1. II: Progress of EDMF, III: comparison/validation of convection schemes I: some other stuff Sander Tijm (HIRLAM) Contributions of: Siebesma, De Rooy, Lenderink, De Roode, Sass, Calvo, Ivarsson, Bengtsson, Malardel, Rontu

2. Hirlam fog problem (2006)

3. Fog improvement Fog problem over the sea Too much fog and too low temperature Especially in spring and summer Runaway effect of cloud top cooling -> more cloud water -> larger emissivity -> stronger cloud top cooling Until in equilibrium with surface flux

4. Fog improvement

5. Improvement of behaviour through (Bent Hansen Sass): Reduction of cloud water due to raining out of fog layer (fall speed of cloud droplets) Reduction of cooling through adjustment of thin cloud layer emissivity Less cloud top cooling and cloud water formation Less fog and not so cold

6. Fog improvement

7. LW-radiation problem With new surface scheme (quicker reaction to radiation) LW-radiation very important for winter conditions Clear sky, cold and dry LW-down too low Surface LW-up too large Too rapid cooling of surface

8. LW-radiation problem

9. EDMF (TKE) De Roode, De Rooy, Lenderink, Siebesma

10. h (km) x(km) 0 5 1 Use LES to derive updraft model in clear boundary layer. 0 Updraft at height z composed of those grid points that contain the highest p% of the vertical velocities: p=1%,3%,5%:

11. Development 1. EDMF (ECMWF) (Massflux + K- profile) 2. Moist TKE (KNMI) 3. Merge EDMF + TKE

12. Problems (RICO case) 1. mean state not too bad, but …. 2.Lots of noise 3. results extremely dependent on parameters 4.Unpredictable

13. Adjustment of EC-EDMF Strip ECMWF EDMF to basics 1. Some recoding + clean-up 2. Get rid off ECMWF tricks (prescribed entrainment, turn off diffusivity in cloud, etc) 3. Use other closure of MF

14. Modifications in massflux 1. Dry parcel: Reduce initial updraft velocity (reduces mass flux contribution at surface) 2. Moist parcel: Replace massflux profile, by linear profile subcloud + Rooy/Siebesma in cloud Moist parcel entrains 10-20% less than dry one (reduces intermittency)

15. TKE modifications Add dissipation massflux as source of TKE Do correction of length scale formulation TKE for transport massflux dry parcel. Do correction of length scale formulation in case of no shear. Add small backgroud diffussion to avoid instability in solver. Apply simple cloud fraction formulation

16. Results Stable results ! Almost no intermittency. Good results at least for RICO, Dry CBL + FIRE. More cases to test

17. RICO

19. Dry CBL: ED

20. Dry CBL: MF

21. Dry CBL

22. Dry CBL: T-prof

23. FIRE

24. FIRE: ED

25. FIRE: MF

26. Developments Test more cases + including transition cases. Put more ECMWF stuff back ? Make cloud mass flux profile more flexible ?

27. Validation and intercomparison of convection schemes

28. HIRLAM: intercomparison Two convection schemes in HIRLAM Been developed next to each other Development resources necessary for other tasks (mesoscale) Release of Hirlam reference system 7.2 Intercomparison during summer 2007 to choose between schemes

29. Intercomparison: setup 8 months in 4 different seasons Two meteorologically different months per season (e.g. July and August 2006) Special setups (0.05 degrees, 4D-Var, new surface scheme) Initial conditions from ECMWF analysis Surface analysis

30. Objective verification Precipitation (30%), Clouds (20%), Synoptic (20%), Upper air (10%), Special features (10%), Daily Cycle (10%) 8 months: 60% Special cases: 40% Other features (sophistication physics, documentation, coding standards, future improvements: independent experts)

31. Objective verification Use contingency tables for precipitation (threshold) and cloud cover (correct bin) Calculate BIAS, PC, FAR, POFD, ETS, HKS, ORSS Translate scores to 0-100 scale, e.g.: 100*1/BIAS if BIAS > 1; 100*BIAS if BIAS < 1

33. Objective verification Use RMS and bias for synoptic scores Best scheme gets 100 for certain score Second scheme gets 100*RMS(best)/RMS(worst) Bias: 100*(1-bias/RMS(worst))

35. Validation of convection schemes Developments in EDMF important for pbl state, transition to deep convection Compiling dataset to validate shallow convection in mesoscale model output Some deep convection cases included also Observations include: Cabauw tower, Radiosonde, MSG, GPS IWV, 10-min syn obs NL, Radar, PBL from ceilometers

36. Validation archive Archive stored at ECMWF Open for anyone to use Description at: http://www.knmi.nl/~tijm/HARMONIE_cases.html http://www.knmi.nl/~tijm/HARMONIE_cases.html

38. Fair weather cumulus

39. Example: convection dying inland

40. Daily cycle of convection

42. Convection PBL development

43. Outlook In addition to shallow convection validation of deep convection: Strength and depth Physics dynamics interaction Impact of parameterisation on resolved deep convection Impact of initial and boundary conditions Convection over sea (subtle) Combination of standard observations over NL ideal for validation work