Cloudnet meeting, A.Tompkins, ECMWF 11

ECMWF cloud scheme: Validation and DirectionAdrian Tompkins

The MP Question: “What have ECMWF ever done for us”?ECMWF’s minor role in Cloudnet: To provide data and await

feedback…?Due to my lack of time, this puts the data in the “slow

feedback loop”

Data

Model parametrization

2. Validation1. Development

Cloudnet meeting, A.Tompkins, ECMWF 22

ValidationExample: Validation of model versus Meteosat Brightness

Temperatures“Expensive” (human resources) validation for a fixed period

But what if (validation) >> (model cycle updates) ?

i.e. When results arrive they refer to “old” cycle

70°S 70°S

60°S60°S

50°S 50°S

40°S40°S

30°S 30°S

20°S20°S

10°S 10°S

0°0°

10°N 10°N

20°N20°N

30°N 30°N

40°N40°N

50°N 50°N

60°N60°N

70°N 70°N

60°W 40°W 20°W 0° 20°E 40°E 60°E

METEOSAT 7 First Infrared Band Thursday 14 October 2004 0600UTC

70°S 70°S

60°S60°S

50°S 50°S

40°S40°S

30°S 30°S

20°S20°S

10°S 10°S

0°0°

10°N 10°N

20°N20°N

30°N 30°N

40°N40°N

50°N 50°N

60°N60°N

70°N 70°N

60°W 40°W 20°W 0° 20°E 40°E 60°E

RTTOV generated METEOSAT 7 First Infrared Band (10 bit)Tuesday 12 October 2004 12UTC ECMWF Forecast t+42 VT:Thursday 14 October 2004 06UTC

Courtesy of F. Chevallier

Cloudnet meeting, A.Tompkins, ECMWF 33

Uses of ARM

ARM data has been used as a validation toolCloud cover, Cloud ice retrievals from radar

(Janiskova) Simulated Z (Morcrette)Surface radiative fluxes and liquid water paths (JJM)

2D-Var assimilation of radar data to test future cloudsat use (Bennedetti and Lopez)

SGP data used to validate new turbulence model (Neggers and Koehler)

Cases studies and “one-offs”, no routine use in model cycle development

Cloudnet meeting, A.Tompkins, ECMWF 44

DevelopmentDevelopment can mean “using the data to derive / develop

/ tune a parametrization”e.g. Tompkins and Di Giuseppe use cloudnet data to tune

and test a new SW cloud overlap parametrization for solar zenith angle effects on cloud geometry

0 20 40 60 80 100SZA (deg)

-0.15

-0.10

-0.05

-0.00

0.05

0.10

0.15T

OA

Re

flect

ion

Err

or

New SchemeHogan (2.1km)RandomMaximumMax-Ran

ECMWF SW albedo error with respect to

a TIPA benchmark calculation using

over 100 cloud scenes taken over

Chilbolton

Cloudnet meeting, A.Tompkins, ECMWF 55

0 20 40 60 80 100SZA (deg)

-0.15

-0.10

-0.05

-0.00

0.05

0.10

0.15T

OA

Re

flect

ion

Err

or

New SchemeHogan (2.1km)RandomMaximumMax-Ran

DevelopmentHogan Length-scale tuned to give correct Cloud Cover

over Chilbolton, then used for 600 Palaiseau scenes as independent “test”

Experience: Data extremely easy to useReprocessing of ARM site data extremely welcome!!!

ECMWF SW albedo error with respect to

a benchmark calculation using

over 600 cloud scenes taken over

Palaiseau

Cloudnet meeting, A.Tompkins, ECMWF 66

Development

Can also mean a validation tool fast and efficient enough to be included in parametrization tests

ECMWF: T799 L91 medium-range “scores”RMS, AC of Z,T,U

Parametrization Group: “climate suite”3 member 13 month atmosphere only T159L91Validation seasons against: MODIS, ISCCP, Quikscat,

SSMI, TRMM, GPCP, Xie-Arkin, Da-Silva, CERES, ERBE

For parameters of: LWP, TCWV, TCC, 10m winds, rainfall, TOA radn fluxes, surface heat fluxes

Cloudnet meeting, A.Tompkins, ECMWF 77

Example ISCCP Total cloud cover :model cycle 29r1operational early

2005

35

65

65

65

65

65

60°S60°S

30°S 30°S

0°0°

30°N 30°N

60°N60°N

135°W

135°W 90°W

90°W 45°W

45°W 0°

0° 45°E

45°E 90°E

90°E 135°E

135°E

Total Cloud Cover e llu Septem ber 2000 nm onth=12 nens=3 Global Mean: 60.5 50N-S Mean: 58.2

[percent]

5

20

35

50

65

80

93.74

35

65

65

65

65

65

65

6565

60°S60°S

30°S 30°S

0°0°

30°N 30°N

60°N60°N

135°W

135°W 90°W

90°W 45°W

45°W 0°

0° 45°E

45°E 90°E

90°E 135°E

135°E

Total Cloud Cover ISCCP September 2000 nmonth=12 50N-S Mean: 62.2

[percent]

13.50

20

35

50

65

80

95

95.54

10

60°S60°S

30°S 30°S

0°0°

30°N 30°N

60°N60°N

135°W

135°W 90°W

90°W 45°W

45°W 0°

0° 45°E

45°E 90°E

90°E 135°E

135°E

Difference ellu - ISCCP 50N-S Mean err -4.04 50N-S rms 11.3[percent]

-60

-50

-40

-30

-20

-10

10

20

30

37.88

80 60 40 20 0 -20 -40 -60 -80

latitude (deg)

0

20

40

60

80

Zonal Meanmodel obs

-180 -120 -60 0 60 120 180

longitude (deg)

55

60

65

70

75

Extra-Tropicsmodel obs

-180 -120 -60 0 60 120 180

longitude (deg)

40

50

60

70

Tropicsm odel obs

Issue: Cloudnet in slower feedback

loop, but independent and comprehensive

validation (also over points) extremely

important

Cloudnet meeting, A.Tompkins, ECMWF 88

Validation and “tuning”

Data “error”

Model parametrization

Fast validation =“tuned metric”

Slow validation =“Independent” source

Cloudnet meeting, A.Tompkins, ECMWF 99

ECMWF Validation needs: Ice!

Information from cloudnet regarding glaciated clouds is useful

e.g. First comparison of ice water content comparison with microwave limb sounder (Frank Li et al.)

Cloudnet meeting, A.Tompkins, ECMWF 1010

ECMWF validation needs: Higher order moments

Information on subgridscale variability of ice, liquid and water vapour is paramount to developments of statistical cloud cover schemes

Much emphasis has been placed on this, and the Cloudnet results will be central to efforts at ECMWF…

Cloudnet meeting, A.Tompkins, ECMWF 1111

ECMWF Directions, Short term

Numerics have been revised to reduce sensitivity to vertical resolution (moving from T511L60 to T799L91 soon)

Ice sedimentation now a pure advection termIce-to-Snow autoconversion added to modelSimple diagnostic parametrization to allow supersaturation

with respect to iceFinal testing for implementation early 2006

Cloudnet meeting, A.Tompkins, ECMWF 1212

ECMWF Directions, Medium term

Prognostic ice mass mixing ratio

Prognostic ice number concentration

Prognostic moments of total water, with cloud cover derived from a statistical cloud scheme

Interaction between aerosols and microphysics (GEMS)

Attention to numerics

0.5 x Dust

-30 -20 -10 0 10 20 30 40 50 60-15

-5

5

15

25

35

45

latit

ude 6

66

6

612

12

12

12

24

24

30 3036 3648 48 54

g m-2

06121824303642485460

1.5 x Dust

-30 -20 -10 0 10 20 30 40 50 60-15

-5

5

15

25

35

45

latit

ude 6

6

6

6

6 1212

12

12

12

24

24

2430 3030

3636

g m-2

06121824303642485460

Difference

-30 -20 -10 0 10 20 30 40 50 60longitude

-15

-5

5

15

25

35

45

latit

ude

-10-8-6-6-2

-2

-200

000

0

0 0

00

0 00

g m-2

-10-8-6-4-20246810

0.01 IN factor

Reduction in ice water path in response to 3x dust aerosols

over Africa