Due to the chaotic nature of the atmosphere, weather forecasts, even with ever improving numerical weather prediction models, eventually lose all skill.
Meteorologists have a strong desire to better understand this process as they try to trace forecast error back to observational gaps and to provide a means for improvement.
Root mean square error (rms, or its square, the variance distance) is often used to measure differences between simulated and observed fields. In this case, scientists measured the distance between a model forecast field within its grid and the verifying analysis field that represents all real-world observations. [Read more…] about Unraveling positional and structural errors in numerical weather forecast models