The Informational Value of Pressure-Based Single-Station Proxies for Storm Activity

Abstract

Air pressure readings and their variations are commonly used to make inferences about storm activity. More precisely, it is assumed that the variation of annual and seasonal statistics of several pressure-based proxies describes changes in the past storm climate qualitatively, an assumption that has yet to be proven.A systematic evaluation of the informational content of five pressure-based proxies for storm activity based on single-station observations of air pressure is presented. The number of deep lows, lower percentiles of pressure, the frequency of absolute pressure tendencies above certain thresholds, as well as mean values and high percentiles of absolute pressure tendencies is examined. Such an evaluation needs long and homogeneous records of wind speed, something that is not available from observations. Consequently, the proxies are examined by using datasets of ground-level wind speeds and air pressure from the NCEP-driven and spectrally nudged regional model, REMO. The proxies are gauged against the 95th and 99th percentile time series of ground-level wind speeds to quantify the relation between pressure-based proxies and storminess. These analyses rely on bootstrap and binomial hypothesis testing. The analyses of single-station-based proxies indicate that the proxies are generally linearly linked to storm activity, and that absolute pressure tendencies have the highest informational content. Further, it is investigated as to whether the proxies have the potential for describing storminess over larger areas, also with regard to surface conditions. It is found that absolute pressure tendencies have improved informational value when describing storm activity over larger areas, while low pressure readings do not show improved informational value.

Recent Change—Atmosphere

“…4.9, the PC1 over all eight indices captures the calm period of the 1960s and 1970s indicating that it is better to use a number of different indices rather than relying on only one. The highest correlation between HiResAFF annual extreme wind speeds and singlestation proxies was achieved for the pressure tendency over 8 h (r = 0.50) confirming the work of Krueger and von Storch (2012). Remarkably high values for the 8-h pressure tendencies on the one hand and very low values for the number of deep lows on the other hand indicate low confidence in the data before around 1850 probably due to irregular pressure readings.…”

“…Hanna et al (2008) found similar results based on a daily pressure variability index calculated as absolute 24 h pressure differences, that is Dp = |p t+24 − p t+0 |, for the British Isles since 1830 and for Denmark since 1874 confirming increased storminess at the end of the nineteenth century and the 1980s to 1990s, with the 1880s being the stormiest decade. The informational value of five different pressure-based storminess indices including those used by Bärring and von Storch (2004) and Hanna et al (2008) was evaluated by Krueger and von Storch (2012). The authors confirmed the general usefulness of the indices as storminess proxies, with absolute pressure tendencies per six or eight hours containing the highest informational value.…”

Central and Eastern U.S. Surface Pressure Variations Derived from the USArray Network

“…More recent climatologies of synoptic storm tracks rely on pressure data derived from numerical model reanalyses (Thomas and Martin 2007;Nieto Ferreira et al 2013). Pressure tendency (i.e., the change in pressure over a specified duration) has been used frequently to identify preferred geographical locations for cyclogenesis and anticyclogenesis (Sanders and Gyakum 1980;Krueger and von Storch 2012).…”

Recent Change—Atmosphere

“…Even single-station pressure indices such as strong pressure changes over 6-or 24-h periods or the annual number of deep lows provide useful information about long-term variations in the wind and storm climate (Krueger and von Storch 2011). The information content to describe long-term variations in the statistics of pressure and wind is even higher for indices of geostrophic wind speeds calculated from triangles of daily pressure observations (Krueger and von Storch 2012). The correlation of geostrophic wind speeds calculated from station triplets with real model wind speeds is especially high over open terrain and sea areas (i.e.…”