Abstract
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 non-robust 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 ground-based calibration data acquired from a NASA millimeter-wave imaging radiometer (MIR) and a NIST radiometer (NFRad). We demonstrate that the physical interpretation of our new metric is more clear than that of the existing variogram metric. Based on a stochastic model for NFRad, we determine the random uncertainty of our stability metric by a Monte Carlo method.
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 non-robust 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 ground-based calibration data acquired from a NASA millimeter-wave imaging radiometer (MIR) and a NIST radiometer (NFRad). We demonstrate that the physical interpretation of our new metric is more clear than that of the existing variogram metric. Based on a stochastic model for NFRad, we determine the random uncertainty of our stability metric by a Monte Carlo method.