A data record, spanning 24 years, is presented of global atmospheric total aerosol optical depth and also the aerosol optical depth due to fine-mode constituents, typically of anthropogenic origin. Original measurements of reflectance were provided at approximately 1-km resolution by a series of dual-view satellite instruments: the Along-Track Scanning Radiometer 2 (ATSR-2), Advanced Along-Track Scanning Radiometer (AATSR), and Sea and Land Surface Temperature Radiometers (SLSTRs). These were processed to retrieve aerosol properties at 10-km resolution and then collated over daily and monthly timescales on a 1° × 1° latitude-longitude grid. Retrievals are evaluated against ground-based sun-photometer measurements from the Aerosol Robotic Network and Maritime Aerosol Network and compared to other satellite-derived datasets. The data record has implications for directly constraining the Earth's radiation budget, allowing benchmarking and improvement of models to represent aerosol in the climate system, air quality monitoring and adding to the long-term record of emission trends related to sources such as fire, dust and sulphate pollution. After release, the SLSTR datasets will be regularly extended in time.
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http://dx.doi.org/10.1038/s41597-025-04694-6 | DOI Listing |
Sci Data
March 2025
Norwegian Meteorological Institute, Postboks 43, Blindern, 0313, Oslo, Norway.
A data record, spanning 24 years, is presented of global atmospheric total aerosol optical depth and also the aerosol optical depth due to fine-mode constituents, typically of anthropogenic origin. Original measurements of reflectance were provided at approximately 1-km resolution by a series of dual-view satellite instruments: the Along-Track Scanning Radiometer 2 (ATSR-2), Advanced Along-Track Scanning Radiometer (AATSR), and Sea and Land Surface Temperature Radiometers (SLSTRs). These were processed to retrieve aerosol properties at 10-km resolution and then collated over daily and monthly timescales on a 1° × 1° latitude-longitude grid.
View Article and Find Full Text PDFWater Res
March 2025
School of Marine Sciences, Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, China.
Molecular property prediction is an important task in drug discovery. However, experimental data for many drug molecules are limited, especially for novel molecular structures or rare diseases which affect the accuracy of many deep learning methods that rely on large training datasets. To this end, we propose PG-DERN, a novel few-shot learning model for molecular property prediction.
View Article and Find Full Text PDFNeural Netw
November 2023
School of Computer Science, Leeds Trinity University, United Kingdom.
Detecting anomalies in massive volumes of multivariate time series data, particularly in the IoT domain, is critical for maintaining stable systems. Existing anomaly detection models based on reconstruction techniques face challenges in distinguishing normal and abnormal samples from unlabeled data, leading to performance degradation. Moreover, accurately reconstructing abnormal values and pinpointing anomalies remains a limitation.
View Article and Find Full Text PDFWhole slide imaging scans a microscope slide into a high-resolution digital image, and it paves the way from pathology to digital diagnostics. However, most of them rely on bright-field and fluorescence imaging with sample labels. In this work, we designed sPhaseStation, which is a dual-view transport of intensity phase microscopy-based whole slide quantitative phase imaging system for label-free samples.
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