Mobile ground‐based SMART radar observations and wind retrievals during the landfall of Hurricane Harvey (2017)

Abstract

This article describes a unique ground‐based weather radar dataset collected during the landfall of Hurricane Harvey (2017) along the United States Gulf Coast, documents the wind retrievals conducted with this dataset, and reports on the location of the archive of the data and wind retrievals so that others may gain access and use of these data. Datasets from C‐band dual‐polarimetric Shared Mobile Atmospheric Research and Teaching (SMART) radar and the United States National Weather Service WSR‐88D in Corpus Christi, Texas, are presented, along with dual‐Doppler analyses before and during Harvey's landfall on the United States Gulf Coast. The quality assurance and dual‐Doppler wind synthesis procedures are detailed. Nearly 8 hr of dual‐Doppler wind analyses were constructed, providing an unprecedented dataset for use in understanding hurricane dynamics at landfall and validating numerical simulations of Hurricane Harvey. In addition, raw, dual‐polarization data not used in the dual‐Doppler syntheses, but included in each radar dataset, are also summarized.

Hurricane Florence (2018): Long duration single‐ and dual‐Doppler observations and wind retrievals during landfall

“…The data quality control procedures used here were similar to those used for the Harvey dataset documented in Alford et al (2019a) with a few modifications described below. As such, all 30 hr of PPI volumetric SMART radar data collected during Hurricane Florence have been preliminarily quality controlled.…”

“…The region‐based method in Py‐ART was employed to objectively process all data collected between 1424 UTC on 13 September and 1943 UTC on 14 September. For Hurricane Harvey, Alford et al (2019a) used two methods, the region‐based method and the four‐dimensional method (James & Houze, 2001), for dealiazing due to the extreme shear of the horizontal wind in Hurricane Harvey. Using the region‐based method in Harvey, the horizontal wind shear tended to result in a greater number of errors resulting in Doppler velocities being dealiazed into the wrong interval.…”

“…The temporal offset between radar volumes from KLTX and the SMART radar was limited to 5 min maximum but was generally on the order of 2–3 min. As the intent of the data collection was focused on mesoscale processes, the spatial/temporal offset of sub‐kilometre scale features should induce minimal error as argued in Alford et al (2019a). The curvature of the horizontal wind made typical linear‐velocity advection correction (e.g., Ziegler, 2013) impossible.…”

“…The radial velocities were corrected for the influence of terminal velocity of the hydrometeors using density‐weighted power‐law fall speed‐reflectivity relationships for rain and snow separately following Biggerstaff and Houze (1991). As in Alford et al (2019a), the smoothness constraint in Potvin et al was replaced with a one‐step Leise filter (Leise, 1981) applied on the retrieved wind components every 50 iterations of the cost function and is similar to the technique inherent to Jackson et al (2020). The variational technique is advantageous over traditional techniques, since the explicit integration (and associated errors) of the mass continuity equation is not employed.…”

“…The radial velocities were corrected for the influence of terminal velocity of the hydrometeors using densityweighted power-law fall speed-reflectivity relationships for rain and snow separately following Biggerstaff and Houze (1991). As in Alford et al (2019a) The retrieved winds at 0.5 km above mean sea level at the time of the landfall of Hurricane Florence (1256 UTC on 14 September 2018) demonstrates the dual-Doppler analysis procedure (Figure 6). The maximum reflectivity between the two contributing radars and the horizontal wind vectors are shown in Figure 6a.…”