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Weather & Mission Feasibility Analysis
EDA Assignment · March 2026
A decade of atmospheric data analyzed to determine the environmental thresholds that dictate helicopter mission suitability — bridging meteorology and aviation safety.
Video Presentation
The videos are in the required format but do not always work so Here is a backup link if the videos do not work https://huggingface.co/datasets/MichaelYitzchak/Weather_Conditions/resolve/main/%3Cvideo%20src%3D%22...%22%20controls%3D%22controls%22%20style%3D%22max-width%3A%20720px%3B%22%3E%3C/video%3E.mp4
| Dataset | Period | Rows | Features | Questions |
|---|---|---|---|---|
| Szeged Weather · Kaggle | 2006–2016 | 96,453 | 12 | 5 |
#Main Objective
To analyze to what extent atmospheric variables — humidity, altimeter pressure, and wind speed — can evaluate the suitability of environmental conditions for helicopter flight.
This project focuses exclusively on hours with active weather conditions. There are no clear or sunny baseline days in the dataset — every row represents a moment where weather played a role. The engineered Mission_Status variable defines, based on standard aviation thresholds, whether conditions on any given hour were safe enough to fly.
The personal motivation: as a helicopter operator serving in the reserves, multiple missions have been cancelled mid-flight due to weather. A data-driven pre-flight weather assessment framework could save time, manpower, and allow missions to be replanned rather than abandoned.
Mission Status Thresholds
| Status | Condition |
|---|---|
| Operational | Visibility > 4 km AND Wind Speed < 45 km/h |
| Non-Operational | Any breach of the above thresholds |
Dataset Description
Source: Weather in Szeged 2006–2016 — Kaggle
Size: 96,453 rows · 12 features
Features
Numeric: Wind Speed (km/h), Visibility (km), Temperature (C), Apparent Temperature (C), Humidity, Pressure (millibars), Wind Bearing (degrees)
Categorical: Summary, Precip Type, Formatted Date
Engineered: Mission_Status — Operational / Non-Operational
Data Cleaning
| Step | Action | Result |
|---|---|---|
| 1 | Date parsing | Converted to UTC datetime |
| 2 | Duplicate removal | 24 rows removed → 96,429 remaining |
| 3 | Pressure sensor failures | 1,288 zeros (1.34%) replaced with column median |
| 4 | Missing Precipitation Type | 517 rows (0.54%) dropped |
| 5 | Redundant columns | Loud Cover (zero variance) and Daily Summary dropped |
| 6 | Humidity validity | All values confirmed in [0, 1] — no action needed |
| 7 | Visibility ceiling | All values ≤ 16.1 km confirmed — no action needed |
| 8 | Fog/Visibility contradiction | 1,939 rows relabeled from "Foggy" → "Partly Cloudy" where visibility > 2.5 km |
Note: All numeric columns had zero NaN values (confirmed via
df.info()). No general median-fill was required — only the targeted pressure zero replacement above.
Outlier Detection & Handling
Distribution shape was checked before selecting any outlier method — IQR assumes symmetry and fails on skewed variables.
| Column Group | Method | Decision |
|---|---|---|
| Temperature, Apparent Temp, Pressure | IQR (symmetric) | Keep — real weather extremes |
| Wind Speed, Visibility, Humidity | 1st–99th percentile (skewed) | Keep — operationally critical events |
| Wind Bearing | None (circular variable) | Keep — 0° and 360° are the same direction; IQR/percentiles fail on circular data |
Overall decision: Keep all outliers. In aviation weather analysis, extreme readings are not errors — they are the most operationally significant events in the dataset.
Class Balance
| Class | Count | Percentage |
|---|---|---|
| Operational | 8,998 | 90.0% |
| Non-Operational | 1,002 | 10.0% |
Imbalance ratio: 8.98:1 — A naive model predicting "Operational" every time would score 90% accuracy while being completely useless. Future models should use F1-score or precision-recall, not raw accuracy.
Correlation Analysis
Key correlations:
- Humidity ↔ Visibility: –0.37 (negative — high humidity predicts low visibility)
- Wind Speed ↔ Visibility: +0.10 (near-zero — they are independent risk factors)
- Temperature ↔ Apparent Temperature: +0.99 (expected — nearly identical variables)
Research Questions & Findings
Q1 · The Fog Trap
Research Question: To what extent does humidity serve as an indicator for non-operational mission status based on visibility?
Left: Scatter plot of Humidity vs. Visibility with 4 km safety threshold · Right: KDE density of humidity by Mission Status
Finding: A "Fog Trap" was identified at 90% humidity. As humidity approaches 100%, visibility consistently crashes below the 4 km safety threshold — making humidity a primary leading indicator of mission grounding before conditions become critical.
Q2 · Atmospheric Pressure Stability
Research Question: Does atmospheric pressure stability vary between operational and non-operational mission statuses?
Left: Box plot — pressure spread by mission status · Right: Overlaid pressure distributions by mission status
Finding: Non-operational missions show higher pressure volatility (std: 9.78 vs 7.29 for operational). Rapid pressure drops correspond to storm systems; high-pressure outliers indicate anticyclonic conditions. Pressure instability is a meaningful — if subtle — indicator of unsafe conditions.
Q3 · The Wind Speed Ceiling
Research Question: To what extent can extreme wind speed variations predict a transition from operational to non-operational mission status?
Left: Violin plot — wind speed by mission status · Right: Full dataset wind speed distribution
Finding: A hard ceiling exists at 45 km/h — beyond this, 100% of missions are Non-Operational. However, most non-operational events occur below 20 km/h, confirming visibility is the dominant grounding factor, not wind. Wind and visibility are independent (correlation: +0.10) — both must be monitored separately.
Q4 · Categorical Weather Hazards
Research Question: To what extent does categorical weather data (Precipitation Type) serve as a reliable indicator for non-operational mission status?
Left: Mission count by precipitation type · Right: Proportional grounding risk (%) per type
Finding: While Rain is the most frequent hazard in absolute numbers, Snow carries a ~40% higher proportional grounding risk. Most common Non-Operational weather: Foggy. Most common Operational weather: Partly Cloudy. Snow is a disproportionate predictor despite lower frequency.
Q5 · Seasonal Risk Patterns
Research Question: Does the risk of mission grounding follow a predictable seasonal pattern throughout the year?
Left: Monthly grounding risk % (line chart) · Right: Normalized stacked bar — Operational vs. Non-Operational ratio by month
Finding: The data reveals a dramatic U-shaped seasonal curve. January grounding rate: ~27%. December: highest risk. July: lowest risk. Winter missions are roughly 30× more likely to be Non-Operational than summer missions — enabling data-driven strategic fleet planning.
Key Insights
| Insight | Detail |
|---|---|
| Wind & Visibility are independent | Near-zero correlation — either alone can ground a mission. Monitor both separately. |
| Humidity is an early warning signal | Above 90%, visibility reliably drops below 4 km. Predict groundings before they happen. |
| Snow is disproportionately dangerous | ~40% higher grounding risk than rain despite lower frequency. |
| Seasonality is predictable | December/January are ~30× riskier than July. Plan resources accordingly. |
Limitations
Mission_Statusis rule-based, not naturally observed — engineered from predefined thresholds that may not capture all real-world operational nuance.- Results reflect associations, not causal relationships.
- The 90/10 class imbalance means future classifiers need careful metric selection (F1, precision-recall) and should consider resampling techniques.
- Some variables may be indirectly related through confounders — seasonality drives both temperature and humidity, which in turn influence visibility.
Conclusion
This analysis demonstrates that aviation mission feasibility is strongly governed by environmental conditions. Through systematic data cleaning, distribution-aware outlier analysis, feature engineering, and five focused research questions, meaningful and actionable insights were derived from raw weather data.
Wind speed and visibility define hard safety boundaries; humidity serves as an early warning signal; pressure instability flags turbulent conditions; snow is a disproportionate categorical risk; and seasonality enables proactive operational planning. Together, these findings form a robust data-driven foundation for aviation safety decision-making.
Weather in Szeged 2006–2016 · Kaggle Dataset · Python · Pandas · Matplotlib · Seaborn
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