Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Jonas Bhend"'
Autor:
Grigory Nikulin, Shakeel Asharaf, María Eugenia Magariño, Sandro Calmanti, Rita M. Cardoso, Jonas Bhend, Jesús Fernández, María Dolores Frías, Kristina Fröhlich, Barbara Früh, Sixto Herrera García, Rodrigo Manzanas, José Manuel Gutiérrez, Ulf Hansson, Michael Kolax, Mark A. Liniger, Pedro M.M. Soares, Christoph Spirig, Ricardo Tome, Klaus Wyser
Publikováno v:
Climate Services, Vol 9, Iss , Pp 72-85 (2018)
Within the FP7 EUPORIAS project we have assessed the utility of dynamical and statistical downscaling to provide seasonal forecast for impact modelling in eastern Africa. An ensemble of seasonal hindcasts was generated by the global climate model (GC
Externí odkaz:
https://doaj.org/article/6a368e7a27ec49b8a5df1c74556213f9
Autor:
Daniele Nerini, Francesco Zanetta, Mathieu Schaer, Jonas Bhend, Christoph Spirig, Lionel Moret, Mark A. Liniger
Forecasting winds at the local scale can be challenging due to the highly variable and complex nature of wind patterns, particularly in the case of complex terrain. In such cases, the accuracy of numerical weather prediction models (NWPs) is often li
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6241da2dd8da940e92f3019b02d81f47
https://doi.org/10.5194/egusphere-egu23-14973
https://doi.org/10.5194/egusphere-egu23-14973
Autor:
Jonas Bhend, Jonathan Demaeyer, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, Stéphane Vannitsem
Statistical postprocessing of forecasts from numerical weather prediction systems is an important component of modern weather forecasting systems. A growing variety of postprocessing methods has been proposed, but a comprehensive, community-driven co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b01e94cfa3f237dd17f50dde75ee6d30
https://doi.org/10.5194/egusphere-egu23-9328
https://doi.org/10.5194/egusphere-egu23-9328
Autor:
Jonathan Demaeyer, jonas Bhend, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, Stéphane Vannitsem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ffe14bfa90fe34851d398db8b5fdfbdc
https://doi.org/10.5194/essd-2022-465-supplement
https://doi.org/10.5194/essd-2022-465-supplement
Autor:
Jonathan Demaeyer, jonas Bhend, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, Stéphane Vannitsem
Statistical postprocessing of medium-range weather forecasts is an important component of modern forecasting systems. Since the beginning of modern data science, numerous new postprocessing methods have been proposed, complementing an already very di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b6efdafe1d36885be41b79f4c0fa6a3
https://doi.org/10.5194/essd-2022-465
https://doi.org/10.5194/essd-2022-465
Automated forecasting provides the basis for everyday forecast products used by a wide range of users. Continued progress in numerical weather prediction allows to produce local forecasts with considerable accuracy. To further reduce systematic error
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7240cbf56a55ebab7750d027fd7c69bb
https://doi.org/10.5194/ems2022-330
https://doi.org/10.5194/ems2022-330
To mitigate the impacts associated with adverse weather conditions, meteorological services issue weather warnings to the general public. These warning systems rely heavily on forecasts issued by underlying prediction systems. When deciding which pre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b81426888f7e8efa715312a6e49c3dab
https://doi.org/10.5194/ems2022-458
https://doi.org/10.5194/ems2022-458
In weather applications, machine learning is emerging as an innovative technology with the potential to address many of the shortcomings of traditional modelling procedures. The trend is fostered by the growing availability of observational data, com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7769e0929454799f83328e93bed02b4d
https://doi.org/10.5194/ems2022-211
https://doi.org/10.5194/ems2022-211
Objective forecast verification provides the basis to motivate changes to the forecast system. At MeteoSwiss, we are introducing statistical ensemble postprocessing into our automated forecast production. These automated forecasts are accessed by the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::850e07b5b4b8701bb0dbeef09422b34d
https://doi.org/10.5194/ems2022-332
https://doi.org/10.5194/ems2022-332
Autor:
Stephan Hemri, Jonas Bhend, Christoph Spirig, Daniele Nerini, Lionel Moret, Reinhard Furrer, Mark A. Liniger
Probabilistic predictions of precipitation call for rather sophisticated postprocessing approaches due to its low predictability, high spatio-temporal variability and highly positive skewness. Moreover, the large number of zeros makes the generation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3752d7a317c28d7707b188224ccddd4c
https://doi.org/10.5194/ems2022-427
https://doi.org/10.5194/ems2022-427