Zobrazeno 1 - 10
of 1 093
pro vyhledávání: '"A. Zeileis"'
Publikováno v:
Nonlinear Processes in Geophysics, Vol 30, Pp 503-514 (2023)
Physical numerical weather prediction models have biases and miscalibrations that can depend on the weather situation, which makes it difficult to post-process them effectively using the traditional model output statistics (MOS) framework based on pa
Externí odkaz:
https://doaj.org/article/89a00506004f42898cd62978b098af4d
Publikováno v:
Weather and Climate Dynamics, Vol 4, Pp 489-509 (2023)
Meteorological environments favorable for thunderstorms are studied across Europe, including rare thunderstorm conditions from seasons with climatologically few thunderstorms. Using cluster analysis on ERA5 reanalysis data and EUCLID (European Cooper
Externí odkaz:
https://doaj.org/article/7157411e227b4d92a7611ae853e756a0
Publikováno v:
Wind Energy Science, Vol 7, Pp 2393-2405 (2022)
Power ramps are sudden changes in turbine power and must be accurately predicted to minimize costly imbalances in the electrical grid. Doing so requires reliable wind speed forecasts, which can be obtained from ensembles of physical numerical weather
Externí odkaz:
https://doaj.org/article/992b9b8d19564e74ad0ae952f75ca884
Differentiating lightning in winter and summer with characteristics of the wind field and mass field
Publikováno v:
Weather and Climate Dynamics, Vol 3, Pp 361-375 (2022)
Lightning in winter (December–January–February, DJF) is rare compared to lightning in summer (June–July–August, JJA) in central Europe north of the Alps. The conventional explanation attributes the scarcity of lightning in winter to seasonall
Externí odkaz:
https://doaj.org/article/cc66f72871e74862afe26bf910ee2547
Autor:
Groll, Andreas, Hvattum, Lars M., Ley, Christophe, Sternemann, Jonas, Schauberger, Gunther, Zeileis, Achim
In this work, three fundamentally different machine learning models are combined to create a new, joint model for forecasting the UEFA EURO 2024. Therefore, a generalized linear model, a random forest model, and a extreme gradient boosting model are
Externí odkaz:
http://arxiv.org/abs/2410.09068
Autor:
Zeileis, Achim
Many statisticians regularly teach large lecture courses on statistics, probability, or mathematics for students from other fields such as business and economics, social sciences and psychology, etc. The corresponding exams often use a multiple-choic
Externí odkaz:
http://arxiv.org/abs/2409.19522
Autor:
Kosmidis, Ioannis, Zeileis, Achim
We introduce the XBX regression model, a continuous mixture of extended-support beta regressions for modeling bounded responses with or without boundary observations. The core building block of the new model is the extended-support beta distribution,
Externí odkaz:
http://arxiv.org/abs/2409.07233
Autor:
Stauffer, Reto, Zeileis, Achim
The Python colorspace package provides a toolbox for mapping between different color spaces which can then be used to generate a wide range of perceptually-based color palettes for qualitative or quantitative (sequential or diverging) information. Th
Externí odkaz:
http://arxiv.org/abs/2407.19921
Foehn winds, characterized by abrupt temperature increases and wind speed changes, significantly impact regions on the leeward side of mountain ranges, e.g., by spreading wildfires. Understanding how foehn occurrences change under climate change is c
Externí odkaz:
http://arxiv.org/abs/2406.01818
Publikováno v:
Nonlinear Processes in Geophysics, Vol 27, Pp 23-34 (2020)
Non-homogeneous regression is a frequently used post-processing method for increasing the predictive skill of probabilistic ensemble weather forecasts. To adjust for seasonally varying error characteristics between ensemble forecasts and correspondin
Externí odkaz:
https://doaj.org/article/45b44bcb74d14457a9b1a17a7ae05539