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NWP SAF Scatterometer Monitoring

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Use in Numerical Weather Prediction (NWP)

Scatterometer use in NWP at
Environment Canada

Last updated May 2014

Back to use in NWP table

Physical characteristics
Modelling system: Global Environmental Multiscale (GEM) model (Côté et al. 1998)
Global Deterministic Prediction System (GDPS)
  • Grid-point model (1025 E-W x 800 N-S)
  • Horizontal resolution: 25 km at 49° latitude
  • Vertical resolution: 80 levels, hybrid-eta coordinate, lid at 0.1 hPa
  • 10-day forecasts at 00 and 12 Z
Global Ensemble Prediction System (GEPS)
  • Grid-point model (600 E-W x 300 N-S) (Gagnon et al. 2013a, Gagnon et al. 2013b)
  • Horizontal resolution: 66 km at equator
  • Vertical resolution: 40 levels, hybrid-eta coordinate, lid at 2 hPa
  • 16-day, 20-member ensemble forecasts at 00 and 12 Z (32-day on Thursday at 00 Z)
Regional Deterministic Prediction System (RDPS)
  • Grid-point model (996 E-W x 1028 N-S), grid centred on North America
  • Horizontal resolution: 10 km
  • Vertical resolution: 80 levels, hybrid-eta coordinate, lid at 0.1 hPa
  • 2-day forecasts at 00, 06, 12 and 18 Z
Regional Ensemble Prediction System (REPS)
  • Grid-point model (horizontal resolution 15 km, region similar to RDPS) (Erfani A. 2013)
  • Vertical resolution: 48 levels, hybrid-eta coordinate, lid at 10 hPa
  • 21-member forecast ensemble, piloted from the top by GEPS
Data assimilation method
Global Deterministic Prediction System
  • 4D-Var, 6-hr data assimilation window, increment resolution T180 (~100 km) (Charron et al. 2012)
  • Analysis times (T): 00, 06, 12, 18 Z
  • Time window: T ± 3 hrs, divided into twenty 18-min intervals
  • Time constraints (data cut-off time and model runtime):
    • Early analysis and forecast run: T+3 hrs, forecast at 00 and 12Z only.
    • Update analysis run: T+9 hrs at 00 Z, T+8:15 hrs at 12 Z, T+6 hrs at 06 and 18 Z
Global Ensemble Prediction System
  • EnKF, 192 members partitioned into 4 sub-ensembles, stochastic and sequential algorithm, 6-hr data assimilation window (Houtekamer et al. 2014)
  • Analysis times (T): 00, 06, 12, 18 Z
  • Horizontal resolution: 66 km at equator
  • Vertical resolution: 74 levels, hybrid-eta coordinate, lid at 2 hPa
  • Time window: T ± 3 hrs, divided into four 90-min intervals for linear interpolation
Regional Deterministic Prediction System
  • 4D-Var, 6-hr data assimilation window, increment resolution ~100 km (Tanguay et al. 2012)
  • Analysis times (T): 00, 06, 12, 18 Z
  • Time window: T ± 3 hrs, divided into twenty four 15-min intervals
  • Time constraints (data cut-off time and model runtime):
    • Early analysis and forecast run:T+2 hrs, forecast at 00, 06, 12 and 18Z.
    • Update analysis run: T+7 hrs at 00, 06, 12 and 18Z.
Regional Ensemble Prediction System
  • Currently assimilation is performed by GEPS
Scatterometers assimilated
WindSat (if assimilated) should also be included here
Scat name Product Models assimilated
ASCAT-A/B

OSI-SAF Level 2 BUFR 25-km equivalent-neutral wind product produced by KNMI.

GDPS, GEPS, RDPS
 
Monitoring
External monitoring web pages
Generic Quality Control
Blacklisting
  • All wind speeds outside range 4-30 m/s
Ambiguity removal
  • Performed a-priori by using solution identified as most probable by the data provider (OSI-SAF/KNMI).
Bias correction
  • 0.2 m/s is subtracted from the retrieved wind speeds (see table below).
Thinning
  • 25-km retrievals thinned to 100 km by selecting observation closest to centre of 100 km x 100 km grid boxes.
Background check
  • Background check applied in all prediction systems. Observation flagged as suspicious when ratio of squared innovation against sum of background and observation error variances is greater than or equal to 8 but smaller than 14, observation is rejected if the ratio is equal to or greater than 14.
  • Variational QC applied in deterministic systems following Gauthier et al. (2003). Applied starting at 6th iteration of the minimization. Observation weight is reduced or observation is discarded if still departing significantly from the updated analyzed state.
Specific Quality Control
Global model
Scat name ASCAT-A/B
Operational since 31 March 2009 (Metop-A), 23 April 2014 (Metop-B)
Observation error std. dev. (U/V) 1.7 m/s
Wind speed range 4-30 m/s *
Bias corrected? Equivalent-neutral wind vectors derived at KNMI using CMOD5.n GMF are transformed into real wind vectors by subtracting 0.2 m/s from retrieved wind speeds. The correction corresponds to the global average difference between equivalent-neutral and real, e.g. stability-dependent, wind speeds.
Crosstrack cells used All nodes 1-21 and 22-42
QC thresholds Rely on flags determined by data provider (see documentation at http://www.knmi.nl/deliverables/scatterometer/publications/)
* The 4 m/s low wind speed threshold is applied "in-house" to eliminate observations characterized by weak winds while the upper 30 m/s threshold follows QC measures applied by the data provider which flags observations above 30 m/s as less reliable.
 
Observation Operator
Lowest model level
  • Height of lowest model level: ~ 40-50 m
Interpolation
  • Horizontal interpolation of 10-m real wind components calculated on the model grid using Monin-Obukhov formalism with the stability functions of Delage and Girard (1992) for unstable surface layers and Delage (1997) for stable surface layers.
Analysis increments
  • Associated wind analysis increments calculated on a 10-m analysis level from tangent linear/adjoint of the horizontal interpolation operator. Increments at other levels and on other state variables determined through the background error covariance matrix.
History of Changes
The list includes the main scatterometer or model changes implemented operationally in the Environment Canada models.
None  
 
References

Charron, M., and Co-authors, 2012: The stratospheric extension of the Canadian global deterministic medium range weather forecasting system and its impact on tropospheric forecasts. Mon. Wea. Rev., 140, 1924-1944.
Côté, J., S. Gravel, A. Méthot, A. Patoine, M. Roch, and A. Staniforth, 1998: The operational CMC-MRB Global Environmental Multiscale (GEM) model: Part I - Design considerations and formulation. Mon. Wea. Rev., 126, 1373-1395.
Delage, Y. and C. Girard, 1992: Stability functions correct at the free convection limit and consistent for both the surface and Ekman layers. Bound.-Layer Meteor., 58, 19-31.
Delage, Y., 1997: Parameterising sub-grid scale vertical transport in atmospheric models under statically stable conditions. Bound.-Layer Meteor., 82, 23-48.
Erfani, A. ., and Co-authors, 2013: The New Regional Ensemble prediction System at 15 km horizontal grid spacing (REPS 2.0.1) Canadian Meteorological Centre Technical Note. [Available on request from Environment Canada, Centre Météorologique Canadien, division du développement, 2121 route Transcanadienne, 4e étage, Dorval, Québec, H9P1J3 or via the following web site: http://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/lib/technote_reps201_20131204_e.pdf
Gagnon, N., and Co-authors, 2013a: Improvements to the Global Ensemble Prediction System from version 2.0.3 to version 3.0.0. Canadian Meteorological Centre Technical Note. [Available on request from Environment Canada, Centre Météorologique Canadien, division du développement, 2121 route Transcanadienne, 4e étage, Dorval, Québec, H9P1J3 or via the following web site: http://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/lib/op_systems/doc_opchanges/technote_geps300_20130213_e.pdf
Gagnon, N., and Co-authors, 2013b: Improvements to the Global Ensemble Prediction System (GEPS) from version 3.0.0 to version 3.1.0. Canadian Meteorological Centre Technical Note. [Available on request from Environment Canada, Centre Mééorologique Canadien, division du développement, 2121 route Transcanadienne, 4e étage, Dorval, Québec, H9P1J3 or via the following web site: http://collaboration.cmc.ec.gc.ca/cmc/CMOI/product_guide/docs/changes_e.html#20131127_geps_3.1.0
Gauthier, P., C. Chouinard and B. Brasnett, 2003: Quality Control: Methodology and Applications. In Data Assimilation for the Earth System. R. Swinbank, V. Shutyaev and W. A. Lahoz editors, Kluwer Academic Publishers, 388 pp.
Houtekamer, P. L., X. Deng, H. L. Mitchell, S.-J. Baek and N. Gagnon, 2014: Higher resolution in an operational ensemble Kalman filter, Mon. Wea. Rev., 142, 1143-1162.
Tanguay, M., and Co-authors, 2012: Four-Dimensional Variational Data Assimilation for the Canadian Regional Deterministic Prediction System. Mon. Wea. Rev., 140, 5, 1517-1538.