ii: progress of edmf, iii: comparison/validation of convection schemes i: some other stuff

II: Progress of EDMF,III: comparison/validation of

convection schemesI: some other stuff

Sander Tijm (HIRLAM)Contributions of: Siebesma, De Rooy, Lenderink,

De Roode, Sass, Calvo, Ivarsson, Bengtsson, Malardel, Rontu

Hirlam fog problem (2006)

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

Fog improvement

Fog improvement 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

Fog improvement

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

LW-radiation problem

EDMF (TKE)

De Roode, De Rooy, Lenderink, Siebesma

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%:

Development

1. EDMF (ECMWF) (Massflux + K-profile)

2. Moist TKE (KNMI)

3. Merge EDMF + TKE

Problems (RICO case)

1. mean state not too bad, but ….

2. Lots of noise

3. results extremely dependent on parameters

4. Unpredictable

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

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)

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

Results Stable results ! Almost no intermittency.

Good results at least for RICO, Dry CBL + FIRE. More cases to test

RICO

RICO

Dry CBL: ED

Dry CBL: MF

Dry CBL

Dry CBL: T-prof

FIRE

FIRE: ED

FIRE: MF

Developments Test more cases + including transition

cases. Put more ECMWF stuff back ? Make cloud mass flux profile more

flexible ?

Validation and intercomparison of convection schemes

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

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

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)

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

Observations-> F 0.25 0.5 1.0 2.0 4.0 8.0 16.0 32.0 64.0 128.0 >128.0 o 62005 523 607 168 104 63 30 8 1 6 6 r 69250 1160 1225 256 153 85 34 6 2 6 6 e 9647 447 524 128 70 45 11 6 0 0 0 c 6553 562 1084 412 202 83 26 8 0 1 0 a 7449 724 1195 375 195 86 33 3 2 3 0 s 5190 555 1312 677 461 164 44 12 2 1 1 t 4779 746 1801 836 513 199 58 6 2 0 0 3656 426 1310 799 799 432 123 23 1 1 1 2610 411 1386 996 1119 516 149 33 3 0 1 2064 232 887 689 926 696 272 56 5 1 2 1340 153 574 595 1026 982 374 70 7 1 3 1124 95 344 344 649 716 384 122 19 2 1 403 47 144 143 317 537 508 181 18 5 1 387 20 63 71 165 292 347 138 19 3 0 62 3 10 15 36 78 136 119 31 4 0 73 3 14 6 26 40 67 63 20 5 0 6 0 0 0 1 4 4 10 6 1 0 4 1 2 2 0 2 6 8 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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))

Parameter (weight) STRACO KF-RK Objective scores (months, 60%) Precipitation (30%) 52.1 53.5 Clouds (20%) 33.5 37.5 Synops (10%) 92.0 92.4 Synop upper air (10%) 90.9 93.4 Upper air bias (10%) 83.9 85.9 Daily Cycle (10%) - - Special cases (10%) - Small precipitation amounts - Satellite imagery

54.0

-

47.4

- Objective scores (special runs, 40%) Precipitation (30%) 42.8 44.3 Clouds (20%) 34.2 35.4 Synops (10%) 91.6 90.4 Upper air (10%) 92.3 92.8 Other features Sophistication physics +/- + Cost (lower number is more expensive) 100 88 Future improvements + + Code standard + - Documentation +/- +/- EPS ? ?

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

Validation archive Archive stored at ECMWF Open for anyone to use Description at:

http://www.knmi.nl/~tijm/HARMONIE_cases.html

Fair weather cumulus

Example: convection dying inland

Daily cycle of convection

Daily cycle of convection

Convection PBL development

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