  The availability of hyperspectral infrared remote sensing instruments, like
AIRS and IASI, on board of Earth observing satellites opens the possibility of
obtaining high vertical resolution atmospheric profiles. We present an
objective and simple technique to derive the parameters used in the optimal
estimation method that retrieve atmospheric states from the spectra. The
retrievals obtained in this way are optimal in the sense of providing the best
possible validation statistics obtained from the difference between retrievals
and a chosen calibration/validation dataset of atmospheric states. This is
demonstrated analytically. To illustrate this result several real world
examples using IASI retrievals fine tuned to ECMWF analyses are shown. The
analytical equations obtained give further insight into the various
contributions to the biases and errors of the retrievals and the consequences
of using other types of fine tuning. Retrievals using IASI show an error of 0.9
to 1.9 K in temperature and below 6.5 K in humidity dew point temperature in
the troposphere on the vertical radiative transfer model pressure grid
(RTIASI-4.1), which has a vertical spacing between 300 and 400 m. The more
accurately the calibration dataset represents the true state of the atmosphere,
the better the retrievals will be when compared to the true states.
