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Advances in weather, climate and water forecasting technologies

Advances in weather, climate and water forecasting technologies. Alasdair Hainsworth Bureau of Meteorology March 2011. Overview. Recent improvements in Weather and Climate Modelling: Are we making progress? Challenges in Modelling and Improving Forecasts

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Advances in weather, climate and water forecasting technologies

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  1. Advances in weather, climate and water forecasting technologies Alasdair Hainsworth Bureau of Meteorology March 2011

  2. 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

  3. 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

  4. New Observations – satellite data • Obtain temperatures at various levels and from that derive wind strengths. • Still requires some ground truthing for calibration

  5. 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

  6. Forecast Verification - Melbourne Rainfall Higher skill Day 1 Day 3 Day 5 Day 7 Low skill Acknowledgement: Dr Harvey Stern (BoM) & Benjamin Levin (Monash University)

  7. Future of Climate Forecasting: Dynamical models Climate models are dynamical, being based on the laws of physics – conservation of energy, momentum and mass…

  8. Predictive Oceanic Atmosphere Model for Australia (POAMA) Improvements due to • Increased supercomputing • Improved forecast system (model, physics, initialisation strategy) • Improved observing networks V1 V1.5 V3 (ACCESS) V2 • Process of continual, incremental improvement. Each stage relies upon the last

  9. 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

  10. New Products: GFE Grid Overview Illustrating idea of underlying Grid • 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

  11. 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)

  12. Broad project overview and time-line

  13. New Seasonal Streamflow service

  14. 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.

  15. POAMA Seasonal Forecasts Dynamical outlooks provide both probabilistic and deterministic forecasts

  16. Intraseasonal predictions; a possibility? Dec 2010 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)

  17. Seasonal Forecasts: March to May Dynamical Model Statistical Model RAIN Tmax

  18. 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.

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