Effect of GOES-R Image Navigation and Registration Errors on Atmospheric Motion Vectors

Abstract

High temporal frequency imagery from geostationary satellites allows for the continuous monitoring of rapidly changing atmospheric constituents such as smoke, dust, water vapor and clouds. The image sequences are often used to quantify the displacement of image features such as water vapor and clouds to produce atmospheric motion vectors (AMVs) which are used as diagnostic tools and also assimilated into numerical weather forecast models. The basic principle behind the determination of AMVs is the calculation of the physical displacement of features from one image (time) to the next. This process assumes that the features being tracked do not change as a function of time, usually requiring the use of short time interval imagery to minimize substantial change in size and shape of the features being tracked. High spatial resolution imagery also is required for reliable feature identification. While these image resolution and temporal sampling requirements often provide major drivers for space-based instrument design requirements, accurate image navigation and registration, INn (between a sequence of images), is also critical to the derivation of useful AMVs. In this paper and poster to be presented at the conference, the image navigation and registration (INR) accuracy expected for the Advanced Baseline Imager (ABI) on the GOES-R series of satellites will be discussed in light of its impact on AMV accuracy. Significant satellite platform and modeling enhancements are planned which should significantly improve INn performance of the GOES-R instruments. Some of these improvements have been demonstrated for the GOES-13 satellite which was launched in summer of 2006. An analysis of GOES-13 INR data, from the special satellite check out period, will be used in the assessment