Bayesian-based subpixel brightness temperature estimation from multichannel infrared GOES radiometer data

Abstract

In this paper, a new image reconstruction scheme is devised for estimating a high-resolution temperature map of the top of the Earth's atmosphere using the Geostationary Operational Environmental Satellite (GOES) imager infrared channels 4 and 5. By simultaneously interpolating the image while estimating temperature, the proposed algorithm achieves a more accurate estimate of the subpixel temperatures than could be obtained by performing these operations independently of one another. The proposed algorithm differs from other Bayesian-based image interpolation schemes in that it estimates brightness temperature as opposed to image intensity and incorporates a detailed optical model of the GOES multichannel imaging system. In order to test the effectiveness of the proposed technique, high-resolution estimates of cloudtop temperatures using GOES channels 4 and 5 are compared to temperature estimates obtained from the Advanced Very High Resolution Radiometer (AVHRR). This test is achieved by examining sets of infrared data taken simultaneously by the GOES and AVHRR systems over the same geographic area. The AVHRR system collects longwave infrared data with a spatial resolution of 1 km, which is higher than the 4-km spatial resolution the GOES system achieves. In some cases, The estimated temperature differences between these systems are as high as 11.5 K. It is shown in this paper that the proposed algorithm improves the consistency between the cloudtop temperatures estimated with the GOES and AVHRR systems by allowing the GOES system to achieve substantially higher spatial resolution.