Station Data Integrity & Outage Risk

Overview

This pillar acts as the 'injury report' for weather data, assessing the reliability and operational status of key meteorological stations. It identifies risks like sensor malfunction or Urban Heat Island effects that can skew temperature readings and mislead market participants.

What It Does

It systematically analyzes the integrity of weather stations crucial to a prediction market. The pillar evaluates real-time data quality control flags, scrutinizes station maintenance histories, and uses satellite imagery to detect environmental changes around the sensor. This creates a risk score for each station's data output.

Why It Matters

In markets hinging on precise temperature records, a single faulty station can cause a dramatic, incorrect swing in the outcome. This pillar provides a crucial edge by flagging potentially compromised data sources before they are widely accepted, preventing costly misinterpretations.

How It Works

First, the pillar identifies the primary weather stations relevant to a specific market. It then ingests live data feeds to check for automated quality control warnings. Finally, it analyzes historical maintenance logs and recent satellite imagery to score the station's overall reliability and risk of environmental bias.

Methodology

A Station Health Score (SHS) from 0 to 100 is calculated for each relevant station. The formula is SHS = 0.5 * (QC_Flag_Score) + 0.3 * (Maintenance_Score) + 0.2 * (UHI_Risk_Score). QC_Flag_Score is derived from the frequency of error flags in METAR feeds over the past 72 hours. Maintenance_Score is based on the time since last documented calibration. UHI_Risk_Score analyzes changes in impervious surfaces within a 1km radius via satellite data.

Edge & Advantage

This provides an edge by revealing when a market may be overreacting to a temperature reading from a poorly sited or malfunctioning weather station, a factor most traders ignore.

Key Indicators

• QC Flags

  high

  Real-time automated warnings in data feeds (e.g., METAR) indicating potential sensor errors or implausible readings.

• Urban Heat Island (UHI) Risk

  high

  Measures increased urbanization or changes in surrounding land use that can artificially inflate temperature readings.

• Station Maintenance Logs

  medium

  Records of recent calibration, sensor replacement, or documented outages, indicating the station's physical health.

Data Sources

• NOAA MADIS

  Meteorological Assimilation Data Ingest System which provides aggregated observational data with integrated quality control checks.

• WMO OSCAR

  World Meteorological Organization's database of observing stations, providing metadata on station type, location, and history.

• USGS LandSat / Copernicus Sentinel-2

  Satellite imagery archives used to analyze long-term changes in land use around weather stations.

Example Questions This Pillar Answers

• → Will the temperature at London Heathrow (EGLL) exceed 38°C this July?
• → Will a new all-time high temperature record be set in Phoenix, AZ this summer?
• → Will the official temperature reading for Death Valley be flagged as unreliable by NOAA this week?

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