WEATHER FORECAST COMPARISON RECORD - YENRA
Guide: https://yenra.com/a/weather-ai.html
Worksheet edition: September 10, 2026

Choose one location, variable, valid period and lead time. Save forecasts
before observing the outcome. Duplicate the record for each case. This is
a learning and evaluation aid, not a basis for overriding official warnings.

COMPARISON DEFINITION
Question and intended decision:
Location / geographic scale:
Variable / units / level:
Valid period and time zone:
Forecast lead time:
Baseline model or method:
Model names and exact versions:
Observation source and its limitations:
Evaluation period / missing-data rule:
Metric and reason for choosing it:
Any threshold and definition of an event:

CASE RECORD
Forecast issue or initialization time (with time zone):
Valid time or period (with time zone):
Forecast A / source / version:
Forecast B / source / version:
Observed value / source:
Absolute error A = absolute value of (A minus observation):
Absolute error B = absolute value of (B minus observation):
Event occurred? / forecast event probability, if relevant:
Data gaps, quality concerns or unusual conditions:

FICTIONAL TEMPERATURE EXAMPLE - DAILY MAXIMA IN DEGREES CELSIUS
Observations: 20, 24, 28
Model A:      21, 22, 27
Model B:      20, 25, 31
Absolute A:    1,  2,  1
Absolute B:    0,  1,  3
Mean absolute error A = (1 + 2 + 1) / 3 = 1.33 C (rounded).
Mean absolute error B = (0 + 1 + 3) / 3 = 1.33 C (rounded).
At a fictional 30 C threshold, B predicts an event on day 3; the observed
maximum remains below it. Equal average errors can hide different patterns.
Three invented days establish no real model ranking or rare-event skill.

PROBABILITY REVIEW
Event definition / threshold / location / period:
Number of comparable cases:
Probability group (for example forecasts near 30%):
Number of observed events / number of cases in group:
Observed event frequency:
Sampling limitations and uncertainty:
Was the system useful as well as calibrated?

REVIEW
Main result, metric and sample size:
Extreme or high-impact cases considered separately:
What the evidence supports:
What remains uncertain:
Next review trigger, such as a model upgrade:

Source context: original GraphCast/AIFS papers and ECMWF operational
documentation linked in the guide. Retain the exact versions evaluated.