On the accuracy of RTTOV-SCATT for radiative transfer at all-sky microwave and submillimeter frequencies
Journal article, 2022

With the new generation of microwave instruments and, especially, the Ice Cloud Imager covering submillimeter frequencies, it is necessary to evaluate the performance of the operational Radiative Transfer model for TOVS (RTTOV). Thus, an intercomparison study has been conducted between RTTOV and the reference model ARTS (Atmospheric Radiative Transfer Simulator), with an emphasis on cloudy and precipitating conditions, covering frequencies between ≈53.6 and ≈664.0 GHz. Overall a rather good agreement is found between the δ-Eddington solution embedded in the scattering solver of RTTOV, RTTOV-SCATT, and the discrete ordinate solution embedded in ARTS. Under clear-sky conditions, given a consistent spectroscopy, the agreement is within 0.4 K over all frequencies considered. When idealized, homogeneous cloudy conditions are employed, the agreement is mostly ±2 K; this range is exceeded only at high scattering conditions. However, the following weaknesses are identified: the δ-Eddington solution fails to produce deep enough brightness temperature depressions at increasingly high scattering conditions and is not sufficient to capture the phase function structures at size parameters above 2–3; conditions typically found at around 664.0 GHz. When realistic hydrometeor profiles are employed, δ-Eddington leads to a root mean squared error of 1 K, with individual errors between 0 and 4 K. Infrequently, and in localized areas, larger discrepancies are identified, exceeding 10 K. However, these inaccuracies stemming from the simplified physics of RTTOV-SCATT were found at least an order of magnitude smaller than the cloud and precipitation representation errors assigned in data assimilation. Thus, we support the use of RTTOV-SCATT at submillimeter frequencies for operational purposes.

Benchmark results

Radiative transfer

Microwave/submillimeter

Intercomparison

Ice Cloud Imager

Author

Vasileios Barlakas

Chalmers, Space, Earth and Environment, Geoscience and Remote Sensing

V. S. Galligani

Universidad de Buenos Aires

Consejo Nacional de Investigaciones Cientificas y Tecnicas

Alan J. Geer

European Centre for Medium-Range Weather Forecasts

Patrick Eriksson

Chalmers, Space, Earth and Environment, Geoscience and Remote Sensing

Journal of Quantitative Spectroscopy and Radiative Transfer

0022-4073 (ISSN)

Vol. 283 108137

Subject Categories

Computational Mathematics

Meteorology and Atmospheric Sciences

Other Physics Topics

DOI

10.1016/j.jqsrt.2022.108137

More information

Latest update

3/14/2022