The Subseasonal to Seasonal (S2S) Prediction Project Database

Vitart, Frédéric; Ardilouze, Constantin; Bonet, A.; Brookshaw, Anca; Chen, M.; Codorean, C.; Déqué, Michel; Ferranti, Laura; Fucile, Enrico; Fuentes, M.; Hendon, Harry H.; Hodgson, J.M.; Kang, Hyun‐Suk; Kumar, Arun; Lin, Hai; Liu, G.; Liu, X.; Malguzzi, P.; Mallas, I.; Manoussakis, M.; Mastrangelo, Daniele; MacLachlan, Craig; McLean, P.; Minami, Atsushi; Mládek, Richard; Nakazawa, Tetsuo; Najm, S.; Nie, Yu; Rixen, M.; Robertson, Andrew W.; Ruti, Paolo; Cheng, Sun; Takaya, Yuhei; Tolstykh, M. A.; Venuti, Fabio; Waliser, Duane E.; Woolnough, Steven J.; Wu, Tongwen; Won, Duk-Jin; Xiao, Hui; Zaripov, R. B.; Zhang, L.

doi:10.1175/bams-d-16-0017.1

Cited by 702 publications

(738 citation statements)

References 19 publications

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“…For the 10-day sampling frequency (Fig. 12b), which is conventionally used by most models in the ISVHE project and by several models in the S2S project Vitart et al 2017), similar skill variation features are found, indicating that the forecast cases with this sampling frequency can also be used to show the overall forecast skill of MJO.…”

Section: Impact Of Parameter Optimization On Mjo Predictionmentioning

confidence: 68%

“…Particularly, in recent years, the Sub-seasonal to Seasonal (S2S) Prediction Project is ongoing, and many organizations join in this effort to promote scientific research and operational application of sub-seasonal forecast (Vitart et al 2017). Being an important source of sub-seasonal predictability, MJO is listed as one of the key issues for the S2S project.…”

Section: Introductionmentioning

confidence: 99%

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Validity of parameter optimization in improving MJO simulation and prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

Liu

et al. 2018

Clim Dyn

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Using the sub-seasonal to seasonal forecast model of Beijing Climate Center, several key physical parameters are perturbed by the Latin hypercube sampling method to find a better configuration for representation of Madden-Julian oscillation (MJO) in the free-run simulation. We find that although model simulation is especially sensitive to some parameters, there are overall no significant linear relationships between model skill and any one of the parameters, and the optimum performance can be obtained by combined perturbations of multiple parameters. By optimization, MJO's spectrum, intensity, spatial structure and propagation, as well as the mean state and variance, are all improved to some extent, suggesting the correspondence and interrelation of model's performances in simulating different characteristics of MJO. Further, several sets of initialized hindcasts using the optimized parameters are conducted, and their results are compared with the hindcasts using only improved initial conditions. We show that with an optimized model, the forecast of MJO beyond 3-week lead time is not improved, and the maximum useful skill is only slightly increased, implying that a decrease of model error does not always translate into an increase of forecast skill at all lead time. However, the skill is obviously enhanced during lead times of 2-3 weeks for forecasts in most seasons and initial phases except for a few cases. Particularly, the deficiency in forecasting MJO's propagation from the Indian Ocean to the Pacific is relieved, further highlighting the positive contribution of reducing model error compared to previous work that only reduced initial condition error. In this study, we also show benefits of multi-scheme ensemble strategy in describing uncertainties of model error and initial condition error and thus improving MJO forecast.

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Section: Impact Of Parameter Optimization On Mjo Predictionmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Validity of parameter optimization in improving MJO simulation and prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

Liu

et al. 2018

Clim Dyn

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“…Of course, this research should be replicated for other locations and using other forecast systems. In this, the data from the Sub-seasonal to Seasonal Prediction (S2S) project (Vitart et al, 2016) will be useful. In this study the forecasts were deterministic, but in further studies probabilistic methods should also be considered.…”

Section: Discussionmentioning

confidence: 99%

Long-range forecasts for the energy market – a case study

et al. 2017

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Abstract.We examined the feasibility of long-range forecasts of temperature for needs of the energy sector in Helsinki, Finland. The work was done jointly by Finnish Meteorological Institute (FMI) and Helen Ltd, the main Helsinki metropolitan area energy provider, and especially provider of district heating and cooling. Because temperatures govern the need of heating and cooling and, therefore, the energy demand, better longrange forecasts of temperature would be highly useful for Helen Ltd. Heating degree day (HDD) is a parameter that indicates the demand of energy to heat a building. We examined the forecasted monthly HDD values for Helsinki using UK Met Office seasonal forecasts with the lead time up to two months. The long-range forecasts of monthly HDD showed some skill in Helsinki in winter 2015-2016, especially if the very cold January is excluded.

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“…The S2S database includes near real-time ensemble forecasts for up to 60 days ahead, from 11 forecasting centres: Australia's Bureau of Meteorology (BOM); the China Meteorological Administration (CMA); ECMWF; Environment and Climate Change Canada (ECCC); Italy's Institute of Atmospheric Sciences and Climate (CNR-ISAC); the Hydrometeorological Centre of Russia (HMCR); the Japan Meteorological Agency (JMA); the Korea Meteorological Administration (KMA); Météo-France; the US National Centers for Environmental Prediction (NCEP); and the UK Met Office (Vitart et al 2017). …”

Section: Extended-range Ensemblesmentioning

confidence: 99%

Medium- and Extended-Range Ensemble Weather Forecasting

Richardson

2018

Weather &Amp; Climate Services for the Energy Industry

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The chapter provides an overview of ensemble weather forecasting for the medium-and extended-range (days to weeks ahead). It reviews the methods used to account for uncertainties in the initial conditions and in the forecast models themselves. The chapter explores the challenges of making useful forecasts for the sub-seasonal timescale, beyond the typical limit for skilful day-to-day forecasts, and considers some of the sources of predictability such as the Madden-Julian oscillation (MJO) that make this possible. It then introduces some of the ensemble-based forecast products and concludes with a case study for a European heat wave that demonstrates how ensemble weather forecasts can be used to guide decision making for weather-dependent activities.

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The Subseasonal to Seasonal (S2S) Prediction Project Database

Cited by 702 publications

References 19 publications

Validity of parameter optimization in improving MJO simulation and prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

Validity of parameter optimization in improving MJO simulation and prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

Long-range forecasts for the energy market – a case study

Medium- and Extended-Range Ensemble Weather Forecasting

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