Impact of ASOS Real-Time Quality Control on Convective Gust Extremes in the USA
2. Materials and Methods
2.1. The ASOS One-Minute Interval Weather Observations
- Synoptic—comprising non-convective gusts generated by the ABL in synoptic-scale weather systems and near-neutral atmospheric stability. Steady, strong, locally stationary wind with low gust factor; little variation in direction and temperature; linear or no trend in atmospheric pressure; precipitation absent or continuous.
- Storm-burst—comprising short-duration, non-stationary, high gust-factor events in otherwise strong steady winds, sometimes with discernible variation in temperature or pressure. May include downdrafts from deep convection which penetrate through the gust structure of the ABL.
- Front-down—comprising convective downdrafts in the rear flank of active fronts, where the mean wind speed is decreasing from a higher steady value. Mean gust speeds after the peak are lower than before; direction veers or backs; temperature drops rapidly through the event; pressure increases temporarily when the downburst is directly over the station; otherwise, the variation is that expected for the passage of a front.
- Front-up—comprising convective downdrafts in the forward flank of active fronts, where the mean wind speed is increasing to a higher steady value. Mean gust speeds after the peak are lower than before; usually, but not always, associated with a sustained change in mean wind direction; otherwise, like Front-down.
- Thunderstorm—comprising downbursts from isolated thunderstorms, often in relatively light winds, where the initial wind speed and direction are restored after the event. Temporary sharp rise in gust speed over several minutes; temporary change in gust direction; sudden large drop in temperature; temporary increase in pressure when the downburst is directly over the station; otherwise, slight rise due to temperature drop; often a sudden burst of heavy rain.
2.2. The ASOS Real-Time Quality Control
2.3. The XIMIS Method of Extreme-Value Analysis
- Probability estimated from the order statistics are for independent events following a Poisson recurrence process model leading to a Fisher–Tippett Type 1 (FT1) asymptote;
- The test for validity of the Poisson model is exponentially distributed inter-arrival times , which is simple to implement;
- The order statistics are ranked downwards from the highest value, so XIMIS is the only method (apart from ACER ) that works for left-censored data where the population, N, is unknown;
- Classified gust event data from  are left-censored at kn;
- Asymptotic convergence is not a requirement, i.e., XIMIS is a penultimate method;
- Confidence limit outliers are easily detected, and their contribution removed.
2.4. XIMIS Methodology
2.5. XIMIS Charts
2.6. CONUS Mapping
3.1. Observed Effect of Test 10 from 2014–2021
3.2. Simulating Test 10 Using Data before 2014
- The 5 s intervals give 12 opportunities in each minute for the running to fall to 6 kn or less and trigger Test 10, whereas the observations give only one which should lead to fewer simulated culls;
- The values of in the gust events are the largest observed in the previous 1 min, not in the previous 5 s, so should lead to more simulated culls.
3.3. Simulating the Effect of Test 10 on XIMIS Extremes 2000–2013
3.3.1. CONUS Superstation
3.3.2. The Kansas and Illinois State Superstations
3.3.3. KGCK Garden City, KS, and KMLI Moline, IL
- The event at KGCK (left) shows the typical Class 4 characteristics of an active cold front:
- The peak gust occurs at the forward flank of the front and recovers to a sustained value greater than the incident value;
- A sudden sustained change in direction is contemporaneous with the peak gust;
- The temperature falls through the event to a sustained minimum ~15 min after the peak gust, whereas in a typical Class 5 event, e.g., Figure 2, the temperature minimum typically coincides with the peak gust;
- The pressure rises to a sustained value;
- A burst of heavy rain starts at the peak gust and lasts for 10–20 min.
- The 93 kn gust at KMLI occurred during a severe outbreak of thunderstorms and tornados across MO, IL, and AR. It is reported by the NOAA Storm Prediction Center  wind report for the day as a non-tornadic event which destroyed the primary ASOS anemometer but was recorded by the back-up equipment. It differs from the typical Class 5 thunderstorm events in Figure 2 in some important respects:
- The timeseries shape is typical of a Class 5 thunderstorm event but the duration is untypically short;
- The transient change in direction is untypically short;
- There is a transient rise in temperature instead of the expected fall;
- The transient changes in pressure are untypically abrupt;
- The continuous rainfall is interrupted at the time of the peak gust.
3.3.4. Influence of Test10 at All Stations
- At KBKB, the underestimate of 4.3 kn (10.7%) for Class 4 at the 6 kn threshold is principally caused by culling the event, which removes the peak value and demotes the event rank to . The effect is amplified by the small population of Class 4 events at this station and the large statistical variance denoted by the wide confidence limits. Raising the threshold to 13 kn restores the culled peak and is then marginally (1%) overestimated. KBKB is a WMO category 2 station and some of this underestimate may be due to shelter by local obstacles.
- At KTQE, the underestimate of 5.2 kn (5.7%) for Class 5 at both thresholds is principally caused by culling the and 11 events. Raising the threshold to 13 kn does not restore these missing events. KTQE is a WMO category 1 station, which rules out any effect of local shelter.
3.4. Predicted 50-Year Maximum Gust Speeds
Data Availability Statement
Conflicts of Interest
|b||Dispersion of Gumbel (FT1) distribution|
|FT1||Fisher-Tippett Type 1 (Gumbel) distribution|
|m||Rank of observation in descending value|
|MRI||Mean Recurrence Interval (Return period)|
|p||Probability density (PDF)|
|P||Cumulative probability (CDF)|
|Mode of Gumbel (FT1) distribution|
|2-min mean wind speed|
|3-s mean wind speed|
|Peak 3-s mean wind speed in previous minute|
|with predicted MRI = 50 years|
|Weibull shape parameter (in Equation (4))|
|Weighted least-mean-squares method|
|Gumbel plotting position (linearised Φ, Equation (3))|
|Gumbel reduced variate for V (non-dimensional V, Equation (2))|
|Annual probability of exceedance|
|Ensemble average of values corresponding to , e.g.,|
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Cook, N.J. Impact of ASOS Real-Time Quality Control on Convective Gust Extremes in the USA. Meteorology 2023, 2, 276-294. https://doi.org/10.3390/meteorology2020017
Cook NJ. Impact of ASOS Real-Time Quality Control on Convective Gust Extremes in the USA. Meteorology. 2023; 2(2):276-294. https://doi.org/10.3390/meteorology2020017Chicago/Turabian Style
Cook, Nicholas John. 2023. "Impact of ASOS Real-Time Quality Control on Convective Gust Extremes in the USA" Meteorology 2, no. 2: 276-294. https://doi.org/10.3390/meteorology2020017