Constraining effects of aerosol-cloud interaction by accounting for coupling between cloud and land surface.

Aerosol-cloud interactions (ACIs) are vital for regulating Earth's climate by influencing energy and water cycles. Yet, effects of ACI bear large uncertainties, evidenced by systematic discrepancies between observed and modeled estimates. This study quantifies a major bias in ACI determinations, stemming from conventional surface or space measurements that fail to capture aerosol at the cloud level unless the cloud is coupled with land surface. We introduce an advanced approach to determine radiative forcing of ACI by accounting for cloud-surface coupling. By integrating field observations, satellite data, and model simulations, this approach reveals a drastic alteration in aerosol vertical transport and ACI effects caused by cloud coupling. In coupled regimes, aerosols enhance cloud droplet number concentration across the boundary layer more homogeneously than in decoupled conditions, under which aerosols from the free atmosphere predominantly affect cloud properties, leading to marked cooling effects. Our findings spotlight cloud-surface coupling as a key factor for ACI quantification, hinting at potential underassessments in traditional estimates.

The different sensitivities of aerosol optical properties to particle concentration, humidity, and hygroscopicity between the surface level and the upper boundary layer in Guangzhou, China.

This study investigates the impact of aerosol liquid water content (ALWC) and related factors, i.e., relative humidity (RH), aerosol mass concentration (PM2.5), and aerosol hygroscopicity, on aerosol optical properties, based on field measurements made in the Pearl River Delta (PRD) region of China at the surface (1 November 2019 to 21 January 2020) and in the upper boundary layer (the 532-m Guangzhou tower from 1 February to 21 March 2020). In general, temporal variations in the ambient aerosol backscattering coefficient (ßp) and ALWC followed each other. However, the surface ßp and 532-m ßp had generally opposite diurnal variation patterns, caused by dramatic differences in PM2.5 and ambient RH between the surface and the upper boundary layer. The ambient 532-m RH was systematically higher than the surface RH, with the latter having a much pronounced diurnal cycle than the former. The surface PM2.5 concentration was systematically higher than the PM2.5 concentration at 532 m, and their diurnal cycle patterns were overall opposite. These dramatic differences reveal that the atmospheric variables, i.e., ambient RH and the PM2.5 concentration in the upper boundary layer, cannot be directly represented by the same variables at the surface. Vertical variability should be considered. Clear differences in the sensitivities of aerosol light scattering to ambient RH, PM2.5, and aerosol hygroscopicity between the two levels were found and examined. Aerosol chemical composition played a minor role in causing the differences between the two levels. In particular, ßp was more sensitive to PM2.5 at the surface level but more to the ambient RH in the upper boundary layer. The larger contribution of aerosol loading to the variability in ßp at the surface implies that local emission controls can decrease ßp and further improve atmospheric visibility effectively at the surface during winter in the PRD region.

The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China.

Respirable particles with aerodynamic diameters ≤ 10 µm (PM10) have important impacts on the atmospheric environment and human health. Available PM10 datasets have coarse spatial resolutions, limiting their applications, especially at the city level. A tree-based ensemble learning model, which accounts for spatiotemporal information (i.e., space-time extremely randomized trees, denoted as the STET model), is designed to estimate near-surface PM10 concentrations. The 1-km resolution Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product and auxiliary factors, including meteorology, land-use cover, surface elevation, population distribution, and pollutant emissions, are used in the STET model to generate the high-resolution (1 km) and high-quality PM10 dataset for China (i.e., ChinaHighPM10) from 2015 to 2019. The product has an out-of-sample (out-of-station) cross-validation coefficient of determination (CV-R2) of 0.86 (0.82) and a root-mean-square error (RMSE) of 24.28 (27.07) µg/m3, outperforming most widely used models from previous related studies. High levels of PM10 concentration occurred in northwest China (e.g., the Tarim Basin) and the Northern China Plain. Overall, PM10 concentrations had a significant declining trend of 5.81 µg/m3 per year (p < 0.001) over the past five years in China, especially in three key urban agglomerations. The ChinaHighPM10 dataset is potentially useful for future small- and medium-scale air pollution studies by virtue of its higher spatial resolution and overall accuracy.

Abnormally Shallow Boundary Layer Associated With Severe Air Pollution During the COVID-19 Lockdown in China.

After the 2020 Lunar New Year, the Chinese government implemented a strict nationwide lockdown to inhibit the spread of the Coronavirus Disease 2019 (COVID-19). Despite the abrupt decreases in gaseous emissions caused by record-low anthropogenic activities, severe haze pollution occurred in northern China during the COVID lockdown. This paradox has attracted the attention of both the public and the scientific community. By analyzing comprehensive measurements of air pollutants, planetary boundary layer (PBL) height, and surface meteorology, we show that the severe air pollution episode over northern China coincided with the abnormally low PBL height, which had reduced by 45%, triggering strong aerosol-PBL interactions. After dynamical processes initiated the temperature inversion, the Beijing metropolitan area experienced a period with continuously shallow PBLs during the lockdown. This unprecedented event provided an experiment showcasing the role of meteorology, in particular aerosol-PBL interactions in affecting air quality.

Evaluation and uncertainty estimate of next-generation geostationary meteorological Himawari-8/AHI aerosol products.

The next-generation geostationary meteorological Himawari-8 satellite carrying the Advanced Himawari Imager (AHI) allows frequent observations of the atmosphere, the surface, and oceans every 10 min. With its retrieval algorithms recently updated, Himawari-8/AHI Version 2 Level 2 aerosol products are now available. However, these retrievals have not yet undergone a quality assessment. This study aims to comprehensively validate the official aerosol optical properties derived from Himawari-8/AHI over land and ocean. Aerosol Robotic Network and Sun-Sky Radiometer Observation Network ground-based measurements at 98 stations in the Himawari-domain region are used to validate aerosol optical depth (AOD, or τ) retrievals at 500 nm and Ångström exponent (AE) retrievals at 440-675 nm from the year 2016. The AOD retrievals agree well with surface observations (i.e., from linear regression, slope = 0.876, intercept = 0.076, and correlation coefficient = 0.756) with a mean absolute error and a root-mean-square error of 0.168 and 0.293, respectively. On site and regional scales, large uncertainties are seen, especially in Australia (significant overestimation) and South Asia (significant underestimation). The AOD retrievals can correctly capture daily variations and show the best (worst) performance in summer (spring). The AE performance is poorer on all scales, showing overall underestimations, especially in Australia, Southeast Asia, and China. The data quality of AOD retrievals improves as the vegetation coverage and the AE increases. This suggests that the official aerosol retrieval algorithm still faces great challenges over bright surfaces and under coarse-particle-dominated conditions. In general, approximately 61% and 64% of the AOD matchups meet the newly defined expected errors of [0.330 × τ + 0.024; -0.132 × τ - 0.125] and [0.519 × τ + 0.005; -0.007 × τ - 0.194] determined by ground measurements and aerosol retrievals, respectively. The highly variable accuracy of aerosol retrievals raises a concern about the reliability of the current product under different environmental conditions and underlying surfaces. It also sheds light on what future improvements need implementing to the aerosol retrieval algorithm.

Back-projection algorithm in generalized form for circular-scanning-based photoacoustic tomography with improved tangential resolution.

BACKGROUND: The back-projection algorithm is the most common method for the reconstruction of circular-scanning-based photoacoustic tomography (CSPAT) due to its simplicity, computational efficiency, and robustness. It usually can be implemented in two models: one for ideal point detector, and the other for planar transducer with infinite element size. However, because most transducers in CSPAT are planar with a finite size, the off-center targets will be blurred in the tangential direction with these two reconstruction models. METHODS: Here in this paper, we put forward a new model of the back projection algorithm for the reconstruction of CSPAT with finite size planar transducer, in which the acoustic spatial temporal response of the employed finite size transducer is approximated with a virtual detector placed at an optimized distance behind the transducer, and the optimized distance is determined by a phase square difference minimization scheme. Notably, this proposed method can also be suitable for reconstruction with the ideal point detector and infinite planar detector, and thus is a generalized form of the back-projection algorithm. RESULTS: Compared with the two conventional models of the back-projection method and the modified back-projection method, the proposed method in this work can significantly improve the tangential resolution of off-center targets, thus improving the reconstructed image quality. These findings are validated with both simulations and experiments. CONCLUSIONS: We propose a generalized model of the back projection algorithm to restore the elongated tangential resolution in CSPAT in case of a planar transducer of finite size, which can also be applicable for point and large-size planar transducers. This proposed method may also guide the design of CSPAT scanning configurations for potential applications such as human breast imaging for cancer detection.

Finite-element reconstruction of 2D circular scanning photoacoustic tomography with detectors in far-field condition.

The finite-element method (FEM) has been a powerful numerical tool for the reconstruction of 2D circular scanning-based photoacoustic tomography (PAT) for its unrivaled ability to accommodate complex boundary conditions, quantitatively reconstruct different physical parameters, and enable low sampling frequency and fewer detector numbers. To reduce the computation cost, a smaller image domain is commonly used instead of the region surrounded by the transducer scanning trace. Then, the pressure data used for the reconstruction that is defined on the boundary of the image domain is usually obtained by directly time delaying the actual measured data. In this case, distortions will be aroused for targets that are away from the rotation center. In this work, we put forward a new data preprocessing method to overcome this problem with a virtual detector concept, in which the measured data for the virtual point detectors on the boundary of the reconstruction domain are generated by a summation of the signals from nearby true detectors. The complete removal of the distortions using our proposed algorithm was proven with experimental reconstruction results.