Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the SARTA all-sky infrared radiative transfer algorithm
Creators: name: DeSouza-Machado, Sergio
name: Strow, L. Larrabee
name: Tangborn, Andrew
name: Huang, Xianglei
name: Chen, Xiuhong
name: Liu, Xu
name: Wu, Wan
name: Yang, Qiguang
Publisher: name: Maryland Shared Open Access Repository
Year: 2018
Subjects: subject: Weather forecasting
subject: Cloud optical depth
subject: Radiative transfer
subject: weather
subject: clouds
subject: temperature
subject: mathematical models
subject: satellites
subject: Spatial discrimination
subject: Brightness temperature
subject: profiles
subject: representations
subject: computer simulation
subject: errors
subject: fields
subject: Meteorological satellites
subject: scattering
subject: Atmospheric Infrared Sounder
subject: Optical analysis
subject: Spatial resolution
subject: Climate models
subject: Atmospheric models
subject: Geophysics
subject: FOS: Earth and related environmental sciences
subject: Infrared
subject: yield
subject: footprints
subject: sky
subject: degrees of freedom
subject: Surface radiation temperature

description: 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.
description: https://www.atmos-meas-tech.net/11/529/2018/