Development and Implementation Progress of Community Radiative Transfer Model

(CRTM)

Yong HanJCSDA/NESDIS

P. van Delst, Q. Liu, F. Weng, Y. Chen, D. Groff, B. Yan, N. Nalli, R. Treadon, J. Derber and Y. Han at JCSDA

JCSDA Workshop, May 31, 2006

Greenbelt Marriott Hotel

Community Contributions

• Community Research: Radiative transfer science AER. Inc: Optimal Spectral Sampling (OSS) Method NRL – Improving Microwave Emissivity Model (MEM) in deserts NOAA/ETL – Fully polarmetric surface models and microwave radiative transfer

model UCLA – Delta 4 stream vector radiative transfer model UMBC – aerosol scattering UWisc – Successive Order of Iteration CIRA/CU – SHDOMPPDA UMBC SARTA Princeton Univ – snow emissivity model improvement NESDIS/ORA – Snow, sea ice, microwave land emissivity models, vector discrete

ordinate radiative transfer (VDISORT), advanced double/adding (ADA), ocean polarimetric, scattering models for all wavelengths

• Core team (ORA/EMC): Smooth transition from research to operation Maintenance of CRTM (OPTRAN/OSS coeff., Emissivity upgrade) CRTM interface Benchmark tests for model selection Integration of new science into CRTM

Outline

• Major progress

• CRTM-v1 implementation

• Ongoing projects

Major Progress

• CRTM has been integrated into the GSI at NCEP/EMC (Dec. 2005)

• Beta version CRTM has been released to the public

• CRTM with OSS (Optimal Spectral Sampling) has been preliminarily implemented and is being evaluated and improved.

• New postdoc Yong Chen has recently joined CRTM development team.

CRTM-v1 implementation

Four main components– Atmospheric gaseous absorption (AtmAbsorption)

– Scattering and absorption by clouds and aerosols (AtmScatter)

– Surface optics; emissivity and reflectivity (SfcOptics)

– Radiative transfer solution (RTSolution) Four models

– Forward used operationally

– Tangent-linear

– Adjoint

– K-Matrix used operationally

CRTM Major Modules

Forward CRTM

SfcOptics(Surface Emissivity Reflectivity Models)

AerosolScatter(Aerosol Absorption

Scattering Model)

AtmAbsorption(Gaseous Absorption

Model)

CloudScatter(Cloud Absorption Scattering Model)

RTSolution(RT Solver)

Source Functions

public interfaces

CRTM Initialization CRTM DestructionJacobian CRTM

still developing

Gaseous Transmittance Model (AtmAbsorption)Compact OPTRAN

d

Ak

ch

chch

:

)exp(

6

10 )()()())(ln(

jjjch APAcAcAk

10,6,0,)ln()(0

,

njAaAcn

m

mmjj

Surface

A1

An

An-1

A0

K – absorption coefficient of an absorberA – integrated absorber amountPj – predictorsaj – constants obtained from regression

Level 0

Level n-1

Level n

Level 1

• Currently water vapor and ozone are the only variable trace gases and other trace gases are “fixed”.• The model provides good Jacobians and is very efficient in using computer memory

estimate layer transmittance

– spectral response function

Channel transmittance definition

0

0.02

0.04

0.06

0.08

0.1

1 3 5 7 9 11 13 15 17 19AMSU channel number

RM

S f

itti

ng

err

or

TotalDryWater Vapor

0

0.05

0.1

0.15

0.2

0.25

1 3 5 7 9 11 13 15 17 19

HIRS channel number

RM

S f

itti

ng

err

or

(K)

TotalDryWater vaporOzone

Radiance errors due to transmittance model uncertainty

Radiance Jacobians with respect to water vapor, compared with LBLRTM

Surface Emissivity/

Reflectivity Module

IR EM module over land

IR EM module over ocean

IR EM module over Snow

IR EM module over Ice

MW EM module over land

MW EM module over ocean

MW EM module over Snow

MW EM module over Ice

Surface Emissivity/Reflectivity Module and Sub-modules

IR Sea Surface Emission Model (IRSSE)

c0 – c4 are regression coefficients, obtained through regression against Wu-Smith model (1997).

,3

,10

42 ˆ,ˆ,,,, wcwc wcwcwcw

The IRSSE model is aparameterized Wu-Smith model for rough sea surface emissivity

IR emissivity database for land surfaces

Surface Type

Compacted soil Grass scrub

Tilled soil Oil grass

Sand Urban concrete

Rock Pine brush

Irrigated low vegetation Broadleaf brush

Meadow grass Wet soil

Scrub Scrub soil

Broadleaf forest Broadleaf(70)/Pine(30)

Pine forest Water

Tundra Old snow

Grass soil Fresh snow

Broadleaf/Pine forest New ice

Surface types included in the IR emissivity database (Carter et al., 2002):

NESDIS Microwave Land Emission Model (LandEM)

(1) Three layer medium:

)(,,2 2 TBLayer

desert, canopy, …

)1( 120 RI

120RI)1)(,( 210 RI

0I1,1 Layer

3,3 Layer)(31 TBeI

),( 1 I231 ),( RI

0

1

(2) Emissivity derived from a two-stream radiative transfer solution and modified Fresnel equations for reflection and transmission at layer interfaces:

)(22121

)(212

)(2

2112 01

0101

)()1(

])[1(]1)[1()1(

k

kk

eRR

eReRRe

Conditions using LandEM: over land: f < 80 GHz, use LandEM; f >= 80 GHz, e_v = e_h = 0.95 over snow: f < 80 GHz, use LandEM; f >= 80 GHz, e_v = e_h = 0.90

Weng, et al, 2001

(1) Emissivity Database:

cos)( 332

12

110 aTaTaTaaDI SBj

N

jjBj

N

jji

(2) Snow type discriminators are used to pick up snow type and emissivity:

Microwave empirical snow and ice surface emissivity model

Tb,j – e.g. AMSU window channel measurements

(3) Supported sensors: AMSU, AMSRE, SSMI, MSU, SSMIS

Microwave Ocean Emissivity Model

Model inputs: satellite zenith angle, water temperature, surface wind speed, and frequency

Model outputs: emissivity (Vertical polarization) and emissivity (horizontal polarization)

FASTEM-1 (English and Hewison, 1998):

Cloud Absorption/Scattering LUT

• Six cloud types: water, ice, rain, snow, graupel and hail

• NESDIS/ORA lookup table (Liu et al., 2005): mass extinction coefficient, single scattering albedo, asymmetric factor and Legendre phase coefficients. Sources:

IR: spherical water cloud droplets (Simmer, 1994); non-spherical ice cloud particles (Liou and Yang, 1995; Macke, Mishenko et al.; Baum et al., 2001).

MW: spherical cloud, rain and ice particles (Simmer, 1994).

RTSolution: Advanced Doubling-Adding Method (ADA)

AtmOpticsOptical depth, single scattering

Albedo, asymmetry factor,Legendre coefficients for

phase matrix

Planck functionsPlanck_Atmosphere

Planck_Surface

SfcOpticsSurface emissivity

reflectivity

Compute the emitted radiance and reflectance at the surface

(without atmosphere)

Compute layer transmittance,reflectance matrices by doubling

method.

Combine (transmittance, reflectance,upwelling source) current level and added

layers to new level

Output radiance

Loopfrom bottom totop layers

(New algorithm) compute layer sources from above layer transmittance and

Reflectance analytically.

Liu and Weng, 2006

1.7 times faster then VDISORT; 61 times faster than DAMaximum differences between ADA,VDISORT and DA are less than 0.01 K.

Ongoing Development

Zeeman effect (theta = 135, B = 0.5 Gauss), US standard Atmosphere

190

200

210

220

230

240

250

260

270

-0.004 -0.003 -0.002 -0.001 0 0.001 0.002 0.003 0.004

Frequency offset from 60.4348, GHz

Tb

(K

)RC, B = 0.5

LC, B = 0.5

RC, B = 0

LC, B = 0

Weighting function (km-1)

Height (km)

Ch20

Ch19Ch21

Ch22

Ch23

Ch24

Zeeman Effect

SSMIS upper-air soundingChannel weighting functions

Fast RT algorithm for SSMIS upper-Air sounding channels affected by Zeeman-splitting

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

19 20 21 22 23 24

SSMIS channel number

RM

S e

rro

r (K

)

Fitting Error

Independent test

Measurement Error(NEDT)

Predictors for estimating absorption coefficients:

Channels Predictors

19, 20 , cosB, cos2B, cos2B, B-1, B-2, cos2B/B2

21 , cos2B, B-1, B-2, B-3, B-4, cos2B/B2

22, 23, 24 , 2, cos2B, B-1

= 300./T, B – Earth magnetic field magnitudeB – angle between magnetic field and propagation direction.

RMS errors, compared with LBL model:

Nick Nalli's Ensemble IR Ocean Surface Emissivity Model

• Properly accounts for reflected downwelling radiance. Conventional approach to modeling IR surface-leaving radiance results in systematic underestimation of surface leaving radiance.

• The approach shows good agreement with M-AERI from CSP and AEROSE. Amounts to a 0.15-0.3% correction in emissivity; 0.1-0.2K correction in bias.

• Work beginning on integration into the CRTM.

Ongoing Development (Cont.)

• CRTM-OSS improvement; OSS LUT-generation software transfer from AER to JCSDA.

• UMBC SARTA forward algorithm implementation; SARTA TL and AD model development (Dr. Yong Chen)

• RTTOV transmittance module integration (Dr. Roger Saunders)

• OPTRAN-v7 improvement and integration

• Aerosol component development

• Visible component development

• CRTM test and validation

Summary

• CRTM has been successfully integrated in the NCEP/EMC GSI.

• CRTM-v1 is implemented with the following models: OPTRAN, IRSSE, LandE, NESDIS MW snow/ice empirical surface emissivity models and ADA radiative transfer solver.

• CRTM-OSS has been preliminarily implemented, tested and evaluated. Several areas have been identified for improvement. The OSS LUT software is being transferred to JCSDA.

• Ongoing development projects also include: fast RT algorithm for SSMIS Zeeman-affected channels, ensemble IR ocean surface emissivity model, integrations of OPTRAN-v7, SARTA and RTTOV and developments of aerosol and visible components.