Improving Wind Speed Forecasts at Wind Turbine Locations over Northern China through Assimilating Nacelle Winds with WRFDA

Abstract

To improve the wind speed forecasts at turbine locations and at hub-height, this study develops the WRFDA system to assimilate the wind speed observations measured on the nacelle of turbines (hereafter referred as turbine wind speed observations) with both 3DVAR and 4DVAR algorithms. Results exhibit that the developed data assimilation (DA) system helps in greatly improving the analysis and the forecast of wind turbine speed. Among three experiments with no cycling DA, with 2-h cycling DA, and with 4-h cycling DA, the last experiment generates the best analysis, improving the averaged forecasts (T+9 to T+24) of wind speed over all wind farms by 32.5% in the bias and 6.3% in the RMSE. After processing the turbine wind speed observations into super-obs, even bigger improvements are revealed when validating against either the original turbine wind speed observations or the super-obs. Taken the results validated against the super-obs as an example, the bias and RMSE of the forecasts (T+9 to T+24) averaged over all wind farms are reduced by 38.8% and 12.0%, respectively. Compared to the best-performed 3DVAR experiment (4-h cycling and super-obs), the experiment following the same DA strategy but using 4DVAR algorithm exhibits further improvements, especially for the averaged bias in the forecasts of all wind farms, and the changing amount in the forecasts of the enhanced wind farms. When validating against the super-obs, the 4DVAR experiment reduces the bias and RMSE in the forecasts (T+9 to T+24) by 54.6% (0.66 m/s) and 12.7% (0.34 m/s) compared to the control experiment.

Improving Wind and Power Predictions via Four-Dimensional Data Assimilation in the WRF Model: Case Study of Storms in February 2022 at Belgian Offshore Wind Farms

“…Both variational techniques rely on minimizing the difference between model forecasts and observations by optimizing a cost function. One work that exploits the benefits of variational data assimilation is by Sun et al (2022), in which wind speed forecasts are improved when assimilating observations from the nacelle of turbines.…”

Wind observations from hot‐air balloons and the application in an NWP model

“…Sun et al (2022) and Nielsen‐Gammon et al (2007) have shown that local ABL wind observations have a positive impact on the analysis of mesoscale models. So far, wind measurements from HAB tracks have never been assimilated into NWP models, and a feasibility study is recommended.…”

“…The scaling variables change and the turbulence scheme cannot adequately handle the different flow regimes; for instance, too much mixing will not represent sharp gradients of low-level jets (Bosveld et al, 2014). Sun et al (2022) and Nielsen-Gammon et al ( 2007) have shown that local ABL wind observations have a positive impact on the analysis of mesoscale models. So far, wind measurements from HAB tracks have never been assimilated into NWP models, and a feasibility study is recommended.…”

Hot-air balloon wind sensing

“…Third party observations are data collected by other organizations using meteorological sensors. Examples are wind observations from ground based wind turbines (Sun et al, 2022) and data from Air Traffic Control (ATC). A surveillance radar interrogates aircraft and in case of Enhanced Surveillance (EHS) position, identity, airspeed, Mach number are exchanged (De Haan, 2011), in case of Meteorological Routine Airport Report (MRAR) wind speed and temperature are directly transferred (Strajnar et al, 2015).…”

“…For instance too much mixing will not represent sharp gradients of low level jets . Sun et al (2022) and Nielsen-Gammon et al (2007) have shown that local ABL wind observations have a positive impact on the analysis of mesoscale models. So far wind measurements from HAB tracks have never been assimilated in NWP models and a feasibility study is recommended.…”