Advances in weather, climate and water forecasting technologies

Alasdair HainsworthBureau of Meteorology

March 2011

Overview

• Recent improvements in Weather and Climate Modelling: Are we making progress?

• Challenges in Modelling and Improving Forecasts

• New Forecast Products and Services

• Future Plans in Modelling and Services

Progress in Weather Forecasting

Improvements due to

• Increased supercomputing

• Improved forecast system (model, physics, initialisation strategy)

• New observing networks – principally satellite instrumentation

• Incremental change over long periods• Rarely a radical leap in skill• Each model builds upon the last and brings an improvement over time

New Observations – satellite data

• Obtain temperatures at various levels and from that derive wind strengths.

• Still requires some ground truthing for calibration

Forecast Verification: Maximum temperature

Four day forecast now as good today as “tomorrow's” forecast was around 20 years ago

Seven day forecast is now as accurate as “tomorrow's” forecast was around 43 years ago

Forecast Verification - Melbourne Rainfall

% variance explained by Melbourne Rainfall f/cShowing that the skill displayed (variance explained by the QPFs) increased (at all lead times) between 2005-2007 and 2008-2011 & that even some (low level) of skill was displayed beyond 168 hours (Day-7)

0

10

20

30

40

50

60

70

0 24 48 72 96 120 144 168 192 216 240 264 288 312

Lead time (hours)

% v

aria

nce

exp

lain

ed

% variance explained (Aug2005-Aug 2007) - pre-GFE Melbourne CBD QPFs

% variance explained (Nov 2008-Jan2010) - GFE Melbourne CBD QPFs

Linear (% variance explained (Aug2005-Aug 2007) - pre-GFE Melbourne CBD QPFs)

Linear (% variance explained (Nov 2008-Jan2010) - GFE Melbourne CBD QPFs)

Higher skill

Low skill

Day 1

Day 3

Day 5

Day 7

Acknowledgement: Dr Harvey Stern (BoM) & Benjamin Levin (Monash University)

Future of Climate Forecasting: Dynamical models

Climate models are dynamical, being based on the laws of physics – conservation of energy, momentum and mass…

Predictive Oceanic Atmosphere Model for Australia (POAMA)

V1 V1.5 V2 V3 (ACCESS)

Improvements due to

•Increased supercomputing

•Improved forecast system (model, physics, initialisation strategy)

•Improved observing networks

• Process of continual, incremental improvement. Each stage relies upon the last

Dynamical models in 2010

La Niña

• The potential for a La Niña event in 2010 recognised during Autumn.• Possibilities described in the Bureau ENSO Wrap product

New Products: GFE Grid Overview

• Grid underlies the spatial image forecasters look at

• 3/6km resolution

• Current day out to 7.5 days

• Guidance interpolated

• Forecasters define and manipulate gridded fields of sensible weather data

• Worded forecasts are generated automatically from the forecaster-edited grids

Grids at high resolution in Victoria

Illustrating idea of underlying Grid

NexGenFWS for the User

• Point-and-click 7 Day Forecasts for 650 (total number when completed) locations Australia wide

• New forecast presentation graphically via the Forecast Explorer• The 7 day forecasts will be a combination of text and graphics to help

users plan their week around the weather• High resolution forecasts for anywhere in Australia (3 - 6 km resolution)

Broad project overview and time-line

New Seasonal Streamflow service

Future Services: Probabalistic forecasting

• Provides indication of areas likely to exceed certain thresholds – in this case, winds >20 knots in 6 days time.

• Not deterministic as per current forecasts, but highlighting where there may be areas of concern.

POAMA Seasonal Forecasts

Dynamical outlooks provide both probabilistic and deterministic forecasts

Intraseasonal predictions; a possibility?

Dynamical models and ensembles (running one model a number of times and altering initial conditions slightly) – our conduit to filling the gap between 10-day and seasonal forecasts (not realistically possible with statistical models)

Dec 2010

Seasonal Forecasts: March to May

RAIN

Tmax

Statistical Model Dynamical Model

Summary: The Future…

• Continual incremental improvements in forecasts

• GFE rolled out into Vic, NSW. Other states/NT to follow

• The emerging power of dynamical models is increasingly apparent – particularly using probabilistic techniques

• POAMA3/ACCESS will build upon progress with POAMA1.5/2

• There are opportunities to fill the intraseasonal space

• Stakeholder survey’s will guide how information is presented to end users.