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Atmospheric evaporative demand is a key metric for monitoring agricultural drought. Existing ways of estimating evaporative demand in drought indices do not faithfully represent the constraints imposed by land surface characteristics and become less accurate over nonuniform land surfaces. This study proposes incorporating surface vegetation characteristics, such as vegetation dynamics data, aerodynamic parameters, and physiological parameters, into existing potential-evapotranspiration (PET) methods. This approach is implemented across the continental United States (CONUS) for the period from 1981–2017 and is tested using a recently developed drought index, the Standardized Precipitation–Evapotranspiration Index (SPEI). We show that activating realistic maximum surface conductance and aerodynamic conductance could improve the prediction of soil moisture dynamics and drought impacts by 29 %–41 % on average compared to more simple, widely used methods. We also demonstrate that this is especially effective in forests and humid regions, with improvements of 86 %–89 %. Our approach only requires a minimal amount of ancillary data while allowing for both historical reconstruction and real-time drought forecasting. This offers a physically meaningful yet easy-to-implement way to account for vegetation control in drought indices.

Stable water isotopes are natural tracers in the hydrological cycle and have been applied in hydrology, atmospheric science, ecology, and paleoclimatology. However, the factors controlling the isotopic distribution, both at spatial and temporal scales, are debated in low and middle latitude regions, due to the significant influence of large-scale atmospheric circulation and complex sources of water vapor. For the first time, we made in situ observations of near-surface vapor isotopes over a large region (over 10 000 km) across China in both pre-monsoon and monsoon seasons, using a newly designed vehicle-based vapor isotope monitoring system. Combined with daily and multiyear monthly mean outputs from the isotope-incorporated global spectral model (Iso-GSM) and infrared atmospheric sounding interferometer (IASI) satellite to calculate the relative contribution, we found that the observed spatial variations in both periods represent mainly seasonal mean spatial variations, but are influenced by more significant synoptic-scale variations during the monsoon period. The spatial variations of vapor δ18O are mainly controlled by Rayleigh distillation along air mass trajectories during the pre-monsoon period, but are significantly influenced by different moisture sources, continental recycling processes, and convection during moisture transport in the monsoon period. Thus, the North–South gradient observed during the pre-monsoon period is counteracted during the monsoon period. The seasonal variation of vapor δ18O reflects the influence of the summer monsoon convective precipitation in southern China and a dependence on temperature in the North. The spatial and seasonal variations in d-excess reflect the different moisture sources and the influence of continental recycling. Iso-GSM successfully captures the spatial distribution of vapor δ18O during the pre-monsoon period, but the performance is weaker during the monsoon period, maybe due to the underestimation of local or short-term high-frequency synoptic variations. These results provide an overview of the spatial distribution and seasonal variability of water isotopic composition in East Asia and their controlling factors, and they emphasize the need to interpret proxy records in the context of the regional system.

Complex features of transonic flow over a turret make it challenging to use passive flow control to reduce aero-optical effects. In this study, the influence of different flow features on wavefront distortions is numerically investigated through improved delayed detached eddy simulation coupled with a modified sub-grid scale. The proper orthogonal decomposition (POD) method is used to study the spatiotemporal characteristics of the flow features. The flow field changes in the wake along with the motion of the shock. Two features, namely, lateral shift (dominant in modes 1 and 2) and wall-normal fluctuation (dominant in modes 3 and 4) of the wake, are the most dominant in the flow field. All beams share the common feature of transmitting the flow field, in which a large component of optical path difference (OPD) appears at St=0.35, indicating the high impact of wall-normal fluctuation on the distortion of the wavefront. After the different POD modes, which contain 30% of the mode energy, are removed, all beams transmitted through different reconstructed fields show very different features for OPD. The flow features that do not exhibit higher-order modes from modes 21 to 92 affect the OPD slightly, as the OPD components in the low-St region are almost unchanged. With the removal of modes from 3 to 32, wavefront distortions are considerably reduced, particularly at St=0.35. The wavefront distortions are most reduced after the lower order modes from 1 to 20 are removed, as the components of OPD in the low-St region are dramatically reduced. The significant relations between OPD and the flow features reveal that controlling the dominant flow features has significant potential for reducing aero-optical effects.

The shortage of water resources and the decline in soil organic matter (SOM) are critical limiting factors affecting the improvement in rice productivity, while alternate wetting and drying (AWD) irrigation and recycling application of rice straw (S) are considered favorable mitigation measures. However, the impact of such measures on rice yield and greenhouse gas (GHG) emissions, especially nitrous oxide (N2O) emissions, needs to be further clarified to ensure that agronomic practices save water, conserve soil, and reduce GHG emissions. Therefore, we explored the effects of mild AWD irrigation combined with on-site rice straw recycling on N2O emissions and rice yield through rice pot experiments. This experiment included 2 irrigation methods (continuous flooding (CF) irrigation and mild AWD irrigation), 2 nitrogen (N) application levels (0 and 225 kg N ha−1) and 2 rice straw return levels (0 and 9000 kg ha−1), for a total of 10 treatments, and each treatment had 3 replicates. The 15N-urea and 15N-S were added to the soil. The results showed that N2O emissions were primarily affected by urea application and irrigation methods, with urea application being most important. Compared with CF irrigation, mild AWD irrigation increased cumulative N2O emissions, with an average increase of 28.8 %. In addition, adding rice straw to mild AWD irrigation further stimulated N2O emissions by 18.1 %. Under the condition of urea application, compared with CF irrigation, mild AWD irrigation increased the yield-scaled N2O emissions by 17.9 %, and the addition of rice straw further promoted the yield-scaled N2O emissions under mild AWD irrigation by 17.4 % but reduced the global warming potential (GWP) (methane (CH4) + N2O) by 62.9 %. Under the condition of urea application, compared with CF irrigation, mild AWD irrigation reduced the uptake of soil-derived N and aboveground biomass of rice but did not reduce rice yield. Therefore, mild AWD irrigation combined with rice straw return may be a promising agronomic method to maintain rice yield, reduce GHGs, and protect or improve soil fertility.

The physical and heat limits of semiconductor technology require the adaptation of heterogeneous architectures in supercomputers, such as graphics processing units (GPUs) with many-core accelerators and many-core processors with management and computing cores, to maintain a continuous increase in computing performance. The transition from homogeneous multi-core architectures to heterogeneous many-core architectures can produce non-bit-for-bit reproducibility that leads to numerical perturbations and uncertainties in simulation results, which could blend with errors due to coding bugs. The development of a methodology to identify computational perturbations and secure model correctness is a critically important step in model development on computer systems with new architectures. Thus, we have developed a methodology to characterize the uncertainties in the heterogeneous many-core computing environment. This methodology contains a simple multi-column atmospheric model consisting of typical discontinuous physical parameterizations defined by the selection programming structure, an efficient ensemble-based test approach, and an application to the GPU-based high-performance computing (HPC) and Sunway systems. Statistical distributions from ensembles of the heterogeneous systems show quantitative analyses of computational perturbations and acceptable error tolerances. The methodology aims to enable one to fully distinguish between perturbations caused by platforms and discrepancies caused by software bugs, and it provides encouraging references for verifying the reliability of supercomputing platforms and discussing the sensibility of Earth system modeling to the adaptation of new heterogeneous many-core architectures.

Lithium iron phosphate (LiFePO4) with an olivine-type structure has garnered great interest due to its electrochemical activity. It is considered one of the most promising cathode materials for lithium-ion batteries because of its high discharge platform, large theoretical capacity, and excellent cycling performance and stability. However, the typical crystal structure of LiFePO4 restricts the formation of efficient electron conduction pathways and hampers the embedding and removal motion of lithium ions, resulting in low electron conductivity and a low ion diffusion rate. The experimental results show that element doping can reduce the activation energy of diffusion, increase the diffusion rate of lithium ions, improve the rate capability of lithium-ion batteries, and contribute to the improvement of its conductivity. In this work, the electronic structure and diffusion mechanism of a S/Co/Mn co-doped LiFePO4 system are systematically studied. The introduction of S, Co, and Mn atoms leads to the formation of impurity bands, resulting in a significant reduction in the bandgap from 3.593 eV to 0.190 eV, which is beneficial for improving the conductivity of LiFePO4. Moreover, the diffusion activation energy of lithium ions decreases from 0.73 eV to 0.34 eV, and the diffusion rate of lithium ions increases from 2.7 × 109 cm/s to 1.0 × 1016 cm/s. These results demonstrate that co-doping of S, Co, and Mn can effectively enhance the conductivity and diffusion rate of lithium ions in LiFePO4, providing a reliable reference for improving the electrochemical performance of lithium iron phosphate.

Tracer-aided hydrological models integrating water isotope modules into the simulation of runoff generation are useful tools to reduce uncertainty of hydrological modeling in cold basins that are featured by complex runoff processes and multiple runoff components. However, there is little guidance on the strategy of field water sampling for isotope analysis to run tracer-aided hydrological models, which is especially important for large mountainous basins on the Tibetan Plateau (TP) where field water sampling work is highly costly. This study conducted a set of numerical experiments based on the THREW-T (Tsinghua Representative Elementary Watershed - Tracer-aided version) model to evaluate the reliance of the tracer-aided modeling performance on the availability of site measurements of water isotope in the Yarlung Tsangpo river (YTR) basin on the TP. Data conditions considered in the numerical experiments included the availability of glacier meltwater isotope measurement, quantity of site measurements of precipitation isotope, and the variable collecting strategies for stream water samples. Our results suggested that (1) in high-mountain basins where glacier meltwater samples for isotope analysis are not available, estimating glacier meltwater isotope by an offset parameter from the precipitation isotope is a feasible way to force the tracer-aided hydrological model. Using a set of glacier meltwater δ18O that were 2 ‰–9 ‰ lower than the mean precipitation δ18O resulted in only small changes in the model performance and the quantifications of contributions of runoff components (CRCs, smaller than 5 %) to streamflow in the YTR basin. (2) The strategy of field sampling for site precipitation to correct the global gridded isotope product of isoGSM (isotope-incorporated global spectral model) for model forcing should be carefully designed. Collecting precipitation samples at sites falling in the same altitude tends to be worse at representing the ground pattern of precipitation δ18O over the basin than collecting precipitation samples from sites in a range of altitudes. (3) Collecting weekly stream water samples at multiple sites in the wet and warm seasons is the optimal strategy for calibrating and evaluating a tracer-aided hydrological model in the YTR basin. It is highly recommended to increase the number of stream water sampling sites rather than spending resources on extensive sampling of stream water at a sole site for multiple years. These results provide important implications for collecting site measurements of water isotopes for running tracer-aided hydrological models to improve quantifications of CRCs in high-mountain basins.

In deep learning based synthetic aperture radar (SAR) change detection, selecting samples of high quality is a crucial step. In this work, we have proposed a refined sample selection algorithm for unsupervised SAR change detection. The propose and incorporation of volume control factors and multi-hierarchical fuzzy c-means (MH-FCM) algorithm generate samples of large diversity and high confidence, thus satisfying the needs for high quality samples. The method includes two phases: firstly, an enhanced difference image is constructed according to the difference consistency between single pixels and their neighbourhoods, and a triangular threshold segmentation method is then proposed to determine the volume control factors for sample selection. MH-FCM is developed to classify the log mean ratio difference image into 4 classes. Secondly, a dual-channel convolution neural network with an adaptive weighted loss is adopted to learn and predict the input and to obtain the change detection result. Experimental results of the Gaofen-3 dataset in Beijing have validated the effectiveness and usefulness of the proposed method.

Abstract The NCI-MATCH was designed to characterize the efficacy of targeted therapies in histology-agnostic driver mutation-positive malignancies. Sub-protocols F and G were developed to evaluate the role of crizotinib in rare tumors that harbored either ALK or ROS1 rearrangements. Patients with malignancies that progressed following at least one prior systemic therapy were accrued to the NCI-MATCH for molecular profiling, and those with actionable ALK or ROS1 rearrangements were offered participation in sub-protocols F or G, respectively. There were five patients who enrolled on Arm F (ALK) and four patients on Arm G (ROS1). Few grade 3 or 4 toxicities were noted, including liver test abnormalities, and acute kidney injury. For sub-protocol F (ALK), the response rate was 50% (90% CI 9.8–90.2%) with one complete response among the 4 eligible patients. The median PFS was 3.8 months, and median OS was 4.3 months. For sub-protocol G (ROS1) the response rate was 25% (90% CI 1.3–75.1%). The median PFS was 4.3 months, and median OS 6.2 months. Data from 3 commercial vendors showed that the prevalence of ALK and ROS1 rearrangements in histologies other than non-small cell lung cancer and lymphoma was rare (0.1% and 0.4% respectively). We observed responses to crizotinib which met the primary endpoint for ALK fusions, albeit in a small number of patients. Despite the limited accrual, some of the patients with these oncogenic fusions can respond to crizotinib which may have a therapeutic role in this setting.

Issues related to large uncertainty and parameter equifinality have posed big challenges for hydrological modeling in cold regions where runoff generation processes are particularly complicated. Tracer-aided hydrological models that integrate the transportation and fractionation processes of water stable isotope are increasingly used to constrain parameter uncertainty and refine the parameterizations of specific hydrological processes in cold regions. However, the common unavailability of site sampling of spatially distributed precipitation isotopes hampers the practical applications of tracer-aided models in large-scale catchments. This study, taking the precipitation isotope data (isotopes-incorporated global spectral model – isoGSM) derived from the isotopic general circulation models (iGCMs) as an example, explored its utility in driving a tracer-aided hydrological model in the Yarlung Tsangpo River basin (YTR; around 2×105 km2, with a mean elevation of 4875 m) on the Tibetan Plateau (TP). The isoGSM product was firstly corrected based on the biases between gridded precipitation isotope estimates and the limited site sampling measurements. Model simulations driven by the corrected isoGSM data were then compared with those forced by spatially interpolated precipitation isotopes from site sampling measurements. Our results indicated that (1) spatial precipitation isotopes derived from the isoGSM data helped to reduce modeling uncertainty and improve parameter identifiability in a large mountainous catchment on the TP, compared to a calibration method using discharge and snow cover area fraction without any information on water isotopes; (2) model parameters estimated by the corrected isoGSM data presented higher transferability to nested subbasins and produced higher model performance in the validation period than that estimated by the interpolated precipitation isotope data from site sampling measurements; (3) model calibration forced by the corrected isoGSM data successfully rejected parameter sets that overestimated glacier melt contribution and gave more reliable contributions of runoff components, indicating the corrected isoGSM data served as a better choice to provide informative spatial precipitation isotope than the interpolated data from site sampling measurements at the macro scale. This work suggested plausible utility of combining isoGSM data with measurements, even from a sparse sampling network, in improving hydrological modeling in large high mountain basins.

The Korea Strait (KS) is a major navigation passage linking the Japan Sea (JS) to the East China Sea and Yellow Sea. Almost all existing studies of the tides in the KS employed either data analysis or numerical modelling methods; thus, theoretical research is lacking. In this paper, we idealize the KS–JS basin as four connected uniform-depth rectangular areas and establish a theoretical model for the tides in the KS and JS using the extended Taylor method. The model-produced K1 and M2 tides are consistent with the satellite altimeter and tidal gauge observations, especially for the locations of the amphidromic points in the KS. The model solution provides the following insights into the tidal dynamics. The tidal system in each area can be decomposed into two oppositely travelling Kelvin waves and two families of Poincaré modes, with Kelvin waves dominating the tidal system. The incident Kelvin wave can be reflected at the connecting cross section, where abrupt increases in water depth and basin width occur from the KS to JS. At the connecting cross section, the reflected wave has a phase-lag increase relative to the incident wave of less than 180∘, causing the formation of amphidromic points in the KS. The above phase-lag increase depends on the angular velocity of the wave and becomes smaller as the angular velocity decreases. This dependence explains why the K1 amphidromic point is located farther away from the connecting cross section in comparison to the M2 amphidromic point.

Climatic oscillations have been developed through the (Early) Jurassic from marine sedimentary archives but remain unclear from terrestrial records. This work presents investigation of climate-sensitive sediments and carbon and oxygen isotope analyses of lacustrine and pedogenic carbonates for the Early Jurassic Ziliujing Formation taken from the Basin in southwestern China. Sedimentary and stable isotope proxies manifest that an overall secular (semi)arid climate dominated the Sichuan Basin during the Early Jurassic, except for the Hettangian. This climate pattern is similar to the arid climate in the Colorado Plateau region in western North America but is distinct from the relatively warm and humid climate in northern China and at high latitudes in the Southern Hemisphere. The estimated atmospheric CO2 concentration (pCO2) from carbon isotopes of pedogenic carbonates shows a range of 980–2610 ppmv (∼3.5–10 times the pre-industrial value) with a mean of 1660 ppmv. Three phases of pCO2 (the Sinemurian 1500–2000 ppmv, the Pliensbachian 1000–1500 ppmv, and the early Toarcian 1094–2610 ppmv) and two events of pCO2 rapidly falling by ∼1000–1300 ppmv are observed, illustrating the pCO2 perturbation in the Early Jurassic. The perturbation of pCO2 is compatible with seawater temperature and carbon cycle from the coeval marine sediments, suggesting a positive feedback of climate to pCO2 through the Early Jurassic.

With semiconductor technology gradually approaching its physical and thermal limits, recent supercomputers have adopted major architectural changes to continue increasing the performance through more power-efficient heterogeneous many-core systems. Examples include Sunway TaihuLight that has four management processing elements (MPEs) and 256 computing processing elements (CPEs) inside one processor and Summit that has two central processing units (CPUs) and six graphics processing units (GPUs) inside one node. Meanwhile, current high-resolution Earth system models that desperately require more computing power generally consist of millions of lines of legacy code developed for traditional homogeneous multicore processors and cannot automatically benefit from the advancement of supercomputer hardware. As a result, refactoring and optimizing the legacy models for new architectures become key challenges along the road of taking advantage of greener and faster supercomputers, providing better support for the global climate research community and contributing to the long-lasting societal task of addressing long-term climate change. This article reports the efforts of a large group in the International Laboratory for High-Resolution Earth System Prediction (iHESP) that was established by the cooperation of Qingdao Pilot National Laboratory for Marine Science and Technology (QNLM), Texas A&M University (TAMU), and the National Center for Atmospheric Research (NCAR), with the goal of enabling highly efficient simulations of the high-resolution (25 km atmosphere and 10 km ocean) Community Earth System Model (CESM-HR) on Sunway TaihuLight. The refactoring and optimizing efforts have improved the simulation speed of CESM-HR from 1 SYPD (simulation years per day) to 3.4 SYPD (with output disabled) and supported several hundred years of pre-industrial control simulations. With further strategies on deeper refactoring and optimizing for remaining computing hotspots, as well as redesigning architecture-oriented algorithms, we expect an equivalent or even better efficiency to be gained on the new platform than traditional homogeneous CPU platforms. The refactoring and optimizing processes detailed in this paper on the Sunway system should have implications for similar efforts on other heterogeneous many-core systems such as GPU-based high-performance computing (HPC) systems.

Direction-of-arrival (DOA) estimation based on sparse signal reconstruction (SSR) is always vulnerable to off-grid error. To address this issue, an efficient super-resolution DOA estimation algorithm is proposed in this work. Utilizing the Taylor series expansion, the sparse dictionary matrix is constructed under the off-grid model. Then, a polynomial optimization function is established based on the orthogonality principle. By minimizing the given objective function, we derive an efficient closed-form solution of the off-grid errors. Using the estimated off-grid errors, the discretized grid can be iteratively learned and approaches the true DOAs. With the newly learned grid, accurate DOA estimations can be achieved through the SSR scheme. The proposed algorithm converges fast and achieves precise DOA estimations even the step size of the discretized grid is large. The superior performance of the proposed algorithm is demonstrated by the simulation results.

The study of the mechanical properties of sea ice benefits the parameterization of sea-ice numerical models and the optimization of engineering design. Deformation measurement of sea ice has been seen as the essential foundation for the study of these properties. However, this measurement has proved to be difficult due to the complex and nonhomogeneous mechanical properties of sea ice. In this paper, we took advantage of DIC (digital image correlation) to obtain the full-field displacement and strain of sea-ice specimens in a uniaxial compression experiment. Full-field deformations of sea ice under both vertical and horizontal loading were measured. Different mechanical behaviors such as microcracks and failure modes due to the anisotropic properties of sea ice were successfully captured. The nonuniformity and local concentration of the strain field were observed and analyzed. Additionally, we evaluated the displacement and strain field of the specimens to verify the feasibility and accuracy of the method. This successful application provides a convenient and powerful option for the study of sea-ice mechanical properties including failure modes, nonlinear behavior and crack propagation.

The AT motif-binding factor (ATBF1), also known as zinc finger homeobox 3 (ZFXH3), is necessary for activating the POU1F1 gene; thus, the ATBF1 gene greatly affects the grow traits in animals. The objective of this work was to explore novel indel (insertion/deletion) variations and their associations with growth traits in three native Chinese goat breeds. Two indels within the ATBF1 gene were found in the Shaanbei white cashmere goat (SWCG; n = 581), the Guanzhong dairy goat (GZDG; n = 334) and the Hainan black goat (HNBG; n = 270) for the first time using 12 pairs of primers. Association analysis revealed that the P1-12-bp indel was consistently correlated with the body height of the three breeds, and individuals with ID (insertion/deletion) and DD (deletion/deletion) genotypes had a higher body weight than the II (insertion/insertion) genotype (P = 0.036); the P11-6-bp indel was consistently correlated with chest circumference and hip width of the three breeds. Moreover, these two loci were associated with other several growth-related traits in different breeds. Hence, these findings indicated that the goat ATBF1 gene had marked effects on growth traits and the growth-trait-related loci, which would contribute to improving the growth-related traits of local breeds in the goat industry by implementing marker-assisted selection (MAS).

In order to realize the analysis of thermal energy of the objects in 3D vision, the registration approach of thermal infrared images and TLS (Terrestrial Laser Scanner) point cloud was studied. The original data was pre-processed. For the sake of making the scale and brightness contrast of the two kinds of data meet the needs of basic matching, the intensity image of point cloud was produced and projected to spherical coordinate system, histogram equalization processing was done for thermal infrared image.This paper focused on the research of registration approaches of thermal infrared images and intensity images of point cloud based on SIFT，EOH-SIFT and PIIFD operators. The latter of which is usually used for medical image matching with different spectral character. The comparison results of the experiments showed that PIIFD operator got much more accurate feature point correspondences compared to SIFT and EOH-SIFT operators. The thermal infrared image and intensity image also have ideal overlap results by quadratic polynomial transformation. Therefore, PIIFD can be used as the basic operator for the registration of thermal infrared images and intensity images, and the operator can also be further improved by incorporating the iteration method.

In this paper a method “slicing-iteration” is proposed to realize automatic extraction of vehicle-borne laser point cloud, the main steps are as follows: (1) Confirm ground reference surface; (2) Dividing points: taking points at certain height range above reference surface, divide them by certain cell size (such as 0.1 m) and record points number of every cell; (3) Forming polyline: based on the number, for every cell, axially cluster is made, then find a neighbor with most similar number and form the two-point-line, further forming a polyline; (4) Iteration: dividing points and forming polyline can be conduct at different height range and by x/y axis, so as many features aspossible can be extracted. The method can also be used in half-automatic boundary extraction. Experiments show that the method in thepaper is easy to use, highly adaptive, with good extraction result.

Road markings as critical feature in high-defination maps, which are Advanced Driver Assistance System (ADAS) and self-driving technology required, have important functions in providing guidance and information to moving cars. Mobile laser scanning (MLS) system is an effective way to obtain the 3D information of the road surface, including road markings, at highway speeds and at less than traditional survey costs. This paper presents a novel method to automatically extract road markings from MLS point clouds. Ground points are first filtered from raw input point clouds using neighborhood elevation consistency method. The basic assumption of the method is that the road surface is smooth. Points with small elevation-difference between neighborhood are considered to be ground points. Then ground points are partitioned into a set of profiles according to trajectory data. The intensity histogram of points in each profile is generated to find intensity jumps in certain threshold which inversely to laser distance. The separated points are used as seed points to region grow based on intensity so as to obtain road mark of integrity. We use the point cloud template-matching method to refine the road marking candidates via removing the noise clusters with low correlation coefficient. During experiment with a MLS point set of about 2 kilometres in a city center, our method provides a promising solution to the road markings extraction from MLS data.

Vehicle-borne laser scanning (VBLS) is widely used to collect urban data for various mapping and modelling systems. This paper proposes a strategy of feature extraction and 3d model reconstruction for main body of overcrossing bridges based on VBLS point clouds. As the bridges usually have a large span, and the clouds data is often affected by obstacles, we have to use round-trip cloud data to avoid missing part. To begin with, pick out the cloud of the bridge body by an interactive clip-box, and group points by scan-line, then sort the points by scanning angle on each scan line. Since the position under the vehicle have a fixed scan-angle, a virtual path can be obtained. Secondly, extract horizontal line segments perpendicular to the virtual path along adjacent scan-lines, and then cluster line segments into long line-strings, which represent the top and bottom edge. Finally, regularize the line-strings and build 3d surface model of the bridge body. Experimental studies have demonstrated its efficiency and accuracy in case of building bridge model. Modelling the stairs at the both end of the bridge will be the direction of the next step.

The application of classical progressive triangulation filter algorithm for airborne point cloud is very successful, however, there is a big difference between airborne point cloud and vehicle-borne laser point cloud in spatial distribution, density and other aspects. In this paper, a lot of experiments are carried out to improve the filter algorithm for vehicle-borne laser point cloud, which includes as follows: (1) Establish grid index, such as 0.1 meters, only retain the lowest points, which can greatly reduce the number of suspected ground points, and the filtering efficiency is improved significantly; (2) According to the vehicle-borne height and track line, the road face points can be roughly determined. Then the convolution operation is used to ensure the real road points, which are also the ground points. This method cannot have to relax the filter parameters (which will lead to more non-ground points) and ensure the integrity of the road boundary; (3) A method named as "get more and remove some" is proposed for solving the filtering faults at the tail of every points segment caused by the incline scanning face. After the three steps, the filtering is improved obviously on qualification and processing speed.

The North China Plain (NCP) frequently experiences heavy haze pollution, particularly during wintertime. In winter 2015–2016, the NCP region suffered several extremely severe haze episodes with air pollution red alerts issued in many cities. We have investigated the sources and aerosol evolution processes of the severe pollution episodes in Handan, a typical industrialized city in the NCP region, using real-time measurements from an intensive field campaign during the winter of 2015–2016. The average (±1σ) concentration of submicron aerosol (PM1) during 3 December 2015–5 February 2016 was 187.6 (±137.5) µg m−3, with the hourly maximum reaching 700.8 µg m−3. Organic was the most abundant component, on average accounting for 45 % of total PM1 mass, followed by sulfate (15 %), nitrate (14 %), ammonium (12 %), chloride (9 %) and black carbon (BC, 5 %). Positive matrix factorization (PMF) with the multilinear engine (ME-2) algorithm identified four major organic aerosol (OA) sources, including traffic emissions represented by a hydrocarbon-like OA (HOA, 7 % of total OA), industrial and residential burning of coal represented by a coal combustion OA (CCOA, 29 % of total OA), open and domestic combustion of wood and crop residuals represented by a biomass burning OA (BBOA, 25 % of total OA), and formation of secondary OA (SOA) in the atmosphere represented by an oxygenated OA (OOA, 39 % of total OA). Emissions of primary OA (POA), which together accounted for 61 % of total OA and 27 % of PM1, are a major cause of air pollution during the winter. Our analysis further uncovered that primary emissions from coal combustion and biomass burning together with secondary formation of sulfate (mainly from SO2 emitted by coal combustion) are important driving factors for haze evolution. However, the bulk composition of PM1 showed comparatively small variations between less polluted periods (daily PM2. 5 ≤ 75 µg m−3) and severely polluted periods (daily PM2. 5 > 75 µg m−3), indicating relatively synchronous increases of all aerosol species during haze formation. The case study of a severe haze episode, which lasted 8 days starting with a steady buildup of aerosol pollution followed by a persistently high level of PM1 (326.7–700.8 µg m−3), revealed the significant influence of stagnant meteorological conditions which acerbate air pollution in the Handan region. The haze episode ended with a shift of wind which brought in cleaner air masses from the northwest of Handan and gradually reduced PM1 concentration to −3 after 12 h. Aqueous-phase reactions under higher relative humidity (RH) were found to significantly promote the production of secondary inorganic species (especially sulfate) but showed little influence on SOA.

The g-C3N4@BiPO4 nanorod core–shell structure photocatalysts were prepared by a new neutral hydrothermal reaction system combined with low temperature reassembled strategy. The CN precursors were successfully reassembled on the surface of BiPO4 nanorod to form ultrathin g-C3N4 layer (about 1 nm) under low temperature. Due to the formation of the core–shell structure, it can effectively promote the separation of photogenerated charge, thereby greatly improving the photocatalytic activity under UV light irradiation. The photodegradation activity of the phenol by the g-C3N4@BiPO4 nanorod core–shell structure photocatalyst annealing at the optimized conditions exhibits 1.6 times that of pure BiPO4 nanorod. The relationship between core–shell structure and photocatalytic activity under UV light irradiation was profoundly revealed through a comprehensive contrast experiment, and the mechanism of the enhancement activity of core–shell structure photocatalyst was also explored. It was found and detailedly demonstrated that the CN precursors prepared by neutral hydrothermal reaction can be polymerized into graphite-like phase C3N4 on the surface of Bi-based photocatalytic materials via low temperature thermal catalytic reassembled method. The novel method was proved to be a universal method for construction of core–shell structures. The establishment of g-C3N4@BiPO4 core–shell structure can provide blueprints for the construction of other organic–inorganic interface electric field catalytic system and can greatly improve the attractive prospect in practical applications owing to the perfect photocatalytic performance. Keywords: Core–shell structure, Interface photocatalysis, Ultrathin g-C3N4, BiPO4 nanorod

This paper proposes a Minimum Span Tree (MST) based image segmentation method for UAV images in coastal area. An edge weight based optimal criterion (merging predicate) is defined, which based on statistical learning theory (SLT). And we used a scale control parameter to control the segmentation scale. Experiments based on the high resolution UAV images in coastal area show that the proposed merging predicate can keep the integrity of the objects and prevent results from over segmentation. The segmentation results proves its efficiency in segmenting the rich texture images with good boundary of objects.

Ammonia (NH3) plays an increasingly important role in the global biogeochemical cycle of reactive nitrogen as well as in aerosol formation and climate. We present extensive and nearly continuous global ammonia measurements made by the Atmospheric Infrared Sounder (AIRS) from the Aqua satellite to identify and quantify major persistent and episodic sources as well as to characterize seasonality. We examine the 13-year period from September 2002 through August 2015 with a retrieval algorithm using an optimal estimation technique with a set of three, spatially and temporally uniform a priori profiles. Vertical profiles show good agreement (∼ 5–15 %) between AIRS NH3 and the in situ profiles from the winter 2013 DISCOVER-AQ (DISCOVER-Air Quality) field campaign in central California, despite the likely biases due to spatial resolution differences between the two instruments. The AIRS instrument captures the strongest consistent NH3 concentrations due to emissions from the anthropogenic (agricultural) source regions, such as South Asia (India/Pakistan), China, the United States (US), parts of Europe, Southeast (SE) Asia (Thailand/Myanmar/Laos), the central portion of South America, as well as Western and Northern Africa. These correspond primarily to irrigated croplands, as well as regions with heavy precipitation, with extensive animal feeding operations and fertilizer applications where a summer maximum and a secondary spring maximum are reliably observable. In the Southern Hemisphere (SH) regular agricultural fires contribute to a spring maximum. Regions of strong episodic emissions include Russia and Alaska as well as parts of South America, Africa, and Indonesia. Biomass burning, especially wildfires, dominate these episodic NH3 high concentrations.

The South China Sea (SCS) and the Java Sea (JS) are connected through the Karimata Strait, Gaspar Strait, and the southern Natuna Sea, where the tides are often used as open boundary condition for tidal simulation in the SCS or Indonesian seas. Tides, tidal currents, and tidal energy fluxes of the principle constituents K1, O1, Q1, M2, S2, and N2 at five stations in this area have been analyzed using in situ observational data. The results show that the diurnal tides are the dominant constituents in the entire study area. The constituent K1 has the largest amplitude, exceeding 50 cm, whereas the amplitudes of M2 are smaller than 5 cm at all stations. The amplitudes of S2 may exceed M2 in the Karimata and Gaspar straits. Tidal currents are mostly of rectilinear type in this area. The semi-major axes lengths of the diurnal tidal current ellipses are about 10 cm s−1, and those of the semidiurnal tidal currents are smaller than 5 cm s−1. The diurnal tidal energy flows from the SCS to the JS. The semidiurnal tidal energy flows from the SCS to the JS through the Karimata Strait and the eastern part of the southern Natuna Sea but flows in the opposite direction in the Gaspar Strait and the western part of the southern Natuna Sea. Harmonic analysis of sea level and current observation also suggest that the study area is located in the antinodal band of the diurnal tidal waves, and in the nodal band of the semidiurnal tidal waves. Comparisons show that the existing models are basically consistent with the observational results, but further improvements are necessary.

The major problem of many on-line web sites is the presentation of many choices to the client at a time; this usually results to strenuous and time consuming task in finding the right product or information on the site. In this work, we present a study of automatic web usage data mining and recommendation system based on current user behavior through his/her click stream data on the newly developed Really Simple Syndication (RSS) reader website, in order to provide relevant information to the individual without explicitly asking for it. The K-Nearest-Neighbor (KNN) classification method has been trained to be used on-line and in Real-Time to identify clients/visitors click stream data, matching it to a particular user group and recommend a tailored browsing option that meet the need of the specific user at a particular time. To achieve this, web users RSS address file was extracted, cleansed, formatted and grouped into meaningful session and data mart was developed. Our result shows that the K-Nearest Neighbor classifier is transparent, consistent, straightforward, simple to understand, high tendency to possess desirable qualities and easy to implement than most other machine learning techniques specifically when there is little or no prior knowledge about data distribution.

Extremely severe and persistent haze occurred in January 2013 over eastern and northern China. The record-breaking high concentrations of fine particulate matter (PM2.5) of more than 700 μg m−3 on hourly average and the persistence of the episodes have raised widespread, considerable public concerns. During that period, 7 of the top 10 polluted cities in China were within the Hebei Province. The three cities in southern Hebei (Shijiazhuang, Xingtai, and Handan) have been listed as the top three polluted cities according to the statistics for the first half of the year 2013. In this study, the Mesoscale Modeling System Generation 5 (MM5) and the Models-3/Community Multiscale Air Quality (CMAQ) modeling system are applied to simulate the 2013 severe winter regional hazes in East Asia and northern China at horizontal grid resolutions of 36 and 12 km, respectively, using the Multi-resolution Emission Inventory for China (MEIC). The source contributions of major source regions and sectors to PM2.5 concentrations in the three most polluted cities in southern Hebei are quantified by aiming at the understanding of the sources of the severe haze pollution in this region, and the results are compared with December 2007, the haziest month in the period 2001–2010. Model evaluation against meteorological and air quality observations indicates an overall acceptable performance and the model tends to underpredict PM2.5 and coarse particulate matter (PM10) concentrations during the extremely polluted episodes. The MEIC inventory is proven to be a good estimation in terms of total emissions of cities but uncertainties exist in the spatial allocations of emissions into fine grid resolutions within cities. The source apportionment shows that emissions from northern Hebei and the Beijing-Tianjin city cluster are two major regional contributors to the pollution in January 2013 in Shijiazhuang, compared with those from Shanxi and northern Hebei for December 2007. For Xingtai and Handan, the emissions from northern Hebei and Henan are important. The industrial and domestic sources are the most significant local contributors, and the domestic and agricultural emissions from Shandong and Henan are non-negligible regional sources, especially for Xingtai and Handan. Even in the top two haziest months (i.e., January 2013 and December 2007), a large fraction of PM2.5 in the three cities may originate from quite different regional sources. These results indicate the importance of establishing a regional joint framework of policymaking and action system to effectively mitigate air pollution in this area, not only over the Beijing-Tianjin-Hebei area, but also surrounding provinces such as Henan, Shandong, and Shanxi.

This study tests a novel methodology to add value to satellite data sets. This methodology, data fusion, is similar to data assimilation, except that the background model-based field is replaced by a satellite data set, in this case AIRS (Atmospheric Infrared Sounder) carbon monoxide (CO) measurements. The observational information comes from CO measurements with lower spatial coverage than AIRS, namely, from TES (Tropospheric Emission Spectrometer) and MLS (Microwave Limb Sounder). We show that combining these data sets with data fusion uses the higher spectral resolution of TES to extend AIRS CO observational sensitivity to the lower troposphere, a region especially important for air quality studies. We also show that combined CO measurements from AIRS and MLS provide enhanced information in the UTLS (upper troposphere/lower stratosphere) region compared to each product individually. The combined AIRS–TES and AIRS–MLS CO products are validated against DACOM (differential absorption mid-IR diode laser spectrometer) in situ CO measurements from the INTEX-B (Intercontinental Chemical Transport Experiment: MILAGRO and Pacific phases) field campaign and in situ data from HIPPO (HIAPER Pole-to-Pole Observations) flights. The data fusion results show improved sensitivities in the lower and upper troposphere (20–30% and above 20%, respectively) as compared with AIRS-only version 5 CO retrievals, and improved daily coverage compared with TES and MLS CO data.

We study the carbon monoxide (CO) variability in the last decade measured by NASA's Atmospheric InfraRed Sounder (AIRS) on the Earth Observing System (EOS)/Aqua satellite. The focus of this study is to analyze CO variability and short-term trends separately for background CO and fresh CO emissions based on a new statistical approach. The AIRS Level 2 (L2) retrieval algorithm utilizes cloud clearing to treat cloud contaminations in the signals, and this increases the data coverage significantly to a yield of more than 50% of the total measurements. We first study if the cloud clearing affects CO retrievals and the subsequent trend studies by using the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask to identify AIRS clear sky scenes. We then carry out a science analysis using AIRS CO data individually for the clear and cloud-cleared scenes to identify any potential effects due to cloud clearing. We also introduce a new technique to separate background and recently emitted CO observations, which aims to constrain emissions using only satellite CO data. We validate the CO variability of the recent emissions estimated from AIRS against other emission inventory databases (i.e., Global Fire Emissions Database – GFED3 and the MACC/CityZEN UE – MACCity) and calculate that the correlation coefficients between the AIRS CO recently emitted and the emission inventory databases are 0.726 for the Northern Hemisphere (NH) and 0.915 for the Southern Hemisphere (SH). The high degree of agreement between emissions identified using only AIRS CO and independent inventory sources demonstrates the validity of this approach to separate recent emissions from the background CO using one satellite data set.

Methyl vinyl silicone rubber/zinc dimethacrylate (VMQ/ZDMA) composites were prepared through in situ polymerization of ZDMA monomers during the peroxide curing. The polymerization conversion of ZDMA and morphology of the VMQ/ZDMA composites were studied. The results showed that most of the ZDMA monomers participated in the in situ polymerization during the cross-linking of the VMQ matrix and uniform nanophases were formed in the composites. The ‘dissolving-diffusion’ model was used to explain the micro-nano transformation of ZDMA. According to the model, a uniform nano-dispersed structure could be obtained through the in situ reaction even though the initial dispersion of ZDMA in the blends was poor. In addition, tensile tests of VMQ/ZDMA composites showed that ZDMA had a significant reinforcement on the mechanical properties of VMQ, and the best mechanical properties were obtained when the amounts of peroxide and ZDMA were 5 and 40 phr, respectively. The gross crosslink density and ionic crosslink density increased as the amount of ZDMA increased, but the covalent crosslink density decreased slightly. These results indicated that the ionic crosslink structure had a significant effect on the mechanical properties of VMQ/ZDMA composites.

We present in this paper an alternative retrieval algorithm for the Atmospheric Infrared Sounder (AIRS) tropospheric Carbon Monoxide (CO) products using the Optimal Estimation (OE) technique, which is different from the AIRS operational algorithm. The primary objective for this study was to compare AIRS CO, as well as the other retrieval properties such as the Averaging Kernels (AKs), the Degrees of Freedom for Signal (DOFS), and the error covariance matrix, against the Tropospheric Emission Spectrometer (TES) and the Measurement of Pollution in the Troposphere (MOPITT) CO, which were also derived using the OE technique. We also demonstrate that AIRS OE CO results are much more realistic than AIRS V5 operational CO, especially in the lower troposphere and in the Southern Hemisphere (SH). These products are validated with in situ profiles obtained by the Differential Absorption Carbon Monoxide Measurements (DACOM), which took place as part of NASA's Intercontinental Chemical Transport Experiment (INTEX-B) field mission that was conducted over the northern Pacific in Spring 2006. To demonstrate the differences existing in the current operational products we first show a detailed direct comparison between AIRS V5 and TES operational V3 CO for the global datasets from December 2005 to July 2008. We then present global CO comparisons between AIRS OE, TES V3, and MOPITT V4 at selected pressure levels as well as for the total column amounts. We conclude that the tropospheric CO retrievals from AIRS OE and TES V3 agree to within 5–10 ppbv or 5% on average globally and throughout the free troposphere. The agreements in total column CO amounts between AIRS OE and MOPITT V4 have improved significantly compared to AIRS V5 with global relative RMS differences now being 12.7%.

This study linked the Weather Research and Forecasting (WRF) modelling system and the Distributed Hydrology Soil Vegetation Model (DHSVM) to forecast snowmelt runoff. The study area was the 800 km2 Juntanghu watershed of the northern slopes of Tianshan Mountain Range. This paper investigated snowmelt runoff forecasting models suitable for meso-microscale application. In this study, a limited-region 24-h Numeric Weather Forecasting System was formulated using the new generation atmospheric model system WRF with the initial fields and lateral boundaries forced by Chinese T213L31 model. Using the WRF forecasts, the DHSVM hydrological model was used to predict 24 h snowmelt runoff at the outlet of the Juntanghu watershed. Forecasted results showed a good similarity to the observed data, and the average relative error of maximum runoff simulation was less than 15%. The results demonstrate the potential of using a meso-microscale snowmelt runoff forecasting model for forecasting floods. The model provides a longer forecast period compared with traditional models such as those based on rain gauges or statistical forecasting.