The broad‐scale, steering atmospheric circulation in the Northern Hemisphere, represented by the tropospheric circumpolar vortex (CPV), is an important driver of environmental processes. The area and circularity of the CPV are analyzed hereby delineating the leading edge of the CPV at the steepest 500‐hPa geopotential height gradient globally. The daily CPV area and circularity were aggregated to monthly averages for contrast with measurements identified in previous research for the overlapping period of record (1979–2001). Accuracy of representation of the CPV is assessed through correlations to air‐sea teleconnections known to be associated with broad‐scale, extratropical steering circulation. Correlation to monthly teleconnection indices suggests that the new method allows for improvements in the calculation of area and circularity of the 500‐hPa manifestation of the CPV. These improvements justify extension of the calculation of the standardized CPV area and circularity for the 1979–2017 period of record. Results largely mirror those for the shorter time series, with the Arctic Oscillation, North Atlantic Oscillation, and Pacific‐North American teleconnection showing stronger links to CPV area and circularity than El Niño–Southern Oscillation and Pacific Decadal Oscillation. Collectively, these results suggest that the use of a singular indicator isohypse and/or monthly averaged data to represent the CPV may oversimplify analyses, especially for identifying past and future longwave ridges and troughs. This finding is important because the amplitudes and positions of the undulations in the broad‐scale flow exert the most important impacts on variability at both low‐ and high‐frequency time periods.

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Leading flood loss estimation models include Federal Emergency Management Agency’s (FEMA’s) Hazus, FEMA’s Flood Assessment Structure Tool (FAST), and (U.S.) Hydrologic Engineering Center’s Flood Impact Analysis (HEC-FIA), with each requiring different data input. No research to date has compared the resulting outcomes from such models at a neighborhood scale. This research examines the building and content loss estimates by Hazus Level 2, FAST, and HEC-FIA, over a levee-protected census block in Metairie, in Jefferson Parish, Louisiana. Building attribute data in National Structure Inventory (NSI) 2.0 are compared against “best available data” (BAD) collected at the individual building scale from Google Street View, Jefferson Parish building inventory, and 2019 National Building Cost Manual, to assess the sensitivity of input building inventory selection. Results suggest that use of BAD likely enhances flood loss estimation accuracy over existing reliance on default data in the software or from a national data set that generalizes over a broad scale. Although the three models give similar mean (median) building and content loss, Hazus Level 2 results diverge from those produced by FAST and HEC-FIA at the individual building level. A statistically significant difference in mean (median) building loss exists, but no significant difference is found in mean (median) content loss, between building inventory input (i.e., NSI 2.0 vs BAD), but both the building and content loss vary at the individual building scale due to difference in building-inventory-reported foundation height, foundation type, number of stories, replacement cost, and content cost. Moreover, building loss estimation also differs significantly by depth-damage function (DDF), for flood depths corresponding with the longest return periods, with content loss differing significantly by DDF at all return periods tested, from 10 to 500 years. Knowledge of the extent of estimated differences aids in understanding the degree of uncertainty in flood loss estimation. Much like the real estate industry uses comparable home values to appraise a home, flood loss planners should use multiple models to estimate flood-related losses. Moreover, results from this study can be used as a baseline for assessing losses from other hazards, thereby enhancing protection of human life and property.

High winds and storm surges associated with torrential rain from tropical cyclones (TCs) cause massive destruction to property and cost the lives of many people. The coastline of the Bay of Bengal (BoB) ranks as one of the most susceptible to storm surges in the world due to low-lying elevation and a high frequency of TC occurrence. This study uses data from 1885 to 2011 and a bivariate statistical copula to describe the relationship and dependency between empirical TC storm surge and reported wind speed before landfall at the BoB. Among the copulas and their families, an Archimedean, Gumbel copula with margins defined by the empirical distributions is specified as the most appropriate choice for the BoB. The model provides return periods for pairs of TC storm surge and 12-hr pre-landfall wind along the BoB coastline. On the shortest timescale, the BoB can expect a TC with 12-hr pre-landfall winds of at least 24 m/s and surge heights of at least 4.0 m, on average, once every 3.9 years. On the other hand, the long-term, worst case scenario suggests the BoB can expect 12-hr pre-landfall winds of 62 m/s and surge heights of at least 8.0 m, on average, once every 311.8 years. Using a copula to model the combined frequency of cyclone wind speeds along with storm surges along the BoB coastline increases the understanding of the dangerous TC characteristics in this region, which can reduce fatalities and monetary losses.

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 Daily variability of the Northern Hemisphere's circumpolar vortex (NHCPV) is linked to variability in air-sea teleconnection patterns. NHCPV is more strongly correlated with the extratropical AO, NAO, and PNA teleconnections than it is correlated to the tropical ENSO phenomenon. Linkage between the NHCPV and air-sea teleconnection variability is important because the low-and high-frequency variability of the broad-scale flow influences weather systems.

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