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1. Synoptic Characteristics of 14-Day Extreme Precipitation Events across the United States

   https://doi.org/10.1175/JCLI-D-19-0563.1  

   Jennrich, Gregory C. ; Furtado, Jason C. ; Basara, Jeffrey B. ; Martin, Elinor R. ( August 2020 , Journal of Climate)

   null (Ed.)

   Abstract Although significant improvements have been made to the prediction and understanding of extreme precipitation events in recent decades, there is still much to learn about these impactful events on the subseasonal time scale. This study focuses on identifying synoptic patterns and precursors ahead of an extreme precipitation event over the contiguous United States (CONUS). First, we provide a robust definition for 14-day “extreme precipitation events” and partition the CONUS into six different geographic regions to compare and contrast the synoptic patterns associated with events in those regions. Then, several atmospheric variables from ERA-Interim (e.g., geopotential height and zonal winds) are composited to understand the evolution of the atmospheric state before and during a 14-day extreme precipitation event. Common synoptic signals seen during events include significant zonally oriented trough–ridge patterns, an energized subtropical jet stream, and enhanced moisture transport into the affected area. Also, atmospheric-river activity increases in the specific region during these events. Modes of climate variability and lagged composites are then investigated for their potential use in lead-time prediction. Key findings include synoptic-scale anomalies in the North Pacific Ocean and regional connections to modes such as the Pacific–North American pattern and the North Pacific Oscillation. Taken together, our results represent a significant step forward in understanding the evolution of 14-day extreme precipitation events for potential damage and casualty mitigation. 

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2. Synoptic Connections and Impacts of 14-Day Extreme Precipitation Events in the United States

   https://doi.org/10.1175/JAMC-D-21-0174.1  

   Schroers, Melanie ; Martin, Elinor ( July 2022 , Journal of Applied Meteorology and Climatology)

   Abstract Long periods of extreme precipitation can cause costly damages to a region’s infrastructure while also creating a higher risk for the region’s population. Planning for these periods would ideally begin at the subseasonal-to-seasonal time scale, yet prediction of precipitation at this time scale has low skill. In this study, we will use Jennrich et al.’s database of 14-day extreme precipitation events to understand more about the synoptic connections and impacts of these extended extreme events. The synoptic connections of events were examined using the composites of event-day 500-hPa geopotential height and precipitable water anomalies. The combination of these two drivers leads to higher skill in the West Coast and Great Lakes than other regions, with an equitable threat score of 0.137 and 0.084, respectively, and higher conditional probabilities of event occurrence. Therefore, the synoptic patterns associated with events, although significant, are not unique, which poses prediction challenges. Historical impacts of these events, using NCEI storm reports, were assessed to benefit decision-makers in future risk mitigation. A variety of reports were found during events, from winter weather reports in West Coast events to tropical storm reports in Southeast events. Every region has significantly more flooding reports during events than in nonextreme 14-day periods, demonstrating the impacts of such extended events. Although there is still much to learn about extreme precipitation events, this study contributes to the foundational knowledge of synoptic drivers and impacts of events. 

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3. Subseasonal-to-Seasonal Extreme Precipitation Events in the Contiguous United States: Generation of a Database and Climatology

   https://doi.org/10.1175/JCLI-D-20-0580.1  

   Dickinson, Ty A. ; Richman, Michael B. ; Furtado, Jason C. ( September 2021 , Journal of Climate)

   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. 

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4. Spatial bias in medium-range forecasts of heavy precipitation in the Sacramento River basin: Implications for water management

   https://doi.org/https://doi.org/10.1175/JHM-D-19-0226.1  

   Brodeur, Zachary ; Steinschneider, Scott ( June 2020 , Journal of hydrometeorology)

   Forecasts of heavy precipitation delivered by atmospheric rivers (ARs) are becoming increasingly important for both flood control and water supply management in reservoirs across California. This study examines the hypothesis that medium-range forecasts of heavy precipitation at the basin scale exhibit recurrent spatial biases that are driven by mesoscale and synoptic-scale features of associated AR events. This hypothesis is tested for heavy precipitation events in the Sacramento River basin using 36 years of NCEP medium-range reforecasts from 1984 to 2019. For each event we cluster precipitation forecast error across western North America for lead times ranging from 1 to 15 days. Integrated vapor transport (IVT), 500-hPa geopotential heights, and landfall characteristics of ARs are composited across clusters and lead times to diagnose the causes of precipitation forecast biases. We investigate the temporal evolution of forecast error to characterize its persistence across lead times, and explore the accuracy of forecasted IVT anomalies across different domains of the North American west coast during heavy precipitation events in the Sacramento basin. Our results identify recurrent spatial patterns of precipitation forecast error consistent with errors of forecasted synoptic-scale features, especially at long (5–15 days) leads. Moreover, we find evidence that forecasts of AR landfalls well outside of the latitudinal bounds of the Sacramento basin precede heavy precipitation events within the basin. These results suggest the potential for using medium-range forecasts of large-scale climate features across the Pacific–North American sector, rather than just local forecasts of basin-scale precipitation, when designing forecast-informed reservoir operations. 

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5. U.S. Extreme Precipitation Weather Types Increased in Frequency During the 20th Century

   https://doi.org/10.1029/2020JD034287  

   Prein, Andreas F. ; Mearns, Linda O. ( April 2021 , Journal of Geophysical Research: Atmospheres)

   Extreme precipitation has increased in frequency and intensity across the Conterminous U.S. (CONUS). This trend is expected to continue under future climate change. The cause is a combination of thermodynamic (i.e., warmer temperatures increase the atmospheric moisture content) and dynamic changes (e.g., shifts in cyclone frequency and tracks). It is well‐established that thermodynamic changes will intensify extreme precipitation events, but the impacts of dynamic changes are more uncertain. Extreme events are, per definition, rare and occur in unusual weather situations that are distinctly different from regular day‐to‐day weather. We take advantage of this and identify extreme precipitation‐producing weather patterns (XWTs) for all major watersheds across the CONUS by using a novel algorithm. We show that a set of one to four XWTs per watershed are causing extreme precipitation accumulations. These XWTs can be detected based on their synoptic‐scale fingerprint and are associated with West Coast atmospheric rivers, troughing in the desert Southwest, cutoff lows and troughs in the central and northwestern plains, and tropical cyclones along the Gulf and Atlantic coast. The algorithm is flexible enough to provide reliable results for city to major watershed‐scales and can detect extremes that are unprecedented in the training record. Importantly, this approach allows us to assess long‐term trends in extreme precipitation dynamics and reveal that XWT frequencies increased significantly in most U.S. watersheds during the 20th century indicating that changes in the atmospheric dynamics played an important role in historic extreme precipitation increases.

    

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