Slide 1

Report
Towards Coupled data assimilation in an
intra/seasonal forecast system
Oscar Alves
CAWCR (Centre for Australian Weather and Climate Research)
Australian Bureau of Meteorology
Contributors and Collaborators: Patricia Okely, Yonghong Yin,
Debbie Hudson, Peter Oke, Terry O’Kane
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
Outline
1. Current data assimilation and ensemble
generation strategies
2. What coupled covariances may look like
3. New coupled ensemble generation for multiweek prediction
4. Path towards fully coupled assimilation
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
POAMA-2 Ocean Data Assimilation
PEODAS: POAMA Ensemble Ocean Data Assimilation
System (Yin et al 2010)
Perturb
forcing + noise
Ocean Model
Ocean
Observations
•
•
•
ASSIM
ASSIM
Ensemble OI (Oke et al 2005)
Covariances from ensemble
spread (3D multivariate-time
evolving)
Poor-persons EnKF: only assimilate into central member
Provides an ensemble of initial ocean states (11 ensembles, but 100
member lagged used for covariance calculation)
Assimilates in situ ocean temperature and salinity.
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
Example of Ensemble Spread
(Estimate of analysis error)
Temperature
Salinity
From Yin et al 2010
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
Coverting PEODAS to Fully Coupled
Assimilation
Coupled Model
ASSIM
Ocean
Observations
+ atmos anals
•
•
•
•
ASSIM
Ensemble OI (Oke et al 2005)
Covariances from ensemble
spread (3D multivariate-time
evolving)
Assimilate ocean obs and atmos re-analyses
Cross-covariances between ocean and atmos (&ice & land)
This will be done with the next version of our model based on UKMO
UM coupled to MOM4
What are going to be the issues ?
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
What might coupled co-variances look like
Case study:
90 member
ensemble forecast
from Dec 1996
Estimate covariances
from ensemble (e.g.
after two months)
Patricia Okely and Li Shi
Coupled Covariances
Ref.: Temp. 100m
Colour: Temp.
Cont.: Zon. Current
1. Covariances consistent with
intra-seasonal activity
2. Non-local covariances (real or
not, desirable or not)
Ref.: Temp. 100m
Colour: SST
3. Large vertical extent (not
shown)
Vect.: Surf. Wind
Ref.: Temp. 100m
Colour: SST
4. Time/space covariance aliasing
– should we represent this
(past event that triggered
independent event)
Cont.: OLR
Patricia Okely
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
POAMA-2 Seasonal and Multi-week systems
1. PEODAS is the bases of ocean data
assimilation and ensemble perturbations in our
POAMA-2 seasonal prediction system
2. Not suitable for multi-week predictions as no
atmospheric perturbations.
3. Atmospheric initial conditions are taken from a
atmospheric integration nudged to ERA-40
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
Coupled Assimilation Step 1: Coupled
ensemble generation scheme
Coupled Model integrations
Central unperturbed
analyses: PEODAS and ALI
Bred vectors are rescaled
and centred to the central
analyses
1 day
Generates coupled bred perturbations of both the
atmosphere and ocean based on the breeding method
Rescaling – zonal surface wind spread similar to NCEPERA
(Yonghong Yin)
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
MJO Variability
Case study:
CEI Coupled Analysis
30 days to 1st Mar 1997
EQ, 150E
Ensemble
Error
Variability
Coupled Covariances
Ref.: Surf. Temp.
Ref.: Surf. Temp.
Ref.: Surf. Zonal Wind
Colour: Surf. Zonal Wind
Colour: OLR
Colour: Surf. Temp.
Conceptual example of real non-local
covariances
• Suppose you have an MJO error (eg. Speed error or structure error)
•Some time later (e.g. 10 days)– there will be non local
covariances due to different processes but triggered by the same
earlier event
Rossby
KW
MJO error
over Brazil
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
Summary
• Development of coupled breeding scheme for intraseasonal forecasts is
first step towards coupled data assimilation
•Co-variance structures capture ~large scale intra-seasonal dynamics
•Practical issues:
• non local covarariances – real or not
•
Localisation, especially in the vertical
• Ocean and atmosphere on different grids (different time scales)
Future
•Step 2: Semi coupled (PEODAS ocean, nudge atmos in coupled model)
•Step 3: Fully coupled
The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology

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