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The impact of extratropical transition (ET) on tropical cyclone (TC) tornadoes is not fully understood with no prior tornado climatologies for ET cases. Hence, this study investigates how ET impacts tornadoes and convective-scale environments within TCs using multidecadal tornado and radiosonde data from North Atlantic TCs. This research divides ET into three phases: tropical (i.e., pre-ET), transition (i.e., during ET), and extratropical (i.e., post-ET). These results show that the largest portion of tornadoes occurs before and during ET, with the greatest frequencies during ET. As TCs progress through ET, tornado location shifts north and east in the United States but farther south or more strongly downshear right relative to the TC center. Tornadoes also tend to occur later in the day and are more likely to be associated with greater damage. Evaluation of radiosondes shows that the downshear-right quadrant of the TC is frequently the most favorable for tornado production, with sufficient entrainment CAPE (ECAPE) and strong storm-relative helicity (SRH). Specifically, the downshear-right quadrant shows slower decreases in ECAPE (associated with synoptic-scale cooling and drying) and increased SRH and associated lower-tropospheric vertical wind shear through ET, relative to the other quadrants relative to the deep-tropospheric (i.e., 850–200-hPa) vertical wind shear vector. These results inform the physical model and prediction of ET-related TC structure, both in terms of their convective-scale environments and subsequent hazard production. 

Abstract Extreme precipitation over a two-week period can cause significant impacts to life and property. Trustworthy and easy-to-understand forecasts of these extreme periods on the subseasonal-to-seasonal timeframe may provide additional time for planning. The Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods (PRES²iP) project team conducted three workshops over six years to engage with stakeholders to learn what is needed for decision-making for subseasonal precipitation. In this study experimental subseasonal to seasonal (S2S) forecast products were designed, using knowledge gained from previous stakeholder workshops, and shown to decision-makers to evaluate the products for two 14-day extreme precipitation period scenarios. Our stakeholders preferred a combination of products that covered the spatial extent, regional daily values, with associated uncertainty, and text narratives with anticipated impacts for planning within the S2S timeframe. When targeting longer extremes, having information regarding timing of expected impacts was seen as crucial for planning. We found that there is increased uncertainty tolerance with stakeholders when using products at longer lead times that typical skill metrics, such as critical success index or anomaly correlation coefficient, do not capture. Therefore, the use of object-oriented verification, that allows for more flexibility in spatial uncertainty, might be beneficial for evaluating S2S forecasts. These results help to create a foundation for design, verification, and implementation of future operational forecast products with longer lead times, while also providing an example for future workshops that engage both researchers and decision-makers. 

Abstract Extreme precipitation events can cause significant impacts to life, property, and the economy. As forecasting capabilities increase, the subseasonal-to-seasonal (S2S) time scale provides an opportunity for advanced notice of impactful precipitation events. Building on a previous workshop, the Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods (PRES²iP) project team conducted a second workshop virtually in the fall of 2021. The workshop engaged a variety of practitioners, including emergency managers, water managers, tribal environmental professionals, and National Weather Service meteorologists. While the team’s first workshop examined the “big picture” in how practitioners define “extreme precipitation” and how precipitation events impact their jobs, this workshop focused on details of S2S precipitation products, both current and potential future decision tools. Discussions and activities in this workshop assessed how practitioners use existing forecast products to make decisions about extreme precipitation, how they interpret newly developed educational tools from the PRES²iP team, and how they manage uncertainty in forecasts. By collaborating with practitioners, the PRES²iP team plans to use knowledge gained going forward to create more educational and operational tools related to S2S extreme precipitation event prediction, helping practitioners to make more informed decisions. 

Abstract Extreme precipitation across multiple time scales is a natural hazard that creates a significant risk to life, with a commensurately large cost through property loss. We devise a method to create 14-day extreme-event windows that characterize precipitation events in the contiguous United States (CONUS) for the years 1915–2018. Our algorithm imposes thresholds for both total precipitation and the duration of the precipitation to identify events with sufficient length to accentuate the synoptic and longer time scale contribution to the precipitation event. Kernel density estimation is employed to create extreme-event polygons that are formed into a database spanning from 1915 through 2018. Using the developed database, we clustered events into regions using a k -means algorithm. We define the “hybrid index,” a weighted composite of silhouette score and number of clustered events, to show that the optimal number of clusters is 15. We also show that 14-day extreme precipitation events are increasing in the CONUS, specifically in the Dakotas and much of New England. The algorithm presented in this work is designed to be sufficiently flexible to be extended to any desired number of days on the subseasonal-to-seasonal (S2S) time scale (e.g., 30 days). Additional databases generated using this framework are available for download from our GitHub. Consequently, these S2S databases can be analyzed in future works to determine the climatology of S2S extreme precipitation events and be used for predictability studies for identified events.