EGU2020-1981
https://doi.org/10.5194/egusphere-egu2020-1981
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Identifying periods of forecast model confidence for improved subseasonal prediction of precipitation in southern Australia

Doug Richardson, James Risbey, and Didier Monselesan
Doug Richardson et al.
  • CSIRO Oceans & Atmosphere, Hobart, Australia (doug.richardson@csiro.au)

Subseasonal prediction skill of precipitation is typically low. Sometimes, however, forecasts are accurate and it would be useful to end-users to assess a priori if this might be the case. We use a 20-year hindcast data set of the ECMWF S2S prediction system and identify periods of high forecast confidence, evaluating model skill of precipitation forecasts for these periods compared to lower confidence predictions.

From reanalysis data, we derive a set of circulation patterns, called archetypes, that represent the broad-scale atmospheric circulation over Australia. These archetypes are combinations of ridges and troughs, and yield different precipitation patterns depending on the location of these features. In the literature, a typical application of circulation patterns is assigning daily reanalysis fields to the closest-matching pattern, thus obtaining conditional distributions of precipitation corresponding to key modes of atmospheric variability. A problem common to such analyses is that the precipitation distributions associated with the circulation patterns can be too similar; distinct distributions are required in order for the patterns to be useful in estimating precipitation. We show that by subsampling the archetype occurrences only when they are particularly well-matched to the underlying field, the conditional precipitation distributions become more distinct.

We subsample hindcast fields in the same way, obtaining a sample of periods when the model is confident about its prediction of the upcoming archetype. We then calculate model skill in predicting precipitation for three regions in southern Australia during such periods compared to when the model is not confident about the predicted archetype. Our results suggest that during periods of forecast confidence, precipitation skill is greater than normal for shorter leads (up to ten days) in two of the three regions (the Murray Basin and Western Tasmania). Skill for the third region (Southwest Western Australia) is greater during confident periods for lead times greater than one week, although this is marginal.

How to cite: Richardson, D., Risbey, J., and Monselesan, D.: Identifying periods of forecast model confidence for improved subseasonal prediction of precipitation in southern Australia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1981, https://doi.org/10.5194/egusphere-egu2020-1981, 2020

This abstract will not be presented.