Channel-to-channel co-registration is an important performance metric for the Geostationary Operational Environmental Satellite (GOES) Imager, and large co-registration errors can have a significant impact on the reliability of derived products that rely on combinations of multiple infrared (IR) channels. Affected products include the cloud mask, fog and fire detection. This is especially the case for GOES-13, in which the co-registration error between channels 2 (3.9 μm) and 4 (10.7 μm) can be as large as 1 pixel (or ∼4 km) in the east-west direction. The GOES Imager IR channel-to-channel co-registration characterization (GII4C) algorithm is presented, which allows a systematic calculation of the co-registration error between GOES IR channel image pairs. The procedure for determining the co-registration error as a function of time is presented. The algorithm characterizes the co-registration error between corresponding images from two channels by spatially transforming one image using the fast Fourier transformation resampling algorithm and determining the distance of the transformation that yields the maximum correlation in brightness temperature. The GII4C algorithm is an area-based approach which does not depend on a fixed set of control points that may be impacted by the presence of clouds. In fact, clouds are a feature that enhances the correlations. The results presented show very large correlations over the majority of Earth-viewing pixels, with stable algorithm results. Verification of the algorithm output is discussed, and a global spatial-spectral gradient asymmetry parameter is defined. The results show that the spatial-spectral gradient asymmetry is strongly correlated to the co-registration error and can be an effective global metric for the quality of the channel-to-channel co-registration characterization algorithm. Implementation of the algorithm in the GOES ground system is presented. This includes an offline component to determine the time dependence of the co-registration errors and a real-time component to correct the co-registration errors based on the inputs from the offline component.
The image resampling algorithm, fast Fourier transformation resampling (FFTR), is introduced. The FFTR uses a global function in the Fourier expansion form to represent an image, and the image resampling is achieved by the introduction of a phase shift in the Fourier expansion. The comparison with the cubic spline interpolation approach in the image resampling is presented, which shows that FFTR is more accurate in the satellite image resampling. The FFTR algorithm is also generally reversible, because both the resampled and its original images share the same Fourier spectrum. The resampling for the images with hot spots is discussed. The hot spots in an image are the pixels with the second-order derivatives that are order of magnitude larger than the average value. The images with the hot spots are resampled with the introduction of a local Gaussian function to model the hot spot data, so that the remaining data for the Fourier expansion are continuous. Its application to the infrared channel image of Geostationary Operational Environmental Satellite Imager, to mitigate a diurnally changing band co-registration, is presented.
To track the degradation of the Imager visible channel on board NOAA’s Geostationary Operation
Environmental Satellite (GOES), a research program has been developed using the stellar observations
obtained for the purpose of instrument navigation. For monitoring the responsivity of the visible channel, we
use observations of approximately fifty stars for each Imager. The degradation of the responsivity is
estimated from a single time series based on 30-day averages of the normalized signals from all the stars.
Referencing the 30-day averages to the first averaged period of operation, we are able to compute a relative
calibration coefficient relative to the first period. Coupling this calibration coefficient with a GOES-MODIS
intercalibration technique allows a direct comparison of the star-based relative GOES calibration to a
MODIS-based absolute GOES calibration, thus translating the relative star-based calibration to an absolute
star-based calibration. We conclude with a discussion of the accuracy of the intercalibrated GOES Imager
visible channel radiance measurements.
Monitoring the responsivities of the visible channels of the Imagers on GOES satellites is a continuing effort at the National
Environmental Satellite, Data and Information Service of NOAA. At this point, a large part of the initial processing of the
star data depends on the operationalGOES Sensor Processing System(SPS) and GOES Orbit and AttitudeTracking System
(OATS) for detecting the presence of stars and computing the amplitudes of the star signals. However, the algorithms of
the SPS and the OATS are not optimized for calculating the amplitudes of the star signals, as they had been developed to
determine pixel location and observation time of a star, not amplitude. Motivated by our wish to be independent of the SPS
and the OATS for data processing and to improve the accuracy of the computed star signals, we have developed our own
methods for such computations. We describe the principal algorithms and discuss their implementation. Next we show our
monitoring statistics derived from star observations by the Imagers aboard GOES-8, -10, -11, -12 and -13. We give a brief
introduction to a new class of time series that have improved the stability and reliability of our degradation estimates.
Stars are regularly observed in the visible channels of the GOES Imagers for real-time navigation operations. However, we
have been also using star observations off-line to deduce the rate of degradation of the responsivity of the visible channels.
We estimate degradation rates from the time series of the intensities of the Imagers' output signals when viewing stars,
available in the GOES Orbit and Attitude Tracking System (OATS). We begin by showing our latest results in monitoring
the responsivities of the visible channels of the Imagers on GOES-8, -9, -10, -11 and -12. Unfortunately, the OATS
computes the intensities of the star signals with approximations suitable for navigation, not for estimating accurate signal
strengths, and thus we had to develop objective criteria for screening out unsuitable data. With several layers of screening,
our most recent trending method yields smoother time series of star signals, but the time series are populated by a smaller
pool of stars. With the goal of simplifying the task of data selection and to retrieve stars that have been rejected in
the screening, we tested a technique that accessed the raw star measurements before they were processed by the OATS.
We developed formulations that not only produced star signals more suitable for monitoring the changes in the Imager's
outputs from views of constant-irradiance stellar sources, but also gave more information on the radiometric characteristics
of the visible channels. We present specifics of this technique together with sample results. We discuss improvements in
the quality of the time series that allow for more reliable inferences on the gradually changing responsivities of the visible
channels. We describe further contributions of this method to monitoring of other performance characteristics of the visible
channel of an Imager.