Algorithm developments for the EarthCARE MSI
cloud products and the early phase analysis of warmwater clouds by the combined use of the cloud radarand the imager

EarthCARE Workshop in 2023
1

Introduction

Introduction

Satellites• Clouds exert an important influence on the water and energybalances and processes, thus, more observations arerequired for understanding of cloud lifecycle.

Algorithme.g. Randall et al. (1984) pointed out that a mere 4% increase of the Earth’s area
covered by low-level clouds, compensates for a projected 2–3 K rise in global
temperature due to a doubling of CO 2.

CFODD• We have long history of the passive sensing of clouds, using
the NOAA, ADEOS-2, TRMM, Terra/Aqua, and Geostationary
satellites …

Summary• Recently, active sensing open the door toward better
understanding of clouds, in terms of cloud evolution process.
2Introduction

Cloud properties were drastically modified
by volcanic ash

Satellites

Aug, 2007（Mt. Kilauea, Hawaii eruption in 2008）
CDR (Effective particle radius)
CDR
Data ： Terra/ MODIS
Algorithm : CAPCOM Ve4.02

Algorithm

Aug,2008, eruption

CDR

CFODDCDR~1,000km~5,000km

Summary

Due to SO2 release, CDR decreased 15µm à 12µm
- 5 W/m2 change in SW radiation (Eguchi et al. 2011)
http://www.darkroastedblend.com/2007/11/hawaiian-volcanoes-beauty-terror.html

Eguchi, K., I. Uno, K. Yumimoto, T. Takemura, T. Y. Nakajima, M. Uematsu,
and Z. Liu, 2011: Modulation of cloud droplets and radiation over the North
Pacific by sulfate aerosol erupted from Mount Kilauea. SOLA, 7, 77-80.

3Introduction

Directions of the cloud research• Long term record à climate change studySatellites

AVHRR, MODIS, VIIRS, GLI, GCOM, Geostationary…

• 3-D observation à cloud evolution process
CloudSat, Calipso, EarthCARE, + Passive sensors

Algorithm• Observation + Model simulation…

CFODD
Summary4

Introduction

EarthCARE

Satellites

EarthCARE- will be launched in middle 2024.
- has Cloud Profiling Radar (CPR), Multispectral Imager (MSI),
Broad Band Radiometer (BBR)

Algorithm
CFODD

EarthCARE satellite

Summary©JAXA
Illingworth, A., and Coauthors, 2015: THE EARTHCARE SATELLITE: THE NEXT STEP FORWARD IN
GLOBAL MEASUREMENTS OF CLOUDS, AEROSOLS, PRECIPITATION AND RADIATION. Bulletin ofthe American Meteorological Society, 96, 1311-1332.

5Introduction

Strategy

Algorithm

CFODD

Summary6

Introduction

MSI cloud product process
MSI data

Strategy

Calculate the cloud flag andCloud phase using CLAUDIA

Algorithm

Day Water

CFODD

Calculate the COT, CDR, CTP using
the 3ch method of CAPCOM

COT, CTP, CDR

Day IceCalculate the COT, CDR CTP, using
the 3ch method of CAPCOM

Summary

COT, CTP, CDR

7

Introduction

A-Train : CloudSat + Aqua/MODIS

Strategy

Cloud Radar（Vertical structure）

Imager（Horizontal structure）Algorithm
CFODD
Summary8Introduction

CLAUDIA algorithm(for cloud flags)

CAPCOM algorithm(for cloud properties)

Strategy

Algorithm
CFODD
SummaryIshida, H., and T. Y. Nakajima, 2009: Development of an unbiased cloud detection algorithm
for a spaceborne multispectral imager. Journal of Geophysical Research-Atmospheres,
114, doi:10.1029/2008JD010710.
Nakajima, T. Y., T. Tsuchiya, H. Ishida, and H. Shimoda, 2011: Cloud detection performanceof spaceborne visible-to-infrared multispectral imagers. Applied Optics, 50, 2601-2616

Nakajima, T. Y., and T. Nakajima, 1995: Wide-area determination of cloud microphysicalproperties from NOAA AVHRR measurements for FIRE and ASTEX regions. Journal ofthe Atmospheric Sciences, 52, 4043-4059.
Kawamoto, K., T. Nakajima, and T. Y. Nakajima, 2001: A global determination of cloud
microphysics with AVHRR remote sensing. Journal of Climate, 14, 2054-2068.

9

A-Train flight direction à
MODIS CDR (µm)

CFODD

Rain mode

MODIS COT

ic in gGathering
global/area
observations

Drizzle mode

Algorithm

à Sl

Cloud particle

StrategyA case of,
14µm<CDR<16µm

In Cloud Optical Depth

Introduction

New visualization method of the radar reflectivity,
CFODD (Contoured Frequency by Optical Depth Diagram)

CloudSat Radar Reflectivity

Summary

Radar Reflectivity [dBZ]
Nakajima, T. Y., K. Suzuki, and G. L. Stephens, 2010: Droplet growth in warm water
clouds observed by the A-Train. Part II: A Multi-sensor view. J. Atmos. Sci., 67, 1897-1907.

10Introduction

Strategy

Visualizing Cloud
Growth from space
Nakajima et al. (JAS, 2010b), Suzuki et al. (JAS, 2010b)

Algorithm

CFODD
Summary

11Introduction

The Global CFODD obtained by A-Train (CloudSat+Aqua)

Strategy
Algorithm

CFODD

Global (Land)

Global (Ocean)

Summary

Satellite Data (April, 2007 to 2014)
Matsumoto et al. 2022

12Introduction

The Regional CFODD obtained by A-Train (CloudSat+Aqua)
Matsumoto, Y., M. Wang, Y. Sato, and T. Y. Nakajima, 2023: Regional
dependency of the cloud droplet growth process in combined analysisof Aqua MODIS and CloudSat CPR. SOLA, 19, 63-69.

Strategy

East Asianè

Algorithm

Californian è
CFODD
Summary

Peruvian è13

X - 46SUZUKI ET AL.: AEROSOL EFFECT ON PRECIPITATION

Pristine
AI=0.01-0.120

CFODD

A Global Cloud
Resolving model

2.440

2.260-20

2.62.440eAI=0.01-0.1
03.520340

2.5(f) NICAM/AI=0.3-1.0
40

3.5

20

340

2.5

4
3.5

20

340

Summary

Rain formationin the modelis 60faster60
22-20020-20
020-200thanobservation
Z [dBZ]Z [dBZ]Z [dBZ]ee

220

AI=0.3-1.0(e) NICAM/AI=0.1-0.3
42.2e

AI=0.1-0.3(d) NICAM/AI=0.01-0.1

Figure 3.

2.8
2060due to22Signaturesof60 rain suppression
020-20020-200Zincreased[dBZ]
Z [dBZ]Z [dBZ]
aerosols

06032.2e

NICAMSPRINTARS

2.6ICOD

2.44002.8

ICOD

2.6(c) A-Train/AI=0.3-1.03ICOD

ü MODIS Cloud productsü MODIS Aerosol Products
ü CloudSat CPR Reflectivity

AI=0.3-1.0

AI=0.1-0.3

02.820Polluted(b) A-Train/AI=0.1-0.3
3ICOD

Algorithm

A-TrainObservation

Moderate(a) A-Train/AI=0.01-0.10ICOD

Strategy

AI = τ aαICODIntroduction

Diagnosis of the Aerosol Effects using CFODD

2.5220e

Vertical cloud microphysical structures depicted in the form of the contoured

K. J-C.
Golaz,G. L.
Evaluating
frequency diagram of radarSuzuki,
reflectivity
profilerescaled
as Stephens,a function 2013:of in-cloudoptical cloud
depth tuning in aclimate model with satellite observations, Geo. Res. Lett., 40, 4464-4468.
(ICOD) and classified according to aerosol index (AI) into (left to right) pristine (AI=0.01-0.1),

moderate (AI=0.1-0.3) and polluted (AI=0.3-1.0) conditions obtained from (a, b and c) the ATrain observations and (d, e and f) the NICAM-SPRINTARS model. The unit of color shading
is %dBZ 1 .

14

Introduction

Summaryp Clouds exert an important influence on the water and energy balances and processes, thus,
more observations are required for understanding of cloud lifecycle.

Strategye.g. Randall et al. (1984) pointed out that a mere 4% increase of the Earth’s area covered by lowlevel clouds, compensates for a projected 2–3 K rise in global temperature due to a doubling of CO2.

Algorithm
CFODD

Summaryp Need more observations of clouds from satellites for– generating cloud climatology database– investigating cloud evolution process
p The CFODD presents– cloud evolution process, clearly.
– results are consistent with past studies by TRMM, ADEOS2, MODIS.
– useful for model evaluations.
p A Doppler capability of the EarthCARE/CPR improves our understanding of cloud evolution
process (2024-).
p 3rd generation geostationary satellites will observe time-series of cloud evolution, every 2.5
min to 10 min.
15

Introduction

Referencespp

Strategyppp

AlgorithmRandall, D. A., J. A. Coakley, Jr., C. W. Fairall, R. A. Kropfli, and D. H. Lenschow, 1984: Outlook for research on subtropicalmarine stratiform clouds. Bull. Amer. Meteor. Soc., 65, 1290–1301.
Nakajima, T. Y., and T. Nakajima, 1995: Wide-area determination of cloud microphysical properties from NOAA AVHRR
measurements for FIRE and ASTEX regions. Journal of the Atmospheric Sciences, 52, 4043-4059.
Kawamoto, K., T. Nakajima, and T. Y. Nakajima, 2001: A global determination of cloud microphysics with AVHRR remotesensing. Journal of Climate, 14, 2054-2068.
Ishida, H., and T. Y. Nakajima, 2009: Development of an unbiased cloud detection algorithm for a spaceborne multispectral
imager. Journal of Geophysical Research-Atmospheres, 114, doi:10.1029/2008JD010710.
Nakajima, T. Y., K. Suzuki, and G. L. Stephens, 2010: Droplet growth in warm water clouds observed by the A-Train. Part I:
Sensitivity analysis of the MODIS-derived cloud droplet size. J. Atmos. Sci., 67, 1884-1896.

CFODDpNakajima, T. Y., K. Suzuki, and G. L. Stephens, 2010: Droplet growth in warm water clouds observed by the A-Train. Part II:
A Multi-sensor view. J. Atmos. Sci., 67, 1897-1907.

pNakajima, T. Y., T. Tsuchiya, H. Ishida, and H. Shimoda, 2011: Cloud detection performance of spaceborne visible-to-infraredmultispectral imagers. Applied Optics, 50, 2601-2616

p

Eguchi, K., I. Uno, K. Yumimoto, T. Takemura, T. Y. Nakajima, M. Uematsu, and Z. Liu, 2011: Modulation of cloud droplets
and radiation over the North Pacific by sulfate aerosol erupted from Mount Kilauea. SOLA, 7, 77-80Suzuki, K. J-C. Golaz, G. L. Stephens, 2013: Evaluating cloud tuning in a climate model with satellite observations, Geo. Res.
Lett., 40, 4464-4468.
Illingworth, A., and Coauthors, 2015: THE EARTHCARE SATELLITE: THE NEXT STEP FORWARD IN GLOBALMEASUREMENTS OF CLOUDS, AEROSOLS, PRECIPITATION AND RADIATION. Bulletin of the American MeteorologicalSociety, 96, 1311-1332.
Nakajima, T. Y., and Coauthors, 2019: Theoretical basis of the algorithms and early phase results of the GCOM-C (Shikisai)
SGLI cloud products. Prog Earth Planet Sci 6:52.
Matsumoto, Y., M. Wang, Y. Sato, and T. Y. Nakajima, 2023: Regional dependency of the cloud droplet growth process in
combined analysis of Aqua MODIS and CloudSat CPR. SOLA, 19, 63-69.

pp

Summarypp16