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Daniela Spade, Kirsten de Beurs, and Mark Shafer


Evaluation of the standardized precipitation index (SPI) dataset published monthly in the National Oceanic and Atmospheric Administration/National Centers for Environmental Information (NOAA/NCEI) climate divisional database revealed that drought frequency is being mischaracterized in climate divisions across the United States. The 3- and 6-month September SPI values were downloaded from the database for all years between 1931 and 2019; the SPI was also calculated for the same time scales and span of years following the SPI method laid out by NOAA/NCEI. Drought frequency is characterized as the total number of years that the SPI fell below −1. SPI values across 1931–90, the calibration period cited by NOAA/NCEI, showed regional patterns in climate divisions that are biased toward or away from drought, according to the average values of the SPI. For both time scales examined, the majority of the climate divisions in the central, Midwest, and northeastern United States showed negative averages, indicating bias toward drought, whereas climate divisions in the western United States, the northern Midwest, and parts of the Southeast and Texas had positive averages, indicating bias away from drought. The standard deviation of the SPI also differed from the expected value of 1. These regional patterns in the NCEI’s SPI values are the result of a different (sliding) calibration period, 1895–2019, instead of the cited standardized period of 1931–90. The authors recommend that the NCEI modify its SPI computational procedure to reflect the best practices identified in the benchmark papers, namely, a fixed baseline period.

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Darrel M. Kingfield and Kirsten M. de Beurs


Multispectral satellite imagery provides a spaceborne perspective on tornado damage identification; however, few studies have explored how tornadoes alter the spectral signature of different land-cover types. In part 1 of this study, Landsat surface reflectance is used to explore how 17 tornadoes modify the spectral signature, NDVI, and “Tassled Cap” parameters inside forest (N = 16), grassland (N = 10), and urban (N = 17) land cover. Land cover influences the magnitude of change observed, particularly in spring/summer imagery, with most tornado-damaged surfaces exhibiting a higher median reflectance in the visible and shortwave infrared, and a lower median reflectance in the near-infrared spectral ranges. These changes result in a higher median Tasseled Cap brightness, lower Tasseled Cap greenness and wetness, and lower NDVI relative to unaffected areas. Other factors affecting the magnitude of change in reflectance include season, vegetation condition, land-cover heterogeneity, and tornado strength. While vegetation indices like NDVI provide a quick way to identify damage, they have limited utility when monitoring recovery because of the cyclical seasonal vegetation cycle. Since tornado damage provides an analogous spectral signal to that of forest clearing, NDVI is compared with a forest disturbance index (DI) across a 5-yr Landsat climatology surrounding the 27 April 2011 tornado outbreak in part 2 of this study. Preoutbreak DI values remain relatively stable across seasons. In the five tornado-damaged areas evaluated, DI values peak within 6 months followed by a decline coincident with ongoing recovery. DI-like metrics provide a seasonally independent mechanism to fill the gap in identifying damage and monitoring recovery.

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Darrel M. Kingfield, Kristin M. Calhoun, Kirsten M. de Beurs, and Geoffrey M. Henebry


Five years of 0.01° latitude × 0.01° longitude multiradar multisensor grids of composite reflectivity and vertically integrated signals from the maximum expected size of hail (MESH) and vertically integrated liquid (VIL) were created to examine the role of city size on thunderstorm occurrence and strength around four cities: Dallas–Fort Worth, Texas; Minneapolis–St. Paul, Minnesota; Oklahoma City, Oklahoma; and Omaha, Nebraska. A storm-tracking algorithm identified thunderstorm areas every minute and connected them together to form tracks. These tracks defined the upwind and downwind regions around each city on a storm-by-storm basis and were analyzed in two ways: 1) by sampling the maximum value every 10 min and 2) by accumulating the spatial footprint over its lifetime. Beyond examining all events, a subset of events corresponding to favorable conditions for urban modification was explored. This urban favorable (UF) subset consisted of nonsupercells occurring in the late afternoon/evening in the meteorological summer on weak synoptically forced days. When examining all thunderstorm events, regions at variable ranges upwind of all four cities generally had higher areal mean values of reflectivity, MESH, and VIL relative to downwind areas. In the UF subset, the larger cities (Dallas–Fort Worth and Minneapolis–St. Paul) had a 24%–50% increase in the number of downwind thunderstorms, resulting in a higher areal mean reflectivity, MESH, and VIL in this region. The smaller cities (Oklahoma City and Omaha) did not show such a downwind enhancement in thunderstorm occurrence and strength for the radar variables examined. This pattern suggests that larger cities could increase thunderstorm occurrence and intensity downwind of the prevailing flow under unique environmental conditions.

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