TITLE:

Exploring stochastic and multi-scale modeling approaches for a seamless prediction system

DATE:

Friday, February 23rd, 2018

TIME:

3:30 PM

LOCATION:

GMCS-314

SPEAKER:

Dr. Aneesh Subramanian, Scripps Institution of Oceanography, University of California – San Diego.

ABSTRACT:

Stochastic schemes to represent model uncertainty in the European Centre for
Medium-Range Weather Forecasts (ECMWF) ensemble prediction system has helped
improve its probabilistic forecast skill over the past decade by both improving
its reliability and reducing the ensemble mean error. The largest uncertainties
in the model arise from the model physics parameterizations. In the tropics,
the parameterization of moist convection presents a major challenge for the
accurate prediction of weather and climate. Super-parameterisation is a
promising recent alternative strategy for including the effects of moist
convection through explicit turbulent fluxes calculated from a cloud-resolving
model (CRM) embedded within a global climate model (GCM). These two approaches
(stochastic and super-parameterization) in convection parameterization have
emerged as new paths forward and complement the conventional approaches rather
than replace them. We study the impact of these two approaches and a
combination of the two on forecasts from weather to sub-seasonal and climate
timescales. Results from the evaluation of model forecast skill and fidelity
in the Tropics and for organized convective systems such as the MJO will be
presented. We show that the combination of the two approaches helps improve
the reliability of forecasts of certain tropical phenomena, especially in
regions that are affected by deep convective systems. This has implications
for improving conventional convection parameterization using hybrid approaches
for probabilistic earth system forecasting as we await the exascale computing
systems of the future to resolve convective processes in climate models.

HOST:

Dr. Jose Castillo

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