# Presentation: 559993

## Developments  of JCSDA Community Radiative Transfer Model (CRTM)

**Fuzhong Weng**
**NOAA/NESDIS/Office of Research and Applications**

**Also see 6.1 (Bennartz), 6.2 (Moncet), 6.3 (Liu), A37 (van Delst)**

**Acknowledgements: *****Yong Han (ORA), Paul van Delst (CIMSS/EMC), Mark Liu (QSS), Larry McMillin (ORA), Jean-Luc Moncet (AER), Ralf Bennartz (UWisc), Al Gasiewski (ETL), K.N Liu (UCLA), Ben Ruston (NRL), Andy Jones (CIRA),  Xu Liu (NASA/Langley), Bill Smith (HU), and Eric Wood (PU)  ***

**The 14****th**** International TOVS Study Conference, Beijing, China**
**May 25-31, 2005**

## JCSDA Road Map (2002 - 2010)

**Improved JCSDA data assimilation science**

**2002**

**2004**

**2007**

**2008**

**2009    **

**2005**

**2003**

**Advanced JCSDA community radiative transfer model,**
**Advanced data selection techniques for hyperspectral **

**Science Advance**

***By 2010, a numerical weather prediction community will be empowered to effectively assimilate  increasing amounts of advanced satellite observations***

**2010    **

**AMSU, HIRS, SSM/I, Quikscat,**
**AVHRR, TMI, GOES assimilated**

**AIRS, ATMS, CrIS, VIIRS,  IASI, SSM/IS, AMSR, more products  assimilated**

**Pre-JCSDA data assimilation science **

- Radiative transfer model, OPTRAN, ocean microwave emissivity, microwave land emissivity model, and GFS data assimilation system were developed

- The radiances of satellite sounding channels were assimilated into EMC global model under only clear atmospheric conditions. Some satellite surface  products (SST, GVI and snow cover, wind) were used in EMC  models

- A beta version of JCSDA community radiative transfer model (CRTM) transfer model will be developed, including non-raining clouds, snow and sea ice surface conditions

- The radiances from advanced sounders will be used. Cloudy radiances will be tested under rain-free atmospheres, and more products (ozone, water vapor winds) are assimilated

**NPOESS sensors ( CrIS, CMIS, ATMS...)**
** ****GOES-R (HES, ABI)**

- The CRTM includes  scattering & polarization from cloud, precip and surface

- The radiances can be assimilated under all weather conditions with the state-of-the science NWP models

## Requirements for Better RT Models

- Accelerated uses of satellite observations
- Direct radiance assimilation (less dependent on product validation)
- Unified satellite data assimilation infrastructure
- Advanced satellite instruments (NPOESS, GOES-R)
- Interferometer sounding technology with a few thousand channels
- Polarimetric from visible to microwave
- Uses of channels sensitive to surface
- Inclusion of spectral response functions/field of views
- NWP specific drivers
- Speed, accuracy and storage
- Radiances/Jacobians
- Coupling with forecast modeling

## The CRTM Framework

- The radiative transfer problem is split into various components (e.g. gaseous absorption, scattering etc). Each component defines its own structure definition and application modules to facilitate independent development.
- Minimize or eliminate potential software conflicts and redundancies.
- Components developed by different groups can “simply” be dropped into the framework.
- Faster implementation of new science and algorithms
- There are User and Developer interfaces, Shared Data interface, Test Software, Utilities/Feedback

**CRTM_Forward( Atmosphere,                         &**
**                                ****Surface,                                &**
**                                ****GeometryInfo,                     &**
**                                 ****ChannelInfo,                      &**
**                                  ****RTSolution )**
**CRTM_K_Matrix( Atmosphere,   &**
**               ****Surface,      &**
**               ****RTSolution_K, &**
**               ****GeometryInfo, &**
**               ****ChannelInfo,  &**
**               ****Atmosphere_K, &**
**               ****Surface_K,    &**
**               ****RTSolution )**

## Community Radiative Transfer Model

**Aerosol and Cloud Optical Model**

**Surface Emissivity, Reflectivity Models**

**Forward Radiative Transfer Schemes**

**Receiver and Antenna  Transfer Functions**

**Jacobian Schemes**

**Atmospheric State Vectors**

**Surface State Vectors**

**Community Radiative Transfer Model**

## 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
- Langley/Hampton Univ – principal component radiative transfer
- Princeton Univ – snow emissivity model improvement
- NESDIS/ORA – Snow, sea ice, microwave land emissivity models, vector discrete ordinate radiative transfer (VDISORT), 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

## Beta Version CRTM flowchart

**Beta Version CRTM flowchart**

## Fast Gaseous Absorption Model

**OSS**
**Trained with ECMWF set**
**Tested with UMBC set**

**OPTRAN**
**Trained with UMBC set**
**Tested with ECMWF set**

- OSS (Optimal Spectral Sampling) method (Moncet and Uymin, 2003; Moncet *et al.* 2001) models the channel radiance as
- Wavenumber ***i** *(nodes) and weights *w**i* are determined by fitting “exact” calculations (from line-by-line model) for globally representative set of atmospheres (training set)
- Monochromatic RT (using look-up tables of absorption coefficients for relevant species stored at the selected nodes)
- Maximum brightness temperature error with current LUT < 0.05K in infrared and <~0.01K in microwave

**Provided by Y. Han (NESDIS) and  J. Moncet (AER)**

## Computation & Memory Efficiency 

|  | OPTRAN-V7
Forward, Jacobian+Forward | OPTRAN-comp
Forward, Jacobian+Forward | OSS
Jacobian+Forward |
| --- | --- | --- | --- |
| AIRS | 7m20s,   22m36s | 10m33s,   35m12 | 3m10s |
| HIRS | 4s, 13s | 5s, 17s | 9s |

**Time needed to process 48 profiles with 7 observation angles **

|  | OPTRAN-V7
single, double | OPTRAN-comp
double precision | OSS
Single precision |
| --- | --- | --- | --- |
| AIRS | 33,    66 | 5 | 97 |
| HIRS | 0.26,   0.5 | 0.04 | 4 |

**Memory resource required (Megabytes) **

## Water vapor Jacobians at weak absorption channels

**Water vapor Jacobians at weak absorption channels**

## Radiative Transfer Scheme: Successive Order of Iteration (SOI) 

**Provided by**
**Bennartz et al**

## Principal Component Radiative Transfer

**Provided by Xu Liu and Bill Smith**

## AIRS Sensitivity to Cirrus Clouds

**Provided by K.N. Liou and S. Ou**

## Oceanic Emission Model

***Phenomenology.***
- Large gravity waves, whose wavelengths are long compared with the radiation wavelength.
- Small capillary waves, which are riding on top of the large-scale waves, and whose RMS height is small compared with radiation wavelength.
- Sea foam, which arises as a mixture of air and water at the wind roughened ocean surface, and which leads to a general increase in the surface emissivity.

**Two-scale Simulations**

**Aircraft Measurements**

**Notes:**
The four panels compare the simulated U and V from two-scale models with the fitted emissivity based on the aircraft measurements at 37 GHz. 

## Canopy Scattering Model  

- Methodology: geometric optics is applied because the leaf size is typically larger than wavelength
- d  -  leaf thickness
- H -  canopy height
- LAI - leaf area index
- md - dry matter content
-  leaf orientation angle
- incident angle of EM wave

## Emissivity-Soil Moisturewith canopy 

## Snow Emissivity Model

-  Dielectric constant within snow is perturbed and
-   a function of volume fraction of scattering particles
-  Reflection occurs at interface

**Air   ********1**** **

**Snow ********2**** **

**Subsurface ********3**** **

## Optical Properties of Dense Medium

**Single Scattering Albedo, ******

**Frequency (GHz)**

- a = 0.5 mm
- 

- fa  =  0.3

- fa =  0.6

- fa =  0.9

- Strong fluactuation theory (Tsang at al., 1985)

- d

- rp

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

**Sub-surface**

- fa - ice-volume fraction
- d -  snow depth
- a -  snow particle size

- 

- 

- 

- 

- 

- 

## Snow Emissivity Spectra

- Parameters:
- Snow depth
- Volume fraction
- Grain size/bulk density

## Emissivity vs. Snow Depth

- Snow stratification
- Melting/refrozen
- Metamorphosis process

**Need Improvements for:**

## Snow Microwave Emissivity Spectra

**Notes:**
The difference between V and H Polarized emissivity is detected.

Now, to use these emissivity data sets of snow and sea ice and simulate emissivity at a certain frequency, we need to identify the snow type, which is the third step of the models

## Analytic Jacobian

- Jacobian to surface parameters (e.g. surface temperature, soil moisture) can be written as (Weng and Liu, 2003, JAS):

- (1)

- Jacobian to any atmospheric parameters is just a linear sum of the Jacobians to temperature, optical thickness, and phase function/matrix, for example, Jacobian to water vapor,

- (2)

- (3)

- and Jacobian to cloud water,

## Stokes Jacobians at 0.67 micronPreparation for NPOESS/APS

- (Weng and Liu, 2003, JAS)

## Stokes Radiance Simulations at Microwave Wavelength, Preparation for NPOESS/CMIS

- 10.7_V

- 10.7_H

- 10.7_U

- 10.7_4

**Notes:**
In particular, the 3rd and 4th components well reflect the wind vector change. 

## Polarimetric Signals from Clouds 

**Notes:**
This shows another example that shows the Stokes vector distribution against rain rate. 

## 3D Clouds Produce Third Stokes Component at 10.7 GHz

## Polarimetric Signals from Finite Clouds: Physical Process of 3D Effects

- z

- x

- y

-  53

- Tv – Th = 5 K and  U = 0, V= 0 in y-plan (53 degree)

- Tv – Th = 5 K and  U = 4.8, V= 0 in z-plan

**Notes:**
Now, I want to show the effects of cloud macroscale features  (3D) on the Stokes vector measurements. Taking a cloud having a cubic shape that is a highly idealized condition, the satellite sensor viewing its top and side could have the third Stokes component due to the rotation of the Stokes vector from the side plane to the reference plane which is based on the local vertical. The conversion matrix is basically shown in L. Even if the third Stokes component is zero in y-plane, the polarization difference between horizontal and vertical polarization from y-plane (5K) results in U component in z-plane (4.8K).       

## WindSat Measurements for  Hurricane Isabel 

## WindSat Measurements (3rd&4th Components)

**10.7 GHz  TBU**

**10.7 GHz  TB4**

**18.7 GHz  TB4**

**18.7 GHz  TBU**

## Vicarious Assessment of WindSat Measurements over Amazon

**Time series of the mean 3****rd**** and 4th Stokes parameters over Amazon rainforests. **
**the line with diamond, triangle and square corresponds to 10.7, 18.7 and 37 GHz, respectively**

**3****rd**** Stokes component **

**4****th**** Stokes component **

## WindSat Measurements over Snow-Covered Surfaces

## Summary 

- The Community Radiavtive Transfer Model (CRTM) is being developed through the JCSDA satellite data assimilation program
- The CRTM includes vital components required for direct assimilations of current and future operational satellite radiances and will allow for uses of satellite data under all weather conditions in NWP models
- The CRTM is a framework with all interfaces to link university research and is accelerating the transition of new radiative transfer science into US operational NWP data assimilation systems (NASA and DoD are planning to use the same CRTM)

## Outstanding Issues

- Lack of schemes for diagnosing the hydrometeors associated with sub-grid convection
- Lack of high quality dataset to validate CRTM under cloudy conditions
- Consistent assumptions in cloud microphysics from visible, infrared and microwave wavelengths used in CRTM with NWP models
- Limited access to operational forecast models outputs
- Surface scattering/emission related to dense medium materials
- Inclusion of spatial inhomogeneity of clouds and precipitation in CRTM
- Infrared emissivity over deserts
- Sea ice emissivity modeling at microwave frequencies

## Slide 34