Introduction to DART’s support for RTTOV

This document serves as an orientation for DART’s support for satellite radiance assimilation. At the current time, only ECMWF’s RTTOV radiative transfer model is supported.

DART now includes the ability to use the RTTOV forward operators for satellite radiance assimilation. This is a new capability for DART, please submit issues with the DART Issues facility.

Note that DART support for RTTOV does not mean that all issues regarding satellite data assimilation with an ensemble system have been solved. Rather, the DART team hopes to provide the tools necessary for researchers to investigate the relevant issues with multiple models and data assimilation methodologies.

DART supports RTTOV version 12.3. Both RTTOV-direct for visible/infrared/microwave without scattering as well as RTTOV-scatt for microwave computations with full scattering are supported. DART supports all features of RTTOV 12.3 as a pass-through from the models to RTTOV. This includes aerosols, trace gases, clouds, and atmospheric variables. It also includes directly specifying scattering properties.

However, a particular model may not have all of the variables necessary for these functions depending on the model and model setup. In some cases RTTOV default climatologies can be used, but at a minimum the following quantities must be supplied by the model_mod interpolate:

Quantity Description
QTY_PRESSURE atmospheric pressure in hPa at the model levels
QTY_TEMPERATURE atmospheric temperature in K at the model levels
QTY_VAPOR_MIXING_RATIO atmospheric humidity mixing ratio in kg/kg at the model levels
QTY_SURFACE_PRESSURE the surface pressure in hPa
QTY_SURFACE_ELEVATION the surface elevation in km
QTY_2M_TEMPERATURE the atmospheric temperature in K at 2 m above the surface
QTY_SKIN_TEMPERATURE the surface (skin) temperature in K
QTY_SURFACE_TYPE 0 = land, 1 = water, 2 = sea ice

If a DART model_mod cannot provide these required quantities, the RTTOV forward operator will fail and cannot be used. It may be possible to look up surface elevation or surface type through an look-up table or “atlas,” although DART does not yet provide such functionality. 2M temperature in theory could be interpolated based on skin temperature and the lowest-level model temperature.

Beyond these fields, there are many other optional fields (such as clouds, trace gases, and aerosols) that can be specified. See the obs_def_rttov_mod.html page in the observations/forward_operators directory for a complete list of values.

Setting up DART+RTTOV

The RTTOV code and coefficients can be downloaded from this page:

https://www.nwpsaf.eu/site/software/rttov

You will need to register for a free account before downloading the code.

You should read the RTTOV user guide carefully as DART primarily acts as a pass through. Refer to the setup instructions included with the RTTOV documentation.

It may also be useful to refer to:

https://github.com/NCAR/DART/wiki/Getting-Started-with-DART-RTTOV

Once you have successfully installed RTTOV, you should customize the mkmf.template.rttov.gfortran file to your own build system, possibly referring to the other mkmf.template examples for additional information if you are not using gfortran.

There are many namelist options available through input.nml that control the run-time behavior of the RTTOV model. These are documented in obs_def_rttov_mod.html in the observations/forward_operators directory.

To get RTTOV to work with your model, you will need to follow these steps:

  1. Install RTTOV as above
  2. Customize your mkmf.template to include the RTTOV libraries and include directories
  3. Go into the models//work directory for your model of choice
  4. Add your observation types (which are listed in obs_def_rttov_mod.html/f90) to the input.nml namelist (assimilate_ / evaluate_these_obs_types)
  5. Include observations/forward_operators/obs_def_rttov_mod.f90 in the input_files section under &preprocess
  6. In your model of choice, run ./quickbuild.csh and ensure the RTTOV libraries are built
  7. For OSSE runs with perfect_model_obs:
    • Create an observation sequence file using ./create_obs_sequence and ./create_fixed_network_seq as detailed in the DART Getting_Started documentation
    • Run perfect_model_obs
    • Setup your ensemble as appropriate
    • Run filter and analyze the results in the usual way
  8. For OSE runs:
    • Run the observation converter for your desired observations
    • Setup your ensemble as appropriate
    • Run filter and analyze the results in the usual way

Note that currently obervation converters are only provided for AIRS, AMSU/A, GOES, and GMI. These converters can be found in the observations/obs_converters directories. The L1 converters are the appropriate converters for the radiance or brightness temperatures (rather than retrievals). If you need real L1 data for another satellite (as opposed to running an OSSE with perfect_model_obs where you can generate your own data), you may be able to use one of these converters to get you started. We welcome your contributions back to the DART public repository. Please issue a pull request to https://github.com/NCAR/DART.

Note that some of the observation converters may require the HDF-EOS libraries. See the BUILDME script in each directory for help in building these observation converters.

Current list of known issues

DART support for satellite radiances cannot be considered 100% complete. The following details the known issues that are being considered with DART’s support for satellite radiances.

  • DART does not yet provide satellite bias correction capabilities. This will be released in the near future.
  • Cross-channel error correlations are not yet supported. A principal component approach has been discussed. For now, the best bet is to use a subset of channels that are nearly independent of one another.
  • Vertical localization is an issue for satellite radiances. The main choices are to turn off vertical localization, use the maximum peak of the weighting function or the cloud-top may be appropriate, or explore other options. We consider this an open research problem.