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Development of a Convective Scale Ensemble Kalman Filter at Environment Canada

Development of a Convective Scale Ensemble Kalman Filter at Environment Canada. Luc Fillion 1 , Kao-Shen Chung 1 , Monique Tanguay 1 Weiguang Chang 2. Meteorological Research Division, Environment Canada Dept of Atmospheric and Oceanic Sciences, McGill University.

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Development of a Convective Scale Ensemble Kalman Filter at Environment Canada

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  1. Development of a Convective Scale Ensemble Kalman Filter at Environment Canada Luc Fillion1,Kao-Shen Chung1, Monique Tanguay1 Weiguang Chang2 • Meteorological Research Division, Environment Canada • Dept of Atmospheric and Oceanic Sciences, McGill University

  2. High Resolution Ensemble Kalman Filter System ( HR-EnKF ) Add random perturbations (model error) Perturbed observations Observation Add random perturbations Data assimilation Ensemble members Initial guess Analysis step GEM-LAM forecast for all the members. Forecast step A: LAM15 B:LAM2p5 C:LAM1 300x300 (MTL region)

  3. Validation of the HR-EnKF system: Single Observation test (Analysis step) Initial guess at 2010 July/22/0000 UTC Ensemble mean: Temperature (degree) Innovation : 1.0 deg : 1 deg from HPfHT : 0.57 deg Given single observation : temperature at grid point (150,150) around 850hPa

  4. Horizontal Correlations of initial perturbations (80 members) Perturbations are: Homogeneous & Isotropic With limited members: Localization is needed Temperature (degree) 850hPa

  5. Analysis step Increment: Xa-Xf from HPfHT : 0.57 : 1 Localization radius (60 km) 0.2479

  6. Flow dependent single observation test Innovation : 1.0 degree : 1 degree Analysis step (single obs) Forecast step ( 30-min ) Analysis step (single obs) Temperature analysis increment

  7. The performance of ensemble predictions The forecasting error at mesoscale Current set up 1. Initial perturbations: U, V, T, HU, TG and P0 2. Do not consider the model errors 3. No perturbations in hydrometeor variables 4. Cycling hydrometeor variables

  8. QB ( cloud mixing ratio ) QL ( rain mixing ratio ) QN ( snow mixing ratio ) QI ( ice mixing ratio ) QJ ( graupel mixing ratio ) QH ( hail mixing ratio ) Microphysical scheme: Milbrandt and Yau (double moment scheme)

  9. Canada/U.S. Radar Reflectivity Case Study: 2010 July 22 0130 UTC 0030 UTC 0330 UTC 0230 UTC

  10. Radar observations (reflectivity) 11μm (observes the temperature of clouds, land and sea surface) GEM-LAM 1-kmPrecipitation GEM-LAM 2.5km

  11. 15-minForecast Error Correlations (800mb) U V precipitation T HU

  12. 30-minForecast Error Correlations (800mb) V U precipitation T HU

  13. Error correlation in vertical(30-min forecast) Sub-24 Single Obs. test Sub-7 Sub-10

  14. 400mb 600mb physics versus dynamics 800mb Physical processes could be as important as dynamics.

  15. Profile of single observation test En_KF T analysis increment Ensemble mean of physicaltemperature tendency

  16. Sub-24 Time step = 2 Time step = 0 Sub-10

  17. Error correlation of TT profile V.S. Vertical correlation of TT tendency ( Ensemble Forecasts) (stochastic perturbation of SCM)

  18. PR Cloud mixing ratio (600mb)

  19. Summary and Discussion of the next steps • The EnKF system has been modified from global to local area • The single observation validation is done • The results from ensemble forecasts (errors) showed strong • flow dependency and revealed the importance of physical • processes over precipitation areas • Ready to assimilate radar observations (radial winds) • The forward model (observation operator) of Doppler wind • 5. Currently, McGill radar group provides us 15-20 cases to study

  20. Comments and Discussions

  21. Summer case: July / 09 / 2010 Summer case: July / 21 / 2010 REF DOP (elv.#4)

  22. Winter case: Dec. / 12 / 2010 Winter case: Feb / 05 / 2011 REF DOP (elv.#4)

  23. Temperature increment vertical cross-section

  24. Features of the system Sequential processing of batches of observations

  25. Sub-ensemble 1 Sub-ensemble 4 Sub-ensemble 2 Sub-ensemble 3 Partitioning the ensemble Ensemble members (80) Gain matrix K1 Gain matrix K2 Gain matrix K3 Gain matrix K

  26. Model configuration: Optimal Nested scheme RegGEM15 forecast 12 UTC 00 UTC 12 UTC 18 UTC Operational model output IC + LBC LAM15 forecast • Archive output : • Control run • Prepare for • EnKF test 30-h run T+30 6-h Spin-up 18 UTC LAM2.5 forecast 12-h run 6-h Spin-up T+12 LAM1 forecast 00 UTC 6h run T+6

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