The THORPEX Interactive Grand Global Ensemble
By P. Bougeault, Z. Toth, C. Bishop, B. Brown, D. Burridge, D. Chen, B. Ebert, M. Fuentes, Thomas M. Hamill, K. Mylne and 12 more. 2010
449 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/1bb5cc0c9b224319c12a0a18df9bdbff40b7a010
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atmospheric Science
- Tilmann Gneiting
- Sebastian Lerch
- Thordis Linda Thorarinsdottir
- E. Grimit
- Jonathan A. Weyn
- Adrian E. Raftery
- Stephan Rasp
- Peter Guttorp
In Global and Planetary Change
- Sebastian Lerch
- Tilmann Gneiting
- Thordis Linda Thorarinsdottir
- E. Grimit
- Peter Guttorp
- Adrian E. Raftery
- Stephan Rasp
- Michael Scheuerer
Citations in the corpus
- ClimaX: A foundation model for weather and climate (2023, unknown): “For IFS, we down-load the predictions from the TIGGE archive (Bougeault et al., 2010) for the year 2018 2 .”
- Impact of Stochastic Physics in a Convection-Permitting Ensemble (2012, methodology): “PEARP contributes to the international TIGGE database (Park 2008, Bougeault 2010).”
- WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting (2020, methodology): “d Forecast System) model of the European Center for Medium-range Weather Forecasting (ECMWF)4. We downloaded the forecasts for 2017 and 2018 from the THORPEX Interactive Grand Global Ensemble (TIGGE; Bougeault et al., 2010) archive5, which contains the operational forecasts, initialized at 00 and 12”
- The operational medium-range deterministic weather forecasting can be extended beyond a 10-day lead time (2023, unknown): “Figure 5a illustrates the average tracking errors for FengWu, ECMWF (retrieved from the TIGGEarchive 18,33 ),Pangu-Weather 16 ,andGraphCast 17 ,andNCEP(stored intheTIGGEarchive 18,33 )fortrackingoftropicalcyclonesin2022.”
- Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction (2010, background): “The station at Castlegar Airport, BC, is marked by an arrow. comprises ten groups, with 11 to 51 members each, which typically are exchangeable (Park et al. 2008; Bougeault et al. 2010), and thus will share common bias correction parameters as well as common BMA or BMA 1 weights.”
- Neural networks for post-processing ensemble weather forecasts (2018, methodology): “The forecasts are taken from the Interactive Grand Global Ensemble (TIGGE) dataset1 (Bougeault et al., 2010).”