1. MO – Design & Plans UERRA GA 2016 Peter Jermey
Jemma Davie, Amy Doherty, Breo Gomez, Sana Mahmood, Adam Maycock, Richard Renshaw

2. 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

3. Uncertainties in Ensembles of Regional Reanalyses
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
H 4
Y D
B V
R A
I R D 4 D V A R 4 D V A R 4 D V A R UM UM UM UM

4. Hybrid Data Assimilation
Operational for global operational forecasts since July 2010
UERRA – first time used in a LAM context

5. 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

6. Observations
For every observation we have a measurement and an uncertainty estimate...
To obtain several realisations randomly perturb the measurement within the uncertainty estimate...

7. Model To perturb the model we need an estimate of model error...
Assuming the analyses are drawn from the same distribution as the truth.

8. Boundary Conditions * *
*planned

9. Variational Satellite Bias Correction
VarBC
Airmass-dependent bias correction of satellite radiances (based on Harris and Kelly, 2001)
VarBC will give smooth and automatic updating
Predictor hPa thickness
Allow a month spin-up for each instrument
Thanks to Richard Renshaw

10. Use of Observations 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)
Reprocessed data means a consistent processing algorithm - we don’t have to deal with sudden changes in processing, nor differences between processing for different stations.
© Crown copyright Met Office

11. land surface (SURF) soil moisture Extended Kalman Filter
Use 2m T/RH obs to correct model soil moisture
Adapted to work for regional models
EKF currently used only for global NWP. SMC is then interpolated once a day for regional models.
We don’t have UM global SMC going back many years and so are running regional EKF in preference to using a climatology.
Breo Gomez

12. Results from technical test…

13. Initial Test Run (no inflation) T2 & 500H
1. Each member equally likely
2. Mean Error < Ctrl Error
3. En Spread = Mean RMSE
4. Model Freq. = Obs. Freq.
500H T2

14. What went wrong? X’ X’ Reduce TOA to 40km
Use appropriate error covariances
Use appropriate model perturbations
Tune perturbations
Use regional SURF
Use full set of levels from ERA5 X’ X’
Global Soil
Plus Rand P.

15. 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
Global has 80km top
© Crown copyright Met Office

16. Impact on HIRS channels
These are rms simulated radiance differences for the HIRS instrument
calculated from extrapolated profiles compared to the truth.
Truth is a sample set of 80 global profiles on RTTOV levels.
It shows us which channels we should avoid using at all
- those where sensitivity to the atmosphere above 40km
is comparable or larger than observation error.
Amy Doherty
© Crown copyright Met Office

17. Impact on GPSRO Chris Burrows
GPS radio occultation - we assimilate bending angles as measured
between pairs of satellites.
These are differences between simulated bending angle calculated from
80km-top ‘truth’ and 40km-top extrapolated profiles.
For obs above 30km, magnitude of difference becomes comparable with specified observation error.
Chris Burrows
© Crown copyright Met Office

18. Impact on GPSRO Restrict to below 30km Ob error Chris Burrows
GPS radio occultation - we assimilate bending angles as measured
between pairs of satellites.
These are differences between simulated bending angle calculated from
80km-top ‘truth’ and 40km-top extrapolated profiles.
For obs above 30km, magnitude of difference becomes comparable with specified observation error.
Chris Burrows
© Crown copyright Met Office

19. Some results
Perturbing BCs only – not model or obs

20. Improve quality of ensemble
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

21. Thank you for listening