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First 3089 results for ”G.S. Forbes”

1. A climatological benchmark for operational radar rainfall bias reduction

   The presence of significant biases in real-time radar quantitative precipitation estimations (QPE...

   Ruben Imhoff, Claudia Brauer, Klaas-Jan van Heeringen, Hidde Leijnse, Aart Overeem, Albrecht Weerts, Remko Uijlenhoet | Journal: Hydrology and Earth System Sciences | Volume: 25 | Year: 2021 | doi: 10.5194/hess-25-4061-2021

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2. The impact of Aeolus wind retrievals on ECMWF global weather forecasts

   Aeolus is the world’s first spaceborne Doppler Wind Lidar, providing profiles of horizontal line-...

   Michael P. Rennie, Lars Isaksen, Fabian Weiler, Jos de Kloe, Thomas Kanitz, Oliver Reitebuch. | Journal: Quarterly Journal of the Royal Meteorological Society | Volume: 147(740) | Year: 2021 | First page: 3555 | Last page: 3586 | doi: 10.1002/qj.4142

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3. A generalized simulation capability for rotating- beam scatterometers

   Rotating-beam wind scatterometers exist in two types: rotating fan-beam and rotating pencil-beam...

   Zhen Li, Ad Stoffelen, Anton Verhoef | Journal: Atmos. Meas. Tech. | Volume: 12 | Year: 2019 | First page: 3573 | Last page: 3594 | doi: https://doi.org/10.5194/amt-12-3573-2019

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4. Statistical Postprocessing of Wind Speed Forecasts Using Convolutional Neural Networks

   Current statistical postprocessing methods for probabilistic weather forecasting are not capable ...

   Veldkamp, S., Whan, K., Dirksen, S., and Schmeits, M. | Journal: Monthly Weather Review | Volume: 149 | Year: 2021 | First page: 1141 | Last page: 1152 | doi: 10.1175/MWR-D-20-0219.1

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5. dispel4py: An agile framework for data-intensive escience

   We present dispel4py a versatile data-intensive kit presented as a standard Python library. It em...

   Rosa Filgueira, Alessandro Spinuso | Journal: IEEE 11th International Conference on e-Science | Year: 2015 | doi: https://doi.org/10.1109/eScience.2015.40

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