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First 3583 results for ” Maurice van Tiggelen”

1. First results of the polar regional climate model RACMO2.4

   The next version of the polar Regional Atmospheric Climate Model (referred to as RACMO2.4p1) is p...

   Christiaan T. van Dalum, Willem Jan van de Berg, Srinidhi N. Gadde, Maurice van Tiggelen, Tijmen van der Drift, Erik van Meijgaard, Lambertus H. van Ulft, and Michiel R. van den Broeke | Journal: The Cryosphere | Volume: 18 | Year: 2024 | First page: 4065 | Last page: 4088 | doi: https://doi.org/10.5194/tc-18-4065-2024

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2. Automatic curve extraction for digitizing rainfall strip charts

   A method has been developed that largely automates the labor-intensive extraction work for large ...

   HE van Piggelen, T Brandsma, H Manders, JF Lichtenauer | Status: published | Journal: J. Atm. Oceanic Technol. | Volume: 28 | Year: 2011 | First page: 891 | Last page: 906 | doi: 10.1175/2011JTECHA1505.1

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3. Automatic Curve Extraction (ACE) Framework

   Digitization of the information on meteorological strip charts is a time consuming activity. This...

   Hans van Piggelen, Jeroen Lichtenauer, Theo Brandsma | Year: 2010 | Pages: 50

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4. Using Explainable Machine Learning Forecasts to Discover Subseasonal Drivers of High Summer Temperatures in Western and Central Europe

   Reliable subseasonal forecasts of high summer temperatures would be very valuable for society. Al...

   Chiem van Straaten, Kirien Whan, Dim Coumou, Bart van den Hurk, and Maurice Schmeits | Journal: Monthly Weather Review | Volume: 150 | Year: 2022 | First page: 1115 | Last page: 1134 | doi: https://doi.org/10.1175/MWR-D-21-0201.1

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5. Correcting Subseasonal Forecast Errors with an Explainable ANN to Understand Misrepresented Sources of Predictability of European Summer Temperatures

   Subseasonal forecasts are challenging for numerical weather prediction (NWP) and machine learning...

   Chiem van Straaten, Kirien Whan, Dim Coumou, Bart van den Hurk, and Maurice Schmeits | Journal: Artif. Intell. Earth Syst. | Volume: 2 | Year: 2023 | doi: https://doi.org/10.1175/AIES-D-22-0047.1

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