A simplified seasonal forecasting strategy, applied to wind and solar power in Europe

Bett, Philip E.; Thornton, Hazel; Troccoli, Alberto; Felice, Matteo De; Suckling, Emma; Dubus, Laurent; Saint‐Drenan, Yves‐Marie; Brayshaw, David

doi:10.1016/j.cliser.2022.100318

Cited by 11 publications

(4 citation statements)

References 66 publications

(76 reference statements)

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“…In practice, to make a prediction of UK storms based on the predicted mean wind speed, one would perform a linear regression of observed UK storms against predicted mean wind (Bett et al ., 2022). Applying this method to the hindcast, and performing leave‐one‐out cross‐validation such that the hindcast year being predicted is not used for the linear regression, results in very similar correlation skill for DJF storm counts ( r = 0.57 for the cross‐validated result, compared to r = 0.65 for the direct correlation between predicted mean wind and storms).…”

Section: Resultsmentioning

confidence: 99%

“…In 2020, wind energy provided 27% of the UK's electricity supply (National Grid ESO, 2023), and by 2030 the UK Government is planning to increase wind capacity to a level high enough to power every home in Britain (British Energy Security Strategy, 2022). However, wind is highly variable on seasonal time‐scales, and there is a very strong linear relationship between seasonal mean wind speed and wind generation, despite wind power generation being a nonlinear function of sub‐daily wind speed (Bett et al ., 2022). In winter (DJF) 2009/2010, one of the coldest UK winters in recent memory (Cattiaux et al ., 2010; Fereday et al ., 2012), the seasonal average UK mean wind speed was 15% lower than the long‐term winter average, and assuming a homogeneous distribution of wind farms this would have caused the UK wind power generation to fall by 40% compared to a typical winter season.…”

Section: Introductionmentioning

confidence: 99%

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Seasonal prediction of UK mean and extreme winds

Lockwood,

Stringer,

Hodge

et al. 2023

Quart J Royal Meteoro Soc

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Recent studies have shown that seasonal forecasting systems have significant skill in predicting northern European winds and storms in winter, but other seasons have not been so extensively analysed. Given this fact, and coupled with requests from users of the Met Office 3‐month outlook for the United Kingdom (UK), we have investigated the skill in predicting seasonal (3‐month) mean UK wind speed and storms (extreme winds) with a one‐month lead‐time, throughout the year, using a large ensemble from the Met Office's seasonal prediction system, GloSea. We find that seasonal UK storms and mean wind speeds are well correlated, and therefore a single prediction of UK mean wind speed will give an indication of predicted storm counts. Skill for these predictions is highest in winter (December–February), related to predictability of the North Atlantic Oscillation. In contrast, summer (June–August) UK wind skill is not significant and furthermore appears to be negative. We find evidence, in both observations and model members, for a Rossby wave from the tropics influencing UK summer winds and forming a significant predictable component in the model ensemble mean. However, the model predictable signal appears to be out of phase with that observed leading to the negative correlation. Further investigation into summer Rossby wave generation and propagation is necessary to understand whether summer predictions could be improved.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seasonal prediction of UK mean and extreme winds

Lockwood,

Stringer,

Hodge

et al. 2023

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

show abstract

“…Finally, wind speed can be considered a promising variable regarding skill, as it is more closely related to the larger-scale atmospheric circulation than more complex processes like precipitation. According to Bett et al (2022), the wind skill was found to be patchy throughout Europe especially during summer however using the System 4 forecasting system. The high wind skill for Attica empowers the discussion of the next section as the FWI is highly sensitive to wind speed (Karali et al, 2014).…”

Section: Fwi and Isi Predictions Against Fire Occurrencementioning

confidence: 99%

Seasonal fire danger forecasts for supporting fire prevention management in an eastern Mediterranean environment: the case of Attica, Greece

Karali

Varotsos

Giannakopoulos

et al. 2023

Nat. Hazards Earth Syst. Sci.

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Abstract. Forest fires constitute a major environmental and socioeconomic hazard in the Mediterranean. Weather and climate are among the main factors influencing forest fire potential. As fire danger is expected to increase under changing climate, seasonal forecasting of meteorological conditions conductive to fires is of paramount importance for implementing effective fire prevention policies. The aim of the current study is to provide high-resolution (∼9 km) probabilistic seasonal fire danger forecasts, utilizing the Canadian Forest Fire Weather Index (FWI) for the Attica region, one of the most fire-prone regions in Greece and the Mediterranean, employing the fifth-generation ECMWF seasonal forecasting system (SEAS5). Results indicate that, depending on the lead time of the forecast, both the FWI and ISI (Initial Spread Index) present statistically significant high discrimination scores and can be considered reliable in predicting above-normal fire danger conditions. When comparing the year-by-year fire danger predictions with the historical fire occurrence recorded by the Hellenic Fire Service database, both seasonal FWI and ISI forecasts are skilful in identifying years with a high number of fire occurrences. Overall, fire danger and its subcomponents can potentially be exploited by regional authorities in fire prevention management regarding preparedness and resources allocation.

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“…When a new EASM forecast is produced from DePreSys, the prediction interval on the regression at that EASM value provides the rainfall forecast probability distribution. This method of producing probabilistic forecasts corrects for any bias in the mean and variance, and yields calibrated probabilities, by construction (Bett et al, 2022), within the sampling limits given by the number of years in the hindcast. This is an important limitation when using the operational GloSea hindcast, as that only covers 24 years .…”

Section: Measures Of Skill and Regression-based Forecastsmentioning

confidence: 99%

Skilful forecasts of summer rainfall in the Yangtze River Basin from November

Bett¹,

Dunstone²,

Golding³

et al. 2022

Preprint

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Variability in the East Asian Summer Monsoon (EASM) brings the risk of heavy flooding or drought to the Yangtze River Basin, with potentially devastating impacts. Early forecasts of the likelihood of enhanced or reduced monsoon rainfall can enable better management of water and hydropower resources by decision-makers, supporting livelihoods and major economic and population centres across Eastern China. This paper demonstrates that the EASM is predictable in a dynamical forecast model from the preceding November, and that this allows skilful forecasts of summer mean rainfall in the Yangtze River Basin at a lead time of 6 months. Skill for May–June–July rainfall is of a similar magnitude to seasonal forecasts initialised in spring, although the skill in June–July–August is much weaker and not consistently significant. However, there is some evidence for enhanced skill following El Niño events. The potential for decadal-scale variability in forecast skill is also examined, although we find no evidence for significant variation.

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A simplified seasonal forecasting strategy, applied to wind and solar power in Europe

Cited by 11 publications

References 66 publications

Seasonal prediction of UK mean and extreme winds

Seasonal prediction of UK mean and extreme winds

Seasonal fire danger forecasts for supporting fire prevention management in an eastern Mediterranean environment: the case of Attica, Greece

Skilful forecasts of summer rainfall in the Yangtze River Basin from November

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