Surface Wind Monitoring at Small Regional Airport
Abstract
1. Introduction
- What are the dominant wind directions occurring at Svidnik Airport throughout the year?
- Is there an observed occurrence of hazardous crosswinds during training flights?
- How do the meteorological data from the station correlate with safety incidents or changes in air operations?
2. Methodology
2.1. Station Location at Svidnik Airport
2.2. Data Mining and Filtering Method
- Reduction in Matrix Columns—Columns irrelevant for statistical processing were removed from the dataset. These included variables such as maximum and minimum wind speeds, directions of maximum and minimum wind, 2 min averages, and other supplementary parameters not essential for the analysis. The dataset was reduced to include only the 10 min average wind speed and direction, as well as gust speed, aligning with the data structure required for aviation meteorological reports (METAR) [19].
- Removal of Non-Numerical Entries—Rows containing non-numeric or incomplete values were deleted to prevent errors in statistical scripts. This ensured that no incomplete data entries, such as missing wind directions or isolated wind speeds without corresponding direction data, would affect the results.
- Exclusion of Zero-Degree Wind Directions—Data entries showing a wind direction of 0° were excluded. According to meteorological conventions, this value is used to represent calm conditions, which are irrelevant for this study. Calm conditions do not generate turbulence and thus have no impact on operational safety at Svidnik Airport.
- Exclusion of Zero Wind Speeds—Rows with a wind speed of 0 knots were also removed. Although these values might occur during calm conditions, they could also result from technical anomalies, such as the anemometer freezing during liquid precipitation combined with sub-zero temperatures. Such frozen states may produce constant directional readings at zero wind speed—an obvious artifact unsuitable for analysis.
- Filtering Based on Airport Operating Hours (Daytime Only)—As Svidnik Airport operates exclusively under daylight VFR conditions, only data corresponding to daytime hours (between sunrise and sunset) were used in the final dataset [24]. This filtering step was carried out using the “suncalc” function, which calculates sunrise and sunset times based on the GPS coordinates of the airport (published in AIP SR) for each row of the dataset. For comparison purposes, wind roses were also generated for both the complete dataset (including nighttime data) and daytime-only data. Analysis showed that nocturnal easterly winds were rare and generally decayed shortly after sunrise. Thus, daytime filtering preserved operational relevance without excluding significant hazardous cases.
- Identification of Critical Wind Directions for Safety Analysis—Based on preliminary visualizations and analysis of wind roses, critical wind sectors affecting take-offs and landings were identified. Subsequently, pie charts representing the monthly frequency of these critical wind directions were created to identify periods of heightened risk. The most concerning wind directions were identified in the 075–105° sector, corresponding to low-level easterly flow across the terrain ridge east of runway 19. These conditions have been associated with turbulence and student go-arounds according to instructor testimony.
3. Discussion and Results
Hierarchical Data Selection and Its Results
- Step 1—Filtering erroneous matrix data
- Step 2—Filtering data by day/night
- Step 3—Selection of data from hazardous sector
- Average monthly measurements (red numbers):
- In the center of each sector is the average number of east-wind records for that month (averaged over 2023 and 2024).
- The highest average monthly frequency of measurements is in August (~275 measurements), followed by July (~196), June (~206), and May (~236).
- Conversely, the lowest average numbers are in December (~65), January (~50), and February (~76), indicating a winter season with less frequent east winds.
- Length of sectors (colored bar-fences):
- The colored bars start from the center and their “radius” (length) is given by the average number of measurements.
- For example, the August sector is the longest, while the January sector is the shortest.
- Black bars (error bars = 95% CI):
- Each sector has a thin black line that indicates the lower and upper limits of the 95% confidence interval for the two-year average.
- A narrow interval (e.g., in May or June) means that between 2023 and 2024 the number of east-wind measurements was very similar, with low interannual variability.
- A wider interval (e.g., in July and August) indicates a larger difference between the number of measurements for these months in individual years.
- Seasonal tendency:
- Summer months (May–August) dominate: Easterly winds occur significantly more frequently during the day, also visible in the form of a higher number of measurements.
- Winter months (November–February) have a lower frequency.
- Reliability of calculations:
- All averages are based on only two years, so the CI can be quite wide (e.g., august approx. [X–Y] measurements).
- If we had data for more years, the intervals would narrow and the interpretation would be more reliable.
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AGL | Above Ground Level |
AIP SR | Aeronautical Information Publication of the Slovak Republic |
CSV | Comma-Separated Value |
GPS | Global Positioning System |
ICAO | International Civil Aviation Organization |
KEGA | Slovak Grant Scheme |
METAR | Meteorological Aerodrome Report |
NTAD | New Trends in Aviation Development |
R | (Programming Language) R |
VFR | Visual Flight Rules |
WMO | World Meteorological Organization |
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Direction Bin | Count Day | Count Night | Avg Day Speed (m/s) | Avg Night Speed (m/s) | Count Change | Count Change (%) | Speed Change (m/s) | Speed Change (%) |
---|---|---|---|---|---|---|---|---|
0° | 4379 | 2866 | 3.55 | 2.37 | 1513 | 34.6 | 1.18 | 33.24 |
22.5° | 2910 | 3480 | 3.05 | 1.76 | 570 | 19.6 | 1.29 | 42.30 |
45° | 1732 | 3799 | 2.05 | 1.50 | 2067 | 119.3 | 0.55 | 26.83 |
67.5° | 1501 | 3259 | 1.58 | 1.40 | 1758 | 117.1 | 0.18 | 11.39 |
90° | 1823 | 2640 | 1.66 | 1.49 | 817 | 44.8 | 0.17 | 10.24 |
112.5° | 2184 | 2546 | 2.11 | 1.81 | 362 | 16.6 | 0.30 | 14.22 |
135° | 11,943 | 8609 | 3.57 | 3.14 | 3334 | 27.9 | 0.43 | 12.04 |
157.5° | 6762 | 4561 | 3.34 | 3.14 | 2201 | 32.5 | 0.20 | 5.99 |
180° | 1542 | 965 | 2.53 | 1.76 | 577 | 37.4 | 0.77 | 30.43 |
202.5° | 741 | 533 | 1.89 | 1.16 | 208 | 28.1 | 0.73 | 38.62 |
225° | 808 | 701 | 1.53 | 1.07 | 107 | 13.2 | 0.46 | 30.07 |
247.5° | 1333 | 1225 | 1.65 | 1.30 | 108 | 8.1 | 0.35 | 21.21 |
270° | 3339 | 3001 | 2.62 | 2.09 | 338 | 10.1 | 0.53 | 20.23 |
292.5° | 3223 | 4217 | 2.46 | 2.00 | 994 | 30.8 | 0.46 | 18.7 |
315° | 3871 | 4676 | 2.69 | 2.19 | 805 | 20.8 | 0.50 | 18.59 |
337.5° | 5028 | 3907 | 3.28 | 2.58 | 1121 | 22.3 | 0.70 | 21.34 |
Metric | Value |
---|---|
Total daytime records | 53,119 |
East sector records | 3324 |
East sector % of daytime | 6.3% |
Mean east wind speed (m/s) | 1.63 |
Max east wind speed (m/s) | 6.10 |
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Choma, L.; Antosko, M.; Korba, P. Surface Wind Monitoring at Small Regional Airport. Atmosphere 2025, 16, 917. https://doi.org/10.3390/atmos16080917
Choma L, Antosko M, Korba P. Surface Wind Monitoring at Small Regional Airport. Atmosphere. 2025; 16(8):917. https://doi.org/10.3390/atmos16080917
Chicago/Turabian StyleChoma, Ladislav, Matej Antosko, and Peter Korba. 2025. "Surface Wind Monitoring at Small Regional Airport" Atmosphere 16, no. 8: 917. https://doi.org/10.3390/atmos16080917
APA StyleChoma, L., Antosko, M., & Korba, P. (2025). Surface Wind Monitoring at Small Regional Airport. Atmosphere, 16(8), 917. https://doi.org/10.3390/atmos16080917