Develop an Advanced Radiative Transfer Modeling System

(ARMS) for Accelerated Uses of FengYun Satellite Data in

Numerical Weather Prediction Models

Fuzhong Weng

10th Asia-Oceania Meteorological Satellite Users’ Conference

Melbourne, Australia, December 2-7, 2019

Laboratory of Severe Weather, Beijing, China

2

ECMWF Global Medium-Range Forecast Skill

ECWMF started assimilation of satellite radiances in 1999 when the first microwave sounding data were assimilated.

The forecast score in SH and NH in terms of 500 hPa approached to the same level. In SH conventional data void

region, the score increase is largely attributed to uses of satellite observations

3

ECMWF Forecast of Hurricane Irma (2017)

Forecast with satellites

Forecast without satellites

700hPa humidity and wind initial conditions with satellites

700hPa humidity and wind initial conditions without satellites

Red shading humidity > 95%

Source: ECMWF

4

Number of Instruments Monitored and Assimilatedin European Global Forecast System

5

Radiative Transfer for TOVS (RTTOV) Model

Input profile on N

levels, view angles

and surface

parameters

Interpolate profile onto

54 fixed levels and check

Calculate predictors

on 53 layers from profile

Instrument

coefficients

Multiply predictors by

coefficients for each channel

=> layer optical depths for

each channel

Interpolate optical depths

to N user levels

Integrate RT equation

for each channel on user levels

Output radiances

and BTs/Reflectances

Optional surface

emissivity/reflectance

calculation

Optional cloud and

aerosol calculation

Optional solar scattering and

NLTE calculations

6

Community Radiative Transfer Model (CRTM)

• Atmospheric gaseous absorption – Band absorption coeff trained by LBL

spectroscopy data with sensor response functions

– Variable gases ( H2O, CO2, O3 etc) .

– Zeeman splitting effects near 60 GHz

• Cloud/precipitation scattering and emission– Fast LUT optical models at all phases

including non-spherical ice particles

– Gamma size distributions

• Aerosol scattering and emission– GOCART/WRF-CHEM (dust, sea salt,

organic/black carbon)

– Lognormal distributions with 35 bins

• Surface emissivity/reflectivity – Two-scale microwave ocean emissivity

– Large scale wave IR ocean emissivity

– Land mw emissivity including vegetation and snow

– Land IR emissivity data base

• Radiative transfer scheme – Tangent linear and adjoints

– Inputs and outputs at pressure level coordinate

– Advanced double and adding scheme

– Other transfer schemes such as SOI, Delta Eddington

“Technology transfer made possible by CRTM is a shining example for

collaboration among the JCSDA Partners and other organizations, and

has been instrumental in the JCSDA success in accelerating uses of

new satellite data in operations” – Dr. Louis Uccellini, Director of

National Weather Service

6

Fengyun Satellite Programs in Next Five Years

7

2012 FY-2F(Op)

2010 FY-3B (R&D)

2013 FY-3C(Op)2017 FY-3D(Op)

2014 FY-2G(Op) 2016 FY-4A (R&D)

2019 FY-3E(Op)

2021 FY-RM(Op)

2020 FY-4B (Op)

2020 FY-3F(Op)

2022 FY-4C(Op)

2018 FY-2H(Op)

2023 FY-3G(Op)

8

• In the geostationary orbit, FY-4 is carrying onboard the hyperspectral infrared

sounder, vis/IR imager, lightening mapper. The hyperspectral IR sounder is the

world first instrument in the geo-orbit. FY-4M will fly carrying onboard the geo-

microwave sounding imager in 2022.

• In the leo-orbits, FY-3 satellites are carrying onboard both hyperspectral infrared

sounder, microwave sounder and imager, radio occultation instruments, vis/infrared

imager, UV and space weather instruments.

• FY-3E will be the first early morning mission, in constellation with EUMETSAT

and US polar-orbiting satellite system.

• FY-3RM is a global precipitation mission and carries on board both active and

passive instruments to continue.

• Emerging small satellite missions from commercial sectors and non-CMA satellite

missions will require more mission agnostic approaches in developing the products

and applications.

• More importantly, a fast and accurate radiative transfer model must be developed to

support FY data assimilation

New Instruments on board FY Satellites Require

Intensive and Extensive Scientific Efforts

• Large uncertainties remain at large in simulating the radiances in scattering

atmosphere.

• Large biases (O-B) are observed for surface sensitive channels especially over

land at microwave frequencies, and the O-B angular-dependence behaves

differently from one model to another, and sometime is unphysical.

• Radiative transfer schemes solve for radiative intensity. The instruments of UV,

visible, and microwave imagers are sensitive to polarization and thus require a full

vector transfer scheme.

• A reference quality ocean emission and reflection model was a gap in our ability

to provide absolute calibration of the satellite-based observing system

9

Common Issues in Current Fast Radiative

Transfer Models

On April 29 to May 3, 2019, CMA,

EMCWF and JCSRA jointly hosted a joint

workshop on radiative transfer models for

satellite data assimilation at Tianjin, China.

More than 100 scientists from China, US,

UK, Germany and Japan attended the

workshop.

The participants reported the major

progresses in developing the fast radiative

transfer models for satellite data assimilations. In

past, the NWP community primarily uses RTTOV

(Europe) and CRTM (US). Now, China is

developing the Advanced Radiative Transfer

Modeling System (ARMS) for FengYun satellite

data applications. The SSC recognized the

significance of ARMS which will be the third

pillar in supporting NWP satellite data

assimilation after RTTOV and CRTM.

2019 International Workshop on Radiative Transfer Model for Satellite Data Assimilation, Tianjin, China

A science steering committee for radiative

transfer (SSC4RT) was formed and 10

distinguished scientists are selected as SSC

members. Several critical actions will be taken

after the workshop.

12

Advanced Radiative Transfer Modeling System (ARMS）

11

12

CRTM, RTTOV and ARMS

CRTM RTTOV ARMS

Radiative

transfer Solver

Advanced Adding

and Doubling

(ADA)

Delta-Eddington

Approximation,

DISORT, MFEAST

Polarization Two-

Stream, ADA, VDA,

VDISORT, SOI, 6S-V

Scattering

properties

Mie Table as a

function of

frequency,

temperature, radii,

and hydrometeor

type and density

Mie Table as a function

of frequency,

temperature, and

hydrometeor type and

density, Discrete

Dipole Approximation

Mie Table, T-Matrix

as a function of

frequency,

temperature, and

hydrometeor type and

density.

Cloud type Water, ice, rain,

snow, graupel, hail

Water, ice, rain, and

snow

Water, ice, rain, snow,

graupel, hail

Surface models NPOESS IR LUT,

Wu/Smith IR

Ocean EM, MW

LandEM, FASTEM

MW Ocean EM

UWisc IR Emissivity

Database, Cox/Munk

IR Ocean EM,

FASTEM，CNRW

MW，TELSEM MW

Uwisc IR Emissivity

Database, Wu/Smith

IR Ocean EM, MW

LandEM, FASTEM

MW Ocean EM, AIEM,

CNRW MW，TELSEM MW

13

ARMS Supported Instruments

• FY-3A MWTS

• FY-3A MWHS

• FY-3B MWTS

• FY-3B MWHS

• FY-3C MWTS-2

• FY-3C-MWHS-2

• FY-3D MWTS-2

• FY-3D MWHS-2

• FY-3 B/C/D MWRI

• FY-3 B/C VIRR

• FY-3C MERSI

• FY-3C IRAS

• FY-3D MERSI-2

• FY-3D HIRAS

• FY-4A GIIRS

• FY-4A AGRI

• FY-4M GMIS

• NOAA 15 to 19 AMSU-A

• NOAA 18-19 MHS

• NOAA 18-19 HIRS

• NOAA 15-19 AVHRR

• SNPP/NOAA-20 ATMS

• SNPP/NOAA-20 CrIS

• SNPP/NOAA-20 VIIRS

• METOP-A to C IASI

• METOP-A to C IASI

• METOP-A to C AMSU-A

• METOP-A to C AVHRR

• JAXA AMSR2

• NASA GMI

• EOS Aqua AIRS

• EOS Terra/Aqua MODIS

Radiative Transfer Solvers Used in ARMS

• Discrete Ordinate Radiative Transfer (DISORT)

• Vector DISORT (VDISORT)

• Matrix Operator (MO)

• Double and Adding (DA)

• Successive Order of Iteration (SOI)

• Polarization Two Stream Approximation

• Delta Eddington Scheme

• Second Simulation of a Satellite Signal in the Solar

Spectrum (6S)

• Line by Line Radiative Transfer Model (LBLRTM)

• Moderate Resolution Transmission (MODTRAN)

• Discrete Anisotropic Radiative Transfer (DART)

14

1exp[ ( )]l −= − +l l

I A C S

Sl=

m0{B(

l−1) +

B(l−1

) − B(l)

l−1

− l

[Al

−1 + ( −

l−1)]

+ 0[

0A

l+ E]

−1F

0

exp(−

0)}

IL

(L) = B(T

s) + RI

L(

L)

+ R0

F0

exp(−

L

0)

I

l(

l−1) = I

l−1(

l−1)

I

l(0) = I

0

Cloud Optical Property Library Used in ARMS

15

• Developed with the most accurate and

state-of-the-art light scattering

computation methods (T-Matrix [Bi et

al., 2014] and IGOM [Yang et al.,

1996]);

• Wide coverage of the spectrum from

0.2 to 100 um;

• Wide particle size range (maximum

dimension) from 2~104 um;

• Complete scattering phase matrix with

polarization

• Three degrees of ice surface roughness:

Completely Smooth, Moderately

Rough, Severely Rough;

• Extended to the microwave spectrum;

temperature dependence considered;

Ice particle single-scattering property database

Yang et al., 2013, JAS

Aerosol Scattering Database in ARMS

16

Infrared Line by Line (HITRAN)

Spectroscopy Data Base

17

MonoRTM Line by Line Spectroscopy Data Base

18

Atmospheric transmittance as a function of frequency in microwave region. The

black, blue, red and green curve represents the contribution of total, oxygen, water

vapor and ozone to the optical depth

Tra

nsm

itta

nce

Diverse Profiles from ECMWF Model Used

for Training Transmittance Models

19

ARMS Layer Optical Thickness Training

20

T: temperature (K); P : pressure (hPa)

Index w, o, d represent water vapor, ozone and dry gas, respectively

=

=

=

−−−−+=

−−−−+=

−−−+=

l

i

l

i

l

i

liAiAiiTiiTlT

liAiAiAiTiAiTlT

lAiAiAiTiTlT

AAA

A

1

322

1

2

1

))(2/())]1()()(1())(1()()([()(***

)),(2/())]1()()(1())1()()([()*(*

)),(2/())]1()())(1()([()(*

A(i) is the ith level integrated absorber amount for water vapor. The same equations applied to P*,p**,P***.

• A set of 6 predictors varying with

channel is selected from the predictors

pool;

• An exhausting search is performed for

each gas component and channel to

select the best set of predictors;

• The set of predictors with strong

correlations between the selected

predictors is not included.

=

+=6

1

,,0,, )()()())((j

ijiijiiiiich AxAcAcAkLn

)(, iich Ak is the absorption coefficient and Ln() is

the natural logarithm

21

HIRAS Simulation from ARMS vs CRTM

Surface Type: Water; The Local zenith angle is 50.208 degree. Surface temperature=288.0 K.

Wind_Speed: 10.0 m/s. Wind_Direction 10.0 degree. Salinity: 33 ppt

Wu and Smith.IR Water Emissivity

Performance of ARMS HIRAS Transmittance Module

22

Performance of ARMS GIIRS Transmittance Module

23

24

Surface Emissivity Models

Microwave land emissivity model (NESDIS model) (Weng et al, Yan, Grody, 2001), desert microwave emissivity library (Yan and Weng, 2011) TELSEM, and CNRM databases (Prigent, 200x)Vegetation (Chen and Weng, 2014)Surface roughness (Chen and Weng, 2015)

Ocean Sea Ice Snow Canopy (bare soil) Desert

Empirical snow and sea ice microwave emissivity algorithm (Yan and Weng, 2003; 2008)

FASTEM microwave emissivity model (Liu et al., 2010, English , 199x)

IR emissivity model (Wu and Smith, 1991; van Delst et al., 2001; Nalli et al., 2008)

NPOESS Infrared emissivity databaseIASI Land Infrared emissivity databaseUWIREMIS database

25

Group Photo at 2019 Bern Working Group Meeting on “A

reference quality model for ocean surface emissivity and

backscatter from the microwave to the infrared”

Major Gaps in Radiative Transfer Modeling:

A Reference Quality Ocean Emission and Reflection Model

• On November 20-22, 2019, a group of 15

scientists from US, Europe, China and

Japan had a meeting at Bern, Switzerland

to work on the European Commission

Horizon 2020 project (GAIA-CLIM).

• A reference quality ocean emission and

reflection model is needed to provide

absolute calibration of the satellite based

observing system. By reference quality it is

meant that the uncertainty is known, both

in terms of systematic and random error

and can be traced to SI standards.

• The gap and the need to work

collaboratively to address it has therefore

been well documented in many

international scientific fora.

26

Fast Ocean Emissivity Models

Foam-free Reflectivity in H-Polarization due to wind roughness

1 5 2 9

, ,

1.15 10 3.8 10 10

)

/

  

(

 

h foam free h calm s

tr w f

tr t

− − −

−

= +

= −

  

   

1 3 2 9

, ,

1.17 10 2.09 10 (7.32 10 ) 10

/

  

( )

 

v foam free v calm s

tr w exp f

tr t

− − − −

−

= −

= −

3 5 2 7 3 1.0 1.748 10 7.336 10 1.044 10g − − −= − − +

4 5 2 6 3 20 10 1.0 9.946 10 3.218 10 1.187 10 7 10g − − − −= − + − +

9

, 1.0 - (208.0 1.29 10 * ) / ( )

h foam sf t g

− = +

9

, 1.0 - (208.0 1.29 10 * ) / ( )

v foam sf t g

− = +

Foam Reflectivity in H-Polarization

Foam Reflectivity in V-Polarization

Foam-free Reflectivity in V-Polarization due to wind roughness

( ) , ,1p c p foam free c p foamf f− = − +

27

Refractivity or Emissivity for Calm Water Surface

For a specular surface, reflectivity can be calculated by Fresnel law:

( , )p

f Reflectivity

2

h,calm2

cos ( , ) sin

cos ( , ) sin

f

f

− − =

+ −

f Frequency Local zenith angle

2

,2

( , ) cos - ( , ) - sin

( , ) cos ( , ) - sinv calm

f f

f f

=

+

sea surface

28

Foam Coverage vs Wind Speed

Foam is a mixture of air and water

and has a higher emissivity than flat

water

Foam coverage:

231.3

0

61075.7

=

−

V

Vf c

5 2.551.95 10

cf u

−=

Stogryn, 1972

Monahan, 1986

29

Foam Emissivity vs. Incident Angle

30

Two-Scale Ocean Emissivity Model

The large-scale roughness is dependent on the gravity waves and whereas the small irregularities

is affected by capillary waves. There are coherent reflection and incoherent scattering associated

with the waves in both scales

Large scale

Small scale foam

coherent

incoherent

downwind upwind

crosswind

31

There are several well-established methods for

simulation of electromagnetic scattering from

randomly rough surfaces

❑ Kirchhoff Method (KM) based on the

assumption that the wavelength of the incident

wave is much shorter than the horizontal variations

of the surface so that the general solution can be

regarded as the integration of local plane-boundary

reflections.

Tangential Plane Approximation

Stationary Phase Approximation and Geometric

Optics (GO) (FASTEM)

Scalar Approximation and Physical Optics (PO)

❑Small Perturbation Method (SPM) based on the

assumption that the surface correlation length and

its standard deviation are smaller than the

wavelength (low frequencies).

❑Composite Two-scale Model based on the

separation of both the surface and the EM wave into

two distinct scales, e.g., Yueh et al., 1997

Two-Scale MW Emissivity Model

Relationship of BRDF, Bistatic Coeffs and Emissivity

10-3 10-2 10-1 100 101 10 2 103

105

100

10-5

10-10

10-15

Bjerkaas/Riedel (BR) Ocean Roughness Spectrum

32

(Elfouhaily et al., 1997).

33Yueh 1997

FASTEM6/5 and Two-Scale

Comparison of Model Simulations with JPL

WINDRAD Observations (theta=30o)

34

Multisensor Remote-sensing Testbed

Input

Satellite radiance or

Brightness temperature,

geolocation information

One Dimension

Variational (1DVAR)

for

sequential or

simultaneous retrievals

Output

Atmospheric

temperature, moisture,

hydrometeor, aerosol,

trace gases, profiles

Forward/Jacobian

Operators

CRTM

RTTOV

ARMS

Background

Atmospheric and

Surface Parameters

NWP model outputs or

climatology profile

• Algorithm valid in all-weather conditions, over all-surface types

• Model& instrumental errors are input to algorithm

• Background and observation error covariances are scene-dependent.

• Selection of background from climatology, NWP forecasts, and regressions

• Selection of channels to use and parameters to retrieve

35

Comparison of FY-3D and NOAA

Microwave Sounding Capability P

ress

ure

(h

Pa)

Weighting Function

ATMS (22 Channels)

Weighting Function

CMWS (30 channels)

Combined microwave sounding Suite (CMWS) from FY-3D MWTS and MWHS has

a better vertical resolution for atmospheric sounding comparing to ATMS

Comparison of ATMS and CMWS Warm Core

ATMS CMWS-20 CMWS-28

34

Comparison of Atmospheric Profiles Derived

from ATMS and CMWS(MWTS/MWHS)

Validation of atmospheric profiles derived from ATMS using GPS dropsondes for

hurricane Florence shows FY-3 microwave sounding system has better profiling

capabilities in hurricane conditions

HIRAS Channel Selection

38

• Principal component analysis is used for channel selection.

• 450 channels is selected,

39

FY-3D HIRAS Derived Atmospheric Profiles

• Data between June 2018 to May 2019 are used for retrieval, validation with ERA5 reanalysis

• 1DVAR is better than regression and machine learning

• The mean temperature RMS is about 1K between 200hPa and 700hPa

Slide courtesy of He Yanfeng, Anhui Meteorological Bureau

Summary and Conclusions

• Fast and accurate radiative transfer models are required for sensor

simulation, instrument calibration and validation, remote sensing and

NWP data assimilation, and thus are critical for satellite mission

successes

• Currently, RTTOV, CRTM and ARMS are now supporting operational

and research missions for critical satellite data assimilation, and they are

sharing some common modules for many applications

• ARMS is now being integrated with CMA NWP systems (e.g. 1dvar,

GRAPES-4dvar) with focuses on FY data assimilation

• ARMS team is working with the international community to accelerate

its science developments

40

Acknowledgement

• Chinese Academy of Meteorological Sciences (CAMS)

• National Satellite Meteorological Centre (NSMC)

• National Meteorological Centre (NMC)

• Chinese Academy of Sciences (CAS)

• Nanjing University of Information Science & Technology (NUIST)

• Nanjing University (NJU)

• Zhejiang University (ZJU)

• Sun Yat-sen University (SYSU)

• Fudan University (FDU)

• UK Metoffice

• ECMWF

• JCSDA

41