Zobrazeno 1 - 10
of 122
pro vyhledávání: '"M G Schultz"'
Publikováno v:
Geoscientific Model Development, Vol 15, Pp 8913-8930 (2022)
Tropospheric ozone is a secondary air pollutant that is harmful to living beings and crops. Predicting ozone concentrations at specific locations is thus important to initiate protection measures, i.e. emission reductions or warnings to the populatio
Externí odkaz:
https://doaj.org/article/c6005c09a18a400b9f6b739d518da0db
Publikováno v:
Geoscientific Model Development, Vol 15, Pp 8931-8956 (2022)
Numerical weather prediction (NWP) models solve a system of partial differential equations based on physical laws to forecast the future state of the atmosphere. These models are deployed operationally, but they are computationally very expensive. Re
Externí odkaz:
https://doaj.org/article/607a06ca4ff94c2186d8712016b38127
Autor:
C. Betancourt, T. T. Stomberg, A.-K. Edrich, A. Patnala, M. G. Schultz, R. Roscher, J. Kowalski, S. Stadtler
Publikováno v:
Geoscientific Model Development, Vol 15, Pp 4331-4354 (2022)
Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here, we present a data-driven ozone-mapping workflow generating a transparent and reliable product. We map the gl
Externí odkaz:
https://doaj.org/article/3cc58269075f4002b33d4f4561477fab
Publikováno v:
Earth System Science Data, Vol 13, Pp 3013-3033 (2021)
With the AQ-Bench dataset, we contribute to the recent developments towards shared data usage and machine learning methods in the field of environmental science. The dataset presented here enables researchers to relate global air quality metrics to e
Externí odkaz:
https://doaj.org/article/b4d6056825ad4e93887002b9ec4eb735
Publikováno v:
Geoscientific Model Development, Vol 14, Pp 1553-1574 (2021)
With MLAir (Machine Learning on Air data) we created a software environment that simplifies and accelerates the exploration of new machine learning (ML) models, specifically shallow and deep neural networks, for the analysis and forecasting of meteor
Externí odkaz:
https://doaj.org/article/f84e6b7a390349a29377b046115b2292
IntelliO3-ts v1.0: a neural network approach to predict near-surface ozone concentrations in Germany
Publikováno v:
Geoscientific Model Development, Vol 14, Pp 1-25 (2021)
The prediction of near-surface ozone concentrations is important for supporting regulatory procedures for the protection of humans from high exposure to air pollution. In this study, we introduce a data-driven forecasting model named “IntelliO3-ts
Externí odkaz:
https://doaj.org/article/833bceaea8494179a77bd558bbce9559
Autor:
M. G. de Vos, W. Hazeleger, D. Bari, J. Behrens, S. Bendoukha, I. Garcia-Marti, R. van Haren, S. E. Haupt, R. Hut, F. Jansson, A. Mueller, P. Neilley, G. van den Oord, I. Pelupessy, P. Ruti, M. G. Schultz, J. Walton
Publikováno v:
Geoscience Communication, Vol 3, Pp 191-201 (2020)
The need for open science has been recognized by the communities of meteorology and climate science. While these domains are mature in terms of applying digital technologies, the implementation of open science methodologies is less advanced. In a ses
Externí odkaz:
https://doaj.org/article/294b72f0902e455f8a56b54922d031d0
Publikováno v:
Geoscientific Model Development, Vol 13, Pp 1513-1544 (2020)
Global tropospheric ozone reanalyses constructed using different state-of-the-art satellite data assimilation systems, prepared as part of the Copernicus Atmosphere Monitoring Service (CAMS-iRean and CAMS-Rean) as well as two fully independent reanal
Externí odkaz:
https://doaj.org/article/0e91eba89480417493699149c0cbb229
Autor:
C. Heinze, V. Eyring, P. Friedlingstein, C. Jones, Y. Balkanski, W. Collins, T. Fichefet, S. Gao, A. Hall, D. Ivanova, W. Knorr, R. Knutti, A. Löw, M. Ponater, M. G. Schultz, M. Schulz, P. Siebesma, J. Teixeira, G. Tselioudis, M. Vancoppenolle
Publikováno v:
Earth System Dynamics, Vol 10, Pp 379-452 (2019)
Earth system models (ESMs) are key tools for providing climate projections under different scenarios of human-induced forcing. ESMs include a large number of additional processes and feedbacks such as biogeochemical cycles that traditional physical c
Externí odkaz:
https://doaj.org/article/9119662c92b64433b293da9cd3342e52
Autor:
R. Sander, A. Baumgaertner, D. Cabrera-Perez, F. Frank, S. Gromov, J.-U. Grooß, H. Harder, V. Huijnen, P. Jöckel, V. A. Karydis, K. E. Niemeyer, A. Pozzer, H. Riede, M. G. Schultz, D. Taraborrelli, S. Tauer
Publikováno v:
Geoscientific Model Development, Vol 12, Pp 1365-1385 (2019)
We present version 4.0 of the atmospheric chemistry box model CAABA/MECCA that now includes a number of new features: (i) skeletal mechanism reduction, (ii) the Mainz Organic Mechanism (MOM) chemical mechanism for volatile organic compounds, (iii) an
Externí odkaz:
https://doaj.org/article/c82d2ea89ff3455a849dca3c166dcf1d