The Thunderstorm Observation by Radar (ThOR) algorithm is an objective and tunable Lagrangian approach to cataloging thunderstorms. ThOR uses observations from multiple sensors (principally multisite surveillance radar data and cloud-to-ground lightning) along with established techniques for fusing multisite radar data and identifying spatially coherent regions of radar reflectivity (clusters) that are subsequently tracked using a new tracking scheme. The main innovation of the tracking algorithm is that, by operating offline, the full data record is available, not just previous cluster positions, so all possible combinations of object sequences can be developed using all observed object positions. In contrast to Eulerian methods reliant on thunder reports, ThOR is capable of cataloging nearly every thunderstorm that occurs over regional-scale and continental United States (CONUS)-scale domains, thereby enabling analysis of internal properties and trends of thunderstorms. ThOR is verified against 166 manually analyzed cluster tracks and is also verified using descriptive statistics applied to a large (~35 000 tracks) sample. Verification also relied on a benchmark tracking algorithm that provides context for the verification statistics. ThOR tracks are shown to match the manual tracks slightly better than the benchmark tracks. Moreover, the descriptive statistics of the ThOR tracks are nearly identical to those of the manual tracks, suggesting good agreement. When the descriptive statistics were applied to the ~35 000-track dataset, ThOR tracking produces longer (statistically significant), straighter, and more coherent tracks than those of the benchmark algorithm. Qualitative assessment of ThOR performance is enabled through application to a multiday thunderstorm event and comparison to the behavior of the Storm Cell Identification and Tracking (SCIT) algorithm.
Initiation is the part of the convective life cycle that is currently least understood and least well forecast. The inability to properly forecast the timing and/or location of deep convection initiation degrades forecast skill, especially during the warm season. To gain insight into what atmospheric parameters distinguish areas where storms initiate from areas where they do not initiate, over 55 000 thunderstorm initiation points over the central United States from 2005 to 2007 are found and a number of thermodynamic and kinematic parameters are computed from 20-km Rapid Update Cycle (RUC)-2 data. In addition to the initiation points, data are also collected at nearby locations where thunderstorms did not initiate (null points) for comparison. Thunderstorm identification and tracking are done using several tools within the Warning Decision Support Services-Integrated Information (WDSS-II) package and a thunderstorm tracking algorithm called Thunderstorm Observation by Radar (ThOR). The parameters being examined are intended to represent the four main factors governing the behavior of convection: buoyancy, dilution, lift, and inhibition. Statistical analysis of the data shows that there is no threshold of any single parameter that is consistently able to discriminate between initiation and noninitiation. However, case-by-case comparison of the values showed that lift is most often the factor that distinguishes the thunderstorm initiation environment from other areas.
A first-of-its-kind automated thunderstorm tracking algorithm that relies on radar reflectivity data and cloud-to-ground lighting is used to identify the spatiotemporal distribution of thunderstorm initiation over the central United States for [2005][2006][2007]. Nearly 56 000 thunderstorm initiations are identified. High concentrations of thunderstorm initiation are found near prominent topography and near the Gulf coast. The annual distribution exhibits a peak in August and the diurnal cycle exhibits a peak at the local solar noon with the majority of initiations occurring between 2 h prior to and 6 h past the local solar noon. While the results are largely expected, no previous study has documented the distribution of thunderstorm initiation over the geographic extent and period of record used to develop the results presented here.
The complex interactions between soil moisture and precipitation are difficult to observe, and consequently there is a lack of consensus as to the sign, strength, and location of these interactions. Inconsistency between soil moisture–precipitation interaction studies can be attributed to a multitude of factors, including the difficulty of demonstrating causal relationships, dataset differences, and precipitation autocorrelation. The purpose of this study is to explore these potential confounding factors and determine which are most important for consideration when assessing statistical coupling between soil moisture and precipitation. Soil moisture is assessed via three remote sensing datasets: the Advanced Microwave Scanning Radiometer for Earth Observing System, the Tropical Rainfall Measuring Mission Microwave Imager, and the Essential Climate Variable Soil Moisture. Estimates of soil moisture are coupled with afternoon thunderstorm events identified by the Thunderstorm Observation by Radar (ThOR) algorithm, and dry soil or wet soil preferences for convection initiation are determined for over 16 000 thunderstorm events between 2005 and 2007. Differences in soil moisture datasets were found to have the largest impact with regard to determining wet or dry soil preferences. Precipitation autocorrelation is prevalent in the data; however, precipitation autocorrelation did not influence the results with regard to dry or wet soil preferences. Consideration of the convective environment (i.e., weakly or synoptically forced) did result in significant differences in wet/dry soil preference, but only for certain soil moisture datasets. The results suggest that observation-driven soil moisture–precipitation interaction studies should both consider the convective environment and implement multiple soil moisture datasets to assure robust results.
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