Correction and downscaling of NWP wind speed forecasts

Howard, Tom; Clark, Peter A.

doi:10.1002/met.12

Cited by 54 publications

(46 citation statements)

References 8 publications

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“…1(b)). This is a result of the smoothed topography used in MERRA, 2 which leads to artificially low wind speeds for stations residing on the (unresolved) peaks [20]. The smoothed topography may similarly contribute to the small overestimation in wind speed for some low altitude stations.…”

Section: M Altitude Wind Speed Comparisonsmentioning

confidence: 97%

Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain

et al. 2015

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a b s t r a c tWith a rapidly increasing fraction of electricity generation being sourced from wind, extreme wind power generation events such as prolonged periods of low (or high) generation and ramps in generation, are a growing concern for the efficient and secure operation of national power systems. As extreme events occur infrequently, long and reliable meteorological records are required to accurately estimate their characteristics.Recent publications have begun to investigate the use of global meteorological "reanalysis" data sets for power system applications, many of which focus on long-term average statistics such as monthlymean generation. Here we demonstrate that reanalysis data can also be used to estimate the frequency of relatively short-lived extreme events (including ramping on sub-daily time scales). Verification against 328 surface observation stations across the United Kingdom suggests that near-surface wind variability over spatiotemporal scales greater than around 300 km and 6 h can be faithfully reproduced using reanalysis, with no need for costly dynamical downscaling.A case study is presented in which a state-of-the-art, 33 year reanalysis data set (MERRA, from NASA-GMAO), is used to construct an hourly time series of nationally-aggregated wind power generation in Great Britain (GB), assuming a fixed, modern distribution of wind farms. The resultant generation estimates are highly correlated with recorded data from National Grid in the recent period, both for instantaneous hourly values and for variability over time intervals greater than around 6 h. This 33 year time series is then used to quantify the frequency with which different extreme GB-wide wind power generation events occur, as well as their seasonal and inter-annual variability. Several novel insights into the nature of extreme wind power generation events are described, including (i) that the number of prolonged low or high generation events is well approximated by a Poission-like random process, and (ii) whilst in general there is large seasonal variability, the magnitude of the most extreme ramps is similar in both summer and winter.An up-to-date version of the GB case study data as well as the underlying model are freely available for download from our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014/.

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Section: M Altitude Wind Speed Comparisonsmentioning

confidence: 97%

Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain

et al. 2015

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“…Donat et al, 2010;Howard and Clark, 2007). It is caused by the use of an effective roughness parameterisation, which is needed to estimate the effect of subgrid-scale orography on the synoptic scale flow; however, it causes unrealistically slow wind (and hence gust) speeds at 10 m.…”

Section: Evaluation Of Metum Windstorm Footprintsmentioning

confidence: 99%

“…In Howard and Clark (2007) a method was proposed to correct for this effect, by estimating a reference height, h ref , above which the wind speeds are unaffected by the surface, and then assuming a log-profile to interpolate wind speeds back down to 10 m, using the local vegetative roughness, z 0 , rather than the effective roughness.…”

Section: Evaluation Of Metum Windstorm Footprintsmentioning

confidence: 99%

“…Nevertheless, applying the correction to the storm Kyrill gave a clear improvement to the maximum 10 m winds for high-altitude locations, although the underestimation of extreme gust at lower altitudes remained (plot not shown). For this calculation h ref was estimated from orographic data at the resolution of the MetUM, but it is possible to estimate h ref from finerresolution data (as was done in Howard and Clark, 2007), which may further improve the correction.…”

Section: Evaluation Of Metum Windstorm Footprintsmentioning

confidence: 99%

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The XWS open access catalogue of extreme European windstorms from 1979 to 2012

Roberts

Champion

Dawkins

et al. 2014

Nat. Hazards Earth Syst. Sci.

123

138

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Abstract. The XWS (eXtreme WindStorms) catalogue consists of storm tracks and model-generated maximum 3 s wind-gust footprints for 50 of the most extreme winter windstorms to hit Europe in the period 1979-2012. The catalogue is intended to be a valuable resource for both academia and industries such as (re)insurance, for example allowing users to characterise extreme European storms, and validate climate and catastrophe models. Several storm severity indices were investigated to find which could best represent a list of known high-loss (severe) storms. The best-performing index was S ft , which is a combination of storm area calculated from the storm footprint and maximum 925 hPa wind speed from the storm track. All the listed severe storms are included in the catalogue, and the remaining ones were selected using S ft . A comparison of the model footprint to station observations revealed that storms were generally well represented, although for some storms the highest gusts were underestimated. Possible reasons for this underestimation include the model failing to simulate strong enough pressure gradients and not representing convective gusts.A new recalibration method was developed to estimate the true distribution of gusts at each grid point and correct for this underestimation. The recalibration model allows for storm-to-storm variation which is essential given that different storms have different degrees of model bias. The catalogue is available at www.europeanwindstorms.org.

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“…Although 20 schemes are known to result in unrealistically low wind speeds close to the surface [40].…”

Section: Effect Of Forecast Heightmentioning

confidence: 99%

Long-term wind resource assessment for small and medium-scale turbines using operational forecast data and measure–correlate–predict

et al. 2015

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Highlights Wind resource predicted at 37 sites using operational forecast data and MCP  Forecast reference data highly competitive with nearby meteorological observations  Systematic improvement when using forecast data at coastal sites AbstractOutput from a state-of-the-art, 4 km resolution, operational forecast model (UK4) was investigated as a source of long-term historical reference data for wind resource assessment. The data were used to implement measure-correlate-predict (MCP) approaches at 37 sites throughout the United Kingdom (UK). The monthly and hourly linear correlation between the UK4-predicted and observed wind speeds indicates that UK4 is capable of representing the wind climate better than the nearby meteorological stations considered. Linear MCP algorithms were implemented at the same sites using reference data from UK4 and nearby meteorological stations to predict the long-term (10-year) wind resource. To obtain robust error statistics, MCP algorithms were applied using onsite measurement periods of 1-12 months initiated at 120 different starting months throughout an 11 year data record. Using linear regression MCP over 12 months, the average percentage errors in the long-term predicted mean wind speed and power density were 3.0% and 7.6% respectively, using UK4, and 2.8% and 7.9% respectively, using nearby meteorological stations. The results indicate that UK4 is highly competitive with nearby meteorological observations as an MCP reference data source. UK4 was also shown to systematically improve MCP predictions at coastal sites due to better representation of local diurnal effects.2

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Correction and downscaling of NWP wind speed forecasts

Cited by 54 publications

References 8 publications

Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain

Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain

The XWS open access catalogue of extreme European windstorms from 1979 to 2012

Long-term wind resource assessment for small and medium-scale turbines using operational forecast data and measure–correlate–predict

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