• Energy Research

Abstract Wind energy plays a pivotal role in the global effort to mitigate climate change, with China emerging as a leader in renewable energy adoption. The Hami region in northwestern China stands out as a crucial area for wind power development, given its substantial wind resources and strategic importance in China’s energy landscape. However, existing studies on wind energy potential vary widely and involve large uncertainties due to sparse measurements and coarse resolution, highlighting the need for more precise assessments to guide policy decisions and optimize energy utilization. This study leverages high-resolution ERA5 reanalysis data and advanced wind turbine technology to assess wind energy potential in the Hami region, taking into account factors including wind speed patterns, turbine heights, and geographical constraints. The comparison with in-situ data demonstrated that high-resolution ERA5 reanalyzed wind speeds enable to capture multi-year wind speed variations in this region. We find substantial potential for wind energy in Hami, with energy densities exceeding 200 W m−2 in the high-potential wind zones. Importantly, this study identifies a new high-potential area in eastern Hami, termed the East Wind Zone. Our high-resolution assessment of wind energy potential at different heights over the past two decades reveals long-term trends and seasonal variations. Increasing the hub height from 95 m to 140 m raises the average wind power generation potential across Hami by 31.3 GWh yr−1. Our findings highlight the importance of strategic wind farm placement to maximize renewable energy output and provide insights for policy and industry, supporting China’s renewable energy goals.

Abstract Wind energy plays a pivotal role in the global effort to mitigate climate change, with China emerging as a leader in renewable energy adoption. The Hami region in northwestern China stands out as a crucial area for wind power development, given its substantial wind resources and strategic importance in China’s energy landscape. However, existing studies on wind energy potential vary widely and involve large uncertainties due to sparse measurements and coarse resolution, highlighting the need for more precise assessments to guide policy decisions and optimize energy utilization. This study leverages high-resolution ERA5 reanalysis data and advanced wind turbine technology to assess wind energy potential in the Hami region, taking into account factors including wind speed patterns, turbine heights, and geographical constraints. The comparison with in-situ data demonstrated that high-resolution ERA5 reanalyzed wind speeds enable to capture multi-year wind speed variations in this region. We find substantial potential for wind energy in Hami, with energy densities exceeding 200 W m−2 in the high-potential wind zones. Importantly, this study identifies a new high-potential area in eastern Hami, termed the East Wind Zone. Our high-resolution assessment of wind energy potential at different heights over the past two decades reveals long-term trends and seasonal variations. Increasing the hub height from 95 m to 140 m raises the average wind power generation potential across Hami by 31.3 GWh yr−1. Our findings highlight the importance of strategic wind farm placement to maximize renewable energy output and provide insights for policy and industry, supporting China’s renewable energy goals.

Abstract China has realized a 56-fold increase in installed wind capacity, from 5.9 GW in 2007 to 328 GW in 2021. In addition to increasing installed capacity, plans to substantially increase wind energy production for climate change mitigation also depend on future wind speeds, which strongly influences the efficiencies of installed turbines within individual wind farms. A reversal in globally decreasing wind speeds over several decades has been reported previously. However, subsequent studies using other data sources reported only a slight increase or no reversal in China. These uncertainties regarding China’s wind energy production hamper estimates of wind energy production potential. Here, our analysis of quality-controlled wind speed measurements from in-situ stations shows that the wind speed decline in China reversed significantly since 2012 (P < 0.001), but with substantial spatio-temporal variability. We further estimated the capacity factor (CF) growth and the wind power gain solely associated with the changes in wind speed ranges from 31.6 to 56.5 TWh yr−1 based on the 2019 installed capacity. This estimate explains 22.0%–39.3% of the rapid increase in wind generation CF in China during 2012–2019. The result implies that the site selection of wind farms should consider both current wind situation and future wind speed trends. Further studies are needed to understand the driving factor of wind speed recovery in support of the wind energy industry.

Abstract China has realized a 56-fold increase in installed wind capacity, from 5.9 GW in 2007 to 328 GW in 2021. In addition to increasing installed capacity, plans to substantially increase wind energy production for climate change mitigation also depend on future wind speeds, which strongly influences the efficiencies of installed turbines within individual wind farms. A reversal in globally decreasing wind speeds over several decades has been reported previously. However, subsequent studies using other data sources reported only a slight increase or no reversal in China. These uncertainties regarding China’s wind energy production hamper estimates of wind energy production potential. Here, our analysis of quality-controlled wind speed measurements from in-situ stations shows that the wind speed decline in China reversed significantly since 2012 (P < 0.001), but with substantial spatio-temporal variability. We further estimated the capacity factor (CF) growth and the wind power gain solely associated with the changes in wind speed ranges from 31.6 to 56.5 TWh yr−1 based on the 2019 installed capacity. This estimate explains 22.0%–39.3% of the rapid increase in wind generation CF in China during 2012–2019. The result implies that the site selection of wind farms should consider both current wind situation and future wind speed trends. Further studies are needed to understand the driving factor of wind speed recovery in support of the wind energy industry.

AbstractGlobal reanalysis products are important tools across disciplines to study past meteorological changes and are especially useful for wind energy resource evaluations. Studies of observed wind speed show that land surface wind speed declined globally since the 1960s (known as global terrestrial stilling) but reversed with a turning point around 2010. Whether the declining trend and the turning point have been captured by reanalysis products remains unknown so far. To fill this research gap, a systematic assessment of climatological winds and trends in five reanalysis products (ERA5, ERA-Interim, MERRA-2, JRA-55, and CFSv2) was conducted by comparing gridcell time series of 10-m wind speed with observational data from 1439 in situ meteorological stations for the period 1989–2018. Overall, ERA5 is the closest to the observations according to the evaluation of climatological winds. However, substantial discrepancies were found between observations and simulated wind speeds. No reanalysis product showed similar change to that of the global observations, although some showed regional agreement. This discrepancy between observed and reanalysis land surface wind speed indicates the need for prudence when using reanalysis products for the evaluation and prediction of winds. The possible reasons for the inconsistent wind speed trends between reanalysis products and observations are analyzed. The results show that wind energy production should select different products for different regions to minimize the discrepancy with observations.

AbstractGlobal reanalysis products are important tools across disciplines to study past meteorological changes and are especially useful for wind energy resource evaluations. Studies of observed wind speed show that land surface wind speed declined globally since the 1960s (known as global terrestrial stilling) but reversed with a turning point around 2010. Whether the declining trend and the turning point have been captured by reanalysis products remains unknown so far. To fill this research gap, a systematic assessment of climatological winds and trends in five reanalysis products (ERA5, ERA-Interim, MERRA-2, JRA-55, and CFSv2) was conducted by comparing gridcell time series of 10-m wind speed with observational data from 1439 in situ meteorological stations for the period 1989–2018. Overall, ERA5 is the closest to the observations according to the evaluation of climatological winds. However, substantial discrepancies were found between observations and simulated wind speeds. No reanalysis product showed similar change to that of the global observations, although some showed regional agreement. This discrepancy between observed and reanalysis land surface wind speed indicates the need for prudence when using reanalysis products for the evaluation and prediction of winds. The possible reasons for the inconsistent wind speed trends between reanalysis products and observations are analyzed. The results show that wind energy production should select different products for different regions to minimize the discrepancy with observations.

Crop production is a large source of atmospheric ammonia (NH3), which poses risks to air quality, human health and ecosystems1-5. However, estimating global NH3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy4,5. Here we develop a machine learning model for generating crop-specific and spatially explicit NH3 emission factors globally (5-arcmin resolution) based on a compiled dataset of field observations. We show that global NH3 emissions from rice, wheat and maize fields in 2018 were 4.3 ± 1.0 Tg N yr-1, lower than previous estimates that did not fully consider fertilizer management practices6-9. Furthermore, spatially optimizing fertilizer management, as guided by the machine learning model, has the potential to reduce the NH3 emissions by about 38% (1.6 ± 0.4 Tg N yr-1) without altering total fertilizer nitrogen inputs. Specifically, we estimate potential NH3 emissions reductions of 47% (44-56%) for rice, 27% (24-28%) for maize and 26% (20-28%) for wheat cultivation, respectively. Under future climate change scenarios, we estimate that NH3 emissions could increase by 4.0 ± 2.7% under SSP1-2.6 and 5.5 ± 5.7% under SSP5-8.5 by 2030-2060. However, targeted fertilizer management has the potential to mitigate these increases.