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First 780 results for ” S Lienert”

1. Self-supervision, remote sensing and abstraction: representation learning across 3 million locations

   Self-supervision based deep learning classification approaches have received considerable attenti...

   Sachith Seneviratne, Kerry A. Nice, Jasper S. Wijnands, Mark Stevenson, Jason Thompson | Journal: Digital Image Computing: Techniques and Applications (DICTA) | Year: 2022 | doi: https://doi.org/10.1109/DICTA52665.2021.9647061

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2. Extreme Events in the European Renewable Power System: Validation of a Modeling Framework to Estimate Renewable Electricity Production and Demand from Meteorological Data

   With the need to reduce greenhouse gas emissions, the coming decades will see a transition of Eur...

   L van der Most, K van der Wiel, RMJ Benders, PW Gerbens-Leenes, P Kerkmans, R Bintanja | Journal: Renewable and Sustainable Energy Reviews | Volume: 170 | Year: 2022 | First page: 112987 | doi: 10.1016/j.rser.2022.112987

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3. The impact of the COVID-19 pandemic on air pollution: A global assessment using machine learning techniques

   In response to the COVID-19 pandemic, most countries implemented public health ordinances that re...

   Jasper S. Wijnands, Kerry A. Nice, Sachith Seneviratne, Jason Thompson, Mark Stevenson | Journal: Atmospheric Pollution Research | Volume: 13 | Year: 2022 | doi: https://doi.org/10.1016/j.apr.2022.101438

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4. Long-term changes in CH4 emissions: Comparing ΔCH4/ΔCO2 ratios between observation and proved model in East Asia (2010–2020)

   To understand the changes in CH4 emissions in East Asia, mainly in China, we analyzed the synopti...

   Samuel Takele Kenea, Haeyoung Lee, Prabir K. Patra, Shanlan Li, Lev D. Labzovskii, Sangwon Joo | Journal: Atmospheric Environment | Year: 2023 | doi: https://doi.org/10.1016/j.atmosenv.2022.119437

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5. Ground-based validation of the Copernicus Sentinel-5P TROPOMI NO2 measurements with the NDACC ZSL-DOAS, MAX-DOAS and Pandonia global networks

   This paper reports on consolidated ground-based validation results of the atmospheric NO2 data pr...

   Verhoelst, T., Compernolle, S., Pinardi, G., Lambert, J.-C., Eskes, H. J., Eichmann, K.-U., Fjæraa, A. M., Granville, J., Niemeijer, S., Cede, A., Tiefengraber, M., Hendrick, F., Pazmiño, A., Bais, A., Bazureau, A., Boersma, K. F., Bognar, K., Dehn, A., Donner, S., Elokhov, A., Gebetsberger, M., Goutail, F., Grutter de la Mora, M., Gruzdev, A., Gratsea, M., Hansen, G. H., Irie, H., Jepsen, N., Kanaya, Y., Karagkiozidis, D., Kivi, R., Kreher, K., Levelt, P. F., Liu, C., Müller, M., Navarro Comas, M., Piters, A. J. M., Pommereau, J.-P., Portafaix, T., Prados-Roman, C., Puentedura, O., Querel, R., Remmers, J., Richter, A., Rimmer, J., Rivera Cárdenas, C., Saavedra de Miguel, L., Sinyakov, V. P., Stremme, W., Strong, K., Van Roozendael, M., Veefkind, J. P., Wagner, T., Wittrock, F., Yela González, M., and Zehner, C. | Status: accepted | Journal: Atmos. Meas. Tech. | Volume: 14 | Year: 2021 | First page: 481 | Last page: 510 | doi: 10.5194/amt-14-481-2021

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