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@chswx LDM Parser

Build Status (test suite in progress, don't read much into this yet!)

A series of scripts to ingest and store NWS watch/warning/advisory products via Unidata’s LDM. Will eventually expand to forecast and METAR processing as well; baby steps for now, though.

New in 2.0

All responsibilities for outputting the results of parsing out products from LDM will be placed on the Alerter going forward. Thus, a lot of what's new in 2.0 has been more code deletion than anything else. Version 2.0, instead of handling the entire lifecycle of a request from receiving it from LDM to sending it out over Twitter, will just dump everything in a pub-sub-aware database (currently RethinkDB) and let other worker processes figure it out. This should improve performance, scalability, and redundancy quite nicely. We store things in JSON; this makes it easy to send the data to virtually anywhere, including directly over a socket into a Web browser. (Wink wink.)

What’s done

  • VTEC (Valid Time Extent Code) awareness
  • JSON output
  • Impact-based warnings
  • Storm-based warnings
  • Pub/sub architecture based on RethinkDB

What’s in the works

  • Support for parsing Special Weather Statements/Significant Weather Advisories (#13)
  • Support for watch probabilities (WWUS40) (#14)
  • Parsing and geocoding Mesoscale Convective/Precip Discussions from SPC and WPC (#15)
  • SPC outlooks (#16)
  • LSRs (#17)
  • Tropical advisories (#18)
  • Climate products (#23)
  • Awareness of conditions via METAR ingest
  • Awareness of forecast updates

Pie in the sky stuff

  • Configurable UI
  • Portable installation

See it in action

The current version of the LDM ingestor works behind the scenes.

To see its predecessor in action, follow @chswx on Twitter during inclement weather in Charleston, SC to see examples of the bot’s output.

Questions?

File issues on GitHub against this project or contact me on Twitter: @jaredwsmith

Licensing, warranty, etc.

Licensing TBD. Not for use in life or death situations; code is getting better but the level of rigor is below what I would expect for mission-critical things.

Many Thanks

Many thanks to @blairblends, @edarc and @chswx\LDMIngest for infrastructure and seeding the initial code for this project.