Atmospheric Remote Sensing Research Articles

A portable reflected-sunlight spectrometer for CO2 and CH4

Abstract. Mapping the greenhouse gases (GHGs) carbon dioxide (CO2) and methane (CH4) above source regions such as urban areas can deliver insights into the distribution and dynamics of local emission patterns. Here, we present the prototype development and an initial performance evaluation of a portable spectrometer that allows for measuring CO2 and CH4 concentrations integrated along a long (>10 km) horizontal path component through the atmospheric boundary layer above a target region. To this end, the spectrometer is positioned at an elevated site from which it points downward at reflection targets in the region, collecting the reflected sunlight at shallow viewing angles. The path-integrated CO2 and CH4 concentrations are inferred from the absorption fingerprint in the shortwave–infrared (SWIR) spectral range. While mimicking the concept of the stationary California Laboratory for Atmospheric Remote Sensing – Fourier Transform Spectrometer (CLARS-FTS) in Los Angeles, our portable setup requires minimal infrastructure and is straightforward to duplicate and to operate in various locations. For performance evaluation, we deployed the instrument, termed EM27/SCA, side by side with the CLARS-FTS at the Mt. Wilson Observatory (1670 m a.s.l.) above Los Angeles for a 1-month period in April/May 2022. We determined the relative precision of the retrieved slant column densities (SCDs) for urban reflection targets to be 0.36 %–0.55 % for O2, CO2 and CH4, where O2 is relevant for light path estimation. For the partial vertical column (VCD) below instrument level, which is the quantity carrying emission information, the propagated precision errors amount to 0.75 %–2 % for the three gases depending on the distance to the reflection target and solar zenith angle. The comparison to simultaneous CLARS-FTS measurements shows good consistency, but the observed diurnal patterns highlight the need to take light scattering into account to enable detection of emission patterns.

Quality assessment of aerosol lidars at 1064 nm in the framework of the MEMO campaign

Abstract. Aerosol lidar networks can play an important role in revealing structural characteristics of the atmospheric boundary layer, the urban heat island effect, and the spatial distribution of aerosols, especially in relation to the monitoring of atmospheric pollution in megacities. To fulfill the need of the monitoring and numerical forecasting of atmospheric pollution, an aerosol lidar network is proposed by the China Meteorological Administration which serves as an important part of the “MegaCity Experiment on Integrated Meteorological Observation in China” (MEMO). To ensure a high standard of data quality and traceability of measurement error, an inter-comparison campaign, dedicated to the quality assessment of lidar systems from different institutes and manufacturers, was designed and performed at Beijing Southern Suburb Observatory in September 2021. Six Mie–Rayleigh lidar systems at 1064 nm were involved in this campaign. The strategies for lidar self-evaluations and inter-comparisons were predefined. A lidar system at 1064 nm, which was developed by the Atmospheric Remote Sensing group at Wuhan University, was selected as the reference lidar system after passing all strict self-evaluation quality checks. The reference lidar system serves as the cornerstone for evaluating the performance of other lidar systems. After using the Rayleigh fit and signal-to-noise evaluation self-tests for each individual lidar system as a fast check of the data quality, the range-corrected signal and backscatter coefficient obtained from all the lidar systems were inter-compared with a reference lidar system. In the end, the lidar systems passed the quality control/assurance, ensuring that the standard deviation of range-corrected signal could be controlled within 5 % at 500–2000 m and 10 % at 2000–5000 m. For the derived aerosol backscatter coefficients, standard deviations can be controlled within 10 % at 500–2000 and 2000–5000 m. The quality assurance strategy lays down a solid basis for atmospheric lidar at near-infrared wavelengths and will be applied in Chinese lidar network development.

A Generalized Variable Projection Algorithm for Least Squares Problems in Atmospheric Remote Sensing

This paper presents a solution for efficiently and accurately solving separable least squares problems with multiple datasets. These problems involve determining linear parameters that are specific to each dataset while ensuring that the nonlinear parameters remain consistent across all datasets. A well-established approach for solving such problems is the variable projection algorithm introduced by Golub and LeVeque, which effectively reduces a separable problem to its nonlinear component. However, this algorithm assumes that the datasets have equal sizes and identical auxiliary model parameters. This article is motivated by a real-world remote sensing application where these assumptions do not apply. Consequently, we propose a generalized algorithm that extends the original theory to overcome these limitations. The new algorithm has been implemented and tested using both synthetic and real satellite data for atmospheric carbon dioxide retrievals. It has also been compared to conventional state-of-the-art solvers, and its advantages are thoroughly discussed. The experimental results demonstrate that the proposed algorithm significantly outperforms all other methods in terms of computation time, while maintaining comparable accuracy and stability. Hence, this novel method can have a positive impact on future applications in remote sensing and could be valuable for other scientific fitting problems with similar properties.

Remote sensing of atmospheric HDO/H2O in southern California from CLARS-FTS

Atmospheric isotopologues of water vapor (e.g., HDO) are important tracers for understanding Earth's hydrological cycles. Most remote sensing and in-situ measurements of these isotopologues, however, are either column averaged values or sparse in space and time. Measurements targeting the planetary boundary layer (PBL), the part of the atmosphere that has high sensitivity to surface sources of water vapor isotopologues, are much rarer. In this study, we retrieved HDO and H2O columns from observations by the California Laboratory for Atmospheric Remote Sensing Fourier Transform Spectrometer (CLARS-FTS), a mountaintop observatory on Mt. Wilson (1.67 km a.s.l.) overlooking the Los Angeles (LA) basin in southern California. CLARS-FTS observations are highly sensitive to the lower atmosphere due to the long light path along the PBL. Retrievals were conducted using spectral windows between 6000 and 7000 cm−1 from CLARS-FTS observations (2011–2019). The isotopological abundance δD, which represents the relative difference of the HDO/H2O ratio to a standard abundance ratio, is also calculated. The averaged δD retrievals are (−156.1 ± 60.0)‰ with an uncertainty of (6.1 ± 10.2)‰ for LA Basin Survey mode and (−344.7 ± 95.0)‰ with an uncertainty of (42.4 ± 31.6)‰ for Spectralon Viewing Observation mode. In LA, the δD shows a seasonal cycle that is primarily driven by the change of atmospheric humidity. A comparison analysis shows that the δD measurements by CLARS-FTS, a collocated Total Carbon Column Observing Network (TCCON), and the TROPOspheric Monitoring Instrument (TROPOMI) are in good agreement. The difference between CLARS and TCCON δD retrievals can primarily be attributed to the difference in their observation geometries. We envision that the HDO and δD measurements from CLARS-FTS provide high spatial and temporal resolution datasets for further study of hydrological processes, such as the partitioning of the water flux into soil evaporation and transpiration, standing water evaporation, or transport and mixing from the oceans, in the LA megacity.

Principle and Research Progress of Atmospheric Remote Sensing by Intense Femtosecond Lasers

Principle and Research Progress of Atmospheric Remote Sensing by Intense Femtosecond Lasers

An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing

In this paper, we present neural network methods for predicting uncertainty in atmospheric remote sensing. These include methods for solving the direct and the inverse problem in a Bayesian framework. In the first case, a method based on a neural network for simulating the radiative transfer model and a Bayesian approach for solving the inverse problem is proposed. In the second case, (i) a neural network, in which the output is the convolution of the output for a noise-free input with the input noise distribution; and (ii) a Bayesian deep learning framework that predicts input aleatoric and model uncertainties, are designed. In addition, a neural network that uses assumed density filtering and interval arithmetic to compute uncertainty is employed for testing purposes. The accuracy and the precision of the methods are analyzed by considering the retrieval of cloud parameters from radiances measured by the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR).

A Comprehensive Description of Multi-Term LSM for Applying Multiple a Priori Constraints in Problems of Atmospheric Remote Sensing: GRASP Algorithm, Concept, and Applications

Advanced inversion Multi-term approach utilizing multiple a priori constraints is proposed. The approach is used as a base for the first unified algorithm GRASP that is applicable to diverse remote sensing observations and retrieving a variety of atmospheric properties. The utilization of GRASP for diverse remote sensing observations is demonstrated.

Aerosol profiling using radiometric and polarimetric spectral measurements in the O2 near infrared bands: Estimation of information content and measurement uncertainties

Characterization of aerosol vertical distribution in the planetary boundary layer (PBL) using passive remote sensing requires advances in the current state of the art. To quantify the performance of various passive sensor designs within a common framework we developed an aerosol climatology of the Los Angeles basin and applied observing system simulation experiments (OSSEs) to estimate the information content retrievable from a variety of sensors measuring reflected near-infrared solar radiation. In addition to simulating current and planned satellite sensors, we also characterize the sensitivity of the California Laboratory for Atmospheric Remote Sensing – Fourier Transform Spectrometer (CLARS-FTS), located at Mt. Wilson (1.67 km above sea level), which is utilized in this work as a testbed for aerosol profiling remote sensing. We estimate the impacts of spectral coverage, radiance and polarization, spectral resolution, signal to noise ratio (SNR), and number of viewing angles on the information content and retrieval uncertainties of aerosol profiles in the PBL. We found that by adding high spectral resolution (full-width half-maximum of 3 cm−1 or better), polarimetric measurements with a SNR of at least 212 to radiance measurements with SNR of 300 for both O2 A and 1∆ bands, the degrees of freedom for signal (DOFS) of a single CLARS-FTS measurement is raised from 2.1 to 2.8. This improvement is sufficient to simultaneously quantify three key parameters: aerosol optical depth, aerosol peak height, and aerosol layer thickness in the PBL. Current satellite-borne instruments (OCO-2, OCO-3, TEMPO, TROPOMI, and EPIC) and planned instruments (TEMPO, MicroCarb, SPEXone, and MAIA), individually provide a DOFS ≤ 2.25, which is insufficient to simultaneously quantify all three aerosol profiling parameters in the PBL. Joint radiometric and polarimetric measurements of the O2 A and B bands with 3 cm−1 spectral resolution, SNR of 500 for radiance and 353 for polarization, acquired at three viewing angles, can provide sufficient sensitivity to retrieve the three aerosol parameters simultaneously. The inclusion of high spectral resolution radiometric and polarimetric measurements reduces the required number of viewing angles, which is advantageous when the multiangular data are acquired with a pointable instrument. In this case a larger number of viewing angles reduces the spatial coverage that can be achieved for a given target.

Model Selection in Atmospheric Remote Sensing with an Application to Aerosol Retrieval from DSCOVR/EPIC, Part 1: Theory

The retrieval of aerosol and cloud properties such as their optical thickness and/or layer/top height requires the selection of a model that describes their microphysical properties. We demonstrate that, if there is not enough information for an appropriate microphysical model selection, the solution’s accuracy can be improved if the model uncertainty is taken into account and appropriately quantified. For this purpose, we design a retrieval algorithm accounting for the uncertainty in model selection. The algorithm is based on (i) the computation of each model solution using the iteratively regularized Gauss–Newton method, (ii) the linearization of the forward model around the solution, and (iii) the maximum marginal likelihood estimation and the generalized cross-validation to estimate the optimal model. The algorithm is applied to the retrieval of aerosol optical thickness and aerosol layer height from synthetic measurements corresponding to the Earth Polychromatic Imaging Camera (EPIC) instrument onboard the Deep Space Climate Observatory (DSCOVR) satellite. Our numerical simulations show that the heuristic approach based on the thesolution minimizing the residual, which is frequently used in literature, is completely unrealistic when both the aerosol model and surface albedo are unknown.

Model Selection in Atmospheric Remote Sensing with Application to Aerosol Retrieval from DSCOVR/EPIC. Part 2: Numerical Analysis

An algorithm for retrieving aerosol parameters by taking into account the uncertainty in aerosol model selection is applied to the retrieval of aerosol optical thickness and aerosol layer height from synthetic measurements from the EPIC sensor onboard the Deep Space Climate Observatory. The synthetic measurements are generated using aerosol models derived from AERONET measurements at different sites, while other commonly used aerosol models, such as OPAC, GOCART, OMI, and MODIS databases are used in the retrieval. The numerical analysis is focused on the estimation of retrieval errors when the true aerosol model is unknown. We found that the best aerosol model is the one with a value of the asymmetry parameter and an angular variation of the phase function around the viewing direction that is close to the values corresponding to the reference aerosol model.

Tracking the atmospheric pulse of a North American megacity from a mountaintop remote sensing observatory

Atmospheric carbon monoxide (CO) is an effective tracer for monitoring atmospheric transport processes and for detecting pollution sources of anthropogenic origin. However, very few observation systems exist that are capable of providing measurements with high spatial and temporal resolution to identify hotspots for emission control purposes. Here we introduce a mountain-top remote sensing observatory, the California Laboratory for Atmospheric Remote Sensing (CLARS), for mapping the enhancement of CO column-averaged mixing ratio (XCO) over the Los Angeles (LA) megacity. Compared to conventional observation network, CLARS is unique in the following ways: (1) it mimics a geostationary satellite observatory for LA with approximately hourly- and kilometer-scale mapping capability; (2) the free tropospheric background atmosphere is simultaneously measured; and (3) the measurements are highly sensitive to anthropogenic emissions due to the long light path along the planetary boundary layer (PBL). The CO slant column density and XCO are retrieved from reflected sunlight measurements in the 2.3 μm CO band and the 1.27 μm oxygen (O2) band. Data filtering and corrections for aerosol scattering and geometric effects are then implemented to derive the XCO enhancement, which is the XCO excess in the PBL compared to the background value. In the LA megacity, the XCO enhancement shows a distinctive diurnal cycle primarily driven by changes in anthropogenic emissions and sea-breeze circulation. Such diurnal patterns can be reproduced by the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The enhancement also shows a significant weekly cycle resulting from the weekly pattern in anthropogenic CO emissions. On average, the XCO enhancements on Sunday and Saturday are 16.1% and 4.4%, respectively, lower than weekday values. The weekly XCO enhancement patterns also show high correlation with traffic counts. A seasonal pattern of XCO enhancement with high (low) spatial contrast in summer (winter), resulting from changing sea-breeze circulation, can be observed. These diurnal, weekly, and seasonal patterns of XCO enhancement serve as tracers of the atmospheric pulse of the LA megacity. The CLARS observatory can serve as a testbed for future geostationary missions to track anthropogenic emissions in cities.

Remote sensing of angular scattering effect of aerosols in a North American megacity

The angle-dependent scattering effect of aerosols in the atmosphere not only influences climate through radiative forcing effects but also impacts trace gas remote sensing by modifying the path of radiation through the atmosphere. The aerosol phase function, which characterizes the angular signature of scattering, has been continuously monitored from ground-based and space-borne observations. However, the range of scattering angles these instruments can sample is very limited. Here, we report multi-year measurements from a mountain-top remote sensing instrument: the California Laboratory for Atmospheric Remote Sensing Fourier Transform Spectrometer (CLARS-FTS), which overlooks the Los Angeles megacity. The observational geometries of CLARS-FTS provide a wide range of scattering angles, from about 20° (forward) to about 140° (backward), which is larger than the range provided by any existing aerosol remote sensing instrument. We then quantify the aerosol angular scattering effect using the O2 ratio, which is the ratio of retrieved O2 Slant Column Density (SCD) to geometric O2 SCD. The O2 ratio quantifies the light path modification due to aerosol scattering, with a value of 1 representing an aerosol-free scenario. The lower the O2 ratio value than 1, the stronger the aerosol loading. CLARS-FTS measurements are highly sensitive to the angular scattering effect of aerosols in the Los Angeles (LA) urban atmosphere, due to the long light path going through the boundary layer and the wide range of observational angles. The differences in aerosol scattering between different surface reflection points targeted by CLARS-FTS can be explained by differences in their angular scattering geometries. The correlation between measurements at different targets can be used to quantify the strength of the angular dependence of the aerosol phase function. Applying the correlation technique to CLARS-FTS measurements, we find that, from 2011 to 2018, there is no significant trend in the aerosol phase function in the LA megacity. Overall, this study provides a practical observing strategy for quantifying the angular dependence of aerosol scattering in urban atmospheres that could potentially contribute towards improved greenhouse gas remote sensing in megacities.

Coupling Atmospheric and Biological Remote Sensing to Investigate Boundary-Layer Evolution and Animal Flight Behavior as Affected by the 2017 North American Solar Eclipse

The daytime atmospheric boundary layer is characterized by vertical convective motions that are driven by solar radiation. Lift provided by thermal updrafts is sufficiently ubiquitous that some diurnal birds and arthropods have evolved specialized flight behaviors to soar or embed in these atmospheric currents. While the diel periodicity of boundary-layer dynamics and animal flight has been characterized, rare disruptions to this cycle provide a chance to investigate animal behavioral responses to boundary layer motion and photoperiod that are disjointed from their expected circadian rhythm. To analyze these interactions, we couple radar-derived animal observations with co-located lidar measurements of the convective boundary layer over north-central Oklahoma, USA during the solar eclipse of 21 August 2017. Analysis of animal flight behavior confirmed that ascending and descending flight effort did change in the time period encompassing the solar eclipse, however, the response in behavior was coincident with proximate changes in boundary-layer turbulence. Both the animal behavioral response and decrease in atmospheric turbulence lagged changes in solar irradiance by approximately 30 min, suggesting that changes in flight activity were not cued by the eclipse directly, but rather by the modification of vertical air motions caused by the eclipse.

Raman Lidar for Synchronous Water Vapor, Liquid Water and Ice Water Profilings

Water is the only atmospheric parameter with three-phase state. An ultraviolet Raman lidar was developed for synchronous measurements for water vapor, liquid water and ice water in Xi’an University of Technology, Xi’an, China (34.233°N, 108.911°E). An accurate retrieval method on the basis of interference degree is proposed for synchronous three-phase water mixing ratio profiles. Preliminary measurements are carried out in the Laser Radar Center of Remote Sensing of Atmosphere (LRCRSA). Several representative examples are obtained and validated the performance of Raman system. Combined with atmospheric temperature profiles, the synchronous water vapor, liquid water and ice water profiling are retrieved and revealed the variation characteristics in three-phase water. The effective detection can reach up to a height of 5 km under cloudy weather, and synchronized growth in water vapor and liquid water content was obtained in cloud layers. Continuous observations are also made under haze weather condition, and the temporal and spatial evolution trend of three-phase water in clouds at 2 km altitude are successfully realized.

The first Vietnam School of Earth Observation: Atmospheric Remote Sensing and Molecular Spectroscopy

In this review paper, we give an introduction to molecular spectroscopy and its relation to atmospheric remote sensing and examples of recent developments in spectroscopic experimental techniques and modelling. Atmospheric retrieval techniques, based on radiative transfer theories and molecular spectroscopy as well as some atmospheric remote sensing missions using spectroscopic techniques are presented.

Constraining Aerosol Vertical Profile in the Boundary Layer Using Hyperspectral Measurements of Oxygen Absorption

AbstractThis study attempts to infer aerosol vertical structure in the urban boundary layer using passive hyperspectral measurements. A spectral sorting technique is developed to retrieve total aerosol optical depth (AOD) and effective aerosol layer height (ALH) from hyperspectral measurements in the 1.27‐μm oxygen absorption band by the mountaintop Fourier Transform Spectrometer at the California Laboratory for Atmospheric Remote Sensing instrument (1,673 m above sea level) overlooking the LA basin. Comparison to AOD measurements from Aerosol Robotic Network and aerosol backscatter profile measurements from a Mini MicroPulse Lidar shows agreement, with coefficients of determination (r2) of 0.74 for AOD and 0.57 for effective ALH. On average, the AOD retrieval has an error of 24.9% and root‐mean‐square error of 0.013, while the effective ALH retrieval has an error of 7.8% and root‐mean‐square error of 67.01 m. The proposed method can potentially be applied to existing and future satellite missions with hyperspectral oxygen measurements to constrain aerosol vertical distribution on a global scale.

Aerosol scattering effects on water vapor retrievals over the Los Angeles Basin

Abstract. In this study, we propose a novel approach to describe the scattering effects of atmospheric aerosols in a complex urban environment using water vapor (H2O) slant column measurements in the near infrared. This approach is demonstrated using measurements from the California Laboratory for Atmospheric Remote Sensing Fourier Transform Spectrometer on the top of Mt. Wilson, California, and a two-stream-exact single scattering (2S-ESS) radiative transfer (RT) model. From the spectral measurements, we retrieve H2O slant column density (SCD) using 15 different absorption bands between 4000 and 8000 cm−1. Due to the wavelength dependence of aerosol scattering, large variations in H2O SCD retrievals are observed as a function of wavelength. Moreover, the variations are found to be correlated with aerosol optical depths (AODs) measured at the AERONET-Caltech station. Simulation results from the RT model reproduce this correlation and show that the aerosol scattering effect is the primary contributor to the variations in the wavelength dependence of the H2O SCD retrievals. A significant linear correlation is also found between variations in H2O SCD retrievals from different bands and corresponding AOD data; this correlation is associated with the asymmetry parameter, which is a first-order measure of the aerosol scattering phase function. The evidence from both measurements and simulations suggests that wavelength-dependent aerosol scattering effects can be derived using H2O retrievals from multiple bands. This understanding of aerosol scattering effects on H2O retrievals suggests a promising way to quantify the effect of aerosol scattering on greenhouse gas retrievals and could potentially contribute towards reducing biases in greenhouse gas retrievals from space.

Evaluation of 28 nm CMOS Receivers at 183 GHz for Space-borne Atmospheric Remote Sensing

This letter discusses the capability of 28 nm CMOS technology to implement receivers at the 183 GHz band enabling the potential of future radiometers for monitoring atmospheric water vapor. To demonstrate the potential of 28 nm for constructing receivers at these frequencies, a prototype LNA and downconverter chip is demonstrated and measurements of both gain and noise performance are presented. The prototype receiver consumes 191 mW of DC power while providing noise temperatures on the order of 2700 K which demonstrates the potential for use in payload restricted cubesats and other small spacecraft platforms.

XCO2 retrieval error over deserts near critical surface albedo

AbstractLarge retrieval errors in column‐weighted CO2 mixing ratio (XCO2) over deserts are evident in the Orbiting Carbon Observatory 2 version 7 L2 products. We argue that these errors are caused by the surface albedo being close to a critical surface albedo (αc). Over a surface with albedo close to αc, increasing the aerosol optical depth (AOD) does not change the continuum radiance. The spectral signature caused by changing the AOD is identical to that caused by changing the absorbing gas column. The degeneracy in the retrievals of AOD and XCO2 results in a loss of degrees of freedom and information content. We employ a two‐stream‐exact single scattering radiative transfer model to study the physical mechanism of XCO2 retrieval error over a surface with albedo close to αc. Based on retrieval tests over surfaces with different albedos, we conclude that over a surface with albedo close to αc, the XCO2 retrieval suffers from a significant loss of accuracy. We recommend a bias correction approach that has significantly improved the XCO2 retrieval from the California Laboratory for Atmospheric Remote Sensing data in the presence of aerosol loading.

Growth, non-linear optical, photoconductivity and dielectric characterization of pure and EDTA doped tris-glycine zinc chloride single crystals for LiDAR

Non-linear optical crystals which are used as frequency converters to access various spectral ranges like visible or UV are key components of (Light Detection and ranging) LiDAR systems. For this purpose promising non-linear optical crystals like tris-glycine zinc chloride (TGZC) a nonlinear optical material, with the additive Ethylenediaminetetraacetic acid (EDTA) is grown successfully by slow cooling technique. In order to improve the device characteristics of TGZC crystals, organic dopant EDTA were incorporated into the parent crystals. Observation of the crystal growth, behavior and measurement of growth kinetics revealed a strong dependence of growth rate on the additive. The slight change in unit-cell parameters of the doped compound from the pure TGZC were confirmed by single crystal X-ray diffraction analysis. The presence of functional groups is estimated qualitatively by FTIR analysis. The UV–vis spectrum shows a cut-off wavelength less than 300nm. The photocurrent of both pure and doped crystals is more than that of the dark current. Microhardness studies carried out using Leitz Weitzler hardness tester at room temperature showed an increase due to doping. The dielectric loss is low at high frequency and this behavior is of vital importance for NLO applications. In this paper, an attempt has been made on the growth and characterization of nonlinear optical crystal and theoretical aspects of single crystals as frequency convertors in Atmospheric Remote Sensing is discussed.