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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

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

performance of current operational systems with respect to tropical rainfall calls for alternative approaches reaching from convection-permitting resolution ( Pante and Knippertz 2019 ) to methods from statistics and machine learning ( Shi et al. 2015 ; Rasp et al. 2020 ; Vogel et al. 2021 ). Before developing and evaluating new models and approaches, it is essential to establish benchmark forecasts in order to systematically assess forecast improvement. Rasp et al. (2020) recently proposed

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

( Fig. 11c ). As for ECMWF, skill gaps in MSC are largest for Arid Americas and Mountain climates. Somewhat surprisingly, the skill gap grows markedly in 2014 for Arid Asia, leading to an overall significant positive trend. Fig . 11. As in Fig. 10 , but for the MSC model and during 2009–16. 5. Conclusions The quality of precipitation forecasts from two leading operational ensemble predictions systems (ECMWF and MSC) was assessed specifically for the tropics between 30°S and 30°N. TRMM satellite

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

forecasts for the benefit of humanity” ( Bougeault et al. 2010 , p. 1060). Since its start in October 2006, up to 10 global NWP centers have provided their operational ensemble forecasts, which are accessible on a common 0.5° × 0.5° grid. Park et al. (2008) and Bougeault et al. (2010) discuss objectives and the setup of TIGGE, including the participating EPSs, in great detail. They also note early results using the TIGGE ensemble, while Swinbank et al. (2016) report on achievements accomplished

Open access
Michael Maier-Gerber, Michael Riemer, Andreas H. Fink, Peter Knippertz, Enrico Di Muzio, and Ron McTaggart-Cowan

were associated with the TT of Chris, before the results in terms of predictability are presented in section 4 . The findings and conclusions from this study are discussed in section 5 . 2. Data and methods a. Data The present case study is based on gridded, 6-hourly operational analysis and ensemble forecast data from the ECMWF. To assess the evolution of predictability, consecutive ensemble forecasts initialized at 0000 UTC between 10 June and 19 June 2012—equivalent to 9.5 (7) days prior to

Open access
Marlene Baumgart, Paolo Ghinassi, Volkmar Wirth, Tobias Selz, George C. Craig, and Michael Riemer

1. Introduction Weather prediction has improved significantly in the past decades ( Bauer et al. 2015 ). Forecast dropouts, however, do still occur in operational numerical weather prediction models ( Rodwell et al. 2013 , 2018 ). Because of the multiscale nature of atmospheric dynamics, there may always be an intrinsic limit of predictability even if model errors and initial-condition errors occur only on the smallest resolved scale ( Lorenz 1969 ). Small-scale errors associated with moist

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

analysis as in Fröhlich and Knippertz (2008) . The 200-hPa trough axes were calculated from the operational analysis and EPS forecasts using zonal geopotential gradients at 200 hPa as in Knippertz (2004) . 3. Description of the extreme precipitation event Between 1200 UTC 25 July and 1200 UTC 3 August 2015, record-breaking rainfall was observed along the northeastern Vietnamese coast between Ha Long Bay and the border region of Vietnam with China. At five stations in northeastern Vietnam, rainfall

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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