The Centre for Australian Weather and Climate ResearchA partnership between CSIRO and the Bureau of Meteorology

Coupled Breeding for Ensemble Multiweek Prediction

Harry Hendon, Patricia Okely, Debra Hudson, Yonghong Yin, Oscar Alves, Griff Young, Andrew Marshall

plus others

www.cawcr.gov.au

Outline• Motivation for coupled breeding approach

A step toward extending seasonal system to multiweek

lagged ensemble is underdispersive

need a consistent set of atmos/ocean/land perturbations

On the path to coupled ensemble-based assimilation

• Examples of forecast benefits for multiweek leads

• Some (limited) analysis of statistics of bred perturbations

focus on first 10 days comparing bred/lagged

primarily focused on atmos perturbations (not discounting importance of coupling)

2

Developing an intraseasonal forecasting capability from seasonal system

POAMA 1.5b (ABOM1)2007-2011

AGCM T47-L17 OGCM 2-0.5

Basic ocean data assimilation (T only) in offline OGCM

atmos/land initialization (ALI); strongly nudge AGCM to ERA in AMIP run

hindcasts 10 member lagged ensemble (successive 6 hour earlier;

0.25-2.5 days)

realtime: once per day

POAMA M24 (ABOM2) 2013-

Ensemble-based ocean data assimilation; T and S (PEODAS)

ALI atmos/land initialization

Coupled breeding to generate atmos-ocean perturbations

Multi-model (3 versions)

33 member burst ensemble every 5 days (twice per week operationally)

Some useful intraseasonal forecasts

Lagged ensemble cumbersome

Inconsistent realtime/hindcasts

Reliability issues (under-dispersive)

Improved Intraseasonal forecasting capability

Improved reliability and products

Consistency hindcasts/realtime

POAMA: Predictive Ocean Atmosphere Model for Australia

3

POAMA Ensemble Ocean Data Assimilation System Yin et al. 2011

runs in OGCM forced by ERA surface fluxes and SST relaxed strongly to Reynolds OI

Ensemble OI (Oke et al. 2005)

Cross-Covariances from ensemble spread

(3D multi-variate-time evolving)

Assimilation only into central member

ASSIMASSIM

Observations T&S

Compress Ensemble

Nudge to central analysis

Synthetic Perturbed wind forcing

(Alves and Robert 2005)

+ ocean perturbation

Coupled Breeding builds on Ensemble Ocean Assimilation

provides ensemble of ocean states, but not for atmos

Ensemble of OGCM integrations

1 day

4

Coupled Model forecasts

1 dayCentral unperturbed analyses:

PEODAS (ocean) and ALI (atmos)

Coupled Breeding Initialisation System

Member perturbations rescaledseparate norms for ocean and atmosthen centred to the central analyse

s

First Step Towards Coupled Assimilation...

Based on the PEODAS and ALI infrastructures:

Atmos: zonal mean rmsd surface zonal wind=analysis uncertainty (ERA-NCEP)

Ocean: 3-d T/S rmsd = analysis uncertainty (PEODAS)

rescale threshold met ~everyday in midlat atmos; every 4-5 days in tropics and oceans

Output an ensemble of atmos/ocean/land perturbations

5

Coupled breeding

Impact of ensemble generation (and multimodel)

POAMA-1.5

POAMA-2 intraseasonal

Burst ensemble

Time-lagged ensemble

6 hour lagged Atmos IC

Ocean IC

Ensemble spread

NRMSE of ensemble mean

SHEM 500 hPa Geopotential hghts

0 10 20 30d

0 10 20 30d

6

Improved forecast reliability

POAMA-1.5

POAMA-2 (seas)

POAMA-2 (intra)

Weeks 1 and 2

(all forecast start months 1980-2006)

Weeks 3 and 4

Probability of rainfall in upper tercile

All grid points over Australia

7

MJO Forecast skill 1982-2011 (1st each mnth)RMM1 and RMM2

Bred spread

Bred RMSE

ABOM2

Lagged ABOM1

Bivariate RMSE/Spread

Coupled breeding significant improvement over lagged, but still under-dispersed in Tropics

Improvement of ensemble mean over individual members

Courtesy D. Waliser

8

Some analyses of the statistics of the perturbations

Compare lagged to bred, plus a sensitivity exp using jumbled

Jumbled: make a new set of perturbations by randomly sampling the bred perturbations from all other years

should elucidate day-to-day “flow dependence”

•How flow dependent are the bred perturbations? (highly)

•Are “flow of the day” any better than jumbled? (not really)

•How coupled are they, or does coupling matter? (can’t fully answer

yet but apparently not important for longer leads)

•How optimal are the bred perturbations? (certainly better than

lagged but still under-dispersed in Tropics)

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Spread and RMSE (9 member ensemble)DJF Southern Hemisphere Z500

Slight benefit of “flow of the day” perturbations

Black = control bredRed = laggedYellow = old lagged: p15bBlue = jumbled bred

spread

rmse

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Spatial correlation of Initial Perturbations (90S-90N)

Perturbations defined wrt to central member

Bred, Jumbled, Lagged (6hourly)

Mean of absolute correlation of perturbations from member 1 with other 9 members

1st Dec, 1st Jan, and 1st Feb 1982-2011

6 hour lagged mean abs(r)=0.55

Bred 0.18

Jumbled 0.14

0.0

0.7

11

Examining flow-of-day sensitivity: U850 spread composites for ENSO

Initial time

After 10 days

U850 spread/anomaly along equator

warm

cold

warm-cold

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Control vs Jumbled results

Control at initialisation

Jumbled at initialisation

After 10 days

Factor 10 smaller scale

Implication: coupled ocean-atmos perturbations not critical for reliable long lead prediction of ENSO; might not be true for short lead prediction of MJO

13

Association of spread with westerly anomalies is general throughout tropics

Correlation U850 spread (from breeding) with obs U850 anomaly at initialisation

high spread goes with westerly anomalies in tropics> convective regimes

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Comparing Bred vs Lagged (6 hourly: 0.25-2.5 days)

Mean variance of perturbations MSLP initial time

DJF 1982-2011Bred

Lagged

varianceZonal mean amplitude

Variance normalized by zonal mean at each lat.

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Bred Lagged

day 4

day 11

16

Bred lagged

day 1

Perturbation Growth rate

day 5

Wavenumber

0 20

)(

)(ln1

)(tICamp

tFampt

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Summary

• Coupled-breeding ensemble generation has led to increased multiweek skill and reliability in POAMA-2 (ABOM2)

But breeding is still underdispersive in Tropics

•Despite simplicity, lagged ICs are far from optimal for multiweek, esp due to slow growth in Tropics

Lagged initial conditions 6hr apart are too similar and not good sample of analysis uncertainty

Infer> need to be 2-3 days apart but pay accuracy penalty for multiweek

•Next steps Further analysis of perturbations (coupling, MJO-dependence, etc)Refine breeding cycle to target increased tropical spreadImplement weakly coupled assimilation (show tomorrow)Fully coupled assimilation (cross covariance ocean-atmos)

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Define mean perturbation amplitude for initial conditions and forecasts over the M ensemble members at each time as

]))()((1[)( 2 tAtAM

sqrttICamp centrali

Assuming exponential growth , define growth rate:

]))()((1[)( 2 tFtFM

sqrttFamp centrali

)(

)(ln1

)(tICamp

tFampt

Decompose ICamp and Famp as functions of zonal wavenumber

perturbation growth rates as function of zonal scale

)exp()()( tICamptFamp

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