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This paper describes work to improve the understanding of the broad range of factors affecting the occurrence of postfire flooding, with emphasis on an event that occurred in the Alpine Shire, Victoria, Australia, in 2003. Analysis and mesoscale modeling of the conditions surrounding the event suggests that the drivers of the extreme rainfall event were above-average precipitable water in the atmosphere, significant values of CAPE, producing strong updrafts within the thunderstorm capable of supporting large quantities of suspended water droplets, and thunderstorm cell regeneration in the same area. However, atmospheric instability was further enhanced by anabatic breezes, above-average boundary layer moisture, and increased surface heating resulting from reduced surface albedo and soil moisture of the recently burned fire surface. Flash flooding resulted, due to 1) the storm cells likely being pulse wet microbursts, 2) cell regeneration over the same area (very little horizontal movement), and 3) the small catchment size. It is likely that a further contributor to the observed flash flood was the reduced infiltration often observed in recently burned catchments; these factors will be explored in a subsequent hydrologic study. It is intended that the mechanisms elucidated in this study will assist in emergency preparedness in the Alpine Shire. Given the warmer conditions expected with near-term anthropogenic climate change for the Alpine Shire, and the concomitant increase in fires, this causal relationship, even for a relatively rare event, has implications for emergency managers and Alpine Shire residents. [ABSTRACT FROM AUTHOR]

This paper describes work to improve the understanding of the broad range of factors affecting the occurrence of flooding in Barrow, Alaska, using as a basis the series of extreme events that have affected the community over the past 50 years. A numerical weather prediction model and a storm surge inundation model have been applied to the 21 case studies identified in National Weather Service data as high wind events. Based on this simulation work flow, a reduced-form model that adequately describes the flooding response has been developed. Specifically, it was found that when wind is forecast to be greater than 13 m s-1 (30 mph) for at least 20 h, this is the most accurate predictor of the possibility of damaging flood. It was found that wind direction, the magnitude of fetch to the sea ice edge (when present), and maximum wind speed were in contrast relatively small contributors to the likelihood of flooding. [ABSTRACT FROM AUTHOR]

The coastal geography of Barrow, Alaska, makes the city vulnerable to weather events that cause flooding and erosion. This study uses the self-organizing map (SOM) algorithm, an unsupervised learning process that codifies large, multivariate datasets onto a 2-dimensional array, or map, to study large-scale circulation patterns associated with temperature and high wind extremes at Barrow. The analysis first uses the SOM algorithm to produce an automated 55 yr synoptic climatology of daily sea level pressure patterns for the western Arctic for August to November, when the area is potentially ice free. The results are in agreement with previous Arctic climatologies, showing the Aleutian Low to be dominant in southern Alaska, and high pressure prevalent over the Beaufort and Chukchi Seas. The SOM algorithm is then used to study circulation patterns associated with temperature and high wind extremes at Barrow. These results show that high winds are associated with patterns containing a strong pressure gradient between the Aleutian Low and the Beaufort and Chukchi Seas and also with patterns that contain a low pressure system to the north of Barrow. High (low) air temperature extreme anomalies are associated with patterns that produce strong, southerly (northerly) air flow at Barrow. This study demonstrates the utility of using SOMs to investigate the relationship between local weather conditions and large-scale patterns. This approach can be applied to future global climate model (GCM) simulations to investigate the impact of changes in large-scale circulation patterns to local extreme events. [ABSTRACT FROM AUTHOR]