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First 1013 results for ” REM Neubert”

  1. Himawari-8 aerosol optical depth (AOD) retrieval using a deep neural network trained using AERONET observations

    Spectral aerosol optical depth (AOD) estimation from satellite-measured top of atmosphere (TOA) r...

    L She, H Zhang, Z Li, G de Leeuw, B Huang | Status: published | Journal: Remote Sensing | Volume: 12 | Year: 2020 | First page: 1 | Last page: 19 | doi: doi:10.3390/rs12244125

    Publication

  2. From near-neutral to strongly stratified: adequately modelling the clear-sky nocturnal boundary layer at Cabauw

    In this study, the performance of an atmospheric single-column model (SCM) is studied systematica...

    Baas, Wiel, Linden, Bosveld | Status: published | Journal: Bound.-Layer Meteorol. | Year: 2017 | doi: 10.1007/s10546-017-0304-8

    Publication

  3. A neural network radiative transfer model approach applied to the Tropospheric Monitoring Instrument aerosol height algorithm

    To retrieve aerosol properties from satellite measurements of the oxygen A-band in the near-infra...

    S Nanda, M de Graaf, JP Veefkind, M ter Linden, MC Sneep, JF de Haan, PF Levelt | Status: published | Journal: Atmospheric Measurement Techniques | Volume: 12 | Year: 2019 | First page: 6619 | Last page: 6634 | doi: 10.5194/amt-12-6619-2019

    Publication

  4. Deep Learning for Solar Irradiance Nowcasting: A Comparison of a Recurrent Neural Network and Two Traditional Methods

    This paper aims to improve short-term forecasting of clouds to accelerate the usability of solar ...

    Dennis Knol, Fons de Leeuw, Jan Fokke Meirink, Valeria V. Krzhizhanovskaya | Year: 2021

    Publication

  5. Direct Retrieval of NO2 Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network

    Satellite retrievals of columnar nitrogen dioxide (NO2) are essential for the characterization of...

    Li, C., Xu, X., Liu, X., Wang, J., Sun, K., van Geffen, J., Zhu, Q., Ma, J., Jin, J., Qin, K., He, Q., Xie, P., Ren, B. and Cohen, R.C. | Journal: J. Remote Sensing | Volume: 2022 | Year: 2022 | First page: 1 | Last page: 17 | doi: 10.34133/2022/9817134

    Publication