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license: mit
tags:
- weather
- forecasting
- baseline
---
# Persistence baseline
A reference forecast for 2 metre temperature: the analysis at T+0, carried
forward unchanged to every lead hour.
Persistence is the standard trivial reference in weather forecasting. It has no
parameters and no training data. Its purpose is comparison — a forecast system
is only demonstrating skill once it beats persistence, and the margin by which
it does so at each lead time is a more meaningful number than an error score
quoted on its own.
## Behaviour
Skill decays in a predictable way, which is what makes it useful as a control:
| Lead | Typical behaviour |
|---|---|
| +6 h | Hard to beat by much — the atmosphere is strongly autocorrelated at this range |
| +24 h | Clearly beaten by any competent forecast |
| +120 h | Error approaches that of climatology |
Error that does **not** grow with lead time indicates a problem in the
evaluation, not in the forecast.
## Inputs
Reads `2m_temperature` from a gridded analysis in netCDF, with dimensions
`(time, latitude, longitude)`, latitude descending from +90 to −90, and
longitude ascending on a 0–360 convention. It uses the most recent timestep.
## Outputs
`temperature_2m` with dimensions `(lead_hour, latitude, longitude)` on the input
grid, in Kelvin (the unit of the source field).
## Files
- `predict.py` — the model
- `earthboi.yaml` — declared variables, lead hours and grid
## Licence
MIT.
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