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Bureau of Meteorology Activities as a GPC of Long Range Forecasts Dr David Jones Australian Bureau of Meteorology CBS Expert Team on.

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Presentation on theme: "Bureau of Meteorology Activities as a GPC of Long Range Forecasts Dr David Jones Australian Bureau of Meteorology CBS Expert Team on."— Presentation transcript:

1 Bureau of Meteorology Activities as a GPC of Long Range Forecasts Dr David Jones Australian Bureau of Meteorology CBS Expert Team on Extended and Long Range Forecasting Geneva, Switzerland, March 2012 Acknowledgement Oscar Alves, Andrew Watkins, Lynette Bettio, Elise Chandler & Andrew Charles

2 Long Range Forecast Service Current seasonal outlooks for Australia based on a statistical model Probability of rainfall/temperature in Tercile/Above Below Median categories Trial of dynamical model forecasts. Statistical monitoring and prediction of Intraseasonal Variability Current phase and amplitude of the MJO Prediction for winds, rainfall, convection, pressure for the coming weeks Dynamical model predictions for ocean conditions & experimentally for climate variables over land Focus on the Pacific and Indian Oceans Developing direct model forecasts for rainfall and temperature Delivering useful long range predictions for Australia and WMO member countries

3 Recent POAMA 2.4 improvements POAMA2.4 became fully operational in October 2011 T47L17 + improved physics (land surface, radiation, gravity wave drag, cloud microphysics, etc) Land surface scheme (ALI) is more realistic and initialized daily Increased number of ensemble members of hind-cast (30 member) over the last 30 years (updated in real-time), providing better hindcast skill estimates Real-time forecasts (30 ensemble members per month) since July 2011 run twice monthly. Moving to a 1981 to 2010 base period Improved accessibility – OpenDAP server Pseudo multimodel ensemble

4 POAMA-1.5POAMA-2 Model T47L17 Bureau Atmos + GFDL MOM2 T47L17 (about 250km) Bureau Atmos + GFDL MOM2 Ocean data assimilation OI (Univariate Smith Optimum Interpolation) Temperature PEODAS (Multivariate pseudo-Ensemble Kalman Filter) Temperature + Salinity Ensemble generation 10 members Time-lagged atmos. ensemble No ocean perturbations 30 members Multi-model (3 versions) – two convection schemes plus bias correction No time-lagged ensemble Ocean perturbations from PEODAS No atmosphere perturbations in seasonal version POAMA: Further Details

5 POAMA=Predictive Ocean Atmosphere Model for Australia Forecasts run for 9 months Atmospheric model: Horizontal resolution ~250km 17 vertical levels Ocean model: Zonal resolution ~220 km Meridional resolution ~55km (tropics) to ~165 km (poles) 25 vertical levels The Coupled Model: Predictive Ocean Atmosphere Model for Australia POAMA BoM Atmospheric Model (BAM) v3 Australian Community Ocean Model (ACOM) v2 Simple land-surface model = + +

6 P2.4 M2.4 Enhanced ensemble generation M2.4 Enhanced ensemble generation Plans for POAMA/ACCESS versions Done Operational P2.4 Full Seasonal System M2.4 multi-week system in operations Operational M2.4 Seamless Multi-week/Full Seasonal System P2.5 Coupled DA/ensemble generation P2.5 Coupled DA/ensemble generation P3.0 ACCESS Based higher resolution system P3.0 ACCESS Based higher resolution system Future ? Operational P2.5 multi-week system in operations (may bipass) Operational P2.5 Seamless multiweek/seasonal Operational P3.0 multi-week system Operational P3.0 Seamless multiweek/seasonal ~1-3 months ~6-9 months ~6-12months ~2 years ~4 years M2.4 Seamless Multi-week/First Seasonal only System in operations

7 Operational Products: SSTs, NINO 3,3.4,4 & IOD available Public Website

8 Pacific SST skill: Temporal correlation of monthly SSTA POAMA 1.5 & 2 NCEP Climate forecast system V1 & 2 Frontier Research Centre Model (Japan) ECMWF System 3 Forecast Lead time (months) Correlation POAMA-2 POAMA-1.5

9 POAMA skill - SST Correlation with Reynolds SST How well does POAMA do with SSTs? Highest correlation is found in the equatorial Pacific…..

10 POAMA skill - Rainfall Correlation with (CMAP) Rainfall How well does POAMA do with the rainfall patterns? Highest correlation is found in the equatorial Pacific and parts of the southwest Pacific…..

11 POAMA-1.5 POAMA-2 Predicting the MJO Index All seasons:Skillful prediction of the MJO out to….

12 POAMA skill to predict rainfall tercile (LT=0) Tercile forecasts show 40-70% hit rates Rainfall Skill in Pacific

13 Producing Reliable Forecasts: Calibration Raw rainfall forecasts (lower Tercile) across the tropical Pacific Calibrated (IOV) forecasts – better reliability but lower skill.

14 Architecture for Seasonal Forecast Generation and Publication System

15 The Bureau as a GPC Hindcasts verified following the LRFVS Real time forecasts

16 Developing a GPC Seasonal Prediction Portal The Seasonal Prediction Portal provides access to outlooks for Broad scale fields Climate drivers (ENSO) Rainfall and temperature tercile probabilities for selected sites Hindcast skill scores for all outlooks Focus on the Pacific

17 Capacity Building Extensive training of the Pacific NMS personnel during in-country visits PASAP/PI-CPP joint workshops – Auckland, New Zealand (Sept 2010) and Port Vila, Vanuatu (Sept 2011)

18 The Bureau as a GPC The BoM is a producer of LRFs, following a fixed time schedule Products are available to other GPCs, RCCs and NMHSs Forecasts provided to the LRF Lead Centre and APCC Hindcasts have been verified following the Standardized Verification System Systems are scientifically documented The Bureau does not yet have a GPC website The BoM is heavily involved in training and capacity building activities in the South Pacific: Pacific Islands Climate Prediction Project Pacific Adaptation Strategy Assistance Program (delivering dynamical season outlooks for Pacific Island countries) Pacific Climate Change Science Program Opportunity for additional supported projects Developing forecasts for extreme events – TCs, Coral Bleaching, high sea level


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