Accuracy of geophysical parameters derived from Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit as a function of fractional cloud cover

Abstract

[1] AIRS was launched on EOS Aqua on 4 May 2002, together with AMSU A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The sounding goals of AIRS are to produce 1 km tropospheric layer mean temperatures with an RMS error of 1 K, and layer precipitable water with an RMS error of 20%, in cases with up to 80% effective cloud cover. The basic theory used to analyze AIRS/AMSU/HSB data in the presence of clouds, called the atlaunch algorithm, was described previously. Prelaunch simulation studies using this algorithm indicated that these results should be achievable. Some modifications have been made to the at-launch retrieval algorithm as described in this paper. Sample fields of parameters retrieved from AIRS/AMSU/HSB data are presented and validated as a function of retrieved fractional cloud cover. As in simulation, the degradation of retrieval accuracy with increasing cloud cover is small and the RMS accuracy of lowertropospheric temperature retrieved with 80% cloud cover is about 0.5 K poorer than for clear cases. HSB failed in February 2003, and consequently, HSB channel radiances are

Retrieval validation during the European Aqua Thermodynamic Experiment

“…A large difference between Raman lidar and other profiles in that region is due to the difference in vertical and horizontal resolution; the Raman lidar has a higher signal-to-noise ratio and the vertical resolution is about 15 to 30 m. The AIRS water vapour mixing ratio and RH profiles retrieved with A-T and N-T algorithms are in good agreement, i.e. within instrument sounding goals (Smith et al, 2005a;Susskind et al, 2006). In the region near 9 km, the radiosonde indicates a lower humidity than that indicated by all other sensors.…”

“…The mean difference profile is within ±8%. The statistical results are within the requirements of this type of sounder (Smith et al, 2005a;Susskind et al, 2006). These small differences can be explained by the different vertical resolutions achieved by the different retrieval algorithms, which are also manifested in the profile validation over Potenza (Figure 1).…”

“…It is equally important to validate retrievals as well as the retrieval algorithm producing the geophysical products. Two retrieval algorithms are used here for intercomparison; they are described in detail elsewhere Susskind et al, 2003;Susskind et al, 2006). However, the differences and similarities of these retrieval algorithms are addressed here in brief.…”

“…However, it should be noted that the forward radiative transfer models used differ in that the optimal spectral sampling (OSS) fast molecular radiative transfer model is used for the NAST-I retrieval Moncet et al, 2003) while the Stand-alone AIRS Radiative Transfer Algorithm (SARTA) is used for the AIRS retrieval (Strow et al, 2003). The A-T retrieval methodology uses eigenvector regression to obtain initial profiles as well (Goldberg et al, 2003;Susskind et al, 2003;Susskind et al, 2006). The A-T regression is based on a global database of profiles extracted from ECMWF analyses colocated with actual AIRS cloud-cleared radiances using 1680 AIRS spectral channels.…”

“…The AIRS Science Team has developed a retrieval scheme capable of producing atmospheric and surface thermodynamics parameters. Validation of retrieved temperature and moisture profiles has been made through comparisons with the European Centre for Mediumrange Weather Forecasting (ECMWF) model analysis and with matching radiosonde measurements Susskind et al, 2006;Tobin et al, 2006a). More information about the ECMWF can be obtained from the web site http://www.ecmwf.int/.…”