafferner A. , 2008: Cb-TRAM: Tracking and monitoring severe convection from onset over rapid development to mature phase using multi-channel Meteosat-8 SEVIRI data. Meteor. Atmos. Phys., 101, 191210.

    • Search Google Scholar
    • Export Citation
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  • Feidas, H., and Cartarlis C. , 2001: Monitoring mesoscale convective cloud systems associated with heavy storms with the use of Meteosat imagery. J. Appl. Meteor., 40, 491512.

    • Search Google Scholar
    • Export Citation
  • Feidas, H., and Cartarlis C. , 2005: Application of an automated cloud-tracking algorithm on satellite imagery for tracking and monitoring small mesoscale convective cloud systems. Int. J. Remote Sens., 26, 16771698.

    • Search Google Scholar
    • Export Citation
  • Greene, D. R., and Clark R. A. , 1972: Vertically integrated liquid water—A new analysis tool. Mon. Wea. Rev., 100, 548552.

  • Johns, R. H., and Doswell C. A. III, 1992: Severe local storms forecasting. Wea. Forecasting, 7, 588612.

  • Lakshmanan, V., Smith T. , Hondl K. , Stumpf G. J. , and Witt A. , 2006: A real-time, three-dimensional, rapidly updating, heterogeneous radar merger technique for reflectivity, velocity and derived products. Wea. Forecasting, 21, 802823.

    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., Fritz A. , Smith T. , Hondl K. , and Stumpf G. J. , 2007a: An automated technique to quality control radar reflectivity data. J. Appl. Meteor., 46, 288305.

    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., Smith T. , Stumpf G. J. , and Hondl K. , 2007b: The Warning Decision Support System-Integrated Information. Wea. Forecasting, 22, 596612.

    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., and Bedka K. M. , 2006: Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery. Mon. Wea. Rev., 134, 4978.

    • Search Google Scholar
    • Export Citation
  • Menzel, W. P., and Purdom J. F. W. , 1994: Introducing GOES-I: The first of a new generation of Geostationary Observational Environmental Satellites. Bull. Amer. Meteor. Soc., 75, 757781.

    • Search Google Scholar
    • Export Citation
  • Moller, A. R., 2001: Severe local storms forecasting. Severe Convective Storms, Meteor. Monogr., No. 50, Amer. Meteor. Soc., 433–480.

  • Moller, A. R., Doswell C. A. III, Foster M. P. , and Woodall G. R. , 1994: The operational recognition of supercell thunderstorm environments and storm structures. Wea. Forecasting, 9, 327347.

    • Search Google Scholar
    • Export Citation
  • Morel, C., and Senesi S. , 2002: A climatology of mesoscale convective systems over Europe using satellite infrared imagery. I: Methodology. Quart. J. Roy. Meteor. Soc., 128, 19531971.

    • Search Google Scholar
    • Export Citation
  • Mueller, C., Saxen T. , Roberts R. , Wilson J. , Betancourt T. , Dettling S. , Oien N. , and Yee J. , 2003: NCAR Auto-Nowcast System. Wea. Forecasting, 18, 545561.

    • Search Google Scholar
    • Export Citation
  • Negri, A. J., and Adler R. F. , 1981: Relation of satellite-based thunderstorm intensity to radar-estimated rainfall. J. Appl. Meteor., 20, 288300.

    • Search Google Scholar
    • Export Citation
  • Ortega, K. L., Smith T. M. , and Stumpf G. J. , 2006: Verification of multi-sensor, multi-radar hail diagnosis techniques. Preprints, Symp. on the Challenges of Severe Convective Storms, Atlanta, GA, Amer. Meteor. Soc., P1.1. [Available online at https://ams.confex.com/ams/pdfpapers/104885.pdf.]

  • Ortega, K. L., Smith T. M. , Manross K. L. , Scharfenberg K. A. , Witt A. , Kolodziej A. G. , and Gourley J. J. , 2009: The Severe Hazards Analysis and Verification Experiment. Bull. Amer. Meteor. Soc., 90, 15191530.

    • Search Google Scholar
    • Export Citation
  • Pautz, M., 1971: Meso-scale analysis and forecasting. Technical Procedures Branch, Weather Analysis and Forecasting Division, National Weather Service/NOAA, 39 pp.

  • Pavolonis, M. J., 2010: Advances in extracting cloud composition information from spaceborne infrared radiances—A robust alternative to brightness temperatures. Part I: Theory. J. Appl. Meteor. Climatol., 49, 19922012.

    • Search Google Scholar
    • Export Citation
  • Purdom, J. F. W., 1971: Satellite imagery and severe weather warnings. Preprints, Seventh Conf. on Severe Local Storms, Kansas City, MO, Amer. Meteor. Soc., 120–137.

  • Purdom, J. F. W., 1976: Some uses of high-resolution GOES imagery in the mesoscale forecasting of convection and its behavior. Mon. Wea. Rev., 104, 14741483.

    • Search Google Scholar
    • Export Citation
  • Purdom, J. F. W., 1982: Subjective interpretations of geostationary satellite data for nowcasting. Nowcasting, K. Browning, Ed., Academic Press, 149–166.

  • Purdom, J. F. W., 1986: The development and evolution of deep convection. Precipitation Connection, Vol. 2, Satellite Imagery Interpretation for Forecasters, National Weather Association, 4-a-1–4-a-8. [Available from NWA, 4400 Stamp Rd., No. 404, Temple Hills, MD 20748.]

  • Roberts, R. D., and Rutledge S. , 2003: Nowcasting storm initiation and growth using GOES-8 and WSR-88D data. Wea. Forecasting, 18, 562584.

    • Search Google Scholar
    • Export Citation
  • Sieglaff, J. M., Cronce L. M. , Feltz W. F. , Bedka K. M. , Pavolonis M. J. , and Heidinger A. K. , 2011: Nowcasting convective storm initiation using satellite-based box-averaged cloud-top cooling and cloud-type trends. J. Appl. Meteor. Climatol., 50, 110126.

    • Search Google Scholar
    • Export Citation
  • Sieglaff, J. M., Hartung D. C. , Feltz W. F. , Cronce L. M. , and Lakshmanan V. , 2013: A satellite-based convective cloud object tracking and multipurpose data fusion tool with application to developing convection. J. Atmos. Oceanic Technol., 30, 510525.

    • Search Google Scholar
    • Export Citation
  • Stumpf, G. J., Smith T. M. , and Hocker J. , 2004: New hail diagnostic parameters derived by integrating multiple radars and multiple sensors. Preprints, 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., P7.8. [Available online at https://ams.confex.com/ams/pdfpapers/88063.pdf.]

  • Suchman, D., Auvine B. , and Hinton B. , 1981: Determining economic benefits of satellite data in short-range forecasting. Bull. Amer. Meteor. Soc., 62, 14581465.

    • Search Google Scholar
    • Export Citation
  • Vila, D. A., Machado L. A. T. , Laurent H. , and Velasco I. , 2008: Forecasting and Tracking the Evolution of Cloud Clusters (ForTraCC) using satellite infrared imagery: Methodology and validation. Wea. Forecasting, 23, 233245.

    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., and Hobbs P. V. , 2006: Atmospheric Science: An Introductory Survey. 2nd ed. Academic Press, 483 pp.

  • Weaver, J. F., and Purdom J. F. W. , 1995: An interesting mesoscale storm–environment interaction observed just prior to changes in severe storm behavior. Wea. Forecasting, 10, 449453.

    • Search Google Scholar
    • Export Citation
  • Weaver, J. F., Purdom J. F. W. , and Smith S. B. , 1994: Comments on “Nowcasts of thunderstorm initiation and evolution.” Wea. Forecasting, 9, 658662.

    • Search Google Scholar
    • Export Citation
  • Weaver, J. F., Dostalek J. F. , Motta B. C. , and Purdom J. F. W. , 1999: Severe thunderstorms on 31 May 1996: A satellite training case. Natl. Wea. Dig., 23 (4), 319.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 627 pp.

  • Witt, A., Eilts M. D. , Stumpf G. J. , Johnson J. T. , Mitchell E. D. W. , and Thomas K. W. , 1998a: An enhanced hail detection algorithm for the WSR-88D. Wea. Forecasting, 13, 286303.

    • Search Google Scholar
    • Export Citation
  • Witt, A., Eilts M. D. , Stumpf G. J. , Mitchell J. E. D. W. , Johnson J. T. , and Thomas K. W. , 1998b: Evaluating the performance of WSR-88D severe storm detection algorithms. Wea. Forecasting, 13, 513518.

    • Search Google Scholar
    • Export Citation
  • Zinner, T., Mannstein H. , and Tafferner A. , 2008: Cb-TRAM: Tracking and monitoring severe convection from onset over rapid development to mature phase using multi-channel Meteosat-8 SEVIRI data. Meteor. Atmos. Phys., 101, 191210.

    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    Conceptual model of a developing cumulonimbus cloud (a) illustrating how cloud-top temperature decreases with increased vertical growth (maturation) over time in the XZ plane and (b) as observed in the IR brightness temperature field from a geostationary satellite. Note that the brightness temperatures in (b) are slightly warmer than the actual temperature of the cloud top at each stage in (a).

  • Fig. 2.

    Analysis domain over the interior plains of the United Sates (red contour) within which satellite-based cloud objects are used to investigate the relationship between various UWCI-CTC algorithm cooling rates and WSR-88D and NLDN lightning data. This analysis domain is the same as that used by Sieglaff et al. (2011).

  • Fig. 3.

    Comparison of all instantaneous UW cloud-top cooling rates to (a) maximum composite reflectivity (dBZ), (b) VIL, and (c) MESH for cloud objects that had both a UW-CTC rate and associated radar field at some point in their lifetime. UW-CTC rates for cloud objects are binned by intensity [K (15 min)−1] with weak, moderate, and strong convective growth defined as UW-CTC > −10, −10 ≥ UW-CTC > −20, and UW-CTC ≤ −20, respectively. For each boxplot, the median (red line), 25th and 75th percentiles (top and bottom bounds of blue box), and one standard deviation (whiskers) are shown. The medians of different intensity bins are significantly different at the 5% significance level if the widths of the notches centered on the medians do not overlap.

  • Fig. 4.

    As in Fig. 3, but a comparison of maximum UW cloud-top cooling rates to maximum achieved values for the radar fields described in Fig. 3.

  • Fig. 5.

    (a) Normalized frequency of lead times (min) between the time that a cloud object’s weak (light gray), moderate (gray), and strong (black) UW cloud-top cooling rate was observed with respect to when the object developed its maximum composite reflectivity signature on radar. All bins contain unique data points; for example, data that fall in the 0 ≤ L < 15 min bin indicate that a UW-CTC rate was observed at least 0, but less than 15, min before the cloud object achieved its maximum composite reflectivity. (b) As in (a), but for maximum UW cloud-top cooling rate to maximum cloud-object composite reflectivity. (c),(d) As in (a),(b), but for maximum VIL. All lead time values greater than 60 min were placed in the respective tail bin, while values less than −17 min were excluded.

  • Fig. 6.

    As in Fig. 5, but comparing lead times of (a),(c) instantaneous and (b),(d) maximum UW cloud-top cooling rates to when a cloud object achieved (a),(b) a composite reflectivity of 60 dBZ or (c),(d) a VIL of 40 kg m−2 for those that had both a UW-CTC rate and the shown field threshold at some point in their lifetime.

  • Fig. 7.

    As in Fig. 5, but showing lead times of (a) all UW-CTC and (b) maximum cloud object UW-CTC rates to MESH signatures on radar.

  • Fig. 8.

    As in Fig. 6, but showing lead times of a cloud object’s (a) instantaneous and (b) maximum UW cloud-top cooling rates to when a 1.00-in. MESH was achieved at some point in their lifetime.

  • Fig. 9.

    GOES-12 11-μm IR-window (a),(d) brightness temperature (K) and (b),(e) UW-CTC rate [K (15 min)−1] valid at 2215 and 2225 UTC, respectively, on 13 May 2009. (c),(f) Quality controlled NEXRAD composite reflectivity (dBZ) valid at 2216 and 2226 UTC, respectively, on 13 May 2009. The developing convective cloud of interest is located just to the northwest of OKC.

  • Fig. 10.

    GOES-12 11-μm IR-window on 13 May 2009 for (a),(c) brightness temperature (K) valid at 2240 and 2255 UTC, respectively, and (b),(d) quality controlled NEXRAD composite reflectivity (dBZ) valid at 2241 and 2301 UTC, respectively. The developing convective cloud of interest is located just to the northwest of OKC.

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