Articles | Volume 3, issue 4
https://doi.org/10.5194/wcd-3-1273-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-3-1273-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The impact of microphysical uncertainty conditional on initial and boundary condition uncertainty under varying synoptic control
Meteorologisches Institut, Ludwig-Maximilians-Universität, Munich, Germany
Christian Keil
Meteorologisches Institut, Ludwig-Maximilians-Universität, Munich, Germany
Christian Barthlott
Department Troposphere Research, Institute of Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Related authors
Oriol Tintó Prims, Robert Redl, Marc Rautenhaus, Tobias Selz, Takumi Matsunobu, Kameswar Rao Modali, and George Craig
Geosci. Model Dev., 17, 8909–8925, https://doi.org/10.5194/gmd-17-8909-2024, https://doi.org/10.5194/gmd-17-8909-2024, 2024
Short summary
Short summary
Advanced compression techniques can drastically reduce the size of meteorological datasets (by 5 to 150 times) without compromising the data's scientific value. We developed a user-friendly tool called
enstools-compressionthat makes this compression simple for Earth scientists. This tool works seamlessly with common weather and climate data formats. Our work shows that lossy compression can significantly improve how researchers store and analyze large meteorological datasets.
Lina Lucas, Christian Barthlott, Corinna Hoose, and Peter Knippertz
EGUsphere, https://doi.org/10.5194/egusphere-2025-3069, https://doi.org/10.5194/egusphere-2025-3069, 2025
Short summary
Short summary
We studied how climate change and cleaner air could affect severe storms in Central Europe. Using high-resolution weather simulations of past supercell storms under warmer and less polluted conditions, we found that storms may become more intense, with heavier rainfall and larger hailstones. These changes suggest an increased risk of damage in the future. Our findings help improve understanding of how extreme storms may evolve in a changing climate.
Oriol Tintó Prims, Robert Redl, Marc Rautenhaus, Tobias Selz, Takumi Matsunobu, Kameswar Rao Modali, and George Craig
Geosci. Model Dev., 17, 8909–8925, https://doi.org/10.5194/gmd-17-8909-2024, https://doi.org/10.5194/gmd-17-8909-2024, 2024
Short summary
Short summary
Advanced compression techniques can drastically reduce the size of meteorological datasets (by 5 to 150 times) without compromising the data's scientific value. We developed a user-friendly tool called
enstools-compressionthat makes this compression simple for Earth scientists. This tool works seamlessly with common weather and climate data formats. Our work shows that lossy compression can significantly improve how researchers store and analyze large meteorological datasets.
Christian Barthlott, Amirmahdi Zarboo, Takumi Matsunobu, and Christian Keil
Atmos. Chem. Phys., 22, 10841–10860, https://doi.org/10.5194/acp-22-10841-2022, https://doi.org/10.5194/acp-22-10841-2022, 2022
Short summary
Short summary
The relevance of microphysical and land-surface uncertainties for convective-scale predictability is evaluated with a combined-perturbation strategy in realistic convection-resolving simulations. We find a large ensemble spread which demonstrates that the uncertainties investigated here and, in particular, their collective effect are highly relevant for quantitative precipitation forecasting of summertime convection in central Europe.
Christian Barthlott, Amirmahdi Zarboo, Takumi Matsunobu, and Christian Keil
Atmos. Chem. Phys., 22, 2153–2172, https://doi.org/10.5194/acp-22-2153-2022, https://doi.org/10.5194/acp-22-2153-2022, 2022
Short summary
Short summary
The relative impact of cloud condensation nuclei (CCN) concentrations and the shape parameter of the cloud droplet size distribution is evaluated in realistic convection-resolving simulations. We find that an increase in the shape parameter can produce almost as large a variation in precipitation as a CCN increase from maritime to polluted conditions. The choice of the shape parameter may be more important than previously thought for determining cloud radiative characteristics.
Christian Keil, Lucie Chabert, Olivier Nuissier, and Laure Raynaud
Atmos. Chem. Phys., 20, 15851–15865, https://doi.org/10.5194/acp-20-15851-2020, https://doi.org/10.5194/acp-20-15851-2020, 2020
Short summary
Short summary
During strong synoptic control, which dominates the weather on 80 % of the days in the 2-month HyMeX-SOP1 period, the domain-integrated precipitation predictability assessed with the normalized ensemble standard deviation is above average, the wet bias is smaller and the forecast quality is generally better. In contrast, the spatial forecast quality of the most intense precipitation in the afternoon, as quantified with its 95th percentile, is superior during weakly forced synoptic regimes.
Cited articles
Albrecht, B. A.:
Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/SCIENCE.245.4923.1227, 1989. a
Bachmann, K., Keil, C., Craig, G. C., Weissmann, M., and Welzbacher, C. A.:
Predictability of Deep Convection in Idealized and Operational Forecasts: Effects of Radar Data Assimilation, Orography, and Synoptic Weather Regime, Mon. Weather Rev., 148, 63–81, https://doi.org/10.1175/mwr-d-19-0045.1, 2020. a, b
Bannister, R. N.:
A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633, https://doi.org/10.1002/qj.2982, 2017. a
Barthlott, C. and Hoose, C.:
Aerosol effects on clouds and precipitation over central Europe in different weather regimes, J. Atmos. Sci., 75, 4247–4264, https://doi.org/10.1175/JAS-D-18-0110.1, 2018. a
Barthlott, C., Zarboo, A., Matsunobu, T., and Keil, C.:
Impacts of combined microphysical and land-surface uncertainties on convective clouds and precipitation in different weather regimes, Atmos. Chem. Phys., 22, 10841–10860, https://doi.org/10.5194/acp-22-10841-2022, 2022b. a, b, c, d, e, f, g, h, i
Baur, F., Keil, C., and Craig, G. C.:
Soil moisture–precipitation coupling over Central Europe: Interactions between surface anomalies at different scales and the dynamical implication, Q. J. Roy. Meteor. Soc., 144, 2863–2875, https://doi.org/10.1002/qj.3415, 2018. a
Baur, F., Keil, C., and Barthlott, C.:
Combined effects of soil moisture and microphysical perturbations on convective clouds and precipitation for a locally forced case over Central Europe, Q. J. Roy. Meteor. Soc., https://doi.org/10.1002/QJ.4295, 2022. a, b, c, d
Bryan, G. H. and Morrison, H.:
Sensitivity of a simulated squall line to horizontal resolution and parameterization of microphysics, Mon. Weather Rev., 140, 202–225, https://doi.org/10.1175/MWR-D-11-00046.1, 2012. a
Chua, X. R. and Ming, Y.:
Convective Invigoration Traced to Warm-Rain Microphysics, Geophys. Res. Lett., 47, e2020GL089134, https://doi.org/10.1029/2020GL089134, 2020. a
Clark, P., Roberts, N., Lean, H., Ballard, S. P., and Charlton-Perez, C.:
Convection-permitting models: a step-change in rainfall forecasting, Meteorol. Appl., 23, 165–181, https://doi.org/10.1002/met.1538, 2016. a
Craig, G. C., Puh, M., Keil, C., Tempest, K., Necker, T., Ruiz, J., Weissmann, M., and Miyoshi, T.:
Distributions and convergence of forecast variables in a 1,000-member convection-permitting ensemble, Q. J. Roy. Meteor. Soc., 148, 2325–2343, https://doi.org/10.1002/QJ.4305, 2022. a
Dey, S. R., Leoncini, G., Roberts, N. M., Plant, R. S., and Migliorini, S.:
A spatial view of ensemble spread in convection permitting ensembles, Mon. Weather Rev., 142, 4091–4107, https://doi.org/10.1175/MWR-D-14-00172.1, 2014. a, b, c
Fan, J., Yuan, T., Comstock, J. M., Ghan, S., Khain, A., Leung, L. R., Li, Z., Martins, V. J., and Ovchinnikov, M.:
Dominant role by vertical wind shear in regulating aerosol effects on deep convective clouds, J. Geophys. Res., 114, D22206, https://doi.org/10.1029/2009JD012352, 2009. a
Flack, D. L., Gray, S. L., Plant, R. S., Lean, H. W., and Craig, G. C.:
Convective-scale perturbation growth across the spectrum of convective regimes, Mon. Weather Rev., 146, 387–405, https://doi.org/10.1175/MWR-D-17-0024.1, 2018. a, b
Flack, D. L. A., Plant, R. S., Gray, S. L., Lean, H. W., Keil, C., and Craig, G. C.:
Characterisation of convective regimes over the British Isles, Q. J. Roy. Meteor. Soc., 142, 1541–1553, https://doi.org/10.1002/qj.2758, 2016. a
Glassmeier, F. and Lohmann, U.:
Precipitation Susceptibility and Aerosol Buffering of Warm- and Mixed-Phase Orographic Clouds in Idealized Simulations, J. Atmos. Sci., 75, 1173–1194, https://doi.org/10.1175/JAS-D-17-0254.1, 2018. a
Grant, L. D. and van den Heever, S. C.:
Cold Pool and Precipitation Responses to Aerosol Loading: Modulation by Dry Layers, J. Atmos. Sci., 72, 1398–1408, https://doi.org/10.1175/JAS-D-14-0260.1, 2015. a
Hande, L. B., Engler, C., Hoose, C., and Tegen, I.:
Parameterizing cloud condensation nuclei concentrations during HOPE, Atmos. Chem. Phys., 16, 12059–12079, https://doi.org/10.5194/acp-16-12059-2016, 2016. a
Heikenfeld, M., White, B., Labbouz, L., and Stier, P.:
Aerosol effects on deep convection: the propagation of aerosol perturbations through convective cloud microphysics, Atmos. Chem. Phys., 19, 2601–2627, https://doi.org/10.5194/acp-19-2601-2019, 2019. a
Hunt, B. R., Kostelich, E. J., and Szunyogh, I.:
Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D, 230, 112–126, https://doi.org/10.1016/J.PHYSD.2006.11.008, 2007. a
Igel, A. L. and van den Heever, S. C.:
The importance of the shape of cloud droplet size distributions in shallow cumulus clouds. Part II: Bulk microphysics simulations, J. Atmos. Sci., 74, 259–273, https://doi.org/10.1175/JAS-D-15-0383.1, 2017a. a, b
Igel, A. L. and van den Heever, S. C.:
The importance of the shape of cloud droplet size distributions in shallow cumulus clouds. Part I: Bin microphysics simulations, J. Atmos. Sci., 74, 249–258, https://doi.org/10.1175/JAS-D-15-0382.1, 2017b. a
Keil, C., Heinlein, F., and Craig, G. C.:
The convective adjustment time-scale as indicator of predictability of convective precipitation, Q. J. Roy. Meteor. Soc., 140, 480–490, https://doi.org/10.1002/qj.2143, 2014. a
Keil, C., Baur, F., Bachmann, K., Rasp, S., Schneider, L., and Barthlott, C.:
Relative contribution of soil moisture, boundary-layer and microphysical perturbations on convective predictability in different weather regimes, Q. J. Roy. Meteor. Soc., 145, 3102–3115, https://doi.org/10.1002/qj.3607, 2019. a, b, c, d
Kühnlein, C., Keil, C., Craig, G. C., and Gebhardt, C.:
The impact of downscaled initial condition perturbations on convective-scale ensemble forecasts of precipitation, Q. J. Roy. Meteor. Soc., 140, 1552–1562, https://doi.org/10.1002/qj.2238, 2014. a, b
Reinert, D., Prill, F., Denhard, H. F. M., Baldauf, M., C. Schraff, C. G., Marsigli, C., and Zängl, G.:
DWD Database Reference for the Global and Regional ICON and ICON-EPS Forecasting System, Deutscher Wetterdienst, https://doi.org/10.5676/DWD_pub/nwv/icon_2.1.7, 2021. a
Roberts, N. M. and Lean, H. W.:
Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events, Mon. Weather Rev., 136, 78–97, https://doi.org/10.1175/2007MWR2123.1, 2008. a, b
Scheck, L., Weissmann, M., and Bach, L.:
Assimilating visible satellite images for convective-scale numerical weather prediction: A case-study, Q. J. Roy. Meteor. Soc., 146, 3165–3186, https://doi.org/10.1002/QJ.3840, 2020. a
Schneider, L., Barthlott, C., Hoose, C., and Barrett, A. I.:
Relative impact of aerosol, soil moisture, and orography perturbations on deep convection, Atmos. Chem. Phys., 19, 12343–12359, https://doi.org/10.5194/acp-19-12343-2019, 2019. a
Schraff, C., Reich, H., Rhodin, A., Schomburg, A., Stephan, K., Periá nez, A., and Potthast, R.:
Kilometre-scale ensemble data assimilation for the COSMO model (KENDA), Q. J. Roy. Meteor. Soc., 142, 1453–1472, https://doi.org/10.1002/qj.2748, 2016.
a, b
Segal, Y. and Khain, A.:
Dependence of droplet concentration on aerosol conditions in different cloud types: Application to droplet concentration parameterization of aerosol conditions, J. Geophys. Res., 111, D15204, https://doi.org/10.1029/2005JD006561, 2006. a
Seifert, A. and Beheng, K. D.:
A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description, Meteorol. Atmos. Phys., 92, 45–66, https://doi.org/10.1007/s00703-005-0112-4, 2006. a, b, c
Seifert, A., Köhler, C., and Beheng, K. D.:
Aerosol-cloud-precipitation effects over Germany as simulated by a convective-scale numerical weather prediction model, Atmos. Chem. Phys., 12, 709–725, https://doi.org/10.5194/acp-12-709-2012, 2012. a, b
Selz, T. and Craig, G. C.:
Upscale error growth in a high-resolution simulation of a summertime weather event over Europe, Mon. Weather Rev., 143, 813–827, https://doi.org/10.1175/MWR-D-14-00140.1, 2015. a
Tao, W.-K. and Li, X.:
The relationship between latent heating, vertical velocity, and precipitation processes: The impact of aerosols on precipitation in organized deep convective systems, J. Geophys. Res.-Atmos., 121, 6299–6320, https://doi.org/10.1002/2015JD024267, 2016. a
Wang, C.:
A modeling study of the response of tropical deep convection to the increase of cloud condensation nuclei concentration: 1. Dynamics and microphysics, J. Geophys. Res.-Atmos., 110, 1–16, https://doi.org/10.1029/2004JD005720, 2005. a
Wellmann, C., Barrett, A. I., Johnson, J. S., Kunz, M., Vogel, B., Carslaw, K. S., and Hoose, C.:
Comparing the impact of environmental conditions and microphysics on the forecast uncertainty of deep convective clouds and hail, Atmos. Chem. Phys., 20, 2201–2219, https://doi.org/10.5194/acp-20-2201-2020, 2020. a, b, c
Weyn, J. A. and Durran, D. R.:
The scale dependence of initial-condition sensitivities in simulations of convective systems over the southeastern United States, Q. J. Roy. Meteor. Soc., 145, 57–74, https://doi.org/10.1002/QJ.3367, 2019. a
Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.:
The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core, Q. J. Roy. Meteor. Soc., 141, 563–579, https://doi.org/10.1002/qj.2378, 2015. a
Zhang, Y., Fan, J., Li, Z., and Rosenfeld, D.:
Impacts of cloud microphysics parameterizations on simulated aerosol–cloud interactions for deep convective clouds over Houston, Atmos. Chem. Phys., 21, 2363–2381, https://doi.org/10.5194/acp-21-2363-2021, 2021. a
Zimmer, M., Craig, G. C., Keil, C., and Wernli, H.:
Classification of precipitation events with a convective response timescale and their forecasting characteristics, Geophys. Res. Lett., 38, L05802, https://doi.org/10.1029/2010GL046199, 2011. a
Short summary
This study quantifies the impact of poorly constrained parameters used to represent aerosol–cloud–precipitation interactions on precipitation and cloud forecasts associated with uncertainties in input atmospheric states. Uncertainties in these parameters have a non-negligible impact on daily precipitation amount and largely change the amount of cloud. The comparison between different weather situations reveals that the impact becomes more important when convection is triggered by local effects.
This study quantifies the impact of poorly constrained parameters used to represent...