The ECMWF ensemble prediction system: Looking back (more than) 25 years and projecting forward 25 years
By Tim N. Palmer. Quarterly Journal of the Royal Meteorological Society, 2018
191 citations recorded by the source corpus.
This paper has been written to mark 25 years of operational medium‐range ensemble forecasting. The origins of the ECMWF Ensemble Prediction System are outlined, including the development of the precursor real‐time Met Office monthly ensemble forecast system. In particular, the reasons for the development of singular vectors and stochastic physics – particular features of the ECMWF Ensemble Prediction System ‐ are discussed. The author speculates about the development and use of ensemble prediction in the next 25 years.
Subfields: Atmospheric Science, Electrical and Electronic Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/e13b35e8b9834c85c2cf60205782777c4eeabca6
How researchers see this paper
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In Atmospheric Science
- Jonathan A. Weyn
- Anima Anandkumar
- Zong-Yi Li
- Peter Dueben
- Pedram Hassanzadeh
- Shakir Mohamed
- N. Thuerey
- Kamyar Azizzadenesheli
In Electrical and Electronic Engineering
- Anima Anandkumar
- Zong-Yi Li
- Jonathan A. Weyn
- Shakir Mohamed
- Oriol Vinyals
- Alexander Pritzel
- Ferran Alet
- Suman V. Ravuri
In Global and Planetary Change
- Jonathan A. Weyn
- Peter Dueben
- Pedram Hassanzadeh
- Shakir Mohamed
- N. Thuerey
- Anima Anandkumar
- Soukayna Mouatadid
- Karthik Kashinath
Citations in the corpus
- FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators (2022, background): “Ensemble forecasts have become a crucial component of numerical weather prediction [Palmer, 2019], and consume the largest share of compute costs at operational weather forecasting centers [Bauer et al., 2020].”
- WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting (2020, background): “ty of a forecast. In physical NWP this is done by running several forecasts, called an ensemble, from slightly different initial conditions and potentially with different or stochastic model physics (Palmer, 2019). From this Monte Carlo forecast one can then estimate a probability distribution. A di”
- GraphCast: Learning skillful medium-range global weather forecasting (2022, background): “However, our approach should not be regarded as a replacement for traditional weather forecasting methods, which have been developed for decades, rigorously tested in many real-world contexts, and provide probabilistic predictions (Palmer, 2018).”