Joint-Simulator and A-train for evaluating
clouds simulated by a global
cloud resolving model

Tempei Hashino1

Email: hashino@aori.u-tokyo.ac.jp

Mini-Workshop on A-train Science
March 8 2013

Masaki Satoh1, Yuichiro Hagihara2, Takuji Kubota3, Toshihisa Matsui4, Tomoe Nasuno5, Hajime Okamoto2, &Joint-Simulator team
1: Atmosphere and Ocean Research Institute, The University of Tokyo, 2: Research Institute for Applied Mechanics, Kyushu University, 3:
JAXA/EORC, 4: NASA Goddard Space Flight Center, 5: Japan Agency for Marine-earth Science and Technology

What is Joint-Simulator?
• Joint-Simulator solves the 1D radiative transfer problems
given by a cloud resolving model in a consistent way
among the sensor simulators and with the model.

Cloud Resolving
Model Outputs

absorption

scattering
scattering

emissionemissionemission

Satellite Data
Simulator

reflection

emissionemission• Can investigate uncertainties in the CRMs and RTMs.
• Help understanding the signals and retrieved products

Synthetic Satellite
Observation

Applicable sensors:
examplesEarthCARE

http://aqua.nasa.gov/about/instruments.php• Microwave radiometers &sounders*• Radars• Lidars• Visible & infrared imagers• Broadband radiometers
Applicable to
CloudSat CPR &CALIPSO CALIOP
Pamphlet
http://www.jaxa.jp/projects/sat/earthcare/index_e.html

http://www.eorc.jaxa.jp/TRMM/about/mechanism/main_j.htm

Sensorsimulators

•

•

•

•

Blue: SDSU modules(http://precip.hyarc.nagoyau.ac.jp/sdsu/index.html)

Green: NASA Goddard
SDSU extension(http://opensource.gsfc.nasa.gov/pro
jects/G-SDSU/index.php)

Orange: Joint-Simulator
extension(https://sites.google.com/site/jointsi
mulator/home)•

Visible and infrared imager– RSTAR6b (Nakajima & Tanaka 1986, 1988)• Discrete-ordinate method/adding method• K-distribution table with HITRAN2004
Microwave radiometer and sounder– Kummerow (1993)• Eddington approximation
Radar– Masunaga & Kummerow (2005)– EASE (Okamoto et al. 2007, 2008; Nishizawa et al. 2008)
Lidar– Matsui et al. (2009)– EASE
Broadband radiometer– CLIRAD (Chou and Suarez 1994, 1999; Chou et al. 2001)• δελτα−Eddington approximation/adding method (two stream)• K-distribution method with HITRAN1996• 21 bands– MSTRN-X (Sekiguchi and Nakajima 2008)• Discrete-ordinate method/adding method (two stream)• Correlated-k distribution method with HITRAN2004• 18, 29, or 37 bands.

Non-spherical scattering database(web-based)
Text or binary format
1: Format converter
netcdf format
2: RTM input creator
Text format
1. Adjust and organize theoriginal data into theMain collaborators
unified format
Prof. Nakajima’s group at Tokai University (planed next year)
2. Calculate lookup tables
Prof. Okamoto’s group at Kyusyu University
for the sensor simulator of
Dr. Ishimoto at Meteorological Research Institute (included)interest based on user’s
Potential international collaborators for microwave
cloud microphysicalDr. Liu at Florida State University, USA (included)
scheme.
Dr. Petty at University of Wisconsin-Madison, USA

Uncertainty due to non-spherical ice
scattering in CFEDs

Validation of simulated AOD (0.55 µm)•

NICAM-SPRINTARS global simulation provided by Dr.•K. Suzuki@JPL with help of Dr. D. Goto.

JointSimulator

Model output

Carbonaceous AP

Aerosol Optical Depth (AOD) estimatedwith Joint-Simulator is compared against
the outputs from SPRINTARS
Dust

Difference possibly due to differences intreatment of particle size distribution:
SPRINTARS uses multiple bins for DUST &Sea Salt, Joint-Simulator uses lognormal
volume distribution for all the aerosol species.

Good agreement

Approach for evaluating clouds
simulated by CRMs with A-train
Variables evaluatedSatellite instrument

Cloud fraction

CloudSatCPR

CALIPSOCALIOP

Aqua
MODIS

Cloud microphysics

CloudSatCPR

CALIPSOCALIOP

Aqua
MODIS

TOA & SFC
broadband fluxes

Aqua
CERES

Surfaceprecipitation

CloudSatCPR

Aqua
AMSR-E

Aqua
AMSR-E

Key word of this study• Simple• Synergetic uses ofdifferent sensors• Forward approach

Data set for this study
Observation：CloudSAT-CALIPSO merged data set （Hagihara et al. 2010）+ αFour cloud masks
Radar mask (C1); cloud & precipitating particles.
Lidar mask (C2); cloud particles.
Radar and Lidar mask (C3); cloud particles
Radar or Lidar mask (C4); all particles
Spatial resolutions and grids: 240m for vertical, 1.1km for horizontal.
AMSR-E L1B & CERES L2 SSF collocated.

NICAM global simulation：2008 TC Fengshen (Nasuno et al. 2009)• horizontal grid spacing: 3.5 km; # of vertical grids: 40 (0~3.8km)• cloud microphysical parameterization: NSW6 (Tomita 2002)
 1 moment scheme, 6 categories (vapor, cloud, rain, cloud ice, snow, graupel)

Forward calculation by Joint-Simulator
Simulation：CloudSAT & CALIPSO merged data set + αTime period

Observation：2008 June
Simulation：2008 June 17th 00Z ~ 25th 00Z

SIM

IR (10.8 μm) Tb

OBS

CloudSat 95 GHz• North-south asymmetry of
TC is simulated with NICAM.
• The simulated anvils andstratiform clouds are lessspread.
• The altitude of multiple
scattering onset (white lines,
Battaglia et al. 2011) andhigh β532 suggest theconvective profiles.

Convective profiles

CALIPSO 532 nm

Example 1:
TropicalCyclone• Overlap regions of C1 andC2 mask (black lines)
extends up to ~4 km both in
OBS and SIM, but NICAM
shows larger depth (opticallythinner).
• SIM: a lack of radarreflectivity found in theconvective cores wherewater contents are high (noMS parameterization forradar)

Proposed cloud type diagram
Define Cloud Type by separating the domain of Cloud top T and max Ze into seven subdomains. H: High, S: Storm, M: Mixed-phase, L: liquid. p is for precipitating, n is for nonprecipitating.

Mixed

Ice

The backgroundshows a global JointPDF from CloudSat PR.

LiqParticle phase

Cloud Type Diagram

Precipitation

Cloud type condenses
3D information ofradar reflectivity into
2D, facilitating spatialcomparison andnarrowing down
samples.

Example of cloud type; TC Fengshen
OBS

SIM

Cloud Type 0: No clouds, 1: Hn, 2: Hp, 3: Sp, 4: Mn, 5: Mp, 6: Ln, 7: LP

Zonal cloud fraction by cloud type•

Cloudy samples were divided into cloud layers, and then the number of cloud layers for alatitude range was divided by the total observation (multiple layers counted).

OBS

•
•
•

Hn dominate at Antarctic, Northernlatitudes.
Mn & Mp peaks at 60S and Arctic.
Ln & Lp peaks at 15S.

SIM

•
•
•
•

Both of Hn and Hp are overestimated.
Mp occurs more than Mn.
Ln is underestimated.
Lack of Mn contributes less CF at 70S-30S.

BETa-TEmperature Radar-conditioned diagram(BETTER) Hashino et al. (2013, under revision)
Aim: obtain the relative information on size and IWC.
If Ze are the same among two gridboxes, smaller 532 means larger
Reff,m and smaller IWC

Effective radius in terms of
Mass-equivalent radius

Mean Reff,m[μm]

Mean IWC[log10(g/cm3)]

Log10(532)

Log10(532)

Assumption
Both theobservation andsimulation follow asimilar 532 - Reff,mand IWC - Reff,mrelationship for agiven Ze.

Temperature [C]Okamoto et al. (2003)• Reff,m and IWC are
Okamoto et al. (2010)’s
lidar-radar retrievals.
• The diagnosis usuallyvalid for a range of T

Global BETTER Diagramfor High Non-Precipitating clouds (Hn)OBSSIM
Cloud ice

Temperature532 is similar to observation,
which means ice water content(effective radius) is similar toobservation.

Snow532 is smaller than observation,
which means ice water content(effective radius) is larger(smaller) than observation.

It is necessary to understandthis diagram in relation to themicrophysical growth processes.

Log10(532)

Normalized Polarization (P) and
Scattering index (S)Petty & Katsaros (1990, 1992)••

Unlike Tb, P decreases
monotonically with decreasing
visibility of the sea surface.
Sensitive to optical depth (orcolumn transmittance T)

Corresponding hypothetical Tb in
the absence of all cloudPetty (1994)••

Similar to polarization corrected
temperature (PCT), but regarded asa PCT anomaly relative to thebackground value Tc.
No empirical parameters required.

TV,O & TH,O can be estimated from nearby clear-sky observation identified
with CloudSat CPR.

OBS

SIM

Very smalltransmittance inconvective profiles
Large ice scattering signal

P36 vs Cloud Type• Samples taken in tropics (30S - 30N).
• Only profiles with a single cloud layer considered.

OBS

•

•

SIM

Small <- Transmittance -> large

OBS decrease of the transmittance for precipitating clouds with liquid particles:
Storm type (Sp) is the smallest transmittance.
 Liquid may exists in Hn, Hp, Mn, but errors in identifying clear scene arepossible, too.
SIM tends to overestimate the transmittance on average (less liquid water path).

SIM-OBS: mean+: q5 : q25
o: q50: q75 x: q95

OLR vs Cloud Type• Samples taken in tropics (30S - 30N).
• Only profiles with a single cloud layer considered.

OBS

•

•

SIM

OBS
 Hn, Hp, and Sp (CTT<-40C) show a variety of OLR, although the others
are not affected by existence of precipitating particles.
 Symmetric distribution (normal distribution?)SIM Sp, Ln, & Lp underestimate OLR, while Hn, Mp, & Mn overestimate.

SIM-OBS: mean+: q5 : q25
o: q50: q75 x: q95

Summary
Joint-simulator development• Sensor simulators available for visible-IR, passive microwave, radar,lidar, and broadband.
• Universal interface for aerosol and cloud microphysical schemes has
been developed. GCM interface is being added.
• Unified non-spherical scattering library was constructed for
microwave frequency, and visible-IR range is planned next year.
• Open to the public (https://sites.google.com/site/jointsimulator/home).

Diagnosis & Validation dataset• AMSR-E and CERES collocated with CloudSat-CALIPSO Merged Dataset.
• The simple cloud type scheme proposed based on cloud top temperatureand maximum radar reflectivity is useful to boil down the samples.
• Passive microwave provides information on columnar amounts of liquid
and ice hydrometeors.
• Errors in OLR for each cloud type can be associated with the cloud
microphysics.

Extra slides

Structure of Joint-Simulator
DriverRadiative property ofparticles

Sensorsimulator
Surfaceradiative
property

Model interface: I/OJoint-Simulator
algorithm flowchart
Volume extinction, absorption, phasefunction, etc, are tabulated as a functionof effective radius.

How easy to implement Joint-simulator?
• Registration– Go to Joint-Simulator webpage(https://sites.google.com/site/jointsimulator/) and click“Contact & Registration”– Then, you can get a package from a ftp site.

• Requirement– A C-preprocessor, a Fortran compiler, and netcdf library• Preparation of input– Put your model output (winds, thermodynamics, aerosolcloud variables) in a single netcdf file.
 Spherical (lat-lon) or rectangular grid system if the beam
convolution is necessary.

– Edit the configure file, Configure_SDSU.F, for yourexperiments and model assumptions.

Preparing input data
• Grid information
 Latitude, longitude height• Dynamical variables
 Vertical wind (3D)
 10-m horizontal winds• Thermodynamical variables
 Air temperature Total pressure or moist air density
 Vapor mixing ratio or relative humidity• Surface variables (example)
 Land-cover type
 Soil moisture content• Cloud microphysical variables
 Bulk: mass (and number) concentration for each category
 Bin: mass concentration for each bin• Aerosol microphysical variables
 Bulk: same as the above
 Internal mixture and hygroscopic growth of a particle

Current status of single scattering library in
Joint-simulator
VIS-IR
EASE (lidar)
RSTAR6bhydrometeors

NS-DB

Aerosol particles

Et, BsEt, Bs, SSA, g, Dp

re, habitre, habit

All particles

Broadband
MSTRN-X
CliRadKernel.out

Ce, Ca, p
Size parameter, index of refraction

Mie; sphere

Microwave
Kummerow (1993)
Masunaga & Kummerow (2005)} Mie; sphere

EASE (radar)

NS-DB

Et, Bsre, habit

The current sensor
simulators use thedatabase withdifferent formats anddifferent particles.

Adding an option forresponse function(b) NICAM IR (K): without response function• A better agreement between
observation and NICAM can beseen over Saharan desert.
• In general, monochromatic
simulation underestimates Tb.
But, it overestimates Tb for highdeep clouds.
Difference: b-c (K)(c) NICAM IR (K): with response functionPrecipitation rate and Ze
Rain

Snow
Assume inverseexponential distribution,varied mass and # of a
Liu (2008) hydrometeor category,and calculated 94GHz
radar reflectivity. Only
one layer (240m) of acloud is assumed above
the surface.

Matrosov (2007)

Okamoto et al.
(2010)
Wang and Geerts (2003)

Rain: At -10 dBZe, Re>20 µm means precip rate<1 mm/hr
At 8 dBZe, precip rate>0.18 mm/hr
Snow: At -10 dBZe, Re>100 µm means precip rate<0.01 mm/hr
At 8 dBZe, precip rate>0.1 mm/hr
Attenuation is not included, leading to overestimation of Ze.

C3 retrievals vs Ze

Zonal statistics of OLR• Cloudy samples taken over ocean• Radar-or-lidar cloud mask used

OBS

Mean

SIM

SIM-OBS

q95
q75
q
q2550
q5

Mean

 Overestimate OLR in 0 – 20N and 60S – 30S
 Global mean (OBS: 226 W/m2) is overestimated by 8 W/m2,
while the global mean (all scenes) shows a good agreement.

How fast does it run?
CPU: Intel Xeon(L5520) 2.26Ghz (4 cores)
Memory: 4x6 GB• 32 CPUs• 2560x1280x40 grid points– EASE: 26 min– RSTAR6b (one frequency): 40 min– MSTRNX: 13 minSummary••

Sensor simulators include radar, lidar, and broadband simulators that are notin SDSU.
Universal interface that can be applied for various aerosol & cloud
microphysical outputs
 Atmospheric models: NICAM, JMA-NHM, & WRF
 Aerosol microphysical models: SPRINTARS & GOCART Cloud microphysical models: NICAM single, double-moment bulk scheme, Hebrai
University spectral bin model, and WRF microphysical schemes (Lin scheme)
 Particle size distribution, mass-dimensional relationship, and fall velocity are easily
specified with a namelist.

•
•
•
•

Parallel-computation option (Message Passing Interface) is available.
The response function can be applied in the visible-IR simulator.
Write out data necessary for off-line implementation of 3D RTM.
Can be useful for retrieval algorithm development as well.

Future works• netcdf output & multiple snapshots• Construction of non-spherical scattering
database• GCM interface• Add interfaces for 3D RTMs

Comparison of 1D and 3D RTM (Preliminary
results)
High resolution simulation of marine stratocumulus• provided by Mr. Y. Sato (Prof. Nakajima group)• horizontal resolution: 500x500m
Experiment design• solar zenith angle: 60°• viewing zenith angle: 14.3°• difference between solar
azimuth angle and viewing
azimuth angle:0°• no surface reflection (RSTAR
not changed yet)

RSTAR: lower
radiance between
clouds.

1D RTM: RSTAR6b

Can be useful for retrieval
algorithm development
3D RTM: MCARaTS (Dr. Ishida)

Uncertainty due to non-spherical ice scattering in
BETTER

Approach for evaluating clouds
simulated by CRMs•

Cloud microphysical evaluation (Hashino et al. 2013, JGR, under revision)––•

Simple cloud type scheme–•Use co-located passive microwave data
To give a constrain on the column amount of hydrometeors in precipitating clouds

TOA broadband fluxes evaluation–•

Use cloud top temperature and maximum radar reflectivity to define “Cloud Type”

Water path evaluation––•

Synergetic use of CloudSat and CALIPSO for cloud fraction and in-cloud signals
BETa-TEmperature Radar-conditioned diagram (BETTER) for diagnosing water content andeffective radius

Use co-located broadband flux data

Main data set–

CloudSat-CALIPSO Merged Dataset (Hagihara et al. 2010)•–

NICAM 3.5km global simulation (Nasuno et al. 2012)•––

C1 (radar), C2 (lidar), C3 (radar and lidar), C4 (radar or lidar) mask available
One-moment bulk microphyscs scheme, NSW6

Aqua AMSR-E L1B (JAXA EORC)
Aqua CERES L2

Meridional-Temperature distribution

Temperature [C]Temperature [C]C2 cloud top occurrence

C1 cloud fraction

OBS

NICAM

NICAM
• C1 CF: generally good agreementwith OBS (R=0.88).
• Captures the max CF in the tropics.
• Overestimates high clouds at T < -30°C
over most of the latitudes.
 low-level clouds in highlatitudes.
• Underestimates subtropical warm clouds

Further info on cloud types• C2 CTO: poor agreement with OBS.
• Captures the high and low clouds
qualitatively.
• Misses middle clouds (-20 < cloud
top T < -10C) in the tropics andnorthern mid latitudes.
• Polar stratospheric clouds are
simulated.
• Higher relative occurrences ofhigh clouds.

Approach for evaluating clouds
simulated by CRMs with A-train
Variables evaluatedSatellite instrument

Cloud fraction

CloudSatCPR

CALIPSOCALIOP

Cloud microphysics

CloudSatCPR

CALIPSOCALIOP

Mass and size
TOA & SFC
broadband fluxes

Aqua
CERES

Aqua
AMSR-E
Water path

Comparison with cloud scenario
OBS

Frequency of clouds by Cloud Scenario
and Cloud Type, and cloud typeconditional frequency.

0: No clouds
5: Sc
1: Ci
2: As 6: Cu
3: Ac 7: Ns
4: St 8: DC

Good correspondence between Ci
and Hn, DC and Sp, Ac and Mn,
and St and Ln.

Example of collocated Tb
OBS

SIM

The simulated Tb are more depressed in the convective area, which
implies overestimation of ice amount in the model.

S89 vs Cloud Type
OBS

SIM

SIM-OBSP89 vs Cloud Type
OBS

SIM

SIM-OBSExample of collocated TOA fluxes
OBS

SIM

Zonal statistics of OLR
OBS

SIM-OBSZonal statistics of OLR over ocean
OBS

SIM-OBSP36-sorted Joint-PDF of T & Ze in tropics
For each range of P and S, the joint PDF of Ze can be constructed.

Radar reflectivity [dBZ]

OBS

SIM

Temperature

Temperature

OBS

•
•

SIM •

Radar reflectivity [dBZ]

The decrease in P36 is associated with high frequency of large Ze.
Transition of clouds from cirrus+non-precipitating shallow cumulus, precipitating shallow
cumulus, precipitating shallow cumulus, Nimbostratus, and then deep convection can beseen.
Signals from cloud droplets are too often & stratiform precipitation is not apparent.

Meridional-Temperature distribution of Cloud Fractions

Global Contoured Frequency by tEmperature Diagram (CFED)OBSCALIPSO 532nm Lidar

CloudSAT 94GHz radar

50 75

5
25

95 quantile

NICAM

OBS
• one single dominant mode fora given T at T < -10C (z asvertical axis does not show it)
NICAM• More pronounced nonprecipitating regime & large ice .
• Overestimates the occurrence of 0
< dBZe < 10 dB.
• The 95th quantile smaller at T < 40 °C and -20 < T < 10 °C.
• The 50th quantile larger at -35 < T< 0 °C.
• Two modes exist for log10(β532) at
T < -40°C level.
• 75th and 95th quantiles
underestimated for liquid
Contribution of eachhydrometeor category

Cloud ice
Snow
GraupelCloud droplets
Rain

Joint PDF of Cloud top Temperature and max Ze
0-15°N

OBS

SIM

20-40°N

70-82°N

Intro: evaluation of aerosol & clouds
Fig4. Masunaga et al. (2010)

Two approaches for evaluation of aerosolclouds simulated by GCMs and CRMs
• Retrieval approach: microphysical space• Forward approach: radiatvie space

Uncertainty in• Phase of hydrometeors• Particle size distributions• Habits• Ice scattering model

Purpose of this study• Develop a effective way to evaluate clouds simulated with a global
cloud resolving model with combined use of A-train observation.

Zonal relative cloud occurrence by cloud typeSIM

Relative cloud frequency

Relative cloud frequency

OBS

•
•
•

Hn dominate at Antarctic, Northernlatitudes.
Mn & Mp peaks at 60S and Arctic.
Ln & Lp peaks at 15S.

•••

Both of Hn and Hp are overestimated.
Mp occurs more than Mn.
Ln is underestimated.

ISMT-SF1

Snowflakes
Taken from Ishimoto (2008),
Fig. 1.

• Method: Finite DifferenceTime Domain (FDTD)
ISMT-SF2• 1 habits• 3 microwave frequency(GHz) 36, 89, 95, 150• 1 temperature points (K) 250• 9 size parameter points• 181 scattering angles• 6 phase function (F11, F12/F11, F22/F11, F33/F11,
ISMT-SF3-F34/F11, F44/F11)

HEXB

Liu’s SCATDB
Liu (2008), BAMS

SDEN

• Method: Discrete Dipole
Approximation (DDA)• 11 habits• 22 microwave frequency (GHz) 3, 5, 9, 10, 13.405, 15, 19, 24.1,
35.605, 50, 60, 70, 80, 85.5, 90,
94, 118, 150, 166, 183, 220, 340• 5 temperature points
 233.15, 243.15, 253.15, 263.15,
273.15• 20 size parameter points• 37 scattering angles• one phase function (F11)AMSR-E 89 GHz• Comparison with Maxwell-Garnett dielectric mixing formula+ Mie solution (MG-MIE1)• Only snow category use the new table, ISMT-SF2
ISMT-SF2

Tb(ISMT-SF2) – Tb(MG-MIE1)

More Tb depression by ISMT-SF2 due to smaller asymmetry factor (smaller
forward scattering)AMSR-E 89 GHz
Only snow category uses LIU-SDEN (dendrite snowflakes) data.
LIU-SDEN

Tb(LIU-SDEN) – Tb(MG-MIE1)

Less Tb depression by LIU-SDEN probably due to smaller extinction

CloudSat CPR 94GHz• Only snow category use the new table, ISMT-SF2
ISMT-SF2

Z(ISMT-SF2)-Z(MG-MIE1)

More backscattering of large particles with ISMT-SF2