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First 2457 results for ” Michael H Bettenhausen”

  1. A Review of City-Scale Methane Flux Inversion Based on Top-Down Methods

    As urbanization intensifies, the quantification of methane (CH4) emissions at city scales faces ...

    Li, X.; Zhang, Y.; de Leeuw, G.; Yao, X.; He, Z.; Wu, H.; Yang, Z. | Journal: . Remote Sens. | Volume: 17 | Year: 2025 | First page: 1 | Last page: 32 | doi: https://doi.org/10.3390/rs17183152.

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  2. Probabilistic Forecasting of Cloud-Base Height and Visibility Using Quantile Regression Forests, Based on NWP and Observation Features

    We have applied quantile regression forests (QRFs) to generate probabilistic forecasts of weather...

    D. Wolters, M. Schmeits, and K. Whan | Journal: Wea. Forecasting | Volume: 40 | Year: 2025 | First page: 543 | Last page: 559 | doi: https://doi.org/10.1175/WAF-D-23-0231.1

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  3. DISCUSSION ON: Robust validation of trends and cycles in sea level and tidal amplitude in the Dutch North Sea

    The paper “Robust validation of trends and cycles in sea level and tidal amplitude in the Dutch N...

    Dewi Le Bars, Cees de Valk, Iris Keizer, André Jüling, Roderik van de Wal, Sybren Drijfhout | Journal: Journal of Coastal and Hydraulic Structures | Volume: 5 | Year: 2025 | doi: 10.59490/jchs.2025.0042

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  4. A rain effect elimination approach using FengYun-3E WindRAD dual-frequency measurements

    Satellite scatterometers play a vital role in providing sea surface wind observations and can hel...

    Ke Zhao, Ad Stoffelen, Jeroen Verspeek, Anton Verhoef, Zhen Li, Na Xu, Lin Chen, Xiaochun Zhai, Fangli Dou, Yunkai Zhang, Peng Zhang, Tie Dai | Journal: Atmospheric and Oceanic Science Letters | Year: 2025 | doi: https://doi.org/10.1016/j.aosl.2025.100725

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  5. Reduction of Persistent Stress-Equivalent Wind Biases With Machine Learning and Scatterometer Data

    The numerical weather prediction (NWP) stress-equivalent 10-m wind (U10S) forecasts are used as a...

    Makarova, E., Portabella, M., Stoffelen, A. | Journal: IEEE Transactions on Geoscience and Remote Sensing | Volume: 63 | Year: 2025 | doi: https://doi.org/10.1109/TGRS.2025.3586375

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