Data cluster

Thailand Aviation Weather Data.

Airport METAR, TAF, aviation weather, and weather-API layers used to understand airport and route disruption risk.

2 sources Updated 2026-05-11 Download JSON

Best use for travel intelligence

For travel decisions, aviation-weather layers are most useful when they turn METAR, TAF, and warning products into airport-specific delay, diversion, and route-confidence signals.

What this changes for travelers

  • TMD Aeromet: Thai airport weather and aviation forecast context closest to the local operation.
  • NOAA Aviation Weather API: Machine-readable METAR and TAF backup plus archive access.
  • NOTAM context: Weather plus official notices produces a much better disruption explanation than weather alone.
  • Pre-flight timing: Helps travelers judge whether to leave earlier, switch airports, or expect a fragile connection.

Best sources to start with

  • Best Thai airport-weather source: TMD Aeromet for Thai airport METAR, TAF, and aviation-weather context.
  • Best machine-readable backup: NOAA Aviation Weather API for METAR, TAF, SIGMET, and recent archives in API-friendly formats.
  • Best operational notice companion: AEROTHAI NOTAM Thai for airspace and aerodrome notices that change the meaning of weather and operational risk.
  • Best developer starting point: Keep raw METAR/TAF text, parsed weather fields, airport mapping, and impact scoring separate.
Official / agency sources

1 of 2 sources look official or agency-backed.

API/feed candidates

2 sources expose API, feed, CKAN/DataStore, JSON, XML, or similar machine-readable access.

Near-real-time signals

2 sources have live, hourly, event-driven, warning, or frequent update language.

Developer note

Prefer structured METAR/TAF products and issued warnings over page scraping, and keep weather observations separate from the downstream operational impact score.

Last checked and source confidence

Last checked: 2026-05-11.

Source confidence: This cluster is strongest when several sources describe the same traveler problem from different angles: official context, machine-readable feeds, and slower fallback documentation.