Although the visible channel of the Imagers carried by NOAA's operational Geostationary Operational Environmental Satellites (GOES) has no onboard calibration device, the decrease in the responsivity of this channel over time must be known if we are to make the data in this channel useful for detecting trends in the signals from the Earth. Therefore, some external method is required to provide this information. In this paper, we examine an external technique for monitoring responsivity changes based on empirical distribution functions (EDFs) of observations of the Earth's full disk. A time series of instrument outputs (in digital counts) at fixed levels at the tops of the EDFs is produced. A nonlinear least squares technique is then employed to adjust the time series for solar and seasonal effects and to fit it with an exponential, whose argument provides the rate of degradation of the responsivity. This technique assumes that the probabilistic structure of the signal from the earth does not change over time. The resulting time series and estimated responsivity degradation rates for the visible channels of GOES-8 and -10 Imagers will be presented. These results are similar to those obtained earlier with a star-based technique, thus increasing our confidence in the results of both techniques. The EDF technique and the star-based technique are synergistic, as they use very different approaches and data sets. Also, the star based technique works at the low end of the Imager's output signal range, whereas the EDF technique works at the high end.
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, 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 on GOES-8, GOES-10 and GOES-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 supported 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 produced star signals in a manner more suitable for monitoring the conditions of the visible channels. We present specifics of this process together with sample results. We discuss improvements in the quality of the time series that allow for more reliable inferences on the characteristics of the visible channels.
Monitoring the responsivities of the visible channels of the operational Geostationary Operational Environmental Satellites (GOES) is an on-going effort at NOAA. Various techniques are being used. In this paper we describe the technique based on the analysis of star signals that are used in the GOES Orbit and Attitude Tracking System (OATS) for satellite attitude and orbit determination. Time series of OATS star observations give information on the degradation of the detectors of a visible channel. Investigations of star data from the past three years have led to several modifications of the method we initially used to calculate the exponential degradation coefficient of a star-signal time series. First we observed that different patterns of detector output versus time result when star images drift across the detector array along different trajectories. We found that certain trajectories should be rejected in the data analysis. We found also that some detector-dependent weighting coefficients used in the OATS analysis tend to scatter the star signals measured by different detectors. We present a set of modifications to our star monitoring algorithms for resolving such problems. Other simple enhancements on the algorithms will also be described. With these modifications, the time series of the star signals show less scatter. This allows for more confidence in the estimated degradation rates and a more realistic statistical analysis on the extent of uncertainty in those rates. The resulting time series and estimated degradation rates for the visible channels of GOES-8 and GOES-10 Imagers will be presented.
Techniques which use empirical distribution functions have been used successfully to intercalibrate detectors on the same instrument or channels on different instruments. In this paper the procedure is developed as a statistical technique for estimating a function. Since it is a statistical procedure, it is possible to use classical statistical techniques to evaluate the accuracy of the results. Confidence regions can be constructed for the estimated parameters. Given a required degree of accuracy it is possible to estimate the required sample sizes. Using hypotheses testing techniques questions about possible changes in the inter-calibration functions can be answered. The theory, which uses quantiles from the distributions of random variable, is outlined. The large-sample properties of the estimators of these quantiles are presented. The theory requires that the data be continuous. However, it is discrete. Preliminary simulation results indicate that the theory for continuous data can be applied to the discrete data with little loss of accuracy.
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