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Michael Maier-Gerber, Andreas H. Fink, Michael Riemer, Elmar Schoemer, Christoph Fischer, and Benedikt Schulz

1. Introduction For decades, there has been a parallel development of predictions for individual tropical cyclones (TCs) made by operational forecast centers for lead times of a few days on the one hand, and seasonal predictions of integrated TC activity on the other. This coexistence is due to the subseasonal predictability gap ( Vitart et al. 2012 ; Robertson et al. 2020 ), which has raised broad attention and efforts to bridge only in recent years. Because of the lack of skillful models

Open access
Eva-Maria Walz, Marlon Maranan, Roderick van der Linden, Andreas H. Fink, and Peter Knippertz

1. Introduction Over the last more than 60 years, scientific and technical advances have tremendously improved numerical weather prediction (NWP) worldwide ( Bauer et al. 2015 ; Alley et al. 2019 ). The quasi-exponential growth in computing power enabled the implementation of ensemble prediction systems (EPSs) in the 1990s, where each member is started from slightly different initial conditions to allow quantifying forecast uncertainty ( Molteni et al. 1996 ). EPSs are well in line with recent

Open access
Stephan Rasp and Sebastian Lerch

1. Introduction Numerical weather prediction based on physical models of the atmosphere has improved continuously since its inception more than four decades ago ( Bauer et al. 2015 ). In particular, the emergence of ensemble forecasts—simulations with varying initial conditions and/or model physics—added another dimension by quantifying the flow-dependent uncertainty. Yet despite these advances the raw forecasts continue to exhibit systematic errors that need to be corrected using statistical

Open access
Kevin Bachmann, Christian Keil, George C. Craig, Martin Weissmann, and Christian A. Welzbacher

1. Introduction Convection-permitting numerical weather prediction (NWP) models underpin a step change for operational forecasting centers in their struggle to predict thunderstorms and convective precipitation ( Clark et al. 2016 ) as they allow some key issues to be addressed. First, the intrinsically limited predictability of the small scales, including convection, necessitates the use of ensembles to generate probabilistic forecasts and assess their confidence ( Lorenz 1969 ; Slingo and

Free access
Marlene Baumgart, Michael Riemer, Volkmar Wirth, Franziska Teubler, and Simon T. K. Lang

1. Introduction Numerical weather prediction has improved remarkably over the last decades (e.g., Bauer et al. 2015 ). Occasionally, however, very poor medium-range forecasts do still occur ( Rodwell et al. 2013 ). Forecast errors arise due to errors in the initial conditions and due to model deficiencies (e.g., Palmer and Hagedorn 2006 ). After 1–2 forecast days, localized errors may form that start to affect the synoptic-scale flow (e.g., Davies and Didone 2013 ; Martínez-Alvarado et al

Open access
Peter Vogel, Peter Knippertz, Andreas H. Fink, Andreas Schlueter, and Tilmann Gneiting

1. Introduction The bulk of precipitation in the tropics is related to moist convection, in contrast to the frontal-dominated extratropics. Because of the small-scale processes involved in the triggering and growth of convective systems, quantitative precipitation forecasts are known to have overall poorer levels of skill in tropical latitudes ( Haiden et al. 2012 ). This can be monitored in quasi–real time via the World Meteorological Organization (WMO) Lead Centre on Verification of Ensemble

Open access
Roderick van der Linden, Andreas H. Fink, Joaquim G. Pinto, and Tan Phan-Van

with thin, dashed lines. The orange dot denotes the location of the center of the ECMWF grid box that was used in the evaluation of the ensemble forecast (cf. Fig. 11 ). The green dot indicates the location of the Phu Lien radar station, and the blue dots denote the locations of the Bach Long Vy (WMO station ID 48839) and Beihai (WMO station ID 59644) radiosonde stations. According to D.-Q. Nguyen et al. (2014) , northeastern Vietnam can be separated into two distinct climate zones, termed N2 and

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Peter Vogel, Peter Knippertz, Andreas H. Fink, Andreas Schlueter, and Tilmann Gneiting

1. Introduction Numerical weather prediction (NWP) has steadily improved over the last decades, allowing a multitude of socioeconomic benefits to be realized ( Bauer et al. 2015 ; Alley et al. 2019 ). While progress is unmistakable for 500-hPa geopotential heights and mean sea level pressure in the extratropics, improvements in the predictions of many other parameters are more variable ( Navascués et al. 2013 ). For example, forecasts of European cloud cover have hardly improved over the last

Open access
Tobias Selz

1. Introduction Forecast skill has improved continuously over the last 40 years. The rate of improvement reached about one forecast day per decade, which means that a 6-day forecast today is as good as a 5-day forecast was 10 years ago. This considerable improvement together with its high socioeconomic impact has been recognized as a “quiet revolution” by Bauer et al. (2015) . However, such studies of past successes immediately raise the question of how far this progress will go on in the

Open access
Jan Wandel, Julian F. Quinting, and Christian M. Grams

of forecast errors and uncertainties from small to large scales ( Grams et al. 2018 ). On the medium range, the representation of WCBs in NWP models was first evaluated by Madonna et al. (2015) for three winter periods [December–February (DJF)] in the operational high resolution deterministic forecast of the ECMWF Integrated Forecasting System (IFS) model. They used a novel feature-based verification technique that was originally developed to verify precipitation forecasts ( Wernli et al. 2008

Open access