Abstract
We evaluate the retrieval performance of the automated, unsupervised inversion algorithm, Tikhonov Advanced Regularization Algorithm (TiARA), which is used for the autonomous retrieval of microphysical parameters of anthropogenic and natural pollution particles. TiARA (version 1.0) has been developed in the past 10 years and builds on the legacy of a data-operator-controlled inversion algorithm used since 1998 for the analysis of data from multiwavelength Raman lidar. The development of TiARA has been driven by the need to analyze in (near) real time large volumes of data collected with NASA Langley Research Center's high-spectral-resolution lidar (HSRL-2). HSRL-2 was envisioned as part of the NASA Aerosols-Clouds-Ecosystems mission in response to the National Academy of Sciences (NAS) Decadal Study mission recommendations 2007. TiARA could thus also serve as an inversion algorithm in the context of a future space-borne lidar. We summarize key properties of TiARA on the basis of simulations with monomodal logarithmic-normal particle size distributions that cover particle radii from approximately 0.05 μm to 10 μm. The real and imaginary parts of the complex refractive index cover the range from non-absorbing to highly light-absorbing pollutants. Our simulations include up to 25% measurement uncertainty. The goal of our study is to provide guidance with respect to technical features of future space-borne lidars, if such lidars will be used for retrievals of microphysical data products, absorption coefficients, and single-scattering albedo. We investigate the impact of two different measurement-error models on the quality of the data products. We also obtain for the first time, to the best of our knowledge, a statistical view on systematic and statistical uncertainties, if a large volume of data is processed. Effective radius is retrieved to 50% accuracy for 58% of cases with an imaginary part up to 0.01i and up to 100% of cases with an imaginary part of 0.05i. Similarly, volume concentration, surface-area concentration, and number concentrations are retrieved to 50% accuracy in 56%-100% of cases, 99%-100% of cases, and 54%-87% of cases, respectively, depending on the imaginary part. The numbers represent measurement uncertainties of up to 15%. If we target 20% retrieval accuracy, the numbers of cases that fall within that threshold are 36%-76% for effective radius, 36%-73% for volume concentration, 98%-100% for surface-area concentration, and 37%-61% for number concentration. That range of numbers again represents a spread in results for different values of the imaginary part. At present, we obtain an accuracy of (on average) 0.1 for the real part. A case study from the ORCALES field campaign is used to illustrate data products obtained with TiARA.
Highlights
NASA’s Aerosol-Clouds-Ecosystem (ACE) mission called for the development of active and passive remote sensors for investigating climate forcing by aerosol particles and clouds
We present the results of the inversion of optical data taken with the high-spectral-resolution lidar (HSRL)-2 during the ObseRvations of Aerosols above CLouds and their IntEractionS (ORACLES) mission
We presented the main results of extensive simulation studies carried out with our new, autonomous inversion algorithm, Tikhonov Advanced RegularizationAlgorithm (TiARA)
Summary
NASA’s Aerosol-Clouds-Ecosystem (ACE) mission (https://acemission.gsfc.nasa.gov/whitepapers.html) called for the development of active and passive remote sensors for investigating climate forcing by aerosol particles and clouds. These instruments are flown on aircraft and serve as research instruments and testing platforms for a potential space-borne HSRL. In this contribution, we present a summary of the performance characteristics of the Tikhonov Advanced Regularization. TiARA allows us to infer uncertainty bars (in terms of bias and noise) from measurements of atmospheric aerosols with HSRL-2. In this first stage of the algorithm development, we have focused on optimizing TiARA for the case of spherical particle geometry, mainly for the lack of a light-scattering model that allows for a trustworthy description of light-scattering at 180° of non-spherical particles. We analyzed backscatter coefficients only at nm, 532 nm, and 1064 nm, and extinction coefficients at 355 nm and 532 nm, which we denote as “3β 2α” or “3 + 2” configuration
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