MO – Design & Plans
UERRA GA 2016
Peter Jermey
Jemma Davie, Amy Doherty, Breo Gomez, Sana Mahmood,
Adam Maycock, Richard Renshaw
Met Office UERRA reanalyses
• Satellite era (1978 – present)
• Ensemble using
static 4DVAR
• Provides lower
resolution fields with
uncertainty
estimation
• i.e. mean and
spread at 24km
• Production start:
Dec 2015
• Deterministic reanalysis
using hybrid 4DVAR
• Uses ensemble reanalysis
uncertainty to improve
assimilation (B)
• Provides higher resolution
deterministic fields at 12km
• Production start:
3rd quarter 2016
Uncertainties in Ensembles of
Regional Reanalyses
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Ensemble & Deterministic systems coupled
4DVAR minimises weighted sum of differences with background & obs
Weights are dependent on background error covariance matrix (B)
Ensemble uses fixed bg error cov (B=Bc)
Ensemble provides EOTD to ensemble
EDA - "hybrid" 4DVAR - weighted sum of bg error covs (B=bcBc+beBe)
Bc + Be
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Hybrid Data Assimilation
• Operational for global operational forecasts since July 2010
• UERRA – first time used in a LAM context
Ensemble System
• Represent every uncertainty in the system via perturbations
• Uncertainty in
• Observations
• Model
• Boundary Conditions
• Each ensemble member has a different realisation of these
• Set of (input) realisations represent the span of all possible
realisations
• Therefore (output) spread should estimate uncertainty in the
system
Observations
• For every observation we have a measurement and an
uncertainty estimate...
• To obtain several realisations randomly perturb the measurement
within the uncertainty estimate...
Model
• To perturb the model we need an estimate of model error...
• Assuming the analyses are drawn from the same distribution as
the truth.
Boundary Conditions
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*planned
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Variational Satellite Bias
Correction
• VarBC
•Airmass-dependent bias correction of
satellite radiances (based on Harris and
Kelly, 2001)
n
bias = c
scan
+  ci air f(xb )
i=1
•VarBC will give smooth and
automatic updating
•Predictor 850-300hPa thickness
•Allow a month spin-up for each instrument
Thanks to Richard Renshaw
Use of Observations
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Conventional Observations from the ECMWF archive
Satellite Radiances (level 1b) – at least TOVS, ATOVS, AIRS, IASI from ECMWF
Reprocessed (consistent) satwinds – EUMETSAT & CIMSS
Reprocessed (consistent) scatterometer winds – KNMI (Ocean SAF)
Reprocessed (consistent) GPSRO - UCAR
Reprocessed (consistent) Ground based GPS
- Rosa Pacione at Agenzia Spaziale Italiano with Gemma Halloran (MO)
- Reformatted to BUFR (400 European stations)
© Crown copyright Met Office
land surface (SURF)
soil moisture
Extended Kalman Filter
• Use 2m T/RH obs to correct model soil moisture
• Adapted to work for regional models
Breo Gomez
Results from
technical test…
Initial Test Run (no inflation)
T2 & 500H
2. Mean Error < Ctrl Error
3. En Spread = Mean RMSE
4. Model Freq. = Obs. Freq.
T2
500H
1. Each member equally likely
What went wrong?
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Reduce TOA to 40km
Use appropriate error covariances
Use appropriate model perturbations
Tune perturbations
Use regional SURF
Use full set of levels from ERA5
Global Soil
Plus Rand P.
X’
Lower 40km model top
Good:
• Avoid problems specifying background error correlations around stratopause
• Allows more of the CPU to be dedicated to troposphere
Bad:
• Some observations are sensitive to the atmosphere above 40km
Solution:
• Extrapolate model background where needed, using the observations themselves
© Crown copyright Met Office
Impact on HIRS channels
© Crown copyright Met Office
Amy Doherty
Impact on GPSRO
© Crown copyright Met Office
Chris Burrows
Impact on GPSRO
Restrict to
below 30km
Ob error
© Crown copyright Met Office
Chris Burrows
Some
results
• Perturbing BCs
only – not
model or obs
What’s Next ..?
• Improve quality of ensemble
• Ensemble production aim to start soon - dependent on ERA5
• Regional hybrid
• VarBC
• Deterministic production aim to start Q3 2016
Thank you for listening