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

Abstract

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 modelsimulated 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.

Evaluation of Radiative Transfer Models With Clouds

“…The cirrus cloud scattering parameters are based on Baum et al (2011), and the ice effective particle size is estimated from a temperature-based parametrization by Ou et al (1995Ou et al ( , 2013, where the ECMWF temperature profile is used to associate the ice cloud slab top pressure with a cloud top temperature. The effective absorption due to each slab is then calculated using PCLSAM (Chou et al, 1999) scattering code and used in the SARTA TwoSlab RTM (DeSouza- Machado et al, 2018). Each pixel is then divided into four columns.…”

“…A cloud fraction for each case is then chosen such that all of the ice cloud and a random portion of the water cloud is seen from TOA, such that the ECMWF-specified tcc is satisfied. Details are summarized in Machado and Strow (2016) and in DeSouza- Machado et al (2018). The difference between SARTA_TwoSlab(C) and SARTA_TwoSlab(P) is due to the difference in the way the boundaries of the thick slabs are calculated.…”

Single-footprint retrievals of temperature, water vapor and cloud properties from AIRS

“…Advances in efficient cloud-scattering algorithms and ever increasing computing power hold the promise of incorporating explicit cloud effects in forward models for routine operational retrievals. Several methods and software packages have been developed for the calculation of outgoing radiance in the presence of clouds: optimal spectral sampling (OSS; Moncet et al, 2015), The Havermann-Taylor Fast Radiative Transfer Code (HT-FRTC;Havermann et al, 2006), the principal component-based radiative transfer model (PCRTM; Liu et al, 2005), discrete ordinate algorithm for radiative transfer (DISORT; Laszlo et al, 2016), vector linearized discrete ordinate radiative transfer (VLIDORT; Spurr, 2006), the community radiative transfer model (CRTM; Han et al, 2005), the Radiative Transfer for the Television Infrared Observation Satellite (TIROS) operational vertical sounder algorithm (RTTOV; Saunders et al, 2013), the standalone AIRS radiative transfer algorithm (SARTA) coupled to a parameterization of clouds for longwave scattering in atmospheric models (PCLSAM; SARTA two-slab; DeSouzaMachado et al, 2017) and, as used in this work, a SARTA with delta-four-stream approximation (SARTA-D4S; Ou et al, 2013).…”

“…Among them are combining channel radiances into "super-channels" using empirical orthogonal functions ), neural networks (Blackwell, 2005) and parameterizations of frequency-dependent nonscattering optical depths (Kulawik et al, 2006a). Recently, DeSouza-Machado et al (2018) presented an optimal estimation scheme using their SARTA two-slab forward model. Here we describe a new retrieval scheme, an optimal estimation retrieval system (AIRS-OE) that can use the level 1b radiances of single AIRS footprints, without cloud clearing for the retrieval of temperature profiles (T atm ), H 2 O volume mixing ratio profiles, skin temperatures (T sfc ), effective cloud optical depths over an AIRS field of view (τ eff ), cloud-top temperatures (T cldtop ) and effective particle radii (r eff ).…”

“…A better a priori τ cld at nighttime may be made by comparing brightness temperatures in the window channels to the a priori surface temperature, similarly to Kulawik et al (2006a). Noting the work of DeSouza-Machado et al (2018), it may be useful to combine MODIS and numerical weather prediction cloud data to determine an a priori profile for the cloudtop height, particle size, optical depth (or liquid/ice column) and horizontal and vertical extent of cloud in the AIRS FOV. Improved cloud results will hopefully better leverage and compliment the spatial coverage and horizontal resolution of MODIS, and the vertical precision and detail of CloudSat/CALIPSO.…”