Impact of weather regimes on wind power variability in western Europe

Garrido-Perez, José M.; Ordóñez, Carlos; Barriopedro, David; García‐Herrera, Ricardo; Paredes, Daniel

doi:10.1016/j.apenergy.2020.114731

Cited by 47 publications

(35 citation statements)

References 51 publications

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“…For example, the NAO− regime is commonly associated with anomalously high demand and low wind power generation over much of Central and Northern Europe (see Figure 2, left column). We note that there are multiple methods to calculate WRs, which can have more than four patterns (e.g., Garrido‐Perez et al, 2020; Grams et al, 2017). Here a method resulting in four patterns is used for complementary with the definition of the TCTs (which are designed using a variation on the Cassou (2008) method).…”

Section: Methodsmentioning

confidence: 99%

“…Green box: A conditional pattern forecast example for WRs where a weekly-mean pattern (see orange box) is 'kept' if 50% or more ensemble members agree on a pattern. The weekly-mean grid-point energy forecasts for these situations (see blue box) are used as the conditional pattern forecast more than four patterns (e.g., Garrido-Perez et al, 2020;Grams et al, 2017). Here a method resulting in four patterns is used for complementary with the definition of the TCTs (which are designed using a variation on the Cassou (2008) method).…”

Section: Energy Variables From Wr Hindcastsmentioning

confidence: 99%

“…Despite the popularity of pattern-based methods to understand and predict the large-scale atmospheric circulation, they are associated with the key caveat that skill in forecasting the large-scale meteorological pattern (typically observed in 500 hPa geopotential height or surface pressure) does not in itself guarantee skill in terms of predicting a desired surface impact response (e.g., nearsurface wind speeds or temperatures, wind power generation or electricity demand). Recent work using historic reanalyses has, however, convincingly demonstrated that many Euro-Atlantic weather patterns (with various methods of construction) relate to relevant surface weather conditions (Bloomfield, Brayshaw, & Charlton-Perez, 2020a;Cortesi et al, 2019;Drücke et al, 2020;Garrido-Perez et al, 2020;Grams et al, 2017;Thornton et al, 2017;van der Wiel et al, 2019) and to European electricity demand, wind and solar power generation (Bloomfield, Brayshaw, & Charlton-Perez, 2020a;Grams et al, 2017;Lled o et al, 2020;van der Wiel et al, 2019). This suggests that, given these patterns can be forecast at lead times out to 2 weeks and 3 months for WRs and teleconnections respectively, they could potentially offer benefits over grid-point forecasts.…”

Section: Introductionmentioning

confidence: 99%

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Pattern‐based conditioning enhances sub‐seasonal prediction skill of European national energy variables

Bloomfield

Brayshaw

Gonzalez

et al. 2021

Meteorological Applications

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Sub-seasonal forecasts are becoming more widely used in the energy sector to inform high-impact, weather-dependent decisions. Using pattern-based methods (such as weather regimes) is also becoming commonplace, although until now an assessment of how pattern-based methods perform compared with gridded model output has not been completed. We compare four methods to predict weekly-mean anomalies of electricity demand and demand-net-wind across 28 European countries. At short lead times (days 0-10) grid-point forecasts have higher skill than pattern-based methods across multiple metrics. However, at extended lead times (day 12+) pattern-based methods can show greater skill than grid-point forecasts. All methods have relatively low skill at weekly-mean national impact forecasts beyond day 12, particularly for probabilistic skill metrics. We therefore develop a method of pattern-based conditioning, which is able to provide windows of opportunity for prediction at extended lead times: when at least 50% of the ensemble members of a forecast agree on a specific pattern, skill increases significantly. The conditioning is valuable for users interested in particular thresholds for decision-making, as it combines the dynamical robustness in the large-scale flow conditions from the pattern-based methods with local information present in the grid-point forecasts.

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Section: Methodsmentioning

confidence: 99%

Section: Energy Variables From Wr Hindcastsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pattern‐based conditioning enhances sub‐seasonal prediction skill of European national energy variables

Bloomfield

Brayshaw

Gonzalez

et al. 2021

Meteorological Applications

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“…Local subsidence (which is not 1 Introduction This paper reviews current understanding of the factors that are responsible for the damaging floods affecting the Venice city centre and for their future evolution. The events of 4 November 1966, with estimated damages of EUR 400 million (De Zolt et al, 2006), and of 12 November 2019 (Cavaleri et al, 2020), with estimated damages above EUR 460 million (Godlewski et al, 2020) and extensive global media coverage, highlight the risks that future extreme floods bring. Potential damages have often been linked to future relative sea level (RSL) rise.…”

mentioning

confidence: 99%

Extreme floods of Venice: characteristics, dynamics, past and future evolution (review article)

Lionello

Barriopedro

Ferrarin

et al. 2021

Nat. Hazards Earth Syst. Sci.

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Abstract. Floods in the Venice city centre result from the superposition of several factors: astronomical tides; seiches; and atmospherically forced fluctuations, which include storm surges, meteotsunamis, and surges caused by atmospheric planetary waves. All these factors can contribute to positive water height anomalies individually and can increase the probability of extreme events when they act constructively. The largest extreme water heights are mostly caused by the storm surges produced by the sirocco winds, leading to a characteristic seasonal cycle, with the largest and most frequent events occurring from November to March. Storm surges can be produced by cyclones whose centres are located either north or south of the Alps. Historically, the most intense events have been produced by cyclogenesis in the western Mediterranean, to the west of the main cyclogenetic area of the Mediterranean region in the Gulf of Genoa. Only a small fraction of the inter-annual variability in extreme water heights is described by fluctuations in the dominant patterns of atmospheric circulation variability over the Euro-Atlantic sector. Therefore, decadal fluctuations in water height extremes remain largely unexplained. In particular, the effect of the 11-year solar cycle does not appear to be steadily present if more than 100 years of observations are considered. The historic increase in the frequency of floods since the mid-19th century is explained by relative mean sea level rise. Analogously, future regional relative mean sea level rise will be the most important driver of increasing duration and intensity of Venice floods through this century, overcompensating for the small projected decrease in marine storminess. The future increase in extreme water heights covers a wide range, largely reflecting the highly uncertain mass contributions to future mean sea level rise from the melting of Antarctica and Greenland ice sheets, especially towards the end of the century. For a high-emission scenario (RCP8.5), the magnitude of 1-in-100-year water height values at the northern Adriatic coast is projected to increase by 26–35 cm by 2050 and by 53–171 cm by 2100 with respect to the present value and is subject to continued increase thereafter. For a moderate-emission scenario (RCP4.5), these values are 12–17 cm by 2050 and 24–56 cm by 2100. Local subsidence (which is not included in these estimates) will further contribute to the future increase in extreme water heights. This analysis shows the need for adaptive long-term planning of coastal defences using flexible solutions that are appropriate across the large range of plausible future water height extremes.

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“…10 The dynamics of wind resources are primarily driven by large-scale circulation systems. 11 On the local scale, surface roughness and orography then modify the largescale wind resource patterns. 12 As a result, exposed mountain ranges and areas where the surface roughness is low, often provide sufficient wind resource for wind turbine installations.…”

Section: Introductionmentioning

confidence: 99%

The role of the power law exponent in wind energy assessment: A global analysis

Jung

Schindler

2021

Int J Energy Res

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The power law is most often applied to extrapolate the near-surface wind speed to the wind turbine hub height. Due to variations of the meteorological conditions, the power law exponent varies over time. Usually, no long-term wind speed measurements from multiple heights are available which would allow time-dependent and spatially explicit power law exponent estimations. Instead, often the mean of the power law exponent or a constant value of 0.14 is assumed. The goal of this study was to quantify the error in wind potential assessments resulting from applying the mean of the power law exponent or a value of 0.14. The data base for this study are the hourly wind speed time series at 10 and 100 m above ground available for the period 2007 to 2018 from the ERA5 reanalysis project at a global 0.25 × 0.25 grid. The errors in the estimation of the wind power density and the capacity factor were calculated. It was found that, onshore, the global median of the absolute percentage error related to the wind power density using the mean of the power law exponent is 7.5%. Assuming a constant value of 0.14, the power law is less accurate (absolute percentage error: 37.1%). For the estimation of the capacity factor the absolute percentage errors are 5.5% and 36.9%. Based on the results of this study, the use of time-dependent and spatially explicit power law exponents is suggested. In the absence of long-term wind speed measurements from multiple heights, the results provide a comprehensive global overview of the errors to be expected from using the mean of the power law exponent or assuming a value of 0.14. In many regions where the wind resource is abundant, using the mean of the power law exponent only leads to minor errors in capacity factor estimation.

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Impact of weather regimes on wind power variability in western Europe

Cited by 47 publications

References 51 publications

Pattern‐based conditioning enhances sub‐seasonal prediction skill of European national energy variables

Pattern‐based conditioning enhances sub‐seasonal prediction skill of European national energy variables

Extreme floods of Venice: characteristics, dynamics, past and future evolution (review article)

The role of the power law exponent in wind energy assessment: A global analysis

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