Microwave Radiometer Instability Due to Infrequent Calibration.

We directly quantify the effect of infrequent calibration on the stability of microwave radiometer temperature measurements (where a power measurement for the unknown source is acquired at a fixed time, but calibration data are acquired at variable earlier times) with robust and nonrobust implementations of a new metric. Based on our new metric, we also determine a component of uncertainty in a single measurement due to infrequent calibration effects. We apply our metric to experimental data acquired from experimental ground-based calibration data acquired from a NASA millimeter-wave imaging radiometer and a NIST radiometer (Noise Figure Radiometer-NFRad). Based on a stochastic model for the NFRad, we determine the random uncertainty of an empirical prediction model of our stability metric by a Monte Carlo method. For comparison purposes, we also present a secondary metric that quantifies stability for the case where calibration data are acquired at a fixed time, but power measurements for the unknown source are acquired at variable later times.

Analysis of Nonstationary Radiometer Gain Using Ensemble Detection.

Radiometer gain is generally a nonstationary random process, even though it is assumed to be strictly or weakly stationary. Since the radiometer gain signal cannot be observed independently, analysis of its nonstationary properties would be challenging. However, using the time series of postgain voltages to form an ensemble set, the radiometer gain may be characterized via radiometer calibration. In this article, the ensemble detection algorithm is presented by which the unknown radiometer gain can be analytically characterized when it is following a Gaussian model (as a strictly stationary process) or a 1st order autoregressive, AR(1) model (as a weakly stationary process). In addition, in a particular radiometer calibration scheme, the nonstationary gain can also be represented as either Gaussian or AR(1) process, and parameters of such an equivalent gain model can be retrieved. However, unlike stationary or weakly stationary gain, retrieved parameters of the Gaussian and AR(1) processes, which describe the nonstationary gain, highly depend on the calibration setup and timings.

ANALYSIS OF NON-STATIONARY RADIOMETER GAIN VIA ENSEMBLE DETECTION.

Although considered as stationary and Gaussian in general, radiometer gain is usually a fluctuating signal with non-stationary properties. Analyses of such non-stationary features is challenging as the radiometer signal cannot be observed independently. On the other hand, time series of post-gain voltages constitute an ensemble set for the radiometer gain which can be used to characterize the radiometer gain. This paper presents a novel technique called "Ensemble Detection" which can analytically retrieve the standard deviation of stationary Gaussian radiometer gain or find an equivalent stationary Gaussian process which represents the non-stationary radiometer gain under different calibration schemes. It has been found that the equivalent Gaussian process for non-stationary radiometer gain heavily depends on the calibration structure and the observation times of the measurand and the calibration references.