-
Comparing Area Probability Forecasts of (Extreme) Local Precipitation Using Parametric and Machine Learning Statistical Postprocessing Methods
Probabilistic forecasts, which communicate forecast uncertainties, enable users to make better we...
KRP Whan, MJ Schmeits | Status: published | Journal: Mon. Wea. Rev. | Volume: 146 | Year: 2018 | First page: 3651 | Last page: 3673 | doi: 10.1175/MWR-D-17-0290.1
Publication
-
Crowdsourcing urban air temperatures through smartphone battery temperatures in São Paulo, Brazil
Crowdsourcing as a method to obtain and apply vast datasets is rapidly becoming prominent in mete...
Droste, Pape, Overeem, Leijnse, Steeneveld, Delden, Uijlenhoet | Status: published | Journal: J. Atm. Oceanic Technol. | Volume: 34 | Year: 2017 | First page: 1853 | Last page: 1866 | doi: 10.1175/JTECH-D-16-0150.1
Publication
-
Improving potential wind for extreme wind statistics
To correct measured wind for local roughness at the station location, exposure correction factors...
N Wever, G Groen | Year: 2009 | Pages: 114
Publication
-
Short-Term Forecasting of High-Impact Weather with Physics-Guided Machine Learning : Technical Report
Extreme weather events such as heavy storms and heavy precipitation have a large impact on our so...
C. A. Severijns & G. A. Pagani
| Year: 2023 | Pages: 12
Publication
-
Updating the calculation of ammonium particle formation in the Operational Priority Substances (OPS) source-receptor model
In this report we describe the methodology for calculating a new parameterization associated with...
J. E. Williams, E. van der Swaluw,
F. Sauter, W. de Vries and A. van Pul | Year: 2014 | Pages: 40
Publication
