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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 1 | Copyright
Atmos. Meas. Tech., 11, 529-550, 2018
https://doi.org/10.5194/amt-11-529-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 25 Jan 2018

Research article | 25 Jan 2018

Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the SARTA all-sky infrared radiative transfer algorithm

Sergio DeSouza-Machado1, L. Larrabee Strow1,2, Andrew Tangborn1, Xianglei Huang3, Xiuhong Chen3, Xu Liu4, Wan Wu5, and Qiguang Yang5 Sergio DeSouza-Machado et al.
  • 1JCET, University of Maryland, Baltimore County, Baltimore, Maryland, USA
  • 2Department of Physics, University of Maryland, Baltimore County, Baltimore, Maryland, USA
  • 3University of Michigan, Ann Arbor, Michigan, USA
  • 4NASA Langley Research Center, Langley, Virginia, USA
  • 5Science Systems and Applications, Inc, Hampton, Virginia, USA

Abstract. One-dimensional variational retrievals of temperature and moisture fields from hyperspectral infrared (IR) satellite sounders use cloud-cleared radiances (CCRs) as their observation. These derived observations allow the use of clear-sky-only radiative transfer in the inversion for geophysical variables but at reduced spatial resolution compared to the native sounder observations. Cloud clearing can introduce various errors, although scenes with large errors can be identified and ignored. Information content studies show that, when using multilayer cloud liquid and ice profiles in infrared hyperspectral radiative transfer codes, there are typically only 2–4 degrees of freedom (DOFs) of cloud signal. This implies a simplified cloud representation is sufficient for some applications which need accurate radiative transfer. Here we describe a single-footprint retrieval approach for clear and cloudy conditions, which uses the thermodynamic and cloud fields from numerical weather prediction (NWP) models as a first guess, together with a simple cloud-representation model coupled to a fast scattering radiative transfer algorithm (RTA). The NWP model thermodynamic and cloud profiles are first co-located to the observations, after which the N-level cloud profiles are converted to two slab clouds (TwoSlab; typically one for ice and one for water clouds). From these, one run of our fast cloud-representation model allows an improvement of the a priori cloud state by comparing the observed and model-simulated radiances in the thermal window channels. The retrieval yield is over 90%, while the degrees of freedom correlate with the observed window channel brightness temperature (BT) which itself depends on the cloud optical depth. The cloud-representation and scattering package is benchmarked against radiances computed using a maximum random overlap (RMO) cloud scheme. All-sky infrared radiances measured by NASA's Atmospheric Infrared Sounder (AIRS) and NWP thermodynamic and cloud profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF) forecast model are used in this paper.

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Thermodynamic fields retrieved from orbiting infrared sounders use a derived set of measurements as their starting point, rather than the actual observations. This leads to problems with noise and sampling. We have developed a fast accurate model with a simple vertical representation of clouds in the atmosphere for use in retrievals, which allows us to use all the actual low-noise measurements at full resolution. These should eventually help produce more accurate weather forecasts.
Thermodynamic fields retrieved from orbiting infrared sounders use a derived set of...
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