# On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Data Description

## 3. Forecasting Methodology

#### 3.1. Predictive Wind Speed Densities

#### 3.1.1. Optimization of the Wind Turbine Area of Influence

#### 3.1.2. Evaluation of Predicted Wind Speeds

#### 3.2. Predictive Wind Power Densities

## 4. Results

#### 4.1. Probabilistic Forecast Evaluation

#### 4.1.1. Individual Wind Turbine Power

#### 4.2. Analysis on Limited Radar Availability

#### 4.2.1. Wind Farm Row Aggregated Power Output

#### 4.3. Evaluation of Single Point Predictions

#### 4.3.1. Individual Wind Turbine Power

#### 4.3.2. Wind Farm Row Aggregated Power Output

## 5. Discussion

#### 5.1. On Further Use of Doppler Radar Measurements for Wind Power Forecasting

#### 5.2. On Extension of the Forecasting Horizon

#### 5.3. On Data Quality

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

DD | Dual-Doppler |

R&D | Research and development |

D | Wind turbine rotor diameter |

SCADA | Supervisory Control and Data Acquisition |

${P}_{n}$ | Nominal power |

CRPS | Average Continous Ranked Probability Score |

IEC | International Electrotechnical Commission |

$ecdf$ | Empirical cumulative distribution function |

RF | Remote sensing-based forecast |

${P}_{i}^{RF}$ | Predictive densities of power |

$w{s}_{i}^{RF}$ | Predictive densities of wind speed |

${P}_{agg}$ | Average aggregated power |

RMSE | Root-mean-square error |

NRMSE | Normalised root-mean-square error |

## References

- Cutululis, N.; Litong-Palima, M.; Sørensen, P. North Sea Offshore Wind Power Variability in 2020 and 2030. In Proceedings of the 11th International Workshop on Large-Scale Integration of Wind Power into Power Systems, Lisbon, Portugal, 13–15 November 2012. [Google Scholar]
- 50Hertz; Amprion; Tennet; TransnetBW. Leitfaden zur Präqualikation von Windenergieanlagen zur Erbringung von Minutenreserveleistung im Rahmen einer Pilotphase/ Guidelines for Prequalification of Wind Turbines to Provide Minute Reserves during a Pilot Phase; Technical Report; German Transmission System Operators: Berlin, Germany, 2016. [Google Scholar]
- Borggrefe, F.; Neuhoff, K. Balancing and Intraday Market Design: Options for Wind Integration; DIW Discussion Papers 1162; German Institute for Economic Research: Berlin, Germany, 2011. [Google Scholar]
- EPEXSPOT. Intraday Lead Times. 2017. Available online: https://www.epexspot.com/en/product-info/intradaycontinuous/intraday_lead_time (accessed on 4 February 2018).
- Giebel, G.; Kariniotakis, G. Wind power forecasting-a review of the state of the art. In Renewable Energy Forecasting: From Models to Applications; Woodhead Publishing: Cambridge, UK, 2017; pp. 59–109. [Google Scholar]
- Cavalcante, L.; Bessa, R.J.; Reis, M.; Browell, J. LASSO vector autoregression structures for very short—Term wind power forecasting. Wind. Energy
**2016**, 20, 657–675. [Google Scholar] [CrossRef] - Erdem, E.; Shi, J. ARMA based approaches for forecasting the tuple of wind speed and direction. Appl. Energy
**2011**, 88, 1405–1414. [Google Scholar] [CrossRef] - Pinson, J.W.M.P. Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting. Int. J. Forecast.
**2018**, in press. [Google Scholar] [CrossRef] - Blonbou, R. Very short-term wind power forecasting with neural networks and adaptive Bayesian learning. Renew. Energy
**2011**, 36, 1118–1124. [Google Scholar] [CrossRef] - Carpinone, A.; Giorgio, M.; Langella, R.; Testa, A. Markov chain modeling for very-short-term wind power forecasting. Electr. Power Syst. Res.
**2015**, 122, 152–158. [Google Scholar] [CrossRef] - Pinson, P.; Madsen, H. Adaptive modelling and forecasting of offshore wind power fluctuations with Markov—Switching autoregressive models. J. Forecast.
**2012**, 31, 281–313. [Google Scholar] [CrossRef] [Green Version] - Hu, J.; Wang, J.; Ma, K. A hybrid technique for short-term wind speed prediction. Energy
**2015**, 81, 563–574. [Google Scholar] [CrossRef] - Pinson, P. Very-short-term probabilistic forecasting of wind power with generalized logit-normal distributions. J. R. Stat. Soc. Ser. C
**2012**, 61, 555–576. [Google Scholar] [CrossRef] - Dowell, J.; Pinson, P. Very-short-term probabilistic wind power forecasts by sparse vector autoregression. IEEE Trans. Smart Grid
**2016**, 7, 763–770. [Google Scholar] [CrossRef] - Alessandrini, S.; Davò, F.; Sperati, S.; Benini, M.; Delle Monache, L. Comparison of the economic impact of different wind power forecast systems for producers. Adv. Sci. Res.
**2014**, 11, 49–53. [Google Scholar] [CrossRef] [Green Version] - Clifton, A.; Clive, P.; Gottschall, J.; Schlipf, D.; Simley, E.; Simmons, L.; Stein, D.; Trabucchi, D.; Vasiljevic, N.; Würth, I. IEA Wind Task 32: Wind Lidar—Identifying and mitigating barriers to the adoption of wind lidar. Remote Sens.
**2018**, 10, 406. [Google Scholar] [CrossRef] - Kameyama, S.; Sakimura, T.; Watanabe, Y.; Ando, T.; Asaka, K.; Tanaka, H.; Yanagisawa, T.; Hirano, Y.; Inokuchi, H. Wind sensing demonstration of more than 30 km measurable range with a 1.5 μm coherent Doppler lidar which has the laser amplifier using Er, Yb:glass planar waveguide. In Lidar Remote Sensing for Environmental Monitoring XIII; SPIE: Kyoto, Japan, 2012; Volume 8526, p. 85260E. [Google Scholar]
- Würth, I.; Brenner, A.; Wigger, M.; Cheng, P. How far do we see? Analysis of the measurement range of long-range lidar data for wind power forecasting. In Proceedings of the German Wind Energy Conference (DEWEK), Bremen, Germany, 17–18 October 2017. [Google Scholar]
- Vasiljević, N.; Palma, J.M.L.M.; Angelou, N.; Carlos Matos, J.; Menke, R.; Lea, G.; Mann, J.; Courtney, M.; Frölen Ribeiro, L.; Gomes, V.M.M.G.C. Perdigão 2015: Methodology for atmospheric multi-Doppler lidar experiments. Atmos. Meas. Tech.
**2017**, 10, 3463–3483. [Google Scholar] [CrossRef] - Floors, R.; Peña, A.; Lea, G.; Vasiljević, N.; Simon, E.; Courtney, M. The RUNE Experiment—A Database of Remote-Sensing Observations of Near-Shore Winds. Remote. Sens.
**2016**, 8, 884. [Google Scholar] [CrossRef] - Van Dooren, M.F.; Trabucchi, D.; Kühn, M. A Methodology for the Reconstruction of 2D Horizontal Wind Fields of Wind Turbine Wakes Based on Dual-Doppler Lidar Measurements. Remote Sens.
**2016**, 8, 809. [Google Scholar] [CrossRef] - Valldecabres, L.; Peña, A.; Courtney, M.; von Bremen, L.; Kühn, M. Very short-term forecast of near-coastal flow using scanning lidars. Wind Energy Sci.
**2018**, 3, 313–327. [Google Scholar] [CrossRef] - Trombe, P.J.; Pinson, P.; Vincent, C.; Bøvith, T.; Cutululis, N.A.; Draxl, C.; Giebel, G.; Hahmann, A.N.; Jensen, N.E.; Jensen, B.P.; et al. Weather radars—The new eyes for offshore wind farms? Wind. Energy
**2014**, 17, 1767–1787. [Google Scholar] [CrossRef] [Green Version] - Meischner, P.; Hagen, M. Weather radars in Europe: Potential for advanced applications. Phys. Chem. Earth Part B Hydrol. Oceans Atmos.
**2000**, 25, 813–816. [Google Scholar] [CrossRef] - Hirth, B.D.; Schroeder, J.L.; Gunter, W.S.; Guynes, J.G. Coupling Doppler radar-derived wind maps with operational turbine data to document wind farm complex flows. Wind. Energy
**2015**, 18, 529–540. [Google Scholar] [CrossRef] - Nygaard, N.G.; Newcombe, A.C. Wake behind an offshore wind farm observed with dual-Doppler radars. J. Phys. Conf. Ser.
**2018**, 1037, 072008. [Google Scholar] [CrossRef] - Marathe, N.; Swift, A.; Hirth, B.; Walker, R.; Schroeder, J. Characterizing power performance and wake of a wind turbine under yaw and blade pitch. Wind. Energy
**2016**, 19, 963–978. [Google Scholar] [CrossRef] - Hirth, B.D.; Schroeder, J.L.; Irons, Z.; Walter, K. Dual-Doppler measurements of a wind ramp event at an Oklahoma wind plant. Wind. Energy
**2016**, 19, 953–962. [Google Scholar] [CrossRef] - Valldecabres, L.; Nygaard, N.; von Bremen, L.; Kühn, M. Very short-term probabilistic forecasting of wind power based on dual-Doppler radar measurements in the North Sea. J. Phys. Conf. Ser.
**2018**, 1037, 052010. [Google Scholar] [CrossRef] - Vignaroli, A.; Svensson, E.; Courtney, M.; Vasiljevic, N.; Lea, G.; Wagner, R.; Nygaard, N. How accurate is the BEACon radar? In Proceedings of the WindEurope Conference, Amsterdam, The Netherlands, 28–30 November 2017. [Google Scholar]
- Germann, U.; Zawadzki, I. Scale-Dependence of the Predictability of Precipitation from Continental Radar Images. Part I: Description of the Methodology. Mon. Weather Rev.
**2002**, 130, 2859–2873. [Google Scholar] [CrossRef] - Germann, U.; Zawadzki, I. Scale Dependence of the Predictability of Precipitation from Continental Radar Images. Part II: Probability Forecasts. J. Appl. Meteorol.
**2004**, 43, 74–89. [Google Scholar] [CrossRef] - International Electrotechnical Commission (IEC). Wind Energy Generation Systems—Part 12-1: Power Performance Measurements of Electricity Producing Wind Turbines; IEC: Geneva, Switzerland, 2017. [Google Scholar]
- Medici, D.; Ivanell, S.; Dahlberg, J.; Alfredsson, P.H. The upstream flow of a wind turbine: Blockage effect. Wind. Energy
**2011**, 14, 691–697. [Google Scholar] [CrossRef] - Gneiting, T.; Balabdaoui, F.; Raftery, A.E. Probabilistic forecasts, calibration and sharpness. J. R. Stat. Soc. Ser. B
**2007**, 69, 243–268. [Google Scholar] [CrossRef] [Green Version] - Gneiting, T. Quantiles as optimal point forecasts. Int. J. Forecast.
**2011**, 27, 197–207. [Google Scholar] [CrossRef] - Ahsbahs, T.; Badger, M.; Karagali, I.; Larsén, X. Validation of sentinel-1A SAR coastal wind speeds against scanning LiDAR. Remote. Sens.
**2017**, 9, 552. [Google Scholar] [CrossRef] - Lange, B.; Højstrup, J. Estimation of offshore wind resources—The influence of the sea fetch. In Wind Engineering into the 21st Century, Copenhagen, Denmark; CRC Press/Balkema: Rotterdam, The Netherlands, 1999; Volume 3, pp. 2005–2012. [Google Scholar]
- Gonzalez, E.; Stephen, B.; Infield, D.; Melero, J.J. On the use of high-frequency SCADA data for improved wind turbine performance monitoring. J. Phys. Conf. Ser.
**2017**, 926, 12009. [Google Scholar] [CrossRef] [Green Version] - Efron, B. Bootstrap Methods: Another Look Jackknife. Ann. Stat.
**1979**, 7, 1–26. [Google Scholar] [CrossRef] - Hamill, T.M. Reliability Diagrams for Multicategory Probabilistic Forecasts. Weather Forecast.
**1997**, 12, 736–741. [Google Scholar] [CrossRef] - Bröcker, J.; Smith, L.A. Increasing the Reliability of Reliability Diagrams. Weather Forecast.
**2007**, 22, 651–661. [Google Scholar] [CrossRef] - Drew, D.R.; Cannon, D.J.; Barlow, J.F.; Coker, P.J.; Frame, T.H. The importance of forecasting regional wind power ramping: A case study for the UK. Renew. Energy
**2017**, 114, 1201–1208. [Google Scholar] [CrossRef] - Gallego, C.; Costa, A.; Cuerva, A. Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks. Adv. Sci. Res.
**2011**, 6, 55–58. [Google Scholar] [CrossRef] [Green Version] - Larson, K.A.; Westrick, K. Short-term wind forecasting using off-site observations. Wind. Energy
**2006**, 9, 55–62. [Google Scholar] [CrossRef] - Cheng, W.Y.Y.; Liu, Y.; Bourgeois, A.J.; Wu, Y.; Haupt, S.E. Short-term wind forecast of a data assimilation/ weather forecasting system with wind turbine anemometer measurement assimilation. Renew. Energy
**2017**, 107, 340–351. [Google Scholar] [CrossRef]

**Figure 1.**(

**a**) location of the Westermost Rough wind farm ( ), 8 km off the Holderness coast, in the North Sea. The colourbar indicates the height above mean sea level in meters; (

**b**) layout of the wind farm showing the position of the radars (○) and the wind turbines (● and ). Wind turbines used for this analysis ( ) are labeled. The dark and light gray shadowed areas indicate the overlapping dual-Doppler measurement area.

**Figure 3.**Wind rose of one-minute mean wind speeds at the height of 100 m (averaged over the radar domain) observed by the dual-Doppler radar system during the period covered in this analysis.

**Figure 4.**Scheme of the remote sensing probabilistic forecasting model (RF) showing the unmodified wind speed predictive densities ($w{s}_{i}$), the wind speed densities after correcting for induction effects ($w{s}_{i}^{RF}$) and the predictive densities of power (${P}_{i}^{RF}$).

**Figure 5.**Wind speed forecast for $WT4$ (marked in red). (

**a**) Dual-Doppler flow field at the time that the forecast is issued and (

**b**) validated; (

**c**) cloud of wind vectors used to derive the probabilistic forecast for $WT4$ and the respective area of influence (blue circle). Notice the different scales on Figure 5c.

**Figure 6.**Average continuous ranked probability score (CRPS) for the one-minute ahead wind speed predictive densities for different areas of influence ${A}_{i}$ with the remote-sensing forecasting (RF) model (blue dots) and for a probabilistic persistence method (magenta line). The area of influence is expressed in number of rotor diameters (D).

**Figure 7.**(

**a**) predictive histogram distribution of wind speeds for $WT4$ at the time shown in Figure 5b. The blue line represents the mean of the distribution and the magenta line the verifying dual-Doppler wind speed 2.5D upstream of the rotor. N indicates the number of wind field vectors; (

**b**) predictive empirical cumulative distribution function of the normalised power for $WT4$ at the same time. The magenta line indicates the observed power.

**Figure 8.**(

**a**) density scatter plot of the dual-Doppler wind speeds 2.5D upstream of the wind turbine rotor (observed wind speed) and the mean of the five-minute ahead wind speeds distributions without velocity correction due to induction effects, (

**b**) including the velocity correction due to induction effects and (

**c**) for persistence.

**Figure 9.**Normalised wind turbine power curve based on 1656 samples of dual-Doppler wind speeds 2.5D upstream of the rotor, at 100 m height (first row of wind turbines). The line is the binned mean power. The error bars represent the standard deviation in 0.5 m/s wind speed bins.

**Figure 10.**A 60 min episode of five-minute ahead predictions of normalised power for $WT3$ with the remote sensing-based forecasting model (RF). Prediction intervals are shown together with the observed power (red squares).

**Figure 11.**Reliability diagram for all wind turbines (WTs) during available measurements (T) for (

**a**) the remote sensing-based forecasting model (RF) and (

**b**) persistence model, simultaneous periods for (

**c**) the RF and (

**d**) persistence model, a reduced radar availability case for (

**e**) RF and (

**f**) persistence model and (

**g**) the aggregation of $WT1$ to $WT7$ and (

**h**) $WT3$ to $WT5$ for both models. $\overline{N}$ indicates the average number of wind field vectors conforming the wind speed distributions of the RF model. In addition, 95% consistency bars are indicated by the error bars.

**Figure 12.**Wind speeds to be forecasted at the wind turbines (white dots) for a horizon of five minutes, a south-southwesterly wind direction and the radar available distance for (

**a**) full radar availability; (

**b**) limited radar availability.

**Figure 13.**(

**Top**) two radar observed wind fields about five minutes apart; (

**Bottom**) time series of power produced and predicted with the remote sensing-based forecasting (RF) model for the wind turbines $WT1$ and $WT6$. Grey lines indicate the two timestamps above the top panels.

**Table 1.**Average continous ranked probability score (CRPS), in % of the nominal capacity (${P}_{n}$), of the five-minute ahead power forecasts for the seven wind turbines evaluated. Results are shown for the remote sensing-based forecast (RF), persistence and climatology benchmarks. Upper row presents the results for all available measurements (T) of each wind turbine. Lower row provides the results for all simultaneous available measurements. Minimum values are shown in bold.

$\mathit{WT}\mathbf{1}$ | $\mathit{WT}\mathbf{2}$ | $\mathit{WT}\mathbf{3}$ | $\mathit{WT}\mathbf{4}$ | $\mathit{WT}\mathbf{5}$ | $\mathit{WT}\mathbf{6}$ | $\mathit{WT}\mathbf{7}$ | ||
---|---|---|---|---|---|---|---|---|

T | 596 | 903 | 997 | 983 | 960 | 846 | 608 | |

RF | 5.11 | 4.06 | 3.92 | 4.57 | 4.50 | 4.29 | 4.82 | |

CRPS [%] | Persistence | 5.37 | 4.98 | 5.04 | 5.07 | 4.96 | 4.88 | 4.85 |

Climatology | 17.93 | 17.45 | 17.32 | 17.15 | 17.07 | 16.96 | 16.79 | |

T | 343 | 343 | 343 | 343 | 343 | 343 | 343 | |

RF | 6.42 | 5.03 | 4.83 | 5.27 | 4.76 | 4.39 | 4.76 | |

CRPS [%] | Persistence | 6.12 | 5.79 | 5.63 | 5.76 | 5.76 | 5.85 | 5.91 |

Climatology | 16.78 | 16.31 | 15.58 | 15.37 | 15.32 | 15.03 | 14.94 |

**Table 2.**Average continous ranked probability score (CRPS) of five-minute ahead forecasts of power for the seven wind turbines evaluated, in the case of a reduced radar availability (Figure 12b). Results (in % of the nominal capacity (${P}_{n}$)) are shown for the remote sensing-based forecasting (RF) model, persistence and climatology benchmarks.

$\mathit{WT}\mathbf{1}$ | $\mathit{WT}\mathbf{2}$ | $\mathit{WT}\mathbf{3}$ | $\mathit{WT}\mathbf{4}$ | $\mathit{WT}\mathbf{5}$ | $\mathit{WT}\mathbf{6}$ | $\mathit{WT}\mathbf{7}$ | ||
---|---|---|---|---|---|---|---|---|

T | 162 | 162 | 162 | 162 | 162 | 162 | 162 | |

RF | 5.09 | 3.75 | 3.45 | 4.25 | 3.91 | 3.49 | 3.99 | |

CRPS [%] | Persistence | 4.58 | 4.56 | 4.41 | 4.84 | 4.10 | 3.75 | 4.36 |

Climatology | 20.73 | 19.86 | 19.19 | 18.83 | 18.51 | 17.97 | 17.57 |

**Table 3.**Average continous ranked probability score (CRPS), in % of the nominal capacity (${P}_{n}$), for the five-minute ahead predictive densities of aggregated power of different sets of wind turbines. T indicates the sample size evaluated. Minimum values are shown in bold. Results are shown for the remote sensing-based forecasting (RF) model, persistence and climatology benchmarks.

${\overline{\mathit{P}}}_{\mathbf{17}}$ | ${\overline{\mathit{P}}}_{\mathbf{35}}$ | ||
---|---|---|---|

T | 340 | 902 | |

RF | 4.19 | 3.87 | |

CRPS [%] | Persistence | 4.98 | 4.75 |

Climatology | 15.51 | 16.61 |

**Table 4.**Normalised root-mean-square-error (NRMSE), in % of the nominal capacity (${P}_{n}$), of the five-minute ahead forecasts for the seven turbines of the first row. T indicates the number of periods evaluated. Minimum values are shown in bold. Results are shown for the remote sensing-based forecasting (RF) model, persistence and climatology benchmarks.

$\mathit{WT}\mathbf{1}$ | $\mathit{WT}\mathbf{2}$ | $\mathit{WT}\mathbf{3}$ | $\mathit{WT}\mathbf{4}$ | $\mathit{WT}\mathbf{5}$ | $\mathit{WT}\mathbf{6}$ | $\mathit{WT}\mathbf{7}$ | ||
---|---|---|---|---|---|---|---|---|

T | 596 | 903 | 997 | 983 | 960 | 846 | 608 | |

RF | 10.06 | 7.90 | 7.70 | 8.93 | 8.50 | 8.16 | 9.06 | |

NRMSE [%] | Persistence | 9.40 | 8.54 | 8.68 | 8.89 | 8.81 | 8.49 | 8.80 |

Climatology | 31.34 | 30.39 | 30.13 | 29.84 | 29.66 | 29.49 | 29.19 |

**Table 5.**Normalised root-mean-square-error (NRMSE), in % of the nominal capacity (${P}_{n}$), of five-minute ahead forecasts for the seven turbines of the first row, for the case of a reduced radar availability (Figure 12b). Results are shown for the remote sensing-based forecasting (RF) model, persistence and climatology benchmarks.

$\mathit{WT}\mathbf{1}$ | $\mathit{WT}\mathbf{2}$ | $\mathit{WT}\mathbf{3}$ | $\mathit{WT}\mathbf{4}$ | $\mathit{WT}\mathbf{5}$ | $\mathit{WT}\mathbf{6}$ | $\mathit{WT}\mathbf{7}$ | ||
---|---|---|---|---|---|---|---|---|

T | 162 | 162 | 162 | 162 | 162 | 162 | 162 | |

RF | 10.09 | 7.35 | 6.83 | 8.89 | 8.01 | 7.09 | 8.01 | |

NRMSE [%] | Persistence | 8.02 | 7.89 | 7.48 | 8.74 | 7.86 | 6.93 | 8.56 |

Climatology | 35.49 | 34.15 | 33.08 | 32.51 | 32.04 | 31.00 | 30.51 |

**Table 6.**Normalised root-mean-square-error (NRMSE), in % of the nominal capacity (${P}_{n}$), for five-minute ahead forecasts of average aggregated power. Results are shown for the remote sensing-based forecasting (RF) model, persistence and climatology benchmarks.

${\overline{\mathit{P}}}_{\mathbf{17}}$ | ${\overline{\mathit{P}}}_{\mathbf{35}}$ | ||
---|---|---|---|

T | 340 | 902 | |

RF | 8.02 | 8.19 | |

NRMSE [%] | Persistence | 8.50 | 8.22 |

Climatology | 26.24 | 28.88 |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Valldecabres, L.; Nygaard, N.G.; Vera-Tudela, L.; Von Bremen, L.; Kühn, M.
On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts. *Remote Sens.* **2018**, *10*, 1701.
https://doi.org/10.3390/rs10111701

**AMA Style**

Valldecabres L, Nygaard NG, Vera-Tudela L, Von Bremen L, Kühn M.
On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts. *Remote Sensing*. 2018; 10(11):1701.
https://doi.org/10.3390/rs10111701

**Chicago/Turabian Style**

Valldecabres, Laura, Nicolai Gayle Nygaard, Luis Vera-Tudela, Lueder Von Bremen, and Martin Kühn.
2018. "On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts" *Remote Sensing* 10, no. 11: 1701.
https://doi.org/10.3390/rs10111701