Automated, unsupervised inversion of multiwavelength lidar data with TiARA: assessment of retrieval performance of microphysical parameters using simulated data

Improved algorithm for retrieving aerosol optical properties based on multi-wavelength Raman lidar

Multi-wavelength Raman lidar has been widely used in profiling aerosol optical properties. The accuracy of measured aerosol optical properties largely depends on sophisticated lidar data retrieval algorithms. Commonly to retrieve aerosol optical properties of Raman lidar, the extinction-related Ångström exponent (EAE) is assumed (to be 1). This value usually generally differs from the true value (called EAE deviation) and adds uncertainty to the retrieved aerosol optical properties. Lidar-signal noise and EAE-deviation are two important error sources for retrieving aerosol optical properties. As the measurement accuracy of Raman lidar has been greatly improved in recent years, the influence of signal noise on retrieval results becomes relatively small, and the uncertainty of retrieved aerosol optical properties caused by an EAE-deviation becomes nonnegligible, especially in scenes that EAE deviation is large. In this study, an iteration retrieval algorithm is proposed to obtain more reliable EAE based on multi-wavelength Raman lidar. Results from this iteration are more precise values of aerosol optical properties. Three atmospheric scenarios where aerosol distribution and the values of EAE vary widely were simulated with a Monte Carlo method to analyze the characteristics and robustness of the iterative algorithm. The results show that the proposed iterative algorithm can eliminate the systematic errors of aerosol optical properties retrieved by traditional retrieval method. The EAEs after iteration does converge to the true value, and the accuracy of aerosol optical properties can be greatly improved, especially for the particle backscatter coefficient and lidar ratio, which has been improved by more than 10% in most cases, and even more than 30%. In addition, field observations data of a three-wavelength Raman lidar are analyzed to illustrate the necessity and reliability of the proposed iterative retrieval algorithm.

Pre-filter analysis for retrieval of microphysical particle parameters: a quality-assurance method applied to 3 backscatter (β) +2 extinction (α) optical data taken with HSRL/Raman lidar

We analyze the solution space of 3β+2α optical data inferred from lidar measurements, i.e., backscatter coefficients at three wavelengths and extinction coefficients at two wavelengths. These optical data are governed by microphysical parameters that can be expressed in terms of particle size distribution, effective radius, and complex refractive index (CRI). In our analysis, we consider two scenarios of the solution space. First, it can be expressed in terms of monomodal particle size distributions represented either by fine modes or by coarse modes. Secondly, the particle size distributions contain a fine mode as well as a coarse mode. Consideration of both scenarios and different values of the effective radius and CRI allows us to find synthetic 3β+2α optical data and corresponding intensive parameters (IPs) such as lidar ratios, backscatter- and extinction-related Ångström exponents at the available measurement wavelengths. Based on interdependencies between synthetic IPs and various microphysical properties, the qualitative and quantitative criteria for the optical data quality-assurance tool are developed. We derive the conditions of smoothness, closeness, convergence, and stability of the solution space for the quantitative criteria to test the quality of the 3β+2α optical data. Our novel methodology, to the best of our knowledge, can be used not only for particles of spherical shape, but also for cases in which particles are irregularly shaped. Another strength of our methodology is that it also works for the case of a size-dependent and wavelength-dependent CRI. We show the potential of this methodology for a measurement case from the ORACLES campaign. Data were taken with NASA Langley's airborne HSRL-2 instrument on September 24, 2016.

LITES: rotational Raman spectra of air molecules measured by high-resolution-spectroscopy lidar

We present the first, to the best of our knowledge, measurements from a new lidar facility that was designed and built at the University of Hertforshire since 2012. LITES (Lidar Innovations for Technologies and Environmental Sciences) allows testing, developing, and measuring of a multitude of climate-change relevant parameters of atmospheric particulate pollution and photochemically reactive trace gases. The core of LITES consists of a lidar spectroscopy instrument. In this first contribution, for example, we present the design and specifications of this instrument, its performance, and potential applications. First, we show examples of the measurements of range-resolved pure rotational Raman spectra and rotational-vibrational Raman spectra of air molecules with a spectral resolution better than 5cm^-1. We also present day-time temperature profiles obtained from pure rotational spectroscopic lidar signals. In future work, we aim to explore the potential of our multi-channel high-resolution spectrometric lidar to obtain vertically resolved chemical characterization of aerosols and trace gases.

The confounding effect of multi-type human papillomavirus infections on type-specific natural history parameter identification

The natural history of human papillomavirus (HPV) from infection to cervical cancer differs between HPV types. Accordingly, type-specific natural history parameters are crucial for the mathematical models used to optimize the nearly life-long series of disease prevention measures. These parameters are estimated from genotyped data from trials and population level screening programs, typically one type at a time, which requires projecting the multiple-type data to the single type. To analyze impacts of such projection methods on the estimates, we compared estimating one type at a time using different projection methods with estimating all types together. We simulated genotyped data with chosen parameter values for two HPV types and analyzed the identifiability of the chosen values using the different estimation methods. We found the success of estimating one type at a time to be excessively sensitive to the data projection method, with potential to falsely identify the parameters at wrong values. Estimating all types together identified the parameters well. Our results were consistent both when trial and population level data were used. In conclusion, the potential confounding by multi-type infections has to be considered when choosing an estimation method for type-specific natural history parameters.