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 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

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 * * *planned

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 850-300hPa thickness Allow a month spin-up for each instrument Thanks to Richard Renshaw

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

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

Results from technical test…

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

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.

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

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

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

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

Some results Perturbing BCs only – not model or obs

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

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