Comment on amt-2022-262

Recent studies utilizing satellite retrievals have shown a strong correlation between aerosol optical depth (AOD) and cloud cover. However, these retrievals from passive sensors are subject to many limitations, including cloud adjacency (or three‐dimensional) effects, possible cloud contamination, uncertainty in the AOD retrieval. Some of these limitations do not exist in High Spectral Resolution Lidar (HSRL) observations; for instance, HSRL observations are not affected by cloud adjacency effects, are less prone to cloud contamination, and offer accurate aerosol property measurements (backscatter coefficient, extinction coefficient, lidar ratio, backscatter Angstrom exponent, and aerosol optical depth) at a fine spatial resolution (<100 m) in the vicinity of clouds. Hence the HSRL provides an important data set for studying aerosol and cloud interaction. In this study, we statistically analyze aircraft‐based HSRL profiles according to their distance from the nearest cloud, assuring that all profile comparisons are subject to the same large‐scale meteorological conditions. Our results indicate that AODs from HSRL are about 8–17% higher in the proximity of clouds (∼100 m) than far away from clouds (4.5 km), which is much smaller than the reported cloud three‐dimensional effect on AOD retrievals. The backscatter and extinction coefficients also systematically increase in the vicinity of clouds, which can be explained by aerosol swelling in the high relative humidity (RH) environment and/or aerosol growth through in‐cloud processing (albeit not conclusively). On the other hand, we do not observe a systematic trend in lidar ratio; we hypothesize that this is caused by the opposite effects of aerosol swelling and aerosol in‐cloud processing on the lidar ratio. Finally, the observed backscatter Angstrom exponent (BAE) does not show a consistent trend because of the complicated relationship between BAE and RH. We demonstrate that BAE should not be used as a surrogate for Angstrom exponent, especially at high RH.

A simple high spectral resolution lidar technique using a multi-longitudinal mode laser is proposed for measuring aerosol extinction and backscattering coefficients. A scanning interferometer having the same free spectral range as the mode spacing of the laser is used to separate Rayleigh from Mie scattering. Scanning the interferometer in the span of one fringe, the lidar signals at the minimum and maximum Mie-scattering transmission are measured. The Rayleigh scattering signal is analyzed from these signals, and the aerosol extinction coefficient is derived. The interferometer transmittance for Mie scattering is calibrated with the reference signals taken with a portion of the transmitted laser beam.

The LIDAR equation contains four unknown variables in a two-component atmosphere where the effects caused by both molecules and aerosols have to be considered. The inversion of LIDAR returns to retrieve aerosol extinction profiles, thus, calls for some functional relationship to be assumed between these two. The Klett's method, assumes a functional relationship between the extinction and backscatter. In this paper, we apply a different technique, called the optical depth solution, where we made use of the total optical depth or transmittance of the atmosphere along the LIDAR-measurement range. This method provides a stable solution to the LIDAR equation. In this study, we apply this technique to the data obtained using a micro pulse LIDAR (MPL, model 1000, Science and Engineering Services Inc) to retrieve the vertical distribution of aerosol extinction coefficient. The LIDAR is equipped with Nd-YLF laser at an operating wavelength of 523.5 nm and the data were collected over Bangalore. The LIDAR data are analyzed to get to weighted extinction coefficient profiles or the weighted sum of aerosol and molecular extinction coefficient profiles. Simultaneous measurements of aerosol column optical depth (at 500 nm) using a Microtops sun photometer were used in the retrievals. The molecular extinction coefficient is determined assuming standard atmospheric conditions. The aerosol extinction coefficient profiles are determined by subtracting the molecular part from the weighted extinction coefficient profiles. The details of the method and the results obtained are presented.

Quantitative measurements of atmospheric aerosol optical properties are required for studies of the Earth’s radiation budget and climate change. Taking advantage of the broad spectrum of the Cabannes-Brillouin scattering from atmospheric molecules, the high spectral resolution lidar (HSRL) technique employs a narrow spectral filter to reject the aerosol Mie scattering component in the lidar return signals. Therefore, an HSRL can directly measure the extinction and backscatter coefficient as well as the lidar ratio. Since the backscattering signal is proportional to 1/λ4, it presents high requirements for the spectral filter to build a near-infrared HSRL. The atomic/molecular absorption filters are limited by the wavelength and it is also challenging for Fabry-Perot interferometers (FPI) due to their small field of view(FOV). The field-widened Michelson interferometer, which has a large FOV, is considered to be a good candidate for the spectral filter of near-infrared HSRL. A polarized near-infrared HSRL instrument, which employs a field-widened Michelson interferometer as the spectral filter, is under development at the Zhejiang University (ZJU), China. In this paper, the methodology and design process of the instrument will be described in detail. The capability of the HSRL in determining the properties of the atmosphere will be addressed. The retrieval of the aerosol optical properties, such as extinction-to-backscatter ratio and aerosol depolarization ratio, will be presented. Sensitivity of the aerosol retrieval to errors in characterizing the spectral filter will also be investigated.

Conventional lidar methods for deriving particle optical properties suffer from the fact that two unknowns (backscatter and extinction coefficients) need to be determined from only one lidar equation. Thus, additional assumptions (constant lidar ratio or Ångström relationship) have to be introduced to settle this problem. In contrast, a single-line-extracted pure-rotational-Raman (PRR) lidar method allows the strict retrieval of backscatter and extinction coefficients without additional assumptions. Based on the observations of our single-line-extracted PRR lidar from February 2016 to December 2017, the optical properties (backscatter coefficient, extinction coefficient and lidar ratio) of continental polluted aerosols, dust aerosols, and cirrus cloud particles over Wuhan (30.5°N, 114.4°E) are well characterized. The mean values of the measured lidar ratios are respectively 60 ± 7 sr for continental polluted aerosols, 47 ± 4 sr for dust aerosols and 22 ± 4 sr for cirrus cloud particles. The backscatter and extinction coefficients measured by the single-line-extracted PRR lidar deviate as a whole by 7-13% and 13-16%, respectively, from those retrieved by the traditional Fernald method. The optical properties measured by the single-line-extracted PRR lidar can serve as observational standards for particle optical properties (backscatter/extinction coefficient and lidar ratio) at 532 nm wavelength.

<p>The Aladin instrument on-board the ESA Earth Explorer satellite Aeolus is a UV high spectral resolution Doppler Wind Lidar. The main mission product is profiles of horizontally projected line-of-sight winds, and the instrument design is therefore optimized to measure Doppler shifts of the atmospheric backscatter signals compared to the UV light emitted at ~355 nm (ESA, 2008; Stoffelen, 2005). Since the lidar backscatter contains information on the location of optically thin aerosol and cloud layers and cloud tops, spin-off products have been developed to retrieve aerosol and cloud backscatter and extinction coefficient and lidar ratio profile products (ESA, 2008; Flamant, 2008; Flamant, 2017). The advantage of a high spectral resolution lidar is that it measures molecular and particle backscatter separately in two dedicated channels. Still, some contributions from molecular backscatter exists in the measurements from the Fizeau channel and vice versa. This channel cross-talk requires correction during the product retrieval.</p><p>The Aeolus L2A operational aerosol and cloud retrieval algorithm is applying the so-called high spectral resolution retrieval method for the calculation of the particle and extinction backscatter coefficient products. The algorithm, developed at IPSL and Météo-France, is called the Standard Correct Algorithm (SCA) (Flamant, 2008; Flamant, 2017). High signal noise is obtained due to ever-decreasing laser energies and instrument receive path transmission. As a result, the Aeolus SCA optical properties retrieval is hampered. Particularly the ill-posed particle extinction coefficient retrieval is severely affected. In the past, attempts were made to mitigate nonphysical optical properties by measures like zero-flooring or signal accumulation in even coarser range gates (Flamant, 2017). Their success was limited.</p><p>An alternative noise suppression approach by Maximum Likelihood Estimation has therefore been prototyped that permits the retrieval of extinction coefficients and lidar ratios solely within pre-defined physical bounds. The optical properties are fitted to the 24 Aeolus atmospheric range gates within single atmospheric columns, minimizing the corresponding distance to the observed L1B useful signals measured by both spectrometers. This up to 48-dimensional non-linear regression problem is solved by means of the L-BFGS-B algorithm (Zhu, 1997). The method has proven its usefulness in noise suppression with astonishing efficiency. Particularly, the retrieved extinction coefficient profiles are less noisy, clearly revealing atmospheric layers also visible in the L1B useful signal profiles. The method is validated on end-to-end simulations and in-orbit observations.</p><p><strong>References<br></strong></p><p>ESA, ADM-Aeolus Science Report. ESA SP-1311, ESA Communication Production Office, 121 pp., 2008, available on http://www.esa.int/aeolus.</p><p>Flamant, P. H., Cuesta, J., Denneulin, M.-L., Dabas, A., Huber, D. ADM-Aeolus retrieval algorithms for aerosol and cloud products, Tellus, 60A, 273-286, 2008, https://doi.org/10.1111/j.1600-0870.2007.00287.x.</p><p>Flamant, P. et al. ADM-Aeolus L2A Algorithm Theoretical Baseline Document, 2017, available on https://earth.esa.int/aos/AeolusCalVal.</p><p>Stoffelen, A. et al. The atmospheric dynamics mission for global wind field measurement, Bulletin of the American Meteorological Society, 86, 73-88, 2005, https://doi.org/10.1175/BAMS-86-1-73.</p><p>Zhu, C., Byrd R. H. and Nocedal, J. L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization, ACM Transactions on Mathematical Software, 23 (4), 550-560, 1997, https://doi.org/10.1145/279232.279236. <br><br></p>

Regular aerosol extinction and backscatter measurements using a UV Raman lidar have been performed from January 2001 to December 2004 at Thessaloniki, Greece (40.5°N, 22.9°E), in the framework of the European Aerosol Research Lidar Network (EARLINET). Profiles of the aerosol extinction coefficient, backscatter coefficient, and extinction‐to‐backscatter ratio (so‐called “lidar ratio”) were acquired under nighttime conditions and have been used for statistical investigations. The statistical analysis was made both for the planetary boundary layer and for the free troposphere. It was found that the 4‐year mean boundary layer particle optical depth at 355 nm was 0.44 ± 0.18, and the total aerosol optical depth was 0.63 ± 0.27. Free tropospheric particles account on the average for 30% of the total aerosol optical depth, ranging from 5% (clean free troposphere conditions) to 55% (mainly Saharan dust events). For the cases examined, the integral of the lidar‐derived extinction coefficient was in good agreement with colocated aerosol optical depth measurements at 355 nm obtained with a Brewer spectroradiometer. The mean value of the lidar ratio at 355 nm derived, for the period of measurements at Thessaloniki, was 40 sr with a standard deviation of 21 sr. Mean height profiles of the particle lidar ratio, extinction, and backscatter coefficients are shown along with their seasonal dependence, showing a significant seasonal variability in the free troposphere. An analysis of the data using back trajectories showed also a dependence of the aerosol optical depth and the lidar ratio on the origin of the air mass, with higher values mostly corresponding to air masses originating from the northeast Balkans and eastern Europe.

Abstract. The Atmospheric Environment Monitoring Satellite (AEMS), also called Daqi-1 or DQ-1, was launched in April 2022; one of its main payloads is a high-spectral-resolution lidar (HSRL) system. This new system enables the accurate measurements of global aerosol optical properties, which can be used in the geoscientific community after the retirement of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. Developing a suitable retrieval algorithm and validating retrieved results are necessary. This research demonstrates a retrieval algorithm for aerosol optical properties using the DQ-1 HSRL system. This method has retrieved the aerosol linear depolarization ratio, backscatter coefficient, extinction coefficient, and optical depth. For validation purposes, we compared retrieved results with those obtained through CALIPSO. The results indicate that the profiles of the two datasets are in good agreement, with DQ-1 showing an improved signal-to-noise ratio (SNR). Optical property profiles from National Aeronautics and Space Administration (NASA) Micro-Pulse Lidar Network (MPLNET) stations were selected for validation with the DQ-1 measurements, resulting in a relative error of 25 %. Between June 2022 and December 2022, aerosol optical depth measurements using the DQ-1 satellite and the AErosol RObotic NETwork (AERONET) were correlated and yielded a value of R2 equal to 0.803. We use the DQ-1 dataset to initially investigate the transport processes of the Saharan dust and the South Atlantic volcanic aerosols. These validations and applications show that the DQ-1 HSRL system can accurately measure global aerosols and has significant potential for Earth scientific applications.

The objective of this project was to provide vertically and horizontally resolved data on aerosol optical properties to assess and ultimately improve how models represent these aerosol properties and their impacts on atmospheric radiation. The approach was to deploy the NASA Langley Airborne High Spectral Resolution Lidar (HSRL) and other synergistic remote sensors on DOE Atmospheric Science Research (ASR) sponsored airborne field campaigns and synergistic field campaigns sponsored by other agencies to remotely measure aerosol backscattering, extinction, and optical thickness profiles. Synergistic sensors included a nadir-viewing digital camera for context imagery, and, later in the project, the NASA Goddard Institute for Space Studies (GISS) Research Scanning Polarimeter (RSP). The information from the remote sensing instruments was used to map the horizontal and vertical distribution of aerosol properties and type. The retrieved lidar parameters include profiles of aerosol extinction, backscatter, depolarization, and optical depth. Products produced in subsequent analyses included aerosol mixed layer height, aerosol type, and the partition of aerosol optical depth by type. The lidar products provided vertical context for in situ and remote sensing measurements from other airborne and ground-based platforms employed in the field campaigns and was used to assess the predictions of transport models. Also, the measurements provide a data base for future evaluation of techniques to combine active (lidar) and passive (polarimeter) measurements in advanced retrieval schemes to remotely characterize aerosol microphysical properties. The project was initiated as a 3-year project starting 1 January 2005. It was later awarded continuation funding for another 3 years (i.e., through 31 December 2010) followed by a 1-year no-cost extension (through 31 December 2011). This project supported logistical and flight costs of the NASA sensors on a dedicated aircraft, the subsequent analysis and archival of the data, and the presentation of results in conferences, workshops, and publications. DOE ASR field campaigns supported under this project included - MAX-Mex /MILAGRO (2006) - TexAQS 2006/GoMACCS (2006) - CHAPS (2007) - RACORO (2009) - CARE/CalNex (2010) In addition, data acquired on HSRL airborne field campaigns sponsored by other agencies were used extensively to fulfill the science objectives of this project and the data acquired have been made available to other DOE ASR investigators upon request.

Determination of the boundary value of the aerosol extinction coefficient and its effects on the extinction coefficient profile of aerosol in lower atmosphere

High Spectral Resolution Lidar (HSRL) is a powerful tool for retrieving aerosol microphysical properties. This paper proposes a modified algorithm based on Look-Up Table (LUT) that enhances inversion stability and accuracy for fine-mode aerosols, particularly in lidar configurations such as 3β+2α, 3β+1α and 2β+1α. Compared to conventional methods, the proposed algorithm significantly reduces retrieval errors, especially for non-grid data, and improves robustness by mitigating ill-posedness. Furthermore, this study extends the application of retrieved aerosol microphysical properties to estimate PM2.5 mass concentration profiles, providing a novel approach for deriving PM2.5 vertical distributions from HSRL measurements. The PM2.5 mass concentration is obtained by integrating the volume concentration of particles with diameters below 2.5 μm and applying an assumed aerosol density. Validation using ground-based lidar networks and monitoring stations demonstrates that the 3β+2α configuration achieves high consistency with ground-truth PM2.5 measurements. These results highlight the potential of HSRL in air quality monitoring and address the research gap in PM2.5 vertical distribution inversion.

In using lidar to measure quantitatively the extinction and backscatter coefficients in an inhomogeneous atmosphere, a second equation is required to invert the lidar signal. This additional information is obtained when a second lidar system is added, pointing in the opposite direction with respect to the first. From the returns of the two lidar systems both the spatial extinction and backscatter coefficients can be retrieved, supplemented by the normally missing interval close to the lidar system. The proposed method requires two calibrated lidar systems and assumes the single scattering model.

The atmospheric molecular number density can be obtained from atmospheric temperature and pressure profiles and is a significant input parameter for the inversion of lidar measurements. When measurements of vertical profiles of temperature and pressure are not available, atmospheric models are typically considered a valid alternative option. This paper investigates the influence of different atmospheric models (forecast and reanalysis) on the retrieval of aerosol optical properties (extinction and backscatter coefficients) by applying Raman and elastic-only methods to lidar measurements, to assess their use in lidar data processing. In general, reanalyzes are more accurate than forecasts, but, typically, they are not delivered in time for allowing near-real-time lidar data analysis. However, near-real-time observation is crucial for real-time monitoring of the environment and meteorological studies. The forecast models used in the paper are provided by the Integrated Forecasting System operated by the European Centre for Medium-Range Weather Forecasts (IFS_ECMWF) and the Global Data Assimilation System (GDAS), whereas the reanalysis model is obtained from the fifth-generation European Centre for Medium-Range Weather Forecasts ReAnalysis v5 (ERA5). The lidar dataset consists of measurements collected from four European Aerosol Research Lidar Network (EARLINET) stations during two intensive measurement campaigns and includes more than 200 cases at wavelengths of 355 nm, 532 nm, and 1064 nm. We present and discuss the results and influence of the forecast and reanalysis models in terms of deviations of the derived aerosol optical properties. The results show that the mean relative deviation in molecular number density is always below ±3%, while larger deviations are shown in the derived aerosol optical properties, and the size of the deviation depends on the retrieval method together with the different wavelengths. In general, the aerosol extinction coefficient retrieval is more dependent on the model used than the aerosol backscatter retrievals are. The larger influence on the extinction retrieval is mainly related to the deviation in the gradient of the temperature profile provided by forecast and reanalysis models rather than the absolute deviation of the molecular number density. We found that deviations in extinction were within ±5%, with a probability of 83% at 355 nm and 60% at 532 nm. Moreover, for aerosol backscatter coefficient retrievals, different models can have a larger impact when the backscatter coefficient is retrieved with the elastic method than when the backscatter coefficient is calculated using the Raman method at both 355 nm and 532 nm. In addition, the atmospheric aerosol load can also influence the deviations in the aerosol extinction and backscatter coefficients, showing a larger impact under low aerosol loading scenarios.

<strong class="journal-contentHeaderColor">Abstract.</strong> NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of 2023, will carry a hyperspectral scanning radiometer named the Ocean Color Instrument (OCI) and two multi-angle polarimeters (MAPs): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). The MAP measurements contain rich information on the microphysical properties of aerosols and hydrosols and therefore can be used to retrieve accurate aerosol properties for complex atmosphere and ocean systems. Most polarimetric aerosol retrieval algorithms utilize vector radiative transfer models iteratively in an optimization approach, which leads to high computational costs that limit their usage in the operational processing of large data volumes acquired by the MAP imagers. In this work, we propose a deep neural network (NN) forward model to represent the radiative transfer simulation of coupled atmosphere and ocean systems for applications to the HARP2 instrument and its predecessors. Through the evaluation of synthetic datasets for AirHARP (airborne version of HARP2), the NN model achieves a numerical accuracy smaller than the instrument uncertainties, with a running time of 0.01 <span class="inline-formula">s</span> in a single CPU core or 1 <span class="inline-formula">ms</span> in a GPU. Using the NN as a forward model, we built an efficient joint aerosol and ocean color retrieval algorithm called FastMAPOL, evolved from the well-validated Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm. Retrievals of aerosol properties and water-leaving signals were conducted on both the synthetic data and the AirHARP field measurements from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign in 2017. From the validation with the synthetic data and the collocated High Spectral Resolution Lidar (HSRL) aerosol products, we demonstrated that the aerosol microphysical properties and water-leaving signals can be retrieved efficiently and within acceptable error. Comparing to the retrieval speed using a conventional radiative transfer forward model, the computational acceleration is <span class="inline-formula">10³</span> times faster with CPU or <span class="inline-formula">10⁴</span> times with GPU processors. The FastMAPOL algorithm can be used to operationally process the large volume of polarimetric data acquired by PACE and other future Earth-observing satellite missions with similar capabilities.

<strong class="journal-contentHeaderColor">Abstract.</strong> NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of 2023, will carry a hyperspectral scanning radiometer named the Ocean Color Instrument (OCI) and two multi-angle polarimeters (MAPs): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). The MAP measurements contain rich information on the microphysical properties of aerosols and hydrosols and therefore can be used to retrieve accurate aerosol properties for complex atmosphere and ocean systems. Most polarimetric aerosol retrieval algorithms utilize vector radiative transfer models iteratively in an optimization approach, which leads to high computational costs that limit their usage in the operational processing of large data volumes acquired by the MAP imagers. In this work, we propose a deep neural network (NN) forward model to represent the radiative transfer simulation of coupled atmosphere and ocean systems for applications to the HARP2 instrument and its predecessors. Through the evaluation of synthetic datasets for AirHARP (airborne version of HARP2), the NN model achieves a numerical accuracy smaller than the instrument uncertainties, with a running time of 0.01 <span class="inline-formula">s</span> in a single CPU core or 1 <span class="inline-formula">ms</span> in a GPU. Using the NN as a forward model, we built an efficient joint aerosol and ocean color retrieval algorithm called FastMAPOL, evolved from the well-validated Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm. Retrievals of aerosol properties and water-leaving signals were conducted on both the synthetic data and the AirHARP field measurements from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign in 2017. From the validation with the synthetic data and the collocated High Spectral Resolution Lidar (HSRL) aerosol products, we demonstrated that the aerosol microphysical properties and water-leaving signals can be retrieved efficiently and within acceptable error. Comparing to the retrieval speed using a conventional radiative transfer forward model, the computational acceleration is <span class="inline-formula">10³</span> times faster with CPU or <span class="inline-formula">10⁴</span> times with GPU processors. The FastMAPOL algorithm can be used to operationally process the large volume of polarimetric data acquired by PACE and other future Earth-observing satellite missions with similar capabilities.