Determination of the best optimal estimation parameters for validation of infrared hyperspectral sounding retrievals
Xavier Calbet

TL;DR
This paper introduces an analytical method to optimize parameter estimation in infrared hyperspectral sounding retrievals, improving atmospheric profile accuracy from satellite data like AIRS and IASI.
Contribution
It presents a simple, objective technique for deriving optimal estimation parameters that enhance validation accuracy of atmospheric retrievals from hyperspectral infrared instruments.
Findings
Retrieval errors are between 0.9 and 1.9 K for temperature.
Humidity dew point temperature errors are below 6.5 K.
Optimized parameters improve validation statistics of atmospheric profiles.
Abstract
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…
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Taxonomy
TopicsAtmospheric Ozone and Climate · Atmospheric and Environmental Gas Dynamics · Calibration and Measurement Techniques
