Publikationen am Institut für Meteorologie und Geophysik
Zeige Ergebnisse 81 - 100 von 793
Keshtgar, B., Voigt, A., Hoose, C., Riemer, M., & Mayer, B. (2023). Cloud-radiative impact on the dynamics and predictability of an idealized extratropical cyclone. Weather and Climate Dynamics, 4(1), 115 - 132. https://doi.org/10.5194/wcd-4-115-2023
Boyer, M., Aliaga, D., Pernov, J. B., Angot, H., Quelever, L. L. J., Dada, L., Heutte, B., Dall'Osto, M., Beddows, D. C. S., Brasseur, Z., Beck, I., Bucci, S., Dütsch, M., Stohl, A., Laurila, T. M., Asmi, E., Massling, A., Thomas, D. C., Nøjgaard, J. K., ... Jokinen, T. (2023). A full year of aerosol size distribution data from the central Arctic under an extreme positive Arctic Oscillation: insights from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Atmospheric Chemistry and Physics, 23(1), 389–415. [23]. https://doi.org/10.5194/acp-23-389-2023
Necker, T., Hinger, D., Griewank, P., Miyoshi, T., & Weissmann, M. (2023). Guidance on how to improve vertical covariance localization based on a 1000-member ensemble. Nonlinear Processes in Geophysics, 30(1), 13-29. https://doi.org/10.5194/npg-30-13-2023
Hu, G., Dance, S. L., Bannister, R. N., Chipilski, H. G., Guillet, O., Macpherson, B., Weissmann, M., & Yussouf, N. (2023). Progress, challenges, and future steps in data assimilation for convection-permitting numerical weather prediction: Report on the virtual meeting held on 10 and 12 November 2021. Atmospheric Science Letters, 24(1), [e1130]. https://doi.org/10.1002/asl.1130
Nomokonova, T., Griewank, P., Loehnert, U., Miyoshi, T., Necker, T., & Weissmann, M. (2023). Estimating the benefit of Doppler wind lidars for short-term low-level wind ensemble forecasts. Quarterly Journal of the Royal Meteorological Society, 149(750), 192-210. https://doi.org/10.1002/qj.4402
Schutt, D. L., Porritt, R. W., Esteve, C., Audet, P., Gosselin, J. M., Schaeffer, A. J., Aster, R. C., Freymueller, J. T., & Cubley, J. F. (2023). Lithospheric S Wave Velocity Variations Beneath the Mackenzie Mountains and Northern Canadian Cordillera. Journal of Geophysical Research - Solid Earth, 128(1), [e2022JB025517]. https://doi.org/10.1029/2022JB025517
Soto Bravo, F. A., & Zhang, C. (2023). A critical mini-review on the low-field nuclear magnetic resonance investigation of pore coupling effects in near-surface environments. Frontiers in Water, 5, [1059128]. https://doi.org/10.3389/frwa.2023.1059128
Boyce, A., Liddell, M. V., Pugh, S., Brown, J., McMurchie, E., Parsons, A., Esteve, C., Burdick, S., Darbyshire, F. A., Cottaar, S., Bastow, I. D., Schaeffer, A. J., Audet, P., Schutt, D. L., & Aster, R. C. (2023). A new P-wave Tomographic Model (CAP22) for North America: Implications for the Subduction and Cratonic Metasomatic Modification History of Western Canada and Alaska. Journal of Geophysical Research - Solid Earth, 128(3), [e2022JB025745]. https://doi.org/10.1029/2022JB025745
Gasparini, B., Quante, M., & Lohmann, U. (2023). Ausdünnung der Cirrusbewölkung um dem Klimawandel entgegenzuwirken? in J. L. Lozán, H. Graßl, S-W. Breckle, D. Kasang, & M. Quante (Hrsg.), WARNSIGNAL-KLIMA: Hilft Technik gegen die Erderwärmung?: Climate Engineering in der Diskussion (S. 256-263). Wissenschaftliche Auswertungen in Kooperation mit GEO Magazin-Hamburg. WARNSIGNAL-KLIMA https://doi.org/10.25592/warnsignal.klima.climate.engineering.39
Farokhmanesh, F., Höhlein, K., Necker, T., & Weissmann, M. (2023). Deep Learning–Based Parameter Transfer in Meteorological Data. Artificial Intelligence for the Earth Systems, 2(1). https://doi.org/10.1175/AIES-D-22-0024.1
Brunner, L., & Sippel, S. (2023). Identifying climate models based on their daily output using machine learning. Environmental Data Science, 2, [e22]. https://doi.org/10.1017/eds.2023.23
Farokhmanesh, F., Höhlein, K., Neuhauser, C., Necker, T., Weissmann, M., Miyoshi, T., & Westermann, R. (2023). Neural Fields for Interactive Visualization of Statistical Dependencies in 3D Simulation Ensembles. Beitrag in VMV 2023, Vision, Modeling, and Visualization. The Eurographics Association, Deutschland. https://doi.org/10.2312/vmv.20231229
Liu, Y., Yao, Y., Liu, D., & Zhang, C. (2023). Nuclear Magnetic Resonance Investigation of Forced Imbibitions in Longmaxi Shales: Consideration of Different Boundary Conditions. Energy & Fuels, 37(8), 5853-5866. https://doi.org/10.1021/acs.energyfuels.3c00404
Eckhardt, S., Pisso, I., Evangeliou, N., Groot Zwaaftink, C., Plach, A., McConnell, J. R., Sigl, M., Ruppel, M. M., Zdanowicz, C., Lim, S., Chellman, N. J., Opel, T., Meyer, H., Steffensen, J. P., Schwikowski, M., & Stohl, A. (2023). Revised historical Northern Hemisphere black carbon emissions based on inverse modeling of ice core records. Nature Communications, 14(1), [271]. https://doi.org/10.1038/s41467-022-35660-0
Quante, M., Gasparini, B., & Belge, B. (2023). Vom Regenmachen zur Klimaintervention – Ein Blick auf die Ideen- und Entwicklungsgeschichte des Climate Engineering. in J. L. Lozán, H. Graßl, S-W. Breckle, D. Kasang, & M. Quante (Hrsg.), WARNSIGNAL-KLIMA: Hilft Technik gegen die Erderwärmung?: Climate Engineering in der Diskussion (S. 34-42). Wissenschaftliche Auswertungen in Kooperation mit GEO Magazin-Hamburg. https://doi.org/10.25592/warnsignal.klima.climate.engineering.05
Liptai, N., Gráczer, Z., Szanyi, G., Cloetingh, S. A. P. L., Süle, B., Aradi, L., Falus, G., Bokelmann, G., Timkó, M., Timár, G., Szabó, C., Istvan, K., & AlpArray Working Group (2022). Seismic anisotropy in the mantle of a tectonically inverted extensional basin: A shear-wave splitting and mantle xenolith study on the western Carpathian-Pannonian region. Tectonics, 845, [229643]. https://doi.org/10.1016/j.tecto.2022.229643
Baier, K., Dütsch, M., Mayer, M., Bakels, L., Haimberger, L., & Stohl, A. (2022). The Role of Atmospheric Transport for El Niño-Southern Oscillation Teleconnections. Geophysical Research Letters, 49(23), [e2022GL100906]. https://doi.org/10.1029/2022GL100906
Badgeley, J. A., Steig, E. J., & Dütsch, M. (2022). Uncertainty in Reconstructing Paleo‐Elevation of the Antarctic Ice Sheet From Temperature‐Sensitive Ice Core Records. Geophysical Research Letters, 49(23), [e2022GL100334]. https://doi.org/10.1029/2022GL100334
Krüger, K., Schäfler, A., Wirth, M., Weissmann, M., & Craig, G. C. (2022). Vertical structure of the lower-stratospheric moist bias in the ERA5 reanalysis and its connection to mixing processes. Atmospheric Chemistry and Physics, 22(23), 15559-15577. https://doi.org/10.5194/acp-22-15559-2022
Vojta, M., Plach, A., Thompson, R. L., & Stohl, A. (2022). A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions. Geoscientific Model Development, 15(22), 8295-8323. [22]. https://doi.org/10.5194/gmd-15-8295-2022
Zeige Ergebnisse 81 - 100 von 793