Construction of a Langevin model from time series with a periodical correlation function: Application to wind speed data

2013 ◽  
Vol 392 (22) ◽  
pp. 5592-5603 ◽  
Author(s):  
Zbigniew Czechowski ◽  
Luciano Telesca
Keyword(s):  
Time Series ◽  
Correlation Function ◽  
Wind Speed ◽  
Langevin Model ◽  
Wind Speed Data

scholarly journals A "Global Radiosonde and tracked-balloon Archive on Sixteen Pressure levels" (GRASP) going back to 1905 – Part 2: homogeneity adjustments for pilot balloon and radiosonde wind data

2014 ◽  
Vol 6 (2) ◽  
pp. 297-316 ◽  
Author(s):  
L. Ramella Pralungo ◽  
L. Haimberger
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Direction ◽  
Sampling Bias ◽  
Observation Time ◽  
Early Years ◽  
Wind Speed Data ◽  
Wind Speeds ◽  
Measurement Biases ◽  
The 1960S

Abstract. This paper describes the comprehensive homogenization of the "Global Radiosonde and tracked balloon Archive on Sixteen Pressure levels" (GRASP) wind records. Many of those records suffer from artificial shifts that need to be detected and adjusted before they are suitable for climate studies. Time series of departures between observations and the National Atmospheric and Oceanic Administration 20th-century (NOAA-20CR) surface pressure only reanalysis have been calculated offline by first interpolating the observations to pressure levels and standard synoptic times, if needed, and then interpolating the gridded NOAA-20CR standard pressure level data horizontally to the observation locations. These difference time series are quite sensitive to breaks in the observation time series and can be used for both automatic detection and adjustment of the breaks. Both wind speed and direction show a comparable number of breaks, roughly one break in three stations. More than a hundred artificial shifts in wind direction could be detected at several US stations in the period 1938/1955. From the 1960s onward the wind direction breaks are less frequent. Wind speed data are not affected as much by measurement biases, but one has to be aware of a large fair-weather sampling bias in early years, when high wind speeds were much less likely to be observed than after 1960, when radar tracking was already common practice. This bias has to be taken into account when calculating trends or monthly means from wind speed data. Trends of both wind speed and direction look spatially more homogeneous after adjustment. With the exception of a widespread wind direction bias found in the early US network, no signs of pervasive measurement biases could be found. The adjustments can likely improve observation usage when applied during data assimilation. Alternatively they can serve as a basis for validating variational wind bias adjustment schemes. Certainly, they are expected to improve estimates of global wind trends. All the homogeneity adjustments are available in the PANGAEA archive with associated doi:10.1594/PANGAEA.823617.

Extreme Value Statistics of Wind Speed Data by the POT and AER Methods

2008 ◽  
Author(s):  
A. Naess ◽  
E. Haug
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
New Method ◽  
Extreme Value Statistics ◽  
Peaks Over Threshold ◽  
Threshold Method ◽  
Wind Speed Data

The paper describes a new method for predicting the appropriate extreme value distribution derived from an observed time series. The method is based on introducing a cascade of conditioning approximations to the exact extreme value distribution. This allows for a rational way of capturing dependence effects in the time series. The performance of the method is compared with that of the peaks over threshold method.

scholarly journals A global radiosonde and tracked balloon archive on 16 pressure levels (GRASP) back to 1905 – Part 2: Homogeneity adjustments for PILOT and radiosonde wind data

2014 ◽  
Vol 7 (1) ◽  
pp. 335-383 ◽  
Author(s):  
L. Ramella Pralungo ◽  
L. Haimberger
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Direction ◽  
Sampling Bias ◽  
Observation Time ◽  
Early Years ◽  
Wind Speed Data ◽  
Wind Speeds ◽  
Measurement Biases ◽  
The 1960S

Abstract. This paper describes the comprehensive homogenization of the GRASP wind records. Many of those records suffer from artificial shifts that need to be detected and adjusted before they are suitable for climate studies. Time series of departures between observations and the National Atmospheric and Oceanic Administration 20th century (NOAA-20CR) surface pressure only reanalysis have been calculated offline by first interpolating the observations to pressure levels and standard synoptic times, if needed, and then interpolating the gridded NOAA-20CR standard pressure level data horizontally to the observation locations. These difference time series are quite sensitive to breaks in the observation time series and can be used for both automatic detection and adjustment of the breaks. Both wind speed and direction show a comparable number of breaks, roughly one break in three stations. More than hundred artificial shifts in wind direction could be detected at several US stations in the period 1938/1955. From the 1960s onward the wind direction breaks are less frequent. Wind speed data are not so much affected by measurement biases but one has to be aware of a large fair weather sampling bias in early years when high wind speeds were much less likely to be observed than after 1960 when RADAR tracking was already common practice. It has to be taken into account when calculating trends or monthly means from wind speed data. Trends of both wind speed and direction look spatially more homogeneous after adjustment. With the exception of a widespread wind direction bias found in the early US network no signs of pervasive measurement biases could be found. The adjustments can likely improve observation usage when applied during data assimilation. Alternatively they can serve as basis for validating variational wind bias adjustment schemes. Certainly they are expected to improve estimates of global wind trends. All the homogeneity adjustments are available in the PANGAEA archive with the associated DOI doi:10.1594/PANGAEA.823617.

scholarly journals Univariate Time Series Prediction of Wind speed with a case study of Yanbu, Saudi Arabia

2021 ◽  
Vol 10 (1) ◽  
pp. 257-264
Keyword(s):  
Time Series ◽  
Saudi Arabia ◽  
Wind Speed ◽  
Wind Energy ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Wind Speed Data ◽  
Absolute Percentage Error ◽  
Univariate Time Series

Wind energy is a promising alternativefor renewable source of energy pursued world-wide to reduce carbon emissions for a green future. The prediction of wind speed is a challenging subject and plays an instrumental role in development of wind power systems (particularly grid connected renewable energy systems where predicting wind speed facilitates manipulation of the load on the grid). Modern machine learning techniques including neural networks have been widely utilized for this purpose. Literature indicates availability of several models for estimation of the wind speed one hour ahead and the hourly wind speed data profile one day ahead. This paper considers the prediction of wind energy as a univariate time series (UVT) prediction problem and employs major prediction algorithms including the K-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Regression (SVR), Holt-Winter and ARIMA method. Forecasting a univariate time series depends only on past wind speed data values, rather than use of external data attributes like wind direction or weather forecast for prediction algorithm. In the present study (as a case-study), 13 years of hourly average wind speed data (of the period 1970-1982) of Yanbu, Saudi Arabia has been utilized to evaluate the performance of selected algorithms. Yanbu is an industrial city that plays a major role in the economy of Saudi Arabia. The findings showed that SVR, RF and ARIMA methods exhibit a better forecastingperformance in relation to four evaluation parameters of Mean Absolute Percentage Error(MAPE),Symmetric Mean Absolute Percentage Error (sMAPE),Mean Absolute Error (MAE) and Mean Absolute Scaled Error (MASE).

scholarly journals Spatial ARMA model for wind speed data

2012 ◽  
Vol 53 ◽  
Author(s):  
Laura Paulionienė
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Dependence ◽  
Arma Model ◽  
Time Series Model ◽  
Seasonal Component ◽  
Wind Speed Data ◽  
Spatial Weights ◽  
Univariate Time Series ◽  
New Location

Spatial time series model for wind speed data is proposed. Based on few similar papers, first at each location univariate time series model, containing seasonal component and higher order autoregressive component is fitted. After eliminating time dependence in time series, empirical semivariogram based on time independent residuals is fitted and spatial weights for new location are calculated.

scholarly journals Deterministic nature of the underlying dynamics of surface wind fluctuations

2012 ◽  
Vol 30 (10) ◽  
pp. 1503-1514 ◽  
Author(s):  
R. C. Sreelekshmi ◽  
K. Asokan ◽  
K. Satheesh Kumar
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Structural Engineering ◽  
Deterministic Model ◽  
Surface Wind ◽  
Wind Speed Data ◽  
Mean Wind Speed ◽  
Low Dimensional ◽  
Short Term Prediction ◽  
Speed Fluctuations

Abstract. Modelling the fluctuations of the Earth's surface wind has a significant role in understanding the dynamics of atmosphere besides its impact on various fields ranging from agriculture to structural engineering. Most of the studies on the modelling and prediction of wind speed and power reported in the literature are based on statistical methods or the probabilistic distribution of the wind speed data. In this paper we investigate the suitability of a deterministic model to represent the wind speed fluctuations by employing tools of nonlinear dynamics. We have carried out a detailed nonlinear time series analysis of the daily mean wind speed data measured at Thiruvananthapuram (8.483° N,76.950° E) from 2000 to 2010. The results of the analysis strongly suggest that the underlying dynamics is deterministic, low-dimensional and chaotic suggesting the possibility of accurate short-term prediction. As most of the chaotic systems are confined to laboratories, this is another example of a naturally occurring time series showing chaotic behaviour.

Extreme Value Statistics of Wind Speed Data by the POT and ACER Methods

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
A. Naess ◽  
E. Haug
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Extreme Value Statistics ◽  
Peaks Over Threshold ◽  
Threshold Method ◽  
Wind Speed Data ◽  
Novel Method

The paper describes a novel method for predicting the appropriate extreme value distribution derived from an observed time series. The method is based on introducing a cascade of conditioning approximations to the exact extreme value distribution. This allows for a rational way of capturing dependence effects in the time series. The performance of the method is compared with that of the peaks-over-threshold method.

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