- Link:
- http://hdl.handle.net/10204/4285
- Collection:
-
- Subjects
- Atmospheric modelling Time scales Weather forecasts El Niño oscillations La Niña oscillations CSIR Conference 2010
- Creators:
- Park, R Landman, WA Lötter, D Engelbrecht, F Bopape, M
- Publisher
- CSIR
- Type
- Presentation
- Language
- en
- Description
- Daily weather forecasts are skilful and are
initial-value problems, while at the seasonal time scale, slowly
varying surface forcing, for example from the tropical Pacific
Ocean, can induce atmospheric anomalies that are sufficiently
long-lasting and predictable. While the chaotic nature of the
atmosphere imposes a finite limit to the predictability of weather
to less than two weeks, El Niño and La Niña oscillations and their
impact on atmospheric anomalies could be predictable several months
in advance. There is a growing trend internationally towards the
development of operational prediction systems that will bridge the
gap between initial-value problem-based weather forecasts and
boundary-value problem-based seasonal forecasts. One of the ways to
bridge this gap is through the use of common forecast systems to
predict for multiple time scales. The time scales considered here
are from short-range forecasts of a few days ahead through to the
longer-range forecasting of seasonal anomalies several months
ahead. The geographical area considered is southern Africa.
Skillfully predicting rainfall and temperatures and their extremes
over various time scales have societal and economic benefits for
the region, and knowing well ahead of time whether or not a coming
summer season may be subjected to El Niño-related droughts can
assist in risk aversion planning. The Atmospheric Modelling
Strategic Initiative (AMSI) of CSIR Natural Resources and the
Environment was established in 2009. This initiative is tasked with
the development of dynamic simulation and forecast systems that are
based on the principles that govern the evolution of the atmosphere
and the ocean, and the interactions between the two. These systems
are required to provide skillful operational weather to seasonal
forecasts, and produce multi-decadal climate change projections.
This paper focuses on the shorter time-range from days to seasons.
The conformal-cubic atmospheric model (CCAM) is an atmospheric
global circulation model (AGCM) that can operate at quasi-uniform
horizontal resolution or alternatively in stretched-grid
variable-resolution mode, to provide high-resolution forecasts over
an area of interest. The CCAM code has been formulated with
computational efficiency in mind, and may be integrated using
parallel processing capabilities on computer clusters. This model
is currently running on the multi-processor machines of AMSI, and
provides high-resolution (~15 km) weather forecasts 7 days ahead,
and coarse-resolution (~200 km) longer-range forecasts up to 5
months ahead. The weather forecasts provide an estimate of
impending weather extremes such as cold snaps and flash floods
associated with deep convection. The CCAM’s seasonal forecasts are
also combined with seasonal forecasts produced locally and abroad,
but model combination lies outside the scope of this paper, which
focuses on the CCAM model only. Forecasts should be produced
probabilistically in order to provide users with the required
forecast uncertainties. The paper introduces the CCAM as a
forecasting system administered by AMSI and discusses forecast
skill estimates.
- Description
- CSIR 3rd Biennial Conference 2010. Science Real and
Relevant. CSIR International Convention Centre, Pretoria, South
Africa, 30 August – 01 September 2010
- Access:
- Instructions in case access is denied
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