The added value of high resolution regional reanalyses for wind power applications

Frank, Christopher; Pospichal, Bernhard; Wahl, Sabrina; Keller, Jan D.; Hense, Andreas; Crewell, Susanne

doi:10.1016/j.renene.2019.09.138

Cited by 42 publications

(41 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Atmospheric reanalyses combine prior knowledge of physical processes captured in the models with observations from a diverse range of instruments, to form spatially complete representations of the historical atmospheric conditions. They are therefore invaluable for revisiting the local processes, climate signals or events that were not fully observed, for applications such as climate monitoring and change assessments (Kendon et al, 2017;2019), renewable energy assessment (e.g., Frank et al, 2020), and hazard management (e.g., Vitolo et al, 2019). Global-scale reanalyses have advanced in quality and quantity during the past three decades with improvements to models, data assimilation methods, number of observations and ensemble methods (Kalnay et al, 1996;Ebita et al, 2011;Gelaro et al, 2017;Dee et al, 2011), and with increasing spatial resolutions.…”

mentioning

confidence: 99%

BARRA v1.0: Kilometre-scale downscaling of an Australian regional atmospheric reanalysis over four midlatitude domains

Su¹,

Eizenberg²,

Jakob³

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract. The development of convection-permitting models (CPMs) in numerical weather prediction has facilitated the creation of km-scale (1–4 km) regional reanalysis and climate projections. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) also aims to realise the benefits of these high-resolution models over Australian sub-regions for applications such as fire danger research, by nesting them in BARRA's 12 km regional reanalysis (BARRA-R). Four mid-latitude sub-regions are centred on Perth in Western Australia, Adelaide in South Australia, Sydney in New South Wales (NSW), and Tasmania. The resulting 29-year 1.5 km downscaled reanalyses (BARRA-C) are assessed for their added skill over BARRA-R and global reanalyses for near-surface parameters (temperature, wind and precipitation) at observation locations and against independent 5 km gridded analyses. BARRA-C demonstrates better agreement with point observations for temperature and wind, particularly in topographically complex regions and coastal regions. BARRA-C also improves upon BARRA-R in terms of intensity and timing of precipitation during the thunderstorm seasons in NSW, and spatial patterns of sub-daily rain fields during storm events. However, as a hindcast-only system, BARRA-C largely inherits the domain-averaged biases and temporal variations of biases from BARRA-R. Further, BARRA-C reflects known issues of CPMs: overestimation of heavy rain rates and rain cells, and underestimation of light rain occurrence.

show abstract

mentioning

confidence: 99%

BARRA v1.0: Kilometre-scale downscaling of an Australian regional atmospheric reanalysis over four midlatitude domains

Su¹,

Eizenberg²,

Jakob³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…To extrapolate measured wind speed to hub-height, there are several mathematical expressions. Among these are the logarithmic law and the power law [22]. The logarithmic law is used here to characterize the impact of the roughness of the earth's surface on the windspeed and it is expressed by the following equation:…”

Section: Methodsmentioning

confidence: 99%

Wind Energy Assessment during High-Impact Winter Storms in the Iberian Peninsula

Gonçalves

Liberato

Nieto

2020

The 3rd International Electronic Conference on Atmospheric Sciences

View full text Add to dashboard Cite

Electricity production through renewable energy sources, such as wind energy, is dependent of the variability of weather conditions. Thus, this work aims to assess the wind resource available and the wind energy potential (WEP) during recent December months (the three years 2017, 2018, 2019). These winters were characterized by the occurrence of high impact storms. To understand the effect of the strong winds associated with the passage of the storms during these months, 10m wind components (10-meter U and V wind components) are used. The fields were extracted at 00, 06, 12 and 18 UTC (6-hourly data), for the 2017, 2018 and 2019 December months over a geographical sector that covers the Iberian Peninsula (IP) region (35°N-45°N;10°W-4°E) and compared to climatological values for the 1981-2010 period. The obtained results show an increase of wind intensity of up to 1.2 m.s-1 in IP during December 2017 and 2019 and a decrease of 1.2 m.s-1 in December 2018, when compared with the respective climatology for the 1981-2010 period. This increase corresponds to an increase in the corresponding WEP. Therefore, our results agree with the values of the wind energy produced during the analyzed months for the two countries, Portugal and Spain. Finally, it is notable that the highest values of wind energy production occurred on the days of the storms passage through the IP.

show abstract

“…The respective density function has a density mass at u that represents the probability P r(Y ≤ u) = G u (u; µ, σ, ξ ). This procedure is similar to the censored representation of rainfall in Scheuerer (2013) or Friederichs (2010).…”

Section: Cosmo-rea6 Regional Reanalysismentioning

confidence: 99%

“…The COSMO-REA6 regional reanalysis (Bollmeyer et al, 2014) represents one such highresolution (grid spacing of about 6 km) reanalysis for Europe that is currently available for the period from 1995 to 2017 1 and has already provided guidance for renewable energy applications (e.g. Frank et al, 2019). Due to the shortterm nature of gusts -following World Meteorological Organization (2018) gusts are defined as the maximum of 3 s averaged wind speeds -their direct simulation is not possible within a NWP model.…”

Section: Introductionmentioning

confidence: 99%

Vertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theory

Steinheuer

Friederichs

2020

Nonlin. Processes Geophys.

View full text Add to dashboard Cite

Abstract. Many applications require wind gust estimates at very different atmospheric height levels. For example, the renewable energy sector is interested in wind and gust predictions at the hub height of a wind power plant. However, numerical weather prediction models typically only derive estimates for wind gusts at the standard measurement height of 10 m above the land surface. Here, we present a statistical post-processing method to derive a conditional distribution for hourly peak wind speed as a function of height. The conditioning variables are taken from the COSMO-REA6 regional reanalysis. The post-processing method was trained using peak wind speed observations at five vertical levels between 10 and 250 m from the Hamburg Weather Mast. The statistical post-processing method is based on a censored generalized extreme value (cGEV) distribution with non-homogeneous parameters. We use a least absolute shrinkage and selection operator to select the most informative variables. Vertical variations of the cGEV parameters are approximated using Legendre polynomials, such that predictions may be derived at any desired vertical height. Further, the Pickands dependence function is used to assess dependencies between gusts at different heights. The most important predictors are the 10 m gust diagnostic, the barotropic and the baroclinic mode of absolute horizontal wind speed, the mean absolute horizontal wind at 700 hPa, the surface pressure tendency, and the lifted index. Proper scores show improvements of up to 60 % with respect to climatology, especially at higher vertical levels. The post-processing model with a Legendre approximation is able to provide reliable predictions of gusts' statistics at non-observed intermediate levels. The strength of dependency between gusts at different levels is non-homogeneous and strongly modulated by the vertical stability of the atmosphere.

show abstract

The added value of high resolution regional reanalyses for wind power applications

Cited by 42 publications

References 34 publications

BARRA v1.0: Kilometre-scale downscaling of an Australian regional atmospheric reanalysis over four midlatitude domains

BARRA v1.0: Kilometre-scale downscaling of an Australian regional atmospheric reanalysis over four midlatitude domains

Wind Energy Assessment during High-Impact Winter Storms in the Iberian Peninsula

Vertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theory

Contact Info

Product

Resources

About