Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence. To capture the spatial and temporal dependence simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is in combination with the graph convolutional network (GCN) and gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures to capture spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data to capture temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://github.com/lehaifeng/T-GCN.

[1] We perform an observationally based evaluation of the cloud ice water content (CIWC) and path (CIWP) of present-day GCMs, notably 20th century CMIP5 simulations, and compare these results to CMIP3 and two recent reanalyses. We use three different CloudSat + CALIPSO ice water products and two methods to remove the contribution from the convective core ice mass and/or precipitating cloud hydrometeors with variable sizes and falling speeds so that a robust observational estimate can be obtained for model evaluations. The results show that for annual mean CIWP, there are factors of 2-10 in the differences between observations and models for a majority of the GCMs and for a number of regions. However, there are a number of CMIP5 models, including CNRM-CM5, MRI, CCSM4 and CanESM2, as well as the UCLA CGCM, that perform well compared to our past evaluations. Systematic biases in CIWC vertical structure occur below the mid-troposphere where the models overestimate CIWC, with this bias arising mostly from the extratropics. The tropics are marked by model differences in the level of maximum CIWC ($250-550 hPa). Based on a number of metrics, the ensemble behavior of CMIP5 has improved considerably relative to CMIP3, although neither the CMIP5 ensemble mean nor any individual model performs particularly well, and there are still a number of models that exhibit very large biases despite the availability of relevant observations. The implications of these results on model representations of the Earth radiation balance are discussed, along with caveats and uncertainties associated with the observational estimates, model and observation representations of the precipitating and cloudy ice components, relevant physical processes and parameterizations. F., et al. (2012), An observationally based evaluation of cloud ice water in CMIP3 and CMIP5 GCMs and contemporary reanalyses using contemporary satellite data,

The tree of life is highly reticulate, with the history of population divergence emerging from populations of gene phylogenies that reflect histories of introgression, lineage sorting and divergence. In this study, we investigate global patterns of oak diversity and test the hypothesis that there are regions of the oak genome that are broadly informative about phylogeny.We utilize fossil data and restriction-site associated DNA sequencing (RAD-seq) for 632 individuals representing nearly 250 Quercus species to infer a time-calibrated phylogeny of the world's oaks. We use a reversible-jump Markov chain Monte Carlo method to reconstruct shifts in lineage diversification rates, accounting for among-clade sampling biases. We then map the > 20 000 RAD-seq loci back to an annotated oak genome and investigate genomic distribution of introgression and phylogenetic support across the phylogeny.Oak lineages have diversified among geographic regions, followed by ecological divergence within regions, in the Americas and Eurasia. Roughly 60% of oak diversity traces back to four clades that experienced increases in net diversification, probably in response to climatic transitions or ecological opportunity.The strong support for the phylogeny contrasts with high genomic heterogeneity in phylogenetic signal and introgression. Oaks are phylogenomic mosaics, and their diversity may in fact depend on the gene flow that shapes the oak genome.

[1] A profiling retrieval algorithm for ice cloud properties, such as effective radius (r e ), ice water content (IWC), and an extinction coefficient, has been developed to use combined CloudSat radar reflectivity factor (Ze) and CALIPSO attenuated backscattering coefficient measurements based on an optimal estimation framework. Developed as an operational standard data product for the CloudSat project, the algorithm can treat a wide range of ice cloud situations from optically tenuous cirrus in the upper troposphere to geometrically and optically thick anvil clouds. It is designed to consider the attenuation of thick clouds in the radar and lidar forward model equations and multiple scattering in the lidar data. An optimal estimation approach allows for inversion of the forward model equations so that the uncertainty due to the assumptions can be evaluated. A sensitivity study shows that lidar multiple scattering has to be accounted for carefully. As for all ice cloud retrieval algorithms, assumptions regarding particle habits and size distribution shapes are critical to the accuracy of the results. The deviation in simulated Ze among different size distribution assumptions is smaller than among different habit assumptions, which indicates that the uncertainty due to particle habits is larger than the size distribution assumption. Those uncertainties are included in the forward model error covariance matrix to analyze the retrieval error. The algorithm is applied to CloudSat-CALIPSO data as well as lidar and radar data collected by the ER-2 during the Tropical Composition, Cloud and Climate Coupling Experiment mission on 22 July 2007. The retrieved r e , IWC, and extinction are shown to compare favorably with coincident in situ measurements collected by instruments on the NASA DC-8. This algorithm is expected to be complementary to the set of standard data products that is already being produced by the CloudSat project.

In this study several ice cloud retrieval products that utilize active and passive A-Train measurements are evaluated using in situ data collected during the Small Particles in Cirrus (SPARTICUS) field campaign. The retrieval datasets include ice water content (IWC), effective radius r e , and visible extinction s from CloudSat level-2C ice cloud property product (2C-ICE), CloudSat level-2B radar-visible optical depth cloud water content product (2B-CWC-RVOD), radar-lidar (DARDAR), and s from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). When the discrepancies between the radar reflectivity Z e derived from 2D stereo probe (2D-S) in situ measurements and Z e measured by the CloudSat radar are less than 10 dBZ e , the flight mean ratios of the retrieved IWC to the IWC estimated from in situ data are 1.12, 1.59, and 1.02, respectively for 2C-ICE, DARDAR, and 2B-CWC-RVOD. For r e , the flight mean ratios are 1.05, 1.18, and 1.61, respectively. For s, the flight mean ratios for 2C-ICE, DARDAR, and CALIPSO are 1.03, 1.42, and 0.97, respectively. The CloudSat 2C-ICE and DARDAR retrieval products are typically in close agreement. However, the use of parameterized radar signals in ice cloud volumes that are below the detection threshold of the CloudSat radar in the 2C-ICE algorithm provides an extra constraint that leads to slightly better agreement with in situ data. The differences in assumed mass-size and area-size relations between CloudSat 2C-ICE and DARDAR also contribute to some subtle difference between the datasets: r e from the 2B-CWC-RVOD dataset is biased more than the other retrieval products and in situ measurements by about 40%. A slight low (negative) bias in CALIPSO s may be due to 5-km averaging in situations in which the cirrus layers have significant horizontal gradients in s.