16065 research outputs found
Sort by
A neural network to retrieve cloud cover from all-sky cameras: A case of study over Antarctica
Get PDFWe present a new model based on a convolutional neural network (CNN) to predict daytime cloud cover (CC) from sky images captured by all-sky cameras, which is called CNN-CC. A total of 49,016 daytime sky images, recorded at different Spanish locations (Valladolid, La Palma, and Izaña) from two different all-sky camera types, are manually classified into different CC (oktas) values by trained researchers. Subsequently, the images are randomly split into a training set and a test set to validate the model. The CC values predicted by the CNN-CC model are compared with the observations made by trained people on the test set, which serve as reference.The research has been supported by the Ministeriode Ciencia e Innovación (MICINN), with Grant no.PID2021-127588OB-I00, and the Junta of Castilla y León (JCyL) with Grant no. VA227P20. This work ispart of the project TED2021-131211B-I00 funded byMCIN/AEI/10.13039/501100011033 and the EuropeanUnion, “NextGenerationEU”/PRTR
Constraining Present‐Day Anthropogenic Total Iron Emissions Using Model and Observations
Get PDFIron emissions from human activities, such as oil combustion and smelting, affect the Earth's climate and marine ecosystems. These emissions are difficult to quantify accurately due to a lack of observations, particularly in remote ocean regions. In this study, we used long-term, near-source observations in areas with a dominance of anthropogenic iron emissions in various parts of the world to better estimate the total amount of anthropogenic iron emissions. We also used a statistical source apportionment method to identify the anthropogenic components and their sub-sources from bulk aerosol observations in the United States. We find that the estimates of anthropogenic iron emissions are within a factor of 3 in most regions compared to previous inventory estimates. Under- or overestimation varied by region and depended on the number of sites, interannual variability, and the statistical filter choice. Smelting-related iron emissions are overestimated by a factor of 1.5 in East Asia compared to previous estimates. More long-term iron observations and the consideration of the influence of dust and wildfires could help reduce the uncertainty in anthropogenic iron emissions estimates.SDR and TCB were supported by the United States Department of Energy (DE-Sc0016362) Collaborative Proposal “Fire, Dust, Air, and Water: Improving Aerosol Biogeochemistry Interactions in ACME.” DSH gratefully acknowledges support from NASA (Grant 80NSSC24K0446). We would like to acknowledge high-performance computing support from Cheyenne https://doi.org/10.5065/D6RX99HX; Computational and Information Systems Laboratory, 2017) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. RVM acknowledges support from NSF (Grant 2020673). Iron measurements in Houston, Texas were made possible by grants from the Texas Air Research Center to SC. This work contributes to the Scientific Committee on Oceanic Research International Working Group 167: Reducing Uncertainty in Soluble Aerosol Trace Element Deposition. CJG would like to thank NASA MRI Grant 2215875
