Waghorn wrote:
...The best way to improve forecasts in these situations is either to 1)
improve your estimate of the inital state of the atmosphere at that time
-this can be done through an improved data assimilation system or by
targetting extra observations to be taken in this region. or 2) to take
account of the uncertainty through using an ensemble forecast...
Admirable sentiments overall-
caveats-
recent work has suggested that the important factors in errors out to 7-10 days may not be chaos but
ongoing deficits in the models .ie there's still lots of room for imrovements.
Ensemble forecasts do not always pick up outlying ,severe events.What do you invest yr
computational/financial resources in-
improving model resolution,ensemble forecasting,and/or data aquisition and assimilation?


-yes, I agree, obviously the models are not perfect. Ideally, an
ensmeble forecast should take into account ALL of the uncertainty
involved in the forecast ie. model error, initial condition error and
data assimilation error. At the moment most ensemble forecasts
concentrate on initial condition error as that is judged to be a
significant source of error. I don't doubt that model error may be just
as significant (if not more so in some cases). However, taking account
of this model error properely is not an easy task.

If you have taken into account all of the sources of error mentioned
above then all outliers or severe events would be picked up. But, I
suppose the downside of taking into account all of this uncertainty is
that your forecast will just show a very large range of possibilities
and you will be left none-the-wiser as to what is actually going to happen!

IMHO, the best improvements in short-medium range forecasts would be
made by investing in a better observation network that would include the
possibility of targetting specific sensitive regions (using aircraft,
dropsondes, etc.) each day to reduce the analysis error. That and an
improved data assimilation system would (I believe) really make a
difference to situations like those experienced over the last few days.

I suppose in the end, where you invest your resources depends on what
timescale you are interested in forecasting...

I'd love to discuss this subject further if anyone has any opinions or
comments...