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DRAFT – Page 1 – January 14, 2016 Development of a Convective Scale Ensemble Kalman Filter at Environment Canada Luc Fillion 1, Kao-Shen Chung 1, Monique.

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Presentation on theme: "DRAFT – Page 1 – January 14, 2016 Development of a Convective Scale Ensemble Kalman Filter at Environment Canada Luc Fillion 1, Kao-Shen Chung 1, Monique."— Presentation transcript:

1 DRAFT – Page 1 – January 14, 2016 Development of a Convective Scale Ensemble Kalman Filter at Environment Canada Luc Fillion 1, Kao-Shen Chung 1, Monique Tanguay 1 Weiguang Chang 2 1.Meteorological Research Division, Environment Canada 2.Dept of Atmospheric and Oceanic Sciences, McGill University

2 DRAFT – Page 2 – January 14, 2016 Initial guess Ensemble members Add random perturbations Data assimilation Observation Perturbed observations GEM-LAM forecast for all the members. Add random perturbations (model error) Analysis step Forecast step High Resolution Ensemble Kalman Filter System ( HR-EnKF ) A: LAM15 B: LAM2p5 C: LAM1 300x300 (MTL region)

3 DRAFT – Page 3 – January 14, 2016 Validation of the HR-EnKF system: Single Observation test (Analysis step) Initial guess at 2010 July/22/0000 UTC Ensemble mean: Temperature (degree) Given single observation : temperature at grid point (150,150) around 850hPa Innovation : 1.0 deg : 1 deg from HP f H T : 0.57 deg

4 DRAFT – Page 4 – January 14, 2016 Horizontal Correlations of initial perturbations (80 members) Temperature (degree) 850hPa With limited members: Localization is needed Perturbations are: Homogeneous & Isotropic

5 DRAFT – Page 5 – January 14, 2016 : 1 from HP f H T : 0.57 Increment: X a -X f Localization radius (60 km) 0.2479 Analysis step

6 DRAFT – Page 6 – January 14, 2016 Flow dependent single observation test Analysis step (single obs) Forecast step ( 30-min ) Analysis step (single obs) Temperature analysis increment Innovation : 1.0 degree : 1 degree

7 DRAFT – Page 7 – January 14, 2016 The performance of ensemble predictions 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 The forecasting error at mesoscale

8 DRAFT – Page 8 – January 14, 2016 Microphysical scheme: Milbrandt and Yau (double moment scheme) QB ( cloud mixing ratio ) QL ( rain mixing ratio ) QN ( snow mixing ratio ) QI ( ice mixing ratio ) QJ ( graupel mixing ratio ) QH ( hail mixing ratio )

9 DRAFT – Page 9 – January 14, 2016 Canada/U.S. Radar Reflectivity 0030 UTC 0130 UTC 0230 UTC 0330 UTC Case Study: 2010 July 22

10 DRAFT – Page 10 – January 14, 2016 GEM-LAM 1-km Precipitation GEM-LAM 2.5km Radar observations (reflectivity) 11μm (observes the temperature of clouds, land and sea surface)

11 DRAFT – Page 11 – January 14, 2016 15-min Forecast Error Correlations (800mb) U V T HU precipitation

12 DRAFT – Page 12 – January 14, 2016 U V T HU precipitation 30-min Forecast Error Correlations (800mb)

13 DRAFT – Page 13 – January 14, 2016 2124 16 11 6710 1 Sub-7 Sub-10 Sub-24 Error correlation in vertical (30-min forecast) Single Obs. test

14 DRAFT – Page 14 – January 14, 2016 400mb 600mb 800mb physics versus dynamics Physical processes could be as important as dynamics.

15 DRAFT – Page 15 – January 14, 2016 Profile of single observation test En_KF T analysis increment Ensemble mean of physical temperature tendency

16 DRAFT – Page 16 – January 14, 2016 Time step = 0 2124 16 11 6710 1 Time step = 2 Sub-24 Sub-10

17 DRAFT – Page 17 – January 14, 2016 Error correlation of TT profile V.S. Vertical correlation of TT tendency ( Ensemble Forecasts) (stochastic perturbation of SCM)

18 DRAFT – Page 18 – January 14, 2016 21 16 11 6 1 Cloud mixing ratio (600mb) PR

19 DRAFT – Page 19 – January 14, 2016 Summary and Discussion of the next steps 1.The EnKF system has been modified from global to local area 2.The single observation validation is done 3.The results from ensemble forecasts (errors) showed strong flow dependency and revealed the importance of physical processes over precipitation areas 4.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 DRAFT – Page 20 – January 14, 2016 Comments and Discussions

21 DRAFT – Page 21 – January 14, 2016 Summer case: July / 09 / 2010 Summer case: July / 21 / 2010 REF DOP (elv.#4)

22 DRAFT – Page 22 – January 14, 2016 Winter case: Feb / 05 / 2011 Winter case: Dec. / 12 / 2010 REF DOP (elv.#4)

23 DRAFT – Page 23 – January 14, 2016 Temperature increment vertical cross-section

24 DRAFT – Page 24 – January 14, 2016 Features of the system Sequential processing of batches of observations

25 DRAFT – Page 25 – January 14, 2016 Sub-ensemble 1 Sub-ensemble 2 Sub-ensemble 3 Ensemble members (80) Sub-ensemble 4 K1 Gain matrix K1 K2 Gain matrix K2 K3 Gain matrix K3 K Gain matrix K Partitioning the ensemble

26 DRAFT – Page 26 – January 14, 2016 RegGEM15 forecast LAM15 forecast LAM2.5 forecast 12 UTC 00 UTC 12 UTC 18 UTC Model configuration: Optimal Nested scheme Operational model output T+30 30-h run IC + LBC 18 UTC 6-h Spin-up 12-h run 6-h Spin-up 00 UTC LAM1 forecast 6h run T+12 T+6 Archive output : 1.Control run 2.Prepare for EnKF test

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