Baran, S., and S. Lerch: Combining predictive distributions for the statistical post-processing of ensemble forecasts, Int. J. Forecast., 34, 477-496. (Link to online article) Fundamental discussion of how we can combine information from different observational sources (e.g. different weather stations) to better correct for biases and dispersion errors in forecasts.
· Baumgart, M., M. Riemer, V. Wirth, F. Teubler, S. T. K. Lang: Potential-vorticity dynamics of forecast errors: A quantitative case study, Mon. Wea. Rev., early online, DOI:10.1175/MWR-D-17-0196.1. (Link to online article) Illustrative example of how a recently developed potential vorticity error tendency equation can be used to better analyse contributions to forecast errors from different dynamical processes.
· Craig, G. C., and T. Selz: Mesoscale dynamical regimes in the midlatitudes, Geophys. Res. Lett., 45, DOI:10.1002/2017GL076174. (Link to online article) Introduction and discussion of five dynamical regimes (quasi‐geostrophic, propagating/stationary gravity waves, acoustic modes, diabatic heating) based on governing equations and spectral analysis.
· Fragkoulidis, G., Wirth, V., Bossmann, P. and Fink, A.H.: Linking Northern Hemisphere temperature extremes to Rossby wave packets, Q.J.R. Meteorol. Soc., 144, 553-566, doi:10.1002/qj.3228 (Link to online article) Statistical analysis of how strongly warm and cold temperature extremes are related to particular characteristics of Rossby waves (e.g. large amplitude) and their longitudinal extent.
· 1) Gentine, P., M. Pritchard, S. Rasp, G. Reinaudi and G. Yacalis, 2018: Could machine learning break the convection parameterization deadlock?, Geophys. Res. Lett. (Link to online article) 2) Rasp, S., M. S. Pritchard, and P. Gentine: Deep learning to represent sub-grid processes in climate models. Proc. Natl. Acad. Sci., doi: 10.1073/pnas.1810286115. (Link to online article) Two papers discussing how new approaches from computer science can be used in atmospheric science
to improve longstanding problems with parametrizations.
· 1) Kern M., T. Hewson, A. Schäfler, R. Westermann, and M. Rautenhaus: Interactive 3D Visual Analysis of Atmospheric Fronts, IEEE Transactions on Visualization and Computer Graphics, doi:10.1109/tvcg.2018.2864806. (Link to online article) 2) Rautenhaus, M., M. Böttinger, S. Siemen, R. Hoffman, R.M. Kirby, M. Mirzargar, N. Röber, and R. Westermann: Visualization in Meteorology - A Survey of Techniques and Tools for Data Analysis Tasks, IEEE Transactions on Visualization and Computer Graphics, doi: 10.1109/TVCG.2017.2779501 (Link to online article) 3) Kumpf, A., B. Tost, M. Baumgart, M. Riemer, R. Westermann, and M. autenhaus: Visualizing confidence in cluster-based ensemble weather forecast analyses, IEEE Transactions on Visualization and Computer Graphics, 24, doi: 10.1109/TVCG.2017.2745178. (Link to online article) Three papers illustrating and summarising the opportunities provided by new developments in visualisation to better identify atmospheric features, understand dynamics of weather systems and to analyse forecast uncertainty interactively.
· Pantillon, F., Lerch, S., Knippertz, P. and Corsmeier, U.: Forecasting wind gusts in winter storms using a calibrated convection‐permitting ensemble. Q. J. R. Meteorol. Soc. Accepted Author Manuscript. doi:10.1002/qj.3380. (Link to online article) Illustration of quality of gust forecasts in a convection-permitting regional ensemble forecast system including the scope for improvements using postprocessing.
· Schneider, L., C. Barthlott, A.I. Barrett, C. Hoose: The precipitation response to variable terrain forcing over low‐mountain ranges in different weather regimes, Q. J. Roy. Meteorol. Soc., doi:10.1002/qj.3250 (Link to online article) Very high resolution (500m grid-spacing) sensitivity experiments investigating the role of hilly terrain on convective triggering and evolution as well as on frontal rainfall.
· Vogel, P., P. Knippertz, A. Fink, A. Schlueter, and T. Gneiting: Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall over Northern Tropical Africa, Wea. Forecasting., 33, 369-388, doi:10.1175/WAF-D-17-0127.1 (Link to online article) Demonstration that even sophisticated ensemble postprocessing and multi-model approaches applied to global state-of-the-art NWP models hardly outperform a simple climatological rainfall forecast in tropical Africa.
· Wirth, V., M. Riemer, E. K. M. Chang, O. Martius: Rossby Wave Packets on the Midlatitude Waveguide – A Review, Mon. Wea. Rev., doi:10.1175/MWR-D-16-0483.1, in press. (Link to online article) Broad discussion on where science stands today with respects to Rossby wave analysis, physical understanding and relevance for weather prediction.
· There is also an AMS special issue for W2W at https://journals.ametsoc.org/topic/W2W, currently containing 11 papers.