A model-based climatology analysis of wind power resources at 100-m height over the Bohai Sea and the Yellow Sea

Li, Delei; Geyer, Beate; Bisling, Peter

doi:10.1016/j.apenergy.2016.07.010

Cited by 37 publications

(23 citation statements)

References 26 publications

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“…With respect to mesoscale atmospheric processes, CCLM outperforms ERAI in resolving detailed temporal and spatial structures for the phenomena of a typhoon, a coastal atmospheric front, and a vortex street. The data set has also been applied to the study of the climatology, variability, and extremes of wind energy over the BYS (Li, Geyer, & Bisling, ). In the following, we sought to assess how about the quality of CCLM in reproducing vertical profiles of the wind.…”

Section: Evaluation Of the Model Data Setmentioning

confidence: 99%

“…The model output has been applied to investigate present surface wind climate and added value to the description of winds by downscaling over the BYS (Li, 2017;Li, Geyer, & Bisling, 2016;Li, von Storch, & Geyer, 2016a, 2016b. Simulated surface winds have been assessed by comparing against satellite and in situ observation data both on land and over water.…”

Section: Evaluation Of the Model Data Setmentioning

confidence: 99%

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Low‐Level Jets Over the Bohai Sea and Yellow Sea: Climatology, Variability, and the Relationship With Regional Atmospheric Circulations

Storch

Yin

et al. 2018

JGR Atmospheres

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The present study reveals climate features of low‐level jets (LLJs) over the Bohai Sea and Yellow Sea (BYS) based on a 35‐year (1979–2013) high‐resolution (7 km) atmospheric hindcast. The regional climate model COSMO‐CLM driven by the ERA‐Interim reanalysis data set was used to obtain the hindcast. Through comparison with observations, the hindcast was proved to robustly reproduce the climatology, the diurnal cycle, the variability of wind profiles, and specific LLJ cases. LLJs over the BYS feature a strong diurnal cycle, intra‐annual, and interannual variability but weak decadal variability. LLJs are more frequent in April, May, and June (LLJ season) and less frequent in winter over the Bohai Sea and western coastal areas of the Yellow Sea, which is due to the intra‐annual variations of large‐scale circulation and local land‐sea thermal contrast. In the LLJ season, the heights of jet cores are generally lower than 500 m above sea level. The maximum wind speed of LLJs is mostly in the range of 10–16 m/s, and prevailing wind directions are southerly and southwesterly. The LLJs are of the nocturnal type, with the highest occurrence frequency at approximately 2300 local time. Furthermore, a low‐frequency link between anomalies of LLJ occurrence and regional large‐scale barotropic circulation was identified using canonical correlation analysis and associated correlation patterns. Pressure systems over the East Asia‐northwest Pacific region are significantly correlated with the variations of LLJ occurrence over the BYS in terms of the intra‐annual and interannual variability.

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Section: Evaluation Of the Model Data Setmentioning

confidence: 99%

Section: Evaluation Of the Model Data Setmentioning

confidence: 99%

Low‐Level Jets Over the Bohai Sea and Yellow Sea: Climatology, Variability, and the Relationship With Regional Atmospheric Circulations

Storch

Yin

et al. 2018

JGR Atmospheres

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“…The simulation was run for obtaining a long‐term atmospheric database to derive past and current climate statistics, including their variability, climatology, and extremes, to contribute to the assessment of climate change impact over the BYS areas. The high‐resolution atmospheric data set is also vital in forcing ocean models and in applications such as potential wind energy assessments [ Li et al ., ].…”

Section: Cases and Methodsmentioning

confidence: 99%

“…When planning for maintenance of offshore structures, when designing ships to withstand environmental stresses, when assessing the risk of very high storm surges [ Weisse et al ., , , ] or the potential of wind energy [ Geyer et al ., ; Li et al ., ], of the variability of regional upwelling [ Tim et al ., ], implications on permafrost thermal state, ecology, and biochemical cycles through snow cover and depth [ Klehmet et al ., ], realistic descriptions of the regionally detailed weather stream for a sufficiently long time are needed. Such descriptions or analyses cannot be constructed directly from observations simply because of scarcity of observational data.…”

Section: Introductionmentioning

confidence: 99%

Regional reanalysis without local data: Exploiting the downscaling paradigm

Storch

Feser

Geyer³

et al. 2017

JGR Atmospheres

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This paper demonstrates two important aspects of regional dynamical downscaling of multidecadal atmospheric reanalysis. First, that in this way skillful regional descriptions of multidecadal climate variability may be constructed in regions with little or no local data. Second, that the concept of large‐scale constraining allows global downscaling, so that global reanalyses may be completed by additions of consistent detail in all regions of the world. Global reanalyses suffer from inhomogeneities. However, their large‐scale componenst are mostly homogeneous; Therefore, the concept of downscaling may be applied to homogeneously complement the large‐scale state of the reanalyses with regional detail—wherever the condition of homogeneity of the description of large scales is fulfilled. Technically, this can be done by dynamical downscaling using a regional or global climate model, which's large scales are constrained by spectral nudging. This approach has been developed and tested for the region of Europe, and a skillful representation of regional weather risks—in particular marine risks—was identified. We have run this system in regions with reduced or absent local data coverage, such as Central Siberia, the Bohai and Yellow Sea, Southwestern Africa, and the South Atlantic. Also, a global simulation was computed, which adds regional features to prescribed global dynamics. Our cases demonstrate that spatially detailed reconstructions of the climate state and its change in the recent three to six decades add useful supplementary information to existing observational data for midlatitude and subtropical regions of the world.

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“…Data derived from climate models are also frequently used for WPD estimation [15][16][17][18][19][20]. Climate models often only provide low sampling and generally generate temporally averaged outputs because this preserves the climate information without the storage burden.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Averaging, Sampling, and Time Series Length on Wind Power Density Estimations

2020

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The Wind Power Density (WPD) is widely used for wind resource characterization. However, there is a significant level of uncertainty associated with its estimation. Here, we analyze the effect of sampling frequencies, averaging periods, and the length of time series on the WPD estimation. We perform this analysis using four approaches. First, we analytically evaluate the impact of assuming that the WPD can simply be computed from the cube of the mean wind speed. Second, the wind speed time series from two meteorological stations are used to assess the effect of sampling and averaging on the WPD. Third, we use numerical weather prediction model outputs and observational data to demonstrate that the error in the WPD estimate is also dependent on the length of the time series. Finally, artificial time series are generated to control the characteristics of the wind speed distribution, and we analyze the sensitivity of the WPD to variations of these characteristics. The WPD estimation error is expressed mathematically using a numerical-data-driven model. This numerical-data-driven model can then be used to predict the WPD estimation errors at other sites. We demonstrate that substantial errors can be introduced by choosing too short time series. Furthermore, averaging leads to an underestimation of the WPD. The error introduced by sampling is strongly site-dependent.

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A model-based climatology analysis of wind power resources at 100-m height over the Bohai Sea and the Yellow Sea

Cited by 37 publications

References 26 publications

Low‐Level Jets Over the Bohai Sea and Yellow Sea: Climatology, Variability, and the Relationship With Regional Atmospheric Circulations

Low‐Level Jets Over the Bohai Sea and Yellow Sea: Climatology, Variability, and the Relationship With Regional Atmospheric Circulations

Regional reanalysis without local data: Exploiting the downscaling paradigm

The Effect of Averaging, Sampling, and Time Series Length on Wind Power Density Estimations

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