A validation of the multivariate and minimum residual method for cloud retrieval using radiance from multiple satellites
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The Multivariate and Minimum Residual (MMR) cloud detection and retrieval algorithm, previously developed and tested on simulated observations and Advanced Infrared Sounder radiance, was explored and validated using various radiances from multiple sensors. For validation, the cloud retrievals were compared to independent cloud products from CloudSat, MODIS (Moderate Resolution Imaging Spectroradiometer), and GOES (Geostationary Operational Environmental Satellites). We found good spatial agreement within a single instrument, although the cloud fraction on each pixel was estimated independently. The retrieved cloud properties showed good agreement using radiances from multiple satellites, especially for the vertically integrated cloud mask. The accuracy of the MMR scheme in detecting mid-level clouds was found to be higher than for higher and lower clouds. The accuracy in retrieving cloud top pressures and cloud profiles increased with more channels from observations. For observations with fewer channels, the MMR solution was an “overly smoothed” estimation of the true vertical profile, starting from a uniform clear guess. Additionally, the retrieval algorithm showed some meaningful skill in simulating the cloudy radiance as a linear observation operator, discriminating between numerical weather prediction (NWP) error and cloud effects. The retrieval scheme was also found to be robust when different radiative transfer models were used. The potential application of the MMR algorithm in NWP with multiple radiances is also discussed.
Key wordscloud retrieval radiance cloud fraction observation operator
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