Model bias might be regime dependent. For example, a model could

show warm bias under one type of weather regime such as high pressure

systems in some days but cold bias under low pressure systems over a

same location in other days. Therefore, it's necessary and important

to remove those regime-dependent model bias in order to steadily reduce

total forecast error in daily NWP operation. In another word, there

are rooms to improve in traditional statistically-based bias correction

approaches which are usually a sort of averaging of past forecast errors

without distinguishing weather regimes.

A weather-regime dependent bias correction method was proposed and tested

with the NCEP Short Range Ensemble Forecast (SREF) system. Different

regime was assigned different weight in the process of averaging past

errors. As a first step, the method was applied only to the first moment

(ensemble mean) and showed promising results. How to apply the method to

the 2nd (ensemble spread) and higher moments (probability distribution)

needs to be tested further. Regime-dependent method might be important

particularly for limited-area forecasts.