Gust Factors in Aerodrome Weather and Climate Assessment

1. Introduction

Wind gustiness is important in piloting for many fixed-wing and rotorcraft operations. Aircraft accidents in mountainous or high elevation locations with impactful gusts include both mountain wave-related and on-airport crosswind cases [1]. The gust factor in aviation, also referred to as the gust spread, is defined as the difference between the sustained wind and the gust [2]. An often-recommended technique for fixed-wing aircraft pilots during the short final approach is to add one-half the gust factor to the normal approach speed [2,3,4].

The subset of gusty conditions that may impact aviators at low altitude can include those created by mechanical turbulence caused by local terrain, land-cover, and obstructions such as buildings [5]. Documented aircraft incidents that specifically include mention of mechanical turbulence in the vicinity of airports are infrequent though there are many in which gusts are a factor. National Transportation Safety Board (NTSB) reports, which note mechanical turbulence from terrain or buildings in fixed-wing aircraft incidents on takeoff or landing are included in Table 1. An incident at the Hutchinson County Airport (BGD), Borger, Texas, for example, notably included reference to "strong updrafts from unusual landforms” on short final approach on runway 21 [6]. The NASA Aviation Safety Reporting System (ASRS) [7] additionally has reports which include references to mechanical and orographic turbulence in the vicinity of airports and are included in Table A1 for fixed-wing aircraft, though such references do not necessarily indicate the mechanical turbulence was a contributing factor to the reported event.

The meteorological usage of the gust factor, particularly when characterizing surface roughness, is formulated as a ratio between the gust and the sustained wind and is applied to many applications, ranging from tropical cyclone winds to fire weather forecasting [8,9,10,11]. Natural or human-made obstructions can significantly impact the representativeness of observed sustained winds and gusts. When local obstructions affect wind measurements, the World Meteorological Organization (WMO) and the National Oceanic and Atmospheric Administration (NOAA) recommend siting the wind sensor higher than the standard level of approximately 10 m [12,13]. Gust factors in this context have been used to filter out unrepresentative observations [14]. Additionally, gust reporting from ASOS systems differ in terms of what is transmitted via Aviation Routine Weather Reports in METAR and SPECI formats versus the higher temporal resolution one-minute ASOS observations. The METAR/SPECI reports gust reporting has, amongst other criteria, a minimum allowable gust report of 14 knots [15], whereas the one-minute ASOS wind gust reports the mean wind and gust without restriction at 1 kt resolution. Additionally, the one-minute ASOS reports the wind directions at 1 deg resolution versus the 10 deg resolution in METAR/SPECI reports.

2. Materials and Methods

2.1. ASOS Data

Analysis of gustiness was achieved using six years (2019-2024) of one-minute ASOS observations of average and peak 3-s wind from 20 stations arbitrarily selected from across the contiguous U.S. (Table 2). 2023 National Land Cover Database (NCLD) data was obtained via the Multi-Resolution Land Characteristics (MRLC) Consortium (https://www.mrlc.gov/data) and was used to calculate the percentage of land cover that was the combined low, medium, and high-intensity developed regions, as well as the combined percentage of forested areas within 3 km of the ASOS location. These values , included in Table 2, are meant to give a general impression of the roughness in the vicinity of the ASOS. The one-minute ASOS data were accessed from the National Centers for Environmental Information (NCEI, https://www.ncei.noaa.gov/pub/data/asos-onemin/).

¹ NLCD land cover values are for the combined low, medium, and high intensity developed areas and the combined areas defined as forested within 3km of the ASOS location.

2.2. Aviation and Meteorological Gust Factors

Aviation gust factors (GF_avn) and meteorological gust factors (GF_met) were determined from the sustained and peak wind. A traditional approach was used to defining the GF_avn as the difference between the peak (U_max) and average wind (U).

$${\mathrm{G}\mathrm{F}}_{\mathrm{a}\mathrm{v}\mathrm{n}}=\mathrm{p}\mathrm{e}\mathrm{a}\mathrm{k}\mathrm{w}\mathrm{i}\mathrm{n}\mathrm{d}-\mathrm{a}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{g}\mathrm{e}\mathrm{w}\mathrm{i}\mathrm{n}\mathrm{d}={\mathrm{U}}_{\mathrm{m}\mathrm{a}\mathrm{x}}-\mathbf{U}.$$

(1)

For GF_met, a modified approach was taken beginning with an expression in Yu and Chowdhurdy [16] where a peak factor (g), wind speed standard deviation (SD_u), and average wind (U)are used to obtain the difference between the peak and average wind:

$${{\mathrm{U}}_{\mathrm{m}\mathrm{a}\mathrm{x}}-\mathbf{U}=\mathrm{G}\mathrm{F}}_{\mathrm{a}\mathrm{v}\mathrm{n}}=\mathrm{g}·\mathbf{U}{·\mathrm{S}\mathrm{D}}_{\mathrm{u}}$$

(2)

Dividing Equation (2) by the average wind and defining GF_met the ratio of the peak wind to average wind minus one, we obtain the following:

$${\mathrm{G}\mathrm{F}}_{\mathrm{m}\mathrm{e}\mathrm{t}}=\left[{\displaystyle \frac{\mathrm{p}\mathrm{e}\mathrm{a}\mathrm{k}\mathrm{w}\mathrm{i}\mathrm{n}\mathrm{d}}{\mathbf{U}}}-1\right]={\displaystyle \frac{{\mathrm{G}\mathrm{F}}_{\mathrm{a}\mathrm{v}\mathrm{n}}}{\mathbf{U}}}=\mathrm{g}·{\mathrm{S}\mathrm{D}}_{\mathrm{u}}.$$

(3)

With these definitions, GF_met is simply GF_avn normalized by the average wind. Additionally, each gust factor will approach zero as the difference between the peak wind and average wind approach 0, whereas in a traditional formulation the meteorological gust factor limit is 1. Gust factors were averaged by direction in 10-degree bins only if the number of observations were greater than or equal to 10. Also, gust factor estimates were also filtered by minimum gust values (1, 5, 10, 15, 20, and 25 kt) to test the impact of filtering out lower gusts, progressively, akin to the filtered gusts that appear in METAR reports.

3. Results

SOME KIND OF INTRO

3.1. Unfiltered Average Gust Factors by Direction

Four locations, three of which have notable impacts from nearby obstacles, are used as a first examination of the differences between average GF_met and GF_avn: KABQ, KDFW, KGLS, and KPAE (Fig. 1). The DFW East Air Traffic Control Tower is about 290 m east of the ASOS. KGLS, which is less developed than KDFW (Table 2), has a VOR located about 345 m to the west/southwest of the ASOS. KPAE has a large fencing structure 80 m to the southeast of the ASOS. The average GF_met is relatively high (Fig. 2) in each of those directions at each airport because of those obstructions. KABQ has a grouping of hangars and other buildings to the southwest of the ASOS and another zone of buildings to the northeast that account for the broader directions of high GF_met. The lowest values of GF_met at each airport are in directions where the upwind flow has a significant fetch along a runway. A full listing of isolated and/or impactful siting issues are included in Table A2. Figures depicting GF_met at the remaining locations are included in Appendix B.

The GF_avn is quite different, directionally, at each airport compared to the GF_met (Fig. 3). KABQ has an exceptionally dramatic increase in GF_avn directly to the east. This enhanced gustiness is presumed to occur from gap winds that flow from Tijeras Canyon [17]. The highest GF_avn values at the other locations are in line with prevailing wind patterns (unshown). Indications of increased GF_avn in the directions with obstructions are noted for KGLS and KPAE. Figures depicting GF_met at the remaining locations are included in Appendix C. KONT is another location in which the GF_avn has high values in a specific direction, from the northeast, which is likely due to infrequent Santa Ana wind events (Fig. C.3).

Figure 1. Satellite views of ASOS locations at a) KABQ, b) KDFW, c) KGLS, and d) KPAE. Map data: Google. Created with QGIS: http://qgis.osgeo.org.

Figure 1. Satellite views of ASOS locations at a) KABQ, b) KDFW, c) KGLS, and d) KPAE. Map data: Google. Created with QGIS: http://qgis.osgeo.org.

Figure 2. Average GF_met by direction [2019-2004] for a) KABQ, b) KDFW, c) KGLS, and d) KPAE.

Figure 2. Average GF_met by direction [2019-2004] for a) KABQ, b) KDFW, c) KGLS, and d) KPAE.

Figure 3. Average GF_avn by direction [2019-2004] for a) KABQ, b) KDFW, c) KGLS, and d) KPAE.

Figure 3. Average GF_avn by direction [2019-2004] for a) KABQ, b) KDFW, c) KGLS, and d) KPAE.

3.2. Filtered Average Gust Factors by Direction

Given that the impact of the obstructions near the ASOS units is less noticeable with GF_avn and that the overall gustiness patterns are dissimilar between the two gust factors, filtering of the gusts was conducted by removing gusts under threshold values of 1, 5, 10, 15, 20, and 25 kt. A threshold value of 1 is equivalent to no filtering. A threshold near 15 kt is near the 14 kt minimum gust value in ASOS-generated METAR reports. The impact of filtering of GF_met by 5 kt and greater produced significant differences to the unfiltered estimates, particularly in the direction of developed land cover (Fig. 4). KABQ had large reductions in GF_met, for example, from broad portions of the northeast and southwest sectors. KDFW also has noticeable reductions in GF_met from the west in the direction of terminal development. On the other hand, some of the GF_met values in the direction of prominent obstructions at KDFW, KGLS, and KPAE are still noticeable, and as the filtered value of gusts is increased, GF_met values are not available and are indicative of the obstructions both increasing gustiness but also reducing wind speeds.

Filtering of GF_avn by gust speeds (Fig. 5) produces less dramatic changes in values compared to GF_met. Given that GF_avn values aren’t normalized, the values increase as the gust filter value is increased. At KABQ, the gustiness in association with gap winds from the east are noticeable in all levels of filtering. At KDFW, the gustiness associated with the ATC tower to the east becomes more noticeable with increased filtering until gust values stop being reported.

Figure 4. Average GF_met by direction [2019-2004] for a) KABQ, b) KDFW, c) KGLS, and d) KPAE filtered by gust values: no filtering (blue box), 5+ kt (green triangle), 10+ kt (purple downward triangle), 20+ kt (red plus), and 25+ kt (black circle).

Figure 4. Average GF_met by direction [2019-2004] for a) KABQ, b) KDFW, c) KGLS, and d) KPAE filtered by gust values: no filtering (blue box), 5+ kt (green triangle), 10+ kt (purple downward triangle), 20+ kt (red plus), and 25+ kt (black circle).

Figure 5. Average GF_avn by direction [2019-2004] for a) KABQ, b) KDFW, c) KGLS, and d) KPAE filtered by gust values: no filtering (blue box), 5+ kt (green triangle), 10+ kt (purple downward triangle), 20+ kt (red plus), and 25+ kt (black circle).

Figure 5. Average GF_avn by direction [2019-2004] for a) KABQ, b) KDFW, c) KGLS, and d) KPAE filtered by gust values: no filtering (blue box), 5+ kt (green triangle), 10+ kt (purple downward triangle), 20+ kt (red plus), and 25+ kt (black circle).

3.3. Correlation Between GF_met and GF_avn as a Function of Gust Filtering

Given the impacts of filtering on gust factor values, the correlation between GF_met and GF_avn was assessed using the Pearson correlation coefficient for all locations. Results for KABQ, KDFW, KGLS, and KPAE are included in Figure 6 and the remaining locations are included in Appendix D. As the level of filtering is increased, the correlations trend more positive. Peak correlation coefficents for all locations occur with filtering of at least 15 kt or greater with 17 locations having peak correlations coeffients greater than +0.95. The slopes of the best fit lines are also noted to increase with the increased filterng of gusts. This may be simple result of Equation 3, when rearranged, shows that the ratio of GF_avn to GFmet would be equal to the mean wind and which would increase with increased filtering of lower gusts.

Five of the locations (KAOO, KDFW, KIDA, KONT, and KTCC), with no filtering, have robust negative correlations (between -0.5 and -0.7). We speculate that the cause of this negative correlation stems from the following scenario. ASOS systems are typically sited near the landing zone of the primary instrumented runway. This runway will often be well aligned with the prevailing winds, which is often the direction of larger gusts and would produce high GF_avn in the directions that the ASOS has a long upwind fetch along the runway and in which GF_met would be minimal. If there is significant development and/or forests to the sides of the runway, the GF_met would be large with fetches perpendicular to the runway. Thus, as GF_met increases, GF_avn would decrease in this scenario.

3.4. NTSB Case CEN18LA172 Borger, Texas

The NTSB report for the incident at KBGR on 11 May 2018, which was classified as a loss of control on ground, included the following text:

During a visual approach to runway 21 (3,897 feet by 100 feet, dry asphalt), the pilot stated that he flew an upwind pattern entry and on short final, experienced "strong updrafts from unusual landforms". The pilot landed the tailwheel equipped airplane on runway 21, near the intersection of runway 17/35 (about 1,650 feet down runway 21), and "experienced strong wind gusts causing swerving".

The report also indicated winds were from 220° (southwest) between 23 and 32 kt. Topographic features in the vicinity of the airport in Figure 7, including to the west and southwest of the intersection of the two runways. Unfiltered GF_met (Fig. B.1) and GF_avn (Fig. C.1) do not indicate high values from the southwest, but GF_met is high from the northwest, which is in the general direction of the terrain features that might have been producing the mechanical turbulence in this incident. The problem is that the high GF_met values observed by the ASOS form the northwest are due to the hangars only 50 m in that direction. Thus, ASOS siting issues can limit the utility for assessing mechanical turbulence for natural obstruction in the vicinity of the airport.

3.4. ASOS Location Change at Manchester-Boston Regional Airport (KMHT)

The ASOS unit at KMHT was relocated as part of airport facilities construction in 2022. The old location was positioned on the west side of runway 17/35 with the terminal just to the west, while the new location is positioned further south on the east side of runway 17/35 with major obstructions just to the east (Fig. 8). Analysis of the gustiness in 2021, before the relocation, and in 2025 after the relocation was conducted to test the impacts on the two gust factors. GF_met shows a significant change between the two years with high gustiness values shifting from the west to the east consistent with the change in direction of significant obstructions relative to the ASOS (Fig. 9). On the other hand, GF_avn is much less impacted by the relocation with the directional patterns in gustiness largely preserved.

Figure 8. Satellite view of Manchester-Boston Regional Airport (KMHT), Manchester, New Hampshire. Map data: Google. Runway data: FAA (https://adds-faa.opendata.arcgis.com/datasets/faa::runways/about). Created with QGIS: http://qgis.osgeo.org. Runways (orange) with numbers labeled in yellow. The present and previous ASOS locations are labeled with a red circle.

Figure 8. Satellite view of Manchester-Boston Regional Airport (KMHT), Manchester, New Hampshire. Map data: Google. Runway data: FAA (https://adds-faa.opendata.arcgis.com/datasets/faa::runways/about). Created with QGIS: http://qgis.osgeo.org. Runways (orange) with numbers labeled in yellow. The present and previous ASOS locations are labeled with a red circle.

Figure 9. KMHT gustiness changes due to an ASOS relocation: a) average GF_met by direction for 2021, b) average GF_met by direction for 2024, c) average GF_avn by direction for 2021, d) average GF_avn by direction for 2024.

Figure 9. KMHT gustiness changes due to an ASOS relocation: a) average GF_met by direction for 2021, b) average GF_met by direction for 2024, c) average GF_avn by direction for 2021, d) average GF_avn by direction for 2024.

4. Discussion

Gust factors are used in piloting, in particular by fixed-wing aircraft on final approach, and large values may be caused by mechanical turbulence produced by natural and/or human-made obstructions. Meteorological gust factors are often used to estimate surface roughness conditions for providing standardized wind speed and gusts, for example, in tropical cyclones. Meteorological (GF_met) and aviation gust factors (GF_avn) were analyzed from six years of one-minute ASOS observations at 20 airports, including small, medium, large, and non-hub facilities.

• A modified version of the meteorological gust factor is used and is shown to be the aviation gust factor normalized by the sustained wind speed. This version allows both gust factors to have minimum values of zero when the gust speed approaches the sustained wind speed.
• The averaged GF_met, by direction, is sensitive to local obstructions ranging from smaller nearby airport equipment and small buildings to larger generalized areas of airport development. The averaged GF_avn, by direction, is not as responsive to local obstructions, but cases of severe obstruction impact on GF_avn are observed to occur. Local obstructions in the vicinity of the ASOS Unit can make the evaluation of mechanical turbulence by natural features of airport development difficult.
• GF_met and GF_avn, without filtering by gust values, are often poorly correlated, but a subset of airport locations have substantive negative correlations that may be caused by a specific ASOS siting scenario. GF_met and GF_avn, when filtered by including only gusts of at least 15 knots, have a generally high correlation.
• A case of an ASOS relocation indicated that GF_met directional variation was significantly impacted, but the GF_avn directional variation was largely preserved and supports the notion that the GFavn is more representative of larger-scale roughness and/or meteorologically driven gust events.

These findings may be important to the climatological analysis and interpretation of gustiness at aerodromes to advise aviators, attribution of gusts for specific aviation incidents, and assessment of ASOS equipment siting issues.