Potential of Simulator Assessments led by GRP H IRO M ASUNAGA Hydrospheric Atmospheric Research...

Potential of Simulator Assessments led by GRP

HIRO MASUNAGAHydrospheric Atmospheric Research Center, Nagoya University

?

Satellite data simulator

Sep 1, 2011GRP 22nd meeting, Tokyo

Satellite data simulators Simulate satellite data, of course.

T, qv, qr, … → satellite measured radiance

Radiative transfer code + user interfaces Sub-grid cloud generator (COSP) Antenna pattern convolution, PSD library (SDSU)

Growing need for multiple sensor package TRMM/GPM: PR/DPR + TMI/GMI (+ VIRS) A-Train: CPR + CALIOP + MODIS + AMSR-E + AMSU + AIRS

…

Applications Climate/Cloud-resolving model evaluation Retrieval algorithm development for future missions Radiance based data assimilation …

Multi-sensor simulator packages


COSP: CFMIP Observation Simulator Package CFMIP (http://cfmip.metoffice.com/COSP.html)

CRTM: Community Radiative Transfer Model NOAA (http://www.star.nesdis.noaa.gov/smcd/spb/CRTM/)

ECSIM: EarthCARE Simulator ESA (Voors et al, 2007)

J-simulator: Joint Simulator for Satellite Sensors JAXA/U Tokyo (http://www22.atwiki.jp/j-simulator/pages/14.html)

RTTOV: Radiative Transfer Model for TOVS UK MetOffice/ECMWF (Matricardi et al. 2004; Bauer et al., 2006)

SDSU: Satellite Data Simulator Unit Nagoya U (http://precip.hyarc.nagoya-u.ac.jp/sdsu/)

Goddard SDSU NASA GSFC (http://atmospheres.gsfc.nasa.gov/cloud_modeling/sdsu.html)

ISSARS: Instrument Simulator Suite for Atmos Remote Sensing

JPL (under development)

Who would need it?


Algorithm developers? Most likely have their own RT codes already.

GCM/CRM developers Would be happy if user-friendly simulators are

available. Best (or least?) motivated to diagnose and refine

model performance. GCM/CRM users

Would be also happy with simulators. Best available to spend time on model

assessment.Satellite simulators have potential user demands primarily from the modeling communities.

Why do we need it?


N(D), ρp,…

N(D), ρp,…

Masunaga et al., BAMS (2010)


Goddard SDSU applied to aWRF simulation

- AMSR-E 36.5 GHz V (top)- MODIS 11 m (middle)- CloudSat dBZ (bottom)

Masunaga et al., BAMS (2010)

Cloud and Precip Top Heights (CTH and PTH)


MJO wet

MJO wet

MJO wet

MJO wet

MJO dry

MJO dry

MJO dry

MJO dry

? ?

CloudSat CPR

NICAM+SDSU

CPR 10-dBZ heightCP

R ech

o-t

op

h

eig

ht

PTH

CTH

TRMM PR&VIRS

NICAM+SDSU

PR echo-top height

Infr

are

d T

b

PTH

CTH

Too much snowIn the cloud model

Missing 94-GHz Echoes above 8 km


The 94-GHz back-scattering coefficient begins to be saturated due to non-Rayleigh scattering as snow content increases.

94

-GH

z


3/1

3

4

3

3

4

N

WrNrW

WdW

dN

WrN

N

s

s

,

4

2

4

6

3/1

,

3/122 )(

WdW

d

NWNr

N

s

s

Rayleigh regimeWavelength >> 2πr

Geometric optics regimeWavelength << 2πr

2r

A Modification to snow microphysics


Snowflake mass spectrum = m(D)n(D)=aDb N0 exp(-lD)where a=2.5x10-2 kg m-2 and b=2 (original=Grabowski, 1998) a=5x10-4 kg m-1 and b=1 (modified)

SWC=1g/m3

0.1g/m3

Smaller snowflakes

Less saturated

PSD Impact on the CTH/PTH Histogram


TRMM

NICAM

MJO wet

MJO wet

MJO wet

MJO wet

MJO dry

MJO dry

MJO dry

MJO dryOriginal

MJO wet MJO wetMJO dry MJO dryModified

Model

Grid resolution,PBL schemes,…

Model assessment with simulators


GCM cumulus/cloud

parameterizations, …

CRM cloud microphysics,

…

Satellite data simulator

Particle Scattering Size distribution, Crystal habit, …

Measuring principles Vis, IR, or Microwave

Passive or active

Tasks


Close communication is crucial among scientists with different background (modeling vs. remote sensing) to foster new ideas to develop assessment metrics. Simulator-related sessions in int’l conferences

ex.) AGU fall meetings in a past few years.

GRP led efforts for simulator-based assessment …

Potential of Simulator Assessments led by GRP H IRO M ASUNAGA Hydrospheric Atmospheric Research...

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