Improved cloud detection in GOES scenes over the oceans

Abstract Accurate cloud detection in GOES data over the ocean is a difficult task complicated by poor spatial resolution (4 km) in the GOES IR data, relatively coarse quantization (6 bits) for GOES VIS data, a visible sensing region of the spectrum not ideally suited for cloud versus ocean segmentation, and relative small oceanic signal dynamic range compared to that of either cloud or land structures found in a typical GOES scene. The GOES Adapted LDTNLR Ocean Cloud Mask (GALOCM) algorithm for cloud detection in GOES scenes over the oceans provides a computationally efficient, scene-specific way to circumvent these difficulties. The algorithm consists of four steps: 1) generate a cloud mark using the Local Dynamic Threshold Non-Linear Rayleigh (LDTNLR) algorithm of Simpson and Humphrey (1990); 2) generate a second cloud mask using an adaptive threshold: 3) divide the pixels in the scene into three groups (both methods agree that pixel is ocean, pixel is cloud, or the pixel is in contention); and 4) iteratively apply an adaptive threshold to the contested pixels. Convergence occurs when pixels are no longer in contention based on statistical criteria. Results show that the GALOCM method produces accurate cloud masks over the oceans which are neither regionally dependent nor temporally specific. GOES scenes containing ocean, cloud, and land are best cloud screened using a combination of the GOES Split-and-Merge Clustering (Simpson and Gobat, 1995) and the GALOCM algorithms.