Ken: Sub-seasonal forecasting doesn't have a
specific definition but typically we
think of it as the time period beyond
where a standard weather forecast
reaches and then out to the season just
like are our three month seasons would
be so it's an intermediate time scale
and it's about weeks 3 through maybe
eight to twelve.
Jeremy: Forecasting the sub-
seasonal timeframe is difficult because
precipitation and temperature forecast
that we get from the weather service
trend toward climatology, so basically
what we end up with is looking at just
historical patterns and trying to assess
how that that's going to impact the
initial state of the snowpack and in
various other variables that we have.
Robert: The challenge is that, it's a gap in our
sort of predictability if you want to
have a seamless suite of predictability
we can do weather forecast fairly
consistently at short timescales going
out to a week or two and then we hit
this gap and then we can start doing
seasonal forecast.
Roger: Right now we deal a
lot with trying to get a sense of what
we call multi-model ensembles and what
that means in English is basically: many
models running at once with different
starting points and we tend to average
over those. Jeremy: So that the the sub seasonal
Jeremy: So that the sub-seasonal
forecasting then becomes challenging
because you essentially have to try to
merge or extend your your physical
models beyond what the predictive
capability are for the precipitation in
temperature
Robert: The limitations of these current
methods is in terms of this skill and
reliability being able to provide
information that is actually useful in
terms of making decisions on the week's
three through six is very hard
Roger: What we need to do is understand the
dynamics behind those a lot better and
so right now the efforts behind seasonal
to sub-seasonal forecasting are actually
a hodgepodge of methods and the effort
that we're talking about in this context
is to be more systematic and
understanding.
Ken: we're looking for improvements to sub
seasonal forecasts in maybe two senses
one we're trying to improve and extend
the methods we have. There's a lot of
frameworks that are relied on and so to
the extent that we can move better
information through those frameworks
that's exactly what we want to do so
that we're not creating a whole new
system but rather enhancing existing
systems that can involve better inputs
better ways to post-process the
forecasts and then communicate those
results.
Roger: It's really linking the various bits of
understanding of weather systems on the
longer-term seasonal forecasts and
mapping and matching our understanding
on both of those timescales.
Jeremy: The benefits
to the public are that we can hopefully
maximize the beneficial uses of the
reservoirs and the projects and for
water managers what this means is that
decisions can hopefully be made in a
more timely manner
Ken: It's an area where we're hoping to not
only get better products but also raise
awareness that we can do more to advance
this type of forecast and get better
skill and show people that there is a
lot of benefit to a variety of folks and
and hopefully this challenge will bring
people together and help realize
that our collective interest is actually
pretty great.
Ken: We know there's a lot of work
going on in this field of forecasting
sub-seasonal forecasting and we want
to reach out to the solver communities,
want to reach out to the general public,
but we also want to reach out to anyone
else who's working on this, whether
they're eligible or not for some of our
cash prizes and so this competition is
open to anyone who wants to submit and
we're really hoping that it'll be in
addition to a competition for cash
prizes venue to showcase folks'
techniques and their forecasts and if
they have something great, it will show and
hopefully that will be something that
people are interested in.
To learn more go to: www.usbr.gov/research/challenges