Predict Tomorrow's Wind Speed for Flight Planning

Before touching code, learn what a METAR report looks like. A typical report reads: METAR KSEA 081753Z 21012G18KT 10SM FEW040 SCT250 18/08 A3012. This encodes the station (KSEA = Seattle-Tacoma), time, wind direction and speed (210° at 12 knots gusting 18), visibility, cloud layers, temperature/dewpoint, and altimeter setting.

Pick a local airport with good data availability. Towered airports (Class C or D) report METAR observations hourly and are excellent choices. Understand the units: wind is in knots, visibility in statute miles, temperature in Celsius.

Use the Iowa Environmental Mesonet (IEM) ASOS download service at mesonet.agron.iastate.edu. This free archive lets you download years of METAR data as CSV for any U.S. airport. Request at least 2–3 years of data to capture seasonal patterns.

Download the data and load it into a pandas DataFrame. Key columns include: timestamp, wind speed (knots), wind direction (degrees), temperature, dewpoint, pressure, and visibility. Check for missing values — weather sensors occasionally fail, and you'll need to handle gaps.

Use pandas to handle missing values (drop or interpolate), convert timestamps, and resample hourly observations to daily summaries. For each day, compute: mean wind speed, max wind speed, mean temperature, mean pressure, and mean dewpoint.

Create exploratory plots with matplotlib: wind speed over time (do you see seasonal patterns?), a histogram of daily max wind (what's "normal"?), and a scatter plot of temperature vs. wind speed. These patterns are what your model will learn to exploit.

Your model needs to predict tomorrow's wind speed from information available today. Create lagged features: yesterday's wind speed, the 3-day rolling average wind speed, yesterday's pressure change (falling pressure often precedes storms), temperature change, and day-of-year (to capture seasonal effects).

Use pandas shift() to create lagged columns and rolling() for moving averages. Drop the first few rows where lags create NaN values. This feature engineering step is where domain knowledge matters most — think about what weather patterns precede windy days.

Split your data chronologically — use the first 80% for training and the last 20% for testing. Never shuffle time-series data randomly, because that leaks future information into training.

Start with a Random Forest regressor from scikit-learn. Train it and evaluate using Mean Absolute Error (MAE) — this tells you the average prediction error in knots. An MAE of 3–4 knots on daily average wind is a reasonable target for a first model. Compare against a naive baseline: "tomorrow's wind will be the same as today's."

Plot your predicted wind speeds against actual values as a time series. Where does the model do well? Where does it fail? Models typically struggle with sudden weather changes — cold fronts and storm systems that bring rapid wind shifts.

Use scikit-learn's feature_importances_ attribute to see which inputs matter most. You'll likely find that yesterday's wind speed and recent pressure changes are the top predictors — this matches meteorological intuition perfectly.

Create a simple decision tool: given today's weather, predict tomorrow's wind and flag whether a student pilot (limited to, say, 15 knots) should plan to fly. Calculate how often your model would have correctly advised "go" or "no-go" compared to actual conditions.

Document your results with plots and accuracy metrics. Discuss the cost of errors: a false "go" (predicting calm when it's actually windy) is a safety issue, while a false "no-go" just means a missed flying day. In aviation, we always err on the side of caution.