Error analysis for mesospheric temperature profiling by absorptive occultation sensors
Abstract. An error analysis for mesospheric profiles retrieved from absorptive occultation data has been performed, starting with realistic error assumptions as would apply to intensity data collected by available high-precision UV photodiode sensors. Propagation of statistical errors was investigated through the complete retrieval chain from measured intensity profiles to atmospheric density, pressure, and temperature profiles. We assumed unbiased errors as the occultation method is essentially self-calibrating and straight-line propagation of occulted signals as we focus on heights of 50–100 km, where refractive bending of the sensed radiation is negligible. Throughout the analysis the errors were characterized at each retrieval step by their mean profile, their covariance matrix and their probability density function (pdf). This furnishes, compared to a variance-only estimation, a much improved insight into the error propagation mechanism. We applied the procedure to a baseline analysis of the performance of a recently proposed solar UV occultation sensor (SMAS – Sun Monitor and Atmospheric Sounder) and provide, using a reasonable exponential atmospheric model as background, results on error standard deviations and error correlation functions of density, pressure, and temperature profiles. Two different sensor photodiode assumptions are discussed, respectively, diamond diodes (DD) with 0.03% and silicon diodes (SD) with 0.1% (unattenuated intensity) measurement noise at 10 Hz sampling rate. A factor-of-2 margin was applied to these noise values in order to roughly account for unmodeled cross section uncertainties. Within the entire height domain (50–100 km) we find temperature to be retrieved to better than 0.3 K (DD) / 1 K (SD) accuracy, respectively, at 2 km height resolution. The results indicate that absorptive occultations acquired by a SMAS-type sensor could provide mesospheric profiles of fundamental variables such as temperature with unprecedented accuracy and vertical resolution. A major part of the error analysis also applies to refractive (e.g., Global Navigation Satellite System based) occultations as well as to any temperature profile retrieval based on air density or major species density measurements (e.g., from Rayleigh lidar or falling sphere techniques).
Key words. Atmospheric composition and structure (pressure, density, and temperature; instruments and techniques) – Radio science (remote sensing)