Caio A. S. Coelho, S. Pezzulli, M. Balmaseda (), F. J. Doblas-Reyes () and D. B. Stephenson Forecast calibration and combination: A simple Bayesian approach.

Slides:

Advertisements

Similar presentations

Caio A. S. Coelho Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) Instituto Nacional de Pesquisas Espaciais (INPE) CPTEC-IRI.

Advertisements

WCRP OSC 2011: Strategies for improving seasonal prediction © ECMWF Strategies for improving seasonal prediction Tim Stockdale, Franco Molteni, Magdalena.

© Crown copyright Met Office The Met Office high resolution seasonal prediction system Anca Brookshaw – Monthly to Decadal Variability and Prediction,

Caio A. S. Coelho Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) Instituto Nacional de Pesquisas Espaciais (INPE) 1 st EUROBRISA.

Mark Cane Lamont-Doherty Earth Observatory of Columbia University El Niño and the Southern Oscillation (ENSO): An Introduction.

Bayesian methods for combining climate forecasts (*): Department of Meteorology, The University of Reading 1.Introduction 2.Conditioning and Bayes’ theorem.

Creating probability forecasts of binary events from ensemble predictions and prior information - A comparison of methods Cristina Primo Institute Pierre.

Assessment of CFSv2 hindcast (seasonal mean) CPC/NCEP/NOAA Jan 2011.

THE IMPACT OF DIFFERENT SEA-SURFACE TEMPERATURE PREDICTION SCENARIOS ON SOUTHERN AFRICAN SEASONAL CLIMATE FORECAST SKILL Willem A. Landman Asmerom Beraki.

1 The EUROBRISA Project David Stephenson University of Exeter 3 rd EUROBRISA workshop, Barcelona, December 2010.

1 The EUROBRISA Project David Stephenson University of Exeter 2 nd EUROBRISA workshop, Dartmoor, UK, July 2009.

1 Visualising Seasonal Climate Forecasts Rachel Lowe - - EUROBRISA workshop - 17 Mar 2008 In collaboration with David Stephenson (University.

Statistics, data, and deterministic models NRCSE.

Consolidated Seasonal Rainfall Guidance for Global Tropics, October 2014 Initial Conditions Issued 14 October 2014 Forecast Background – ENSO update –

Consolidated Seasonal Rainfall Guidance for Africa, July 2014 Initial Conditions Issued 14 July 2014 Forecast Background – ENSO update – Current State.

© Crown copyright Met Office Andrew Colman presentation to EuroBrisa Workshop July Met Office combined statistical and dynamical forecasts for.

Caio A. S. Coelho Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) Instituto Nacional de Pesquisas Espaciais (INPE) 3.

Caio A. S. Coelho Supervisors: D. B. Stephenson, F. J. Doblas-Reyes (*) Thanks to CAG, S. Pezzulli and M. Balmaseda.

Historical trends and multi-model ensemble forecasting of extreme events Dr. Caio A. S. Coelho University of Reading, U.K.

Caio A. S. Coelho Department of Meteorology University of Reading Met Office, Exeter (U.K.), 20 February 2006 PLAN OF TALK.

1 The EUROBRISA Project David Stephenson University of Exeter 1 st EUROBRISA workshop, Paraty, Brazil March 2008.

Dr Mark Cresswell Dynamical Forecasting 2 69EG6517 – Impacts & Models of Climate Change.

Recent developments in seasonal forecasting at French NMS Michel Déqué Météo-France, Toulouse.

Intraseasonal TC prediction in the southern hemisphere Matthew Wheeler and John McBride Centre for Australia Weather and Climate Research A partnership.

Assessing Predictability of Seasonal Precipitation for May-June-July in Kazakhstan Tony Barnston, IRI, New York, US.

The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Summary/Future Re-anal.

The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Red - ECMWF Green - Sintex Navy - POAMA-2.0.

EUROBRISA Workshop – Beyond seasonal forecastingBarcelona, 14 December 2010 INSTITUT CATALÀ DE CIÈNCIES DEL CLIMA Beyond seasonal forecasting F. J. Doblas-Reyes,

FORECAST SST TROP. PACIFIC (multi-models, dynamical and statistical) TROP. ATL, INDIAN (statistical) EXTRATROPICAL (damped persistence)

CCA MADE EASY. The Science of Seasonal Climate Forecasting is all about connecting forcing and response. Once a forcing mechanism is identified the response.

Water Year Outlook. Long Range Weather Forecast Use a combination of long term predictors –Phase of Pacific Decadal Oscillation (PDO) –Phase of Atlantic.

The South American Monsoon System: Recent Evolution and Current Status Update prepared by Climate Prediction Center / NCEP 1 October 2012 For more information,

EUROBRISA WORKSHOP, Paraty March 2008, ECMWF System 3 1 The ECMWF Seasonal Forecast System-3 Magdalena A. Balmaseda Franco Molteni,Tim Stockdale.

Caio A. S. Coelho, D. B. Stephenson, F. J. Doblas-Reyes (*) and M. Balmaseda (*) Department of Meteorology, University of Reading and ECMWF (*)

Seasonal forecasting from DEMETER to ENSEMBLES21 July 2009 Seasonal Forecasting From DEMETER to ENSEMBLES Francisco J. Doblas-Reyes ECMWF.

Verification of IRI Forecasts Tony Barnston and Shuhua Li.

Caio A. S. Coelho Is a group of forecasts better than one? Department of Meteorology, University of Reading Introduction Combination in meteorology Bayesian.

Caio A. S. Coelho Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) Instituto Nacional de Pesquisas Espaciais (INPE) 2.

Franco Molteni, Frederic Vitart, Tim Stockdale,

Course Evaluation Closes June 8th.

Crop yield predictions using seasonal climate forecasts Simone M. S. Costa and Caio A. S. Coelho Instituto Nacional de Pesquisas Espaciais – INPE, São.

The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Oscar Alves and the POAMA Team CAWCR (Centre.

© Crown copyright Met Office Standard Verification System for Long-range Forecasts (SVSLRF) Richard Graham, Met Office Hadley Centre. With acknowledgements.

The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Verification and Metrics (CAWCR)

Two extra components in the Brier Score Decomposition David B. Stephenson, Caio A. S. Coelho (now at CPTEC), Ian.T. Jolliffe University of Reading, U.K.

The South American Monsoon System: Recent Evolution and Current Status Update prepared by Climate Prediction Center / NCEP 6 February 2012 For more information,

The South American Monsoon System: Recent Evolution and Current Status Update prepared by Climate Prediction Center / NCEP 7 November 2011 For more information,

Caio A. S. Coelho Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) Instituto Nacional de Pesquisas Espaciais (INPE) 10 th International.

The South American Monsoon System: Recent Evolution and Current Status Update prepared by Climate Prediction Center / NCEP 18 November 2012 For more information,

The South American Monsoon System: Recent Evolution and Current Status Update prepared by Climate Prediction Center / NCEP 18 May 2015 For more information,

The South American Monsoon System: Recent Evolution and Current Status Update prepared by Climate Prediction Center / NCEP 21 November 2011 For more information,

Caio A. S. Coelho, S. Pezzulli, M. Balmaseda (*), F. J. Doblas-Reyes (*) and D. B. Stephenson Bayesian combination of ENSO forecasts Department of Meteorology,

Fifth Session of the South Asian Climate Outlook Forum (SASCOF-5) JMA Seasonal Prediction of South Asian Climate for Summer 2014 Hitoshi Sato Climate Prediction.

The South American Monsoon System: Recent Evolution and Current Status Update prepared by Climate Prediction Center / NCEP 4 June 2012 For more information,

1/39 Seasonal Prediction of Asian Monsoon: Predictability Issues and Limitations Arun Kumar Climate Prediction Center

The South American Monsoon System: Recent Evolution and Current Status Update prepared by Climate Prediction Center / NCEP 2 November 2015 For more information,

JMA Seasonal Prediction of South Asian Climate for OND 2017

IRI Multi-model Probability Forecasts

Course Evaluation Now online You should have gotten an with link.

Course Evaluation Now online You should have gotten an with link.

GPC CPTEC: Seasonal forecast activities update

Preliminary Consensus Forecast for the 2017 NE Monsoon Season

El Nino-Southern Oscillation

Course Evaluation Now online You should have gotten an with link.

Forecast Assimilation: A Unified Framework for the

2013 Macau Rainy Season Forecast

Seasonal prediction of South Asian summer monsoon 2010: Met Office

Seasonal Prediction Activities at the South African Weather Service

Improved skill of ENSO coupled model probability forecasts by Bayesian combination with empirical forecasts Caio A. S. Coelho, S. Pezzulli, M. Balmaseda.

ECMWF Seasonal Forecast Group Meeting: 24 July 2002

Presentation transcript:

Caio A. S. Coelho, S. Pezzulli, M. Balmaseda (*), F. J. Doblas-Reyes (*) and D. B. Stephenson Forecast calibration and combination: A simple Bayesian approach Department of Meteorology, University of Reading and ECMWF (*) Aim: To create reliable probability forecasts from ensembles of predictions

Empirical forecasts of Niño-3.4 Well-calibrated: Most observations in the 95% prediction interval (P.I.) 95% P.I.

ECMWF coupled model ensemble forecasts  Observations not within the 95% prediction interval!  Coupled model forecasts need calibration m=9 DEMETER: 5-month lead

Conceptual framework Data Assimilation “Forecast Assimilation”

Prior: Univariate X and Y Posterior: Likelihood: Bayes’ theorem:

Modelling the likelihood p(X|Y) y

Combined forecasts  Note: most observations within the 95% prediction interval!

All forecasts ForecastMAE (  C) Skill Score (%) Uncert. (  C) Climatolog y Empirical Coupled Combined Skill Score = [1- MAE/MAE(climatology)]*100% Empirical Coupled Combined

Prior: Likelihood: Posterior: Multivariate X and Y bias Matrices

Equatorial Pacific SST anomalies ForecastBrier Score (B) BSS (%) Climatology0.250 Multi-model FA SST anomalies: Y (°C) Forecast probabilities: p DEMETER: 7 coupled models 6-month lead Climatology: BSS = [1- B/B(clim.)]*100%

South American DJF rainfall anomalies Obs Multi-model Bayesian combined (mm/day) DEMETER: 7 coupled models 1-month lead Start: Nov -> DJF ENSO composites: El Ni ñ o 82/83 86/87 87/88 90/91 91/92 92/93 93/94 94/95 97/98 La Ni ñ a 83/84 84/85 88/89 95/96 98/99 99/00 00/01 r=0.58 r=0.94 r=0.39r=0.79

Conclusions Forecast Assimilation: improves the skill of probability forecasts can be extended to deal with multi-model spatial forecasts improves the skill of seasonal precipitation forecasts in South America Three publications available at