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RO assimilation at the Meteorological Service of Canada

RO assimilation at the Meteorological Service of Canada. Josep M. Aparicio Godelieve Deblonde Aug 23/24, 2005. Overview. We have analyzed the properties of the RO data, its coherence vs MSC models, and explored the impact of a future operational assimilation

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RO assimilation at the Meteorological Service of Canada

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  1. RO assimilation at theMeteorological Service of Canada Josep M. Aparicio Godelieve Deblonde Aug 23/24, 2005

  2. Overview • We have analyzed the properties of the RO data, its coherence vs MSC models, and explored the impact of a future operational assimilation • Some assimilation cycles have been performed • Systematic biases present (data & model) • Consistently positive impact will depend on a good choice of bias description

  3. Comparison models • 2 models have been compared • GEM (Global Environmental Multiscale) • Standard version • Currently operational, now in 4DVar mode • 400x200x28 grid • Low lid (10 hPa) • Stratospheric extension • Undergoing tests • 400x200x80 grid • High lid (0.1 hPa) • Mesoscale extension • Undergoing tests • 800x600x58 grid • Low lid (10 hPa) • CMAM (Canadian Middle Atmosphere Model) • Research model • 96x48x72 grid • Very high lid (0.000575 hPa) • Total of 4 versions

  4. Objectives • Comparison models are different • Cover some range of horiz/vert resolutions • Different vertical coordinates • Terrain-following η • Hybrid • terrain-following (low altitude) • non-following (high altitude) • Several approaches for physical parameterizations • Study of statistical coherence of RO across models • Testing of the observation operator with realistic data and atmosphere properties against subtle effects & coding errors • Example of issues identified • Ellipsoidal vs spherical latitude • Geoid offset • Gravity dependence with height • Compressibility of WV • Non-gaussian statistics • Properties of the new info fed during assimilation

  5. Observation theoretical accuracy • The obs error is smaller than Obs-Model scatter. • 0.2-0.5% strato • 1% low tropo • The rest is model error: • Obs useful to assimilate Image by E.R.Kursinski

  6. Obs-Forecast (6h) I Low lid models: GEM-operational & GEM-mesoscale FRAC: fraction of profiles covering a given height

  7. Obs-Forecast (6h) II High lid models: GEM-stratospheric & CMAM (mid-atmosphere) FRAC: fraction of profiles covering a given height

  8. Obs-Forecast (6h) III • Each model has a characteristic bias signature • O-P seems to be dominated by model resolution (not data accuracy!) • Refractivity ok. No benefit from bending angle. • Mesoscale version seems the less biased & more accurate a-priori • Some common features (data biases?) • Negative low tropo bias • Negative tropopause bias • Negative low strato bias

  9. Obs-Forecast (6h) IV Against GEM mesoscale

  10. Obs-Forecast (6h) V Against GEM mesoscale

  11. Some thoughts • Low tropo bias appears in wide class of models • Two smaller biased areas around tropopause & low strato • Bias and STD seem related with WV

  12. Dependences • O-F distribution is not symmetric: • Positively skewed at very low saturation • Negatively skewed at very large saturation • Skewness concentrated at low tropo

  13. Skewness bias • In a skewed distribution • The mean is usually not at the point of maximum probability (=mode) • The assimilation process works by maximizing the probability of the analysis field (estimates the mode)

  14. Skewness bias estimation • If we assume: • underlying distribution gaussian • WV above saturation not observable • Mean-Mode offset

  15. CMC tests on Assimilation of ROVariational impact • 2 Runs of 3DVar: Control and RO • Control = current operational • Experiment performed over 1 month (Jan 2004). • Here: typical impact of 1 cycle of 6h (with RO obs wrt without RO obs) • Notably: • consistence in T increments in independent obs in near areas. • Larger corrections in southern hemisphere • Most corrections in ocean & unpopulated areas. • Obs in populated areas agree Example: Increments of Moisture at 1000hPa and Temperature at 500hPa wrt operational 3DVar, due to occultation data.

  16. Correlation anomaly I Control: Blue RO: Red

  17. Correlation anomaly II Control: Blue RO: Red

  18. Time Series Forecast Consistency: 48h FCST vs later Analysis • Higher Short FCST consistency: • Less bias • Less STD Control: Blue RO: Red

  19. Current status at MSC • Extensive O-P analysis against 4 models • Each model presents characteristic signatures ~0.5% • Common features: • Negative O-P bias in low tropo • Negative O-P bias above tropopause (sp. tropics) • Large O-P STD in tropical tropo • Small O-P STD around tropopause • Small O-P STD in polar strato • Data impact maximization: • Identify data bias • Optimized a-priori STD (situation-dependent STD) • Assimilated N • O-P still too big to benefit from bending angle • More obvious positive impacts observed • Generalized positive impacts require finer tuning • Large analysis of O-P statistics performed • WV saturation seems to produce some artifacts. Skewness bias seems to be present.

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