A simulation-experiment-based assessment of retrievals of above-cloud temperature and water vapor using a hyperspectral infrared sounder

Abstract

Abstract. Measuring atmospheric conditions above convective storms using spaceborne instruments is challenging. The operational retrieval framework of current hyperspectral infrared sounders adopts a cloud-clearing scheme that is unreliable in overcast conditions. To overcome this issue, previous studies have developed an optimal estimation method that retrieves the temperature and humidity above high thick clouds by assuming a slab of cloud. In this study, we find that variations in the effective radius and density of cloud ice near the tops of convective clouds lead to non-negligible spectral uncertainties in simulated infrared radiance spectra. These uncertainties cannot be fully eliminated by the slab-cloud assumption. To address this problem, a synergistic retrieval method is developed here. This method retrieves temperature, water vapor, and cloud properties simultaneously by incorporating observations from active sensors in synergy with infrared radiance spectra. A simulation experiment is conducted to evaluate the performance of different retrieval strategies using synthetic radiance data from the Atmospheric Infrared Sounder (AIRS) and cloud data from CloudSat/CALIPSO. In this experiment, we simulate infrared radiance spectra from convective storms through a combination of a numerical weather prediction model and a radiative transfer model. The simulation experiment shows that the synergistic method is advantageous, as it shows high retrieval sensitivity to the temperature and ice water content near the cloud top. The synergistic method more than halves the root-mean-square errors in temperature and column integrated water vapor compared to prior knowledge based on the climatology. It can also improve the quantification of the ice water content and effective radius compared to prior knowledge based on retrievals from active sensors. Our results suggest that existing infrared hyperspectral sounders can detect the spatial distributions of temperature and humidity anomalies above convective storms.

Impacts of tropical cyclones on the thermodynamic conditions in the tropical tropopause layer observed by A-Train satellites

“…The high spectral resolution in the mid-infrared makes AIRS sensitive to temperature, water vapor, and also clouds. However, the standard AIRS retrieval is not sensitive to the water vapor signal from the UTLS (Fetzer et al, 2008;Gettelman et al, 2004;Read et al, 2007) because of the weak absorption and/or emission of the dry UTLS relative to the troposphere and a cloud-clearing algorithm (Susskind et al, 2003) adopted by the AIRS standard retrieval. This algorithm infers temperature and trace gases of the clear-sky portion of adjacent 3 × 3 FOVs with varying cloud amounts by contrasting the nine FOVs, assuming a uniform distribution of these atmospheric states.…”

“…Using the wavenumber-dependent cloud extinction coefficients in the mid-infrared channels, this method marginally updates the concentration and effective particle size of ice clouds relative to the collocated cloud observations of active sensors. The effective radius is esti-mated as a vertically averaged value that produces the most reasonable mid-infrared emission spectra of thick high-level clouds (Feng et al, 2021). Through a simulation experiment, Feng et al (2021) demonstrated that the synergistic method is sensitive to spatial variability in thermodynamic conditions above deep convection.…”

“…Feng and Huang (2018) found that the retrievability of temperature and water vapor is improved by an underlying cloud layer because the cloud layer reduces the degeneracy caused by nonmonotonic vertical temperature variations and the smearing effect of lower-level water vapor, and they proposed a cloud-assisted retrieval algorithm that can be applied to infrared hyperspectral measurements, such as those from the Atmospheric Infrared Sounder (AIRS; Chahine et al, 2006) aboard Aqua (Parkinson, 2003). Feng et al (2021) further developed a synergistic method that incorporates cloud measurements of collocated active cloud profilers. By conducting a simulation experiment, Feng et al (2021) demonstrated that this method can capture the variability in temperature and humidity above tropical convective storms and improve the retrievals near the cloud top through the incorporation of information from active sensors.…”

“…Feng et al (2021) further developed a synergistic method that incorporates cloud measurements of collocated active cloud profilers. By conducting a simulation experiment, Feng et al (2021) demonstrated that this method can capture the variability in temperature and humidity above tropical convective storms and improve the retrievals near the cloud top through the incorporation of information from active sensors.…”

“…In this study, we aim to quantify the effect of tropical cyclones on TTL temperature, water vapor, and clouds using A-Train satellite observations, specifically hyperspectral infrared measurements from AIRS and cloud profiles from CloudSat (Stephens et al, 2008) and CALIPSO (Cloud-Aerosol Lidar and the Infrared Pathfinder Satellite Observation; Winker et al, 2010). Tropical cyclones are of particular interest here because they constitute a large fraction of the most energetic (overshooting) convective clouds in the tropics (Romps and Kuang, 2009) and provide vertically extended dense high clouds that enable the above-cloud temperature and humidity retrieval method developed by Feng and Huang (2018) and Feng et al (2021). By using satellite observational datasets, which are introduced in Sect.…”