The performance of numerical weather prediction models has improved dramatically recently. However, model biases remain a fundamental limitation prohibiting the direct implementation of model results. There are several ways to describe wind speed data. The Weibull and lognormal distributions are used to obtain the best-fit model for the wind speed data. This study aims to develop a statistical post-processing method based on the distribution-based scaling (DBS) approach, which scales NWP data to fit the distribution derived using recorded wind speed at that site location. The performance of the suggested method was evaluated using four different error measures. The optimal model is anticipated to have the lowest Mean Bias Error (MBE), Mean Absolute Error (MAE), Root Mean square Error (RMSE), and variance (s2) values. It was determined that employing a DBS strategy significantly improved the NWP by at least 75%.
AkyuzHEGamgamH (2017) Statistical analysis of wind speed data with Weibull, lognormal and gamma distributions. Cumhuriyet Science Journal38: 68–76.
2.
BurlagaLFLazarusAJ (2000) Lognormal distributions and spectra of solar wind plasma fluctuations: Wind 1995–1998. Journal of Geophysical Research105(A2): 2357–2364.
3.
DhimanHSDebD (2021) Probability distribution functions for short-term wind power forecasting. In: BalasVJainLBalasMetal. (eds) Soft Computing Applications. SOFA 2018, Advances in Intelligent Systems and Computing, vol. 1221: 60–69. Cham: Springer.
4.
DhimanHSDebDFoleyAM (2020) Bilateral Gaussian wake model formulation for wind farms: A forecasting based approach. Renewable and Sustainable Energy Reviews127: 109873. DOI:10.1016/j.rser.2020.109873.
5.
DhimanHSDebDGuerreroJM (2019) Hybrid machine intelligent SVR variants for wind forecasting and ramp events. Renewable and Sustainable Energy Reviews108: 369–379.
6.
GilbertCMessnerJWPinsonP, et al. (2020) Statistical post-processing of turbulence-resolving weather forecasts for offshore wind power forecasting. Wind Energy23: 884–897.
7.
IndhumathyDSeshaiahCVSukkiramathiK (2014) Estimation of Weibull parameters for wind speed calculation at Kanyakumari in India. International Journal of Innovative Research in Science Engineering and Technology3(1): 8340–8345.
8.
JeongJLeeSJ (2018) A statistical parameter correction technique for WRF medium-range prediction of near-surface temperature and wind speed using generalized linear model. Atmosphere9(8): 291.
9.
KatinasVGeceviciusGMarciukaitisM (2018) An investigation of wind power density distribution at location with low and high wind speeds using statistical model. Applied Energy218: 442–451.
10.
LiHZhangFLiuHD, et al. (2014) Summary on wind speed distribution and its parameter estimation. Advanced Materials Research986–987: 689–693. Available at: www.scientific.net/AMR.986-987.689
11.
LynchP (2008) The origins of computer weather prediction and climate modeling. Journal of Computational Physics227: 3431–3444.
12.
MendozaPARajagopalanBClarkMP, et al. (2015) Statistical post-processing of high-Resolution Regional Climate Model Output. Monthly Weather Review143(5): 1533–1553.
NeriniDZanettaFSchaerM, et al. (2023) Operational machine learning for the post-processing of surface wind forecasts, Paper presented at EGU General Assembly 2023, Vienna, Austria, April 24–28, 2023 (EGU23-14973). DOI:10.5194/egusphere-egu23-14973.
15.
PatlakasPDrakakiEGalanisG, et al. (2017) Wind gust estimation by combining a numerical weather prediction model and statistical post-processing. Energy Procedia125: 190–198.
16.
PérezIASánchezMLGarcíaMÁ (2007) Weibull wind speed distribution: Numerical considerations and use with sodar data. Journal of Geophysical Research112: D20112.
17.
PerezRLorenzEPellandS, et al. (2013) Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe. Solar Energy94: 305–326.
18.
Pishgar-KomlehSHKeyhaniASefeedpariP (2015) Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran). Renewable and Sustainable Energy Reviews42: 313–322.
19.
PowersJGKlempJBSkamarockWC, et al. (2017) The weather research and forecasting model: Overview, system efforts, and future directions. Bulletin of the American Meteorological Society98(8): 1717–1737.
20.
RamírezPCartaJA (2005) Influence of the data sampling interval in the estimation of the parameters of the Weibull wind speed probability density distribution: A case study. Energy Conversion and Management46: 2419–2438.
21.
RangarajAKrithikaVSrinathY, et al. (2019) Validation and bias correction techniques to improve Numerical Weather Prediction wind speed data. In: 2nd Int’l conference on large-scale grid integration of renewable energy in India, New Delhi, India, 4–6 September.
22.
RaspSLerchS (2018) Neural networks for postprocessing ensemble weather forecasts. Waves to Weather146(11): 3885–3900.
23.
RobertsonDEShresthaDLWangQJ (2013) Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting. Hydrology and Earth System Sciences17(9): 3587–3603.
24.
RuslauMHelgaS (2018) Analysis of wind speed distribution at the Mopah Airport in Merauke. In: Proceedings of the international conference on science and technology (ICST 2018). Bali Nusa Dua Convention: Atlantis Press. DOI:10.2991/icst-18.2018.193
25.
SarkarAGuglianiGDeepS (2017) Weibull model for wind speed data analysis of different locations in India. KSCE Journal of Civil Engineering21: 2764–2776.
26.
StewartDAEssenwangerOM (1978) Frequency distribution of wind speed near the surface. Journal of Applied Meteorology and Climatology17(11): 1633–1642.
27.
SuksamosornSHoonchareonNSongsiriJ (2021) Post-processing of NWP forecasts using Kalman filtering with operational constraints for Day-Ahead Solar Power Forecasting in Thailand. IEEE Access9: 105409–105423.
28.
TizguiIEl GuezarFBouzahirH, et al. (2019) Wind speed distribution modeling for wind power estimation: Case of Agadir in Morocco. Wind Engineering43(2): 190–200.
29.
YangHYanJLiuY, et al. (2022) Statistical downscaling of numerical weather prediction based on convolutional neural networks. Global Energy Interconnection5(2): 217–225.
30.
YangWAndréassonJPhil GrahamL, et al. (2010) Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impacts studies. Hydrology Research41(3–4): 211–229.
31.
ZaharimARazaliAMRoslanA, et al. (2009) Fitting of statistical distributions to wind speed data in Malaysia. European Journal of Scientific Research26: 6–12.
