Winter atmospheric circulation patterns and their relationship with the meteorological conditions in Greece

Philippopoulos, Kostas; Deligiorgi, Despina; Mavrakou, Thaleia; Cheliotis, Ioannis

doi:10.1007/s00703-014-0310-z

Cited by 5 publications

(7 citation statements)

References 26 publications

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“…The SOM implementation used here is based on a 4‐step SOM clustering framework proposed in Philippopoulos and Deligiorgi, which has also been applied in a climatological context over Greece to examine the relationship of wintertime meteorological conditions with atmospheric circulation . In Philippopoulos et al, the above framework is used to examine the association of atmospheric patterns with extreme wind power events in the UK.…”

Section: Case Studymentioning

confidence: 99%

“…The resulting catalogue of atmospheric circulation patterns may then be visualised and studied as per the user's application. The application here is to condition VAR models on atmospheric modes and that is the focus of the remainder of this paper; for more detail on the the SOM process, please see [23,24,22].…”

Section: Atmospheric Classificationmentioning

confidence: 99%

“…For a given region, there may be a wide range of possible large‐scale meteorological conditions due to variations in the strength and location of synoptic‐scale weather features, defined as atmospheric motion with a typical spatial scale of many hundreds of kilometres, such as extratropical cyclones or high‐pressure systems. () Unsupervised learning, or clustering, techniques may be used to codify these large‐scale atmospheric circulation patterns in terms of a relatively small number of distinct modes() defined based on the fields of mean sea‐level pressure (SLP) and geopotential height, for example, for each time instant of interest. To reduce the number of modes for specific applications, a second clustering stage may be applied, akin to Weia and Mohanb and Ohba et al In the meteorological literature, this process is sometimes referred to as “classification,” but we avoid the use of that term here as in broader usage, this term implies a form of supervised learning.…”

Section: Introductionmentioning

confidence: 99%

“…For a given region there may be a wide range of possible large-scale meteorological conditions due to variations in the strength and location of synoptic-scale weather features such as extratropical cyclones or high pressure systems [24]. Classification techniques may be used to codify these large-scale atmospheric cir-culation patterns in terms of a relatively small number of distinct modes [17,23] defined based on the fields of mean sea level pressure and geopotential height, for example, for each time instant of interest.…”

Section: Introductionmentioning

confidence: 99%

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Improved very short‐term spatio‐temporal wind forecasting using atmospheric regimes

2018

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We present a regime-switching vector autoregressive method for very short-term wind speed forecasting at multiple locations with regimes based on large-scale meteorological phenomena.Statistical methods for wind speed forecasting based on recent observations outperform numerical weather prediction for forecast horizons up to a few hours, and the spatio-temporal interdependency between geographically dispersed locations may be exploited to improve forecast skill. Here, we show that conditioning spatio-temporal interdependency on "atmospheric modes" derived from gridded numerical weather data can further improve forecast performance. Atmospheric modes are based on the clustering of surface wind and sea-level pressure fields, and the geopotential height field at the 5000-hPa level. The data fields are extracted from the MERRA-2 reanalysis dataset with an hourly temporal resolution over the UK; atmospheric patterns are clustered using self-organising maps and then grouped further to optimise forecast performance. In a case study based on 6 years of measurements from 23 weather stations in the UK, a set of 3 atmospheric modes are found to be optimal for forecast performance. The skill of 1-to 6-hour-ahead forecasts is improved at all sites compared with persistence and competitive benchmarks. Across the 23 test sites, 1-hour-ahead root mean squared error is reduced by between 0.3% and 4.1% compared with the best performing benchmark and by an average of 1.6% over all sites; the 6-hour-ahead accuracy is improved by an average of 3.1%.

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Section: Case Studymentioning

confidence: 99%

Section: Atmospheric Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved very short‐term spatio‐temporal wind forecasting using atmospheric regimes

2018

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“…The area of study is the Hellinikon site in Athens, Greece at the Southeastern Mediterranean region. The study employs hourly wind data and a daily catalogue of atmospheric circulation patterns (Philippopoulos et al 2014) from January 1979 to December 2009. The SOM classification method resulted into 32 distinct atmospheric circulation patterns, organized into an 8x4 map and a daily catalogue that associates each day with a single pattern.…”

Section: Methods Area Of Study and Datamentioning

confidence: 99%

Daily wind gusts in relation to mean daily wind speed and atmospheric circulation

DELIGIORGI¹,

PHILIPPOPOULOS²,

GRANAKIS³

Global NEST International Conference on Environmental Science &Amp; Technology

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An important aspect in the design of wind farms and consequently in wind power production, is the sensitivity of wind turbines to wind gusts. This study aims at estimating the probability of occurrence of daily wind gusts for different atmospheric circulation regimes. The analysis is performed for a 30-year period (1979-2009) for the Hellinikon station in Athens, Greece. The proposed methodology estimates the probability of exceeding a wind gust speed value from the mean daily wind speed observations for a given atmospheric circulation pattern. The large-scale atmospheric circulation classification is based on a two-stage clustering approach, using Self-Organizing Maps as a clustering methodology. The classification is based on the Sea Level Pressure, the geopotential at 500hPa, the zonal and meridional wind components at 10m and at 850hPa, the specific humidity at 700hPa and the air and dew-point temperature at 2m. Following the gust factor method, different statistical models are trained for each of the eight identified atmospheric modes. The results demonstrate the suitability of the lognormal distribution for associating wind gusts and mean wind speeds. The adopted gust factor method provides accurate estimates of daily wind gust speeds and that the atmospheric circulation enhances the precision of the statistical models.

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An extreme wind speed climatology – Atmospheric driver identification using neural networks

Blougouras

Philippopoulos

Tzanis

2023

Science of The Total Environment

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Winter atmospheric circulation patterns and their relationship with the meteorological conditions in Greece

Cited by 5 publications

References 26 publications

Improved very short‐term spatio‐temporal wind forecasting using atmospheric regimes

Improved very short‐term spatio‐temporal wind forecasting using atmospheric regimes

Daily wind gusts in relation to mean daily wind speed and atmospheric circulation

An extreme wind speed climatology – Atmospheric driver identification using neural networks

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