As the largest hydroelectric project worldwide, previous studies indicate that the Three Gorges Dam (TGD) affects the local climate because of the changes of hydrological cycle caused by the ...impounding and draining of the TGD. However, previous studies do not analyze the long-term precipitation changes before and after the impoundment, and the variation characteristics of local precipitation remain elusive. In this study, we use precipitation anomaly data derived from the CN05.1 precipitation dataset between 1988 and 2017 to trace the changes of precipitation before and after the construction of the TGD (i.e., 1988–2002 and 2003–2017), in the Three Gorges Reservoir Area (TGRA). Results showed that the annual and dry season precipitation anomaly in the TGRA presented an increasing trend, and the precipitation anomaly showed a slight decrease during the flood season. After the impoundment of TGD, the precipitation concentration degree in the TGRA decreased, indicating that the precipitation became increasingly uniform, and the precipitation concentration period insignificantly increased. A resonance phenomenon between the monthly average water level and precipitation anomaly occurred in the TGRA after 2011 and showed a positive correlation. Our findings revealed the change of local precipitation characteristics before and after the impoundment of TGD and showed strong evidence that this change had a close relationship with the water level.
Geospatial three-dimensional (3D) raster data have been widely used for simple representations and analysis, such as geological models, spatio-temporal satellite data, hyperspectral images, and ...climate data. With the increasing requirements of resolution and accuracy, the amount of geospatial 3D raster data has grown exponentially. In recent years, the processing of large raster data using Hadoop has gained popularity. However, data uploaded to Hadoop are randomly distributed onto datanodes without consideration of the spatial characteristics. As a result, the direct processing of geospatial 3D raster data produces a massive network data exchange among the datanodes and degrades the performance of the cluster. To address this problem, we propose an efficient group-based replica placement policy for large-scale geospatial 3D raster data, aiming to optimize the locations of the replicas in the cluster to reduce the network overhead. An overlapped group scheme was designed for three replicas of each file. The data in each group were placed in the same datanode, and different colocation patterns for three replicas were implemented to further reduce the communication between groups. The experimental results show that our approach significantly reduces the network overhead during data acquisition for 3D raster data in the Hadoop cluster, and maintains the Hadoop replica placement requirements.
Accurate PM2.5 concentration prediction is crucial for protecting public health and improving air quality. As a popular deep learning model, deep belief network (DBN) for PM2.5 concentration ...prediction has received increasing attention due to its effectiveness. However, the DBN structure parameters that have a significant impact on prediction accuracy and computation time are hard to be determined. To address this issue, a modified grey wolf optimization (MGWO) algorithm is proposed to optimize the DBN structure parameters containing number of hidden nodes, learning rate, and momentum coefficient. The methodology modifies the basic grey wolf optimization (GWO) algorithm using the nonlinear convergence and position update strategies, and then utilizes the training error of the DBN to calculate the fitness function of the MGWO algorithm. Through the multiple iterations, the optimal structure parameters are obtained, and a suitable predictor is finally generated. The proposed prediction model is validated on a real application case. Compared with the other prediction models, experimental results show that the proposed model has a simpler structure but higher prediction accuracy.
Thunderstorms are usually accompanied by heavy precipitation and other disasters. Therefore, it is very important to study the thermodynamic and dynamic mechanisms that control the occurrence and ...development of storms. However, the relatively coarse resolution of the reanalysis may limit the evaluation and application of its derived convective parameters at regional and local scales, especially in high mountains. Given that the effect of grid averaging on ERA5 can be well-reflected in the monthly mean, the paper proposes a methodological framework to achieve the statistical downscaling of convective parameters (SDCP). After the convection-related variables are decomposed, we introduce a multi-scale geographically weighted regression (MGWR) model and take the Normalized Difference Vegetation Index (NDVI) and the Digital Elevation Model (DEM) as explanatory variables to improve the spatial resolution of monthly-scale convective parameters. The convective parameters chosen for the downscaling experiments are closely related to thunderstorm occurrence, involving instability, moisture, and wind shear. ERA5 and SDCP datasets’ quality is evaluated based on measurements available from 11 radiosonde stations over 10 years. We also explore the climatology of thunderstorm environments and the corresponding trends. The results show that the accuracy of the SDCP data obtains additional added value in complex-terrain areas. Compared with ERA5, the downscaled results have a finer spatial structure. A strong but spatially and scale-varying relationship exists between the monthly mean convective parameter and the resampled NDVI and DEM. Considering the very high predictive ability of the MGWR model, the performance of the SDCP algorithm mainly depends on the accuracy of the ERA5 data. Our study demonstrates the possibility of high spatial resolution land surface features observed by remote sensing satellites in improving the spatial resolution of thermodynamic parameters.
Retrieving thunderstorm activity through specific thermodynamic and kinematic parameters is paramount for predicting deep convective weather and investigating long‐term climatology of storm. However, ...the reliability of the relationship between parameters and convective events is restricted by the modelling methods and sampling of thunderstorm activity. There is no objective definition of a thunderstorm, so the clustering method is applied to the cloud‐to‐ground (CG) lightning stroke data in Central China to identify lightning clusters. These clusters are then gridded and associated with environmental variables derived from ERA5 reanalysis. Finally, machine learning (ML) technologies are applied to model the occurrence of thunderstorms. In addition, ERA5 is also evaluated. The parameters related to moisture and lapse rate calculated by ERA5 are close to sounding measurements, such as Td850, PW, LR700_400 and KI, whose correlation coefficients exceed 0.90. ERA5 has a good estimation of some parameters that are susceptible to the influence of the boundary layer. Compared with the lightning strike‐based scheme, our scheme obtains the best performance index values. An agreement between observations and predictions based on lightning clusters is also evident from the diurnal cycle of thunderstorm probabilities. Although thunderstorm activity on complex terrain is underestimated, the created ML model can explain 61.4% of the variance in the observed frequency. The results of the significance test reveal that there are statistically significant differences between the soundings corresponding to some isolated CG strikes and the thunderstorm class, but the distribution is the same as that of the non‐thunderstorm class. Solar radiation, topographic features and lake distribution play a major role in promoting the occurrence and development of thunderstorms.
The diagram illustrates the feasibility of using ERA5, lightning clusters and machine learning to model deep convective activity climatology. We focused on the difference between the lightning strike‐based modeling scheme and the clustering scheme, and the performance of ERA5 in Central China was evaluated. The Mann–Whitney–Wilcoxon test demonstrated that the soundings/model soundings associated with lightning clusters can well represent thunderstorm environments.
PDF
