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ESA-JAXA Pre-Launch EarthCARE
Science and Validation Workshop
13 – 17 November 2023 | ESA-ESRIN, Frascati (Rome), Italy

Development of a new simulator on COSP2
for vertical doppler velocity of EarthCARE CPR
Yuhi Nakamura*1, Kentaroh Suzuki1, Hiroaki Horie2
1: Atmosphere and Ocean Research Institute, the University of Tokyo
2: National Institute of Information and Communications Technology, Japan

See also Poster #11

1/11

Newer observation by EarthCARE CPR
and satellite simulator on COSP2
size distribution back scatter fall velocity

vertical air motion
radar reflectivity factor
Picture from JAXA webpage (14 Nov. 2022 browsed)
https://www.eorc.jaxa.jp/EARTHCARE/about/hardware_j.html• Higher sensitivity than CloudSat• Doppler velocity observation→ Dynamics of cloud microphysics• Retrieval formulation• What is needed for the new simulator…
→ droplet fall velocity→ vertical air motion• For size distribution and back scatter,use codes already implemented in COSP2.

Contributions to climate models
expected from new doppler velocity observation• Droplet fall velocity was often considered as a tuning parameter.
It affected the performance of models, both GCMs and CRMs.
→ Risk of a physically meaningless setting.
✓ New observation is expected to constraint the fall velocity of microphysics.
• Observations of cumulus mass flux itself were very limited.
✓ Quantitative estimation will be provided by EarthCARE doppler velocity.
→ But CPR attenuation would be a difficult issue…
To maximize these benefits from EarthCARE,
it is necessary to ensure a basis for comparing models with observation.
1. To develop a new simulator for doppler velocity on COSP2.
2. To compare ground-based doppler radar and GCM.
3. Further discussion of an impact for climate from droplet fall velocity
and use of doppler velocity statistics.

2/11

Ground-based radar
1. NICT(National Institute of Information and Communications Technology)

provided by Horie-san (NICT).
In Koganei City, Tokyo. (30km west of Senso-ji temple, Asakusa)

2. MOSAiC(Multidisciplinary drifting Observatory for the Study of Arctic Climate)details: https://mosaic-expedition.org

provided by ARM, ship-borne
drifting on sea ice in Arctic Sea

Both datasets arein Aug. 2020.

GCM setup

3/11MIROC6 (Tatebe et al. 2019; GMD)
• Prognostic precipitation scheme(Michibata et al. 2019; JAMES)

This bulk scheme prognoses mass andnumber of falling hydrometeors: cloud, rain,and snow.

• about 100 km resolution (t85 l40h)• JJA 2020
✓ 5×5 grids centered on Tokyo (NICT)✓ 30W-20E, 75N-88N (MOSAiC)

COSP2• CFMIP Observation Simulator Package• Doppler velocity simulator is developed.
• #subcolumn = 140

Simulator design: prognostic precipitation scheme

4/11

vertical air motion

Numbermx. ratio

droplet fall velocitydiagnosed in dynamics

microphys

microphys

microphys

All variables needed for Doppler velocity simulator
is provided by 2-moment bulk prognostic scheme.

size distribution
microphys

𝑹𝒆𝒇𝒇microphys↑ GCM↓ COSP2

size distribution

Consistent formulation

COSP2

Radar reflectivity
COSP2 (quickbeam)

Doppler velocity

droplet fall velocity
COSP2

5/11

Construction of droplet fall velocity 𝒗𝒇

𝒗𝒇 = viscous drag × formulation• viscous dragThe higher the air density, the greater the viscous drag and the slower the fall speed.

𝝆𝟎 /𝝆: Square root of the ratio of air density 𝜌 to the standard value (𝜌0; 1013 hPa, 0℃)
on/off switchable• formulation
2 types of functions of droplet diameter 𝐷 for 𝑣𝑓 formulation

1. Power law: 𝒂𝑫𝒃
2. PL08: 𝒃𝟏 − 𝒃𝟐 𝐞𝐱𝐩 −𝒃𝟑 𝑫 + 𝒃𝟐 − 𝒃𝟏 𝐞𝐱𝐩 −𝟓𝒃𝟑 𝑫
Posselt and Lohmann (2008; ACP)

ctrllarge-scale condensation

cumulus convection

dropletcloud liq.

cloud ice

rain

snow

graupelcloud liq.

cloud ice

rain

snow

viscous drag×××××××〇〇

PL08〇×〇××〇

×××

Power law; 𝑎-1.107-

3.321

19.3-1.107842

4.84

Power law; 𝑏-0.22

-0.22

0.37

-0.22

0.8

0.25

PL08: 𝒃𝟏 = 𝟗. 𝟔𝟓, 𝒃𝟐 = 𝟏𝟎. 𝟒𝟑, 𝒃𝟑 = 𝟔𝟎𝟎

Comparison between NICT obs. and MIROC6
𝒗𝒅 CFAD

NICT

2020/08
observation

6/11

𝒁𝒆 CFAD𝒁𝒆 -𝒗𝒅 2dPDF

snow

rain

rain

2020 JJAMIROC ctrl

icesnow
snow

rain

rain

• Slower 𝑣𝑑 in MIROC6, especially rain particle• Discrete distribution, ice-snow• Overestimating 𝑍𝑒✓ Often seen bias

2020/08
observation

Comparison between MOSAiC obs. and MIROC6
𝒗𝒅 CFAD𝒁𝒆 CFAD𝒁𝒆 -𝒗𝒅 2dPDF

MOSAiC

snow

snow
rain

rain

2020 JJAMIROC ctrl

ice

icesnowsnow• Slower 𝑣𝑑 , smaller variance in MIROC6.
• Lack of faster falling droplets below 2km• Overestimating 𝑍𝑒• Distribution is quite different.

7/11

𝒗𝒅 CFAD

NICT

2020/08
observation

Tuning to match 𝒗𝒅 in MIROC6 with NICT obs.
𝒁𝒆 CFAD𝒁𝒆 -𝒗𝒅 2dPDF

snow

rain

rain

2020 JJAMIROC ctrl

icesnow
snow

rain

rain

• Tuned to faster 𝑣𝑑 and larger variance• 𝑍𝑒 becomes decreased• better 2dPDF, but separated

8/11

Impact on climate of 𝒗𝒅 tuning
anomaly of cloud fraction

5-year run from Jun. 2020
Anomalies from control run are shown.

OLR anomaly

OSR anomaly• Upper clouds strongly decreased.
→ Due to faster fall and shorter lifetime.
→ Cooling• Lower clouds increased.

How to reduce this perturbation?
for liquid: autoconversion ratio, background CCN
for ice: INP, shape, WBF process and mode…

9/11

𝒁𝒆 -𝒗𝒅 relation on 2dPDF of NICT observation

10/11✓ 𝑍𝑒 -𝑣𝑑 relation can be made in the similar way as 𝑍-𝑅 relation.

• with Rayleigh scattering limit and power law for droplet fall velocity

modified gamma

log normal(employed in MIROC6)

𝑏 = 0.5
𝑏 = 1.0𝑏 = 2.0
𝑏 = 1.5

𝑏 = 0.5
𝑏 = 2.0
𝑏 = 1.5

𝑏 = 1.0• This line fitting contributes to estimate the scale 𝑏 of droplet fall velocity.
• Different scale is suggested: 𝑏 ≈ 0.5 for snow, 𝑏 ≈ 1.0 or 1.5 for rain.

𝒗𝒇 = 𝒂𝑫𝒃

Summary
1. New simulator for doppler velocity is developed on COSP2.
• Droplet fall velocity and vertical air motion are newly handled.

2. Distribution of 𝒗𝒅 in MIROC6 is not good, actually.
• slower 𝑣𝑑 and smaller its variance, overestimated 𝑍𝑒• separated by categories of cloud/precipitation droplet

3. Significant impact on climate by tuning of droplet fall velocity• Large modification of cloud fraction and strong cooling effect.
• Cloud microphysics schemes still have much room for improvement.

4. Estimation of scale of fall velocity by 𝒁𝒆 -𝒗𝒅 relation• Different scale among cloud, rain, snow droplets?
• Implication for shape of ice particles?

✓ We are now preparing to publish this new simulator for COSP2.
✓ I’d like to see this simulator used with EarthCARE observation tounderstand model variability and to improve schemes related to clouds.

11/11

12/11

Supplement

13/11

Details of tuning setup
PL08: 𝒃𝟏 − 𝒃𝟐 𝐞𝐱𝐩 −𝒃𝟑 𝑫 + 𝒃𝟐 − 𝒃𝟏 𝐞𝐱𝐩 −𝟓𝒃𝟑 𝑫
ctrl

or

large-scale condensation

Power law: 𝒂𝑫𝒃cumulus convection

dropletcloud liq.

cloud ice

rain

snow

cloud liq.

cloud ice

rain

snow

viscous drag××××××〇〇

PL08〇×〇×〇

×××

Power law; 𝑎-1.107-

3.321-

1.107

842

4.84

Power law; 𝑏-

0.22-

0.22-

0.22

0.8

0.25

𝒃𝟏 = 𝟗. 𝟔𝟓, 𝒃𝟐 = 𝟏𝟎. 𝟒𝟑, 𝒃𝟑 = 𝟔𝟎𝟎
tuninglarge-scale condensation

cumulus convection

dropletcloud liq.

cloud ice

rain

snow

cloud liq.

cloud ice

rain

snow

viscous drag

〇

〇

〇

〇

〇

〇

〇

〇

PL08〇

×〇

×〇×××

Power law; 𝑎-300-

100-

300

842

4.84

Power law; 𝑏-

0.8-

0.5-

0.8

0.8

0.25

𝒃𝟏 = 𝟏𝟎. 𝟎, 𝒃𝟐 = 𝟏𝟎. 𝟒𝟑, 𝒃𝟑 = 𝟏𝟓𝟎𝟎

14/11

clwp

ciwp

rwp

swp

15/11cwv

rain + snow

rainsnow16/11cloud fraction @ 300hPa

cloud fraction @ 500hPa

cloud fraction @ 850hPa

2m temperatureSimulator design: diagnostic precipitation scheme

17/11

vertical air motion

Numbermx. ratio

precip. flux

droplet fall velocity

diagnosed in dynamics

cloudscloudsrain / snow

microphys

size distribution
microphys• Droplet fall velocity is parametrized.
• Flux conversion is required.

𝑹𝒆𝒇𝒇
microphys↑ GCM↓ COSP2

size distribution

flux ↔ mx. ratio

COSP2

conversion routine

Radar reflectivity
COSP2 (quickbeam)Doppler velocity

droplet fall velocity
COSP2

18/11

Simulator design: cumulus scheme
cumulus mass flux

mx. ratio

precip. flux

droplet fall velocity

cumulus scheme

cumulus cloudcumulus scheme

microphys

size distribution
microphys𝑹𝒆𝒇𝒇
microphys↑ GCM↓ COSP2• Missing variables in cumulus scheme without
cloud microphysics should be assumed in COSP2.
• It is also necessary to convert precipitation flux tomixing ratio of hydrometeors.

size distribution

flux ↔ mx. ratio

COSP2

conversion routine

vertical air motion

Radar reflectivityconverted in COSP2

COSP2 (quickbeam)

Doppler velocity

droplet fall velocity
COSP2𝒗𝒅 simulator on cumulus precipitation in MIROC6𝒁𝒆 CFADMIROC6𝒗𝒅 CFAD

19/11

corresponding area to NICT obs.

cumulus mass flux

Not comparable to NICT monthly statistics.
We should pick up cumulus cloud case.

𝑍𝑒 in COSP2 ...
𝑍𝑒= 1018

𝜆4
න 𝐶𝑏𝑘 𝑛 𝐷 𝑑𝐷
𝜋5𝐾

Rayleigh Limit ...
𝜋5𝐾 6𝐶𝑏𝑘 = 4 𝐷
𝜆→𝑍𝑒 = 1018 න 𝐷6 𝑛 𝐷 𝑑𝐷 = 1018 𝑁𝑀6𝑀𝑘 =1
න 𝐷𝑘 𝑛 𝐷 𝑑𝐷
𝑁𝑍𝑒 = 1018

𝜋𝑚 = 𝜌𝑁𝑀361 𝜋 𝑀3= 𝜌𝑁 6 𝑚
6𝑚 1
𝑁=𝜋 𝜌 𝑀3
6 𝑀6 𝑚𝜋 𝑀3 𝜌𝑑𝐵𝑍𝑒 = 10 log10 𝑍𝑒𝑑𝐵𝑍𝑒
𝑍𝑒 = 10 10𝑣𝑓 = 𝑎𝐷𝑏𝑎𝑀6+𝑏𝑣𝑑 =𝑀61 6𝑚
𝑍𝑒 𝜋 𝑀36 × 1018
𝑀6+𝑏 1=𝑎𝑚
𝜋
𝑀3 𝑍𝑒
6 × 1018
Γ 𝜇+6+𝑏 13+𝑏=𝑎𝐷0 𝑚𝜋
Γ 𝜇 + 3 𝑍𝑒𝑣𝑑 = 𝑎𝑀6+𝑏 1018

mod. gamma
𝑁𝐷𝑛 𝐷 =
Γ 𝜇 𝐷 𝐷0𝜇

𝐷exp −𝐷0

𝑁𝑀𝑘 = න 𝐷𝑘 𝑛 𝐷 𝑑𝐷
𝑀𝑘 = 𝐷0𝑘

Γ 𝜇+𝑘
Γ 𝜇
𝑎𝑀6+𝑏𝑀6
Γ 𝜇+6+𝑏= 𝑎𝐷0𝑏
Γ 𝜇+6𝑣𝑑 Γ 𝜇 + 6𝐷0𝑏 =𝑎 Γ 𝜇+6+𝑏𝑣𝑑 =6 𝑀6 𝑚𝑍𝑒𝜋 𝑀3 𝜌6𝑚 3Γ 𝜇+6= 1018
𝐷𝜋 𝜌 0 Γ 𝜇+3𝜋 𝜌 Γ 𝜇+3𝐷03 = 10−18𝑍6𝑚Γ 𝜇 +6 𝑒= 1018

𝑣𝑑 = 𝑎Γ 𝜇+6+𝑏Γ 𝜇+610−18𝜋 𝜌 Γ 𝜇+3𝑍6𝑚Γ 𝜇 +6 𝑒𝑏3

log normal
1𝑁log 𝐷 − log 𝜇 2𝑛 𝐷 =exp −𝐷 2𝜋 log 𝜎2 log 𝜎 2
𝑁𝑀𝑘 = න 𝐷𝑘 𝑛 𝐷 𝑑𝐷

= 101827
6 𝑀6 𝑚6 𝑚 −1218= 10𝜇 5 𝐷𝑒𝑓𝑓 5𝜋 𝑀3 𝜌𝜋𝜌

𝑏 𝑏+1227𝜋 𝜌 12−18510𝜇 𝑍𝑒6𝑚𝑏(𝑏+12) 𝑏(𝑏+12)𝑏(𝑏+3)𝜋𝜌27= 𝑎𝜇 − 910−18𝑍𝑒 276𝑚𝑏(𝑏+7)−5𝑣𝑑 = 𝑎𝜇𝑀352𝑅𝑒𝑓𝑓 == 𝜇 explog 𝜎 2𝑀222𝑅𝑒𝑓𝑓2log 𝜎 2 = log
5𝜇2𝑅𝑒𝑓𝑓𝑘2
𝑀𝑘 = exp 𝑘 log 𝜇 + log
5𝜇2𝑅𝑒𝑓𝑓
𝑘=𝜇 ∙𝜇6+𝑏 2 −62
5𝑏(𝑏+7) 𝑏 𝑏+12−
55 𝐷= 𝑎𝜇𝑒𝑓𝑓

𝑍𝑒𝑘 2 log 𝜎 2
𝑀𝑘 = exp 𝑘 log 𝜇 +2𝑘2
56+𝑏 2 −62
𝑎𝑀6+𝑏𝑏−5𝑣𝑑 ==𝑎𝜇𝐷𝑒𝑓𝑓
𝑀62𝑘2 𝑘𝑘− 55=𝜇𝐷𝑒𝑓𝑓

𝑏 𝑏+12𝑣𝑑 = 𝑎𝐷𝑒𝑓𝑓 5𝑏 𝑏+3− 4= 𝑎𝐷𝑒𝑓𝑓

27𝜋𝜌−510−18𝐷 𝑍
6 𝑚 𝑒𝑓𝑓 𝑒𝜋𝜌
10−186𝑚𝑏 𝑏+712𝑏 𝑏+712𝑏 𝑏+7𝑍𝑒 12

23/11
MIROC6 ctrl
red: NICT
blue: MOSAiC

JRA3Q (reanalysis)
NICTAug. 2020 monthly

MOSAiCSounding
Aug. 2020 monthly

about 600 hPa

Altitude (km)pressure (hPa)about 300 hPa

surface pressure

temperature (℃)

temperature (℃)

temperature (℃)

24/11

25/11

Simulator design: *****
vertical air motion

Numbermx. ratio

precip. flux

droplet fall velocity

diagnosed in dynamics

microphys

microphys

cumulus scheme

microphys

size distribution
microphys𝑹𝒆𝒇𝒇
microphys↑ GCM↓ COSP2

size distribution

flux ↔ mx. ratio

COSP2

conversion routine

vertical air motion

Radar reflectivity

converted in COSP2

COSP2 (quickbeam)

merged ver.
フォーマット調整用

Doppler velocity

droplet fall velocity
COSP2