27.4 Basic equations of the approximate stochastic dynamic method 691
The terms involving
˙
X
k
and ˙µ
k
can be eliminated with the help of (27.50) and
(27.66). This gives a fairly involved expression, which we are not going to state
explicitly. The interested reader may find this equation in explicit form in Epstein
(1969), Fortak (1973), or elsewhere.
Had we written out this equation, we would have found that new unknown
quantities appear in the form of the next higher moments τ
ij k
. Formally, we could
easily obtain a differential equation for τ
ij k
containing as new unknowns still higher
moments. We could go on indefinitely deriving prognostic equations for any order
of moments, but these would always contain the unknown next-higher generation
of moments. The same type of closure problem is already known from the theory
of turbulence. Since the deterministic prediction equations are nonlinear, it is
impossible to derive a closed finite set of prognostic equations for the moments. The
numerical evaluation of the third and even higher moments requires prohibitively
large amounts of computer time, so Epstein (1969) was compelled to close the
system of predictive equations consisting of the µ
k
and the σ
kl
by ignoring the
occurrence of the third-order moments τ
ij k
in the σ
kl
equations. Fleming (1971a)
discussed the closure problem very thoroughly. For details we refer to his paper.
Moreover, Fleming (1971a) also applied the stochastic dynamic method to in-
vestigate the effect of uncertainties in the initial conditions on the energetics of
the atmosphere. When one is investigating certain phenomena, the method makes
it possible also to study in a systematic fashion the uncertainties resulting from
the parameterization of neighboring scales. By introducing the quadratic ensem-
ble mean
µ
2
k
one may estimate the most probable energy distribution, which is
called the certain energy. With the help of the variance
σ
kk
or uncertainty, the
so-called uncertain energy can be determined. An atmospheric scale corresponding
to a certain wavenumber k becomes unpredictable whenever the uncertain energy
is as large as or even larger than the certain energy. For details we must refer to the
original literature.
We may easily recognize the advantages of the stochastic dynamic method over
the deterministic forecast. Stochastic dynamic forecasts produce a significantly
smaller mean square error than do deterministic forecasts. As discussed above,
the range of useful forecasts can be extended by applying the stochastic dynamic
method. The main disadvantage of this method is that forecasts require a much
higher level of computational effort than does the deterministic procedure.
Many research papers on the stochastic dynamic method have appeared in the
literature since Fortak (1973). The interested reader will have no difficulty in
finding the proper references. It is beyond the scope of this book to discuss newer
developments aiming to increase the accuracy of the forecast and the period of
useful predictability. Suffice it to say that, at present, even with the best available
weather-prediction models, the predictability on the synoptic scale hardly exceeds