## Abstract

Radiometric calibration relative the Triple-Point-of-Water, tpw, is statistical by nature with an uncertain energy density. The Planck Equation yields specific energy density distribution for the tpw, energy peaks greater than the tpw density distribution are additive while energy peaks less than are subtractive. To assess radiometric calibration traced to the International System of Units, SI, correction for spectral energy density above and below the tpw is required, the excess subtracted and the missing added to estimated energy density. We note instrument, ambient laboratory or operational environment temperatures (spectral energy) greater than the tpw will not be in thermal equilibrium unless they are at the tpw. Less than or greater than, radiative transfer occurs until equilibrium is achieved. This paper is unconventional, an extensive Power Point set of slides, ~160, to illustrate the statistical issues encountered when a mean ambient temperature prevails with the multiple experiment venues. Numerical data analysis uses linear transforms and statistics.

Statistical Calibration Relative to the Triple Point of Water (Visibility, Signal, Noise per pixel per data frame)

Radiometric calibration relative the Triple-Point-of-Water, tpw, is statistical by nature with an uncertain energy density. The Planck Equation yields specific energy density distribution for the tpw, energy peaks greater than the tpw density distribution are additive while energy peaks less than are subtractive. To assess radiometric calibration traced to the International System of Units, SI, correction for spectral energy density above and below the tpw is required, the excess subtracted and the missing added to estimated energy density. We note instrument, ambient laboratory or operational environment temperatures (spectral energy) greater than the tpw will not be in thermal equilibrium unless they are at the tpw. Less than or greater than, radiative transfer occurs until equilibrium is achieved. This paper is unconventional, an extensive Power Point set of slides, ~160, to illustrate the statistical issues encountered when a mean ambient temperature prevails with the multiple experiment venues. Numerical data analysis uses linear transforms and statistics.