1. A practical guide to IR and MW radiative transfer using the RTTOV model and GUI

2. What is atmospheric radiative transfer?
Study of the of the propagation of electromagnetic radiation through the atmosphere which involves interactions with atmospheric constituents (gas molecules, aerosols, clouds, hydrometeors) and the surface.
From a data assimilation perspective an RT model is the observation operator for assimilating passive visible/near- infrared, infrared and microwave satellite radiances into NWP models.
RT models take NWP fields (p, T, q, trace gas profiles and surface variables) as input and calculate top-of- atmosphere (TOA) satellite-seen radiances.

3. Definitions Wavenumber: Radiance:
Energy flux per unit solid angle per wavenumber in direction
All matter with a temperature T>0 Kelvin radiates energy. The radiance emitted by a black-body at wavenumber is given by the Planck function:
Brightness temperature:

4. Radiation-particle interactions
(1) Absorption: radiation attenuation of by energetic modification (heat or chemical reaction)
(3)
(2)
(2) Emission: isotropic increase in radiation by molecular excitation due to absorption (Kirchhoff’s law: emissivity = absorptivity)
(1)
(3) Scattering: radiation attenuation by deviation of radiation from original direction; also increase in radiation by deviation of radiation into direction under consideration

5. What is RTTOV? RTTOV = Radiative Transfer for TOVS
TOVS = TIROS Operational Vertical Sounder
TIROS = Television Infrared Observation Satellite
(RTTOV has been around for ~25 years)
TIROS-1 flew in 1960 carrying a television camera for capturing visible imagery of cloud cover. RTTOV is not quite that old, but the first version was written in the early 90’s.

6. What is RTTOV?
A fast radiative transfer model for passive VIS, IR and MW nadir-viewing instruments.
Funded by EUMETSAT through the NWP SAF, developed by Met Office, Météo-France and ECMWF.
Direct, TL, AD, K models.
Applications: data assimilation, reanalysis, simulated imagery, 1D-VAR, ...
RTTOV v11: >700 users RTTOV v10: >600 users

7. RTTOV inputs Vertical profiles of p, T, q
Other optional trace gases: O3, CO2, CO, N2O, CH4
Viewing geometry: zenith and azimuth angles
Surface variables: skin T, surface pressure, 10m wind u/v
Surface emissivity (optional)

8. extinction (absorption)
Transmittance
extinction (absorption)
transparent
opaque
Transmittance is related to optical depth by = e-(optical depth)

9. Clear-sky RT equation upwelling atmospheric emission
transmittance from TOA*
temperature
Planck function
transmittance from TOA* to surface
frequency
radiance
surface skin temperature
surface emissivity
upwelling atmospheric emission
downwelling atmospheric emission reflected by surface
surface emission
The computationally challenging term to calculate accurately is the transmittance or equivalently the optical depth where = e-(optical depth)
*TOA = top of atmosphere

10. Weighting functions Transmittance varies monotonically with height z.
AMSU-A:
50-57 GHz
channels 4 5 6 7 8 9 10 11 12 13 14
Weighting functions
Transmittance varies monotonically with height z.
We can write the upwelling emission term as:
Weighting function:
The upwelling emission is an integral of the Planck function weighted by w(z).
The largest contribution comes from the region where w(z) is largest i.e. where changes most rapidly with height.

11. Polychromatic channels
Passive IR/MW sensor channels are not monochromatic.
Ideally we would solve the RT equation at many wavelengths and integrate the resulting radiances over the channel spectral response function (SRF).
In practice we integrate transmittances over the SRF and solve the RT equation once per channel.

12. Line-by-line (LBL) models
LBL models embody the physics behind the absorption processes => accurate, but slow.

13. Line-by-line (LBL) models
LBL models embody the physics behind the absorption processes => accurate, but slow.
Key idea:
RTTOV parameterises off-line LBL calculations of optical depths to enable very rapid optical depth calculations for each instrument channel.

14. RTTOV optical depth calculation
83 diverse atmospheric profiles each at 6 zenith angles => 498 training profiles.

16. RTTOV optical depth calculation
83 diverse atmospheric profiles each at 6 zenith angles => 498 training profiles.
Divide atmosphere into 53* layers defined by 54 fixed pressure levels.
*For hi-res sounders we also produce coefficients for 100 layers/101 levels.

17. RTTOV optical depth calculation
83 diverse atmospheric profiles each at 6 zenith angles => 498 training profiles.
Divide atmosphere into 53* layers defined by 54 fixed pressure levels.
Calculate database of LBL optical depths for each layer at high spectral resolution for each training profile.
*For hi-res sounders we also produce coefficients for 100 layers/101 levels.

18. RTTOV optical depth calculation
Define a set of atmospheric “predictors” derived from input profile variables => there are separate sets of predictors for the optical depth due to mixed gases, water vapour and each additional trace gas.

19. The details aren’t important, but it’s useful to give a concrete example of what the predictors look like.

20. RTTOV optical depth calculation
Define a set of atmospheric “predictors” derived from input profile variables => there are separate sets of predictors for the optical depth due to mixed gases, water vapour and each additional trace gas.
Integrate the LBL optical depths in each layer over each instrument channel SRF for every training profile.

21. RTTOV optical depth calculation
Regress layer optical depths onto predictors (pi) for each channel => coefficients (ci) which are stored in a file for each instrument
Optical depth due to mixed gases*
Optical depth due to water vapour*
Optical depth due to ozone*
Total layer optical depth =
Optical depth calculation:
* strictly speaking these are “pseudo” optical depths (RTTOV science and validation reports give more details)

22. RTTOV flow diagram Input profile on N levels and surface parameters
Interpolate profile onto 54 fixed levels
Calculate predictors on 53 layers
internal RTTOV calculations
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
Optional surface emissivity calculation
Output radiances and BTs

23. Implications for accuracy
Sources of error:
Use of polychromatic optical depths
I won’t go into details on this, but will mention that the optical depth prediction scheme involves some subtleties which reduce the error due to the use of polychromatic optical depths: these errors are very small (e.g. below instrument noise for hi-res sounders).

24. Implications for accuracy
Sources of error:
Use of polychromatic optical depths
Optical depth parameterisation (regression)

25. Implications for accuracy
Comparison of TOA BTs from a simple forward RT model (upwelling emission plus surface term with unit emissivity) run with:
LBL channel-integrated optical depths
RTTOV optical depths (from predictor regression)
This plot shows errors due to the optical depth regression (second point from previous slide)

26. Implications for accuracy
Sources of error:
Use of polychromatic optical depths
Optical depth parameterisation (regression)
Discretisation of atmosphere into homogenous layers and associated interpolation
This is a valid approximation for the optical depth calculation and the RT integration (involving the Planck source term) is done on the input levels.

27. Implications for accuracy
Sources of error:
Use of polychromatic optical depths
Optical depth parameterisation (regression)
Discretisation of atmosphere into homogenous layers and associated interpolation
Input profiles values (including zenith angle) lying beyond the limits of the training set

28. Jacobian (K) model
This calculates the derivatives of the simulated radiances or BTs with respect to each profile variable. For example:
for 1 <= i <= nlevels
profile variables:
and surface parameters:
It tells us how sensitive the satellite-seen radiance is to each individual profile variable.

29. RTTOV capabilities Clear-sky visible/near-IR, IR and MW radiances
Internal sea surface emissivity and reflectance models
Land surface emissivity and reflectance atlases
Aerosol- and cloud-affected IR radiances
Cloud- and precipitation-affected MW radiances
Simulated Principal Components for hi-res IR sounders
and more...

30. How to get RTTOV Freely available, simply register here:
Coefficient files are available here:
RTTOV forum:

31. Thanks for listening. Any questions?