SMS scnews item created by Alex Casella at Sun 30 Apr 2017 2131
Type: Seminar
Distribution: World
Expiry: 30 Jul 2017
Calendar1: 1 May 2017 1700-1800
CalLoc1: Carslaw 535A
CalTitle1: MaPSS: Mathematical Postgraduate Seminar Series
Auth: casella@pa49-180-148-128.pa.nsw.optusnet.com.au (acas5565) in SMS-WASM

# MaPSS: Mathematical Postgraduate Seminar Series: Brent Giggins (Sydney University) -- How to Predict the Weather - An introduction to Chaos, Data Assimilation and Ensemble Forecasting

Dear All,

We are delighted to present the MaPSS Seminar topic of Monday 01/05; please see the
abstract below.

**This Semester the Seminar will always run on Monday, at 5:00pm in 535A**

Following the talk, there will be pizza on offer.

Speaker: Brent Giggins (Sydney University)

Title: How to Predict the Weather - An introduction to Chaos, Data Assimilation and
Ensemble Forecasting

Abstract: In 1961, Edward Lorenz discovered that the atmosphere has a finite limit of
predictability, even if we have a perfect model of the atmosphere and the initial
conditions are known almost perfectly.  This was a catalyst for the fields of numerical
weather prediction and chaos theory, which is the study of dynamical systems that
exhibit sensitive dependence to small perturbations in the initial conditions - often
referred to in popular culture as the "butterfly effect".  In this talk, we will examine
what it means for a dynamical system to be "chaotic" and look at ways to characterise
chaotic behaviour both globally and locally.  We will look at this through the context
of weather and climate forecasting - the main example of chaotic behaviour in natural
systems - and summarise the basic components needed for numerical weather prediction.
In particular, we will examine the topics of Data Assimilation and Ensemble Forecasting
in generating optimal initial conditions for a weather or climate model and consider the
practical problems that arise.  Finally, we will look multi-scale dynamical systems and
illustrate the challenges of forecasting over multiple time and length scales.