scholarly journals Numerical Simulation of Near-Surface Wind during a Severe Wind Event in a Complex Terrain by Multisource Data Assimilation and Dynamic Downscaling

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
De Zhang ◽  
Luyuan Chen ◽  
Feimin Zhang ◽  
Juan Tan ◽  
Chenghai Wang
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Surface Wind ◽  
Wrf Model ◽  
Surface Wind Speed ◽  
Dynamic Downscaling ◽  
Near Surface ◽  
Wind Event ◽  
Weak Wind

Accurate forecast and simulation of near-surface wind is a great challenge for numerical weather prediction models due to the significant transient and intermittent nature of near-surface wind. Based on the analyses of the impact of assimilating in situ and Advanced Tiros Operational Vertical Sounder (ATOVS) satellite radiance data on the simulation of near-surface wind during a severe wind event, using the new generation mesoscale Weather Research and Forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation system, the dynamic downscaling of near-surface wind is further investigated by coupling the microscale California Meteorological (CALMET) model with the WRF and its 3DVAR system. Results indicate that assimilating in situ and ATOVS radiance observations strengthens the airflow across the Alataw valley and triggers the downward transport of momentum from the upper atmosphere in the downstream area of the valley in the initial conditions, thus improving near-surface wind simulations. Further investigations indicate that the CALMET model provides more refined microtopographic structures than the WRF model in the vicinity of the wind towers. Although using the CALMET model achieves the best simulation of near-surface wind through dynamic downscaling of the output from the WRF and its 3DVAR assimilation, the simulation improvements of near-surface wind speed are mainly within 1 m s−1. Specifically, the mean improvement proportions of near-surface wind speed are 64.8% for the whole simulation period, 58.7% for the severe wind period, 68.3% for the severe wind decay period, and 75.4% for the weak wind period. The observed near-surface wind directions in the weak wind conditions are better simulated in the coupled model with CALMET downscaling than in the WRF and its 3DVAR system. It is concluded that the simulation improvements of CALMET downscaling are distinct when near-surface winds are weak, and the downscaling effects are mainly manifested in the simulation of near-surface wind directions.

scholarly journals Covariability of Near-Surface Wind Speed Statistics and Mesoscale Sea Surface Temperature Fluctuations

2018 ◽  
Vol 48 (3) ◽  
pp. 465-478 ◽  
Author(s):  
Johannes Gemmrich ◽  
Adam Monahan
Keyword(s):  
Wind Speed ◽  
Cold Water ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Western Boundary ◽  
Near Surface ◽  
Water Entrainment ◽  
Speed Fluctuations ◽  
Coupling Processes

AbstractThe atmospheric (ABL) and ocean (OBL) boundary layers are intimately linked via mechanical and thermal coupling processes. In many regions over the world’s oceans, this results in a strong covariability between anomalies in wind speed and SST. At oceanic mesoscale, this coupling can be driven either from the atmosphere or the ocean. Gridded SST and wind speed data at 0.25° resolution show that over the western North Atlantic, the ABL mainly responds to the OBL, whereas in the eastern North Pacific and in the Southern Ocean, the OBL largely responds to wind speed anomalies. This general behavior is also verified by in situ buoy observations in the Atlantic and Pacific. A stochastic, nondimensional, 1D coupled air–sea boundary layer model is utilized to assess the relative importance of the coupling processes. For regions of little intrinsic SST fluctuations (i.e., most regions of the world’s oceans away from strong temperature fronts), the inclusion of cold water entrainment at the thermocline is crucial. In regions with strong frontal activities (e.g., the western boundary regions), the coupling is dominated by the SST fluctuations, and the frontal variability needs to be included in models. Generally, atmospheric and ocean-driven coupling lead to an opposite relationship between SST and wind speed fluctuations. This effect can be especially important for higher wind speed quantiles.

scholarly journals Applying Satellite Data Assimilation to Wind Simulation of Coastal Wind Farms in Guangdong, China

2020 ◽  
Vol 12 (6) ◽  
pp. 973
Author(s):  
Wenqing Xu ◽  
Like Ning ◽  
Yong Luo
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Satellite Data ◽  
Surface Wind ◽  
Wrf Model ◽  
Wind Farms ◽  
Resource Assessment ◽  
Near Surface ◽  
Wind Resource Assessment ◽  
Wind Resource

With the development of the wind power industry in China, accurate simulation of near-surface wind plays an important role in wind-resource assessment. Numerical weather prediction (NWP) models have been widely used to simulate the near-surface wind speed. By combining the Weather Research and Forecast (WRF) model with the Three-dimensional variation (3DVar) data assimilation system, our work applied satellite data assimilation to the wind resource assessment tasks of coastal wind farms in Guangdong, China. We compared the simulation results with wind speed observation data from seven wind observation towers in the Guangdong coastal area, and the results showed that satellite data assimilation with the WRF model can significantly reduce the root-mean-square error (RMSE) and improve the index of agreement (IA) and correlation coefficient (R). In different months and at different height layers (10, 50, and 70 m), the Root-Mean-Square Error (RMSE) can be reduced by a range of 0–0.8 m/s from 2.5–4 m/s of the original results, the IA can be increased by a range of 0–0.2 from 0.5–0.8 of the original results, and the R can be increased by a range of 0–0.3 from 0.2–0.7 of the original results. The results of the wind speed Weibull distribution show that, after data assimilation was used, the WRF model was able to simulate the distribution of wind speed more accurately. Based on the numerical simulation, our work proposes a combined wind resource evaluation approach of numerical modeling and data assimilation, which will benefit the wind power assessment of wind farms.

scholarly journals The Analysis of the Spatiotemporal Variations and Mechanisms for the Near-Surface Wind Speed Over China in the Last 40 Years

2021 ◽  
Author(s):  
Xia Li ◽  
Yongjie Pan ◽  
Yingsha Jiang
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
The Other ◽  
Strong Wind ◽  
Reanalysis Dataset ◽  
Near Surface ◽  
Wind Speeds ◽  
Dispersion Degree ◽  
Weak Wind

Abstract Near-surface wind speed is of great significance in many aspects of the human production and living. This study analyses the spatiotemporal characteristics of the near-surface wind speed and wind speed percentiles with meteorological station observations in China from 1979 to 2019. Furthermore, the mechanisms of the wind speed variations are also investigated with ERA-Interim reanalysis dataset. Spatially, the wind speeds in the northern and eastern regions of China are larger than that in the central and southern regions. Seasonally, the wind speed in spring is significantly larger than that in the other seasons. The dispersion degree of wind speed in spring is larger than that in the other seasons both spatially and temporally. The near-surface wind speed in China shows significantly decreasing trends during 1979–2019, particularly in 1979–1999, but the wind speed trend reversed after 2000. After dividing the wind speed into different percentiles, it recognizes that the decreasing trend of stronger winds are more significant than that of weaker winds. The weaker the wind speed, the more significant increasing trend after 2000. Therefore, the decreasing wind speed trend before 2000 is mainly caused by the significant reduction of strong wind, while the reversal trend after 2000 results from the increase of weak wind. The variations of the wind speed over China attributed to both the U and V wind components, and the variations of zonal wind is closely related to the weakened upper westerly wind field and the uneven warming between high and low latitudes.

scholarly journals Impact of Satellite Data Assimilation in Atmospheric Reanalysis on the Marine Wind and Wave Climate

2016 ◽  
Vol 29 (17) ◽  
pp. 6351-6361 ◽  
Author(s):  
Wataru Sasaki
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Southern Ocean ◽  
Satellite Data ◽  
North Pacific ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Surface Winds ◽  
Near Surface ◽  
The Impact

Abstract This study investigated the impact of assimilating satellite data into atmospheric reanalyses on trends in ocean surface winds and waves. Two experiments were performed using a numerical wave model forced by near-surface winds: one derived from the Japanese 55-year Reanalysis (JRA-55; experiment A) and the other derived from JRA-55 using assimilated conventional observations only (JRA-55C; experiment B). The results showed that the satellite data assimilation reduced upward trends of the annual mean of wave energy flux (WEF) in the midlatitude North Pacific and southern ocean (30°–60°S), south of Australia, from 1959 to 2012. It was also found that the assimilation of scatterometer winds reduced the near-surface wind speed in the midlatitude North Pacific after the mid-1990s, which resulted in the reduced trend in WEF from 1959 to 2012. By contrast, assimilation of the satellite radiances for 1973–94 increased near-surface wind speed in the southern ocean, south of Australia, whereas the assimilation of the scatterometer winds after the mid-1990s reduced wind speed. The latter led to the reduced trend in WEF south of Australia from 1959 to 2012.

Recurrence Characteristics Analysis of Near-Surface Wind Speed Signal with Different Sampling Frequencies

2014 ◽  
Vol 599-601 ◽  
pp. 1605-1609 ◽  
Author(s):  
Ming Zeng ◽  
Zhan Xie Wu ◽  
Qing Hao Meng ◽  
Jing Hai Li ◽  
Shu Gen Ma
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Recurrence Plot ◽  
Sampling Frequency ◽  
Near Surface ◽  
Gas Leakage ◽  
Time Period ◽  
Characteristics Analysis ◽  
Quantification Analysis

The wind is the main factor to influence the propagation of gas in the atmosphere. Therefore, the wind signal obtained by anemometer will provide us valuable clues for searching gas leakage sources. In this paper, the Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) are applied to analyze the influence of recurrence characteristics of the wind speed time series under the condition of the same place, the same time period and with the sampling frequency of 1hz, 2hz, 4.2hz, 5hz, 8.3hz, 12.5hz and 16.7hz respectively. Research results show that when the sampling frequency is higher than 5hz, the trends of recurrence nature of different groups are basically unchanged. However, when the sampling frequency is set below 5hz, the original trend of recurrence nature is destroyed, because the recurrence characteristic curves obtained using different sampling frequencies appear cross or overlapping phenomena. The above results indicate that the anemometer will not be able to fully capture the detailed information in wind field when its sampling frequency is lower than 5hz. The recurrence characteristics analysis of the wind speed signals provides an important basis for the optimal selection of anemometer.

A preliminary study on the influence of Beijing urban spatial morphology on near-surface wind speed

Urban Climate ◽  
2020 ◽  
Vol 34 ◽  
pp. 100703
Author(s):  
Yonghong Liu ◽  
Yongming Xu ◽  
Fangmin Zhang ◽  
Wenjun Shu
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Near Surface ◽  
Spatial Morphology ◽  
Preliminary Study

scholarly journals Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 738 ◽  
Author(s):  
Wenqing Xu ◽  
Like Ning ◽  
Yong Luo
Keyword(s):  
Wind Speed ◽  
Decision Tree ◽  
Wind Power ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Gradient Boosting ◽  
Post Processing ◽  
Near Surface ◽  
Wind Power Forecasting ◽  
Numerical Weather

With the large-scale development of wind energy, wind power forecasting plays a key role in power dispatching in the electric power grid, as well as in the operation and maintenance of wind farms. The most important technology for wind power forecasting is forecasting wind speed. The current mainstream methods for wind speed forecasting involve the combination of mesoscale numerical meteorological models with a post-processing system. Our work uses the WRF model to obtain the numerical weather forecast and the gradient boosting decision tree (GBDT) algorithm to improve the near-surface wind speed post-processing results of the numerical weather model. We calculate the feature importance of GBDT in order to find out which feature most affects the post-processing wind speed results. The results show that, after using about 300 features at different height and pressure layers, the GBDT algorithm can output more accurate wind speed forecasts than the original WRF results and other post-processing models like decision tree regression (DTR) and multi-layer perceptron regression (MLPR). Using GBDT, the root mean square error (RMSE) of wind speed can be reduced from 2.7–3.5 m/s in the original WRF result by 1–1.5 m/s, which is better than DTR and MLPR. While the index of agreement (IA) can be improved by 0.10–0.20, correlation coefficient be improved by 0.10–0.18, Nash–Sutcliffe efficiency coefficient (NSE) be improved by −0.06–0.6. It also can be found that the feature which most affects the GBDT results is the near-surface wind speed. Other variables, such as forecast month, forecast time, and temperature, also affect the GBDT results.

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