RTTOV v12.2 (due for release March 2018)
- Incorporate MFASIS fast visible scattering model for clouds into RTTOV
- New visible/IR optical property parameterisation for cloud liquid water in terms of LWC and Deff
- Option to treat explicit cloud/aerosol optical properties as active variables in TL/AD/K for visible/IR scattering simulations
- New visible/IR aerosol optical property files for CAMS aerosol species (these are already available and are compatible with v12.1)
- New sea surface solar BRDF model with reduced bias
- New PC-RTTOV coefficients supporting additional variable trace gases and aerosols
- New liquid water permittivity options for “clear-sky” MW cloud liquid water absorption
- New liquid water permittivity options for RTTOV-SCATT Mietables
- Enable RTTOV-SCATT simulations for MetopSG ICI (frequencies above 200GHz)
- New RTTOV-SCATT option to carry out RT calculation on radiances instead of brightness temperatures
- New optional RTTOV-SCATT output structure containing variables to allow dynamic all-sky emissivity retrievals
- Parallel (OpenMP) interfaces to RTTOV-SCATT
- Update wrapper to be compatible with Python 2 and 3
- Allow compilation of RTTOV against an external LAPACK library instead of using the supplied lapack.f source
- Improve and extend the interface to the HT-FRTC fast model (optimisation, treatment of surface consistent with RTTOV, enable RTTOV options for gas units, use RTTOV interpolator for profile interpolation, support for additional hyperspectral sensors)
- Support for lightly-apodised MTG-IRS simulations via HT-FRTC-in-RTTOV
On-going general developments
- Keep up to date with latest visible, IR and MW spectroscopy and LBL models
- Updates to the RTTOV GUI to support new capabilities
- Updates to the Python/C++ wrapper to support new capabilities
- Code optimisation to increase speed (for scalar architectures) and reduce memory usage
The plans outlined below may be subject to change in particular in light of newly identified user requirements
RTTOV v12.3 (due for release March 2019)
- Improvements to accuracy of SO2 optical depth prediction
- Investigate fast visible aerosol scattering capability using MFASIS
- Create a tool to allow users to generate their own RTTOV aerosol optical property files
- Implement alternative (more efficient) cloud overlap option(s) for visible/IR scattering simulations
- Rewrite the RTTOV-SCATT Mietable file generation code to make it easier for users to generate their own cloud/hydrometeor optical property files
- Further improvements to the interface to HT-FRTC (optional trace gases, cloud simulations, aerosol simulations, solar radiation, non-LTE, add TL and AD models)
RTTOV v13 (due for release 2020)
- Investigate improvements to the gaseous optical depth parameterisation
- Improved treatment of Rayleigh scattering in visible simulations
- UV simulation capability
- Investigate HDO as a variable gas species
- Enable retrieval of aerosol particle size by making this an input variable
- Investigate inclusion of 3D effects in MW, visible and IR scattering simulations
- Capability to simulate active MW sensors in RTTOV-SCATT
- Develop new physical MW emissivity model to replace FASTEM
- Updates to the CAMEL IR emissivity atlas
We have had requests for the following capabilities/updates, but we currently do not plan to implement them for reasons of complexity and/or prioritisation of resources:
Making coefficient generation software available to users
This software is complex and would require considerable resources to support users in running it which we feel would be better spent developing RTTOV itself. We also prefer to be able to retain control over the official RTTOV coefficients that are available in order to maintain consistent quality among them. The NWP SAF is responsive to all user requests for new coefficients. This includes coefficients for theoretical instruments which may involve multiple variations of channel specifications: we are actively supporting a number of users in this regard currently.
Use of NetCDF rather than HDF5 for large coefficient files and emissivity/BRDF atlases
We want to minimise the number of external library dependencies of RTTOV and RTTOV v12 now has only one: the HDF5 library. HDF5 was chosen because RTTOV had a lot of pre-existing infrastructure based on HDF5 including an extensive set of HDF5 I/O subroutines for reading/writing many RTTOV data structures. The GUI, for example, makes extensive use of these. It would require a significant development effort to switch to an alternative format such as NetCDF. Furthermore the NetCDF4 library is based on HDF5 so the latter must be installed in any case if NetCDF4 is installed.
Fully polarised simulations for RTTOV or RTTOV-SCATT
This would require very significant development effort and we feel it is currently better to focus resources on other capabilities. This may be reconsidered in the future depending on user requirements.
Simulation of ground-based sensors
Clear-sky simulations of ground-based MW sensors are possible using the RTTOVgb package which is based on RTTOV v11.2. We hope to make RTTOVgb available for download via the NWP SAF website soon (Q2 2018). This software is developed and supported by CETEMPS.
Simulation of airborne sensors
This was investigated and proved to be impractical with the current optical depth parameterisation used by RTTOV. This could be revisited in the future if an alternative optical depth parameterisation is implemented.
Implement RTTOV wrapper for language X
Currently the wrapper allows much RTTOV capability (direct and Jacobian model clear-sky and scattering simulations including use of land surface emissivity/BRDF atlases) to be called from C, C++ or Python code. The current wrapper capability will be maintained in future versions and will be extended to support new RTTOV capabilities. However the implementation of the wrapper for a given language requires a significant development effort with additional effort required for maintenance as RTTOV develops. Therefore we do not plan to support additional languages in the wrapper.
Support for the RTTOV TL/AD models in the Python/C++ wrapper
These would require considerable development effort to implement and maintain. If required the TL and AD can be computed from the Jacobian (K) model wrapper, though this is less efficient than computing them directly.