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 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.
Highlights
Since the early 2000s, a number of high-spectral-resolution, low-noise, very stable new generation hyperspectral infrared (IR) sounders have been deployed onboard Earth-orbiting satellites, providing daily global top-of-atmosphere (TOA) radiance spectra
The complexity of true cloud structures cannot be retrieved with hyperspectral IR radiances, and we have shown that only a maximum of 2–4 cloud parameters can be derived from a single scene, suggesting that only a simple radiative transfer algorithm (RTA) is needed
The two slab clouds (TwoSlab) model can be an order of magnitude faster than typical implementations of MRO and has nearly the same accuracy, both in terms of mean spectral radiances and radiance probability distribution functions (PDFs)
Summary
Since the early 2000s, a number of high-spectral-resolution, low-noise, very stable new generation hyperspectral infrared (IR) sounders have been deployed onboard Earth-orbiting satellites, providing daily global top-of-atmosphere (TOA) radiance spectra. In principle these TOA radiances can be inverted to estimate atmospheric temperature and humidity profiles, minor gas concentration, surface temperature and some clouds parameters. Cloud-cleared radiances are synthesized using the differences in cloud amounts in a (typically) 3-by-3 set of adjacent fields of view (FOVs) to produce a single effective estimate of the clear-sky radiance This process increases the retrieval yield (to well above 10 %) and provides some error estimates but simultaneously reduces the spatial resolution by a factor of 3. Infrared Atmospheric Sounding Interferometer (IASI): NUCAPS, using cloud clearing from a 2 × 2 set of footprints (Gambacorta, 2013)
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