Why
do ensemble prediction systems so often possess an apparent
under-dispersiveness?
Ake
Johansson
Swedish Meteorological and
Hydrological Institute
2:30 pm in Room 2155
Abstract:
The relationship between spread and skill is an important indicator of
the quality of an ensemble prediction system (EPS). When the commonly
used spread-skill condition is used to quantify whether an EPS is
under- or over-dispersive, there has historically been a clear bias
towards under-dispersiveness. Arguments are given that suggests that an
alternative to the commonly used spread-skill condition is needed to
properly address the true relationship between spread and skill. Such a
relationship is derived and given the name U2I spread-skill condition.
Its properties are described both analytically and geometrically and
the relationship to the commonly used condition is demonstrated and
discussed. It is argued that it would provide not only a more
appropriate and sharper tool to assess the spread-skill relationship,
but also that it indicates how to improve upon the present design of an
EPS. In particular, the presently prevalent practice of centering the
perturbed ensemble members around the control analysis is shown to be
substantially detrimental to the performance of an EPS and therefore
should be replaced by a methodology that constructs equally likely
ensemble members with the same quality as the control member. A recent
experiment with a limited area ensemble prediction system is used to
quantify the differences in perceived quality that is obtained by
employing the two different spread-skill conditions. The differences
are found to be substantial.