Multi-Decadal Change of Atmospheric Aerosols and their Effect on Surface Radiation

We present an investigation on multi-decadal changes of atmospheric aerosols and their effects on surface radiation using a global chemistry transport model, GOCART, along with the near-term to long-term data records. We focus on a 28-year time period of satellite era from 1980 to 2007 during which a suite of aerosol data from satellite observations, ground-based measurements, and intensive field experiments have become available. Particularly: (1) We compare the model calculated clear sky downward radiation at the surface with surface network data from BSRN and CMA (2) We compare the model and surface data with satellite derived downward radiation products from ISCCP and SRS (3) We analyze the long-term global and regional aerosol trends in major anthropogenic source regions (North America, Europe, Asia) that have been experiencing considerable changes of emissions during the three decades, dust and biomass burning regions that have large interannual variability, downwind regions that are directly affected by the changes in the source area, and remote regions that are considered to representing "background" conditions. The comparisons and methods from this study can be applied to multiple model analysis in the AeroCom framework

Multi-Decadal Change of Atmospheric Aerosols and Their Effect on Surface Radiation

We present an investigation on multi-decadal changes of atmospheric aerosols and their effects on surface radiation using a global chemistry transport model along with the near-term to long-term data records. We focus on a 28-year time period of satellite era from 1980 to 2007, during which a suite of aerosol data from satellite observations and ground-based remote sensing and in-situ measurements have become available. We analyze the long-term global and regional aerosol optical depth and concentration trends and their relationship to the changes of emissions" and assess the role aerosols play in the multi-decadal change of solar radiation reaching the surface (known as "dimming" or "brightening") at different regions of the world, including the major anthropogenic source regions (North America, Europe, Asia) that have been experiencing considerable changes of emissions, dust and biomass burning regions that have large interannual variabilities, downwind regions that are directly affected by the changes in the source area, and remote regions that are considered to representing "background" conditions

Seasonally Transported Aerosol Layers Over Southeast Atlantic are Closer to Underlying Clouds than Previously Reported

From June to October, low-level clouds in the southeast (SE) Atlantic often underlie seasonal aerosol layers transported from African continent. Previously, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) 532 nm lidar observations have been used to estimate the relative vertical location of the above-cloud aerosols (ACA) to the underlying clouds. Here we show new observations from NASA's Cloud-Aerosol Transport System (CATS) lidar. Two seasons of CATS 1064 nm observations reveal that the bottom of the ACA layer is much lower than previously estimated based on CALIPSO 532 nm observations. For about 60% of CATS nighttime ACA scenes, the aerosol layer base is within 360 m distance to the top of the underlying cloud. Our results are important for future studies of the microphysical indirect and semidirect effects of ACA in the SE Atlantic region

A HTAP Multi-Model Assessment of the Influence of Regional Anthropogenic Emission Reductions on Aerosol Direct Radiative Forcing and the Role of Intercontinental Transport

In this study, we assess changes of aerosol optical depth (AOD) and direct radiative forcing (DRF) in response to the reduction of anthropogenic emissions in four major pollution regions in the northern hemisphere by using results from 10 global chemical transport models in the framework of the Hemispheric Transport of Air Pollution (HTAP). The multi-model results show that on average, a 20% reduction of anthropogenic emissions in North America, Europe, East Asia and South Asia lowers the global mean AOD and DRF by about 9%, 4%, and 10% for sulfate, organic matter, and black carbon aerosol, respectively. The impacts of the regional emission reductions on AOD and DRF extend well beyond the source regions because of intercontinental transport. On an annual basis, intercontinental transport accounts for 10-30% of the overall AOD and DRF in a receptor region, with domestic emissions accounting for the remainder, depending on regions and species. While South Asia is most influenced by import of sulfate aerosol from Europe, North America is most influenced by import of black carbon from East Asia. Results show a large spread among models, highlighting the need to improve aerosol processes in models and evaluate and constrain models with observations

Contrastive Triple Extraction with Generative Transformer

Triple extraction is an essential task in information extraction for natural language processing and knowledge graph construction. In this paper, we revisit the end-to-end triple extraction task for sequence generation. Since generative triple extraction may struggle to capture long-term dependencies and generate unfaithful triples, we introduce a novel model, contrastive triple extraction with a generative transformer. Specifically, we introduce a single shared transformer module for encoder-decoder-based generation. To generate faithful results, we propose a novel triplet contrastive training object. Moreover, we introduce two mechanisms to further improve model performance (i.e., batch-wise dynamic attention-masking and triple-wise calibration). Experimental results on three datasets (i.e., NYT, WebNLG, and MIE) show that our approach achieves better performance than that of baselines.Comment: Accepted by AAAI 202

COMPATIBILITY EVALUATION OF BZ25-1 CRUDE OILS IN BOHAI BAY, CHINA

ABSTRACT BZ25-1 oilfield is located in the southeast of Bohai bay which geographically lies between 119 o 00′to 119 o 15′east longitude and 38 o 10′to 38 o 20′north latitude. It has two oil blocks, including Shahejie (SHJ) waxy oil and Minghuazhen (MHZ) heavy oil, with six wellhead platforms WHPA~WHPF and six submarine pipelines. Therein, the WHPC-WHPB and WHPB-SPM (Single Point Mooring) pipelines transport the mixture of the two produced crude oils. However, the mixing of the two oils will certainly bring out a change in their components and properties, which directly affects the safe operation of the submarine pipelines and offshore production facilities. Therefore, this paper compounds three kinds of MHZ/SHJ mixed oils with blending ratios of 1:1, 3:1 and 9:1, mainly studies how the components, rheological and thermophysical properties of the oil mixtures change with the blending ratio. The major objective of this study is to evaluate the compatibility of the two crude oils and provide a theoretical basis for the production optimization and risk elusion of the oilfield. The results of the study show that the components and properties of SHJ crude oil are quite different from those of MHZ oil, the flow behavior of SHJ oil is more sensitive to temperature. As MHZ oil in the compounds increases, the contents of asphaltene, resin, sulfur and carbon residue will increase except wax contents, their viscosities, densities and flash points will also increase, but their pour points, yield stresses, calorific values and other major thermophysical parameters will decrease. A blending ratio of 2~7:1 for MHZ to SHJ crude oil can be concluded to make the properties of the compounds meet the safe and economic requirements of the subsea pipeline and offshore facility operations and ensure the compatibility of the mixed oils. In actuality, the field operations have confirmed that the recommended blending ratio is reasonable and practicable

Learning to Ask for Data-Efficient Event Argument Extraction

Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles. In this study, we cast EAE as a question-based cloze task and empirically analyze fixed discrete token template performance. As generating human-annotated question templates is often time-consuming and labor-intensive, we further propose a novel approach called "Learning to Ask," which can learn optimized question templates for EAE without human annotations. Experiments using the ACE-2005 dataset demonstrate that our method based on optimized questions achieves state-of-the-art performance in both the few-shot and supervised settings.Comment: work in progres

Quantitative Analysis of Molecular Transport in the Extracellular Space Using Physics-Informed Neural Network

The brain extracellular space (ECS), an irregular, extremely tortuous nanoscale space located between cells or between cells and blood vessels, is crucial for nerve cell survival. It plays a pivotal role in high-level brain functions such as memory, emotion, and sensation. However, the specific form of molecular transport within the ECS remain elusive. To address this challenge, this paper proposes a novel approach to quantitatively analyze the molecular transport within the ECS by solving an inverse problem derived from the advection-diffusion equation (ADE) using a physics-informed neural network (PINN). PINN provides a streamlined solution to the ADE without the need for intricate mathematical formulations or grid settings. Additionally, the optimization of PINN facilitates the automatic computation of the diffusion coefficient governing long-term molecule transport and the velocity of molecules driven by advection. Consequently, the proposed method allows for the quantitative analysis and identification of the specific pattern of molecular transport within the ECS through the calculation of the Peclet number. Experimental validation on two datasets of magnetic resonance images (MRIs) captured at different time points showcases the effectiveness of the proposed method. Notably, our simulations reveal identical molecular transport patterns between datasets representing rats with tracer injected into the same brain region. These findings highlight the potential of PINN as a promising tool for comprehensively exploring molecular transport within the ECS

Impact of Asian Dust on Climate and Air Quality

Dust generated from Asian permanent desert and desertification areas can be efficiently transported around the globe, making significant radiative impact through their absorbing and scattering solar radiation and through their deposition on snow and ice to modify the surface albedo. Asian dust is also a major concern of surface air quality not only in the source and immediate downwind regions but also areas thousands of miles away across the Pacific. We present here a global model, GOCART, analysis of data from satellite remote sensing instrument (MODIS, MISR, CALIPSO, OMI) and other observations on Asian dust sources, transport, and deposition, and use the model to assess the Asian dust impact on global climate and air quality

LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training

Natural language generation from structured data mainly focuses on surface-level descriptions, suffering from uncontrollable content selection and low fidelity. Previous works leverage logical forms to facilitate logical knowledge-conditioned text generation. Though achieving remarkable progress, they are data-hungry, which makes the adoption for real-world applications challenging with limited data. To this end, this paper proposes a unified framework for logical knowledge-conditioned text generation in the few-shot setting. With only a few seeds logical forms (e.g., 20/100 shot), our approach leverages self-training and samples pseudo logical forms based on content and structure consistency. Experimental results demonstrate that our approach can obtain better few-shot performance than baselines.Comment: Work in progres