Integrative fitting of absorption line profiles with high accuracy, robustness, and speed

Abstract

The principle of the integrative evaluation of absorption line profiles relies on the numeric integration of absorption line signals to retrieve absorber concentrations, e.g., of trace gases. Thus, it is a fast and robust technique. However, previous implementations of the integrative evaluation principle showed shortcomings in terms of accuracy and the lack of a fit quality indicator. This has motivated the development of an advanced integrative (AI) fitting algorithm. The AI fitting algorithm retains the advantages of previous integrative implementations—robustness and speed—and is able to achieve high accuracy by introduction of a novel iterative fitting process. A comparison of the AI fitting algorithm with the widely used Levenberg–Marquardt (LM) fitting algorithm indicates that the AI algorithm has advantages in terms of robustness due to its independence from appropriately chosen start values for the initialization of the fitting process. In addition, the AI fitting algorithm shows speed advantages typically resulting in a factor of three to four shorter computational times on a standard personal computer. The LM algorithm on the other hand retains advantages in terms of a much higher flexibility, as the AI fitting algorithm is restricted to the evaluation of single absorption lines with precomputed line width. Comparing both fitting algorithms for the specific application of in situ laser hygrometry at 1,370 nm using direct tunable diode laser absorption spectroscopy (TDLAS) suggests that the accuracy of the AI algorithm is equivalent to that of the LM algorithm. For example, a signal-to-noise ratio of 80 and better typically yields a deviation of <1 % between both fitting algorithms. The properties of the AI fitting algorithm make it an interesting alternative if robustness and speed are crucial in an application and if the restriction to a single absorption line is possible. These conditions are fulfilled for the 1,370 nm TDLAS hygrometry at the aerosol and cloud chamber aerosol interactions and dynamics in the atmosphere (AIDA)—a unique large-scale facility to study atmospheric processes. The robustness of the AI fitting algorithm has been validated for typical AIDA conditions encompassing strong transmission fluctuations during the formation of droplet or ice clouds inside AIDA. Under these conditions, the stability of the AI algorithm remained virtually unaffected. Thus, the AI algorithm presents an alternative technique for a fast, reliable, and accurate online data evaluation of the humidity measurements at AIDA.