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To meet the requirement of the wind energy resource assessment and utilization, the statistical characteristics of wind climate in the China mainland were analyzed in this study by using observation wind dataset for 1971–2017 from the China Meteorological Administration (CMA). A series of key factors closely related to wind power development were analyzed, systematically including the spatial and temporal characteristic of wind speed, the distribution of wind speed at different density grades, the density of the cumulative distribution, strong wind days and weak wind days. The characteristics of wind patterns in different regions are also compares. Results indicate that: (1) the regions with the highest wind speed in China centrally locate in Northwest China, North China and Northeast China with the highest wind speed occurs in spring. The annual variation of wind speed in northern China is bimodal while in southern China it is unimodal. (2) The wind speed probability density distribution shows a significant spatial–temporal difference. The wind speed probability distribution is the most concentrated in spring for almost all regions. For each season, the concentrated and stable wind speed occurs in Xinjiang region in spring. While it appears in the region from Northwest China to the northern part of North China for summer and autumn, and in the southern part of North China and Central China for winter. In the case of equal cumulative probability, the highest wind speed locates in Northeast China and the lowest in Sichuan Basin. (3) For 1971–2017, the wind speed in all regions of China showed a decreasing trend on the whole. But this trend slowed down since 1990s. It is​ worth noting that the wind speed in some regions even increased since 2000. (4) The numbers of strong wind day have been decreasing in all regions, but the numbers of weak wind day demonstrate an evolutionary feature of a growth followed by decrease. In most regions, the numbers of weak wind day reached the maximum in the vicinity of 2000 and then decreased rapidly.

Wind energy is a fluctuating source for power systems, which poses challenges to grid planning for the wind power industry. To improve the short-term wind forecasts at turbine height, the bias correction approach Kalman filter (KF) is applied to 72-h wind speed forecasts from the WRF model in Zhangbei wind farm for a period over two years. The KF approach shows a remarkable ability in improving the raw forecasts by decreasing the root-mean-square error by 16% from 3.58 to 3.01 m s−1, the mean absolute error by 14% from 2.71 to 2.34 m s−1, the bias from 0.22 to − 0.19 m s−1, and improving the correlation from 0.58 to 0.66. The KF significantly reduces random errors of the model, showing the capability to deal with the forecast errors associated with physical processes which cannot be accurately handled by the numerical model. In addition, the improvement of the bias correction is larger for wind speeds sensitive to wind power generation. So the KF approach is suitable for short-term wind power prediction.

Background Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is threatening the world with the symptoms of seasonal influenza. This study was conducted to investigate the patient characteristics and clinical value of blood markers to assess the severity of coronavirus disease 2019 (COVID-19). Methods 187 patients, diagnosed with COVID-19 (non-severe and severe cases) and admitted to hospital between January 27th and March 8th of 2020, were enrolled in the present study. Results A higher proportion of clinical symptoms, including cough, expectoration, myalgia, and fatigue were observed in the non-severe group. The level of white blood cell count, neutrophils, CRP, IL-6 and IL-8 were significantly increased, while the platelet count was remarkedly decreased in the severe group. The risk model based on lymphocyte, IL-6, IL-8, CRP and platelet counts had the highest area under the receiver operator characteristic curve (AUROC). The baseline of IL-6, IL-8 and CRP was positively correlated with other parameters except in the cases of lymphocyte, hemoglobin and platelet counts. The baseline of the platelet count was negatively correlated with other parameters except in the lymphocyte and hemoglobin counts. Additionally, there was no connection between the severity of COVID-19 and cultures of blood, sputum or catheter secretion. Conclusions The present study suggested that high leucocyte and low platelets counts were independent predictive markers of the severity of COVID-19.