Impact of ASOS real-time quality control on convective gust extremes in the USA

Abstract

Most damage, in terms of number and total cost, to buildings across the contiguous United States is caused by gusts in convective events associated with thunderstorms. Assessment of the risk posed by these events is reliant on the integrity of the source meteorological observations. This study examines the impact on risk due to the loss of the valid gust observations in convective downbursts which are erroneously culled by the real-time quality control algorithm of the US Automated Surface Observation System (ASOS) after 2013.  ASOS data before 2014 is used to simulate the effect of this algorithm on the 50-year mean recurrence interval (MRI) gust speeds in convective gust events from isolated thunderstorms and active cold fronts at 450 well exposed stations distributed across the contiguous USA. The peak gust is culled in around 10% of these events, but the impact on 50-year MRI gust speeds is mitigated by the contributions of the unaffected events to the extremes. Nevertheless, significant underestimates occur when values are culled from the upper tail of the distribution of extreme gusts. The full ASOS record, 2000-2021, is used to estimate and map the 50-year MRI for all 450 stations by the XIMIS method. It is concluded that recovery of erroneously culled observations is not possible. Spatial smoothing would spread any underestimate over a wider area and would also reduce legitimately high values. The only practical option to eliminate the risk of underestimation is to ensure that the 50-year mean recurrence interval (MRI) gust speed at any given station is not less than the mean for nearby surrounding stations. This also affects stations with legitimately lower values than their neighbours, which represents the price that must be paid to eliminate unacceptable risk.