- United States of America (berner@ucar.edu)
Recently, there has been pronoued interest in predictability on the
subseasonal-to-seasonal (S2S) timescale. Skill at this forecast range is
only positive, if the lead-time dependent forecast bias is removed.
Recently, Chapman and Berner, 2024, developed an online bias-correction
from nudging tendencies and saw a bias reduction for surface and
free-atmosphere variables of up to 60% in climate simulations. Here, we
quantify the performance of this model-error scheme against
post-processing in S2S-forecasts. We find that the online bias-correction
reduces the bias, but less so than removing the lead-time dependent bias.
Together, they perform better than reducing the lead-time dependent bias
alone.
How to cite: Berner, J., Jaye, A., and Chapman, W. E.: Benefits of online bias-correction versus postproessing methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14345, https://doi.org/10.5194/egusphere-egu25-14345, 2025.
