/pdf/short-term-nacelle-orientation-forecasting-using-bilinear-1rhbmmxudz.pdf

Short-Term Nacelle Orientation Forecasting Using Bilinear Transformation and ICEEMDAN Framework

Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks

A novel machine learning-based electricity price forecasting model based on optimal model selection strategy

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Short-term wind speed forecasting using recurrent neural networks with error correction

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: 13 Aerosols are a complex compound with a great effect on the global radiation 14 balance and climate system even human health, and concurrently are a large uncertain 15 source in the numerical simulation process. The arid and semi-arid area has a fragile 16 ecosystem, with abundant dust, but lacks related aerosol data or data accuracy. To solve 17 these problems, we use the bagging trees ensemble model, based on 1 km aerosol 18 optical depth (AOD) data and multiple environmental covariates, to produce monthly 19 advanced-performance, full-coverage, and high-resolution (250 m) AOD products 20 (named FEC AOD, Fusing Environmental Covariates AOD) in the arid and semi-arid 21 areas. Then, based on FEC AOD, we analyzed the spatiotemporal pattern of AOD and 22 further discussed the interpretation of environmental covariates to AOD. The result 23 shows that the bagging trees ensemble model has a good performance, with its 24 verification R 2 always keeping at 0.90 and the R 2 being 0.79 for FEC AOD compared 25 with AERONET. The high AOD areas are located in the Taklimakan Desert and the 26 Loess Plateau, and the low AOD area is concentrated in the south of Qinghai province. 2 Taklimakan Desert, while the AOD in the southern Qinghai province almost shows no 30 significant change between 2000 and 2019. The annual variation characteristics present 31 that AOD is the largest in spring (0.267) and the smallest in autumn (0.147); the AOD 32 pattern in Gansu province is bimodal, but unimodal in other provinces. The farmland 33 and construction land are at high AOD levels compared with other land cover types. 34 The meteorological factors demonstrate a maximum interpretation of AOD on all set 35 temporal scales, followed by the terrain factors, and the surface properties are the 36 smallest, i.e., 77.1%, 59.1%, and 50.4% respectively on average. The capability of the 37 environmental covariates for explained AOD varies with season, with an sequence 38 being winter (86.6%) > autumn (80.8%) > spring (79.9%) > summer (72.5%). In this 39 research, we pathbreakingly provide high spatial resolution (250 m) and long time 40 series (2000-2019) FEC AOD dataset in arid and semi-arid regions to support the 41 atmosphere and related study in northwest China, with the full data available at 42 Network ground observation the and MxD08 AOD satellite products collected accuracy FEC AOD; spatiotemporal change is analyzed; environmental of FEC AOD

Full-coverage 250 m monthly aerosol optical depth dataset 1 (2000-2019) emended with environmental covariates by the 2 ensemble machine learning model over the arid and semi-arid 3 areas, NW China

Under the background of global warming and excessive human activities, much surface water in drylands is experiencing rapid degradation or shrinkage in recent years. The shrinkage of surface water, especially the degradation of lakes and their adjacent wetlands in drylands, may lead to the emergence of new salt dust storm hotspots, which causes greater danger. In this paper, based on high spatial resolution global surface water (GSW) and multiangle implementation of atmospheric correction (MAIAC) AOD data, we systematically analyze the dynamic characteristics of surface water and aerosols in typical drylands (Central Asia, CA) between 2000 and 2018. Simultaneously, combined with auxiliary environment variables, we explore the driving mechanisms of surface water on the regional salt/sand aerosols on different spatial scales. The results show that the seasonal surface water features an increasing trend, especially a more dramatic increase after 2015, and the permanent surface water indicates an overall decrease, with nearly 54.367 % at risk of receding and drying up. In typical lakes (Aral Sea and Ebinur Lake), the interannual change feature of the surface water area (WA) is that a continuous decrease during the study period occurs in Aral Sea area, yet a significant improvement has occurred in Ebinur Lake after 2015, and the degradation of Ebinur Lake takes place later and its recovery earlier than Aral Sea. The aerosol optical depth (AOD) in CA shows obvious seasonal variation, with the largest in spring (0.192 ± 0173), next in summer (0.169 ± 0.106), and the smallest in autumn (0.123 ± 0.065). The interannual variation of AOD exhibits an increase from 2000 to 2018 in CA, with high AOD areas mainly concentrated in the Taklamakan Desert and some lake beds resulting from lake degradation, including Aral Sea and Ebinur Lake. The AOD holds a similar trend between Aral Sea and Ebinur Lake on an interannual scale. And the AOD over Ebinur Lake is lower than that over Aral Sea in magnitude and lags behind in reaching the peak compared with Aral Sea. The WA change can significantly affect aerosol variation directly or indirectly on the aerosol load or mode size, but there are obvious differences in the driving mechanisms, acting paths, and influence magnitude of WA on aerosols on different spatial scales. In addition, the increase of WA can significantly directly suppress the increase of Ångström exponent (AE), and the effects of WA on AOD are realized majorly by an indirect approach. From the typical lake perspective, the effects of WA on aerosol in Aral Sea are achieved via an indirect path; and the decrease of WA can indirectly promote the AOD rise, and directly stimulate the AE growth in Ebinur Lake. 

Jikai Duan

Papers

Short-term wind speed forecasting using recurrent neural networks with error correction

A combined short-term wind speed forecasting model based on CNN-RNN and linear regression optimization considering error

A multistep short-term solar radiation forecasting model using fully convolutional neural networks and chaotic aquila optimization combining WRF-Solar model results

Full-coverage 250 m monthly aerosol optical depth dataset 1 (2000-2019) emended with environmental covariates by the 2 ensemble machine learning model over the arid and semi-arid 3 areas, NW China

Surface water and aerosol spatiotemporal dynamics and influence mechanisms over drylands