Weather Data

Overview

Weather data captures environmental conditions at the circuit, recorded approximately once per minute throughout the session. These conditions have a profound impact on car performance, tire behavior, and race strategy.

Weather affects lap times dramatically. Cold air reduces engine power, hot tracks reduce tire grip, rain changes braking distances, and wind direction affects DRS effectiveness and cornering stability.

File Location

Weather data is stored once per session:

Pre-Season Testing 2/Practice 3/weather.json

Example:

Pre-Season Testing 2/Practice 3/weather.json
Bahrain Grand Prix/Qualifying/weather.json
Monaco Grand Prix/Race/weather.json

Data Structure

The JSON file contains parallel arrays with environmental readings sampled at ~60 second intervals:

{
  "wT": [19.345, 79.336, 139.346, 199.334, ...],
  "wAT": [28.5, 28.7, 29.1, 29.3, ...],
  "wH": [45.2, 44.8, 44.3, 43.9, ...],
  "wP": [1013.2, 1013.1, 1013.0, 1012.9, ...],
  "wR": [false, false, false, false, ...],
  "wTT": [42.3, 43.1, 44.2, 45.5, ...],
  "wWD": [135, 138, 142, 145, ...],
  "wWS": [2.3, 2.5, 2.8, 3.1, ...]
}

Field Reference

array<number>

required

Session time when this weather sample was recorded, in seconds from the start of the session.Unit: seconds
Sampling frequency: ~60 seconds
Example: [19.345, 79.336, 139.346, 199.334, ...]

wAT

array<number>

required

Ambient air temperature around the track.Unit: °C (Celsius)
Typical range: 15-45°C
Example: [28.5, 28.7, 29.1, 29.3, ...]

Cold air is denser, providing more oxygen for combustion and better engine performance. However, it also makes tires harder to bring up to optimal operating temperature.

array<number>

required

Relative humidity of the air.Unit: % (percentage)
Range: 0-100%
Example: [45.2, 44.8, 44.3, 43.9, ...]

High humidity affects engine cooling and can slightly reduce power output. It also influences how quickly the track dries after rain.

array<number>

required

Atmospheric air pressure.Unit: mbar (millibars)
Typical range: 990-1030 mbar
Example: [1013.2, 1013.1, 1013.0, 1012.9, ...]

Lower pressure (high altitude circuits like Mexico City) means less oxygen, reducing engine power. Higher pressure provides better aerodynamic grip.

array<boolean>

required

Boolean flag indicating whether it was raining at this moment.Values: true (raining) or false (dry)
Example: [false, false, false, true, true, false, ...]

Rain triggers immediate strategic decisions: switch to intermediate or wet tires, adjust brake balance, reduce downforce for wet conditions.

wTT

array<number>

required

Temperature of the asphalt track surface.Unit: °C (Celsius)
Typical range: 20-60°C
Example: [42.3, 43.1, 44.2, 45.5, ...]

Track temperature is the single most important factor for tire performance. Too cold and tires won’t reach operating temperature; too hot and they overheat and lose grip. Optimal track temp varies by compound: softs prefer cooler tracks, hards work better in heat.

wWD

array<number>

required

Direction the wind is coming from, measured as compass bearing.Unit: ° (degrees, 0-359)
Convention: 0° = North, 90° = East, 180° = South, 270° = West
Example: [135, 138, 142, 145, ...] (wind from Southeast)

Wind direction affects downforce and drag. Headwinds increase drag and downforce, tailwinds reduce both. Crosswinds can destabilize the car through high-speed corners.

wWS

array<number>

required

Wind speed - how fast the wind is blowing.Unit: m/s (meters per second)
Conversion: 1 m/s ≈ 3.6 km/h ≈ 2.24 mph
Example: [2.3, 2.5, 2.8, 3.1, ...] (light breeze at ~8-11 km/h)

Strong winds (>5 m/s or 18 km/h) significantly impact DRS effectiveness and cornering stability. Drivers must adjust their racing lines and braking points accordingly.

Real Data Example

Here’s actual weather data from Pre-Season Testing 2, Practice 3 in Bahrain (first 5 samples):

{
  "wT": [19.345, 79.336, 139.346, 199.334, 259.322],
  "wAT": [28.0, 28.0, 28.0, 28.0, 28.0],
  "wH": [44.0, 44.0, 44.0, 44.0, 44.0],
  "wP": [1014.0, 1014.0, 1014.0, 1014.0, 1014.0],
  "wR": [false, false, false, false, false],
  "wTT": [35.0, 35.0, 35.0, 35.0, 35.0],
  "wWD": [80, 80, 80, 80, 80],
  "wWS": [2.1, 2.1, 2.1, 2.1, 2.1]
}

Analysis of this sample:

Consistent 28°C air temperature - optimal for engine cooling
Track temperature at 35°C - good for medium/soft compounds
44% humidity - relatively dry desert conditions
Wind from 80° (East) at 2.1 m/s (7.6 km/h) - light breeze, minimal impact
No rainfall - ideal dry racing conditions
Stable pressure at 1014 mbar - sea level pressure, good air density

Use Cases

Performance Analysis

Tire Strategy
Engine Performance
DRS Effectiveness

Track Temperature Impact on Tire Compounds

20-30°C: HARD compound struggles, SOFT is optimal
30-40°C: MEDIUM compound sweet spot
40-50°C: HARD compound works best, SOFT overheats
>50°C: All compounds degrade rapidly, strategy shifts to shorter stints

Correlate wTT with lap times from the Lap Times dataset to validate these patterns.

Air Temperature and Density EffectsUse wAT and wP to calculate air density:

Air Density ≈ (wP × 100) / (287.05 × (wAT + 273.15))

Compare speed trap data from Lap Times with air density to quantify power unit performance in different conditions.

Wind Impact on Straight-Line SpeedCombine wWD and wWS with track orientation to determine headwind/tailwind components:

Headwind on DRS straight: Reduced top speed, greater DRS benefit
Tailwind on DRS straight: Higher top speed, less DRS benefit
Crosswind: Stability issues, drivers may avoid using DRS

Cross-reference with Telemetry DRS status and speed data.

Strategic Decision Making

Rain Detection and Tire Changes

# Detect rain start and duration
rain_periods = []
raining = False
rain_start = None

for i, (time, is_rain) in enumerate(zip(weather['wT'], weather['wR'])):
    if is_rain and not raining:
        rain_start = time
        raining = True
    elif not is_rain and raining:
        rain_periods.append((rain_start, time))
        raining = False

# Correlate with pit stop timing from lap times data

Track Evolution Modeling

# Model grip level based on track temperature
import numpy as np

track_temps = np.array(weather['wTT'])
optimal_temp = 40.0  # Depends on tire compound

# Grip coefficient (simplified model)
grip = 1.0 - 0.01 * np.abs(track_temps - optimal_temp)

# Correlate with cornering speeds from telemetry

Session Comparison

Weather Evolution Across Sessions

Compare FP1 (morning) vs FP2 (afternoon) vs Qualifying vs Race conditions
Identify teams that adapt better to changing temperatures
Predict race pace based on expected race day weather

Year-over-Year Analysis

Compare weather at the same circuit across seasons
Identify unusually hot/cold/wet events
Contextualize lap time records with weather conditions

Weather Impact Reference

Air Temperature Effects

Condition	Engine Power	Tire Warm-up	Cooling
Cold (below 20°C)	+1-2%	Difficult	Excellent
Optimal (20-30°C)	Baseline	Good	Good
Hot (above 35°C)	-1-2%	Easy	Challenging

Track Temperature Effects

Condition	SOFT	MEDIUM	HARD	Degradation
Cold (below 30°C)	Optimal	Struggles	Very Poor	Low
Warm (30-45°C)	Good	Optimal	Good	Medium
Hot (above 50°C)	Poor (overheats)	Challenging	Best	Very High

Wind Speed Impact

Speed	Description	Impact on Lap Time	DRS Effect
0-2 m/s	Calm	Negligible	Full benefit
2-5 m/s	Light breeze	±0.1s	Moderate
5-8 m/s	Moderate wind	±0.3s	Variable
>8 m/s	Strong wind	±0.5-1.0s	Reduced/avoided

Data Quality Notes

Weather data is sampled at approximately 60-second intervals. Conditions can change between samples, especially during rapidly developing rain showers. For critical analysis, consider interpolating between samples or using external weather data sources.

Wind direction and speed are typically measured at a single point on the circuit (usually near the main straight). Actual wind conditions may vary in different parts of the track due to terrain and structures.

Lap Times - Correlate weather with lap time performance
Telemetry - Analyze tire grip and engine performance under different conditions
Race Control - Track status changes due to weather (rain flags)
Drivers - Driver information for performance analysis

Core Data Types

Field Specifications

Overview

File Location

Data Structure

Field Reference

Real Data Example

Use Cases

Performance Analysis

Strategic Decision Making

Session Comparison

Weather Impact Reference

Air Temperature Effects

Track Temperature Effects

Wind Speed Impact

Data Quality Notes

Build docs developers (and LLMs) love

Core Data Types

Field Specifications

​Overview

​File Location

​Data Structure

​Field Reference

​Real Data Example

​Use Cases

​Performance Analysis

​Strategic Decision Making

​Session Comparison

​Weather Impact Reference

​Air Temperature Effects

​Track Temperature Effects

​Wind Speed Impact

​Data Quality Notes

​Related Pages

Build docs developers (and LLMs) love

Overview

File Location

Data Structure

Field Reference

Real Data Example

Use Cases

Performance Analysis

Strategic Decision Making

Session Comparison

Weather Impact Reference

Air Temperature Effects

Track Temperature Effects

Wind Speed Impact

Data Quality Notes

Related Pages