7 Most damage, in terms of number and total cost, t o buildings across the contiguous United States is 8 caused by gusts in convective events associated with thunderstorms. Assessment of the risk posed by 9 these events is reliant on the integrity of the source meteorological observations. This study examines 10 the impact on risk due to the loss of the valid gust observations in convective downbursts which are 11 erroneously culled by the real -time quality control algorithm of the US Automated Surface 12 Observation System (ASOS) after 2013 . ASOS data before 2014 is us ed to simulate the effect of this 13 algorithm on the 50 -year mean recurrence interval (MRI) gust speeds in convective gust events from 14 isolated thunderstorms and active cold fronts at 450 well exposed stations distributed across the 15 contiguous USA. The peak gust is culled in around 10% of these events, but the impact on 50 -year 16 MRI gust speeds is mitigated by the contributions of the unaffected events to the extremes. 17 Nevertheless, significant underestimates occur when values are culled from the upper tail of the 18 distribution of extreme gusts. The full ASOS record, 2000 -2021, is used to estimate and map the 50 - 19 year MRI for all 450 stations by the XIMIS method . It is concluded that recovery of erroneously 20 culled observations is not possible . Spatial smoothing would spread any underestimate over a wider 21 area and would also reduce legitimately high values. T he only practical option to eliminate the risk of 22 underestimat ion is to ensure that the 50 -year mean recurrence interval (MRI) gust speed at any given 23 station is not less than the mean for nearby surrounding stations. This also affects stations with 24 2 legitimately lower values than the ir neighbours, which represents the price that must be paid to 25 eliminate unacceptable risk. 26 Introduction 27 Most damage , in terms of number and total cost, to buildings across the contiguous United States 28 (CONUS) is caused by gusts from thunderstorm downbursts (Lombardo et. al. 2014) . Assessment of 29 design gust values for codes of practice and standards is reliant on the integrity of the source 30 meteorological observations. The principal purpose of this study was to examine how the US 31 Automated Surface Observation System ( ASOS ) resolves extreme wind gusts in convective events 32 and to assess the impact on safety due to loss of valid observations culled by the real -time quality 33 control (QC) algorithm which is applied to the observations at the source station before onward 34 transmission and archiving . 35 The ASOS network provides weather observation s at 1 -minute intervals at 860 stations distributed 36 across CONUS that are archived in the TD6405 and TD6406 database s at the National Center s for 37 Environmental Information ( NCEI ). This is a most valuable resource because it permit s study of 38 many more meso scale events over much longer observational periods than is possible with targeted 39 measurement campaigns. Previous studies found the precise location and World Meteorological 40 Organisation ( WMO ) exposure classification of each anemometer mast (Cook 2021 ) and curated the 41 observations (Cook 2022) to correct or remove common acquisition, transmission and archiving errors 42 (Cook 2014) . All gust events >20kn between 2000 and 2021 at 450 WMO class 1 and 2 stations were 43 sorted into six characteristic Classes (Cook 2 023) and assigned a presumed causal mechanism, 44 including downbursts from active fronts and from isolated thunderstorms . 45 Initially, from 2000, ASOS stations used Belfort cup and vane anemometers with a nominal 5s 46 gust response, most at 10m above ground, but some at 26 feet (7.9m) . Between 2005 and 2010 they 47 were gradually replaced by Vaisala sonic anemometers averaged to give the WMO standard 3s gust. 48 Having no moving parts, and heated in the winter to prevent icing, the sonic anemometers provide 49 ideal perches for birds (Avery and Genchi 2004; Schwartz and Kays 2001) which block the acoustic 50 3 path, causing data loss, and which generate spurious large “spikes” of gust speed on landing and take - 51 off. 52 The principal r ole of ASOS is to transmit accurate current weather information in real time from 53 the station to aircraft. Accordingly, QC procedures are applied continuously before transmission. 54 These real -time data are also the primary source for the routine METAR weath er reports and the 55 derived daily, monthly, etc., summaries. The previous studies (Cook 2022 and 2023) highlighted 56 serious concerns about excessive false positive QC alerts by Test 10: the “low -speed non - 57 meteorological event” detection algorithm (NOAA 2013) that le ads to loss of valid data in meso -scale 58 convective events. The locations of the 450 ASOS stations with WMO class 1 or 2 exposures that 59 were addressed by these studies are shown in Fig. 1. 60 The ASOS Real -time Quality Control 61 The ASOS system reports mean and gust wind speeds in integer values of knots (1 kn = 0.5144 ms-1) 62 and it is essential for understanding how the QC works, as well as to avoid unit bias, to retain these 63 original units. 64 At the start of 2014 NOAA added a new QC test algorithm – Test 10 – to identify high gust speed 65 events of non -meteorological origin oc curring at low mean wind speeds . This was specifically to 66 address the issue of bird -generated gusts but also to find the numerous “spike ” artefacts caused by 67 acquisition or transmission glitches . Test 10 works by m onitoring the running 2 -minute mean speed, 68 𝑉̅2min, and running 3 -s mean speed, 𝑉̅3𝑠, at 5s intervals and raises an alert when: 69 𝑉̅2min <= 6 kn & 𝑉̂3𝑠 > 6 kn & 𝑉̂3𝑠>2.5×𝑉̅2min (1) 70 after which wind speed and direction observations are suppressed for 5 minutes. NOAA reported 71 (NOAA 2013) a 97% drop in erroneous reports at the cost of 0.033% loss of valid data. In late 2017 72 the 𝑉̂3𝑠 threshold in (1) was raised from >6kn to >13kn to reduce the rate of false positives. Although 73 the lost valid data is a very small fraction of the whole, its action in suppressing sudden gusts is 74 strongly biased towards thunderstorm downbursts. 75 4 Fig. 2 presents two examples of the ASOS 1 -minute interval timeseries through the two 76 strongest recorded thunderstorm downbursts at Jackson International Airport, MS, both occur ring 77 while Test 10 applie d at the 13kn 𝑉̂3𝑠 threshold. (Note that the chart sub -headings indicate ranks 19 78 and 23, respectively, because ranks 1:18 and 20:2 2 are all non -meteorological artefacts.) The peak 79 gust speed, 𝑉̂3𝑠, is shown by the (black) linked o symbols relative to the left -hand scale and the 2 - 80 minute mean direction by the (green) unlinked o symbols relative to the right -hand scale. Also shown 81 are the contemporaneous atmospheric pressure (scale ±0.05in Hg) and temperature (scale ±10°F) 82 anomalies and the amount of rain (none/light/medium/heavy). 83 There is no loss of gust speed data, 𝑉̂3𝑠, in (a) because 𝑉̅2min>6kn prior to the downburst, 84 but data are culled from (b) because 𝑉̅2min<6kn. Note that the maximum 𝑉̂3𝑠 of both events are 85 concurrent with the minimum temperature and maximum pressure anomaly, so it is likely that the 86 maximum 𝑉̂3𝑠 in (b) may be the true maximum for this event. However, in the more general case, 87 where the true maximum was excised by the cull , its value is unknown, and this is the main concern in 88 assessing the impact of Test 10 false positives. 89 The pioneering insight of G omes and Vickery (1978) established the need to sort extreme events 90 in mixed climates into classes by causal mechanism for separate analysis . Of the 5 event Classes 91 defined for the USA in (Cook 20 23) only Class 4: Front -up events, which correspond to active cold 92 fronts, and Class 5: Thunderstorm events are potentially sensitive to Test 10 by virtue of low initial 93 𝑉̅2min values . Fig. 3 presents the en semble -averaged gust speed, temperature and pressure timeseries 94 of these two Classes. For both Classes there is a rapid fall in temperature and rise in pressure 95 concurrent with the peak gust. The gust speed timeseries differ in that the speed quickly recov ers to 96 the initial value for Class 5 but remains high much longer for Class 4. The individual gust events, e.g., 97 Fig. 2, are much more variable than these ensemble -averages suggest . Those recovering slower are 98 clearly Class 4 , those recovering faster are clearly Class 5 , but there is some uncertainty in the Class 99 of intermediate events . The overall classification accuracy of the neural network in (Cook 20 23), 100 which inte rprets the gust speed, temperature and pressure timeseries, is claimed to be better than 95%. 101 5 On average, the initial 𝑉̅2min>6kn for both Classes, so only subsets of these events with low 102 initial speeds will be affected by Test 10: potentially, 0.76% of Class 4 and 0.65% of Class 5 at the 103 6kn 𝑉̅2min threshold ; 0.23% of Class 4 and 0.33% of Class 5 at the 13kn threshold . These percentages 104 are small but are an order of magnitude greater than the 0.033% overall loss of valid data (NOAA 105 2013), highlighting the bias in Test 10 false positives towards convective gust events. 106 Observed e ffect of Test 10 from 2014 -2021 107 Observations from 2 014 to late 2017 were subject to Test 10 at the 6kn 𝑉̂3s threshold, and those from 108 2018 onwards to the 13kn 𝑉̂3s threshold. A Test 10 false positive culls 5 minutes of valid wind speed 109 and direction data, sometimes longer if 𝑉̅2min falls below its trigger threshold again. This should not 110 affect the timeseries of temperature and pressure. The Class 4 and 5 gust events over the whole period 111 2000 -2021 w ere searched for gaps in speed and direction greater than 4 minutes . Those gaps 112 immediately preceded by 𝑉̅2min≤6kn and where temperature and pressure remained continuous were 113 counted as a Test 10 false positive, while those >6kn were counted as due to other causes. The 114 average annual rates of gaps per station for the Class 4 and 5 events are plotted in Fig. 4. This shows a 115 sharp rise in the rate of gaps on the introduction of Test 10 which continues rising slowly thereafter, 116 while the rate due to other causes remains reasonably constant. Some may have been caused by Test 117 10 after 𝑉̅2min fell below the 6kn threshold briefly in the 1 -minute overlap between observations. 118 The annual rate s of Test 10 culls of valid Class 4 and 5 events is mapped across CONUS in 119 Fig. 5 for 2014 -2017 and for 2018 -2021, representing each threshold. The highest rates occur in the 120 high-altitude stations of UT, CO, AZ and NM, with the maximum at KASE Aspen, CO . The annual 121 rates of data gaps from other causes shown in Fig. 6 are an order of magnitude lower and more 122 randomly distributed. 123 Simulating Test 10 using data before 2014 . 124 The major issue with the Test 10 false positives after 2013 is that the culled observations are 125 unrecoverable, so it is unc ertain whether this includes the peak gust and, if it was, how much it was 126 6 underestimated by the surviving observations. The gust events which occurred before the introduction 127 of Test 10 are suitable for retrospectively assessing its effect at both 6kn and 13kn thresholds. The 128 only significant difference is that the reported observations are at 1 -minute intervals, whereas Test 10 129 operate s at 5s intervals. This has two potential counteracting consequences : 130 1. The 5s intervals give 12 opportunities in each minut e for the running 𝑉̅2min to fall to 6kn or 131 less and trigger Test 10, whereas the observations give only one which should lead to fewer 132 simulated culls . 133 2. The values of 𝑉̂3𝑠 in the gust events are the largest observed in the previous 1 minute, not the 134 in just the previous 5s , and should lead to more simulated culls . 135 The simulated annual rates of Class 4 and 5 false positives at either Test 10 threshold are mapped 136 across CO NUS in Fig. 7. At the 6kn 𝑉̂3𝑠 threshold (left), the highest rates again occur in the high- 137 altitude stations of UT, CO, AZ and NM, with the maximum at KASE Aspen, CO. At the 13kn 𝑉̂3𝑠 138 threshold (right), the rates are considerably reduced. While the geographical distributions are the same 139 as observed in Fig. 5, but the trend in rate between thresholds is reversed . 140 The period culled by Test 10 does not always include the peak gust . The distribution of the 141 simulated annual rate of Class 4 and 5 𝑉̂3𝑠 values that are underestimated by culling the peak is shown 142 in Fig. 8 to have a similar geographical pattern as Fig. 5, but at annual rates around 10 times smal ler. 143 The simulated rate is lower for the 13kn 𝑉̂3𝑠 threshold which was the intent of the threshold change. 144 The underestimat ed values of 𝑉̂3𝑠 caused by the ~10% of simulated Test 10 false positives that cull 145 the peak are shown in the Q -Q plots of Fig. 9 for both Classes and thresholds. As the se are in integer 146 knots, each point may represent multiple events. The solid line shows the li near regression, and the 147 chained line denotes 1:1 correspondence. The scatter is too large and random to allow any reasonable 148 correction. It is therefore necessary to directly assess how these false -positive culls would affect the 149 distribution of extremes. 150 7 The XIMIS method of e xtreme -value analysis 151 The XIMIS method (Harris, 2009) of extreme -value analysis (EVA), fitted by weighted least -mean - 152 squares (wLMS) and displayed on Gumbel axes , was preferred over other methods for the following 153 reasons: 154 1. Probability estimate d from the order statistics are for independent events following a Poisson 155 recurrence process model leading to a Fisher -Tippett Type 1 (FT1) asymptote . 156 2. The test for validity of the Poisson model is exponentially distributed inter -arriva l times 157 (Brabson and Palutikof 2009) which is simple to implement . 158 3. The order statistics are ranked downwards from the highest value , so XIMIS is the only 159 method (apart from ACER (Karpa and Naess, 2013)) that works for left-censored data where 160 the populat ion, N, is unknown. 161 4. Gust event data from (Cook 20 23) are left-censored at 𝑉̂3𝑠>20kn. 162 5. Asymptotic convergence is not a requirement , i.e., XIMIS is a penultimate method. 163 6. Confidence limit outliers are easily detected, and their contribution removed. 164 7. Any extreme model can be fitted using any fitting method, but wLMS was used here with the 165 recommended fitting weights (Harris, 2009) to account for statistical variance. 166 XIMIS methodology 167 The variate, 𝑉, is normalised by the Gumbel “reduced variate” (Gumbel 1958), 𝑦𝑉: 168 𝑦𝑉=(𝑉−𝑈)/𝑏 (2) 169 where 𝑈 is the mode and 𝑏 is the dispersion . The annual probability of exceedance, 𝛷𝑚, for the 𝑚-th 170 rank from the top is linearised in the FT1 model by the Gumbel “plotting position” (Gumbel 1958) , 171 𝑦𝑚: 172 𝑦𝑚=−ln⁡(−ln(𝛷𝑚)) (3) 173 8 so that a plot of 𝑦𝑉 as abscissa against 𝑦𝑚⁡as ordinate presents the FT1 asymptote as a straight line of 174 slope 1 through the origin . In practice, 𝑉 is usually plotted as abscissa, whe reupon the slope is 𝑏 and 175 the intercept is 𝑈. Deviation from the asymptote due to non -convergence resolves as a curve which 176 may be fitted by any method but , when necessary, XIMIS seeks to linearise this by the 177 transformation : 178 𝑦𝑉=(𝑉𝑤−𝑈𝑤)/𝑏𝑤 (4) 179 which is exact when the upper tail of the parent distribution of 𝑉 has Weibull equivalence with shape 180 parameter w. 181 XIMIS uses the mean plotting position , 𝑦̅, given (Gumbel 1958, Eq.4.2.1(11) ) for each 𝑚 by 182 the recursive formula: 183 𝑦̅𝑚+1=𝑦̅𝑚−1/𝑚 with 𝑦̅1=𝛾+ln⁡(𝑅) (5) 184 where  = 0.5772… (Euler’s constant) and 𝑅 is the observation period in years. Equation (5) is exact 185 for an Exponential parent ( 𝑤=1) but, when 𝑤≠1, progressively accumulates error at large 𝑚 186 which is avoided by left censoring . The corresponding variances, 𝜎𝑚2(𝑦), are given in a similar 187 manner (Gumbel 1958, Eq.4.2.1(13) ) by: 188 𝜎𝑚+12(𝑦)=𝜎𝑚2(𝑦)−1/𝑚2 with 𝜎12=𝜋2/6 (6) 189 The distributions of plott ing position around the mean, 𝑦̅𝑚, are also the distributions of the m-th 190 highest values for repeated samples of the observation period, 𝑅. In practice, as there is only one 191 observation period, the distributions of the m-th highest values are used to assess the confidence that 192 can be placed in the one sample. The PDF of the m-th highest values is given (Gumbel, 1958) , 193 Eq.5.3.1(2) ) by: 194 𝑝𝑚(𝑦𝑚)=𝑚𝑚 (𝑚−1)!exp⁡(−𝑚𝑦𝑚−𝑚𝑒−𝑦𝑚) (7) 195 and t he corresponding CDF of the m-th highest values is given (Gumbel 1958, Eq.5.3.2(3)) by: 196 9 𝑃𝑚(𝑦𝑚)=𝑃1⁡𝑚(𝑦𝑚)∑𝑚𝑣𝑒−𝑣𝑦𝑚 𝑣!𝑚−1 𝑣=0 197 (8) 198 Note that 𝑝𝑚(𝑦) and 𝑃𝑚(𝑦) are continuous functions for each m-th extreme but are evaluated by ( 7) 199 and ( 8) only for the discrete ranks . Confidence limits for an XIMIS model are evaluated by solving 200 (8) for 𝑦𝑚 at each desired confidence level 𝑃𝑚 and for the median, 𝑦̆𝑚=𝑦𝑚(𝑃𝑚=0.5), then 201 applying their differences, 𝛿𝑦𝑚(𝑃𝑚)= 𝑦𝑚(𝑃𝑚)−𝑦̆𝑚, either side of the model. Owing to the huge 202 powers of 𝑚 encountered when solving ( 8), the practical range using 64 -bit floating point arithmetic 203 is limited to m  100. 204 XIMIS charts 205 All the XIMIS charts which follow are presented on the conventional Gumb el axes (Gumbel 1958) , 206 with maximum gust speed 𝑉̂3s as abscissa and 𝑦=−ln⁡(−ln(𝛷)) as ordinate. When all the integer 207 𝑉̂3s values are plotted, the many tied values create a staircase effect with steps wider than the 208 confidence limits. For clarity of present ation, only one point was plotted for each integer 𝑉̂3s at the 209 ensemble mean plotting position of each set of ties, 〈𝑦̅𝑚〉𝑉̂. All observations above the lowest 210 indicated speed were included in the FT1 model fit shown by the straight lines which was fitted by 211 wLMS using 1/𝜎𝑚2 as the fitting weights (Harris, 2009) . Confidence limits of 5% and 95% on 𝑉̂3s are 212 shown by the chained curves . Strictly, the confidence is in the value of 𝑦𝑚 assigned to each rank 213 (Harris 2009) , so indicates the uncertainty in the abscissa. But f ollowing the convention of Gumbel 214 (1958) , here they are applied through (2) to indicate the uncertainty in the ordinate 𝑉̂3s at the mean 215 plotting position s. 216 10 Simulat ing the effect of Test 10 on XIMIS extr emes 2000 -2013 217 CONUS superstation 218 The ensemble of all independent events at a group of stations representative of an area into a 219 “superstation” was used by Peterka (1992) to reduce the sampling error from short records of fastest - 220 mile wind speeds across th e US Midwest. Fig. 10 presents the XIMIS charts for Class 4 and 5 events 221 amalgamated from all 450 stations, corresponding to 8360 station -years of observations . The ch arts 222 show the fit for the original observations and for both Test 10 thresholds. Both charts show a good 223 linear fit to the FT1 asymptote , requiring no correction for non -convergence offered by (4). A ll 224 observations lie within the confidence limits. If the Class characteristics were uniform across CONUS 225 – which, of course, they are not – the highest observed 𝑉̂3s in each Class could be interpreted as 226 having an 8360 -year mean recurrence interval (MRI) . The validity of the superstation concept 227 depends on two contradictory conditions: 1) that the individual stations are well separated to ensure 228 independence ; and 2) that the area they represent is sufficiently small for uniformity. 229 The principal conclusions from Fig. 10 are that the FT1 asymptote is a good model for these 230 strongly convective gust events and that the impact of Test 10 on the 50 -year MRI gust speed 𝑉̂50 is 231 minimal for the C ONUS superstation. The provenance of these conclusions is tracked for the highest 232 observed 𝑉̂3s, via the corresponding US state superstations , to the individual ASOS stations in the 233 following sections. 234 The Kansas and Illinois state superstations 235 The highes t observed 𝑉̂3s occurred in Kansas for Class 4 and to Illinois for Class 5. Fig. 11 presents 236 the corresponding XIMIS charts for KS (290 station -years) and IL (194 station -years) state 237 superstations. Again, both charts show a good linear fit to the FT1 asymptote with all poin ts within 238 the confidence limits, no discernible influence of Test 10 on 𝑉̂50. The highest observed 𝑉̂ now lie 239 above the fitted asymptote and p rojecti on onto the fitted model gives an MRI of 667 years for Class 4 240 in KS and 686 years for Class 5 in IL, a lar ge reduction from the CONUS value s. 241 11 KGCK Garden City, KS, and KMLI Moline, IL 242 The highest observed 𝑉̂3s= 82kn for Class 4 occurred at KGCK Garden City, KS, and 𝑉̂3s= 93kn 243 for Class 5 at KMLI Moline, IL . Fig. 12 presents the XIMIS charts for these two stations. Allowing 244 for the higher statistical variance due to the smaller populations, both charts still show a good linear 245 fit to the FT1 asymptote with all points just within the confidence limits and no discernible influence 246 of Test 10 on 𝑉̂50. The corresponding MRI of the highest observed 𝑉̂3s are now 36 years and 213 247 years, respectively, with the latter only just within the confidence limits. Fig. 13 presents the 1 -minute 248 interval timeseries in the same f ormat as Fig. 2. 249 • The event at KGCK (left) shows typical the Class 4 characteristics of an active cold front: 250 o The peak gust occurs at the forward flank of the front an d recovers to a sustained 251 value greater than the incident value. 252 o A sudden sustained change in direction is contemporaneous with the peak gust. 253 o The temperature falls through the event to a sustained minimum ~15 minutes after the 254 peak gust , whereas in a typi cal Class 5 event, e.g., Fig. 2, the temperature minimum 255 typically coincides with the peak gust. 256 o The pressure rises to a sustained value. 257 o A burst of heavy rain starts at the peak gust and lasts for 10 -20 minutes. 258 • The 93kn gust at KMLI occurred during a severe outbreak of thunderstorms and tornados 259 across MO, IL and AR. It is reported by the NOAA Storm Prediction Center ( SPC 2006) 260 wind report for the day as a non -tornadic event which destroyed the primary ASOS 261 anemometer but was recorded by the back -up equipment. It differs from the typical Class 5 262 thunderstorm event s in Fig. 2 in some important respects: 263 o The 𝑉̂3s timeseries shape is typical of a Class 5 thunderstorm event but the duration is 264 untypi cally short . 265 o The transient change in direction is untypically short. 266 o There is a transient rise in temperature instead of the expected fall. 267 12 o The transient changes in pressure are untypically abrupt. 268 o The continuous rainfall is inte rrupted at the time of the peak gust. 269 This is cited as an example of a “warm” event by Cook (20 23) which include local thermally 270 induced events and represent 7.3% of Class 5 . The characteristics of this event are sufficiently 271 atypical of the training set for both neural networks of Cook (20 23) for the event to have been 272 classified as non -meteorological in origin (TNNDT = 6 and XTNNDT = 6 in the chart 273 subheadings) . Reclassification to Class 5 was made by visual inspection of the timeseries and 274 confirmation b y SPC (2006) . 275 Influence of Test10 at all stations 276 Although simulating Test 10 appears to have no discernible influence on majority of stations, about 277 10% are affected. Fig. 14 shows the distribution of error in the values of 𝑉̂50⁡ predicted by XIMIS for 278 both Classes and thresholds. These distributions are skewed towards underestimation, with the largest 279 underestimate of 𝑉̂50 for Class 4 at KBKB Fort Polk Fullerton , LA, and for Class 5 at KTQE 280 Tekamah , NE. Raising the threshold to 13kn r educes the frequency of errors for both Classes , and the 281 range of errors for Class 4 . 282 Underestimation is a concern for the design of safe structures. The XIMIS charts for the largest 283 underestimate of each Class are presented in Fig. 15. 284 • At KBKB the underestimate of 4.3kn (10.7%) for Class 4 at the 6kn threshold is principally 285 caused by culling the 𝑚=1 event, which remov es the peak value and demot es the event rank 286 to 𝑚=22. The effect is amplified by the small population of Class 4 events at this station 287 and the large statistical variance denoted by the wide confidence limits. Raising the threshold 288 to 13kn restores the culled peak and 𝑉̂50 is then marginally (1%) overestimated. 289 • At KTQE the underestimate of 5.2kn (5.7%) for Class 5 at both threshold s is principally 290 caused by culling the 𝑚=3 and 11 event s. Raising the threshold t o 13kn does not restore 291 these missing events. 292 13 The distribution of 𝑉̂50 underestimates for both Classes and thresholds are mapped across CONUS in 293 Fig. 16. The locations of errors appear confined to individual randomly located stations, although the 294 larger errors resolve as patches around each affected station due to the barycentric linear inte rpolation. 295 There is no discernible correlation between the locations of Class 4 and Class 5 locations . The 296 reduction in errors on raising the threshold from 6kn to 13kn is evident, more so for Class 4 than 297 Class 5. 298 Results 299 The values of 𝑉̂50 predicted by XIMIS for all stations over the observation period 2000 -2021 are 300 mapped across CONUS in Fig. 17. These are designated as “raw” in that no corrections have been 301 applied to compensate for the effect of Test 10 false positives. Again, u nderestimates resolve as 302 patches around each affected station, although these do not correlate well with the locations in Fig. 16. 303 Locations of underestimates that would have occurred in 2000 -2013 , had QC Test 10 operated then, 304 do not predict where underestimates occur in 2014 -2021 . Neither do the locations where the observed 305 rate of culls are high, Fig. 5, illustrating the inherent uncertainty of the impact of Test 10. There is a 306 complex balance between militating effects, such as the rate of all culls , and miti gating effects , such 307 as the small proportion that result in underestimates and the lower weighting given to the smaller 308 ranks (higher values) at each station. Stations with the highest values are least influenced by Test 10, 309 implying that the highest peaks start from initial mean values higher that the threshold. The effect on 310 risk of exceedance by culling the highest value at a station is the same as if that value ha d, by chance, 311 not occurred and therefore it has the smallest weighting in EVA . The larger underestimates occur 312 when there are several culls from the upper tail and the rate of gust events is low. 313 For the safe design of structures, underestimation must be avoided at all costs. Overestimates 314 are an economic issue, but Fig. 14 shows the effect on 𝑉̂50 is limited to about 1kn which is acceptable. 315 Reduction of the underestimates by s imple geographical smoothing is not an adequate solution 316 because, while the under estimate at the affected station is reduce d, it is redistribute d over a larger 317 surrounding area . Smoothing also reduces valid peak values. One solution is to clip the value at a 318 14 station by the mean of other surrounding stations within a given radius, i.e. , by taking the larger of the 319 mean at stations within a doughnut shape d area around the station or the original station value. The 320 results of using a 3° radius are shown in Fig. 18, in which ~40% of stations have been clipped for 321 each Class . The frequency of each level of underestimate correction is plotted in Fig. 19, together 322 with the names of stations where the correction is greater than 10kn. Of these, only four are common 323 to both Classes: Aspen, CO; Fort Polk Fullerton, LA; Harrison Boone, AR; and Hayward, WI. The 324 proportion o f stations corrected in this way is much higher than the 10% expected from the simulation 325 of Test 10 because it includes stations where 𝑉̂50 is legitimately lower than its neighbours. 326 Nevertheless, it indicates that reliance on a single station without ver ification by its neighbours is 327 unsafe. 328 The estimates of 𝑉̂50 presented in Fig. 18 should be regarded as a preliminary “proof of 329 concept”, because they lack full consideration of statistical independence, asymptotic convergence 330 and local exposure. These considerations will be addressed in a later study and these preliminary 331 estimates serve to inform how this study should proceed. 332 Conclusions 333 As gusts from thunderstorm downbursts are the principal cause of damage, in terms of numbers and 334 overall cost, to structures in the co ntiguous USA, the accurate assessment of risk posted by these 335 events is reliant on the suitability, availability and integrity of the relevant meteorological 336 observations. The ASOS network provides observations at 1 -minute intervals which are sufficient to 337 resolve these mesoscale convective events, but observations after 2013 are compromised by false - 338 positive alerts by the introduction of a real-time quality control algorithm that cull s valid data at the 339 source stations before it is transmitted and archived . Although the number of false alerts is very small, 340 they are biased towards these important mesoscale events. Simulation of the algorithm on 341 observations before 2014 suggests that the peak gust is culled in about 10% of these alerts and its 342 value cannot b e estimated from the surviving values . The missing data are therefore unrecoverable, 343 but their impact on the assessment of extremes is diluted by the contributions from the unaffected 344 15 events. As there is no reasonable prospect of retrospectively correcting the action of the algorithm on 345 past observations, attention must be focussed in mitigating its impact on extreme value analysis, 346 represented here by 𝑉̂50, and the consequential risk to structures . The QC simulation predicted that the 347 locations of underest imating stations are randomly distributed across CONUS and not predictable 348 from the observed rates of culls. The mitigation proposed here is remove underestimates by ensuring 349 that 𝑉̂50 predicted at a ny given station is not less than the average value of other stations within a 350 radius (here, 3°) around the station. This also impacts on stations where 𝑉̂50 is legitimately lower than 351 the neighbours, which represents the price that must be paid to eliminate unacc eptable risk. 352 Acknowledgements 353 The work reported in th is paper is the result of unfunded, curiosity -driven research undertaken at the 354 author’s pleasure. 355 Notation 356 The following symbols are used in this paper : 357 𝑏 Dispersion of Gumbel (FT1) distribution 358 𝑚 Rank of observation in descending value 359 MRI Mean Recurrence Interval (Return period) 360 𝑝 Probability density (PDF) 361 𝑃 Cumulative probability (CDF) 362 𝑈 Mode of Gumbel (FT1) distribution 363 𝑉 Wind speed 364 𝑉̅2min 2-minute mean wind speed 365 𝑉̂3s Peak 3-second mean wind speed in previous minute 366 16 𝑉̂50 Peak 𝑉̂3s with predicted MRI = 50 years 367 𝑤 Weibull shape parameter (in Eqn(4)) 368 𝑦𝑚 Gumbel plotting position (linearised 𝛷, Eq (3)) 369 𝑦𝑉 Gumbel reduced variate for V (non-dimensional 𝑉, Eq (2)) 370 𝛷 Annual probability of exceedance 371 𝜎 Standard deviation 372 〈∙〉𝑥 Ensemble average of values corresponding to 𝑥, e.g., 〈𝑦̅𝑚〉𝑉̂ 373 Data Availability Statement 374 The meteorological observations used in this study, TD6405 and TD6406 files from 2000 to 375 December 2021, may be downloaded from NCEI by FTP or HTTP . NCEI has lately transit ioned to 376 HTTP only, with observations from January 2022 onwards available here, updated monthly. The R 377 scripts and instructions to extract gust events from any ASOS station an d classify by the TNNDT 378 method in Cook (2023) are available from Mendeley at URL: https://doi.org/10.17632/88jp3swkn6.1 . 379 R scripts to replicate the analyses in this paper are available from the author, on application by email . 380