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AI agent for meteorologists

Forecast Verification Agent

Clear, regular feedback on forecast accuracy and biases

Forecast Verification Agent: what goes in, what the agent does and what you get

What it does

Forecasters improve when they know where their forecasts go wrong, but scoring by hand rarely happens. Each day this agent compares issued forecasts with observations for temperature, precipitation and wind across stations. Before scoring, it checks observations for bad data such as stuck sensors or impossible jumps. If a station's data is invalid, it excludes that station and logs why. It then computes error scores by forecaster, lead time, season and weather type, and looks for repeat biases, such as overnight lows that run warm on clear, calm nights. It writes a weekly summary with the clearest patterns and charts. You decide on any training or method change. Edge case: a station with a broken sensor is excluded and logged so it does not drag down anyone's scores.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueApprovedNo 1 STARTS WHEN Daily scoring 2 USES A TOOL Pull forecasts and observations 3 CHECKS THE RESULT Are observations valid? If not: exclude bad stations and log them. Back to step2. 4 DOES Score forecasts 5 DOES Find persistent biases 6 YOU APPROVE Lead forecaster reviews summary 7 RESULT Weekly verification report
Read the steps as a list
  1. Daily scoring
  2. Pull forecasts and observations
  3. Are observations valid?If not: exclude bad stations and log them. Back to step 2.
  4. Score forecasts
  5. Find persistent biases
  6. Lead forecaster reviews summaryThe agent waits here for your OK.
  7. Weekly verification report

How it decides

It scores forecasts against quality-checked observations and flags biases that persist over the set number of cases.

  • Exclude bad stations
  • Group by weather type
  • Flag persistent biases

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Variables to score
  • Bias threshold
  • Report day
  • Station list

What keeps you in control

It always asks you first

  • Method changes

Hard limits

  • Scores used for learning, not ranking staff

It stops when

  • Done: report shared
  • Stop: data missing

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensOn January 15 the agent pulled forecasts and observations for 42 stations. Its validity check found station KLM reporting 0 F for six hours, far from its neighbors. It excluded those hours and rescored. Over the last 30 days the overnight low forecasts in valley stations ran 3.1 F warm on clear, calm nights at day 2 lead time. The lead forecaster reviewed the bias summary and shared it at the weekly meeting.

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