East Asian Study of Tropospheric Aerosols and their Impact on Regional Clouds, Precipitation, and Climate (EAST-AIR_(CPC))

Aerosols have significant and complex impacts on regional climate in East Asia. Cloud‐aerosol‐precipitation interactions (CAPI) remain most challenging in climate studies. The quantitative understanding of CAPI requires good knowledge of aerosols, ranging from their formation, composition, transport, and their radiative, hygroscopic, and microphysical properties. A comprehensive review is presented here centered on the CAPI based chiefly, but not limited to, publications in the special section named EAST‐AIRcpc concerning (1) observations of aerosol loading and properties, (2) relationships between aerosols and meteorological variables affecting CAPI, (3) mechanisms behind CAPI, and (4) quantification of CAPI and their impact on climate. Heavy aerosol loading in East Asia has significant radiative effects by reducing surface radiation, increasing the air temperature, and lowering the boundary layer height. A key factor is aerosol absorption, which is particularly strong in central China. This absorption can have a wide range of impacts such as creating an imbalance of aerosol radiative forcing at the top and bottom of the atmosphere, leading to inconsistent retrievals of cloud variables from space‐borne and ground‐based instruments. Aerosol radiative forcing can delay or suppress the initiation and development of convective clouds whose microphysics can be further altered by the microphysical effect of aerosols. For the same cloud thickness, the likelihood of precipitation is influenced by aerosols: suppressing light rain and enhancing heavy rain, delaying but intensifying thunderstorms, and reducing the onset of isolated showers in most parts of China. Rainfall has become more inhomogeneous and more extreme in the heavily polluted urban regions

Confronting the Challenge of Modeling Cloud and Precipitation Microphysics

In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth\u27s atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle‐based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next‐generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process‐level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle‐based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods

Application of remotely-sensed cloud properties for climate studies

Clouds play a vital role in Earth’s energy balance by modulating atmospheric processes, thus it is crucial to have accurate information on their spatial and temporal variability. Furthermore, clouds are relevant in those processes involved in aerosol-cloud-radiation interactions. The work conducted and presented herein concentrates on the retrievals of cloud properties, as well as their application for climate studies. While remote sensing observation systems have been used to analyze the atmosphere and observe its changes for the last decades, climate models predict how climate will change in the future. Altogether, these sources of observations are needed to better understand cloud processes and their impact on climate. In this thesis aerosol and cloud properties from the three above mentioned sources are applied to evaluate their potential in representing cloud properties and applicability in climate studies on local, regional and global scales. One aim of this thesis focuses on evaluating cloud parameters from ground-based remote-sensing sensors and from climate models using the MODerate Imaging Spectroradiometer (MODIS) data as a reference dataset. It is found that ground-based measurements of liquid clouds are in good agreement with MODIS cloud droplet size while poor correlation is found in the amount of cloud liquid water due to the management of drizzle. The comparison of the cloud diagnostic from three climate models with MODIS data, enabled through the application of a satellite simulator, helped to understand discrepancies among models, as well as discover deficiencies in their simulation processes. These findings are important to further improve the parametrization of atmospheric constituents in climate models, therefore enhancing the accuracy of climate projections. In this thesis it is also assessed the impact of aerosol particles on clouds. Satellite data can be used to derive climatically crucial quantities that are otherwise not directly retrieved (such as aerosol index and cloud droplet number concentration) which can be used to infer the sensitivity of clouds to aerosols changes. Results on the local and regional scales show that contrasting aerosol backgrounds indicate a higher sensitivity of clouds to aerosol changes in cleaner ambient air and a lower sensitivity in polluted areas, further corroborating the notion that anthropogenic emission modify clouds. On the global scale, the estimates of the aerosol-cloud interaction present, overall, a good agreement between the satellite- and model-based values which are in line with the results from other models

Satellite-Based Fog Detection: A Dynamic Retrieval Method for Europe Based on Machine Learning

Fog has many economic as well as ecological impacts and it directly affects human life in many ways. The large number of fog influence factors shows that a comprehensive understanding of its causes and a precise mapping of the spatio-temporal distribution patterns are of great interest. Since there are justifiable concerns about the general applicability of existing fog retrieval methods, this thesis investigates new techniques of satellite based fog detection and the derivation of spatio-temporal information on fog distribution in Europe. The central novelties of this study are: - No static assumptions about microphysical properties were used during fog retrieval. - A novel hybrid approach based on machine learning methods was developed that can be continuously applied 24 hours a day. - The algorithm covers all fog types. Areas of different fog types could also be differentiated indirectly from the generated product due to their typical diurnal and annual frequency cycles. - For the first time, fog frequency maps for Europe could be produced for different general weather situations separately for each fog type

Satellite-based PM2.5 Exposure Estimation and Health Impacts over China

Exposure to suspended fine particulate matter (PM2.5) has been proven to adversely impact public health through increased risk of cardiovascular and respiratory mortality. Assessing health impacts of PM2.5 and its long-term variations requires accurate estimates of large-scale exposure data. Such data include mass concentration and particle size, the latter of which may be an effect modifier on PM2.5 attributable health risks. The availability of these exposure data, however, is limited by sparse ground-level monitoring networks. In this dissertation, an optical-mass relationship was first developed based on aerosol microphysical characteristics for ground-level PM2.5 retrieval. This method quantifies PM2.5 mass concentrations with a theoretical basis, which can simultaneously estimate large-scale particle size. The results demonstrate the effectiveness and applicability of the proposed method and reveal the spatiotemporal distribution of PM2.5 over China. To explore the spatial variability and population exposure, particle radii of PM2.5 are then derived using the developed theoretical relationship along with a statistical model for a better performance. The findings reveal the prevalence of exposure to small particles (i.e. PM1), identify the need for in-situ measurements of particle size, and motivate further research to investigate the effects of particle size on health outcomes. Finally, the long-term impacts of PM2.5 on health and environmental inequality are assessed by using the satellite-retrieved PM2.5 estimates over China during 2005-2017. Premature mortality attributable to PM2.5 exposure increased by 31% from 2005 to 2017. For some causes of death, the burden fell disproportionately on provinces with low-to-middle GDP per capita. As a whole, this work contributes to bridging satellite remote sensing and long-term exposure studies and sheds light on an ongoing need to understand the effects of PM2.5, including both concentrations and other particle characteristics, on human health

Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains

Lightning is affected by many factors, many of which are not routinely measured, well understood, or accounted for in physical models. Several commonly used machine learning (ML) models have been applied to analyze the relationship between Atmospheric Radiation Measurement (ARM) data and lightning data from the Earth Networks Total Lightning Network (ENTLN) in order to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains (SGP) ARM site during the summer months (June, July, August and September) of 2012 to 2020. Testing various ML models, we found that the random forest model is the best predictor among common classifiers. When convective clouds were detected, it predicts lightning occurrence with an accuracy of 76.9 % and an area under the curve (AUC) of 0.850. Using this model, we further ranked the variables in terms of their effectiveness in nowcasting lightning and identified geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors. The contrast in meteorological variables between no-lightning and frequent-lightning periods was examined for hours with CAPE values conducive to thunderstorm formation. Besides the variables considered for the ML models, surface variables and mid-altitude variables (e.g., equivalent potential temperature and minimum equivalent potential temperature, respectively) have statistically significant contrasts between no-lightning and frequent-lightning hours. For example, the minimum equivalent potential temperature from 700 to 500 hPa is significantly lower during frequent-lightning hours compared with no-lightning hours. Finally, a notable positive relationship between the intracloud (IC) flash fraction and the square root of CAPE (CAPE) was found, suggesting that stronger updrafts increase the height of the electrification zone, resulting in fewer flashes reaching the surface and consequently a greater IC flash fraction.</p

Multi-View Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size

Tomography aims to recover a three-dimensional (3D) density map of a medium or an object. In medical imaging, it is extensively used for diagnostics via X-ray computed tomography (CT). We define and derive a tomography of cloud droplet distributions via passive remote sensing. We use multi-view polarimetric images to fit a 3D polarized radiative transfer (RT) forward model. Our motivation is 3D volumetric probing of vertically-developed convectively-driven clouds that are ill-served by current methods in operational passive remote sensing. Current techniques are based on strictly 1D RT modeling and applied to a single cloudy pixel, where cloud geometry defaults to that of a plane-parallel slab. Incident unpolarized sunlight, once scattered by cloud-droplets, changes its polarization state according to droplet size. Therefore, polarimetric measurements in the rainbow and glory angular regions can be used to infer the droplet size distribution. This work defines and derives a framework for a full 3D tomography of cloud droplets for both their mass concentration in space and their distribution across a range of sizes. This 3D retrieval of key microphysical properties is made tractable by our novel approach that involves a restructuring and differentiation of an open-source polarized 3D RT code to accommodate a special two-step optimization technique. Physically-realistic synthetic clouds are used to demonstrate the methodology with rigorous uncertainty quantification

Estimating the urban atmospheric boundary layer height from remote sensing applying machine learning techniques

This work was supported by the Spanish Ministry of Economy and Competitiveness through projects CGL2015- 73250-JIN, CGL2016-81092-R, CGL2017-83538-C3-1-R ,CGL2017-90884-REDT and PID2020-120015RB-I00 and by the University of Granada through “Plan Propio. Programa 9 Convocatoria 2013. The financial support for EARLINET in the ACTRIS Research Infrastructure Project by the European Union’s Horizon 2020 research and innovation program through project ACTRIS-2 (grant agreement No 654109). The authors thankfully acknowledge the FEDER program for the instrumentation used in this work and the University of Granada that supported this study through the Excellence Units Program. COST Action TOPROF (ES1303), supported by497 COST (European Cooperation in Science and Technology), is also acknowledged.This study proposes a new methodology to estimate the Atmospheric Boundary Layer Height (ABLH), discriminating between Convective Boundary Layer and Stable Boundary Layer heights, based on the machine learning algorithm known as Gradient Boosting Regression Tree. The algorithm proposed here uses a first estimation of the ABLH derived applying the gradient method to a ceilometer signal and several meteorological variables to obtain ABLH values comparable to those derived from a microwave radiometer. A deep analysis of the model configuration and its inputs has been performed in order to avoid the model overfitting and ensure its applicability. The hourly and seasonal values and variability of the ABLH values obtained with the new algorithm have been analyzed and compared with the initial estimations obtained using only the ceilometer signal. Mean Relative Errors (MRE) between the ABLH estimated with the new algorithm and microwave radiometer show a daily pattern with their highest values during the night-time (stable situations) and their lowest values along the day-time (convective situations). This pattern has been observed for all the seasons with MRE ranging between −5% and 35%. This result notably improves those ABLH values derived by applying the gradient method to ceilometer data during convective situations and enables the Stable Boundary Layer height detection at night and early morning, instead of only Residual Layer top height. Finally, the model performance has been directly validated in three particular cases: clear-sky day, presence of low-clouds and dust outbreak event. In these three particular situations, ABLH values obtained with the new algorithm follow the pattern obtained with the microwave radiometer presenting very similar values, thus confirming the good model performance. In this way it is feasible by the combination of the proposed method with gradient method, to estimate Convective, Stable and Residual Boundary Layer height from ceilometer data and surface meteorological data in extended network that include ceilometer profiling.Spanish Ministry of Economy and Competitiveness through projects CGL2015-73250-JIN, CGL2016-81092-R, CGL2017-83538-C3-1-R, CGL2017-90884-REDT and PID2020-120015RB-I00COST Action TOPROF (ES1303), supported by COST (European Cooperation in Science and Technology