Wind energy, as a vital renewable energy source, also plays a significant role in reducing carbon emissions and mitigating climate change. It is therefore of utmost necessity to evaluate ocean wind energy resources for electricity generation and environmental management. Ocean wind distribution around the globe can be obtained from satellite observations to compensate for limited in situ measurements. However, previous studies have largely ignored uncertainties in ocean wind energy resources assessment with multiple satellite data. It is against this background that the current study compares mean wind speeds (MWS) and wind power densities (WPD) retrieved from scatterometers (QuikSCAT, ASCAT) and radiometers (WindSAT) and their different combinations with National Data Buoy Center (NDBC) buoy measurements at heights of 10 m and 100 m (wind turbine hub height) above sea level. Our results show an improvement in the accuracy of wind resources estimation with the use of multiple satellite observations. This has implications for the acquisition of reliable data on ocean wind energy in support of management policies.

The development of machine learning (ML) provides new means and methods for accurate climate analysis and prediction. This study focuses on summer precipitation prediction using ML algorithms. Based on BCC CSM1.1, ECMWF SEAS5, NCEP CFSv2, and JMA CPS2 model data, we conducted a multi-model ensemble (MME) prediction experiment using three tree-based ML algorithms: the decision tree (DT), random forest (RF), and adaptive boosting (AB) algorithms. On this basis, we explored the applicability of ML algorithms for ensemble prediction of seasonal precipitation in China, as well as the impact of different hyperparameters on prediction accuracy. Then, MME predictions based on optimal hyperparameters were constructed for different regions of China. The results showed that all three ML algorithms had an optimal maximum depth less than 2, which means that, based on the current amount of data, the three algorithms could only predict positive or negative precipitation anomalies, and extreme precipitation was hard to predict. The importance of each model in the ML-based MME was quantitatively evaluated. The results showed that NCEP CFSv2 and JMA CPS2 had a higher importance in MME for the eastern part of China. Finally, summer precipitation in China was predicted and tested from 2019 to 2021. According to the results, the method provided a more accurate prediction of the main rainband of summer precipitation in China. ML-based MME had a mean ACC of 0.3, an improvement of 0.09 over the weighted average MME of 0.21 for 2019–2021, exhibiting a significant improvement over the other methods. This shows that ML methods have great potential for improving short-term climate prediction.

The rainstorm intensity formula and the design of rainstorm hyetographs are important aspects in drainage design standards. Against the backdrop of climate change, in most cities in Jiangsu Province of China, significant trends of increasing intensities of heavy rainfall are apparent. The parameters of the rainstorm intensity formula are no longer applicable in the current context of significantly stronger rainstorms. To adapt to this change, we first contrasted the fitting accuracy of the Gumbel distribution, the exponential distribution, and the Pearson III distribution for the rainstorm intensity formula in Jiangsu. It was found that the Gumbel distribution has the smallest relative mean square error in most cities, proving that it provides the best estimation of rainstorm intensity formula parameters. Therefore, the rainstorm intensity formula parameters for 13 cities was revised using the Gumbel distribution based on the rainfall data from 1991 to 2020. Then, the precipitation with a 100-year return period was calculated using the revised formula. Moreover, to compensate for the lack of storm hyetographs that have been designed for Jiangsu, we designed short-duration rainstorm hyetographs for 13 cities using the Chicago hyetograph method and the Pilgrim and Cordery (PC) method. The results show that most of the short-duration rainstorms lasted between 45 and 120 min and were dominated by single-peaked patterns, with the peak position typically occurring in the first half of the rainfall cycle. The peak coefficient distribution of short-duration rainstorms shows that short-duration rainstorms in the south reached their peak rainfall intensity earlier than those in the north. On this basis, using the Chicago method and PC method, short-duration storm hyetographs were designed, which could be used in the design of drainage systems to provide support in effectively reducing urban flood threats. By comparing the hyetographs with real short-duration rainstorm patterns, it was found that the precipitation process designed using the PC method was most similar to the actual precipitation process. However, the PC method was found to be highly dependent on local precipitation data, whereas only the rain peak coefficient is required to design the Chicago rainstorm hyetograph. Therefore, we primarily recommend hyetographs designed using the PC method for Jiangsu Province’s 13 major cities, while we recommend the Chicago hyetograph for the surrounding areas of the 13 cities that have no meteorological stations or lack data.