Use in the Met Office model


Physical characteristics

Global Model (coupled atmosphere-ocean)

  • GC4 global coupled atmosphere-land-ocean-seaice
  • GA8 atmospheric configuration
    • Grid-point model: 2560×1920 grid points
    • Horizontal resolution: Forecast N1280 (~10 km in mid-latitudes)
    • Vertical resolution: 70 vertical levels, hybrid-eta configuration, lid ~80 km
    • Forecast range: out to T+168 (7 days)
  • GL9 land configuration
  • GO6 ocean (with GSI8 sea ice) configuration at ORCA025 tri-polar grid 1/4 deg resolution

UKV Model

  • Grid-point model: 622×810 grid points (inner), 950×1025 grid (total)
  • Horizontal resolution: variable stretching from ~1.5 km (outer domain) to ~4 km (inner)
  • Vertical resolution: 70 vertical levels, lid ~40 km (different levels from global)
  • Forecasts to T+120 at 03/15 UTC, T+54 or T+12 otherwise

Data assimilation method

Global Model

  • Type: Hybrid incremental 4D-Var, Errors of the day (EOTD) information provided by global ensemble (N640 ensemble differences reconfigured to modes at N320) every 6 hours. EOTD now uses the lagging-and-shifting technique with a one lag and two shifts configuration – see Fig 12 of Lorenc (2017)
  • Resolution: N320 L70 (~40 km, PF timestep = 10 mins)
  • Analysis times (T): 00, 06, 12, 18 Z
  • Time window: 6 hrs, T ± 3 hr
  • Time constraints (model runtime):
  • Main forecast run: 20 min before time window ends
  • Update run: 3 hr 15 min after time window ends

UKV Model

  • Type: Incremental 4D-Var (except for radar rainrate)
  • Resolution: ~4.5 km fixed resolution
  • Analysis times (T): 00, 01, 02,..,21,22,23 Z
  • Time window: 1 hour (centred on nominal analysis time i.e. 0830-0930 for 09 UTC run).
  • Time constraints (model runtime):
  • T+45 mins from analysis time (e.g. 0945 for 09 UTC run)
  • T+80 mins from analysis time for 11 UTC and 23 UTC

Scatterometers assimilated

Scat name Product Models assimilated
ASCAT-B OSI-SAF Level 2 BUFR 25-km wind product produced by KNMI (Global and EARS stream). Global
OSI-SAF Level 2 BUFR Coastal 12.5-km wind product produced by KNMI (Global and EARS stream). UKV
ASCAT-C OSI-SAF Level 2 BUFR 25-km wind product produced by KNMI (Global and EARS stream). Global
OSI-SAF Level 2 BUFR Coastal 12.5-km wind product produced by KNMI (Global and EARS stream). UKV

Generic Quality Control


  • All wind speeds outside range 2-25 m/s
  • All observations made over ice according to the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA)
  • All winds in the Southern Ocean below 72S
  • All observations with SST less than 273.15 K according to OSTIA

Ambiguity removal

  • No prior ambiguity removal
  • Scatterometer cost function contains a term for each ambiguous wind so no dealiasing is required prior to assimilation. Essentially the ambiguity removal is performed within the analysis and so uses other independent observations and the latest model state to choose the correct wind (Candy, 2001).

Bias correction

Wind speed bias correction based on the mean scatterometer and model wind speed (Cotton, 2018)


In the global model observations are thinned separately for each instrument.

  • Thinning distance 80-km in global model
  • Chosen wind is that closest to centre of grid box
  • Max 1 wind per instrument in each thinning box

In the UKV ASCAT observations are thinned together

  • Thinning distance 46-km in UKV model
  • Chosen wind is that closest to centre of grid box
  • Thinning priority set so EARS data are used to fill in the gaps where no global data received in time.

Background check

  • Background check: none
  • Variational quality control applied to scatterometer cost function during assimilation. Initially the a-priori probability of gross error is set to zero then, after about half the iterations in the analysis, this is set non-zero which activates the quality control.

Specific Quality Control

Scat name ASCAT-B/C
Operational since April 2013 (Metop-B) Dec-2020 (Metop-C)
Observation error U/V 2.0 m/s
Wind speed range 2-25 m/s
Bias corrected? Mean Wind Speed correction
Crosstrack cells used All Nodes
QC thresholds Check supplied wind vector QC flag

*see Keogh and Offiler (2006)

Observation Operator

Lowest model level

The UM uses a terrain following height coordinate with the wind (and density) levels vertically staggered from the potential temperature (theta) levels. Whilst the Global and UKV models both have 70 levels, the UKV uses a different set designed to give most benefit to short-range forecasts, i.e. a lower model lid allowing greater resolution in the boundary layer and troposphere. The heights of the lowest horizontal wind levels over sea (zero orography) are

  • Global: 10 m
  • UKV: lowest wind level is 2.5m, second lowest is 13.33 m


The UM surface exchange scheme assumes that Monin-Obukhov similarity theory is valid for the surface layer, with surface variables interpolated to standard observation heights.

  • Scatterometer winds are assimilated as 10m neutral winds (Cotton, 2018)
  • Model wind fields are horizontally interpolated to the observation location using standard linear interpolation of the surrounding 4 grid points. In the global model, if any of the 4 grid points are coast then the model value at the nearest grid point is used instead.


Beljaars, A. C. M. and Holtslag, A. A. M., 1991: Flux parameterisation over land surfaces for atmospheric models. J. Appl. Meteor., 30, 327-341.
Candy, B., 2001: The Assimilation of Ambiguous Scatterometer Winds Using a Variational Technique: Method and Forecast Impact. Met Office Forecasting Research Technical Report 349.
Cotton, J., 2018: Update on surface wind activities at the Met Office. Proceedings of the 14th International Winds Workshop, Jeju, South Korea, 23 April – 27 April, 2018.
Davies, T., M.J.P. Cullen, A.J. Malcolm, M.H. Mawson, A. Staniforth, A.A. White and N. Wood, 2005: A new dynamical core for the Met Office’s global and regional modelling of the atmosphere Q. J. R. Met. Soc., 131, Pages 1759-1782.
Dyer, A. J., 1974: A review of flux-profile relationships. Bound. Layer Meteor., 7, 363-372.
Keogh, S.J. and D. Offiler, 2006: ERS-2 scatterometer – reintroduction into the Met Office global model. Met Office Forecasting Research Technical Report 473.
Lorenc, A.C. (2017), Improving ensemble covariances in hybrid variational data assimilation without increasing ensemble size. Q.J.R. Meteorol. Soc., 143: 1062-1072.