The TIGGE Project and Its Achievements

Swinbank, Richard; Kyouda, Masayuki; Buchanan, Piers; Froude, Lizzie S. R.; Hamill, Thomas M.; Hewson, Tim; Keller, Julia; Matsueda, Mio; Methven, John; Pappenberger, Florian; Scheuerer, Michael; Titley, Helen; Wilson, Laurence J.; Yamaguchi, Munehiko

doi:10.1175/bams-d-13-00191.1

Cited by 216 publications

(157 citation statements)

References 57 publications

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“…These poor scores could be related to the low predictability of Mesoscale Convective Systems (MCS) generally leading to heavy precipitation events over the region but which are not well predicted in current EPS (Swinbank et al, 2016). RPSS values for week 3 and week 4 are near zero or negative everywhere except for small patches near the coasts in NCEP and ECMWF models.…”

Section: Weekly Averagesmentioning

confidence: 99%

Subseasonal Predictability of Boreal Summer Monsoon Rainfall from Ensemble Forecasts

Vigaud

Robertson

Tippett

et al. 2017

Front. Environ. Sci.

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Subseasonal forecast skill over the broadly defined North American (NAM), West African (WAM) and Asian (AM) summer monsoon regions is investigated using three Ensemble Prediction Systems (EPS) at sub-monthly lead times. Extended Logistic Regression (ELR) is used to produce probabilistic forecasts of weekly and week 3-4 averages of precipitation with starts in May-Aug, over the 1999-2010 period. The ELR tercile category probabilities for each model gridpoint are then averaged together with equal weight. The resulting Multi-Model Ensemble (MME) forecasts exhibit good reliability, but have generally low sharpness for forecasts beyond 1 week; Multi-model ensembling largely removes negative values of the Ranked Probability Skill Score (RPSS) seen in individual forecasts, and broadly improves the skill obtained in any of the three individual models except for the AM. The MME week 3-4 forecasts have generally higher RPSS and comparable reliability over all monsoon regions, compared to week 3 or week 4 forecast separately. Skill is higher during La Niña compared to El Niño and ENSO-neutral conditions over the 1999-2010 period, especially for the NAM. Regionally averaged RPSS is significantly correlated with the Maden-Julian Oscillation (MJO) for the AM and WAM. Our results indicate potential for skillful predictions at subseasonal time-scales over the three summer monsoon regions of the Northern Hemisphere.

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Section: Weekly Averagesmentioning

confidence: 99%

Subseasonal Predictability of Boreal Summer Monsoon Rainfall from Ensemble Forecasts

Vigaud

Robertson

Tippett

et al. 2017

Front. Environ. Sci.

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“…Second, a better knowledge of uncertainties is necessary to reduce model error by using data assimilation techniques to correct the simulated snowpack state with ground-based or remotely sensed snow observations. In other disciplines, such as meteorology and hydrology, ensemble approaches are now generalized (Swinbank et al, 2016), especially for the furthest prediction lead times when the uncertainty is increasing. Ensemble simulations also increase confidence in future climate simulations (IPCC, 2013).…”

Section: Introductionmentioning

confidence: 99%

A multiphysical ensemble system of numerical snow modelling

Lafaysse¹,

Cluzet²,

Dumont³

et al. 2017

The Cryosphere

109

135

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Abstract. Physically based multilayer snowpack models suffer from various modelling errors. To represent these errors, we built the new multiphysical ensemble system ES-CROC (Ensemble System Crocus) by implementing new representations of different physical processes in the deterministic coupled multilayer ground/snowpack model SUR-FEX/ISBA/Crocus. This ensemble was driven and evaluated at Col de Porte (1325 m a.s.l., French alps) over 18 years with a high-quality meteorological and snow data set. A total number of 7776 simulations were evaluated separately, accounting for the uncertainties of evaluation data. The ability of the ensemble to capture the uncertainty associated to modelling errors is assessed for snow depth, snow water equivalent, bulk density, albedo and surface temperature. Different sub-ensembles of the ESCROC system were studied with probabilistic tools to compare their performance. Results show that optimal members of the ESCROC system are able to explain more than half of the total simulation errors. Integrating members with biases exceeding the range corresponding to observational uncertainty is necessary to obtain an optimal dispersion, but this issue can also be a consequence of the fact that meteorological forcing uncertainties were not accounted for. The ESCROC system promises the integration of numerical snow-modelling errors in ensemble forecasting and ensemble assimilation systems in support of avalanche hazard forecasting and other snowpack-modelling applications.

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Section: Introductionmentioning

confidence: 99%

“…For example, Pappenberger et al (2008) found that if the grand THORPEX International Grand Global Ensemble (TIGGE) forecasts had been used, flood warnings could be issued 8 days before the event, whereas the warning based on a single ensemble system would only allow for a lead time of 4 days (Swinbank et al, 2016). The continuation of the TIGGE project (Swinbank et al, 2016) will further benefit the flood forecasting. Similarly, the seasonal climate prediction from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) project (Palmer et al, 2004) was used to improve the hydrological forecasting over the Ohio River basin during the first 2 months (Luo and Wood, 2008).…”

Section: Introductionmentioning

confidence: 99%

An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models

Yuan

2016

Hydrol. Earth Syst. Sci.

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Abstract. This is the second paper of a two-part series on introducing an experimental seasonal hydrological forecasting system over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, the added value from eight North American Multimodel Ensemble (NMME) climate forecast models with a grand ensemble of 99 members is assessed in this paper, with an implicit consideration of human-induced uncertainty in the hydrological models through a post-processing procedure. The forecast skill in terms of anomaly correlation (AC) for 2 m air temperature and precipitation does not necessarily decrease over leads but is dependent on the target month due to a strong seasonality for the climate over the Yellow River basin. As there is more diversity in the model performance for the temperature forecasts than the precipitation forecasts, the grand NMME ensemble mean forecast has consistently higher skill than the best single model up to 6 months for the temperature but up to 2 months for the precipitation. The NMME climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model regionalized over the Yellow River basin to produce forecasts of soil moisture, runoff and streamflow. And the NMME/VIC forecasts are compared with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month hindcast experiments for each calendar month during 1982-2010. As verified by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC for the soil moisture forecasts, and the former has higher skill than the latter only for the forecasts at long leads and for those initialized in the rainy season. The forecast skill for runoff is lower for both forecast approaches, but the added value from NMME/VIC is more obvious, with an increase of the average AC by 0.08-0.2. To compare with the observed streamflow, both the hindcasts from NMME/VIC and ESP/VIC are post-processed through a linear regression model fitted by using VIC offline-simulated streamflow. The post-processed NMME/VIC reduces the root mean squared error (RMSE) from the post-processed ESP/VIC by 5-15 %. And the reduction occurs mostly during the transition from wet to dry seasons. With the consideration of the uncertainty in the hydrological models, the added value from climate forecast models is decreased especially at short leads, suggesting the necessity of improving the large-scale hydrological models in human-intervened river basins.

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The TIGGE Project and Its Achievements

Cited by 216 publications

References 57 publications

Subseasonal Predictability of Boreal Summer Monsoon Rainfall from Ensemble Forecasts

Subseasonal Predictability of Boreal Summer Monsoon Rainfall from Ensemble Forecasts

A multiphysical ensemble system of numerical snow modelling

An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models

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