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 modelearthboi.yaml— declared variables, lead hours and grid
Licence
MIT.