1. Introduction
Landfalling tropical cyclones (TCs) can produce significant destruction and mortality, and have been estimated to kill upward of 500 million people since 1492 (Rappaport 2000). While damaging winds pose a threat to life and property near the TC landfall point, freshwater flooding can result in human fatalities hundreds of kilometers inland (e.g., Rappaport 2000; Jarrell et al. 2001). Thus, it is important to understand the characteristics of excessive precipitation in landfalling TCs well away from coastal regions.
May and June 2015 produced unprecedented rainfall in portions of Oklahoma and Texas, including an all-time high rainfall total of 594 mm for the month of May at the Norman Mesonet site (e.g., Brock et al. 1995; McPherson et al. 2007; Duchon et al. 2017). As a result, catastrophic urban and river flooding occurred during this period due to excessive precipitation, runoff, and saturated soils, resulting in 11 fatalities. Tropical Storm Bill further contributed to the excessive precipitation event over the region as it tracked over Texas and Southern Oklahoma in June 2015.
Previous studies (e.g., Clark and Arritt 1995; Lynn et al. 1998) have shown the importance of soil moisture on generating deep convection through enhanced latent heat fluxes which serves to increase boundary layer moisture. The influence of soil moisture on local weather and climate extremes is most pronounced in continental regions characterized by a transition zone from humid to drier climates (e.g., Guo et al. 2006; Koster et al. 2006), such as the southern Great Plains (SGP). In this region, evapotranspiration displays a greater sensitivity to changes in both soil moisture and atmospheric demand (e.g., Guo et al. 2006; Koster et al. 2011; Wei et al. 2016). The connection between these continental land–atmosphere feedbacks and TCs is not entirely obvious at first. However, observations of TC reintensification over land have recently given rise to the concept of the “brown ocean effect,” which hypothesizes that anomalously moist soils can mimic an oceanic surface by providing fluxes of heat and moisture to the TC (e.g., Emanuel et al. 2008; Andersen and Shepherd 2014).
The first paper of this study (Wakefield et al. 2021, hereafter Part I) found that the brown ocean effect played a role in maintaining Tropical Storm Bill over land through above average latent heat fluxes, which increases total precipitable water and vertically integrated relative humidity. The reintensification of Tropical Storm Erin (2007) over Oklahoma has been attributed to this particular phenomenon (e.g., Arndt et al. 2009; Monteverdi and Edwards 2010; Evans et al. 2011; Kellner et al. 2012; Andersen and Shepherd 2014). Nair et al. (2019) recently attributed the historic flooding in Louisiana associated with an unnamed tropical system to the brown ocean effect. TC maintenance and/or reintensification events, otherwise known as TCMI events, have been observed globally (Andersen and Shepherd 2014), and are typically associated with above-normal latent heat flux in the 3 weeks prior to the TC’s landfall.
Andersen and Shepherd (2014) used a 900–600-hPa thermal wind calculation to categorize landfalling TCs after progressing inland as having a warm core, neutral (hybrid), or cold core. From the 227 cases examined, 45 TCs were found to have reintensified over land, primarily due to large positive heat fluxes over a warm and moist land surface. Other important factors that were found to be conducive to TCMI over land are weak deep-layer wind shear and a lack of a horizontal temperature gradient. While synoptic-scale features and land surface characteristics were found to dictate TCMIs over land (e.g., Andersen and Shepherd 2014; Yoo et al. 2020), the microphysical precipitation processes remain to be explored in these events. Specifically, the evolution and quantification of microphysical processes have yet to be systematically analyzed in cases of inland TC reintensification or maintenance. Griffin et al. (2014) performed an in-depth ground-based polarimetric radar analysis of Tropical Storm Erin’s reintensification over central Oklahoma. Didlake and Kumjian (2017) examined the interaction between storm asymmetries, vertical wind shear, and precipitation processes using polarimetric radar observations in Hurricane Arthur (2014), and found that vertical profiles of ZH and ZDR in the downshear half of the eyewall exhibited signatures associated with collision–coalescence. Feng and Bell (2019) performed a similar analysis in Hurricane Harvey (2017) and discussed size-sorting signatures in the eyewall as the maximum in KDP and ZH remained downwind from the maximum in ZDR. Polarimetric radar observations from the WSR-88D network (Crum and Alberty 1993) provide additional insight into the evolution of precipitation processes, and for example can be used to diagnose the extent of the low-echo centroid, warm rain processes (i.e., collision–coalescence) that are expected in a TC environment (e.g., Ryzhkov et al. 2005b; Vitale and Ryan 2013; Kumjian and Prat 2014; Didlake and Kumjian 2017).
Polarimetric radar observations at essentially unattenuated frequencies provide physical insight into precipitation processes at a high temporal resolution (e.g., Medlin et al. 2007; Didlake and Kumjian 2018), and can provide valuable insight into precipitation microphysics and drop size distribution characteristics that can ultimately improve the accuracy of quantitative precipitation estimation (e.g., Seliga and Bringi 1976; Herzegh and Jameson 1992; Zrnić and Ryzhkov 1996; Ryzhkov et al. 2005a; Giangrande and Ryzhkov 2008; Cifelli et al. 2011). However, ground radars are often limited in sampling the vertical dimension that is critical for precipitation microphysics, due to discrete elevation angles and increasing beam elevation with range, combined with beam broadening and nonuniform beam filling (Kirstetter et al. 2013). Other limitations include calibration uncertainty (e.g., Gorgucci et al. 1992; Bechini et al. 2008), the presence of mixed-phase precipitation (e.g., Gray et al. 2006; Kumjian 2013a), and partial beam filling (e.g., Ryzhkov 2007; Zhang et al. 2013). On the other hand, satellite-based radars provide a more regular and a finer vertical sampling as well as calibration stability, but they operate at attenuated frequencies. Thus, it is useful to jointly examine ground-based radar observations and satellite-borne radar retrievals to quantify microphysical processes (e.g., Smalley et al. 2017; Porcacchia et al. 2019). The synergy between ground-based radar observations and space-borne radar retrievals provides a novel framework for identifying instances of TCMI in Tropical Storm Bill by identifying profiles of collision–coalescence processes hundreds of kilometers inland from the landfall point. The objective of this study is to identify whether warm rain processes that are commonly observed in TCs existed well away from the landfall point during the periods of TCMI.
2. Data and methods
a. Event background
Tropical Storm Bill made landfall at 1645 UTC 16 June 2015 near Matagorda Island, Texas, with an estimated maximum sustained wind speed of 26 m s−1 (50 kt) and a minimum central pressure of 997 hPa. Bill then progressed north over north Texas and into southeastern Oklahoma while maintaining tropical depression status before being classified as an extratropical cyclone as it moved east into Arkansas, Missouri, and Kentucky (Fig. 1). Bill produced three distinct maxima in rainfall, with accumulations near the landfall point over south Texas near 300 mm, and a secondary maximum over north Texas and southern Oklahoma of 400 mm, and a third maximum over southern Illinois of 225 mm (Fig. 2). From here on, TCMI1 will refer to the period of tropical cyclone maintenance over north Texas and southern Oklahoma from 1200 to 1800 UTC 17 June, and TCMI2 will refer to the reintensification of Bill over southern Missouri, Illinois, and western Kentucky from 1200 UTC 19 June to 1200 UTC 20 June (Part I).
Hurricane Database (HURDAT2) best track plot of Tropical Storm Bill from 16 to 21 Jun 2015. Each point is spaced apart in 6-h time increments. The green box represents the location of TCMI1 from 1200 to 1800 UTC 17 Jun, and the black box represents the location of TCMI2 from 1200 UTC 19 Jun to 1200 UTC 20 Jun.
Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0151.1
(a),(b) PRISM daily accumulated precipitation over Texas and Oklahoma from 16 to 17 Jun; (c) total accumulated precipitation from 16 to 20 Jun; (d),(e) daily accumulated precipitation over Missouri and Illinois from 19 to 20 Jun, and (f) total accumulated precipitation from 16 to 20 Jun.
Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0151.1
b. Polarimetric radar data
Tropical Storm Bill offers the first opportunity to examine TCMI over land and the entire microphysical evolution of the cyclone using polarimetric radar observations since the WSR-88D network was upgraded with dual-polarization technology in 2010. Thus, Tropical Storm Erin (2007) was not captured due to a limited radar network that contained dual-polarization capabilities. This study uses Level-II WSR-88D data from the National Centers for Environmental Information (NOAA/NWS/Radar Operations Center 1991), which are then processed using the Gridded NEXRAD WSR-88D (GridRad) software (Bowman and Homeyer 2017). These data have a temporal resolution of 5 min and have an azimuthal resolution of 0.5° for the lowest four elevation angles, and a 1° azimuthal resolution for other angles (Crum and Alberty 1993).
The polarimetric radar variables that are analyzed include the horizontal reflectivity factor (ZH), differential reflectivity (ZDR), specific differential phase (KDP), and the copolar correlation coefficient (ρhv). The horizontal reflectivity factor ZH is proportional to the integration of the diameter of scatterers raised to the sixth power and provides information regarding the size and concentration of precipitation-sized hydrometeors that satisfy the Rayleigh regime (e.g., Austin 1987; Herzegh and Jameson 1992; Zrnić and Ryzhkov 1999; Vitale and Ryan 2013). Differential reflectivity ZDR is defined as the difference between the horizontal and vertical reflectivity factors, and provides information about the size, shape, and orientation of hydrometeors (e.g., Seliga and Bringi 1976; Herzegh and Jameson 1992). The ZDR observations can be biased if mixed-phase precipitation is present within a resolution volume which can lead to nonuniform beam filling (e.g., Bringi et al. 1990; Testud et al. 2000; Ryzhkov 2007; Giangrande and Ryzhkov 2008), or if the radar is miscalibrated (e.g., Gorgucci et al. 1992; Bechini et al. 2008). Specific differential phase KDP is influenced by the number concentration of hydrometeors within a volume (e.g., Kumjian 2013b). This is because large drops are oblate spheroids; therefore, the horizontal polarization will encounter more of a phase shift compared to the vertical polarization, resulting in positive KDP (e.g., Herzegh and Jameson 1992; Zrnić and Ryzhkov 1999; Ryzhkov et al. 2005b; Kumjian 2013a). Thus, one advantage of using KDP is that it is independent of radar calibration and is immune to propagation attenuation, which makes it useful for estimating heavy rainfall (e.g., Seliga and Bringi 1978; Jameson 1985; Wang and Chandrasekar 2009). The copular correlation coefficient ρhv is a measure of the similarity of scatters in a resolution volume (e.g., Herzegh and Jameson 1992; Zrnić and Ryzhkov 1999; Ryzhkov et al. 2005a,b; Kumjian 2013a). A homogeneous particle size distribution will yield a ρhv close to 1, whereas mixed-phased precipitation will result in a ρhv < 0.9 (e.g., Herzegh and Jameson 1992; Zrnić and Ryzhkov 1999; Ryzhkov et al. 2005a,b; Kumjian 2013a).
Rainfall in TCs is characterized by a larger concentration of smaller drops (e.g., Cao et al. 2008; Brauer et al. 2020; DeHart and Bell 2020). Thus, ZH tends to be lower than that of rainfall in the midlatitudes due to the dependence of ZH on drop size (e.g., Austin 1987; Herzegh and Jameson 1992; Zrnić and Ryzhkov 1999). Further, due to the large number concentration of small drops found in TCs (e.g., Squires 1956; Ulbrich and Atlas 2007; Xu et al. 2008), ZDR tends to range from 0 to 1 dB and KDP tends to be positive (e.g., Brown et al. 2016; Didlake and Kumjian 2017). In terms of vertical structure, the warm rain events associated with TCs that are characterized by the aforementioned polarimetric radar signatures are typically dominated by collision–coalescence (CC) below the −10°C isotherm because supercooled liquid water can still contribute to drop growth via the CC mechanism (e.g., Vitale and Ryan 2013; Schroeder et al. 2016). Signatures of CC below the −10°C isotherm are identified by ZH and ZDR increasing toward the surface (e.g., Xu et al. 2008; Kumjian and Prat 2014; Carr et al. 2017; Porcacchia et al. 2019).
c. GPM dual frequency precipitation radar data
The Global Precipitation Measurements (GPM) mission was launched in 2014 as a successor to the Tropical Rainfall Measurement Mission (TRMM), which ended in 2015 (e.g., Hou et al. 2014; Skofronick-Jackson et al. 2017). On board the GPM Core Observatory is the active dual-frequency precipitation radar (DPR). The GPM DPR is generally well calibrated, has a higher sensitivity than S-band radars such as the WSR-88D network, and can provide snapshots of vertical profiles of reflectivity at a high vertical resolution and a low temporal resolution (e.g., Kozu et al. 2001; Hou et al. 2014). The GPM DPR is also capable of estimating precipitation at the surface when rainfall rates exceed 0.5 mm h−1 (e.g., Kozu et al. 2001; Hou et al. 2014). Although the GPM DPR is specifically prone to attenuation, it allows for a complementary source of identification and quantification of precipitation processes in addition to the ground-based radar network.
Alternatively, the GPM DPR operates at both Ku and Ka bands (13.6 GHz), which allows for the detection of lighter rainfall and ice hydrometeors due to the higher sensitivity of the Ka band (12 dBZ). This is particularly useful for precipitation estimation at higher latitudes where frozen precipitation and stratiform systems are more common (e.g., Skofronick-Jackson et al. 2017; Porcacchia et al. 2019). The GPM DPR has a horizontal resolution of 5 km and a vertical resolution of 250 m. In 2015, the swath widths were 245 km at Ku band and 120 km at Ka band (e.g., Hou et al. 2014; Skofronick-Jackson et al. 2017).
d. Miscellaneous data
The Hurricane Database (HURDAT2) best-track data were used to plot the track of Tropical Storm Bill from 16 to 21 June 2015 (NHC et al. 1993). The ECMWF ERA-5 dataset has a horizontal grid spacing 31 km, 137 vertical levels, and a 3-h temporal resolution (Hersbach et al. 2019), and was used to generate longitude–height cross sections of potential vorticity, potential temperature, and vertical velocity during both periods of the TCMIs (i.e., TCMI1 and TCMI2). The Parameter-Elevation Regressions on Independent Model (PRISM; Daly et al. 1994), which uses a 4-km grid resolution was used for daily precipitation accumulation from 16 to 20 June over Oklahoma and Texas. Additionally, the University of Wyoming sounding database was used to plot skew T–logp diagrams using MetPy plotting software (May et al. 2017) at Springfield, Missouri, from 1200 UTC 18 June to 1200 UTC 19 June.
3. Results
a. near landfall
Figure 3 displays vertical profiles of ZH, ZDR, KDP, ρhv, and drop size on 16 June over El Campo, Texas, as Tropical Storm Bill made landfall. Because the ρhv field provides information regarding hydrometeor diversity, regions of reduced ρhv can be used to detect the melting layer (e.g., Herzegh and Jameson 1992; Zrnić and Ryzhkov 1999; Ryzhkov et al. 2005a,b; Kumjian 2013a). In this case the melting layer was located between 4.5 and 5 km, which is consistent with polarimetric radar observations of other landfalling tropical cyclones such as Hurricane Harvey in 2017 (Brauer et al. 2020). Values of ZH ranged from 25 to 45 dBZ in the liquid phase after 1500 UTC, with the highest values occurring after 2000 UTC. Values of ZDR of 1–1.5 dB existed from 1500 to 1700 UTC, implying a slightly larger drop size when compared to the values of ZDR of 0.5–1 dB that were observed later in the day after 1800 UTC. From the same 1500–1700 UTC period, values of KDP were less than 0.25° km−1, whereas later in the day values ranged from 0.25° to 0.5° km−1. When combining the ZH and ZDR observations with KDP, it can be seen that a small number concentration of larger drops existed from 1500 to 1700 UTC, whereas after 2000 UTC, there was a larger number concentration of smaller drops, consistent with tropical rainfall driven by CC (e.g., Squires 1956; Ulbrich and Atlas 2007; Carr et al. 2017). While the drop size appears to have increased toward the surface throughout the entire period (consistent with CC), the largest increase in drop size occurred after 2100 UTC 16 June.
Time–height curtains of (a) ZH, (b) ZDR, (c) KDP, (d) ρhv, and (e) drop size near the landfall point over El Campo, TX, on from 1200 UTC 16 Jun to 0000 UTC 17 Jun, and images of 2-km ZH at 1200 and 1800 UTC 16 Jun.
Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0151.1
b. TCMI1: Southern Oklahoma
After Tropical Storm Bill progressed inland across north Texas and southern Oklahoma, it maintained its tropical precipitation characteristics. Figure 4 displays time–height curtains of the polarimetric radar variables and drop size at Grady, Oklahoma, which is near the time and location of TCMI1. The drop size was similar to that over El Campo, with values of ZDR ranging from 1 to 1.5 dB in the liquid phase after 1800 UTC, and smaller values before this time. Similarly, values of KDP of 0°–0.25° km−1 for the majority of the period, with higher values close to 0.5° km−1 around 2200 UTC. At this time, ZDR values were largest and ZH was approximately 45 dBZ, implying that convection was responsible for the larger number concentration of larger drops. The ρhv field suggests that the melting layer height decreased slightly from the previous day over El Campo, ranging from 3.5 to 4.5 km, with an upward displace ment during the period of convection at approximately 2200 UTC. This may be due to stronger updrafts inducing latent heat release, which subsequently increased the height of the 0°C isotherm. The drop size profile over Grady was similar to that over El Campo, with drop size that increased toward the surface below the melting layer, indicative of CC and warm rain. This can also be seen via Fig. 5, which shows that the vertical distributions of ZH and ZDR over El Campo and Grady were consistent with low-echo centroid precipitation systems and are characterized by the majority of reflectivity remaining within the warm cloud layer (e.g., Vitale and Ryan 2013; Schroeder et al. 2016). Similarly, ZDR also increased toward the surface at both locations below the melting layer which is indicative of CC. The ZDR distribution was also shifted toward values between 0 and 1 dB, implying a small mean drop size at El Campo and Grady (e.g., Squires 1956; Ulbrich and Atlas 2007; Carr et al. 2017) Fig. 5 also illustrates the frequency of KDP and ρhv values with height at the same two locations. The KDP values from 0° to 0.5° km−1 occurred below the melting layer at El Campo between 30 and 40 different radar scans, indicating a large concentration of small drops (e.g., Brown et al. 2016; Didlake and Kumjian 2017; Brauer et al. 2020). Last, the high frequency of ρhv < 0.98 between 4.5 and 5.5 km ASL implies mixed-phase precipitation and the approximate location of the melting layer.
Time–height curtains of (a) ZH, (b) ZDR, (c) KDP, (d) ρhv, and (e) drop size during TCMI1 over Grady, OK, on from 1200 UTC 17 Jun to 0000 UTC 18 Jun, and images of 2-km ZH at 1200 UTC and 1800 UTC 17 Jun.
Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0151.1
Contoured frequency by altitude diagrams (CFADs) of ZH, ZDR, KDP, and ρhv at (a) El Campo, TX, on 16 Jun and (b) Grady, OK, on 17 Jun during TCMI1.
Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0151.1
Figure 6 shows along-track vertical profiles of reflectivity at Ku band from the GPM DPR at 0538 and 1454 UTC 17 June. Although no additional overpass was available further north and east over Oklahoma, the 1454 UTC overpass provides a sense of the evolution of the reflectivity field as Bill progressed inland post-landfall. The DPR retrievals confirm the findings with the ground-based polarimetric radar observations. At 0538 UTC, a brightband signature was evident at approximately 4.5–5 km, which is indicative of a melting layer at this altitude and is consistent with the 0°C isotherm that was extracted from the GPM DPR. Below this level, the reflectivity increased toward the surface consistent with CC occurring within the warm cloud layer. Further, the retrieved DM generally increases toward the surface, consistent with Porcacchia et al. (2019). As Bill tracked inland over north-central Texas, the melting level was located slightly lower near 4.5 km; however, there were upward displacements evident in the melting layer collocated with convection and associated values of reflectivity near 50 dBZ. Similarly, reflectivity predominantly increased below the melting layer, implying the maintenance of CC-dominant precipitation after Bill progressed hundreds of kilometers inland from the landfall point. Mean drop sizes (DM) ranged from 0.75 to 1.5 mm, with higher values of 2 mm in regions of convection. Such observations were consistent with larger values seen in convection in other TCs such as Hurricane Harvey (2017) (Brauer et al. 2020; DeHart and Bell 2020). Finally, high drop number concentrations [log10(NW)] between 3 and 5 m−3 mm−1 occurred during both times, with the highest values occurring in convective cores.
GPM DPR along-track vertical profiles of ZM(Ku), DM, and log10(NW) at (a) 0538 UTC and (b) 1454 UTC on 17 Jun 2015 over Texas. The dashed line represents the 0°C isotherm. Along-track cross sections are plotted beginning from the star.
Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0151.1
c. TCMI2: Southern Missouri, Illinois
As Bill continued to move north and east over southern Missouri, Illinois, and Kentucky from 19 to 20 June, the second TCMI occurred (TCMI2) at approximately 0000 UTC 20 June. Figures 7 and 8 show time–height curtains of ZH, ZDR, KDP, ρhv, and drop size from the WSR-88D network at Cape Girardeau, Missouri, and Cairo, Illinois, on 19 June, respectively. Values of ZH at the surface ranged from 30 to 40 dBZ after 2100 UTC at Cape Girardeau, with slightly lower values of 25–35 dBZ at Cairo, with distinctive bursts of weak convection after 1200 UTC, which explains the gaps in meteorological scatterers as ρhv ≤ 0.9. Values of ZDR were considerably lower than TCMI1, with values ranging from 0 to 1 dB at Cape Girardeau and from 0 to 0.5 dB at Cairo, compared to 0.5–1.5 dB at El Campo and Grady. These lower values of ZDR translate to a smaller drop size (e.g., Brown et al. 2016; Didlake and Kumjian 2017) and were likely due to CC or a balance between CC and drop breakup, as expected in a tropical environment (e.g., Kumjian and Prat 2014; Didlake and Kumjian 2017; Brauer et al. 2020). Additionally, signatures with an enhancement in hydrometeor number concentration in areas of weak convection (KDP values near 0.25° km−1) occurred after 2100 UTC at both Cape Girardeau and Cairo. The vertical profiles of ρhv indicated that the melting layer height ranged from 4.0 to 5.5 km at both locations, and was located higher in altitude than Grady, Oklahoma.
Time–height curtains of (a) ZH, (b) ZDR, (c) KDP, (d) ρhv, and (e) drop size during TCMI2 over Cape Girardeau, MO, on from 1800 UTC 19 Jun to 0000 UTC 20 Jun, and images of 2-km ZH at 1800 UTC 19 Jun and 0000 UTC 20 Jun.
Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0151.1
Time–height curtains of (a) ZH, (b) ZDR, (c) KDP, (d) ρhv, and (e) drop size during TCMI2 over Cairo, IL, on from 1800 UTC 19 Jun to 0000 UTC 20 Jun, and images of 2-km ZH at 1200 and 1800 UTC 19 Jun.
Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0151.1
Figure 9 illustrates the frequency of ZH, ZDR, KDP, and ρhv with height at Cape Girardeau and Cairo to provide information regarding the dominant precipitation processes during TCMI2. From the framework used in Kumjian and Prat (2014), Carr et al. (2017), and Porcacchia et al. (2019), ZH increased toward the surface while ZDR decreased toward the surface below the melting layer at both locations. Such results indicate size sorting and evaporation, which may be due to enhanced vertical wind shear, leading to more dry air entrainment into the core of Bill, which is known to disrupt the structure of tropical cyclones (e.g., Gray 1968; DeMaria and Kaplan 1994; Hanley et al. 2001; Corbosiero and Molinari 2002). Although size sorting and evaporation were likely the dominant processes, the drop size distribution was still skewed toward a smaller drop size as ZDR remained below 1 dB at both locations for the majority of the event. Similarly, echo tops associated with the weak convection were below 12 km ASL, and similar features are known to produce the most extreme rainfall rates rather than deep convection with high values of ZH (Hamada et al. 2015). There were also instances where locations saw an enhancement in drop concentration as KDP between 0.25° and 0.5° km−1 were observed.
Contoured frequency by altitude diagrams (CFADs) of ZH, ZDR, KDP, and ρhv at (a) Cape Girardeau, MO, on 19 Jun and (b) Cairo, IL on 19 Jun during TCMI 2.
Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0151.1
Although ground-based radar observations show evidence of size sorting and evaporation being the dominant processes, retrievals from the GPM DPR during an overpass at 0436 UTC 20 June show evidence of CC or a balance between CC and drop breakup below the melting layer (Fig. 10). The melting layer was identified between 4 and 5 km on the cross section of Ku-band reflectivity and denoted by the enhancement of reflectivity due to melting hydrometeors. Further, the 0°C isotherm was also located at 5 km, indicating a deep warm cloud layer. Below this level, reflectivity increased from 25 to 35 dBZ at an along-track distance of 100 km, which is a signal of CC or a CC-breakup balance (Fig. 10b). Mean drop sizes DM also increased from 0.75 to 1.2 mm at this location, with a larger mean drop size ranging from 1.25 to 1.75 mm within the weak convection (Fig. 10c). The vertical profiles of log10(NW) show a drop concentration of 4.5 m−3 mm−1 in the aforementioned region of CC, with slightly lower concentrations of 3.5–4.0 m−3 mm−1 in the region of weak convection (Fig. 10d). The GPM DPR estimated a rainfall rate of 5–10 mm h−1 in the stratiform precipitation regions and enhanced rainfall rates of 20–35 mm h−1 in the embedded regions of weak convection (Fig. 10e). Last, Fig. 10f shows regions of convection embedded in a broader region of stratiform precipitation. The aforementioned brightband signature is likely a result of an area of stratiform precipitation within areas of weak convection.
(a) GPM DPR near-surface reflectivity, (b) GPM DPR along-track vertical profiles of ZH (Ku), (c) surface rainfall rate, (d) DM, (e) log10(NW), and (f) associated regions of stratiform and convective precipitation at 0436 UTC on 20 Jun 2015 over Illinois. The dashed line represents the 0°C isotherm. Along-track cross sections are plotted beginning from the star.
Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0151.1
d. Dynamics
While Bill certainly maintained tropical rainfall characteristics during TCMI2 over Southern Missouri and Illinois, the dynamics associated with Bill were investigated to determine how the large-scale structure evolved during the reintensification period. The primary feature of TCs is the presence of a low-level potential vorticity (PV) anomaly due to large amounts of latent heat release in convection (e.g., Möller and Smith 1994; Möller and Montgomery 2000; Trenberth and Fasullo 2007). This PV anomaly in TCs differs from extratropical cyclones, in which positive PV anomalies are typically found in the upper troposphere (e.g., Hoskins et al. 1985; Hoskins 2006). Figure 11 shows longitude–height cross sections of PV and potential temperature at a constant latitude of 38°N using the ERA-5 data from 2100 UTC 19 June to 1200 UTC 20 June during TCMI2 over Southern Illinois and Kentucky. Before the onset of TCMI2, the positive PV anomaly existed in the mid troposphere between 600 and 400 hPa, with a gradual lowering and intensification of the positive PV anomaly analyzed by 0300 UTC 20 June. By 0600 UTC 20 June, the positive PV anomaly was located in the lower troposphere between 900 and 800 hPa, characteristic of low-level positive PV anomalies that are typically found in TCs.
Longitude–height cross sections of potential vorticity (shaded) and potential temperature (contours) along 38°N from 2100 UTC 19 Jun to 1200 UTC 20 Jun.
Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0151.1
Longitude–height cross sections of vertical velocity and potential temperature were also plotted using the ERA-5 data along a constant latitude of 34°N from 1200 to 2100 UTC 17 June (Fig. 12) during TCMI1, and along a constant latitude of 38°N from 2100 UTC 19 June to 1200 UTC 20 June (Fig. 13) during TCMI2. Maximum ascent rates of 3 Pa s−1 occurred near 600 hPa during TCMI1, whereas maximum ascent rates were considerably stronger during TCMI2, nearing values of 5 Pa s−1. Vertical velocity can be related to convective available potential energy (CAPE) (e.g., List and Lozowski 1970; Blanchard 1998), and the vertical distribution of CAPE can be directly related to updraft speed. Moist adiabatic profiles that are often frequently observed in tropical environments are characterized by “skinny” CAPE profiles and are indicative of slow ascent rates (e.g., Davis 2001; Jessup and DeGaetano 2008; Vitale and Ryan 2013; Schroeder et al. 2016), whereas “fat” CAPE profiles are associated with stronger updraft speeds and are more common in the midlatitudes. These weaker ascent rates are known to increase in-cloud residence time of hydrometeors, allowing for more efficient growth via CC (e.g., Vitale and Ryan 2013; Schroeder et al. 2016). In the case of Bill during TCMI1 and TCMI2, the magnitude of ascent was considerably less than vertical velocities captured in midlatitude convection by ERA-5, which could be as high as 15 Pa s−1 as was seen in a midlatitude mesoscale convective system prior to Bill over the same region. The combination of low-echo centroid precipitation, shallow echo tops, and weak ascent rates further illustrates that Bill maintained tropical characteristics inland over southern Oklahoma, Missouri, southern Illinois, and Kentucky. Figure 14 shows observed soundings at Springfield, Missouri, from 1200 UTC 18 August to 1200 UTC 19 August, which displays deep, moist adiabatic profiles and associated skinny CAPE which characterized the environment of Bill as it progressed northeast over Missouri and Kentucky. It can also be seen that there is considerable speed and directional shear at all three times, perhaps explaining the dominant presence of size sorting and evaporation as Bill moved over this region.
(a) Longitude–height cross sections of vertical velocity (shaded) and potential temperature (contours) along 34°N from 1200 to 2100 UTC 17 Jun during TCMI1, with (b) snapshots of 2-km ZH and cross-section locations.
Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0151.1
(a) Longitude–height cross sections of vertical velocity (shaded) and potential temperature (contours) along 38°N from 2100 UTC 19 Jun to 1200 UTC 20 Jun during TCMI2, with (b) snapshots of 2-km ZH and cross-section locations.
Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0151.1
Observed soundings from Springfield, MO, from 1200 UTC 18 Jun to 1200 UTC 19 Jun.
Citation: Journal of Hydrometeorology 22, 10; 10.1175/JHM-D-20-0151.1
4. Discussion
Tropical cyclones that maintain their structure over land can cause flooding and damaging winds hundreds of kilometers from the landfall point (e.g., Arndt et al. 2009; Andersen and Shepherd 2014). Tropical Storm Bill (2015) experienced two distinct TCMI events over 1) southern Oklahoma and 2) Missouri, southern Illinois, and Kentucky as it produced upward of 400 mm of precipitation over this region from 16 to 20 June (Fig. 1). An important aspect of the inland maintenance of warm cloud microphysics and precipitation associated with tropical rainfall is that they are highly efficient processes to convert tropospheric water vapor to precipitation (i.e., precipitation efficiency). Further, these precipitation systems have a deep warm cloud layer (e.g., Davis 2001; Vitale and Ryan 2013; Schroeder et al. 2016; Brauer et al. 2020) dominated by CC or a CC-drop breakup balance and are known to account for excessive precipitation events in the midlatitudes (e.g., Mohd Anip and Market 2007; Carr et al. 2017; Porcacchia et al. 2019). A novel aspect of TC Bill is that its TCMIs occurred during a period of available polarimetric radar observations from ground-based radars along with observations from the newly launched GPM DPR in 2014 (e.g., Hou et al. 2014; Skofronick-Jackson et al. 2017). Such datasets allowed for a more in-depth analysis and quantification of precipitation processes during the TCMI events that were not possible with prior events. These observational datasets can further benefit and improve the numerical modeling of landfalling TCs since, compared to radiation and PBL/surface schemes, microphysics schemes play the more critical role in the numerical model simulations of TCMIs (Yoo et al. 2020). Yoo et al. (2020) found that the TCMI of TC Kelvin was driven by moisture transport from the intertropical convergence zone, rather than latent heat fluxes from coupling to from warm, sandy soils. Thus, the inferred precipitation microphysics from the polarimetric radar observations and GPM DPR retrievals can be used to adjust the microphysical parameterization schemes accordingly in numerical simulations of TCMI to deliver model output that is more consistent with observations, and determine the role of precipitation microphysics of TCMI.
While inland over southern Oklahoma, Bill maintained dual-polarization radar signatures consistent with tropical rainfall and characterized by a large number concentration of small drops (Fig. 4) (e.g., Squires 1956; Ulbrich and Atlas 2007; Xu et al. 2008; Brauer et al. 2020). Values of ZDR of 0.5–1.25 dB in addition to KDP > 0.5° km−1 allow the classification of tropical rainfall, whereas ZH alone is more sensitive to hydrometeor size (e.g., Austin 1987; Herzegh and Jameson 1992; Zrnić and Ryzhkov 1999; Kumjian 2013a). GPM DPR observations (Fig. 6) during TCMI1 also showed an increase in drop size and Ku-band reflectivity below the melting layer, which is consistent with CC-dominant precipitation (e.g., Huang and Chen 2019; Porcacchia et al. 2019).
As Bill progressed inland over Missouri, southern Illinois, and Kentucky on 19–20 June, signatures of tropical precipitation were maintained during TCMI2, but were not as pronounced as when Bill was closer to the landfall point during TCMI1. Figure 9 illustrates signatures associated with evaporation and size sorting as ZH increased toward the surface and ZDR decreased toward the surface (e.g., Kumjian and Prat 2014; Carr et al. 2017; Porcacchia et al. 2019). However, the values of ZDR ranging from 0.5 to 1 dB, and KDP as high as 0.25° km−1 (Figs. 7 and 8) implies tropical rainfall characteristics similar to TCMI1 and shortly after the landfall point near El Campo, TX. The GPM DPR overpass over southern Illinois at 0436 UTC 20 June also identified Ku-band reflectivity and drop size increasing toward the surface, indicating CC-dominant precipitation or a balance between CC and drop breakup. These features are consistent with warm rain processes associated with tropical rainfall (Fig. 10). One possible reason for the occurrence of TCMI2 was the presence of anomalous mean latent heat fluxes of 105 W m−2 over the region, with the land surface obtaining oceanic influences on the reintensification of Bill (Part I).
5. Conclusions
The inland progression of Tropical Storm Bill over Texas and Oklahoma followed a two month period with record high precipitation throughout the region, which provided a unique opportunity to explore the microphysical evolution using polarimetric radar observations from the WSR-88D network and the GPM DPR. The exceptional precipitation during the 45 days prior to Bill resulted in anomalously high soil moisture and latent heat fluxes over the region, acting to increase boundary layer moisture and increase the warm cloud depth through latent heat release. As a result, Bill maintained tropical, warm rain characteristics as it tracked inland over southern Oklahoma and produced over 400 mm of rainfall in the aforementioned four day period during TCMI1. The polarimetric radar observations and GPM DPR measurements showed increasing reflectivity toward the surface below the melting layer, which is consistent with CC-dominant precipitation and/or a balance between CC and drop breakup. These signatures are consistent with tropical cyclone environments.
As Bill progressed inland over Missouri, southern Illinois, and Kentucky, an additional TCMI occurred. While dominant precipitation signatures were found to be associated with size sorting and evaporation below the melting layer, there were still signatures of CC in the WSR-88D observations and the GPM DPR retrievals. Additionally, investigation of atmospheric dynamics during TCMI2 illustrates ascent rates that were similar to those in shallow, tropical convection, and low level positive PV anomalies indicative of low and midlevel latent heat release found in TCs. This further demonstrates that Bill maintained tropical characteristics from a dynamical framework several days post-landfall.
Limitations of this work include that the GPM DPR was only able to extract vertical profiles of reflectivity and drop size distribution moments at snapshots in time, limiting the extent in which a TCMI was observed from spaceborne radar. The echo top heights in the ground-based radar observations were also 2 km higher than the GPM DPR retrievals, which may be due to regridding of the WSR-88D data. Additional uncertainties arise with the ERA-5 reanalysis being unable to fully resolve the spatial details in the PV and vertical velocity fields, which may explain the vertical discontinuity in midlevel PV as shown in Fig. 12.
Future work should examine more places throughout the inland progression of Tropical Storm Bill as it moved into Missouri and northeastern Oklahoma to determine the temporal extent to which Bill maintained tropical rainfall characteristics. Additionally, it would be useful to compare this event to other less pronounced TCMI cases using the GPM DPR on a global scale and using ground-based radar measurements where available. Future analyses could also incorporate the use of disdrometer data to more precisely quantify the drop size distribution moments to compare to the GPM DPR algorithms that are used to estimate DM and log10(NW) from space. Another area that can be explored in future work is the impacts of latent heating on precipitation microphysics during periods of TCMI. Last, future research could perform a modeling study of the dynamics and thermodynamics associated with the TCMI periods to account for the uncertainties in the ERA-5 reanalysis.
Acknowledgments
Funding for this study was provided, in part by NASA Grant 80NSSC19K0681 and the NASA Modeling, Analysis, and Prediction (MAP) program (16-MAP16-013). This work was also supported by the Future Investigators in NASA Earth Space Science and Technology (FINNEST) Award 80NSSC19K1365. We would also like to acknowledge Dr. David Considine and the generous support from the NASA MAP Program Grant 80NSSC17K0264. This material is also supported by the National Science Foundation under Grant OIA-1946093. The authors thank the three anonymous reviewers who provided useful feedback and comments to improve the quality of this study. Additionally, the authors thank Jordan Christian, Melanie Schroers, Ryan Lagerquist, Tomer Burg, and Ty Dickinson for their valuable insight, guidance, and encouragement that was necessary to complete this work. The authors also thank Randy Chase and Steve Nesbitt at the University of Illinois at Urbana–Champaign for providing source code used to complete this work.
Data availability statement
The WSR-88D Level II polarimetric radar data used in this study can be accessed at https://www.ncdc.noaa.gov/nexradinv/. Due to agreements with research collaborators, the GridRad data used in this study cannot be made openly available. The GPM DPR data used in this work can be found at https://search.earthdata.nasa.gov/search?fp=GPM&fi=DPR. The ERA-5 reanalysis dataset is openly available at https://cds.climate.copernicus.eu/cdsapp#!/ dataset/reanalysis-era5-pressure-levels?tab=form, and the upper-air data that were used in this study can be accessed via http://weather.uwyo.edu/upperair/sounding.html.
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