Using Optical Flow Temporal Interpolation of Satellite Imagery to Assist Multi-Sensor Global Cloud Product Composites

Large domain cloud-product composites important for climate research, civil and military aviation, maritime, and renewable energy operations require the synthesis of products retrieved from different satellites scanning at different times around the globe. Common cloud-product composites simply use a pixel within the nearest scan time (with similar sensor zenith angles) to select as the valid cloud state for any given image. Such an assumption leaves clouds out of valid time position, especially when large temporal differences exist between the composite valid time and imagery scan time over fast-moving clouds (i.e. cirrus). Within this presentation, we introduce a method for accounting for cloud motions between different scanned imagers before composite synthesis, which we term as “Temporal Correction.” The method uses an advanced dense (every image pixel) optical flow retrieval technique coupled with a simple, occlusion reasoning warping method commonly used for temporal resolution enhancement. The optical flow retrieval was tuned using a large comparison between temporally interpolated full-disk geostationary satellite imagery to corresponding fine-temporal resolution 1-min scans. It is demonstrated through six separate case studies that the temporal correction methodology improves the correspondence between retrieved cloud-top heights from various geostationary and low-earth orbiting imagers by 7-16% in reduction of root mean squared error after such products are parallax corrected and remapped to a common rectilinear grid. Similar analysis shows the same warping preserves characteristics relevant to 3D cloud-product retrieval, such as cloud-base height, without decimating the originally sensed fields.