Validation of "AW3D" Global Dsm Generated from Alos Prism

NASA Astrophysics Data System (ADS)

Takaku, Junichi; Tadono, Takeo; Tsutsui, Ken; Ichikawa, Mayumi

2016-06-01

Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM), one of onboard sensors carried by Advanced Land Observing Satellite (ALOS), was designed to generate worldwide topographic data with its optical stereoscopic observation. It has an exclusive ability to perform a triplet stereo observation which views forward, nadir, and backward along the satellite track in 2.5 m ground resolution, and collected its derived images all over the world during the mission life of the satellite from 2006 through 2011. A new project, which generates global elevation datasets with the image archives, was started in 2014. The data is processed in unprecedented 5 m grid spacing utilizing the original triplet stereo images in 2.5 m resolution. As the number of processed data is growing steadily so that the global land areas are almost covered, a trend of global data qualities became apparent. This paper reports on up-to-date results of the validations for the accuracy of data products as well as the status of data coverage in global areas. The accuracies and error characteristics of datasets are analyzed by the comparison with existing global datasets such as Ice, Cloud, and land Elevation Satellite (ICESat) data, as well as ground control points (GCPs) and the reference Digital Elevation Model (DEM) derived from the airborne Light Detection and Ranging (LiDAR).

The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts

Treesearch

L.N. Hudson; T. Newbold; S. Contu

2014-01-01

Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species’ threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that...

Detecting Surgical Tools by Modelling Local Appearance and Global Shape.

PubMed

Bouget, David; Benenson, Rodrigo; Omran, Mohamed; Riffaud, Laurent; Schiele, Bernt; Jannin, Pierre

2015-12-01

Detecting tools in surgical videos is an important ingredient for context-aware computer-assisted surgical systems. To this end, we present a new surgical tool detection dataset and a method for joint tool detection and pose estimation in 2d images. Our two-stage pipeline is data-driven and relaxes strong assumptions made by previous works regarding the geometry, number, and position of tools in the image. The first stage classifies each pixel based on local appearance only, while the second stage evaluates a tool-specific shape template to enforce global shape. Both local appearance and global shape are learned from training data. Our method is validated on a new surgical tool dataset of 2 476 images from neurosurgical microscopes, which is made freely available. It improves over existing datasets in size, diversity and detail of annotation. We show that our method significantly improves over competitive baselines from the computer vision field. We achieve 15% detection miss-rate at 10(-1) false positives per image (for the suction tube) over our surgical tool dataset. Results indicate that performing semantic labelling as an intermediate task is key for high quality detection.

Cohort profile: the TrueNTH Global Registry - an international registry to monitor and improve localised prostate cancer health outcomes.

PubMed

Evans, Sue M; Millar, Jeremy L; Moore, Caroline M; Lewis, John D; Huland, Hartwig; Sampurno, Fanny; Connor, Sarah E; Villanti, Paul; Litwin, Mark S

2017-11-28

Globally, prostate cancer treatment and outcomes for men vary according to where they live, their race and the care they receive. The TrueNTH Global Registry project was established as an international registry monitoring care provided to men with localised prostate cancer (CaP). Sites with existing CaP databases in Movember fundraising countries were invited to participate in the international registry. In total, 25 Local Data Centres (LDCs) representing 113 participating sites across 13 countries have nominated to contribute to the project. It will collect a dataset based on the International Consortium for Health Outcome Measures (ICHOM) standardised dataset for localised CaP. A governance strategy has been developed to oversee registry operation, including transmission of reversibly anonymised data. LDCs are represented on the Project Steering Committee, reporting to an Executive Committee. A Project Coordination Centre and Data Coordination Centre (DCC) have been established. A project was undertaken to compare existing datasets, understand capacity at project commencement (baseline) to collect the ICHOM dataset and assist in determining the final data dictionary. 21/25 LDCs provided data dictionaries for review. Some ICHOM data fields were well collected (diagnosis, treatment start dates) and others poorly collected (complications, comorbidities). 17/94 (18%) ICHOM data fields were relegated to non-mandatory fields due to poor capture by most existing registries. Participating sites will transmit data through a web interface biannually to the DCC. Recruitment to the TrueNTH Global Registry-PCOR project will commence in late 2017 with sites progressively contributing reversibly anonymised data following ethical review in local regions. Researchers will have capacity to source deidentified data after the establishment phase. Quality indicators are to be established through a modified Delphi approach in later 2017, and it is anticipated that reports on performance against quality indicators will be provided to LDCs. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Large Scale Flood Risk Analysis using a New Hyper-resolution Population Dataset

NASA Astrophysics Data System (ADS)

Smith, A.; Neal, J. C.; Bates, P. D.; Quinn, N.; Wing, O.

2017-12-01

Here we present the first national scale flood risk analyses, using high resolution Facebook Connectivity Lab population data and data from a hyper resolution flood hazard model. In recent years the field of large scale hydraulic modelling has been transformed by new remotely sensed datasets, improved process representation, highly efficient flow algorithms and increases in computational power. These developments have allowed flood risk analysis to be undertaken in previously unmodeled territories and from continental to global scales. Flood risk analyses are typically conducted via the integration of modelled water depths with an exposure dataset. Over large scales and in data poor areas, these exposure data typically take the form of a gridded population dataset, estimating population density using remotely sensed data and/or locally available census data. The local nature of flooding dictates that for robust flood risk analysis to be undertaken both hazard and exposure data should sufficiently resolve local scale features. Global flood frameworks are enabling flood hazard data to produced at 90m resolution, resulting in a mis-match with available population datasets which are typically more coarsely resolved. Moreover, these exposure data are typically focused on urban areas and struggle to represent rural populations. In this study we integrate a new population dataset with a global flood hazard model. The population dataset was produced by the Connectivity Lab at Facebook, providing gridded population data at 5m resolution, representing a resolution increase over previous countrywide data sets of multiple orders of magnitude. Flood risk analysis undertaken over a number of developing countries are presented, along with a comparison of flood risk analyses undertaken using pre-existing population datasets.

Developing a Global Network of River Reaches in Preparation of SWOT

NASA Astrophysics Data System (ADS)

Lion, C.; Pavelsky, T.; Allen, G. H.; Beighley, E.; Schumann, G.; Durand, M. T.

2016-12-01

In 2020, the Surface Water and Ocean Topography satellite (SWOT), a joint mission of NASA/CNES/CSA/UK will be launched. One of its major products will be the measurements of continental water surfaces, including the width, height, and slope of rivers and the surface area and elevations of lakes. The mission will improve the monitoring of continental water and also our understanding of the interactions between different hydrologic reservoirs. For rivers, SWOT measurements of slope will be carried out over predefined river reaches. As such, an a priori dataset for rivers is needed in order to facilitate analysis of the raw SWOT data. The information required to produce this dataset includes measurements of river width, elevation, slope, planform, river network topology, and flow accumulation. To produce this product, we have linked two existing global datasets: the Global River Widths from Landsat (GRWL) database, which contains river centerline locations, widths, and a braiding index derived from Landsat imagery, and a modified version of the HydroSHEDS hydrologically corrected digital elevation product, which contains heights and flow accumulation measurements for streams at 3 arcseconds spatial resolution. Merging these two datasets requires considerable care. The difficulties, among others, lie in the difference of resolution: 30m versus 3 arseconds, and the age of the datasets: 2000 versus 2010 (some rivers have moved, the braided sections are different). As such, we have developed custom software to merge the two datasets, taking into account the spatial proximity of river channels in the two datasets and ensuring that flow accumulation in the final dataset always increases downstream. Here, we present our results for the globe.

Multisource Estimation of Long-term Global Terrestrial Surface Radiation

NASA Astrophysics Data System (ADS)

Peng, L.; Sheffield, J.

2017-12-01

Land surface net radiation is the essential energy source at the earth's surface. It determines the surface energy budget and its partitioning, drives the hydrological cycle by providing available energy, and offers heat, light, and energy for biological processes. Individual components in net radiation have changed historically due to natural and anthropogenic climate change and land use change. Decadal variations in radiation such as global dimming or brightening have important implications for hydrological and carbon cycles. In order to assess the trends and variability of net radiation and evapotranspiration, there is a need for accurate estimates of long-term terrestrial surface radiation. While large progress in measuring top of atmosphere energy budget has been made, huge discrepancies exist among ground observations, satellite retrievals, and reanalysis fields of surface radiation, due to the lack of observational networks, the difficulty in measuring from space, and the uncertainty in algorithm parameters. To overcome the weakness of single source datasets, we propose a multi-source merging approach to fully utilize and combine multiple datasets of radiation components separately, as they are complementary in space and time. First, we conduct diagnostic analysis of multiple satellite and reanalysis datasets based on in-situ measurements such as Global Energy Balance Archive (GEBA), existing validation studies, and other information such as network density and consistency with other meteorological variables. Then, we calculate the optimal weighted average of multiple datasets by minimizing the variance of error between in-situ measurements and other observations. Finally, we quantify the uncertainties in the estimates of surface net radiation and employ physical constraints based on the surface energy balance to reduce these uncertainties. The final dataset is evaluated in terms of the long-term variability and its attribution to changes in individual components. The goal of this study is to provide a merged observational benchmark for large-scale diagnostic analyses, remote sensing and land surface modeling.

ESSG-based global spatial reference frame for datasets interrelation

NASA Astrophysics Data System (ADS)

Yu, J. Q.; Wu, L. X.; Jia, Y. J.

2013-10-01

To know well about the highly complex earth system, a large volume of, as well as a large variety of, datasets on the planet Earth are being obtained, distributed, and shared worldwide everyday. However, seldom of existing systems concentrates on the distribution and interrelation of different datasets in a common Global Spatial Reference Frame (GSRF), which holds an invisble obstacle to the data sharing and scientific collaboration. Group on Earth Obeservation (GEO) has recently established a new GSRF, named Earth System Spatial Grid (ESSG), for global datasets distribution, sharing and interrelation in its 2012-2015 WORKING PLAN.The ESSG may bridge the gap among different spatial datasets and hence overcome the obstacles. This paper is to present the implementation of the ESSG-based GSRF. A reference spheroid, a grid subdvision scheme, and a suitable encoding system are required to implement it. The radius of ESSG reference spheroid was set to the double of approximated Earth radius to make datasets from different areas of earth system science being covered. The same paramerters of positioning and orienting as Earth Centred Earth Fixed (ECEF) was adopted for the ESSG reference spheroid to make any other GSRFs being freely transformed into the ESSG-based GSRF. Spheroid degenerated octree grid with radius refiment (SDOG-R) and its encoding method were taken as the grid subdvision and encoding scheme for its good performance in many aspects. A triple (C, T, A) model is introduced to represent and link different datasets based on the ESSG-based GSRF. Finally, the methods of coordinate transformation between the ESSGbased GSRF and other GSRFs were presented to make ESSG-based GSRF operable and propagable.

Cadastral Database Positional Accuracy Improvement

NASA Astrophysics Data System (ADS)

Hashim, N. M.; Omar, A. H.; Ramli, S. N. M.; Omar, K. M.; Din, N.

2017-10-01

Positional Accuracy Improvement (PAI) is the refining process of the geometry feature in a geospatial dataset to improve its actual position. This actual position relates to the absolute position in specific coordinate system and the relation to the neighborhood features. With the growth of spatial based technology especially Geographical Information System (GIS) and Global Navigation Satellite System (GNSS), the PAI campaign is inevitable especially to the legacy cadastral database. Integration of legacy dataset and higher accuracy dataset like GNSS observation is a potential solution for improving the legacy dataset. However, by merely integrating both datasets will lead to a distortion of the relative geometry. The improved dataset should be further treated to minimize inherent errors and fitting to the new accurate dataset. The main focus of this study is to describe a method of angular based Least Square Adjustment (LSA) for PAI process of legacy dataset. The existing high accuracy dataset known as National Digital Cadastral Database (NDCDB) is then used as bench mark to validate the results. It was found that the propose technique is highly possible for positional accuracy improvement of legacy spatial datasets.

Spatially-explicit models of global tree density.

PubMed

Glick, Henry B; Bettigole, Charlie; Maynard, Daniel S; Covey, Kristofer R; Smith, Jeffrey R; Crowther, Thomas W

2016-08-16

Remote sensing and geographic analysis of woody vegetation provide means of evaluating the distribution of natural resources, patterns of biodiversity and ecosystem structure, and socio-economic drivers of resource utilization. While these methods bring geographic datasets with global coverage into our day-to-day analytic spheres, many of the studies that rely on these strategies do not capitalize on the extensive collection of existing field data. We present the methods and maps associated with the first spatially-explicit models of global tree density, which relied on over 420,000 forest inventory field plots from around the world. This research is the result of a collaborative effort engaging over 20 scientists and institutions, and capitalizes on an array of analytical strategies. Our spatial data products offer precise estimates of the number of trees at global and biome scales, but should not be used for local-level estimation. At larger scales, these datasets can contribute valuable insight into resource management, ecological modelling efforts, and the quantification of ecosystem services.

Global climate shocks to agriculture from 1950 - 2015

NASA Astrophysics Data System (ADS)

Jackson, N. D.; Konar, M.; Debaere, P.; Sheffield, J.

2016-12-01

Climate shocks represent a major disruption to crop yields and agricultural production, yet a consistent and comprehensive database of agriculturally relevant climate shocks does not exist. To this end, we conduct a spatially and temporally disaggregated analysis of climate shocks to agriculture from 1950-2015 using a new gridded dataset. We quantify the occurrence and magnitude of climate shocks for all global agricultural areas during the growing season using a 0.25-degree spatial grid and daily time scale. We include all major crops and both temperature and precipitation extremes in our analysis. Critically, we evaluate climate shocks to all potential agricultural areas to improve projections within our time series. To do this, we use Global Agro-Ecological Zones maps from the Food and Agricultural Organization, the Princeton Global Meteorological Forcing dataset, and crop calendars from Sacks et al. (2010). We trace the dynamic evolution of climate shocks to agriculture, evaluate the spatial heterogeneity in agriculturally relevant climate shocks, and identify the crops and regions that are most prone to climate shocks.

Global Optimization Ensemble Model for Classification Methods

PubMed Central

Anwar, Hina; Qamar, Usman; Muzaffar Qureshi, Abdul Wahab

2014-01-01

Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity. PMID:24883382

High resolution population distribution maps for Southeast Asia in 2010 and 2015.

PubMed

Gaughan, Andrea E; Stevens, Forrest R; Linard, Catherine; Jia, Peng; Tatem, Andrew J

2013-01-01

Spatially accurate, contemporary data on human population distributions are vitally important to many applied and theoretical researchers. The Southeast Asia region has undergone rapid urbanization and population growth over the past decade, yet existing spatial population distribution datasets covering the region are based principally on population count data from censuses circa 2000, with often insufficient spatial resolution or input data to map settlements precisely. Here we outline approaches to construct a database of GIS-linked circa 2010 census data and methods used to construct fine-scale (∼100 meters spatial resolution) population distribution datasets for each country in the Southeast Asia region. Landsat-derived settlement maps and land cover information were combined with ancillary datasets on infrastructure to model population distributions for 2010 and 2015. These products were compared with those from two other methods used to construct commonly used global population datasets. Results indicate mapping accuracies are consistently higher when incorporating land cover and settlement information into the AsiaPop modelling process. Using existing data, it is possible to produce detailed, contemporary and easily updatable population distribution datasets for Southeast Asia. The 2010 and 2015 datasets produced are freely available as a product of the AsiaPop Project and can be downloaded from: www.asiapop.org.

High Resolution Population Distribution Maps for Southeast Asia in 2010 and 2015

PubMed Central

Gaughan, Andrea E.; Stevens, Forrest R.; Linard, Catherine; Jia, Peng; Tatem, Andrew J.

2013-01-01

Spatially accurate, contemporary data on human population distributions are vitally important to many applied and theoretical researchers. The Southeast Asia region has undergone rapid urbanization and population growth over the past decade, yet existing spatial population distribution datasets covering the region are based principally on population count data from censuses circa 2000, with often insufficient spatial resolution or input data to map settlements precisely. Here we outline approaches to construct a database of GIS-linked circa 2010 census data and methods used to construct fine-scale (∼100 meters spatial resolution) population distribution datasets for each country in the Southeast Asia region. Landsat-derived settlement maps and land cover information were combined with ancillary datasets on infrastructure to model population distributions for 2010 and 2015. These products were compared with those from two other methods used to construct commonly used global population datasets. Results indicate mapping accuracies are consistently higher when incorporating land cover and settlement information into the AsiaPop modelling process. Using existing data, it is possible to produce detailed, contemporary and easily updatable population distribution datasets for Southeast Asia. The 2010 and 2015 datasets produced are freely available as a product of the AsiaPop Project and can be downloaded from: www.asiapop.org. PMID:23418469

Simulation of Smart Home Activity Datasets

PubMed Central

Synnott, Jonathan; Nugent, Chris; Jeffers, Paul

2015-01-01

A globally ageing population is resulting in an increased prevalence of chronic conditions which affect older adults. Such conditions require long-term care and management to maximize quality of life, placing an increasing strain on healthcare resources. Intelligent environments such as smart homes facilitate long-term monitoring of activities in the home through the use of sensor technology. Access to sensor datasets is necessary for the development of novel activity monitoring and recognition approaches. Access to such datasets is limited due to issues such as sensor cost, availability and deployment time. The use of simulated environments and sensors may address these issues and facilitate the generation of comprehensive datasets. This paper provides a review of existing approaches for the generation of simulated smart home activity datasets, including model-based approaches and interactive approaches which implement virtual sensors, environments and avatars. The paper also provides recommendation for future work in intelligent environment simulation. PMID:26087371

Simulation of Smart Home Activity Datasets.

PubMed

Synnott, Jonathan; Nugent, Chris; Jeffers, Paul

2015-06-16

A globally ageing population is resulting in an increased prevalence of chronic conditions which affect older adults. Such conditions require long-term care and management to maximize quality of life, placing an increasing strain on healthcare resources. Intelligent environments such as smart homes facilitate long-term monitoring of activities in the home through the use of sensor technology. Access to sensor datasets is necessary for the development of novel activity monitoring and recognition approaches. Access to such datasets is limited due to issues such as sensor cost, availability and deployment time. The use of simulated environments and sensors may address these issues and facilitate the generation of comprehensive datasets. This paper provides a review of existing approaches for the generation of simulated smart home activity datasets, including model-based approaches and interactive approaches which implement virtual sensors, environments and avatars. The paper also provides recommendation for future work in intelligent environment simulation.

Evaluation of Ground-Motion Modeling Techniques for Use in Global ShakeMap - A Critique of Instrumental Ground-Motion Prediction Equations, Peak Ground Motion to Macroseismic Intensity Conversions, and Macroseismic Intensity Predictions in Different Tectonic Settings

USGS Publications Warehouse

Allen, Trevor I.; Wald, David J.

2009-01-01

Regional differences in ground-motion attenuation have long been thought to add uncertainty in the prediction of ground motion. However, a growing body of evidence suggests that regional differences in ground-motion attenuation may not be as significant as previously thought and that the key differences between regions may be a consequence of limitations in ground-motion datasets over incomplete magnitude and distance ranges. Undoubtedly, regional differences in attenuation can exist owing to differences in crustal structure and tectonic setting, and these can contribute to differences in ground-motion attenuation at larger source-receiver distances. Herein, we examine the use of a variety of techniques for the prediction of several ground-motion metrics (peak ground acceleration and velocity, response spectral ordinates, and macroseismic intensity) and compare them against a global dataset of instrumental ground-motion recordings and intensity assignments. The primary goal of this study is to determine whether existing ground-motion prediction techniques are applicable for use in the U.S. Geological Survey's Global ShakeMap and Prompt Assessment of Global Earthquakes for Response (PAGER). We seek the most appropriate ground-motion predictive technique, or techniques, for each of the tectonic regimes considered: shallow active crust, subduction zone, and stable continental region.

Setting up a hydrological model based on global data for the Ayeyarwady basin in Myanmar

NASA Astrophysics Data System (ADS)

ten Velden, Corine; Sloff, Kees; Nauta, Tjitte

2017-04-01

The use of global datasets in local hydrological modelling can be of great value. It opens up the possibility to include data for areas where local data is not or only sparsely available. In hydrological modelling the existence of both static physical data such as elevation and land use, and dynamic meteorological data such as precipitation and temperature, is essential for setting up a hydrological model, but often such data is difficult to obtain at the local level. For the Ayeyarwady catchment in Myanmar a distributed hydrological model (Wflow: https://github.com/openstreams/wflow) was set up with only global datasets, as part of a water resources study. Myanmar is an emerging economy, which has only recently become more receptive to foreign influences. It has a very limited hydrometeorological measurement network, with large spatial and temporal gaps, and data that are of uncertain quality and difficult to obtain. The hydrological model was thus set up based on resampled versions of the SRTM digital elevation model, the GlobCover land cover dataset and the HWSD soil dataset. Three global meteorological datasets were assessed and compared for use in the hydrological model: TRMM, WFDEI and MSWEP. The meteorological datasets were assessed based on their conformity with several precipitation station measurements, and the overall model performance was assessed by calculating the NSE and RVE based on discharge measurements of several gauging stations. The model was run for the period 1979-2012 on a daily time step, and the results show an acceptable applicability of the used global datasets in the hydrological model. The WFDEI forcing dataset gave the best results, with a NSE of 0.55 at the outlet of the model and a RVE of 8.5%, calculated over the calibration period 2006-2012. As a general trend the modelled discharge at the upstream stations tends to be underestimated, and at the downstream stations slightly overestimated. The quality of the discharge measurements that form the basis for the performance calculations is uncertain; data analysis suggests that rating curves are not frequently updated. The modelling results are not perfect and there is ample room for improvement, but the results are reasonable given the notion that setting up a hydrological model for this area would not have been possible without the use of global datasets due to the lack of available local data. The resulting hydrological model then enabled the set-up of the RIBASIM water allocation model for the Ayeyarwady basin in order to assess its water resources. The study discussed here is a first step; ideally this is followed up by a more thorough calibration and validation with the limited local measurements available, e.g. a precipitation correction based on the available rainfall measurements, to ensure the integration of global and local data.

High-quality seamless DEM generation blending SRTM-1, ASTER GDEM v2 and ICESat/GLAS observations

NASA Astrophysics Data System (ADS)

Yue, Linwei; Shen, Huanfeng; Zhang, Liangpei; Zheng, Xianwei; Zhang, Fan; Yuan, Qiangqiang

2017-01-01

The absence of a high-quality seamless global digital elevation model (DEM) dataset has been a challenge for the Earth-related research fields. Recently, the 1-arc-second Shuttle Radar Topography Mission (SRTM-1) data have been released globally, covering over 80% of the Earth's land surface (60°N-56°S). However, voids and anomalies still exist in some tiles, which has prevented the SRTM-1 dataset from being directly used without further processing. In this paper, we propose a method to generate a seamless DEM dataset blending SRTM-1, ASTER GDEM v2, and ICESat laser altimetry data. The ASTER GDEM v2 data are used as the elevation source for the SRTM void filling. To get a reliable filling source, ICESat GLAS points are incorporated to enhance the accuracy of the ASTER data within the void regions, using an artificial neural network (ANN) model. After correction, the voids in the SRTM-1 data are filled with the corrected ASTER GDEM values. The triangular irregular network based delta surface fill (DSF) method is then employed to eliminate the vertical bias between them. Finally, an adaptive outlier filter is applied to all the data tiles. The final result is a seamless global DEM dataset. ICESat points collected from 2003 to 2009 were used to validate the effectiveness of the proposed method, and to assess the vertical accuracy of the global DEM products in China. Furthermore, channel networks in the Yangtze River Basin were also extracted for the data assessment.

The GEM Global Active Faults Database: The growth and synthesis of a worldwide database of active structures for PSHA, research, and education

NASA Astrophysics Data System (ADS)

Styron, R. H.; Garcia, J.; Pagani, M.

2017-12-01

A global catalog of active faults is a resource of value to a wide swath of the geoscience, earthquake engineering, and hazards risk communities. Though construction of such a dataset has been attempted now and again through the past few decades, success has been elusive. The Global Earthquake Model (GEM) Foundation has been working on this problem, as a fundamental step in its goal of making a global seismic hazard model. Progress on the assembly of the database is rapid, with the concatenation of many national—, orogen—, and continental—scale datasets produced by different research groups throughout the years. However, substantial data gaps exist throughout much of the deforming world, requiring new mapping based on existing publications as well as consideration of seismicity, geodesy and remote sensing data. Thus far, new fault datasets have been created for the Caribbean and Central America, North Africa, and northeastern Asia, with Madagascar, Canada and a few other regions in the queue. The second major task, as formidable as the initial data concatenation, is the 'harmonization' of data. This entails the removal or recombination of duplicated structures, reconciliation of contrastinginterpretations in areas of overlap, and the synthesis of many different types of attributes or metadata into a consistent whole. In a project of this scale, the methods used in the database construction are as critical to project success as the data themselves. After some experimentation, we have settled on an iterative methodology that involves rapid accumulation of data followed by successive episodes of data revision, and a computer-scripted data assembly using GIS file formats that is flexible, reproducible, and as able as possible to cope with updates to the constituent datasets. We find that this approach of initially maximizing coverage and then increasing resolution is the most robust to regional data problems and the most amenable to continued updates and refinement. Combined with the public, open-source nature of this project, GEM is producing a resource that can continue to evolve with the changing knowledge and needs of the community.

The global coastline dataset: the observed relation between erosion and sea-level rise

NASA Astrophysics Data System (ADS)

Donchyts, G.; Baart, F.; Luijendijk, A.; Hagenaars, G.

2017-12-01

Erosion of sandy coasts is considered one of the key risks of sea-level rise. Because sandy coastlines of the world are often highly populated, erosive coastline trends result in risk to populations and infrastructure. Most of our understanding of the relation between sea-level rise and coastal erosion is based on local or regional observations and generalizations of numerical and physical experiments. Until recently there was no reliable global scale assessment of the location of sandy coasts and their rate of erosion and accretion. Here we present the global coastline dataset that covers erosion indicators on a local scale with global coverage. The dataset uses our global coastline transects grid defined with an alongshore spacing of 250 m and a cross shore length extending 1 km seaward and 1 km landward. This grid matches up with pre-existing local grids where available. We present the latest results on validation of coastal-erosion trends (based on optical satellites) and classification of sandy versus non-sandy coasts. We show the relation between sea-level rise (based both on tide-gauges and multi-mission satellite altimetry) and observed erosion trends over the last decades, taking into account broken-coastline trends (for example due to nourishments).An interactive web application presents the publicly-accessible results using a backend based on Google Earth Engine. It allows both researchers and stakeholders to use objective estimates of coastline trends, particularly when authoritative sources are not available.

Comparing Goldstone Solar System Radar Earth-based Observations of Mars with Orbital Datasets

NASA Technical Reports Server (NTRS)

Haldemann, A. F. C.; Larsen, K. W.; Jurgens, R. F.; Slade, M. A.

2005-01-01

The Goldstone Solar System Radar (GSSR) has collected a self-consistent set of delay-Doppler near-nadir radar echo data from Mars since 1988. Prior to the Mars Global Surveyor (MGS) Mars Orbiter Laser Altimeter (MOLA) global topography for Mars, these radar data provided local elevation information, along with radar scattering information with global coverage. Two kinds of GSSR Mars delay-Doppler data exist: low 5 km x 150 km resolution and, more recently, high (5 to 10 km) spatial resolution. Radar data, and non-imaging delay-Doppler data in particular, requires significant data processing to extract elevation, reflectivity and roughness of the reflecting surface. Interpretation of these parameters, while limited by the complexities of electromagnetic scattering, provide information directly relevant to geophysical and geomorphic analyses of Mars. In this presentation we want to demonstrate how to compare GSSR delay-Doppler data to other Mars datasets, including some idiosyncracies of the radar data. Additional information is included in the original extended abstract.

Does a global DNA barcoding gap exist in Annelida?

PubMed

Kvist, Sebastian

2016-05-01

Accurate identification of unknown specimens by means of DNA barcoding is contingent on the presence of a DNA barcoding gap, among other factors, as its absence may result in dubious specimen identifications - false negatives or positives. Whereas the utility of DNA barcoding would be greatly reduced in the absence of a distinct and sufficiently sized barcoding gap, the limits of intraspecific and interspecific distances are seldom thoroughly inspected across comprehensive sampling. The present study aims to illuminate this aspect of barcoding in a comprehensive manner for the animal phylum Annelida. All cytochrome c oxidase subunit I sequences (cox1 gene; the chosen region for zoological DNA barcoding) present in GenBank for Annelida, as well as for "Polychaeta", "Oligochaeta", and Hirudinea separately, were downloaded and curated for length, coverage and potential contaminations. The final datasets consisted of 9782 (Annelida), 5545 ("Polychaeta"), 3639 ("Oligochaeta"), and 598 (Hirudinea) cox1 sequences and these were either (i) used as is in an automated global barcoding gap detection analysis or (ii) further analyzed for genetic distances, separated into bins containing intraspecific and interspecific comparisons and plotted in a graph to visualize any potential global barcoding gap. Over 70 million pairwise genetic comparisons were made and results suggest that although there is a tendency towards separation, no distinct or sufficiently sized global barcoding gap exists in either of the datasets rendering future barcoding efforts at risk of erroneous specimen identifications (but local barcoding gaps may still exist allowing for the identification of specimens at lower taxonomic ranks). This seems to be especially true for earthworm taxa, which account for fully 35% of the total number of interspecific comparisons that show 0% divergence.

Global Simulation of Bioenergy Crop Productivity: Analytical Framework and Case Study for Switchgrass

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kang, Shujiang; Kline, Keith L; Nair, S. Surendran

A global energy crop productivity model that provides geospatially explicit quantitative details on biomass potential and factors affecting sustainability would be useful, but does not exist now. This study describes a modeling platform capable of meeting many challenges associated with global-scale agro-ecosystem modeling. We designed an analytical framework for bioenergy crops consisting of six major components: (i) standardized natural resources datasets, (ii) global field-trial data and crop management practices, (iii) simulation units and management scenarios, (iv) model calibration and validation, (v) high-performance computing (HPC) simulation, and (vi) simulation output processing and analysis. The HPC-Environmental Policy Integrated Climate (HPC-EPIC) model simulatedmore » a perennial bioenergy crop, switchgrass (Panicum virgatum L.), estimating feedstock production potentials and effects across the globe. This modeling platform can assess soil C sequestration, net greenhouse gas (GHG) emissions, nonpoint source pollution (e.g., nutrient and pesticide loss), and energy exchange with the atmosphere. It can be expanded to include additional bioenergy crops (e.g., miscanthus, energy cane, and agave) and food crops under different management scenarios. The platform and switchgrass field-trial dataset are available to support global analysis of biomass feedstock production potential and corresponding metrics of sustainability.« less

Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate

NASA Astrophysics Data System (ADS)

Hobeichi, Sanaa; Abramowitz, Gab; Evans, Jason; Ukkola, Anna

2018-02-01

Accurate global gridded estimates of evapotranspiration (ET) are key to understanding water and energy budgets, in addition to being required for model evaluation. Several gridded ET products have already been developed which differ in their data requirements, the approaches used to derive them and their estimates, yet it is not clear which provides the most reliable estimates. This paper presents a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000-2009. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. The weighting method is based on a technique that provides an analytically optimal linear combination of ET products compared to site data and accounts for both the performance differences and error covariance between the participating ET products. We examine the performance of the weighting approach in several in-sample and out-of-sample tests that confirm that point-based estimates of flux towers provide information on the grid scale of these products. We also provide evidence that the weighted product performs better than its six constituent ET product members in four common metrics. Uncertainty in the ET estimate is derived by rescaling the spread of participating ET products so that their spread reflects the ability of the weighted mean estimate to match flux tower data. While issues in observational data and any common biases in participating ET datasets are limitations to the success of this approach, future datasets can easily be incorporated and enhance the derived product.

Long-term dataset on aquatic responses to concurrent climate change and recovery from acidification

NASA Astrophysics Data System (ADS)

Leach, Taylor H.; Winslow, Luke A.; Acker, Frank W.; Bloomfield, Jay A.; Boylen, Charles W.; Bukaveckas, Paul A.; Charles, Donald F.; Daniels, Robert A.; Driscoll, Charles T.; Eichler, Lawrence W.; Farrell, Jeremy L.; Funk, Clara S.; Goodrich, Christine A.; Michelena, Toby M.; Nierzwicki-Bauer, Sandra A.; Roy, Karen M.; Shaw, William H.; Sutherland, James W.; Swinton, Mark W.; Winkler, David A.; Rose, Kevin C.

2018-04-01

Concurrent regional and global environmental changes are affecting freshwater ecosystems. Decadal-scale data on lake ecosystems that can describe processes affected by these changes are important as multiple stressors often interact to alter the trajectory of key ecological phenomena in complex ways. Due to the practical challenges associated with long-term data collections, the majority of existing long-term data sets focus on only a small number of lakes or few response variables. Here we present physical, chemical, and biological data from 28 lakes in the Adirondack Mountains of northern New York State. These data span the period from 1994-2012 and harmonize multiple open and as-yet unpublished data sources. The dataset creation is reproducible and transparent; R code and all original files used to create the dataset are provided in an appendix. This dataset will be useful for examining ecological change in lakes undergoing multiple stressors.

Agricultural Management Practices Explain Variation in Global Yield Gaps of Major Crops

NASA Astrophysics Data System (ADS)

Mueller, N. D.; Gerber, J. S.; Ray, D. K.; Ramankutty, N.; Foley, J. A.

2010-12-01

The continued expansion and intensification of agriculture are key drivers of global environmental change. Meeting a doubling of food demand in the next half-century will further induce environmental change, requiring either large cropland expansion into carbon- and biodiversity-rich tropical forests or increasing yields on existing croplands. Closing the “yield gaps” between the most and least productive farmers on current agricultural lands is a necessary and major step towards preserving natural ecosystems and meeting future food demand. Here we use global climate, soils, and cropland datasets to quantify yield gaps for major crops using equal-area climate analogs. Consistent with previous studies, we find large yield gaps for many crops in Eastern Europe, tropical Africa, and parts of Mexico. To analyze the drivers of yield gaps, we collected sub-national agricultural management data and built a global dataset of fertilizer application rates for over 160 crops. We constructed empirical crop yield models for each climate analog using the global management information for 17 major crops. We find that our climate-specific models explain a substantial amount of the global variation in yields. These models could be widely applied to identify management changes needed to close yield gaps, analyze the environmental impacts of agricultural intensification, and identify climate change adaptation techniques.

Generating and Visualizing Climate Indices using Google Earth Engine

NASA Astrophysics Data System (ADS)

Erickson, T. A.; Guentchev, G.; Rood, R. B.

2017-12-01

Climate change is expected to have largest impacts on regional and local scales. Relevant and credible climate information is needed to support the planning and adaptation efforts in our communities. The volume of climate projections of temperature and precipitation is steadily increasing, as datasets are being generated on finer spatial and temporal grids with an increasing number of ensembles to characterize uncertainty. Despite advancements in tools for querying and retrieving subsets of these large, multi-dimensional datasets, ease of access remains a barrier for many existing and potential users who want to derive useful information from these data, particularly for those outside of the climate modelling research community. Climate indices, that can be derived from daily temperature and precipitation data, such as annual number of frost days or growing season length, can provide useful information to practitioners and stakeholders. For this work the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset was loaded into Google Earth Engine, a cloud-based geospatial processing platform. Algorithms that use the Earth Engine API to generate several climate indices were written. The indices were chosen from the set developed by the joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI). Simple user interfaces were created that allow users to query, produce maps and graphs of the indices, as well as download results for additional analyses. These browser-based interfaces could allow users in low-bandwidth environments to access climate information. This research shows that calculating climate indices from global downscaled climate projection datasets and sharing them widely using cloud computing technologies is feasible. Further development will focus on exposing the climate indices to existing applications via the Earth Engine API, and building custom user interfaces for presenting climate indices to a diverse set of user groups.

Online Visualization and Analysis of Merged Global Geostationary Satellite Infrared Dataset

NASA Technical Reports Server (NTRS)

Liu, Zhong; Ostrenga, D.; Leptoukh, G.; Mehta, A.

2008-01-01

The NASA Goddard Earth Sciences Data Information Services Center (GES DISC) is home of Tropical Rainfall Measuring Mission (TRMM) data archive. The global merged IR product also known as the NCEP/CPC 4-km Global (60 degrees N - 60 degrees S) IR Dataset, is one of TRMM ancillary datasets. They are globally merged (60 degrees N - 60 degrees S) pixel-resolution (4 km) IR brightness temperature data (equivalent blackbody temperatures), merged from all available geostationary satellites (GOES-8/10, METEOSAT-7/5 and GMS). The availability of data from METEOSAT-5, which is located at 63E at the present time, yields a unique opportunity for total global (60 degrees N- 60 degrees S) coverage. The GES DISC has collected over 8 years of the data beginning from February of 2000. This high temporal resolution dataset can not only provide additional background information to TRMM and other satellite missions, but also allow observing a wide range of meteorological phenomena from space, such as, mesoscale convection systems, tropical cyclones, hurricanes, etc. The dataset can also be used to verify model simulations. Despite that the data can be downloaded via ftp, however, its large volume poses a challenge for many users. A single file occupies about 70 MB disk space and there is a total of approximately 73,000 files (approximately 4.5 TB) for the past 8 years. In order to facilitate data access, we have developed a web prototype to allow users to conduct online visualization and analysis of this dataset. With a web browser and few mouse clicks, users can have a full access to over 8 year and over 4.5 TB data and generate black and white IR imagery and animation without downloading any software and data. In short, you can make your own images! Basic functions include selection of area of interest, single imagery or animation, a time skip capability for different temporal resolution and image size. Users can save an animation as a file (animated gif) and import it in other presentation software, such as, Microsoft PowerPoint. The prototype will be integrated into GIOVANNI and existing GIOVANNI capabilities, such as, data download, Google Earth KMZ, etc will be available. Users will also be able to access other data products in the GIOVANNI family.

Towards a High-Resolution Global Inundation Delineation Dataset

NASA Astrophysics Data System (ADS)

Fluet-Chouinard, E.; Lehner, B.

2011-12-01

Although their importance for biodiversity, flow regulation and ecosystem service provision is widely recognized, wetlands and temporarily inundated landscapes remain poorly mapped globally because of their inherent elusive nature. Inventorying of wetland resources has been identified in international agreements as an essential component of appropriate conservation efforts and management initiatives of these threatened ecosystems. However, despite recent advances in remote sensing surface water monitoring, current inventories of surface water variations remain incomplete at the regional-to-global scale due to methodological limitations restricting truly global application. Remote sensing wetland applications such as SAR L-band are particularly constrained by image availability and heterogeneity of acquisition dates, while coarse resolution passive microwave and multi-sensor methods cannot discriminate distinct surface water bodies. As a result, the most popular global wetland dataset remains to this day the Global Lake & Wetland Database (Lehner and Doll, 2004) a spatially inconsistent database assembled from various existing data sources. The approach taken in this project circumvents the limitations of current global wetland monitoring methods by combining globally available topographic and hydrographic data to downscale coarse resolution global inundation data (Prigent et al., 2007) and thus create a superior inundation delineation map product. The developed procedure downscales inundation data from the coarse resolution (~27km) of current passive microwave sensors to the finer spatial resolution (~500m) of the topographic and hydrographic layers of HydroSHEDS' data suite (Lehner et al., 2006), while retaining the high temporal resolution of the multi-sensor inundation dataset. From the downscaling process emerges new information on the specific location of inundation, but also on its frequency and duration. The downscaling algorithm employs a decision tree classifier trained on regional remote sensing wetland maps, to derive inundation probability followed by a seeded region growing segmentation process to redistribute the inundated area at the finer resolution. Assessment of the algorithm's performance is accomplished by evaluating the level of agreement between its outputted downscaled inundation maps and existing regional remote sensing inundation delineation. Upon completion, this project's will offer a dynamic globally seamless inundation map at an unprecedented spatial and temporal scale, which will provide the baseline inventory long requested by the research community, and will open the door to a wide array of possible conservation and hydrological modeling applications which were until now data-restricted. Literature Lehner, B., K. Verdin, and A. Jarvis. 2008. New global hydrography derived from spaceborne elevation data. Eos 89, no. 10. Lehner, B, and P Doll. 2004. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296, no. 1-4: 1-22. Prigent, C., F. Papa, F. Aires, W. B. Rossow, and E. Matthews. 2007. Global inundation dynamics inferred from multiple satellite observations, 1993-2000. Journal of Geophysical Research 112, no. D12: 1-13.

Dual Coordination of Post Translational Modifications in Human Protein Networks

PubMed Central

Woodsmith, Jonathan; Kamburov, Atanas; Stelzl, Ulrich

2013-01-01

Post-translational modifications (PTMs) regulate protein activity, stability and interaction profiles and are critical for cellular functioning. Further regulation is gained through PTM interplay whereby modifications modulate the occurrence of other PTMs or act in combination. Integration of global acetylation, ubiquitination and tyrosine or serine/threonine phosphorylation datasets with protein interaction data identified hundreds of protein complexes that selectively accumulate each PTM, indicating coordinated targeting of specific molecular functions. A second layer of PTM coordination exists in these complexes, mediated by PTM integration (PTMi) spots. PTMi spots represent very dense modification patterns in disordered protein regions and showed an equally high mutation rate as functional protein domains in cancer, inferring equivocal importance for cellular functioning. Systematic PTMi spot identification highlighted more than 300 candidate proteins for combinatorial PTM regulation. This study reveals two global PTM coordination mechanisms and emphasizes dataset integration as requisite in proteomic PTM studies to better predict modification impact on cellular signaling. PMID:23505349

1-km Global Anthropogenic Heat Flux Database for Urban Climate Studies

NASA Astrophysics Data System (ADS)

Dong, Y.; Varquez, A. C. G.; Kanda, M.

2016-12-01

Among various factors contributing to warming in cities, anthropogenic heat emission (AHE), defined by heat fluxes arising from human consumption of energy, has the most obvious influence. Despite this, estimation of the AHE distribution is challenging and assumed almost uniform in investigations of the regional atmospheric environment. In this study, we introduce a top-down method for estimating a global distribution of AHE (see attachment), with a high spatial resolution of 30 arc-seconds and temporal resolution of 1 hour. Annual average AHE was derived from human metabolic heating and primary energy consumption, which was further divided into three components based on consumer sector: heat loss, heat emissions from industrial-related sectors and heat emissions from commercial, residential and transport sectors (CRT). The first and second components were equally distributed throughout the country and populated areas, respectively. Bulk AHE from the CRT was proportionally distributed using a global population dataset with a nighttime lights adjustment. An empirical function to estimate monthly fluctuations of AHE based on monthly temperatures was derived from various city measurements. Finally, a global AHE database was constructed for the year 2013. Comparisons between our proposed AHE and other existing datasets revealed that a problem of AHE underestimation at central urban areas existing in previous top-down models was significantly mitigated by the nighttime lights adjustment. A strong agreement in the monthly profiles of AHE between our database and other bottom-up datasets further proved the validity of our current methodology. Investigations of AHE in the 29 largest urban agglomerations globally highlighted that the share of heat emissions from CRT sectors to the total AHE at the city level was 40-95%, whereas the share of metabolic heating varied closely depending on the level of economic development in the city. Incorporation of our proposed AHE data into climate models will provide a more realistic representation of urban atmospheric environment, leading to a deeper understanding of urban climate change. Acknowledgment: This research was supported by the Environment Research and Technology Development Fund (S-14) of the Ministry of the Environment, Japan

Evaluation and Applications of Cloud Climatologies from CALIOP

NASA Technical Reports Server (NTRS)

Winker, David; Getzewitch, Brian; Vaughan, Mark

2008-01-01

Clouds have a major impact on the Earth radiation budget and differences in the representation of clouds in global climate models are responsible for much of the spread in predicted climate sensitivity. Existing cloud climatologies, against which these models can be tested, have many limitations. The CALIOP lidar, carried on the CALIPSO satellite, has now acquired over two years of nearly continuous cloud and aerosol observations. This dataset provides an improved basis for the characterization of 3-D global cloudiness. Global average cloud cover measured by CALIOP is about 75%, significantly higher than for existing cloud climatologies due to the sensitivity of CALIOP to optically thin cloud. Day/night biases in cloud detection appear to be small. This presentation will discuss detection sensitivity and other issues associated with producing a cloud climatology, characteristics of cloud cover statistics derived from CALIOP data, and applications of those statistics.

Sampling biases in datasets of historical mean air temperature over land.

PubMed

Wang, Kaicun

2014-04-10

Global mean surface air temperature (Ta) has been reported to have risen by 0.74°C over the last 100 years. However, the definition of mean Ta is still a subject of debate. The most defensible definition might be the integral of the continuous temperature measurements over a day (Td0). However, for technological and historical reasons, mean Ta over land have been taken to be the average of the daily maximum and minimum temperature measurements (Td1). All existing principal global temperature analyses over land rely heavily on Td1. Here, I make a first quantitative assessment of the bias in the use of Td1 to estimate trends of mean Ta using hourly Ta observations at 5600 globally distributed weather stations from the 1970s to 2013. I find that the use of Td1 has a negligible impact on the global mean warming rate. However, the trend of Td1 has a substantial bias at regional and local scales, with a root mean square error of over 25% at 5° × 5° grids. Therefore, caution should be taken when using mean Ta datasets based on Td1 to examine high resolution details of warming trends.

Implementing microscopic charcoal in a global climate-aerosol model

NASA Astrophysics Data System (ADS)

Gilgen, Anina; Lohmann, Ulrike; Brügger, Sandra; Adolf, Carole; Ickes, Luisa

2017-04-01

Information about past fire activity is crucial to validate fire models and to better understand their deficiencies. Several paleofire records exist, among them ice cores and sediments, which preserve fire tracers like levoglucosan, vanillic acid, or charcoal particles. In this work, we implement microscopic charcoal particles (maximum dimension 10-100 μm) into the global climate-aerosol model ECHAM6.3HAM2.3. Since we are not aware of any reliable estimates of microscopic charcoal emissions, we scaled black carbon emissions from GFAS to capture the charcoal fluxes from a calibration dataset. After that, model results were compared with a validation dataset. The coarse model resolution (T63L31; 1.9°x1.9°) impedes the model to capture local variability of charcoal fluxes. However, variability on the global scale is pronounced due to highly-variable fire emissions. In future, we plan to model charcoal fluxes in the past 1-2 centuries using fire emissions provided from fire models. Furthermore, we intend to compare modelled charcoal fluxes from prescribed fire emissions with those calculated by an interactive fire model.

Data for polar-regions research

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jenne, R.L.

1992-03-01

Datasets available for polar research on global change topics are summarized. Emphasis is given to data that define the large, including rawinsonde data, surface meteorological observations, cloud drift winds, atmospheric analyses, sea ice, planetary radiation, and ocean forcing. Plans are discussed for making improved atmospheric analyses, using existing data. The use of CD-ROMs and DAT technologies for data distribution is discussed and selected CD-ROMs are listed.

Measuring river from the cloud - River width algorithm development on Google Earth Engine

NASA Astrophysics Data System (ADS)

Yang, X.; Pavelsky, T.; Allen, G. H.; Donchyts, G.

2017-12-01

Rivers are some of the most dynamic features of the terrestrial land surface. They help distribute freshwater, nutrients, sediment, and they are also responsible for some of the greatest natural hazards. Despite their importance, our understanding of river behavior is limited at the global scale, in part because we do not have a river observational dataset that spans both time and space. Remote sensing data represent a rich, largely untapped resource for observing river dynamics. In particular, publicly accessible archives of satellite optical imagery, which date back to the 1970s, can be used to study the planview morphodynamics of rivers at the global scale. Here we present an image processing algorithm developed using the Google Earth Engine cloud-based platform, that can automatically extracts river centerlines and widths from Landsat 5, 7, and 8 scenes at 30 m resolution. Our algorithm makes use of the latest monthly global surface water history dataset and an existing Global River Width from Landsat (GRWL) dataset to efficiently extract river masks from each Landsat scene. Then a combination of distance transform and skeletonization techniques are used to extract river centerlines. Finally, our algorithm calculates wetted river width at each centerline pixel perpendicular to its local centerline direction. We validated this algorithm using in situ data estimated from 16 USGS gauge stations (N=1781). We find that 92% of the width differences are within 60 m (i.e. the minimum length of 2 Landsat pixels). Leveraging Earth Engine's infrastructure of collocated data and processing power, our goal is to use this algorithm to reconstruct the morphodynamic history of rivers globally by processing over 100,000 Landsat 5 scenes, covering from 1984 to 2013.

Robust continuous clustering

PubMed Central

Shah, Sohil Atul

2017-01-01

Clustering is a fundamental procedure in the analysis of scientific data. It is used ubiquitously across the sciences. Despite decades of research, existing clustering algorithms have limited effectiveness in high dimensions and often require tuning parameters for different domains and datasets. We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. The presented algorithm optimizes a smooth continuous objective, which is based on robust statistics and allows heavily mixed clusters to be untangled. The continuous nature of the objective also allows clustering to be integrated as a module in end-to-end feature learning pipelines. We demonstrate this by extending the algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a continuous global objective. The presented approach is evaluated on large datasets of faces, hand-written digits, objects, newswire articles, sensor readings from the Space Shuttle, and protein expression levels. Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank. PMID:28851838

A high-resolution European dataset for hydrologic modeling

NASA Astrophysics Data System (ADS)

Ntegeka, Victor; Salamon, Peter; Gomes, Goncalo; Sint, Hadewij; Lorini, Valerio; Thielen, Jutta

2013-04-01

There is an increasing demand for large scale hydrological models not only in the field of modeling the impact of climate change on water resources but also for disaster risk assessments and flood or drought early warning systems. These large scale models need to be calibrated and verified against large amounts of observations in order to judge their capabilities to predict the future. However, the creation of large scale datasets is challenging for it requires collection, harmonization, and quality checking of large amounts of observations. For this reason, only a limited number of such datasets exist. In this work, we present a pan European, high-resolution gridded dataset of meteorological observations (EFAS-Meteo) which was designed with the aim to drive a large scale hydrological model. Similar European and global gridded datasets already exist, such as the HadGHCND (Caesar et al., 2006), the JRC MARS-STAT database (van der Goot and Orlandi, 2003) and the E-OBS gridded dataset (Haylock et al., 2008). However, none of those provide similarly high spatial resolution and/or a complete set of variables to force a hydrologic model. EFAS-Meteo contains daily maps of precipitation, surface temperature (mean, minimum and maximum), wind speed and vapour pressure at a spatial grid resolution of 5 x 5 km for the time period 1 January 1990 - 31 December 2011. It furthermore contains calculated radiation, which is calculated by using a staggered approach depending on the availability of sunshine duration, cloud cover and minimum and maximum temperature, and evapotranspiration (potential evapotranspiration, bare soil and open water evapotranspiration). The potential evapotranspiration was calculated using the Penman-Monteith equation with the above-mentioned meteorological variables. The dataset was created as part of the development of the European Flood Awareness System (EFAS) and has been continuously updated throughout the last years. The dataset variables are used as inputs to the hydrological calibration and validation of EFAS as well as for establishing long-term discharge "proxy" climatologies which can then in turn be used for statistical analysis to derive return periods or other time series derivatives. In addition, this dataset will be used to assess climatological trends in Europe. Unfortunately, to date no baseline dataset at the European scale exists to test the quality of the herein presented data. Hence, a comparison against other existing datasets can therefore only be an indication of data quality. Due to availability, a comparison was made for precipitation and temperature only, arguably the most important meteorological drivers for hydrologic models. A variety of analyses was undertaken at country scale against data reported to EUROSTAT and E-OBS datasets. The comparison revealed that while the datasets showed overall similar temporal and spatial patterns, there were some differences in magnitudes especially for precipitation. It is not straightforward to define the specific cause for these differences. However, in most cases the comparatively low observation station density appears to be the principal reason for the differences in magnitude.

Generation of the 30 M-Mesh Global Digital Surface Model by Alos Prism

NASA Astrophysics Data System (ADS)

Tadono, T.; Nagai, H.; Ishida, H.; Oda, F.; Naito, S.; Minakawa, K.; Iwamoto, H.

2016-06-01

Topographical information is fundamental to many geo-spatial related information and applications on Earth. Remote sensing satellites have the advantage in such fields because they are capable of global observation and repeatedly. Several satellite-based digital elevation datasets were provided to examine global terrains with medium resolutions e.g. the Shuttle Radar Topography Mission (SRTM), the global digital elevation model by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER GDEM). A new global digital surface model (DSM) dataset using the archived data of the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS, nicknamed "Daichi") has been completed on March 2016 by Japan Aerospace Exploration Agency (JAXA) collaborating with NTT DATA Corp. and Remote Sensing Technology Center, Japan. This project is called "ALOS World 3D" (AW3D), and its dataset consists of the global DSM dataset with 0.15 arcsec. pixel spacing (approx. 5 m mesh) and ortho-rectified PRISM image with 2.5 m resolution. JAXA is also processing the global DSM with 1 arcsec. spacing (approx. 30 m mesh) based on the AW3D DSM dataset, and partially releasing it free of charge, which calls "ALOS World 3D 30 m mesh" (AW3D30). The global AW3D30 dataset will be released on May 2016. This paper describes the processing status, a preliminary validation result of the AW3D30 DSM dataset, and its public release status. As a summary of the preliminary validation of AW3D30 DSM, 4.40 m (RMSE) of the height accuracy of the dataset was confirmed using 5,121 independent check points distributed in the world.

Assessment of the NASA-USGS Global Land Survey (GLS) Datasets

USGS Publications Warehouse

Gutman, Garik; Huang, Chengquan; Chander, Gyanesh; Noojipady, Praveen; Masek, Jeffery G.

2013-01-01

The Global Land Survey (GLS) datasets are a collection of orthorectified, cloud-minimized Landsat-type satellite images, providing near complete coverage of the global land area decadally since the early 1970s. The global mosaics are centered on 1975, 1990, 2000, 2005, and 2010, and consist of data acquired from four sensors: Enhanced Thematic Mapper Plus, Thematic Mapper, Multispectral Scanner, and Advanced Land Imager. The GLS datasets have been widely used in land-cover and land-use change studies at local, regional, and global scales. This study evaluates the GLS datasets with respect to their spatial coverage, temporal consistency, geodetic accuracy, radiometric calibration consistency, image completeness, extent of cloud contamination, and residual gaps. In general, the three latest GLS datasets are of a better quality than the GLS-1990 and GLS-1975 datasets, with most of the imagery (85%) having cloud cover of less than 10%, the acquisition years clustered much more tightly around their target years, better co-registration relative to GLS-2000, and better radiometric absolute calibration. Probably, the most significant impediment to scientific use of the datasets is the variability of image phenology (i.e., acquisition day of year). This paper provides end-users with an assessment of the quality of the GLS datasets for specific applications, and where possible, suggestions for mitigating their deficiencies.

Global relationships in river hydromorphology

NASA Astrophysics Data System (ADS)

Pavelsky, T.; Lion, C.; Allen, G. H.; Durand, M. T.; Schumann, G.; Beighley, E.; Yang, X.

2017-12-01

Since the widespread adoption of digital elevation models (DEMs) in the 1980s, most global and continental-scale analysis of river flow characteristics has been focused on measurements derived from DEMs such as drainage area, elevation, and slope. These variables (especially drainage area) have been related to other quantities of interest such as river width, depth, and velocity via empirical relationships that often take the form of power laws. More recently, a number of groups have developed more direct measurements of river location and some aspects of planform geometry from optical satellite imagery on regional, continental, and global scales. However, these satellite-derived datasets often lack many of the qualities that make DEM=derived datasets attractive, including robust network topology. Here, we present analysis of a dataset that combines the Global River Widths from Landsat (GRWL) database of river location, width, and braiding index with a river database extracted from the Shuttle Radar Topography Mission DEM and the HydroSHEDS dataset. Using these combined tools, we present a dataset that includes measurements of river width, slope, braiding index, upstream drainage area, and other variables. The dataset is available everywhere that both datasets are available, which includes all continental areas south of 60N with rivers sufficiently large to be observed with Landsat imagery. We use the dataset to examine patterns and frequencies of river form across continental and global scales as well as global relationships among variables including width, slope, and drainage area. The results demonstrate the complex relationships among different dimensions of river hydromorphology at the global scale.

Using Multiple Big Datasets and Machine Learning to Produce a New Global Particulate Dataset: A Technology Challenge Case Study

NASA Astrophysics Data System (ADS)

Lary, D. J.

2013-12-01

A BigData case study is described where multiple datasets from several satellites, high-resolution global meteorological data, social media and in-situ observations are combined using machine learning on a distributed cluster using an automated workflow. The global particulate dataset is relevant to global public health studies and would not be possible to produce without the use of the multiple big datasets, in-situ data and machine learning.To greatly reduce the development time and enhance the functionality a high level language capable of parallel processing has been used (Matlab). A key consideration for the system is high speed access due to the large data volume, persistence of the large data volumes and a precise process time scheduling capability.

The effects of spatial population dataset choice on estimates of population at risk of disease

PubMed Central

2011-01-01

Background The spatial modeling of infectious disease distributions and dynamics is increasingly being undertaken for health services planning and disease control monitoring, implementation, and evaluation. Where risks are heterogeneous in space or dependent on person-to-person transmission, spatial data on human population distributions are required to estimate infectious disease risks, burdens, and dynamics. Several different modeled human population distribution datasets are available and widely used, but the disparities among them and the implications for enumerating disease burdens and populations at risk have not been considered systematically. Here, we quantify some of these effects using global estimates of populations at risk (PAR) of P. falciparum malaria as an example. Methods The recent construction of a global map of P. falciparum malaria endemicity enabled the testing of different gridded population datasets for providing estimates of PAR by endemicity class. The estimated population numbers within each class were calculated for each country using four different global gridded human population datasets: GRUMP (~1 km spatial resolution), LandScan (~1 km), UNEP Global Population Databases (~5 km), and GPW3 (~5 km). More detailed assessments of PAR variation and accuracy were conducted for three African countries where census data were available at a higher administrative-unit level than used by any of the four gridded population datasets. Results The estimates of PAR based on the datasets varied by more than 10 million people for some countries, even accounting for the fact that estimates of population totals made by different agencies are used to correct national totals in these datasets and can vary by more than 5% for many low-income countries. In many cases, these variations in PAR estimates comprised more than 10% of the total national population. The detailed country-level assessments suggested that none of the datasets was consistently more accurate than the others in estimating PAR. The sizes of such differences among modeled human populations were related to variations in the methods, input resolution, and date of the census data underlying each dataset. Data quality varied from country to country within the spatial population datasets. Conclusions Detailed, highly spatially resolved human population data are an essential resource for planning health service delivery for disease control, for the spatial modeling of epidemics, and for decision-making processes related to public health. However, our results highlight that for the low-income regions of the world where disease burden is greatest, existing datasets display substantial variations in estimated population distributions, resulting in uncertainty in disease assessments that utilize them. Increased efforts are required to gather contemporary and spatially detailed demographic data to reduce this uncertainty, particularly in Africa, and to develop population distribution modeling methods that match the rigor, sophistication, and ability to handle uncertainty of contemporary disease mapping and spread modeling. In the meantime, studies that utilize a particular spatial population dataset need to acknowledge the uncertainties inherent within them and consider how the methods and data that comprise each will affect conclusions. PMID:21299885

Outcomes of a NASA Workshop to Develop a Portfolio of Low Latency Datasets for Time-Sensitive Applications

NASA Technical Reports Server (NTRS)

Davies, Diane K.; Brown, Molly E.; Green, David S.; Michael, Karen A.; Murray, John J.; Justice, Christopher O.; Soja, Amber J.

2016-01-01

It is widely accepted that time-sensitive remote sensing data serve the needs of decision makers in the applications communities and yet to date, a comprehensive portfolio of NASA low latency datasets has not been available. This paper will describe the NASA low latency, or Near-Real Time (NRT), portfolio, how it was developed and plans to make it available online through a portal that leverages the existing EOSDIS capabilities such as the Earthdata Search Client (https:search.earthdata.nasa.gov), the Common Metadata Repository (CMR) and the Global Imagery Browse Service (GIBS). This paper will report on the outcomes of a NASA Workshop to Develop a Portfolio of Low Latency Datasets for Time-Sensitive Applications (27-29 September 2016 at NASA Langley Research Center, Hampton VA). The paper will also summarize findings and recommendations from the meeting outlining perceived shortfalls and opportunities for low latency research and application science.

The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Arkin, Philip; Chang, Alfred; Ferraro, Ralph; Gruber, Arnold; Janowiak, John; McNab, Alan; Rudolf, Bruno; Schneider, Udo

1997-01-01

The Global Precipitation Climatology Project (GPCP) has released the GPCP Version 1 Combined Precipitation Data Set, a global, monthly precipitation dataset covering the period July 1987 through December 1995. The primary product in the dataset is a merged analysis incorporating precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit -satellite infrared data, and rain gauge observations. The dataset also contains the individual input fields, a combination of the microwave and infrared satellite estimates, and error estimates for each field. The data are provided on 2.5 deg x 2.5 deg latitude-longitude global grids. Preliminary analyses show general agreement with prior studies of global precipitation and extends prior studies of El Nino-Southern Oscillation precipitation patterns. At the regional scale there are systematic differences with standard climatologies.

In-situ databases and comparison of ESA Ocean Colour Climate Change Initiative (OC-CCI) products with precursor data, towards an integrated approach for ocean colour validation and climate studies

NASA Astrophysics Data System (ADS)

Brotas, Vanda; Valente, André; Couto, André B.; Grant, Mike; Chuprin, Andrei; Jackson, Thomas; Groom, Steve; Sathyendranath, Shubha

2014-05-01

Ocean colour (OC) is an Oceanic Essential Climate Variable, which is used by climate modellers and researchers. The European Space Agency (ESA) Climate Change Initiative project, is the ESA response for the need of climate-quality satellite data, with the goal of providing stable, long-term, satellite-based ECV data products. The ESA Ocean Colour CCI focuses on the production of Ocean Colour ECV uses remote sensing reflectances to derive inherent optical properties and chlorophyll a concentration from ESA's MERIS (2002-2012) and NASA's SeaWiFS (1997 - 2010) and MODIS (2002-2012) sensor archives. This work presents an integrated approach by setting up a global database of in situ measurements and by inter-comparing OC-CCI products with pre-cursor datasets. The availability of in situ databases is fundamental for the validation of satellite derived ocean colour products. A global distribution in situ database was assembled, from several pre-existing datasets, with data spanning between 1997 and 2012. It includes in-situ measurements of remote sensing reflectances, concentration of chlorophyll-a, inherent optical properties and diffuse attenuation coefficient. The database is composed from observations of the following datasets: NOMAD, SeaBASS, MERMAID, AERONET-OC, BOUSSOLE and HOTS. The result was a merged dataset tuned for the validation of satellite-derived ocean colour products. This was an attempt to gather, homogenize and merge, a large high-quality bio-optical marine in situ data, as using all datasets in a single validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. An inter-comparison analysis between OC-CCI chlorophyll-a product and satellite pre-cursor datasets was done with single missions and merged single mission products. Single mission datasets considered were SeaWiFS, MODIS-Aqua and MERIS; merged mission datasets were obtained from the GlobColour (GC) as well as the Making Earth Science Data Records for Use in Research Environments (MEaSUREs). OC-CCI product was found to be most similar to SeaWiFS record, and generally, the OC-CCI record was most similar to records derived from single mission than merged mission initiatives. Results suggest that CCI product is a more consistent dataset than other available merged mission initiatives. In conclusion, climate related science, requires long term data records to provide robust results, OC-CCI product proves to be a worthy data record for climate research, as it combines multi-sensor OC observations to provide a >15-year global error-characterized record.

A method to derive vegetation distribution maps for pollen dispersion models using birch as an example

NASA Astrophysics Data System (ADS)

Pauling, A.; Rotach, M. W.; Gehrig, R.; Clot, B.

2012-09-01

Detailed knowledge of the spatial distribution of sources is a crucial prerequisite for the application of pollen dispersion models such as, for example, COSMO-ART (COnsortium for Small-scale MOdeling - Aerosols and Reactive Trace gases). However, this input is not available for the allergy-relevant species such as hazel, alder, birch, grass or ragweed. Hence, plant distribution datasets need to be derived from suitable sources. We present an approach to produce such a dataset from existing sources using birch as an example. The basic idea is to construct a birch dataset using a region with good data coverage for calibration and then to extrapolate this relationship to a larger area by using land use classes. We use the Swiss forest inventory (1 km resolution) in combination with a 74-category land use dataset that covers the non-forested areas of Switzerland as well (resolution 100 m). Then we assign birch density categories of 0%, 0.1%, 0.5% and 2.5% to each of the 74 land use categories. The combination of this derived dataset with the birch distribution from the forest inventory yields a fairly accurate birch distribution encompassing entire Switzerland. The land use categories of the Global Land Cover 2000 (GLC2000; Global Land Cover 2000 database, 2003, European Commission, Joint Research Centre; resolution 1 km) are then calibrated with the Swiss dataset in order to derive a Europe-wide birch distribution dataset and aggregated onto the 7 km COSMO-ART grid. This procedure thus assumes that a certain GLC2000 land use category has the same birch density wherever it may occur in Europe. In order to reduce the strict application of this crucial assumption, the birch density distribution as obtained from the previous steps is weighted using the mean Seasonal Pollen Index (SPI; yearly sums of daily pollen concentrations). For future improvement, region-specific birch densities for the GLC2000 categories could be integrated into the mapping procedure.

A global dataset of crowdsourced land cover and land use reference data.

PubMed

Fritz, Steffen; See, Linda; Perger, Christoph; McCallum, Ian; Schill, Christian; Schepaschenko, Dmitry; Duerauer, Martina; Karner, Mathias; Dresel, Christopher; Laso-Bayas, Juan-Carlos; Lesiv, Myroslava; Moorthy, Inian; Salk, Carl F; Danylo, Olha; Sturn, Tobias; Albrecht, Franziska; You, Liangzhi; Kraxner, Florian; Obersteiner, Michael

2017-06-13

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general.

A global dataset of crowdsourced land cover and land use reference data

PubMed Central

Fritz, Steffen; See, Linda; Perger, Christoph; McCallum, Ian; Schill, Christian; Schepaschenko, Dmitry; Duerauer, Martina; Karner, Mathias; Dresel, Christopher; Laso-Bayas, Juan-Carlos; Lesiv, Myroslava; Moorthy, Inian; Salk, Carl F.; Danylo, Olha; Sturn, Tobias; Albrecht, Franziska; You, Liangzhi; Kraxner, Florian; Obersteiner, Michael

2017-01-01

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general. PMID:28608851

Developing a global crop model for maize, wheat, and soybean production

NASA Astrophysics Data System (ADS)

Deryng, D.; Ramankutty, N.; Sacks, W. J.

2008-12-01

Recently, the world food supply has faced a crisis due to increasing food prices driven by rising food demand, increasing fuel prices, poor harvests due to climate factors, and the use of crops such as maize and soybean to produce biofuel. In order to assess the future of global food availability, there is a need for understanding the factors underlying food production. Farmer management practices along with climatic conditions are the main elements directly influencing crop yield. As a consequence, estimations of future world food production require the use of a global crop model that simulates reasonably the effect of both climate and management practices on yield. Only a few global crop models have been developed to date, and currently none of them represent management factors adequately, principally due to the lack of spatially explicit datasets at the global scale. In this study, we present a global crop model designed for maize, wheat, and soybean production that incorporates planting and harvest decisions, along with irrigation options based on newly available data. The crop model is built on a simple water-balance algorithm based on the Penman- Monteith equation combined with a light use efficiency approach that calculates biomass production under non-nutrient-limiting conditions. We used a world crop calendar dataset to develop statistical relationships between climate variables and planting dates for different regions of the world. Development stages are defined based on total growing degree days required to reach the beginning of each phase. Irrigation options are considered in regions where water stress occurs and irrigation infrastructures exist. We use a global dataset on irrigated areas for each crop type. The quantity of water applied is then calculated in order to avoid water stress but with an upper threshold derived from total irrigation withdrawal quantity estimated by the global water use model WaterGAP 2. Our analysis will present the model sensitivity to different scenarios of management practices, e.g. planting date and water supply, under non-nutrient limited conditions. With this study, we hope to clarify the importance of planting date and irrigation versus climate for crop yield.

Regional-scale calculation of the LS factor using parallel processing

NASA Astrophysics Data System (ADS)

Liu, Kai; Tang, Guoan; Jiang, Ling; Zhu, A.-Xing; Yang, Jianyi; Song, Xiaodong

2015-05-01

With the increase of data resolution and the increasing application of USLE over large areas, the existing serial implementation of algorithms for computing the LS factor is becoming a bottleneck. In this paper, a parallel processing model based on message passing interface (MPI) is presented for the calculation of the LS factor, so that massive datasets at a regional scale can be processed efficiently. The parallel model contains algorithms for calculating flow direction, flow accumulation, drainage network, slope, slope length and the LS factor. According to the existence of data dependence, the algorithms are divided into local algorithms and global algorithms. Parallel strategy are designed according to the algorithm characters including the decomposition method for maintaining the integrity of the results, optimized workflow for reducing the time taken for exporting the unnecessary intermediate data and a buffer-communication-computation strategy for improving the communication efficiency. Experiments on a multi-node system show that the proposed parallel model allows efficient calculation of the LS factor at a regional scale with a massive dataset.

Global Lakes Sentinel Services: Water Quality Parameters Retrieval in Lakes Using the MERIS and S3-OLCI Band Sets

NASA Astrophysics Data System (ADS)

Peters, Steef; Alikas, Krista; Hommersom, Annelies; Latt, Silver; Reinart, Anu; Giardino, Claudia; Bresciani, Mariano; Philipson, Petra; Ruescas, Ana; Stelzer, Kerstin; Schenk, Karin; Heege, Thomas; Gege, Peter; Koponen, Sampsa; Kallio, Karri; Zhang, Yunlin

2015-12-01

The European collaborative project GLaSS aims to prepare for the use of the data streams from Sentinel 2 and Sentinel 3. Its focus is on inland waters, since these are considered to be sentinels for land-use- and climate change and need to be monitored closely. One of the objectives of the project is to compare existing water quality algorithms and parameterizations on the basis of in-situ spectral observations and Hydrolight simulations. A first achievement of the project is the collection of over 400 Rrs spectra with accompanying data on CHL, TSM, aCDOM and Secchi depth. Especially the dataset on Lake CDOM measurements represents a rather unique reference dataset.

MANOVA for distinguishing experts' perceptions about entrepreneurship using NES data from GEM

NASA Astrophysics Data System (ADS)

Correia, Aldina; Costa e Silva, Eliana; Lopes, Isabel C.; Braga, Alexandra

2016-12-01

Global Entrepreneurship Monitor is a large scale database for internationally comparative entrepreneurship that includes information about many aspects of entrepreneurship activities, perceptions, conditions, national and regional policy, among others, of a large number of countries. This project has two main sources of primary data: the Adult Population Survey and the National Expert Survey. In this work the 2011 and 2012 National Expert Survey datasets are studied. Our goal is to analyze the effects of the different type of entrepreneurship expert specialization on the perceptions about the Entrepreneurial Framework Conditions. For this purpose the multivariate analysis of variance is used. Some similarities between the results obtained for the 2011 and 2012 datasets were found, however the differences between experts still exist.

Five year global dataset: NMC operational analyses (1978 to 1982)

NASA Technical Reports Server (NTRS)

Straus, David; Ardizzone, Joseph

1987-01-01

This document describes procedures used in assembling a five year dataset (1978 to 1982) using NMC Operational Analysis data. These procedures entailed replacing missing and unacceptable data in order to arrive at a complete dataset that is continuous in time. In addition, a subjective assessment on the integrity of all data (both preliminary and final) is presented. Documentation on tapes comprising the Five Year Global Dataset is also included.

Downscaling global precipitation for local applications - a case for the Rhine basin

NASA Astrophysics Data System (ADS)

Sperna Weiland, Frederiek; van Verseveld, Willem; Schellekens, Jaap

2017-04-01

Within the EU FP7 project eartH2Observe a global Water Resources Re-analysis (WRR) is being developed. This re-analysis consists of meteorological and hydrological water balance variables with global coverage, spanning the period 1979-2014 at 0.25 degrees resolution (Schellekens et al., 2016). The dataset can be of special interest in regions with limited in-situ data availability, yet for local scale analysis particularly in mountainous regions, a resolution of 0.25 degrees may be too coarse and downscaling the data to a higher resolution may be required. A downscaling toolbox has been made that includes spatial downscaling of precipitation based on the global WorldClim dataset that is available at 1 km resolution as a monthly climatology (Hijmans et al., 2005). The input of the down-scaling tool are either the global eartH2Observe WRR1 and WRR2 datasets based on the WFDEI correction methodology (Weedon et al., 2014) or the global Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset (Beck et al., 2016). Here we present a validation of the datasets over the Rhine catchment by means of a distributed hydrological model (wflow, Schellekens et al., 2014) using a number of precipitation scenarios. (1) We start by running the model using the local reference dataset derived by spatial interpolation of gauge observations. Furthermore we use (2) the MSWEP dataset at the native 0.25-degree resolution followed by (3) MSWEP downscaled with the WorldClim dataset and final (4) MSWEP downscaled with the local reference dataset. The validation will be based on comparison of the modeled river discharges as well as rainfall statistics. We expect that down-scaling the MSWEP dataset with the WorldClim data to higher resolution will increase its performance. To test the performance of the down-scaling routine we have added a run with MSWEP data down-scaled with the local dataset and compare this with the run based on the local dataset itself. - Beck, H. E. et al., 2016. MSWEP: 3-hourly 0.25° global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-236, accepted for final publication. - Hijmans, R.J. et al., 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978. - Schellekens, J. et al., 2016. A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data Discuss., doi:10.5194/essd-2016-55, under review. - Schellekens, J. et al., 2014. Rapid setup of hydrological and hydraulic models using OpenStreetMap and the SRTM derived digital elevation model. Environmental Modelling&Software - Weedon, G.P. et al., 2014. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resources Research, 50, doi:10.1002/2014WR015638.

Global Expanded Nutrient Supply (GENuS) Model: A New Method for Estimating the Global Dietary Supply of Nutrients

PubMed Central

Golden, Christopher D.; Mozaffarian, Dariush

2016-01-01

Insufficient data exist for accurate estimation of global nutrient supplies. Commonly used global datasets contain key weaknesses: 1) data with global coverage, such as the FAO food balance sheets, lack specific information about many individual foods and no information on micronutrient supplies nor heterogeneity among subnational populations, while 2) household surveys provide a closer approximation of consumption, but are often not nationally representative, do not commonly capture many foods consumed outside of the home, and only provide adequate information for a few select populations. Here, we attempt to improve upon these datasets by constructing a new model—the Global Expanded Nutrient Supply (GENuS) model—to estimate nutrient availabilities for 23 individual nutrients across 225 food categories for thirty-four age-sex groups in nearly all countries. Furthermore, the model provides historical trends in dietary nutritional supplies at the national level using data from 1961–2011. We determine supplies of edible food by expanding the food balance sheet data using FAO production and trade data to increase food supply estimates from 98 to 221 food groups, and then estimate the proportion of major cereals being processed to flours to increase to 225. Next, we estimate intake among twenty-six demographic groups (ages 20+, both sexes) in each country by using data taken from the Global Dietary Database, which uses nationally representative surveys to relate national averages of food consumption to individual age and sex-groups; for children and adolescents where GDD data does not yet exist, average calorie-adjusted amounts are assumed. Finally, we match food supplies with nutrient densities from regional food composition tables to estimate nutrient supplies, running Monte Carlo simulations to find the range of potential nutrient supplies provided by the diet. To validate our new method, we compare the GENuS estimates of nutrient supplies against independent estimates by the USDA for historical US nutrition and find very good agreement for 21 of 23 nutrients, though sodium and dietary fiber will require further improvement. PMID:26807571

Global Expanded Nutrient Supply (GENuS) Model: A New Method for Estimating the Global Dietary Supply of Nutrients.

PubMed

Smith, Matthew R; Micha, Renata; Golden, Christopher D; Mozaffarian, Dariush; Myers, Samuel S

2016-01-01

Insufficient data exist for accurate estimation of global nutrient supplies. Commonly used global datasets contain key weaknesses: 1) data with global coverage, such as the FAO food balance sheets, lack specific information about many individual foods and no information on micronutrient supplies nor heterogeneity among subnational populations, while 2) household surveys provide a closer approximation of consumption, but are often not nationally representative, do not commonly capture many foods consumed outside of the home, and only provide adequate information for a few select populations. Here, we attempt to improve upon these datasets by constructing a new model--the Global Expanded Nutrient Supply (GENuS) model--to estimate nutrient availabilities for 23 individual nutrients across 225 food categories for thirty-four age-sex groups in nearly all countries. Furthermore, the model provides historical trends in dietary nutritional supplies at the national level using data from 1961-2011. We determine supplies of edible food by expanding the food balance sheet data using FAO production and trade data to increase food supply estimates from 98 to 221 food groups, and then estimate the proportion of major cereals being processed to flours to increase to 225. Next, we estimate intake among twenty-six demographic groups (ages 20+, both sexes) in each country by using data taken from the Global Dietary Database, which uses nationally representative surveys to relate national averages of food consumption to individual age and sex-groups; for children and adolescents where GDD data does not yet exist, average calorie-adjusted amounts are assumed. Finally, we match food supplies with nutrient densities from regional food composition tables to estimate nutrient supplies, running Monte Carlo simulations to find the range of potential nutrient supplies provided by the diet. To validate our new method, we compare the GENuS estimates of nutrient supplies against independent estimates by the USDA for historical US nutrition and find very good agreement for 21 of 23 nutrients, though sodium and dietary fiber will require further improvement.

A comparison of U.S. geological survey seamless elevation models with shuttle radar topography mission data

USGS Publications Warehouse

Gesch, D.; Williams, J.; Miller, W.

2001-01-01

Elevation models produced from Shuttle Radar Topography Mission (SRTM) data will be the most comprehensive, consistently processed, highest resolution topographic dataset ever produced for the Earth's land surface. Many applications that currently use elevation data will benefit from the increased availability of data with higher accuracy, quality, and resolution, especially in poorly mapped areas of the globe. SRTM data will be produced as seamless data, thereby avoiding many of the problems inherent in existing multi-source topographic databases. Serving as precursors to SRTM datasets, the U.S. Geological Survey (USGS) has produced and is distributing seamless elevation datasets that facilitate scientific use of elevation data over large areas. GTOPO30 is a global elevation model with a 30 arc-second resolution (approximately 1-kilometer). The National Elevation Dataset (NED) covers the United States at a resolution of 1 arc-second (approximately 30-meters). Due to their seamless format and broad area coverage, both GTOPO30 and NED represent an advance in the usability of elevation data, but each still includes artifacts from the highly variable source data used to produce them. The consistent source data and processing approach for SRTM data will result in elevation products that will be a significant addition to the current availability of seamless datasets, specifically for many areas outside the U.S. One application that demonstrates some advantages that may be realized with SRTM data is delineation of land surface drainage features (watersheds and stream channels). Seamless distribution of elevation data in which a user interactively specifies the area of interest and order parameters via a map server is already being successfully demonstrated with existing USGS datasets. Such an approach for distributing SRTM data is ideal for a dataset that undoubtedly will be of very high interest to the spatial data user community.

A global distributed basin morphometric dataset

NASA Astrophysics Data System (ADS)

Shen, Xinyi; Anagnostou, Emmanouil N.; Mei, Yiwen; Hong, Yang

2017-01-01

Basin morphometry is vital information for relating storms to hydrologic hazards, such as landslides and floods. In this paper we present the first comprehensive global dataset of distributed basin morphometry at 30 arc seconds resolution. The dataset includes nine prime morphometric variables; in addition we present formulas for generating twenty-one additional morphometric variables based on combination of the prime variables. The dataset can aid different applications including studies of land-atmosphere interaction, and modelling of floods and droughts for sustainable water management. The validity of the dataset has been consolidated by successfully repeating the Hack's law.

Open Earth Observation Data for Measuring Anthropogenic Development in Coastal Zones at Continental Scales

NASA Astrophysics Data System (ADS)

Du, X.; Leinenkugel, P.; Guo, H.; Kuenzer, C.

2017-12-01

During the recent decades, global coasts are undergoing tremendous change due to accelerating socio-economic growth, which has severe effects on the functioning of global coastal systems. In view of this, accurate, timely, and area-wide global information on natural as well as anthropogenic processes in the coastal zone are of paramount importance for sustainable coastal development. A broad range of freely available satellite derived products, and open geo-datasets, as well as statistics with global coverage exist that have not yet been fully exploited to evaluate human development patterns in coastal areas. In this study, we demonstrate the potential of freely and openly available EO and GEO data sets for characterizing and evaluating human development in coastal zones on large scales. Therefore, different geo-spatial dataset such as Global Urban Footprint (GUF), Open Street Map (OSM), time series of Global Human Settlement Layer (GHSL) and Climate Change Initiative (CCI) Land cover were acquired for the entire continental coast of Asia, defined as the terrestrial area 100 km from the coastline. In order to extract indices for the coastline, a reference structure was developed allowing the integration of a 2D spatial pattern of a given parameter to a certain location along the coast line. Based on this reference structure statistics for the coast were calculated every 5 km parallel to the coast line as well as for four different distance intervals from the coast. The results demonstrate the highly unequal distribution of coastal development with respect to urban and agricultural usage in Asia, with large differences between and within different countries. China coasts show the highest overall patterns of urban development, while countries such as Pakistan and Myanmar show comparably low levels with nearly no development evident absence from coastal metropolitan areas. Furthermore, a clear trend of decreasing urban development is evident with increasing distance from the coast. This study highlights the potential of global geo-spatial data products for deriving anthropogenic development indicators that can support the evaluation and monitoring for sustainable development of coastal zones, while also discussing the shortcomings of these datasets for such purposes.

Annual global tree cover estimated by fusing optical and SAR satellite observations

NASA Astrophysics Data System (ADS)

Feng, M.; Sexton, J. O.; Channan, S.; Townshend, J. R.

2017-12-01

Tree cover defined structurally as the proportional, vertically projected area of vegetation (including leaves, stems, branches, etc.) of woody plants above a given height affects terrestrial energy and water exchanges, photosynthesis and transpiration, net primary production, and carbon and nutrient fluxes. Tree cover provides a measurable attribute upon which forest cover may be defined. Changes in tree cover over time can be used to monitor and retrieve site-specific histories of forest disturbance, succession, and degradation. Measurements of Earth's tree cover have been produced at regional, national, and global extents. However, most representations are static, and those for which multiple time periods have been produced are neither intended nor adequate for consistent, long-term monitoring. Moreover, although a substantial proportion of change has been shown to occur at resolutions below 250 m, existing long-term, Landsat-resolution datasets are either produced as static layers or with annual, five- or ten-year temporal resolution. We have developed an algorithms to retrieve seamless and consistent, sub-hectare resolution estimates of tree-canopy from optical and radar satellite data sources (e.g., Landsat, Sentinel-2, and ALOS-PALSAR). Our approach to estimation enables assimilation of multiple data sources and produces estimates of both cover and its uncertainty at the scale of pixels. It has generated the world's first Landsat-based percent tree cover dataset in 2013. Our previous algorithms are being adapted to produce prototype percent-tree and water-cover layers globally in 2000, 2005, and 2010—as well as annually over North and South America from 2010 to 2015—from passive-optical (Landsat and Sentinel-2) and SAR measurements. Generating a global, annual dataset is beyond the scope of this support; however, North and South America represent all of the world's major biomes and so offer the complete global range of environmental sources of error and uncertainty.

Methods for mapping and monitoring global glaciovolcanism

NASA Astrophysics Data System (ADS)

Curtis, Aaron; Kyle, Philip

2017-03-01

The most deadly (Nevado del Ruiz, 1985) and the most costly (Eyjafjallajökull, 2010) eruptions of the last 100 years were both glaciovolcanic. Considering its great importance to studies of volcanic hazards, global climate, and even astrobiology, the global distribution of glaciovolcanism is insufficiently understood. We present and assess three algorithms for mapping, monitoring, and predicting likely centers of glaciovolcanic activity worldwide. Each algorithm intersects buffer zones representing known Holocene-active volcanic centers with existing datasets of snow, ice, and permafrost. Two detection algorithms, RGGA and PZGA, are simple spatial join operations computed from the Randolph Glacier Inventory and the Permafrost Zonation Index, respectively. The third, MDGA, is an algorithm run on all 15 available years of the MOD10A2 weekly snow cover product from the Terra MODIS satellite radiometer. Shortcomings and advantages of the three methods are discussed, including previously unreported blunders in the MOD10A2 dataset. Comparison of the results leads to an effective approach for integrating the three methods. We show that 20.4% of known Holocene volcanic centers host glaciers or areas of permanent snow. A further 10.9% potentially interact with permafrost. MDGA and PZGA do not rely on any human input, rendering them useful for investigations of change over time. An intermediate step in MDGA involves estimating the snow-covered area at every Holocene volcanic center. These estimations can be updated weekly with no human intervention. To investigate the feasibility of an automatic ice-loss alert system, we consider three examples of glaciovolcanism in the MDGA weekly dataset. We also discuss the potential use of PZGA to model past and future glaciovolcanism based on global circulation model outputs. Combined, the three algorithms provide an automated system for understanding the geographic and temporal patterns of global glaciovolcanism which should be of use for hazard assessment, the search for extreme microbiomes, climate models, and implementation of ice-cover-based volcano monitoring systems.

Reconstruction of spatially detailed global map of NH4+ and NO3- application in synthetic nitrogen fertilizer

NASA Astrophysics Data System (ADS)

Nishina, Kazuya; Ito, Akihiko; Hanasaki, Naota; Hayashi, Seiji

2017-02-01

Currently, available historical global N fertilizer map as an input data to global biogeochemical model is still limited and existing maps were not considered NH4+ and NO3- in the fertilizer application rates. This paper provides a method for constructing a new historical global nitrogen fertilizer application map (0.5° × 0.5° resolution) for the period 1961-2010 based on country-specific information from Food and Agriculture Organization statistics (FAOSTAT) and various global datasets. This new map incorporates the fraction of NH4+ (and NO3-) in N fertilizer inputs by utilizing fertilizer species information in FAOSTAT, in which species can be categorized as NH4+- and/or NO3--forming N fertilizers. During data processing, we applied a statistical data imputation method for the missing data (19 % of national N fertilizer consumption) in FAOSTAT. The multiple imputation method enabled us to fill gaps in the time-series data using plausible values using covariates information (year, population, GDP, and crop area). After the imputation, we downscaled the national consumption data to a gridded cropland map. Also, we applied the multiple imputation method to the available chemical fertilizer species consumption, allowing for the estimation of the NH4+ / NO3- ratio in national fertilizer consumption. In this study, the synthetic N fertilizer inputs in 2000 showed a general consistency with the existing N fertilizer map (Potter et al., 2010) in relation to the ranges of N fertilizer inputs. Globally, the estimated N fertilizer inputs based on the sum of filled data increased from 15 to 110 Tg-N during 1961-2010. On the other hand, the global NO3- input started to decline after the late 1980s and the fraction of NO3- in global N fertilizer decreased consistently from 35 to 13 % over a 50-year period. NH4+-forming fertilizers are dominant in most countries; however, the NH4+ / NO3- ratio in N fertilizer inputs shows clear differences temporally and geographically. This new map can be utilized as input data to global model studies and bring new insights for the assessment of historical terrestrial N cycling changes. Datasets available at doi:10.1594/PANGAEA.861203.

Inter-comparison of daily precipitation products for large-scale hydro-climatic applications over Canada

NASA Astrophysics Data System (ADS)

Wong, Jefferson S.; Razavi, Saman; Bonsal, Barrie R.; Wheater, Howard S.; Asong, Zilefac E.

2017-04-01

A number of global and regional gridded climate products based on multiple data sources are available that can potentially provide reliable estimates of precipitation for climate and hydrological studies. However, research into the consistency of these products for various regions has been limited and in many cases non-existent. This study inter-compares several gridded precipitation products over 15 terrestrial ecozones in Canada for different seasons. The spatial and temporal variability of the errors (relative to station observations) was quantified over the period of 1979 to 2012 at a 0.5° and daily spatio-temporal resolution. These datasets were assessed in their ability to represent the daily variability of precipitation amounts by four performance measures: percentage of bias, root mean square error, correlation coefficient, and standard deviation ratio. Results showed that most of the datasets were relatively skilful in central Canada. However, they tended to overestimate precipitation amounts in the west and underestimate in the north and east, with the underestimation being particularly dominant in northern Canada (above 60° N). The global product by WATCH Forcing Data ERA-Interim (WFDEI) augmented by Global Precipitation Climatology Centre (GPCC) data (WFDEI [GPCC]) performed best with respect to different metrics. The Canadian Precipitation Analysis (CaPA) product performed comparably with WFDEI [GPCC]; however, it only provides data starting in 2002. All the datasets performed best in summer, followed by autumn, spring, and winter in order of decreasing quality. Findings from this study can provide guidance to potential users regarding the performance of different precipitation products for a range of geographical regions and time periods.

Shifts of Start and End of Season in Response to Air Temperature Variation Based on Gimms Dataset in Hyrcanian Forests

NASA Astrophysics Data System (ADS)

Kiapasha, K. H.; Darvishsefat, A. A.; Zargham, N.; Julien, Y.; Sobrino, J. A.; Nadi, M.

2017-09-01

Climate change is one of the most important environmental challenges in the world and forest as a dynamic phenomenon is influenced by environmental changes. The Hyrcanian forests is a unique natural heritage of global importance and we need monitoring this region. The objective of this study was to detect start and end of season trends in Hyrcanian forests of Iran based on biweekly GIMMS (Global Inventory Modeling and Mapping Studies) NDVI3g in the period 1981-2012. In order to find response of vegetation activity to local temperature variations, we used air temperature provided from I.R. Iran Meteorological Organization (IRIMO). At the first step in order to remove the existing gap from the original time series, the iterative Interpolation for Data Reconstruction (IDR) model was applied to GIMMS and temperature dataset. Then we applied significant Mann Kendall test to determine significant trend for each pixel of GIMMS and temperature datasets over the Hyrcanian forests. The results demonstrated that start and end of season (SOS & EOS respectively) derived from GIMMS3g NDVI time series increased by -0.16 and +0.41 days per year respectively. The trends derived from temperature time series indicated increasing trend in the whole of this region. Results of this study showed that global warming and its effect on growth and photosynthetic activity can increased the vegetation activity in our study area. Otherwise extension of the growing season, including an earlier start of the growing season, later autumn and higher rate of production increased NDVI value during the study period.

Filling a missing link between biogeochemical, climate and ecosystem studies: a global database of atmospheric water-soluble organic nitrogen

NASA Astrophysics Data System (ADS)

Cornell, Sarah

2015-04-01

It is time to collate a global community database of atmospheric water-soluble organic nitrogen deposition. Organic nitrogen (ON) has long been known to be globally ubiquitous in atmospheric aerosol and precipitation, with implications for air and water quality, climate, biogeochemical cycles, ecosystems and human health. The number of studies of atmospheric ON deposition has increased steadily in recent years, but to date there is no accessible global dataset, for either bulk ON or its major components. Improved qualitative and quantitative understanding of the organic nitrogen component is needed to complement the well-established knowledge base pertaining to other components of atmospheric deposition (cf. Vet et al 2014). Without this basic information, we are increasingly constrained in addressing the current dynamics and potential interactions of atmospheric chemistry, climate and ecosystem change. To see the full picture we need global data synthesis, more targeted data gathering, and models that let us explore questions about the natural and anthropogenic dynamics of atmospheric ON. Collectively, our research community already has a substantial amount of atmospheric ON data. Published reports extend back over a century and now have near-global coverage. However, datasets available from the literature are very piecemeal and too often lack crucially important information that would enable aggregation or re-use. I am initiating an open collaborative process to construct a community database, so we can begin to systematically synthesize these datasets (generally from individual studies at a local and temporally limited scale) to increase their scientific usability and statistical power for studies of global change and anthropogenic perturbation. In drawing together our disparate knowledge, we must address various challenges and concerns, not least about the comparability of analysis and sampling methodologies, and the known complexity of composition of ON. We need to discuss and develop protocols that work for diverse research needs. The database will need to be harmonized or merged into existing global N data initiatives. This presentation therefore launches a standing invitation for experts to contribute and share rain and aerosol ON and chemical composition data, and jointly refine the preliminary database structure and metadata requirements for optimal mutual use. Reference: Vet et al. (2014) A global assessment of precipitation chemistry… Atmos Environ 93: 3-100

Continuation of the NVAP Global Water Vapor Data Sets for Pathfinder Science Analysis

NASA Technical Reports Server (NTRS)

VonderHaar, Thomas H.; Engelen, Richard J.; Forsythe, John M.; Randel, David L.; Ruston, Benjamin C.; Woo, Shannon; Dodge, James (Technical Monitor)

2001-01-01

This annual report covers August 2000 - August 2001 under NASA contract NASW-0032, entitled "Continuation of the NVAP (NASA's Water Vapor Project) Global Water Vapor Data Sets for Pathfinder Science Analysis". NASA has created a list of Earth Science Research Questions which are outlined by Asrar, et al. Particularly relevant to NVAP are the following questions: (a) How are global precipitation, evaporation, and the cycling of water changing? (b) What trends in atmospheric constituents and solar radiation are driving global climate? (c) How well can long-term climatic trends be assessed or predicted? Water vapor is a key greenhouse gas, and an understanding of its behavior is essential in global climate studies. Therefore, NVAP plays a key role in addressing the above climate questions by creating a long-term global water vapor dataset and by updating the dataset with recent advances in satellite instrumentation. The NVAP dataset produced from 1988-1998 has found wide use in the scientific community. Studies of interannual variability are particularly important. A recent paper by Simpson, et al. that examined the NVAP dataset in detail has shown that its relative accuracy is sufficient for the variability studies that contribute toward meeting NASA's goals. In the past year, we have made steady progress towards continuing production of this high-quality dataset as well as performing our own investigations of the data. This report summarizes the past year's work on production of the NVAP dataset and presents results of analyses we have performed in the past year.

China CO2 emission accounts 1997–2015

PubMed Central

Shan, Yuli; Guan, Dabo; Zheng, Heran; Ou, Jiamin; Li, Yuan; Meng, Jing; Mi, Zhifu; Liu, Zhu; Zhang, Qiang

2018-01-01

China is the world’s top energy consumer and CO2 emitter, accounting for 30% of global emissions. Compiling an accurate accounting of China’s CO2 emissions is the first step in implementing reduction policies. However, no annual, officially published emissions data exist for China. The current emissions estimated by academic institutes and scholars exhibit great discrepancies. The gap between the different emissions estimates is approximately equal to the total emissions of the Russian Federation (the 4th highest emitter globally) in 2011. In this study, we constructed the time-series of CO2 emission inventories for China and its 30 provinces. We followed the Intergovernmental Panel on Climate Change (IPCC) emissions accounting method with a territorial administrative scope. The inventories include energy-related emissions (17 fossil fuels in 47 sectors) and process-related emissions (cement production). The first version of our dataset presents emission inventories from 1997 to 2015. We will update the dataset annually. The uniformly formatted emission inventories provide data support for further emission-related research as well as emissions reduction policy-making in China. PMID:29337312

China CO2 emission accounts 1997-2015

NASA Astrophysics Data System (ADS)

Shan, Yuli; Guan, Dabo; Zheng, Heran; Ou, Jiamin; Li, Yuan; Meng, Jing; Mi, Zhifu; Liu, Zhu; Zhang, Qiang

2018-01-01

China is the world's top energy consumer and CO2 emitter, accounting for 30% of global emissions. Compiling an accurate accounting of China's CO2 emissions is the first step in implementing reduction policies. However, no annual, officially published emissions data exist for China. The current emissions estimated by academic institutes and scholars exhibit great discrepancies. The gap between the different emissions estimates is approximately equal to the total emissions of the Russian Federation (the 4th highest emitter globally) in 2011. In this study, we constructed the time-series of CO2 emission inventories for China and its 30 provinces. We followed the Intergovernmental Panel on Climate Change (IPCC) emissions accounting method with a territorial administrative scope. The inventories include energy-related emissions (17 fossil fuels in 47 sectors) and process-related emissions (cement production). The first version of our dataset presents emission inventories from 1997 to 2015. We will update the dataset annually. The uniformly formatted emission inventories provide data support for further emission-related research as well as emissions reduction policy-making in China.

A global analysis of the urban heat island effect based on multisensor satellite data

NASA Astrophysics Data System (ADS)

Xiao, J.; Frolking, S. E.; Milliman, T. E.; Schneider, A.; Friedl, M. A.

2017-12-01

Human population is rapidly urbanizing. In much of the world, cities are prone to hotter weather than surrounding rural areas - so-called `urban heat islands' - and this effect can have mortal consequences during heat waves. During the daytime, when the surface energy balance is driven by incoming solar radiation, the magnitude of urban warming is strongly influenced by surface albedo and the capacity to evaporate water (i.e., there is a strong relationship between vegetated land fraction and the ratio of sensible to latent heat loss or Bowen ratio). At nighttime, urban cooling is often inhibited by the thermal inertia of the built environment and anthropogenic heat exhaust from building and transportation energy use. We evaluated a suite of global remote sensing data sets representing a range of urban characteristics against MODIS-derived land-surface temperature differences between urban and surrounding rural areas. We included two new urban datasets in this analysis - MODIS-derived change in global urban extent and global urban microwave backscatter - along with several MODIS standard products and DMSP/OLS nighttime lights time series data. The global analysis spanned a range of urban characteristics that likely influence the magnitude of daytime and/or nighttime urban heat islands - urban size, population density, building density, state of development, impervious fraction, eco-climatic setting. Specifically, we developed new satellite datasets and synthesizing these with existing satellite data into a global database of urban land surface parameters, used two MODIS land surface temperature products to generate time series of daytime and nighttime urban heat island effects for 30 large cities across the globe, and empirically analyzed these data to determine specifically which remote sensing-based characterizations of global urban areas have explanatory power with regard to both daytime and nighttime urban heat islands.

Ontology-based meta-analysis of global collections of high-throughput public data.

PubMed

Kupershmidt, Ilya; Su, Qiaojuan Jane; Grewal, Anoop; Sundaresh, Suman; Halperin, Inbal; Flynn, James; Shekar, Mamatha; Wang, Helen; Park, Jenny; Cui, Wenwu; Wall, Gregory D; Wisotzkey, Robert; Alag, Satnam; Akhtari, Saeid; Ronaghi, Mostafa

2010-09-29

The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today. We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets. Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.

Mutual information estimation reveals global associations between stimuli and biological processes

PubMed Central

Suzuki, Taiji; Sugiyama, Masashi; Kanamori, Takafumi; Sese, Jun

2009-01-01

Background Although microarray gene expression analysis has become popular, it remains difficult to interpret the biological changes caused by stimuli or variation of conditions. Clustering of genes and associating each group with biological functions are often used methods. However, such methods only detect partial changes within cell processes. Herein, we propose a method for discovering global changes within a cell by associating observed conditions of gene expression with gene functions. Results To elucidate the association, we introduce a novel feature selection method called Least-Squares Mutual Information (LSMI), which computes mutual information without density estimaion, and therefore LSMI can detect nonlinear associations within a cell. We demonstrate the effectiveness of LSMI through comparison with existing methods. The results of the application to yeast microarray datasets reveal that non-natural stimuli affect various biological processes, whereas others are no significant relation to specific cell processes. Furthermore, we discover that biological processes can be categorized into four types according to the responses of various stimuli: DNA/RNA metabolism, gene expression, protein metabolism, and protein localization. Conclusion We proposed a novel feature selection method called LSMI, and applied LSMI to mining the association between conditions of yeast and biological processes through microarray datasets. In fact, LSMI allows us to elucidate the global organization of cellular process control. PMID:19208155

Dataset of MIGRAME Project (Global Change, Altitudinal Range Shift and Colonization of Degraded Habitats in Mediterranean Mountains)

PubMed Central

Pérez-Luque, Antonio Jesús; Zamora, Regino; Bonet, Francisco Javier; Pérez-Pérez, Ramón

2015-01-01

Abstract In this data paper, we describe the dataset of the Global Change, Altitudinal Range Shift and Colonization of Degraded Habitats in Mediterranean Mountains (MIGRAME) project, which aims to assess the capacity of altitudinal migration and colonization of marginal habitats by Quercus pyrenaica Willd. forests in Sierra Nevada (southern Spain) considering two global-change drivers: temperature increase and land-use changes. The dataset includes information of the forest structure (diameter size, tree height, and abundance) of the Quercus pyrenaica ecosystem in Sierra Nevada obtained from 199 transects sampled at the treeline ecotone, mature forest, and marginal habitats (abandoned cropland and pine plantations). A total of 3839 occurrence records were collected and 5751 measurements recorded. The dataset is included in the Sierra Nevada Global-Change Observatory (OBSNEV), a long-term research project designed to compile socio-ecological information on the major ecosystem types in order to identify the impacts of global change in this mountain range. PMID:26491387

TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015.

PubMed

Abatzoglou, John T; Dobrowski, Solomon Z; Parks, Sean A; Hegewisch, Katherine C

2018-01-09

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.

TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015

NASA Astrophysics Data System (ADS)

Abatzoglou, John T.; Dobrowski, Solomon Z.; Parks, Sean A.; Hegewisch, Katherine C.

2018-01-01

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.

The Status of the NASA MEaSUREs Combined ASTER and MODIS Emissivity Over Land (CAMEL) Products

NASA Astrophysics Data System (ADS)

Borbas, E. E.; Feltz, M.; Hulley, G. C.; Knuteson, R. O.; Hook, S. J.

2017-12-01

As part of a NASA MEaSUREs Land Surface Temperature and Emissivity project, the University of Wisconsin, Space Science and Engineering Center and the NASA's Jet Propulsion Laboratory have developed a global monthly mean emissivity Earth System Data Record (ESDR). The CAMEL ESDR was produced by merging two current state-of-the-art emissivity datasets: the UW-Madison MODIS Infrared emissivity dataset (UWIREMIS), and the JPL ASTER Global Emissivity Dataset v4 (GEDv4). The dataset includes monthly global data records of emissivity, uncertainty at 13 hinge points between 3.6-14.3 µm, and Principal Components Analysis (PCA) coefficients at 5 kilometer resolution for years 2003 to 2015. A high spectral resolution algorithm is also provided for HSR applications. The dataset is currently being tested in sounder retrieval algorithm (e.g. CrIS, IASI) and has already been implemented in RTTOV-12 for immediate use in numerical weather modeling and data assimilation. This poster will present the current status of the dataset.

Defining pyromes and global syndromes of fire regimes.

PubMed

Archibald, Sally; Lehmann, Caroline E R; Gómez-Dans, Jose L; Bradstock, Ross A

2013-04-16

Fire is a ubiquitous component of the Earth system that is poorly understood. To date, a global-scale understanding of fire is largely limited to the annual extent of burning as detected by satellites. This is problematic because fire is multidimensional, and focus on a single metric belies its complexity and importance within the Earth system. To address this, we identified five key characteristics of fire regimes--size, frequency, intensity, season, and extent--and combined new and existing global datasets to represent each. We assessed how these global fire regime characteristics are related to patterns of climate, vegetation (biomes), and human activity. Cross-correlations demonstrate that only certain combinations of fire characteristics are possible, reflecting fundamental constraints in the types of fire regimes that can exist. A Bayesian clustering algorithm identified five global syndromes of fire regimes, or pyromes. Four pyromes represent distinctions between crown, litter, and grass-fueled fires, and the relationship of these to biomes and climate are not deterministic. Pyromes were partially discriminated on the basis of available moisture and rainfall seasonality. Human impacts also affected pyromes and are globally apparent as the driver of a fifth and unique pyrome that represents human-engineered modifications to fire characteristics. Differing biomes and climates may be represented within the same pyrome, implying that pathways of change in future fire regimes in response to changes in climate and human activity may be difficult to predict.

Defining pyromes and global syndromes of fire regimes

PubMed Central

Archibald, Sally; Lehmann, Caroline E. R.; Gómez-Dans, Jose L.; Bradstock, Ross A.

2013-01-01

Fire is a ubiquitous component of the Earth system that is poorly understood. To date, a global-scale understanding of fire is largely limited to the annual extent of burning as detected by satellites. This is problematic because fire is multidimensional, and focus on a single metric belies its complexity and importance within the Earth system. To address this, we identified five key characteristics of fire regimes—size, frequency, intensity, season, and extent—and combined new and existing global datasets to represent each. We assessed how these global fire regime characteristics are related to patterns of climate, vegetation (biomes), and human activity. Cross-correlations demonstrate that only certain combinations of fire characteristics are possible, reflecting fundamental constraints in the types of fire regimes that can exist. A Bayesian clustering algorithm identified five global syndromes of fire regimes, or pyromes. Four pyromes represent distinctions between crown, litter, and grass-fueled fires, and the relationship of these to biomes and climate are not deterministic. Pyromes were partially discriminated on the basis of available moisture and rainfall seasonality. Human impacts also affected pyromes and are globally apparent as the driver of a fifth and unique pyrome that represents human-engineered modifications to fire characteristics. Differing biomes and climates may be represented within the same pyrome, implying that pathways of change in future fire regimes in response to changes in climate and human activity may be difficult to predict. PMID:23559374

USGS approach to real-time estimation of earthquake-triggered ground failure - Results of 2015 workshop

USGS Publications Warehouse

Allstadt, Kate E.; Thompson, Eric M.; Wald, David J.; Hamburger, Michael W.; Godt, Jonathan W.; Knudsen, Keith L.; Jibson, Randall W.; Jessee, M. Anna; Zhu, Jing; Hearne, Michael; Baise, Laurie G.; Tanyas, Hakan; Marano, Kristin D.

2016-03-30

The U.S. Geological Survey (USGS) Earthquake Hazards and Landslide Hazards Programs are developing plans to add quantitative hazard assessments of earthquake-triggered landsliding and liquefaction to existing real-time earthquake products (ShakeMap, ShakeCast, PAGER) using open and readily available methodologies and products. To date, prototype global statistical models have been developed and are being refined, improved, and tested. These models are a good foundation, but much work remains to achieve robust and defensible models that meet the needs of end users. In order to establish an implementation plan and identify research priorities, the USGS convened a workshop in Golden, Colorado, in October 2015. This document summarizes current (as of early 2016) capabilities, research and operational priorities, and plans for further studies that were established at this workshop. Specific priorities established during the meeting include (1) developing a suite of alternative models; (2) making use of higher resolution and higher quality data where possible; (3) incorporating newer global and regional datasets and inventories; (4) reducing barriers to accessing inventory datasets; (5) developing methods for using inconsistent or incomplete datasets in aggregate; (6) developing standardized model testing and evaluation methods; (7) improving ShakeMap shaking estimates, particularly as relevant to ground failure, such as including topographic amplification and accounting for spatial variability; and (8) developing vulnerability functions for loss estimates.

DAPAGLOCO - A global daily precipitation dataset from satellite and rain-gauge measurements

NASA Astrophysics Data System (ADS)

Spangehl, T.; Danielczok, A.; Dietzsch, F.; Andersson, A.; Schroeder, M.; Fennig, K.; Ziese, M.; Becker, A.

2017-12-01

The BMBF funded project framework MiKlip(Mittelfristige Klimaprognosen) develops a global climate forecast system on decadal time scales for operational applications. Herein, the DAPAGLOCO project (Daily Precipitation Analysis for the validation of Global medium-range Climate predictions Operationalized) provides a global precipitation dataset as a combination of microwave-based satellite measurements over ocean and rain gauge measurements over land on daily scale. The DAPAGLOCO dataset is created for the evaluation of the MiKlip forecast system in the first place. The HOAPS dataset (Hamburg Ocean Atmosphere Parameter and Fluxes from Satellite data) is used for the derivation of precipitation rates over ocean and is extended by the use of measurements from TMI, GMI, and AMSR-E, in addition to measurements from SSM/I and SSMIS. A 1D-Var retrieval scheme is developed to retrieve rain rates from microwave imager data, which also allows for the determination of uncertainty estimates. Over land, the GPCC (Global Precipitation Climatology Center) Full Data Daily product is used. It consists of rain gauge measurements that are interpolated on a regular grid by ordinary Kriging. The currently available dataset is based on a neuronal network approach, consists of 21 years of data from 1988 to 2008 and is currently extended until 2015 using the 1D-Var scheme and with improved sampling. Three different spatial resolved dataset versions are available with 1° and 2.5° global, and 0.5° for Europe. The evaluation of the MiKlip forecast system by DAPAGLOCO is based on ETCCDI (Expert Team on Climate Change and Detection Indices). Hindcasts are used for the index-based comparison between model and observations. These indices allow for the evaluation of precipitation extremes, their spatial and temporal distribution as well as for the duration of dry and wet spells, average precipitation amounts and percentiles on global scale. Besides, an ETCCDI-based climatology of the DAPAGLOCO precipitation dataset has been derived.

Long-term records of global radiation, carbon and water fluxes derived from multi-satellite data and a process-based model

NASA Astrophysics Data System (ADS)

Ryu, Youngryel; Jiang, Chongya

2016-04-01

To gain insights about the underlying impacts of global climate change on terrestrial ecosystem fluxes, we present a long-term (1982-2015) global radiation, carbon and water fluxes products by integrating multi-satellite data with a process-based model, the Breathing Earth System Simulator (BESS). BESS is a coupled processed model that integrates radiative transfer in the atmosphere and canopy, photosynthesis (GPP), and evapotranspiration (ET). BESS was designed most sensitive to the variables that can be quantified reliably, fully taking advantages of remote sensing atmospheric and land products. Originally, BESS entirely relied on MODIS as input variables to produce global GPP and ET during the MODIS era. This study extends the work to provide a series of long-term products from 1982 to 2015 by incorporating AVHRR data. In addition to GPP and ET, more land surface processes related datasets are mapped to facilitate the discovery of the ecological variations and changes. The CLARA-A1 cloud property datasets, the TOMS aerosol datasets, along with the GLASS land surface albedo datasets, were input to a look-up table derived from an atmospheric radiative transfer model to produce direct and diffuse components of visible and near infrared radiation datasets. Theses radiation components together with the LAI3g datasets and the GLASS land surface albedo datasets, were used to calculate absorbed radiation through a clumping corrected two-stream canopy radiative transfer model. ECMWF ERA interim air temperature data were downscaled by using ALP-II land surface temperature dataset and a region-dependent regression model. The spatial and seasonal variations of CO2 concentration were accounted by OCO-2 datasets, whereas NOAA's global CO2 growth rates data were used to describe interannual variations. All these remote sensing based datasets are used to run the BESS. Daily fluxes in 1/12 degree were computed and then aggregated to half-month interval to match with the spatial-temporal resolution of LAI3g dataset. The BESS GPP and ET products were compared to other independent datasets including MPI-BGC and CLM. Overall, the BESS products show good agreement with the other two datasets, indicating a compelling potential for bridging remote sensing and land surface models.

Global snowfall: A combined CloudSat, GPM, and reanalysis perspective.

NASA Astrophysics Data System (ADS)

Milani, Lisa; Kulie, Mark S.; Skofronick-Jackson, Gail; Munchak, S. Joseph; Wood, Norman B.; Levizzani, Vincenzo

2017-04-01

Quantitative global snowfall estimates derived from multi-year data records will be presented to highlight recent advances in high latitude precipitation retrievals using spaceborne observations. More specifically, the analysis features the 2006-2016 CloudSat Cloud Profiling Radar (CPR) and the 2014-2016 Global Precipitation (GPM) Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) observational datasets and derived products. The ERA-Interim reanalysis dataset is also used to define the meteorological context and an independent combined modeling/observational evaluation dataset. An overview is first provided of CloudSat CPR-derived results that have stimulated significant recent research regarding global snowfall, including seasonal analyses of unique snowfall modes. GMI and DPR global annual snowfall retrievals are then evaluated against the CloudSat estimates to highlight regions where the datasets provide both consistent and diverging snowfall estimates. A hemispheric seasonal analysis for both datasets will also be provided. These comparisons aim at providing a unified global snowfall characterization that leverages the respective instrument's strengths. Attention will also be devoted to regions around the globe that experience unique snowfall modes. For instance, CloudSat has demonstrated an ability to effectively discern snowfall produced by shallow cumuliform cloud structures (e.g., lake/ocean-induced convective snow produced by air/water interactions associated with seasonal cold air outbreaks). The CloudSat snowfall database also reveals prevalent seasonal shallow cumuliform snowfall trends over climate-sensitive regions like the Greenland Ice Sheet. Other regions with unique snowfall modes, such as the US East Coast winter storm track zone that experiences intense snowfall rates directly associated with strong low pressure systems, will also be highlighted to demonstrate GPM's observational effectiveness. Linkages between CloudSat and GPM global snowfall analyses and independent ERA-Interim datasets will also be presented as a final evaluation exercise.

Recent Development on the NOAA's Global Surface Temperature Dataset

NASA Astrophysics Data System (ADS)

Zhang, H. M.; Huang, B.; Boyer, T.; Lawrimore, J. H.; Menne, M. J.; Rennie, J.

2016-12-01

Global Surface Temperature (GST) is one of the most widely used indicators for climate trend and extreme analyses. A widely used GST dataset is the NOAA merged land-ocean surface temperature dataset known as NOAAGlobalTemp (formerly MLOST). The NOAAGlobalTemp had recently been updated from version 3.5.4 to version 4. The update includes a significant improvement in the ocean surface component (Extended Reconstructed Sea Surface Temperature or ERSST, from version 3b to version 4) which resulted in an increased temperature trends in recent decades. Since then, advancements in both the ocean component (ERSST) and land component (GHCN-Monthly) have been made, including the inclusion of Argo float SSTs and expanded EOT modes in ERSST, and the use of ISTI databank in GHCN-Monthly. In this presentation, we describe the impact of those improvements on the merged global temperature dataset, in terms of global trends and other aspects.

Modeling Global Biogenic Emission of Isoprene: Exploration of Model Drivers

NASA Technical Reports Server (NTRS)

Alexander, Susan E.; Potter, Christopher S.; Coughlan, Joseph C.; Klooster, Steven A.; Lerdau, Manuel T.; Chatfield, Robert B.; Peterson, David L. (Technical Monitor)

1996-01-01

Vegetation provides the major source of isoprene emission to the atmosphere. We present a modeling approach to estimate global biogenic isoprene emission. The isoprene flux model is linked to a process-based computer simulation model of biogenic trace-gas fluxes that operates on scales that link regional and global data sets and ecosystem nutrient transformations Isoprene emission estimates are determined from estimates of ecosystem specific biomass, emission factors, and algorithms based on light and temperature. Our approach differs from an existing modeling framework by including the process-based global model for terrestrial ecosystem production, satellite derived ecosystem classification, and isoprene emission measurements from a tropical deciduous forest. We explore the sensitivity of model estimates to input parameters. The resulting emission products from the global 1 degree x 1 degree coverage provided by the satellite datasets and the process model allow flux estimations across large spatial scales and enable direct linkage to atmospheric models of trace-gas transport and transformation.

OneGeology-Europe - The Challenges and progress of implementing a basic geological infrastructure for Europe

NASA Astrophysics Data System (ADS)

Asch, Kristine; Tellez-Arenas, Agnes

2010-05-01

OneGeology-Europe is making geological spatial data held by the geological surveys of Europe more easily discoverable and accessible via the internet. This will provide a fundamental scientific layer to the European Plate Observation System Rich geological data assets exist in the geological survey of each individual EC Member State, but they are difficult to discover and are not interoperable. For those outside the geological surveys they are not easy to obtain, to understand or to use. Geological spatial data is essential to the prediction and mitigation of landslides, subsidence, earthquakes, flooding and pollution. These issues are global in nature and their profile has also been raised by the OneGeology global initiative for the International Year of Planet Earth 2008. Geology is also a key dataset in the EC INSPIRE Directive, where it is also fundamental to the themes of natural risk zones, energy and mineral resources. The OneGeology-Europe project is delivering a web-accessible, interoperable geological spatial dataset for the whole of Europe at the 1:1 million scale based on existing data held by the European geological surveys. Proof of concept will be applied to key areas at a higher resolution and some geological surveys will deliver their data at high resolution. An important role is developing a European specification for basic geological map data and making significant progress towards harmonising the dataset (an essential first step to addressing harmonisation at higher data resolutions). It is accelerating the development and deployment of a nascent international interchange standard for geological data - GeoSciML, which will enable the sharing and exchange of the data within and beyond the geological community within Europe and globally. The geological dataset for the whole of Europe is not a centralized database but a distributed system. Each geological survey implements and hosts an interoperable web service, delivering their national harmonized geological data. These datasets are registered in a multilingual catalogue, who is one the main part of this system. This catalogue and a common metadata profile allows the discovery of national geological and applied geological maps at all scapes, Such an architecture is facilitating re-use and addition of value by a wide spectrum of users in the public and private sector and identifying, documenting and disseminating strategies for the reduction of technical and business barriers to re-use. In identifying and raising awareness in the user and provider communities, it is moving geological knowledge closer to the end-user where it will have greater societal impact and ensure fuller exploitation of a key data resource gathered at huge public expense. The project is providing examples of best practice in the delivery of digital geological spatial data to users, e.g. in the insurance, property, engineering, planning, mineral resource and environmental sectors. The scientifically attributed map data of the project will provide a pan-European base for science research and, importantly, a prime geoscience dataset capable of integration with other data sets within and beyond the geoscience domain. This presentation will demonstrate the first results of this project and will indicate how OneGeology-Europe is ensuring that Europe may play a leading role in the development of a geoscience spatial data infrastructure (SDI) globally.

Fluid Lensing based Machine Learning for Augmenting Earth Science Coral Datasets

NASA Astrophysics Data System (ADS)

Li, A.; Instrella, R.; Chirayath, V.

2016-12-01

Recently, there has been increased interest in monitoring the effects of climate change upon the world's marine ecosystems, particularly coral reefs. These delicate ecosystems are especially threatened due to their sensitivity to ocean warming and acidification, leading to unprecedented levels of coral bleaching and die-off in recent years. However, current global aquatic remote sensing datasets are unable to quantify changes in marine ecosystems at spatial and temporal scales relevant to their growth. In this project, we employ various supervised and unsupervised machine learning algorithms to augment existing datasets from NASA's Earth Observing System (EOS), using high resolution airborne imagery. This method utilizes NASA's ongoing airborne campaigns as well as its spaceborne assets to collect remote sensing data over these afflicted regions, and employs Fluid Lensing algorithms to resolve optical distortions caused by the fluid surface, producing cm-scale resolution imagery of these diverse ecosystems from airborne platforms. Support Vector Machines (SVMs) and K-mean clustering methods were applied to satellite imagery at 0.5m resolution, producing segmented maps classifying coral based on percent cover and morphology. Compared to a previous study using multidimensional maximum a posteriori (MAP) estimation to separate these features in high resolution airborne datasets, SVMs are able to achieve above 75% accuracy when augmented with existing MAP estimates, while unsupervised methods such as K-means achieve roughly 68% accuracy, verified by manually segmented reference data provided by a marine biologist. This effort thus has broad applications for coastal remote sensing, by helping marine biologists quantify behavioral trends spanning large areas and over longer timescales, and to assess the health of coral reefs worldwide.

Mochras Revisited: a new global standard for 25 million years of Jurassic Earth history - A drilling proposal for the International Continental Drilling Programme

NASA Astrophysics Data System (ADS)

Hesselbo, Stephen; Bjerrum, Christian; Hinnov, Linda; Mac Niocaill, Conall; Miller, Kenneth; Riding, James; van de Schootbrugge, Bas; Wonik, Thomas

2014-05-01

The Early Jurassic Epoch (201.4 - 175 Ma) was a time of extreme environmental change. Through this period there are well-documented examples of rapid transitions from cold, or even glacial climates, through to super-greenhouse events, the latter characterized worldwide by hugely enhanced organic carbon burial, multiple large-magnitude isotopic anomalies, global sea-level changes, and mass extinctions. These events not only reflect changes in the global climate system but are also thought to have had significant influence on the evolution of Jurassic marine and terrestrial biota. Furthermore, the events may serve as analogues for present-day and future environmental transitions. Although our knowledge of specific global change events within the Early Jurassic is rapidly improving, a prime case-in-point being the Toarcian Oceanic Anoxic Event (or T-OAE), we have neither documented all the events, nor do we have a comprehensive understanding of their timing, pacing, or triggers. A key factor contributing to our fragmentary knowledge is the scattered and discontinuous nature of the existing datasets. The major goal for this proposed ICDP project is therefore to produce a new global standard for these key 25 million years of Earth history by re-drilling a 45 year old borehole at Mochras Farm on the edge of Cardigan Bay, Wales, and to develop an integrated stratigraphy for the cored material, as well as high-resolution proxy-records of environmental change. The new datasets will be applied to understand fundamental questions about the long- and short-term evolution of the Earth System.

Fast Gaussian kernel learning for classification tasks based on specially structured global optimization.

PubMed

Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen

2014-09-01

For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Data Descriptor: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015

Treesearch

John T. Abatzoglou; Solomon Z. Dobrowski; Sean A. Parks; Katherine C. Hegewisch

2018-01-01

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958–2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from...

The First Global Geological Map of Mercury

NASA Astrophysics Data System (ADS)

Prockter, L. M.; Head, J. W., III; Byrne, P. K.; Denevi, B. W.; Kinczyk, M. J.; Fassett, C.; Whitten, J. L.; Thomas, R.; Ernst, C. M.

2015-12-01

Geological maps are tools with which to understand the distribution and age relationships of surface geological units and structural features on planetary surfaces. Regional and limited global mapping of Mercury has already yielded valuable science results, elucidating the history and distribution of several types of units and features, such as regional plains, tectonic structures, and pyroclastic deposits. To date, however, no global geological map of Mercury exists, and there is currently no commonly accepted set of standardized unit descriptions and nomenclature. With MESSENGER monochrome image data, we are undertaking the global geological mapping of Mercury at the 1:15M scale applying standard U.S. Geological Survey mapping guidelines. This map will enable the development of the first global stratigraphic column of Mercury, will facilitate comparisons among surface units distributed discontinuously across the planet, and will provide guidelines for mappers so that future mapping efforts will be consistent and broadly interpretable by the scientific community. To date we have incorporated three major datasets into the global geological map: smooth plains units, tectonic structures, and impact craters and basins >20 km in diameter. We have classified most of these craters by relative age on the basis of the state of preservation of morphological features and standard classification schemes first applied to Mercury by the Mariner 10 imaging team. Additional datasets to be incorporated include intercrater plains units and crater ejecta deposits. In some regions MESSENGER color data is used to supplement the monochrome data, to help elucidate different plains units. The final map will be published online, together with a peer-reviewed publication. Further, a digital version of the map, containing individual map layers, will be made publicly available for use within geographic information systems (GISs).

Modeling spatial-temporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Z.; Zimmermann, N. E.; Poulter, B.

2015-11-01

Simulations of the spatial-temporal dynamics of wetlands are key to understanding the role of wetland biogeochemistry under past and future climate variability. Hydrologic inundation models, such as TOPMODEL, are based on a fundamental parameter known as the compound topographic index (CTI) and provide a computationally cost-efficient approach to simulate wetland dynamics at global scales. However, there remains large discrepancy in the implementations of TOPMODEL in land-surface models (LSMs) and thus their performance against observations. This study describes new improvements to TOPMODEL implementation and estimates of global wetland dynamics using the LPJ-wsl dynamic global vegetation model (DGVM), and quantifies uncertainties by comparing three digital elevation model products (HYDRO1k, GMTED, and HydroSHEDS) at different spatial resolution and accuracy on simulated inundation dynamics. In addition, we found that calibrating TOPMODEL with a benchmark wetland dataset can help to successfully delineate the seasonal and interannual variations of wetlands, as well as improve the spatial distribution of wetlands to be consistent with inventories. The HydroSHEDS DEM, using a river-basin scheme for aggregating the CTI, shows best accuracy for capturing the spatio-temporal dynamics of wetlands among the three DEM products. The estimate of global wetland potential/maximum is ∼ 10.3 Mkm2 (106 km2), with a mean annual maximum of ∼ 5.17 Mkm2 for 1980-2010. This study demonstrates the feasibility to capture spatial heterogeneity of inundation and to estimate seasonal and interannual variations in wetland by coupling a hydrological module in LSMs with appropriate benchmark datasets. It additionally highlights the importance of an adequate investigation of topographic indices for simulating global wetlands and shows the opportunity to converge wetland estimates across LSMs by identifying the uncertainty associated with existing wetland products.

Application of global datasets for hydrological modelling of a remote, snowmelt driven catchment in the Canadian Sub-Arctic

NASA Astrophysics Data System (ADS)

Casson, David; Werner, Micha; Weerts, Albrecht; Schellekens, Jaap; Solomatine, Dimitri

2017-04-01

Hydrological modelling in the Canadian Sub-Arctic is hindered by the limited spatial and temporal coverage of local meteorological data. Local watershed modelling often relies on data from a sparse network of meteorological stations with a rough density of 3 active stations per 100,000 km2. Global datasets hold great promise for application due to more comprehensive spatial and extended temporal coverage. A key objective of this study is to demonstrate the application of global datasets and data assimilation techniques for hydrological modelling of a data sparse, Sub-Arctic watershed. Application of available datasets and modelling techniques is currently limited in practice due to a lack of local capacity and understanding of available tools. Due to the importance of snow processes in the region, this study also aims to evaluate the performance of global SWE products for snowpack modelling. The Snare Watershed is a 13,300 km2 snowmelt driven sub-basin of the Mackenzie River Basin, Northwest Territories, Canada. The Snare watershed is data sparse in terms of meteorological data, but is well gauged with consistent discharge records since the late 1970s. End of winter snowpack surveys have been conducted every year from 1978-present. The application of global re-analysis datasets from the EU FP7 eartH2Observe project are investigated in this study. Precipitation data are taken from Multi-Source Weighted-Ensemble Precipitation (MSWEP) and temperature data from Watch Forcing Data applied to European Reanalysis (ERA)-Interim data (WFDEI). GlobSnow-2 is a global Snow Water Equivalent (SWE) measurement product funded by the European Space Agency (ESA) and is also evaluated over the local watershed. Downscaled precipitation, temperature and potential evaporation datasets are used as forcing data in a distributed version of the HBV model implemented in the WFLOW framework. Results demonstrate the successful application of global datasets in local watershed modelling, but that validation of actual frozen precipitation and snowpack conditions is very difficult. The distributed hydrological model shows good streamflow simulation performance based on statistical model evaluation techniques. Results are also promising for inter-annual variability, spring snowmelt onset and time to peak flows. It is expected that data assimilation of stream flow using an Ensemble Kalman Filter will further improve model performance. This study shows that global re-analysis datasets hold great potential for understanding the hydrology and snowpack dynamics of the expansive and data sparse sub-Arctic. However, global SWE products will require further validation and algorithm improvements, particularly over boreal forest and lake-rich regions.

McTwo: a two-step feature selection algorithm based on maximal information coefficient.

PubMed

Ge, Ruiquan; Zhou, Manli; Luo, Youxi; Meng, Qinghan; Mai, Guoqin; Ma, Dongli; Wang, Guoqing; Zhou, Fengfeng

2016-03-23

High-throughput bio-OMIC technologies are producing high-dimension data from bio-samples at an ever increasing rate, whereas the training sample number in a traditional experiment remains small due to various difficulties. This "large p, small n" paradigm in the area of biomedical "big data" may be at least partly solved by feature selection algorithms, which select only features significantly associated with phenotypes. Feature selection is an NP-hard problem. Due to the exponentially increased time requirement for finding the globally optimal solution, all the existing feature selection algorithms employ heuristic rules to find locally optimal solutions, and their solutions achieve different performances on different datasets. This work describes a feature selection algorithm based on a recently published correlation measurement, Maximal Information Coefficient (MIC). The proposed algorithm, McTwo, aims to select features associated with phenotypes, independently of each other, and achieving high classification performance of the nearest neighbor algorithm. Based on the comparative study of 17 datasets, McTwo performs about as well as or better than existing algorithms, with significantly reduced numbers of selected features. The features selected by McTwo also appear to have particular biomedical relevance to the phenotypes from the literature. McTwo selects a feature subset with very good classification performance, as well as a small feature number. So McTwo may represent a complementary feature selection algorithm for the high-dimensional biomedical datasets.

Insights and Challenges to Integrating Data from Diverse Ecological Networks

NASA Astrophysics Data System (ADS)

Peters, D. P. C.

2014-12-01

Many of the most dramatic and surprising effects of global change occur across large spatial extents, from regions to continents, that impact multiple ecosystem types across a range of interacting spatial and temporal scales. The ability of ecologists and inter-disciplinary scientists to understand and predict these dynamics depend, in large part, on existing site-based research infrastructures that developed in response to historic events. Integrating these diverse sources of data is critical to addressing these broad-scale questions. A conceptual approach is presented to synthesize and integrate diverse sources and types of data from different networks of research sites. This approach focuses on developing derived data products through spatial and temporal aggregation that allow datasets collected with different methods to be compared. The approach is illustrated through the integration, analysis, and comparison of hundreds of long-term datasets from 50 ecological sites in the US that represent ecosystem types commonly found globally. New insights were found by comparing multiple sites using common derived data. In addition to "bringing to light" many dark data in a standardized, open access, easy-to-use format, a suite of lessons were learned that can be applied to up and coming research networks in the US and internationally. These lessons will be described along with the challenges, including cyber-infrastructure, cultural, and behavioral constraints associated with the use of big and little data, that may keep ecologists and inter-disciplinary scientists from taking full advantage of the vast amounts of existing and yet-to-be exposed data.

Interpretable dimensionality reduction of single cell transcriptome data with deep generative models.

PubMed

Ding, Jiarui; Condon, Anne; Shah, Sohrab P

2018-05-21

Single-cell RNA-sequencing has great potential to discover cell types, identify cell states, trace development lineages, and reconstruct the spatial organization of cells. However, dimension reduction to interpret structure in single-cell sequencing data remains a challenge. Existing algorithms are either not able to uncover the clustering structures in the data or lose global information such as groups of clusters that are close to each other. We present a robust statistical model, scvis, to capture and visualize the low-dimensional structures in single-cell gene expression data. Simulation results demonstrate that low-dimensional representations learned by scvis preserve both the local and global neighbor structures in the data. In addition, scvis is robust to the number of data points and learns a probabilistic parametric mapping function to add new data points to an existing embedding. We then use scvis to analyze four single-cell RNA-sequencing datasets, exemplifying interpretable two-dimensional representations of the high-dimensional single-cell RNA-sequencing data.

Saliency Detection for Stereoscopic 3D Images in the Quaternion Frequency Domain

NASA Astrophysics Data System (ADS)

Cai, Xingyu; Zhou, Wujie; Cen, Gang; Qiu, Weiwei

2018-06-01

Recent studies have shown that a remarkable distinction exists between human binocular and monocular viewing behaviors. Compared with two-dimensional (2D) saliency detection models, stereoscopic three-dimensional (S3D) image saliency detection is a more challenging task. In this paper, we propose a saliency detection model for S3D images. The final saliency map of this model is constructed from the local quaternion Fourier transform (QFT) sparse feature and global QFT log-Gabor feature. More specifically, the local QFT feature measures the saliency map of an S3D image by analyzing the location of a similar patch. The similar patch is chosen using a sparse representation method. The global saliency map is generated by applying the wake edge-enhanced gradient QFT map through a band-pass filter. The results of experiments on two public datasets show that the proposed model outperforms existing computational saliency models for estimating S3D image saliency.

A Metastatistical Approach to Satellite Estimates of Extreme Rainfall Events

NASA Astrophysics Data System (ADS)

Zorzetto, E.; Marani, M.

2017-12-01

The estimation of the average recurrence interval of intense rainfall events is a central issue for both hydrologic modeling and engineering design. These estimates require the inference of the properties of the right tail of the statistical distribution of precipitation, a task often performed using the Generalized Extreme Value (GEV) distribution, estimated either from a samples of annual maxima (AM) or with a peaks over threshold (POT) approach. However, these approaches require long and homogeneous rainfall records, which often are not available, especially in the case of remote-sensed rainfall datasets. We use here, and tailor it to remotely-sensed rainfall estimates, an alternative approach, based on the metastatistical extreme value distribution (MEVD), which produces estimates of rainfall extreme values based on the probability distribution function (pdf) of all measured `ordinary' rainfall event. This methodology also accounts for the interannual variations observed in the pdf of daily rainfall by integrating over the sample space of its random parameters. We illustrate the application of this framework to the TRMM Multi-satellite Precipitation Analysis rainfall dataset, where MEVD optimally exploits the relatively short datasets of satellite-sensed rainfall, while taking full advantage of its high spatial resolution and quasi-global coverage. Accuracy of TRMM precipitation estimates and scale issues are here investigated for a case study located in the Little Washita watershed, Oklahoma, using a dense network of rain gauges for independent ground validation. The methodology contributes to our understanding of the risk of extreme rainfall events, as it allows i) an optimal use of the TRMM datasets in estimating the tail of the probability distribution of daily rainfall, and ii) a global mapping of daily rainfall extremes and distributional tail properties, bridging the existing gaps in rain gauges networks.

Evaluation of Terrestrial Carbon Cycle with the Land Use Harmonization Dataset

NASA Astrophysics Data System (ADS)

Sasai, T.; Nemani, R. R.

2017-12-01

CO2 emission by land use and land use change (LULUC) has still had a large uncertainty (±50%). We need to more accurately reveal a role of each LULUC process on terrestrial carbon cycle, and to develop more complicated land cover change model, leading to improve our understanding of the mechanism of global warming. The existing biosphere model studies do not necessarily have enough major LULUC process in the model description (e.g., clear cutting and residual soil carbon). The issue has the potential for causing an underestimation of the effect of LULUC on the global carbon exchange. In this study, the terrestrial biosphere model was modified with several LULUC processes according to the land use harmonization data set. The global mean LULUC emission from the year 1850 to 2000 was 137.2 (PgC 151year-1), and we found the noticeable trend in tropical region. As with the case of primary production in the existing studies, our results emphasized the role of tropical forest on wood productization and residual soil organic carbon by cutting. Global mean NEP was decreased by LULUC. NEP is largely affected by decreasing leaf biomass (photosynthesis) by deforestation process and increasing plant growth rate by regrowth process. We suggested that the model description related to deforestation, residual soil decomposition, wood productization and plant regrowth is important to develop a biosphere model for estimating long-term global carbon cycle.

Evaluation of the Global Land Data Assimilation System (GLDAS) air temperature data products

USGS Publications Warehouse

Ji, Lei; Senay, Gabriel B.; Verdin, James P.

2015-01-01

There is a high demand for agrohydrologic models to use gridded near-surface air temperature data as the model input for estimating regional and global water budgets and cycles. The Global Land Data Assimilation System (GLDAS) developed by combining simulation models with observations provides a long-term gridded meteorological dataset at the global scale. However, the GLDAS air temperature products have not been comprehensively evaluated, although the accuracy of the products was assessed in limited areas. In this study, the daily 0.25° resolution GLDAS air temperature data are compared with two reference datasets: 1) 1-km-resolution gridded Daymet data (2002 and 2010) for the conterminous United States and 2) global meteorological observations (2000–11) archived from the Global Historical Climatology Network (GHCN). The comparison of the GLDAS datasets with the GHCN datasets, including 13 511 weather stations, indicates a fairly high accuracy of the GLDAS data for daily temperature. The quality of the GLDAS air temperature data, however, is not always consistent in different regions of the world; for example, some areas in Africa and South America show relatively low accuracy. Spatial and temporal analyses reveal a high agreement between GLDAS and Daymet daily air temperature datasets, although spatial details in high mountainous areas are not sufficiently estimated by the GLDAS data. The evaluation of the GLDAS data demonstrates that the air temperature estimates are generally accurate, but caution should be taken when the data are used in mountainous areas or places with sparse weather stations.

A program for handling map projections of small-scale geospatial raster data

USGS Publications Warehouse

Finn, Michael P.; Steinwand, Daniel R.; Trent, Jason R.; Buehler, Robert A.; Mattli, David M.; Yamamoto, Kristina H.

2012-01-01

Scientists routinely accomplish small-scale geospatial modeling using raster datasets of global extent. Such use often requires the projection of global raster datasets onto a map or the reprojection from a given map projection associated with a dataset. The distortion characteristics of these projection transformations can have significant effects on modeling results. Distortions associated with the reprojection of global data are generally greater than distortions associated with reprojections of larger-scale, localized areas. The accuracy of areas in projected raster datasets of global extent is dependent on spatial resolution. To address these problems of projection and the associated resampling that accompanies it, methods for framing the transformation space, direct point-to-point transformations rather than gridded transformation spaces, a solution to the wrap-around problem, and an approach to alternative resampling methods are presented. The implementations of these methods are provided in an open-source software package called MapImage (or mapIMG, for short), which is designed to function on a variety of computer architectures.

The Surface Radiation Budget over Oceans and Continents.

NASA Astrophysics Data System (ADS)

Garratt, J. R.; Prata, A. J.; Rotstayn, L. D.; McAvaney, B. J.; Cusack, S.

1998-08-01

An updated evaluation of the surface radiation budget in climate models (1994-96 versions; seven datasets available, with and without aerosols) and in two new satellite-based global datasets (with aerosols) is presented. All nine datasets capture the broad mean monthly zonal variations in the flux components and in the net radiation, with maximum differences of some 100 W m2 occurring in the downwelling fluxes at specific latitudes. Using long-term surface observations, both from land stations and the Pacific warm pool (with typical uncertainties in the annual values varying between ±5 and 20 W m2), excess net radiation (RN) and downwelling shortwave flux density (So) are found in all datasets, consistent with results from earlier studies [for global land, excesses of 15%-20% (12 W m2) in RN and about 12% (20 W m2) in So]. For the nine datasets combined, the spread in annual fluxes is significant: for RN, it is 15 (50) W m2 over global land (Pacific warm pool) in an observed annual mean of 65 (135) W m2; for So, it is 25 (60) W m2 over land (warm pool) in an annual mean of 176 (197) W m2.The effects of aerosols are included in three of the authors' datasets, based on simple aerosol climatologies and assumptions regarding aerosol optical properties. They offer guidance on the broad impact of aerosols on climate, suggesting that the inclusion of aerosols in models would reduce the annual So by 15-20 W m2 over land and 5-10 W m2 over the oceans. Model differences in cloud cover contribute to differences in So between datasets; for global land, this is most clearly demonstrated through the effects of cloud cover on the surface shortwave cloud forcing. The tendency for most datasets to underestimate cloudiness, particularly over global land, and possibly to underestimate atmospheric water vapor absorption, probably contributes to the excess downwelling shortwave flux at the surface.

Global distribution of urban parameters derived from high-resolution global datasets for weather modelling

NASA Astrophysics Data System (ADS)

Kawano, N.; Varquez, A. C. G.; Dong, Y.; Kanda, M.

2016-12-01

Numerical model such as Weather Research and Forecasting model coupled with single-layer Urban Canopy Model (WRF-UCM) is one of the powerful tools to investigate urban heat island. Urban parameters such as average building height (Have), plain area index (λp) and frontal area index (λf), are necessary inputs for the model. In general, these parameters are uniformly assumed in WRF-UCM but this leads to unrealistic urban representation. Distributed urban parameters can also be incorporated into WRF-UCM to consider a detail urban effect. The problem is that distributed building information is not readily available for most megacities especially in developing countries. Furthermore, acquiring real building parameters often require huge amount of time and money. In this study, we investigated the potential of using globally available satellite-captured datasets for the estimation of the parameters, Have, λp, and λf. Global datasets comprised of high spatial resolution population dataset (LandScan by Oak Ridge National Laboratory), nighttime lights (NOAA), and vegetation fraction (NASA). True samples of Have, λp, and λf were acquired from actual building footprints from satellite images and 3D building database of Tokyo, New York, Paris, Melbourne, Istanbul, Jakarta and so on. Regression equations were then derived from the block-averaging of spatial pairs of real parameters and global datasets. Results show that two regression curves to estimate Have and λf from the combination of population and nightlight are necessary depending on the city's level of development. An index which can be used to decide which equation to use for a city is the Gross Domestic Product (GDP). On the other hand, λphas less dependence on GDP but indicated a negative relationship to vegetation fraction. Finally, a simplified but precise approximation of urban parameters through readily-available, high-resolution global datasets and our derived regressions can be utilized to estimate a global distribution of urban parameters for later incorporation into a weather model, thus allowing us to acquire a global understanding of urban climate (Global Urban Climatology). Acknowledgment: This research was supported by the Environment Research and Technology Development Fund (S-14) of the Ministry of the Environment, Japan.

Harmonization of Multiple Forest Disturbance Data to Create a 1986-2011 Database for the Conterminous United States

NASA Astrophysics Data System (ADS)

Soulard, C. E.; Acevedo, W.; Yang, Z.; Cohen, W. B.; Stehman, S. V.; Taylor, J. L.

2015-12-01

A wide range of spatial forest disturbance data exist for the conterminous United States, yet inconsistencies between map products arise because of differing programmatic objectives and methodologies. Researchers on the Land Change Research Project (LCRP) are working to assess spatial agreement, characterize uncertainties, and resolve discrepancies between these national level datasets, in regard to forest disturbance. Disturbance maps from the Global Forest Change (GFC), Landfire Vegetation Disturbance (LVD), National Land Cover Dataset (NLCD), Vegetation Change Tracker (VCT), Web-enabled Landsat Data (WELD), and Monitoring Trends in Burn Severity (MTBS) were harmonized using a pixel-based data fusion process. The harmonization process reconciled forest harvesting, forest fire, and remaining forest disturbance across four intervals (1986-1992, 1992-2001, 2001-2006, and 2006-2011) by relying on convergence of evidence across all datasets available for each interval. Pixels with high agreement across datasets were retained, while moderate-to-low agreement pixels were visually assessed and either manually edited using reference imagery or discarded from the final disturbance map(s). National results show that annual rates of forest harvest and overall fire have increased over the past 25 years. Overall, this study shows that leveraging the best elements of readily-available data improves forest loss monitoring relative to using a single dataset to monitor forest change, particularly by reducing commission errors.

A New Synthetic Global Biomass Carbon Map for the year 2010

NASA Astrophysics Data System (ADS)

Spawn, S.; Lark, T.; Gibbs, H.

2017-12-01

Satellite technologies have facilitated a recent boom in high resolution, large-scale biomass estimation and mapping. These data are the input into a wide range of global models and are becoming the gold standard for required national carbon (C) emissions reporting. Yet their geographical and/or thematic scope may exclude some or all parts of a given country or region. Most datasets tend to focus exclusively on forest biomass. Grasslands and shrublands generally store less C than forests but cover nearly twice as much global land area and may represent a significant portion of a given country's biomass C stock. To address these shortcomings, we set out to create synthetic, global above- and below-ground biomass maps that combine recently-released satellite based data of standing forest biomass with novel estimates for non-forest biomass stocks that are typically neglected. For forests we integrated existing publicly available regional, global and biome-specific biomass maps and modeled below ground biomass using empirical relationships described in the literature. For grasslands, we developed models for both above- and below-ground biomass based on NPP, mean annual temperature and precipitation to extrapolate field measurements across the globe. Shrubland biomass was extrapolated from existing regional biomass maps using environmental factors to generate the first global estimate of shrub biomass. Our new synthetic map of global biomass carbon circa 2010 represents an update to the IPCC Tier-1 Global Biomass Carbon Map for the Year 2000 (Ruesch and Gibbs, 2008) using the best data currently available. In the absence of a single seamless remotely sensed map of global biomass, our synthetic map provides the only globally-consistent source of comprehensive biomass C data and is valuable for land change analyses, carbon accounting, and emissions modeling.

A daily global mesoscale ocean eddy dataset from satellite altimetry.

PubMed

Faghmous, James H; Frenger, Ivy; Yao, Yuanshun; Warmka, Robert; Lindell, Aron; Kumar, Vipin

2015-01-01

Mesoscale ocean eddies are ubiquitous coherent rotating structures of water with radial scales on the order of 100 kilometers. Eddies play a key role in the transport and mixing of momentum and tracers across the World Ocean. We present a global daily mesoscale ocean eddy dataset that contains ~45 million mesoscale features and 3.3 million eddy trajectories that persist at least two days as identified in the AVISO dataset over a period of 1993-2014. This dataset, along with the open-source eddy identification software, extract eddies with any parameters (minimum size, lifetime, etc.), to study global eddy properties and dynamics, and to empirically estimate the impact eddies have on mass or heat transport. Furthermore, our open-source software may be used to identify mesoscale features in model simulations and compare them to observed features. Finally, this dataset can be used to study the interaction between mesoscale ocean eddies and other components of the Earth System.

A daily global mesoscale ocean eddy dataset from satellite altimetry

PubMed Central

Faghmous, James H.; Frenger, Ivy; Yao, Yuanshun; Warmka, Robert; Lindell, Aron; Kumar, Vipin

2015-01-01

Mesoscale ocean eddies are ubiquitous coherent rotating structures of water with radial scales on the order of 100 kilometers. Eddies play a key role in the transport and mixing of momentum and tracers across the World Ocean. We present a global daily mesoscale ocean eddy dataset that contains ~45 million mesoscale features and 3.3 million eddy trajectories that persist at least two days as identified in the AVISO dataset over a period of 1993–2014. This dataset, along with the open-source eddy identification software, extract eddies with any parameters (minimum size, lifetime, etc.), to study global eddy properties and dynamics, and to empirically estimate the impact eddies have on mass or heat transport. Furthermore, our open-source software may be used to identify mesoscale features in model simulations and compare them to observed features. Finally, this dataset can be used to study the interaction between mesoscale ocean eddies and other components of the Earth System. PMID:26097744

The Global Drivers of Photosynthesis and Light Use Efficiency Seasonality: A Granger Frequency Causality Analysis

NASA Technical Reports Server (NTRS)

Nemani, Ramakrishna R.

2016-01-01

Photosynthesis and light use efficiency (LUE) are major factors in the evolution of the continental carbon cycle due to their contribution to gross primary production (GPP). However, while the drivers of photosynthesis and LUE on a plant or canopy scale can often be identified, significant uncertainties exist when modeling these on a global scale. This is due to sparse observations in regions such as the tropics and the lack of a direct global observation dataset. Although others have attempted to address this issue using correlations (Beer, 2010) or calculating GPP from vegetation indices (Running, 2004), in this study we take a new approach. We combine the statistical method of Granger frequency causality and partial Granger frequency causality with remote sensing data products (including sun-induced fluorescence used as a proxy for GPP) to determine the main environmental drivers of GPP across the globe.

Harmonization of forest disturbance datasets of the conterminous USA from 1986 to 2011

USGS Publications Warehouse

Soulard, Christopher E.; Acevedo, William; Cohen, Warren B.; Yang, Zhiqiang; Stehman, Stephen V.; Taylor, Janis L.

2017-01-01

Several spatial forest disturbance datasets exist for the conterminous USA. The major problem with forest disturbance mapping is that variability between map products leads to uncertainty regarding the actual rate of disturbance. In this article, harmonized maps were produced from multiple data sources (i.e., Global Forest Change, LANDFIRE Vegetation Disturbance, National Land Cover Database, Vegetation Change Tracker, and Web-Enabled Landsat Data). The harmonization process involved fitting common class ontologies and determining spatial congruency to produce forest disturbance maps for four time intervals (1986–1992, 1992–2001, 2001–2006, and 2006–2011). Pixels mapped as disturbed for two or more datasets were labeled as disturbed in the harmonized maps. The primary advantage gained by harmonization was improvement in commission error rates relative to the individual disturbance products. Disturbance omission errors were high for both harmonized and individual forest disturbance maps due to underlying limitations in mapping subtle disturbances with Landsat classification algorithms. To enhance the value of the harmonized disturbance products, we used fire perimeter maps to add information on the cause of disturbance.

Harmonization of forest disturbance datasets of the conterminous USA from 1986 to 2011.

PubMed

Soulard, Christopher E; Acevedo, William; Cohen, Warren B; Yang, Zhiqiang; Stehman, Stephen V; Taylor, Janis L

2017-04-01

Several spatial forest disturbance datasets exist for the conterminous USA. The major problem with forest disturbance mapping is that variability between map products leads to uncertainty regarding the actual rate of disturbance. In this article, harmonized maps were produced from multiple data sources (i.e., Global Forest Change, LANDFIRE Vegetation Disturbance, National Land Cover Database, Vegetation Change Tracker, and Web-Enabled Landsat Data). The harmonization process involved fitting common class ontologies and determining spatial congruency to produce forest disturbance maps for four time intervals (1986-1992, 1992-2001, 2001-2006, and 2006-2011). Pixels mapped as disturbed for two or more datasets were labeled as disturbed in the harmonized maps. The primary advantage gained by harmonization was improvement in commission error rates relative to the individual disturbance products. Disturbance omission errors were high for both harmonized and individual forest disturbance maps due to underlying limitations in mapping subtle disturbances with Landsat classification algorithms. To enhance the value of the harmonized disturbance products, we used fire perimeter maps to add information on the cause of disturbance.

Drought - A Global Assessment

NASA Astrophysics Data System (ADS)

Lackner, S.; Barnwal, P.; von der Goltz, J.

2013-12-01

We investigate the lasting effects of early childhood exposure to drought on economic and health outcomes in a large multi-country dataset. By pooling all Demographic and Health Survey rounds for which household geocodes are available, we obtain an individual-level dataset covering 47 developing countries. Among other impact measures, we collect infant and child mortality data from 3.3m live births and data on stunting and wasting for 1.2m individuals, along with data on education, employment, wealth, marriage and childbearing later in life for similarly large numbers of respondents. Birth years vary from 1893 to 2012. We seek to improve upon existing work on the socio-economic impact of drought in a number of ways. First, we introduce from the hydrological literature a drought measure, the Standardized Precipitation Index (SPI), that has been shown to closely proxy the Palmer drought index, but has far less demanding data requirements, and can be obtained globally and for long time periods. We estimate the SPI for 110 years on a global 0.5° grid, which allows us to assign drought histories to the geocoded individual data. Additionally, we leverage our large sample size to explicitly investigate both how drought impacts have changed over time as adaptation occurred at a varying pace in different locations, and the role of the regional extent of drought in determining impacts.

Regional Contrasts of the Warming Rate over Land Significantly Depend on the Calculation Methods of Mean Air Temperature

NASA Astrophysics Data System (ADS)

Wang, Kaicun; Zhou, Chunlüe

2016-04-01

Global analyses of surface mean air temperature (Tm) are key datasets for climate change studies and provide fundamental evidences for global warming. However, the causes of regional contrasts in the warming rate revealed by such datasets, i.e., enhanced warming rates over the northern high latitudes and the "warming hole" over the central U.S., are still under debate. Here we show these regional contrasts depends on the calculation methods of Tm. Existing global analyses calculated Tm from daily minimum and maximum temperatures (T2). We found that T2 has a significant standard deviation error of 0.23 °C/decade in depicting the regional warming rate from 2000 to 2013 but can be reduced by two-thirds using Tm calculated from observations at four specific times (T4), which samples diurnal cycle of land surface air temperature more often. From 1973 to 1997, compared with T4, T2 significantly underestimated the warming rate over the central U.S. and overestimated the warming rate over the northern high latitudes. The ratio of the warming rate over China to that over the U.S. reduces from 2.3 by T2 to 1.4 by T4. This study shows that the studies of regional warming can be substantially improved by T4 instead of T2.

NetCoffee: a fast and accurate global alignment approach to identify functionally conserved proteins in multiple networks.

PubMed

Hu, Jialu; Kehr, Birte; Reinert, Knut

2014-02-15

Owing to recent advancements in high-throughput technologies, protein-protein interaction networks of more and more species become available in public databases. The question of how to identify functionally conserved proteins across species attracts a lot of attention in computational biology. Network alignments provide a systematic way to solve this problem. However, most existing alignment tools encounter limitations in tackling this problem. Therefore, the demand for faster and more efficient alignment tools is growing. We present a fast and accurate algorithm, NetCoffee, which allows to find a global alignment of multiple protein-protein interaction networks. NetCoffee searches for a global alignment by maximizing a target function using simulated annealing on a set of weighted bipartite graphs that are constructed using a triplet approach similar to T-Coffee. To assess its performance, NetCoffee was applied to four real datasets. Our results suggest that NetCoffee remedies several limitations of previous algorithms, outperforms all existing alignment tools in terms of speed and nevertheless identifies biologically meaningful alignments. The source code and data are freely available for download under the GNU GPL v3 license at https://code.google.com/p/netcoffee/.

Global patterns of current and future road infrastructure

NASA Astrophysics Data System (ADS)

Meijer, Johan R.; Huijbregts, Mark A. J.; Schotten, Kees C. G. J.; Schipper, Aafke M.

2018-06-01

Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. Yet current global road maps are typically outdated or characterized by spatial bias in coverage. In the Global Roads Inventory Project we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. We then related total road length per country to country area, population density, GDP and OECD membership, resulting in a regression model with adjusted R 2 of 0.90, and found that that the highest road densities are associated with densely populated and wealthier countries. Applying our regression model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios, we obtained a tentative estimate of 3.0–4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world’s last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems.

Internationally coordinated glacier monitoring: strategy and datasets

NASA Astrophysics Data System (ADS)

Hoelzle, Martin; Armstrong, Richard; Fetterer, Florence; Gärtner-Roer, Isabelle; Haeberli, Wilfried; Kääb, Andreas; Kargel, Jeff; Nussbaumer, Samuel; Paul, Frank; Raup, Bruce; Zemp, Michael

2014-05-01

Internationally coordinated monitoring of long-term glacier changes provide key indicator data about global climate change and began in the year 1894 as an internationally coordinated effort to establish standardized observations. Today, world-wide monitoring of glaciers and ice caps is embedded within the Global Climate Observing System (GCOS) in support of the United Nations Framework Convention on Climate Change (UNFCCC) as an important Essential Climate Variable (ECV). The Global Terrestrial Network for Glaciers (GTN-G) was established in 1999 with the task of coordinating measurements and to ensure the continuous development and adaptation of the international strategies to the long-term needs of users in science and policy. The basic monitoring principles must be relevant, feasible, comprehensive and understandable to a wider scientific community as well as to policy makers and the general public. Data access has to be free and unrestricted, the quality of the standardized and calibrated data must be high and a combination of detailed process studies at selected field sites with global coverage by satellite remote sensing is envisaged. Recently a GTN-G Steering Committee was established to guide and advise the operational bodies responsible for the international glacier monitoring, which are the World Glacier Monitoring Service (WGMS), the US National Snow and Ice Data Center (NSIDC), and the Global Land Ice Measurements from Space (GLIMS) initiative. Several online databases containing a wealth of diverse data types having different levels of detail and global coverage provide fast access to continuously updated information on glacier fluctuation and inventory data. For world-wide inventories, data are now available through (a) the World Glacier Inventory containing tabular information of about 130,000 glaciers covering an area of around 240,000 km2, (b) the GLIMS-database containing digital outlines of around 118,000 glaciers with different time stamps and (c) the Randolph Glacier Inventory (RGI), a new and globally complete digital dataset of outlines from about 180,000 glaciers with some meta-information, which has been used for many applications relating to the IPCC AR5 report. Concerning glacier changes, a database (Fluctuations of Glaciers) exists containing information about mass balance, front variations including past reconstructed time series, geodetic changes and special events. Annual mass balance reporting contains information for about 125 glaciers with a subset of 37 glaciers with continuous observational series since 1980 or earlier. Front variation observations of around 1800 glaciers are available from most of the mountain ranges world-wide. This database was recently updated with 26 glaciers having an unprecedented dataset of length changes from from reconstructions of well-dated historical evidence going back as far as the 16th century. Geodetic observations of about 430 glaciers are available. The database is completed by a dataset containing information on special events including glacier surges, glacier lake outbursts, ice avalanches, eruptions of ice-clad volcanoes, etc. related to about 200 glaciers. A special database of glacier photographs contains 13,000 pictures from around 500 glaciers, some of them dating back to the 19th century. A key challenge is to combine and extend the traditional observations with fast evolving datasets from new technologies.

The ISLSCP initiative I global datasets: Surface boundary conditions and atmospheric forcings for land-atmosphere studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sellers, P.J.; Collatz, J.; Koster, R.

1996-09-01

A comprehensive series of global datasets for land-atmosphere models has been collected, formatted to a common grid, and released on a set of CD-ROMs. This paper describes the motivation for and the contents of the dataset. In June of 1992, an interdisciplinary earth science workshop was convened in Columbia, Maryland, to assess progress in land-atmosphere research, specifically in the areas of models, satellite data algorithms, and field experiments. At the workshop, representatives of the land-atmosphere modeling community defined a need for global datasets to prescribe boundary conditions, initialize state variables, and provide near-surface meteorological and radiative forcings for their models.more » The International Satellite Land Surface Climatology Project (ISLSCP), a part of the Global Energy and Water Cycle Experiment, worked with the Distributed Active Archive Center of the National Aeronautics and Space Administration Goddard Space Flight Center to bring the required datasets together in a usable format. The data have since been released on a collection of CD-ROMs. The datasets on the CD-ROMs are grouped under the following headings: vegetation; hydrology and soils; snow, ice, and oceans; radiation and clouds; and near-surface meteorology. All datasets cover the period 1987-88, and all but a few are spatially continuous over the earth`s land surface. All have been mapped to a common 1{degree} x 1{degree} equal-angle grid. The temporal frequency for most of the datasets is monthly. A few of the near-surface meteorological parameters are available both as six-hourly values and as monthly means. 26 refs., 8 figs., 2 tabs.« less

Global Contrast Based Salient Region Detection.

PubMed

Cheng, Ming-Ming; Mitra, Niloy J; Huang, Xiaolei; Torr, Philip H S; Hu, Shi-Min

2015-03-01

Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoon, Jin -Ho

Amazon rainfall is subject to year-to-year fluctuation resulting in drought and flood in various intensities. A major climatic driver of the interannual variation of the Amazon rainfall is El Niño/Southern Oscillation. Also, the Sea Surface Temperature over the Atlantic Ocean is identified as an important climatic driver on the Amazon water cycle. Previously, observational datasets were used to support the Atlantic influence on Amazon rainfall. Furthermore, it is found that multiple global climate models do reproduce the Atlantic-Amazon link robustly. However, there exist differences in rainfall response, which primarily depends on the climatological rainfall amount.

Using Global Total Electron Content to Understand Interminimum Changes in Solar EUV Irradiance and Thermospheric Composition

NASA Astrophysics Data System (ADS)

McDonald, S. E.; Emmert, J. T.; Krall, J.; Mannucci, A. J.; Vergados, P.

2017-12-01

To understand how and why the distribution of geospace plasma in the ionosphere/plasmasphere is evolving over multi-decadal time scales in response to solar, heliospheric and atmospheric forcing, it is critically important to have long-term, stable datasets. In this study, we use a newly constructed dataset of GPS-based total electron content (TEC) developed by JPL. The JPL Global Ionosphere Mapping (GIM) algorithm was used to generate a 35-station dataset spanning two solar minimum periods (1993-2014). We also use altimeter-derived TEC measurements from TOPEX-Poseidon and Jason-1 to construct a continuous dataset for the 1995-2014 time period. Both longterm datasets are compared to each other to study interminimum changes in the global TEC (during 1995-1995 and 2008-2009). We use the SAMI3 physics-based model of the ionosphere to compare the simulations of 1995-2014 with the JPL TEC and TOPEX/Jason-1 datasets. To drive SAMI3, we use the Naval Research Laboratory Solar Spectral Irradiance (NRLSSI) model to specify the EUV irradiances, and NRLMSIS to specify the thermosphere. We adjust the EUV irradiances and thermospheric constituents to match the TEC datasets and draw conclusions regarding sources of the differences between the two solar minimum periods.

An Atlas of ShakeMaps and population exposure catalog for earthquake loss modeling

USGS Publications Warehouse

Allen, T.I.; Wald, D.J.; Earle, P.S.; Marano, K.D.; Hotovec, A.J.; Lin, K.; Hearne, M.G.

2009-01-01

We present an Atlas of ShakeMaps and a catalog of human population exposures to moderate-to-strong ground shaking (EXPO-CAT) for recent historical earthquakes (1973-2007). The common purpose of the Atlas and exposure catalog is to calibrate earthquake loss models to be used in the US Geological Survey's Prompt Assessment of Global Earthquakes for Response (PAGER). The full ShakeMap Atlas currently comprises over 5,600 earthquakes from January 1973 through December 2007, with almost 500 of these maps constrained-to varying degrees-by instrumental ground motions, macroseismic intensity data, community internet intensity observations, and published earthquake rupture models. The catalog of human exposures is derived using current PAGER methodologies. Exposure to discrete levels of shaking intensity is obtained by correlating Atlas ShakeMaps with a global population database. Combining this population exposure dataset with historical earthquake loss data, such as PAGER-CAT, provides a useful resource for calibrating loss methodologies against a systematically-derived set of ShakeMap hazard outputs. We illustrate two example uses for EXPO-CAT; (1) simple objective ranking of country vulnerability to earthquakes, and; (2) the influence of time-of-day on earthquake mortality. In general, we observe that countries in similar geographic regions with similar construction practices tend to cluster spatially in terms of relative vulnerability. We also find little quantitative evidence to suggest that time-of-day is a significant factor in earthquake mortality. Moreover, earthquake mortality appears to be more systematically linked to the population exposed to severe ground shaking (Modified Mercalli Intensity VIII+). Finally, equipped with the full Atlas of ShakeMaps, we merge each of these maps and find the maximum estimated peak ground acceleration at any grid point in the world for the past 35 years. We subsequently compare this "composite ShakeMap" with existing global hazard models, calculating the spatial area of the existing hazard maps exceeded by the combined ShakeMap ground motions. In general, these analyses suggest that existing global, and regional, hazard maps tend to overestimate hazard. Both the Atlas of ShakeMaps and EXPO-CAT have many potential uses for examining earthquake risk and epidemiology. All of the datasets discussed herein are available for download on the PAGER Web page ( http://earthquake.usgs.gov/ eqcenter/pager/prodandref/ ). ?? 2009 Springer Science+Business Media B.V.

Hydrodynamic modelling and global datasets: Flow connectivity and SRTM data, a Bangkok case study.

NASA Astrophysics Data System (ADS)

Trigg, M. A.; Bates, P. B.; Michaelides, K.

2012-04-01

The rise in the global interconnected manufacturing supply chains requires an understanding and consistent quantification of flood risk at a global scale. Flood risk is often better quantified (or at least more precisely defined) in regions where there has been an investment in comprehensive topographical data collection such as LiDAR coupled with detailed hydrodynamic modelling. Yet in regions where these data and modelling are unavailable, the implications of flooding and the knock on effects for global industries can be dramatic, as evidenced by the recent floods in Bangkok, Thailand. There is a growing momentum in terms of global modelling initiatives to address this lack of a consistent understanding of flood risk and they will rely heavily on the application of available global datasets relevant to hydrodynamic modelling, such as Shuttle Radar Topography Mission (SRTM) data and its derivatives. These global datasets bring opportunities to apply consistent methodologies on an automated basis in all regions, while the use of coarser scale datasets also brings many challenges such as sub-grid process representation and downscaled hydrology data from global climate models. There are significant opportunities for hydrological science in helping define new, realistic and physically based methodologies that can be applied globally as well as the possibility of gaining new insights into flood risk through analysis of the many large datasets that will be derived from this work. We use Bangkok as a case study to explore some of the issues related to using these available global datasets for hydrodynamic modelling, with particular focus on using SRTM data to represent topography. Research has shown that flow connectivity on the floodplain is an important component in the dynamics of flood flows on to and off the floodplain, and indeed within different areas of the floodplain. A lack of representation of flow connectivity, often due to data resolution limitations, means that important subgrid processes are missing from hydrodynamic models leading to poor model predictive capabilities. Specifically here, the issue of flow connectivity during flood events is explored using geostatistical techniques to quantify the change of flow connectivity on floodplains due to grid rescaling methods. We also test whether this method of assessing connectivity can be used as new tool in the quantification of flood risk that moves beyond the simple flood extent approach, encapsulating threshold changes and data limitations.

A Global Geospatial Ecosystem Services Estimate of Urban Agriculture

NASA Astrophysics Data System (ADS)

Clinton, Nicholas; Stuhlmacher, Michelle; Miles, Albie; Uludere Aragon, Nazli; Wagner, Melissa; Georgescu, Matei; Herwig, Chris; Gong, Peng

2018-01-01

Though urban agriculture (UA), defined here as growing of crops in cities, is increasing in popularity and importance globally, little is known about the aggregate benefits of such natural capital in built-up areas. Here, we introduce a quantitative framework to assess global aggregate ecosystem services from existing vegetation in cities and an intensive UA adoption scenario based on data-driven estimates of urban morphology and vacant land. We analyzed global population, urban, meteorological, terrain, and Food and Agriculture Organization (FAO) datasets in Google Earth Engine to derive global scale estimates, aggregated by country, of services provided by UA. We estimate the value of four ecosystem services provided by existing vegetation in urban areas to be on the order of 33 billion annually. We project potential annual food production of 100-180 million tonnes, energy savings ranging from 14 to 15 billion kilowatt hours, nitrogen sequestration between 100,000 and 170,000 tonnes, and avoided storm water runoff between 45 and 57 billion cubic meters annually. In addition, we estimate that food production, nitrogen fixation, energy savings, pollination, climate regulation, soil formation and biological control of pests could be worth as much as 80-160 billion annually in a scenario of intense UA implementation. Our results demonstrate significant country-to-country variability in UA-derived ecosystem services and reduction of food insecurity. These estimates represent the first effort to consistently quantify these incentives globally, and highlight the relative spatial importance of built environments to act as change agents that alleviate mounting concerns associated with global environmental change and unsustainable development.

A critical evaluation of ecological indices for the comparative analysis of microbial communities based on molecular datasets.

PubMed

Lucas, Rico; Groeneveld, Jürgen; Harms, Hauke; Johst, Karin; Frank, Karin; Kleinsteuber, Sabine

2017-01-01

In times of global change and intensified resource exploitation, advanced knowledge of ecophysiological processes in natural and engineered systems driven by complex microbial communities is crucial for both safeguarding environmental processes and optimising rational control of biotechnological processes. To gain such knowledge, high-throughput molecular techniques are routinely employed to investigate microbial community composition and dynamics within a wide range of natural or engineered environments. However, for molecular dataset analyses no consensus about a generally applicable alpha diversity concept and no appropriate benchmarking of corresponding statistical indices exist yet. To overcome this, we listed criteria for the appropriateness of an index for such analyses and systematically scrutinised commonly employed ecological indices describing diversity, evenness and richness based on artificial and real molecular datasets. We identified appropriate indices warranting interstudy comparability and intuitive interpretability. The unified diversity concept based on 'effective numbers of types' provides the mathematical framework for describing community composition. Additionally, the Bray-Curtis dissimilarity as a beta-diversity index was found to reflect compositional changes. The employed statistical procedure is presented comprising commented R-scripts and example datasets for user-friendly trial application. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Comparative assessment of methods for the fusion transcripts detection from RNA-Seq data

PubMed Central

Kumar, Shailesh; Vo, Angie Duy; Qin, Fujun; Li, Hui

2016-01-01

RNA-Seq made possible the global identification of fusion transcripts, i.e. “chimeric RNAs”. Even though various software packages have been developed to serve this purpose, they behave differently in different datasets provided by different developers. It is important for both users, and developers to have an unbiased assessment of the performance of existing fusion detection tools. Toward this goal, we compared the performance of 12 well-known fusion detection software packages. We evaluated the sensitivity, false discovery rate, computing time, and memory usage of these tools in four different datasets (positive, negative, mixed, and test). We conclude that some tools are better than others in terms of sensitivity, positive prediction value, time consumption and memory usage. We also observed small overlaps of the fusions detected by different tools in the real dataset (test dataset). This could be due to false discoveries by various tools, but could also be due to the reason that none of the tools are inclusive. We have found that the performance of the tools depends on the quality, read length, and number of reads of the RNA-Seq data. We recommend that users choose the proper tools for their purpose based on the properties of their RNA-Seq data. PMID:26862001

A Multi-Decadal Sample Return Campaign Will Advance Lunar and Solar System Science and Exploration by 2050

NASA Technical Reports Server (NTRS)

Neal, C. R.; Lawrence, S. J.

2017-01-01

There have been 11 missions to the Moon this century, 10 of which have been orbital, from 5 different space agencies. China became the third country to successfully soft-land on the Moon in 2013, and the second to successfully remotely operate a rover on the lunar surface. We now have significant global datasets that, coupled with the 1990s Clementine and Lunar Prospector missions, show that the sample collection is not representative of the lithologies present on the Moon. The M3 data from the Indian Chandrayaan-1 mission have identified lithologies that are not present/under-represented in the sample collection. LRO datasets show that volcanism could be as young as 100 Ma and that significant felsic complexes exist within the lunar crust. A multi-decadal sample return campaign is the next logical step in advancing our understanding of lunar origin and evolution and Solar System processes.

GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare.

PubMed

Ali, Rahman; Siddiqi, Muhammad Hameed; Idris, Muhammad; Ali, Taqdir; Hussain, Shujaat; Huh, Eui-Nam; Kang, Byeong Ho; Lee, Sungyoung

2015-07-02

A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset. This paper proposes a global unified data model (GUDM) to provide a global unified data structure for all data sources and generate a unified dataset by a "data modeler" tool. The proposed tool implements user-centric priority based approach which can easily resolve the problems of unified data modeling and overlapping attributes across multiple datasets. The tool is illustrated using sample diabetes mellitus data. The diverse data sources to generate the unified dataset for diabetes mellitus include clinical trial information, a social media interaction dataset and physical activity data collected using different sensors. To realize the significance of the unified dataset, we adopted a well-known rough set theory based rules creation process to create rules from the unified dataset. The evaluation of the tool on six different sets of locally created diverse datasets shows that the tool, on average, reduces 94.1% time efforts of the experts and knowledge engineer while creating unified datasets.

GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare

PubMed Central

Ali, Rahman; Siddiqi, Muhammad Hameed; Idris, Muhammad; Ali, Taqdir; Hussain, Shujaat; Huh, Eui-Nam; Kang, Byeong Ho; Lee, Sungyoung

2015-01-01

A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset. This paper proposes a global unified data model (GUDM) to provide a global unified data structure for all data sources and generate a unified dataset by a “data modeler” tool. The proposed tool implements user-centric priority based approach which can easily resolve the problems of unified data modeling and overlapping attributes across multiple datasets. The tool is illustrated using sample diabetes mellitus data. The diverse data sources to generate the unified dataset for diabetes mellitus include clinical trial information, a social media interaction dataset and physical activity data collected using different sensors. To realize the significance of the unified dataset, we adopted a well-known rough set theory based rules creation process to create rules from the unified dataset. The evaluation of the tool on six different sets of locally created diverse datasets shows that the tool, on average, reduces 94.1% time efforts of the experts and knowledge engineer while creating unified datasets. PMID:26147731

Tracking urban activity growth globally with big location data

PubMed Central

Daggitt, Matthew L.; Noulas, Anastasios; Shaw, Blake; Mascolo, Cecilia

2016-01-01

In recent decades, the world has experienced rates of urban growth unparalleled in any other period of history and this growth is shaping the environment in which an increasing proportion of us live. In this paper, we use a longitudinal dataset from Foursquare, a location-based social network, to analyse urban growth across 100 major cities worldwide. Initially, we explore how urban growth differs in cities across the world. We show that there exists a strong spatial correlation, with nearby pairs of cities more likely to share similar growth profiles than remote pairs of cities. Subsequently, we investigate how growth varies inside cities and demonstrate that, given the existing local density of places, higher-than-expected growth is highly localized while lower-than-expected growth is more diffuse. Finally, we attempt to use the dataset to characterize competition between new and existing venues. By defining a measure based on the change in throughput of a venue before and after the opening of a new nearby venue, we demonstrate which venue types have a positive effect on venues of the same type and which have a negative effect. For example, our analysis confirms the hypothesis that there is large degree of competition between bookstores, in the sense that existing bookstores normally experience a notable drop in footfall after a new bookstore opens nearby. Other place types, such as museums, are shown to have a cooperative effect and their presence fosters higher traffic volumes to nearby places of the same type. PMID:27152210

Tracking urban activity growth globally with big location data.

PubMed

Daggitt, Matthew L; Noulas, Anastasios; Shaw, Blake; Mascolo, Cecilia

2016-04-01

In recent decades, the world has experienced rates of urban growth unparalleled in any other period of history and this growth is shaping the environment in which an increasing proportion of us live. In this paper, we use a longitudinal dataset from Foursquare, a location-based social network, to analyse urban growth across 100 major cities worldwide. Initially, we explore how urban growth differs in cities across the world. We show that there exists a strong spatial correlation, with nearby pairs of cities more likely to share similar growth profiles than remote pairs of cities. Subsequently, we investigate how growth varies inside cities and demonstrate that, given the existing local density of places, higher-than-expected growth is highly localized while lower-than-expected growth is more diffuse. Finally, we attempt to use the dataset to characterize competition between new and existing venues. By defining a measure based on the change in throughput of a venue before and after the opening of a new nearby venue, we demonstrate which venue types have a positive effect on venues of the same type and which have a negative effect. For example, our analysis confirms the hypothesis that there is large degree of competition between bookstores, in the sense that existing bookstores normally experience a notable drop in footfall after a new bookstore opens nearby. Other place types, such as museums, are shown to have a cooperative effect and their presence fosters higher traffic volumes to nearby places of the same type.

Evaluation of Global Observations-Based Evapotranspiration Datasets and IPCC AR4 Simulations

NASA Technical Reports Server (NTRS)

Mueller, B.; Seneviratne, S. I.; Jimenez, C.; Corti, T.; Hirschi, M.; Balsamo, G.; Ciais, P.; Dirmeyer, P.; Fisher, J. B.; Guo, Z.;

Marine Socio-Environmental Covariates: queryable global layers of environmental and anthropogenic variables for marine ecosystem studies.

PubMed

Yeager, Lauren A; Marchand, Philippe; Gill, David A; Baum, Julia K; McPherson, Jana M

2017-07-01

Biophysical conditions, including climate, environmental stress, and habitat availability, are key drivers of many ecological processes (e.g., community assembly and productivity) and associated ecosystem services (e.g., carbon sequestration and fishery production). Furthermore, anthropogenic impacts such as coastal development and fishing can have drastic effects on the structure and function of marine ecosystems. Scientists need to account for environmental variation and human impacts to accurately model, manage, and conserve marine ecosystems. Although there are many types of environmental data available from global remote sensing and open-source data products, some are inaccessible to potential end-users because they exist as global layers in high temporal and spatial resolutions which require considerable computational power to process. Additionally, coastal locations often suffer from missing data or data quality issues which limit the utility of some global marine products for coastal sites. Herein we present the Marine Socio-Environmental Covariates dataset for the global oceans, which consists of environmental and anthropogenic variables summarized in ecologically relevant ways. The dataset includes four sets of environmental variables related to biophysical conditions (net primary productivity models corrected for shallow-water reflectance, wave energy including sheltered-coastline corrections) and landscape context (coral reef and land cover within varying radii). We also present two sets of anthropogenic variables, human population density (within varying radii) and distance to large population center, which can serve as indicators of local human impacts. We have paired global, summarized layers available for download with an online data querying platform that allows users to extract data for specific point locations with finer control of summary statistics. In creating these global layers and online platform, we hope to make the data accessible to a wide array of end-users with the goal of advancing marine ecosystem studies. © 2017 by the Ecological Society of America.

Modeling spatial-temporal dynamics of global wetlands: Comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Z.; Zimmermann, N. E.; Poulter, B.

2015-12-01

Simulations of the spatial-temporal dynamics of wetlands is key to understanding the role of wetland biogeochemistry under past and future climate variability. Hydrologic inundation models, such as TOPMODEL, are based on a fundamental parameter known as the compound topographic index (CTI) and provide a computationally cost-efficient approach to simulate global wetland dynamics. However, there remains large discrepancy in the implementations of TOPMODEL in land-surface models (LSMs) and thus their performance against observations. This study describes new improvements to TOPMODEL implementation and estimates of global wetland dynamics using the LPJ-wsl DGVM, and quantifies uncertainties by comparing three digital elevation model products (HYDRO1k, GMTED, and HydroSHEDS) at different spatial resolution and accuracy on simulated inundation dynamics. We found that calibrating TOPMODEL with a benchmark dataset can help to successfully predict the seasonal and interannual variations of wetlands, as well as improve the spatial distribution of wetlands to be consistent with inventories. The HydroSHEDS DEM, using a river-basin scheme for aggregating the CTI, shows best accuracy for capturing the spatio-temporal dynamics of wetland among three DEM products. This study demonstrates the feasibility to capture spatial heterogeneity of inundation and to estimate seasonal and interannual variations in wetland by coupling a hydrological module in LSMs with appropriate benchmark datasets. It additionally highlight the importance of an adequate understanding of topographic indices for simulating global wetlands and show the opportunity to converge wetland estimations in LSMs by identifying the uncertainty associated with existing wetland products.

Multi-scale mantle structure underneath the Americas from a new tomographic model of seismic shear velocity

NASA Astrophysics Data System (ADS)

Porritt, R. W.; Becker, T. W.; Auer, L.; Boschi, L.

2017-12-01

We present a whole-mantle, variable resolution, shear-wave tomography model based on newly available and existing seismological datasets including regional body-wave delay times and multi-mode Rayleigh and Love wave phase delays. Our body wave dataset includes 160,000 S wave delays used in the DNA13 regional tomographic model focused on the western and central US, 86,000 S and SKS delays measured on stations in western South America (Porritt et al., in prep), and 3,900,000 S+ phases measured by correlation between data observed at stations in the IRIS global networks (IU, II) and stations in the continuous US, against synthetic data generated with IRIS Syngine. The surface wave dataset includes fundamental mode and overtone Rayleigh wave data from Schaeffer and Levedev (2014), ambient noise derived Rayleigh wave and Love wave measurements from Ekstrom (2013), newly computed fundamental mode ambient noise Rayleigh wave phase delays for the continuous US up to July 2017, and other, previously published, measurements. These datasets, along with a data-adaptive parameterization utilized for the SAVANI model (Auer et al., 2014), should allow significantly finer-scale imaging than previous global models, rivaling that of regional-scale approaches, under the USArray footprint in the continuous US, while seamlessly integrating into a global model. We parameterize the model for both vertically (vSV) and horizontally (vSH) polarized shear velocities by accounting for the different sensitivities of the various phases and wave types. The resulting, radially anisotropic model should allow for a range of new geodynamic analysis, including estimates of mantle flow induced topography or seismic anisotropy, without generating artifacts due to edge effects, or requiring assumptions about the structure of the region outside the well resolved model space. Our model shows a number of features, including indications of the effects of edge-driven convection in the Cordillera and along the eastern margin and larger-scale convection due to the subduction of the Farallon slab and along the edge of the Laurentia cratonic margin.

Designing for Global Data Sharing, Designing for Educational Transformation

ERIC Educational Resources Information Center

Adams, Robin S.; Radcliffe, David; Fosmire, Michael

2016-01-01

This paper provides an example of a global data sharing project with an educational transformation agenda. This agenda shaped both the design of the shared dataset and the experience of sharing the common dataset to support multiple perspective inquiry and enable integrative and critically reflexive research-to-practice dialogue. The shared…

Regional climate change study requires new temperature datasets

NASA Astrophysics Data System (ADS)

Wang, K.; Zhou, C.

2016-12-01

Analyses of global mean air temperature (Ta), i. e., NCDC GHCN, GISS, and CRUTEM4, are the fundamental datasets for climate change study and provide key evidence for global warming. All of the global temperature analyses over land are primarily based on meteorological observations of the daily maximum and minimum temperatures (Tmax and Tmin) and their averages (T2) because in most weather stations, the measurements of Tmax and Tmin may be the only choice for a homogenous century-long analysis of mean temperature. Our studies show that these datasets are suitable for long-term global warming studies. However, they may introduce substantial bias in quantifying local and regional warming rates, i.e., with a root mean square error of more than 25% at 5°x 5° grids. From 1973 to 1997, the current datasets tend to significantly underestimate the warming rate over the central U.S. and overestimate the warming rate over the northern high latitudes. Similar results revealed during the period 1998-2013, the warming hiatus period, indicate the use of T2 enlarges the spatial contrast of temperature trends. This because T2 over land only sample air temperature twice daily and cannot accurately reflect land-atmosphere and incoming radiation variations in the temperature diurnal cycle. For better regional climate change detection and attribution, we suggest creating new global mean air temperature datasets based on the recently available high spatiotemporal resolution meteorological observations, i.e., daily four observations weather station since 1960s, These datasets will not only help investigate dynamical processes on temperature variances but also help better evaluate the reanalyzed and modeled simulations of temperature and make some substantial improvements for other related climate variables in models, especially over regional and seasonal aspects.

Regional climate change study requires new temperature datasets

NASA Astrophysics Data System (ADS)

Wang, Kaicun; Zhou, Chunlüe

2017-04-01

Analyses of global mean air temperature (Ta), i. e., NCDC GHCN, GISS, and CRUTEM4, are the fundamental datasets for climate change study and provide key evidence for global warming. All of the global temperature analyses over land are primarily based on meteorological observations of the daily maximum and minimum temperatures (Tmax and Tmin) and their averages (T2) because in most weather stations, the measurements of Tmax and Tmin may be the only choice for a homogenous century-long analysis of mean temperature. Our studies show that these datasets are suitable for long-term global warming studies. However, they may have substantial biases in quantifying local and regional warming rates, i.e., with a root mean square error of more than 25% at 5 degree grids. From 1973 to 1997, the current datasets tend to significantly underestimate the warming rate over the central U.S. and overestimate the warming rate over the northern high latitudes. Similar results revealed during the period 1998-2013, the warming hiatus period, indicate the use of T2 enlarges the spatial contrast of temperature trends. This is because T2 over land only samples air temperature twice daily and cannot accurately reflect land-atmosphere and incoming radiation variations in the temperature diurnal cycle. For better regional climate change detection and attribution, we suggest creating new global mean air temperature datasets based on the recently available high spatiotemporal resolution meteorological observations, i.e., daily four observations weather station since 1960s. These datasets will not only help investigate dynamical processes on temperature variances but also help better evaluate the reanalyzed and modeled simulations of temperature and make some substantial improvements for other related climate variables in models, especially over regional and seasonal aspects.

Mapping Global Ocean Surface Albedo from Satellite Observations: Models, Algorithms, and Datasets

NASA Astrophysics Data System (ADS)

Li, X.; Fan, X.; Yan, H.; Li, A.; Wang, M.; Qu, Y.

2018-04-01

Ocean surface albedo (OSA) is one of the important parameters in surface radiation budget (SRB). It is usually considered as a controlling factor of the heat exchange among the atmosphere and ocean. The temporal and spatial dynamics of OSA determine the energy absorption of upper level ocean water, and have influences on the oceanic currents, atmospheric circulations, and transportation of material and energy of hydrosphere. Therefore, various parameterizations and models have been developed for describing the dynamics of OSA. However, it has been demonstrated that the currently available OSA datasets cannot full fill the requirement of global climate change studies. In this study, we present a literature review on mapping global OSA from satellite observations. The models (parameterizations, the coupled ocean-atmosphere radiative transfer (COART), and the three component ocean water albedo (TCOWA)), algorithms (the estimation method based on reanalysis data, and the direct-estimation algorithm), and datasets (the cloud, albedo and radiation (CLARA) surface albedo product, dataset derived by the TCOWA model, and the global land surface satellite (GLASS) phase-2 surface broadband albedo product) of OSA have been discussed, separately.

Approximating Long-Term Statistics Early in the Global Precipitation Measurement Era

NASA Technical Reports Server (NTRS)

Stanley, Thomas; Kirschbaum, Dalia B.; Huffman, George J.; Adler, Robert F.

2017-01-01

Long-term precipitation records are vital to many applications, especially the study of extreme events. The Tropical Rainfall Measuring Mission (TRMM) has served this need, but TRMMs successor mission, Global Precipitation Measurement (GPM), does not yet provide a long-term record. Quantile mapping, the conversion of values across paired empirical distributions, offers a simple, established means to approximate such long-term statistics, but only within appropriately defined domains. This method was applied to a case study in Central America, demonstrating that quantile mapping between TRMM and GPM data maintains the performance of a real-time landslide model. Use of quantile mapping could bring the benefits of the latest satellite-based precipitation dataset to existing user communities such as those for hazard assessment, crop forecasting, numerical weather prediction, and disease tracking.

Pantheon 1.0, a manually verified dataset of globally famous biographies.

PubMed

Yu, Amy Zhao; Ronen, Shahar; Hu, Kevin; Lu, Tiffany; Hidalgo, César A

2016-01-05

We present the Pantheon 1.0 dataset: a manually verified dataset of individuals that have transcended linguistic, temporal, and geographic boundaries. The Pantheon 1.0 dataset includes the 11,341 biographies present in more than 25 languages in Wikipedia and is enriched with: (i) manually verified demographic information (place and date of birth, gender) (ii) a taxonomy of occupations classifying each biography at three levels of aggregation and (iii) two measures of global popularity including the number of languages in which a biography is present in Wikipedia (L), and the Historical Popularity Index (HPI) a metric that combines information on L, time since birth, and page-views (2008-2013). We compare the Pantheon 1.0 dataset to data from the 2003 book, Human Accomplishments, and also to external measures of accomplishment in individual games and sports: Tennis, Swimming, Car Racing, and Chess. In all of these cases we find that measures of popularity (L and HPI) correlate highly with individual accomplishment, suggesting that measures of global popularity proxy the historical impact of individuals.

Pantheon 1.0, a manually verified dataset of globally famous biographies

PubMed Central

Yu, Amy Zhao; Ronen, Shahar; Hu, Kevin; Lu, Tiffany; Hidalgo, César A.

2016-01-01

We present the Pantheon 1.0 dataset: a manually verified dataset of individuals that have transcended linguistic, temporal, and geographic boundaries. The Pantheon 1.0 dataset includes the 11,341 biographies present in more than 25 languages in Wikipedia and is enriched with: (i) manually verified demographic information (place and date of birth, gender) (ii) a taxonomy of occupations classifying each biography at three levels of aggregation and (iii) two measures of global popularity including the number of languages in which a biography is present in Wikipedia (L), and the Historical Popularity Index (HPI) a metric that combines information on L, time since birth, and page-views (2008–2013). We compare the Pantheon 1.0 dataset to data from the 2003 book, Human Accomplishments, and also to external measures of accomplishment in individual games and sports: Tennis, Swimming, Car Racing, and Chess. In all of these cases we find that measures of popularity (L and HPI) correlate highly with individual accomplishment, suggesting that measures of global popularity proxy the historical impact of individuals. PMID:26731133

The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts

PubMed Central

Hudson, Lawrence N; Newbold, Tim; Contu, Sara; Hill, Samantha L L; Lysenko, Igor; De Palma, Adriana; Phillips, Helen R P; Senior, Rebecca A; Bennett, Dominic J; Booth, Hollie; Choimes, Argyrios; Correia, David L P; Day, Julie; Echeverría-Londoño, Susy; Garon, Morgan; Harrison, Michelle L K; Ingram, Daniel J; Jung, Martin; Kemp, Victoria; Kirkpatrick, Lucinda; Martin, Callum D; Pan, Yuan; White, Hannah J; Aben, Job; Abrahamczyk, Stefan; Adum, Gilbert B; Aguilar-Barquero, Virginia; Aizen, Marcelo A; Ancrenaz, Marc; Arbeláez-Cortés, Enrique; Armbrecht, Inge; Azhar, Badrul; Azpiroz, Adrián B; Baeten, Lander; Báldi, András; Banks, John E; Barlow, Jos; Batáry, Péter; Bates, Adam J; Bayne, Erin M; Beja, Pedro; Berg, Åke; Berry, Nicholas J; Bicknell, Jake E; Bihn, Jochen H; Böhning-Gaese, Katrin; Boekhout, Teun; Boutin, Céline; Bouyer, Jérémy; Brearley, Francis Q; Brito, Isabel; Brunet, Jörg; Buczkowski, Grzegorz; Buscardo, Erika; Cabra-García, Jimmy; Calviño-Cancela, María; Cameron, Sydney A; Cancello, Eliana M; Carrijo, Tiago F; Carvalho, Anelena L; Castro, Helena; Castro-Luna, Alejandro A; Cerda, Rolando; Cerezo, Alexis; Chauvat, Matthieu; Clarke, Frank M; Cleary, Daniel F R; Connop, Stuart P; D'Aniello, Biagio; da Silva, Pedro Giovâni; Darvill, Ben; Dauber, Jens; Dejean, Alain; Diekötter, Tim; Dominguez-Haydar, Yamileth; Dormann, Carsten F; Dumont, Bertrand; Dures, Simon G; Dynesius, Mats; Edenius, Lars; Elek, Zoltán; Entling, Martin H; Farwig, Nina; Fayle, Tom M; Felicioli, Antonio; Felton, Annika M; Ficetola, Gentile F; Filgueiras, Bruno K C; Fonte, Steven J; Fraser, Lauchlan H; Fukuda, Daisuke; Furlani, Dario; Ganzhorn, Jörg U; Garden, Jenni G; Gheler-Costa, Carla; Giordani, Paolo; Giordano, Simonetta; Gottschalk, Marco S; Goulson, Dave; Gove, Aaron D; Grogan, James; Hanley, Mick E; Hanson, Thor; Hashim, Nor R; Hawes, Joseph E; Hébert, Christian; Helden, Alvin J; Henden, John-André; Hernández, Lionel; Herzog, Felix; Higuera-Diaz, Diego; Hilje, Branko; Horgan, Finbarr G; Horváth, Roland; Hylander, Kristoffer; Isaacs-Cubides, Paola; Ishitani, Masahiro; Jacobs, Carmen T; Jaramillo, Víctor J; Jauker, Birgit; Jonsell, Mats; Jung, Thomas S; Kapoor, Vena; Kati, Vassiliki; Katovai, Eric; Kessler, Michael; Knop, Eva; Kolb, Annette; Kőrösi, Ádám; Lachat, Thibault; Lantschner, Victoria; Le Féon, Violette; LeBuhn, Gretchen; Légaré, Jean-Philippe; Letcher, Susan G; Littlewood, Nick A; López-Quintero, Carlos A; Louhaichi, Mounir; Lövei, Gabor L; Lucas-Borja, Manuel Esteban; Luja, Victor H; Maeto, Kaoru; Magura, Tibor; Mallari, Neil Aldrin; Marin-Spiotta, Erika; Marshall, E J P; Martínez, Eliana; Mayfield, Margaret M; Mikusinski, Grzegorz; Milder, Jeffrey C; Miller, James R; Morales, Carolina L; Muchane, Mary N; Muchane, Muchai; Naidoo, Robin; Nakamura, Akihiro; Naoe, Shoji; Nates-Parra, Guiomar; Navarrete Gutierrez, Dario A; Neuschulz, Eike L; Noreika, Norbertas; Norfolk, Olivia; Noriega, Jorge Ari; Nöske, Nicole M; O'Dea, Niall; Oduro, William; Ofori-Boateng, Caleb; Oke, Chris O; Osgathorpe, Lynne M; Paritsis, Juan; Parra-H, Alejandro; Pelegrin, Nicolás; Peres, Carlos A; Persson, Anna S; Petanidou, Theodora; Phalan, Ben; Philips, T Keith; Poveda, Katja; Power, Eileen F; Presley, Steven J; Proença, Vânia; Quaranta, Marino; Quintero, Carolina; Redpath-Downing, Nicola A; Reid, J Leighton; Reis, Yana T; Ribeiro, Danilo B; Richardson, Barbara A; Richardson, Michael J; Robles, Carolina A; Römbke, Jörg; Romero-Duque, Luz Piedad; Rosselli, Loreta; Rossiter, Stephen J; Roulston, T'ai H; Rousseau, Laurent; Sadler, Jonathan P; Sáfián, Szabolcs; Saldaña-Vázquez, Romeo A; Samnegård, Ulrika; Schüepp, Christof; Schweiger, Oliver; Sedlock, Jodi L; Shahabuddin, Ghazala; Sheil, Douglas; Silva, Fernando A B; Slade, Eleanor M; Smith-Pardo, Allan H; Sodhi, Navjot S; Somarriba, Eduardo J; Sosa, Ramón A; Stout, Jane C; Struebig, Matthew J; Sung, Yik-Hei; Threlfall, Caragh G; Tonietto, Rebecca; Tóthmérész, Béla; Tscharntke, Teja; Turner, Edgar C; Tylianakis, Jason M; Vanbergen, Adam J; Vassilev, Kiril; Verboven, Hans A F; Vergara, Carlos H; Vergara, Pablo M; Verhulst, Jort; Walker, Tony R; Wang, Yanping; Watling, James I; Wells, Konstans; Williams, Christopher D; Willig, Michael R; Woinarski, John C Z; Wolf, Jan H D; Woodcock, Ben A; Yu, Douglas W; Zaitsev, Andrey S; Collen, Ben; Ewers, Rob M; Mace, Georgina M; Purves, Drew W; Scharlemann, Jörn P W; Purvis, Andy

2014-01-01

Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species’ threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project – and avert – future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups – including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems – http://www.predicts.org.uk). We make site-level summary data available alongside this article. The full database will be publicly available in 2015. PMID:25558364

The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts.

PubMed

Hudson, Lawrence N; Newbold, Tim; Contu, Sara; Hill, Samantha L L; Lysenko, Igor; De Palma, Adriana; Phillips, Helen R P; Senior, Rebecca A; Bennett, Dominic J; Booth, Hollie; Choimes, Argyrios; Correia, David L P; Day, Julie; Echeverría-Londoño, Susy; Garon, Morgan; Harrison, Michelle L K; Ingram, Daniel J; Jung, Martin; Kemp, Victoria; Kirkpatrick, Lucinda; Martin, Callum D; Pan, Yuan; White, Hannah J; Aben, Job; Abrahamczyk, Stefan; Adum, Gilbert B; Aguilar-Barquero, Virginia; Aizen, Marcelo A; Ancrenaz, Marc; Arbeláez-Cortés, Enrique; Armbrecht, Inge; Azhar, Badrul; Azpiroz, Adrián B; Baeten, Lander; Báldi, András; Banks, John E; Barlow, Jos; Batáry, Péter; Bates, Adam J; Bayne, Erin M; Beja, Pedro; Berg, Åke; Berry, Nicholas J; Bicknell, Jake E; Bihn, Jochen H; Böhning-Gaese, Katrin; Boekhout, Teun; Boutin, Céline; Bouyer, Jérémy; Brearley, Francis Q; Brito, Isabel; Brunet, Jörg; Buczkowski, Grzegorz; Buscardo, Erika; Cabra-García, Jimmy; Calviño-Cancela, María; Cameron, Sydney A; Cancello, Eliana M; Carrijo, Tiago F; Carvalho, Anelena L; Castro, Helena; Castro-Luna, Alejandro A; Cerda, Rolando; Cerezo, Alexis; Chauvat, Matthieu; Clarke, Frank M; Cleary, Daniel F R; Connop, Stuart P; D'Aniello, Biagio; da Silva, Pedro Giovâni; Darvill, Ben; Dauber, Jens; Dejean, Alain; Diekötter, Tim; Dominguez-Haydar, Yamileth; Dormann, Carsten F; Dumont, Bertrand; Dures, Simon G; Dynesius, Mats; Edenius, Lars; Elek, Zoltán; Entling, Martin H; Farwig, Nina; Fayle, Tom M; Felicioli, Antonio; Felton, Annika M; Ficetola, Gentile F; Filgueiras, Bruno K C; Fonte, Steven J; Fraser, Lauchlan H; Fukuda, Daisuke; Furlani, Dario; Ganzhorn, Jörg U; Garden, Jenni G; Gheler-Costa, Carla; Giordani, Paolo; Giordano, Simonetta; Gottschalk, Marco S; Goulson, Dave; Gove, Aaron D; Grogan, James; Hanley, Mick E; Hanson, Thor; Hashim, Nor R; Hawes, Joseph E; Hébert, Christian; Helden, Alvin J; Henden, John-André; Hernández, Lionel; Herzog, Felix; Higuera-Diaz, Diego; Hilje, Branko; Horgan, Finbarr G; Horváth, Roland; Hylander, Kristoffer; Isaacs-Cubides, Paola; Ishitani, Masahiro; Jacobs, Carmen T; Jaramillo, Víctor J; Jauker, Birgit; Jonsell, Mats; Jung, Thomas S; Kapoor, Vena; Kati, Vassiliki; Katovai, Eric; Kessler, Michael; Knop, Eva; Kolb, Annette; Kőrösi, Ádám; Lachat, Thibault; Lantschner, Victoria; Le Féon, Violette; LeBuhn, Gretchen; Légaré, Jean-Philippe; Letcher, Susan G; Littlewood, Nick A; López-Quintero, Carlos A; Louhaichi, Mounir; Lövei, Gabor L; Lucas-Borja, Manuel Esteban; Luja, Victor H; Maeto, Kaoru; Magura, Tibor; Mallari, Neil Aldrin; Marin-Spiotta, Erika; Marshall, E J P; Martínez, Eliana; Mayfield, Margaret M; Mikusinski, Grzegorz; Milder, Jeffrey C; Miller, James R; Morales, Carolina L; Muchane, Mary N; Muchane, Muchai; Naidoo, Robin; Nakamura, Akihiro; Naoe, Shoji; Nates-Parra, Guiomar; Navarrete Gutierrez, Dario A; Neuschulz, Eike L; Noreika, Norbertas; Norfolk, Olivia; Noriega, Jorge Ari; Nöske, Nicole M; O'Dea, Niall; Oduro, William; Ofori-Boateng, Caleb; Oke, Chris O; Osgathorpe, Lynne M; Paritsis, Juan; Parra-H, Alejandro; Pelegrin, Nicolás; Peres, Carlos A; Persson, Anna S; Petanidou, Theodora; Phalan, Ben; Philips, T Keith; Poveda, Katja; Power, Eileen F; Presley, Steven J; Proença, Vânia; Quaranta, Marino; Quintero, Carolina; Redpath-Downing, Nicola A; Reid, J Leighton; Reis, Yana T; Ribeiro, Danilo B; Richardson, Barbara A; Richardson, Michael J; Robles, Carolina A; Römbke, Jörg; Romero-Duque, Luz Piedad; Rosselli, Loreta; Rossiter, Stephen J; Roulston, T'ai H; Rousseau, Laurent; Sadler, Jonathan P; Sáfián, Szabolcs; Saldaña-Vázquez, Romeo A; Samnegård, Ulrika; Schüepp, Christof; Schweiger, Oliver; Sedlock, Jodi L; Shahabuddin, Ghazala; Sheil, Douglas; Silva, Fernando A B; Slade, Eleanor M; Smith-Pardo, Allan H; Sodhi, Navjot S; Somarriba, Eduardo J; Sosa, Ramón A; Stout, Jane C; Struebig, Matthew J; Sung, Yik-Hei; Threlfall, Caragh G; Tonietto, Rebecca; Tóthmérész, Béla; Tscharntke, Teja; Turner, Edgar C; Tylianakis, Jason M; Vanbergen, Adam J; Vassilev, Kiril; Verboven, Hans A F; Vergara, Carlos H; Vergara, Pablo M; Verhulst, Jort; Walker, Tony R; Wang, Yanping; Watling, James I; Wells, Konstans; Williams, Christopher D; Willig, Michael R; Woinarski, John C Z; Wolf, Jan H D; Woodcock, Ben A; Yu, Douglas W; Zaitsev, Andrey S; Collen, Ben; Ewers, Rob M; Mace, Georgina M; Purves, Drew W; Scharlemann, Jörn P W; Purvis, Andy

2014-12-01

Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species' threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project - and avert - future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups - including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems - http://www.predicts.org.uk). We make site-level summary data available alongside this article. The full database will be publicly available in 2015.

Relative Error Evaluation to Typical Open Global dem Datasets in Shanxi Plateau of China

NASA Astrophysics Data System (ADS)

Zhao, S.; Zhang, S.; Cheng, W.

2018-04-01

Produced by radar data or stereo remote sensing image pairs, global DEM datasets are one of the most important types for DEM data. Relative error relates to surface quality created by DEM data, so it relates to geomorphology and hydrologic applications using DEM data. Taking Shanxi Plateau of China as the study area, this research evaluated the relative error to typical open global DEM datasets including Shuttle Radar Terrain Mission (SRTM) data with 1 arc second resolution (SRTM1), SRTM data with 3 arc second resolution (SRTM3), ASTER global DEM data in the second version (GDEM-v2) and ALOS world 3D-30m (AW3D) data. Through process and selection, more than 300,000 ICESat/GLA14 points were used as the GCP data, and the vertical error was computed and compared among four typical global DEM datasets. Then, more than 2,600,000 ICESat/GLA14 point pairs were acquired using the distance threshold between 100 m and 500 m. Meanwhile, the horizontal distance between every point pair was computed, so the relative error was achieved using slope values based on vertical error difference and the horizontal distance of the point pairs. Finally, false slope ratio (FSR) index was computed through analyzing the difference between DEM and ICESat/GLA14 values for every point pair. Both relative error and FSR index were categorically compared for the four DEM datasets under different slope classes. Research results show: Overall, AW3D has the lowest relative error values in mean error, mean absolute error, root mean square error and standard deviation error; then the SRTM1 data, its values are a little higher than AW3D data; the SRTM3 and GDEM-v2 data have the highest relative error values, and the values for the two datasets are similar. Considering different slope conditions, all the four DEM data have better performance in flat areas but worse performance in sloping regions; AW3D has the best performance in all the slope classes, a litter better than SRTM1; with slope increasing, the relative error for the SRTM3 data increases faster than other DEM datasets; so SRTM3 is better than GDEM-v2 in flat regions but worse in sloping regions. As to FSR value, AW3D has the lowest value, 4.37 %; then SRTM1 data, 5.80 %, similar to AW3D data; SRTM3 has higher value, about 8.27 %; GDEM-v2 data has the highest FSR value, about 12.15 %. FSR can represent the performance of correctly creating the earth surface based on DEM data. Hence, AW3D has the best performance, which is approximate to but a little better than SRTM1. The performance of SRTM3 and GDEM-v2 is similar, which is much worse than AW3D and SRTM1, and the performance of GDEM-v2 is the worst of all. Originated from the DEM dataset with 5m resolution, AW3D is regarded as the most precise global DEM datasets up to now, so it may exerts more effect in topographic analysis and geographic research. Through analysis and comparison of the relative error for the four open global DEM datasets, this research will provide reference in open global DEM datasets selection and applications in geosciences and other relevant fields.

Global Distribution of Pyrogenic Carbon

NASA Astrophysics Data System (ADS)

Reisser, Moritz; Abiven, Samuel; Schmidt, Michael W. I.

2016-04-01

Pyrogenic Carbon (PyC) is ubiquitous in the environment and represents presumably one of the most stable compounds of the total organic carbon. Due to its persistence in the soil, it might play an important role in the global carbon cycle. In order to model future CO2 emissions from soils it is thus crucial to know where and how much of PyC exists on a global scale. Yet, only rough estimates for global PyC stocks in soils could be made, and even less is known about the distribution across ecosystems. Therefore we propose here literature analysis of data on PyC concentrations and stocks worldwide. We extracted PyC values in soils from the literature (n = 600) and analysed the percentage of PyC in the soil organic carbon (SOC) as a function of climate (temperature, precipitation), soil parameters (pH, clay content), fire characteristics (fire frequency and fire regime) and land use. Overall, the average contribution of PyC to SOC was 13 %, ranging from 0.1 % up to 60 %. We observed that the PyC content was significantly higher with high clay content, higher pH, and in cultivated land as compared to forest and grassland. We did not observe any relationships between fire activity, frequency or intensity and PyC % at a global scale. When the fire regime was monitored on site (only 12 % of the data we collected), we observed higher PyC concentrations with higher fire frequencies. We hypothesise that the resolution of global fire datasets is neither temporally nor spatially high enough to explain the very local fire history of the soil samples. Data points were not homogeneously distributed on the globe, but rather aggregated in places like Central Europe, the Russian Steppe or North America. Therefore, a global interpolation is not directly possible. We modelled PyC concentrations, based on the five most significant parameters, which were clay content, pH, mean annual temperature and precipitation as well as land use. We then predicted worldwide PyC using global datasets existing for these five variables. We present a global map of PyC concentrations as well as it stocks. In arid ecosystems, where SOC is generally low, stocks of PyC are also low, even though concentrations can be very high. On the other hand, stocks are mostly very large in temperate and boreal ecosystems, even if concentrations are rather low, because total SOC stocks are very high there. Integrating our modelled data, we result in a total global stock of about 230 Pg PyC, corresponding to about 10 % of the total soil organic carbon stock. This value lies well in range with current rule-of-thump estimates of previous studies.

Developing a provisional, international minimal dataset for Juvenile Dermatomyositis: for use in clinical practice to inform research.

PubMed

McCann, Liza J; Arnold, Katie; Pilkington, Clarissa A; Huber, Adam M; Ravelli, Angelo; Beard, Laura; Beresford, Michael W; Wedderburn, Lucy R

2014-01-01

Juvenile dermatomyositis (JDM) is a rare but severe autoimmune inflammatory myositis of childhood. International collaboration is essential in order to undertake clinical trials, understand the disease and improve long-term outcome. The aim of this study was to propose from existing collaborative initiatives a preliminary minimal dataset for JDM. This will form the basis of the future development of an international consensus-approved minimum core dataset to be used both in clinical care and inform research, allowing integration of data between centres. A working group of internationally-representative JDM experts was formed to develop a provisional minimal dataset. Clinical and laboratory variables contained within current national and international collaborative databases of patients with idiopathic inflammatory myopathies were scrutinised. Judgements were informed by published literature and a more detailed analysis of the Juvenile Dermatomyositis Cohort Biomarker Study and Repository, UK and Ireland. A provisional minimal JDM dataset has been produced, with an associated glossary of definitions. The provisional minimal dataset will request information at time of patient diagnosis and during on-going prospective follow up. At time of patient diagnosis, information will be requested on patient demographics, diagnostic criteria and treatments given prior to diagnosis. During on-going prospective follow-up, variables will include the presence of active muscle or skin disease, major organ involvement or constitutional symptoms, investigations, treatment, physician global assessments and patient reported outcome measures. An internationally agreed minimal dataset has the potential to significantly enhance collaboration, allow effective communication between groups, provide a minimal standard of care and enable analysis of the largest possible number of JDM patients to provide a greater understanding of this disease. This preliminary dataset can now be developed into a consensus-approved minimum core dataset and tested in a wider setting with the aim of achieving international agreement.

Developing a provisional, international Minimal Dataset for Juvenile Dermatomyositis: for use in clinical practice to inform research

PubMed Central

2014-01-01

Background Juvenile dermatomyositis (JDM) is a rare but severe autoimmune inflammatory myositis of childhood. International collaboration is essential in order to undertake clinical trials, understand the disease and improve long-term outcome. The aim of this study was to propose from existing collaborative initiatives a preliminary minimal dataset for JDM. This will form the basis of the future development of an international consensus-approved minimum core dataset to be used both in clinical care and inform research, allowing integration of data between centres. Methods A working group of internationally-representative JDM experts was formed to develop a provisional minimal dataset. Clinical and laboratory variables contained within current national and international collaborative databases of patients with idiopathic inflammatory myopathies were scrutinised. Judgements were informed by published literature and a more detailed analysis of the Juvenile Dermatomyositis Cohort Biomarker Study and Repository, UK and Ireland. Results A provisional minimal JDM dataset has been produced, with an associated glossary of definitions. The provisional minimal dataset will request information at time of patient diagnosis and during on-going prospective follow up. At time of patient diagnosis, information will be requested on patient demographics, diagnostic criteria and treatments given prior to diagnosis. During on-going prospective follow-up, variables will include the presence of active muscle or skin disease, major organ involvement or constitutional symptoms, investigations, treatment, physician global assessments and patient reported outcome measures. Conclusions An internationally agreed minimal dataset has the potential to significantly enhance collaboration, allow effective communication between groups, provide a minimal standard of care and enable analysis of the largest possible number of JDM patients to provide a greater understanding of this disease. This preliminary dataset can now be developed into a consensus-approved minimum core dataset and tested in a wider setting with the aim of achieving international agreement. PMID:25075205

Capturing Data Connections within the Climate Data Initiative to Support Resiliency

NASA Astrophysics Data System (ADS)

Ramachandran, R.; Bugbee, K.; Weigel, A. M.; Tilmes, C.

2015-12-01

The Climate Data Initiative (CDI) focuses on preparing the United States for the impacts of climate change by leveraging existing federal climate-relevant data to stimulate innovation and private-sector entrepreneurship supporting national climate-change preparedness. To achieve these goals, relevant data was curated around seven thematic areas relevant to climate change resiliency. Data for each theme was selected by subject matter experts from various Federal agencies and collected in Data.gov at http://climate.data.gov. While the curation effort for each theme has been immensely valuable on its own, in the end, the themes essentially become a long directory or a list. Establishing valuable connections between datasets and their intended use is lost. Therefore, the user understands that the datasets in the list have been approved by the CDI subject matter experts but has less certainty when making connections between the various datasets and their possible applications. Additionally, the intended use of the curated list is overwhelming and can be difficult to interpret. In order to better address the needs of the CDI data end users, the CDI team has been developing a new controlled vocabulary that will assist in capturing connections between datasets. This new vocabulary will be implemented in the Global Change Information System (GCIS), which has the capability to link individual items within the system. This presentation will highlight the methodology used to develop the controlled vocabulary that will aid end users in both understanding and locating relevant datasets for their intended use.

Global distribution of clay-size minerals on land surface for biogeochemical and climatological studies

PubMed Central

Ito, Akihiko; Wagai, Rota

2017-01-01

Clay-size minerals play important roles in terrestrial biogeochemistry and atmospheric physics, but their data have been only partially compiled at global scale. We present a global dataset of clay-size minerals in the topsoil and subsoil at different spatial resolutions. The data of soil clay and its mineralogical composition were gathered through a literature survey and aggregated by soil orders of the Soil Taxonomy for each of the ten groups: gibbsite, kaolinite, illite/mica, smectite, vermiculite, chlorite, iron oxide, quartz, non-crystalline, and others. Using a global soil map, a global dataset of soil clay-size mineral distribution was developed at resolutions of 2' to 2° grid cells. The data uncertainty associated with data variability and assumption was evaluated using a Monte Carlo method, and validity of the clay-size mineral distribution obtained in this study was examined by comparing with other datasets. The global soil clay data offer spatially explicit studies on terrestrial biogeochemical cycles, dust emission to the atmosphere, and other interdisciplinary earth sciences. PMID:28829435

A new global anthropogenic heat estimation based on high-resolution nighttime light data

PubMed Central

Yang, Wangming; Luan, Yibo; Liu, Xiaolei; Yu, Xiaoyong; Miao, Lijuan; Cui, Xuefeng

2017-01-01

Consumption of fossil fuel resources leads to global warming and climate change. Apart from the negative impact of greenhouse gases on the climate, the increasing emission of anthropogenic heat from energy consumption also brings significant impacts on urban ecosystems and the surface energy balance. The objective of this work is to develop a new method of estimating the global anthropogenic heat budget and validate it on the global scale with a high precision and resolution dataset. A statistical algorithm was applied to estimate the annual mean anthropogenic heat (AH-DMSP) from 1992 to 2010 at 1×1 km2 spatial resolution for the entire planet. AH-DMSP was validated for both provincial and city scales, and results indicate that our dataset performs well at both scales. Compared with other global anthropogenic heat datasets, the AH-DMSP has a higher precision and finer spatial distribution. Although there are some limitations, the AH-DMSP could provide reliable, multi-scale anthropogenic heat information, which could be used for further research on regional or global climate change and urban ecosystems. PMID:28829436

Parton Distributions based on a Maximally Consistent Dataset

NASA Astrophysics Data System (ADS)

Rojo, Juan

2016-04-01

The choice of data that enters a global QCD analysis can have a substantial impact on the resulting parton distributions and their predictions for collider observables. One of the main reasons for this has to do with the possible presence of inconsistencies, either internal within an experiment or external between different experiments. In order to assess the robustness of the global fit, different definitions of a conservative PDF set, that is, a PDF set based on a maximally consistent dataset, have been introduced. However, these approaches are typically affected by theory biases in the selection of the dataset. In this contribution, after a brief overview of recent NNPDF developments, we propose a new, fully objective, definition of a conservative PDF set, based on the Bayesian reweighting approach. Using the new NNPDF3.0 framework, we produce various conservative sets, which turn out to be mutually in agreement within the respective PDF uncertainties, as well as with the global fit. We explore some of their implications for LHC phenomenology, finding also good consistency with the global fit result. These results provide a non-trivial validation test of the new NNPDF3.0 fitting methodology, and indicate that possible inconsistencies in the fitted dataset do not affect substantially the global fit PDFs.

The Mpi-M Aerosol Climatology (MAC)

NASA Astrophysics Data System (ADS)

Kinne, S.

2014-12-01

Monthly gridded global data-sets for aerosol optical properties (AOD, SSA and g) and for aerosol microphysical properties (CCN and IN) offer a (less complex) alternate path to include aerosol radiative effects and aerosol impacts on cloud-microphysics in global simulations. Based on merging AERONET sun-/sky-photometer data onto background maps provided by AeroCom phase 1 modeling output and AERONET sun-/the MPI-M Aerosol Climatology (MAC) version 1 was developed and applied in IPCC simulations with ECHAM and as ancillary data-set in satellite-based global data-sets. An updated version 2 of this climatology will be presented now applying central values from the more recent AeroCom phase 2 modeling and utilizing the better global coverage of trusted sun-photometer data - including statistics from the Marine Aerosol network (MAN). Applications include spatial distributions of estimates for aerosol direct and aerosol indirect radiative effects.

Toward global crop type mapping using a hybrid machine learning approach and multi-sensor imagery

NASA Astrophysics Data System (ADS)

Wang, S.; Le Bras, S.; Azzari, G.; Lobell, D. B.

2017-12-01

Current global scale datasets on agricultural land use do not have sufficient spatial or temporal resolution to meet the needs of many applications. The recent rapid increase in public availability of fine- to moderate-resolution satellite imagery from Landsat OLI and Copernicus Sentinel-2 provides a unique opportunity to improve agricultural land use datasets. This project leverages these new satellite data streams, existing census data, and a novel training approach to develop global, annual maps that indicate the presence of (i) cropland and (ii) specific crops at a 20m resolution. Our machine learning methodology consists of two steps. The first is a supervised classifier trained with explicitly labelled data to distinguish between crop and non-crop pixels, creating a binary mask. For ground truth, we use labels collected by previous mapping efforts (e.g. IIASA's crowdsourced data (Fritz et al. 2015) and AFSIS's geosurvey data) in combination with new data collected manually. The crop pixels output by the binary mask are input to the second step: a semi-supervised clustering algorithm to resolve different crop types and generate a crop type map. We do not use field-level information on crop type to train the algorithm, making this approach scalable spatially and temporally. We instead incorporate size constraints on clusters based on aggregated agricultural land use statistics and other, more generalizable domain knowledge. We employ field-level data from the U.S., Southern Europe, and Eastern Africa to validate crop-to-cluster assignments.

Development of a global slope dataset for estimation of landslide occurrence resulting from earthquakes

USGS Publications Warehouse

Verdin, Kristine L.; Godt, Jonathan W.; Funk, Christopher C.; Pedreros, Diego; Worstell, Bruce; Verdin, James

2007-01-01

Landslides resulting from earthquakes can cause widespread loss of life and damage to critical infrastructure. The U.S. Geological Survey (USGS) has developed an alarm system, PAGER (Prompt Assessment of Global Earthquakes for Response), that aims to provide timely information to emergency relief organizations on the impact of earthquakes. Landslides are responsible for many of the damaging effects following large earthquakes in mountainous regions, and thus data defining the topographic relief and slope are critical to the PAGER system. A new global topographic dataset was developed to aid in rapidly estimating landslide potential following large earthquakes. We used the remotely-sensed elevation data collected as part of the Shuttle Radar Topography Mission (SRTM) to generate a slope dataset with nearly global coverage. Slopes from the SRTM data, computed at 3-arc-second resolution, were summarized at 30-arc-second resolution, along with statistics developed to describe the distribution of slope within each 30-arc-second pixel. Because there are many small areas lacking SRTM data and the northern limit of the SRTM mission was lat 60?N., statistical methods referencing other elevation data were used to fill the voids within the dataset and to extrapolate the data north of 60?. The dataset will be used in the PAGER system to rapidly assess the susceptibility of areas to landsliding following large earthquakes.

Toward Robust Climate Baselining: Objective Assessment of Climate Change Using Widely Distributed Miniaturized Sensors for Accurate World-Wide Geophysical Measurements

DOE R&D Accomplishments Database

Teller, E.; Leith, C.; Canavan, G.; Marion, J.; Wood, L.

2001-11-13

A gap-free, world-wide, ocean-, atmosphere-, and land surface-spanning geophysical data-set of three decades time-duration containing the full set of geophysical parameters characterizing global weather is the scientific perquisite for defining the climate; the generally-accepted definition in the meteorological community is that climate is the 30-year running-average of weather. Until such a tridecadal climate baseline exists, climate change discussions inevitably will have a semi-speculative, vs. a purely scientific, character, as the baseline against which changes are referenced will at least somewhat uncertain.

A comparison of two global datasets of extreme sea levels and resulting flood exposure

NASA Astrophysics Data System (ADS)

Muis, Sanne; Verlaan, Martin; Nicholls, Robert J.; Brown, Sally; Hinkel, Jochen; Lincke, Daniel; Vafeidis, Athanasios T.; Scussolini, Paolo; Winsemius, Hessel C.; Ward, Philip J.

2017-04-01

Estimating the current risk of coastal flooding requires adequate information on extreme sea levels. For over a decade, the only global data available was the DINAS-COAST Extreme Sea Levels (DCESL) dataset, which applies a static approximation to estimate extreme sea levels. Recently, a dynamically derived dataset was developed: the Global Tide and Surge Reanalysis (GTSR) dataset. Here, we compare the two datasets. The differences between DCESL and GTSR are generally larger than the confidence intervals of GTSR. Compared to observed extremes, DCESL generally overestimates extremes with a mean bias of 0.6 m. With a mean bias of -0.2 m GTSR generally underestimates extremes, particularly in the tropics. The Dynamic Interactive Vulnerability Assessment model is applied to calculate the present-day flood exposure in terms of the land area and the population below the 1 in 100-year sea levels. Global exposed population is 28% lower when based on GTSR instead of DCESL. Considering the limited data available at the time, DCESL provides a good estimate of the spatial variation in extremes around the world. However, GTSR allows for an improved assessment of the impacts of coastal floods, including confidence bounds. We further improve the assessment of coastal impacts by correcting for the conflicting vertical datum of sea-level extremes and land elevation, which has not been accounted for in previous global assessments. Converting the extreme sea levels to the same vertical reference used for the elevation data is shown to be a critical step resulting in 39-59% higher estimate of population exposure.

Mapping 2000 2010 Impervious Surface Change in India Using Global Land Survey Landsat Data

NASA Technical Reports Server (NTRS)

Wang, Panshi; Huang, Chengquan; Brown De Colstoun, Eric C.

2017-01-01

Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface is subject to seasonal and phenological variations. The overall goal of this paper is to map 200-02010 ISC for India using Global Land Survey datasets and training data only available for 2010. To this end, a method was developed that could transfer the regression tree model developed for mapping 2010 impervious surface to 2000 using an iterative training and prediction (ITP) approach An independent validation dataset was also developed using Google Earth imagery. Based on the reference ISC from the validation dataset, the RMSE of predicted ISC was estimated to be 18.4%. At 95% confidence, the total estimated ISC for India between 2000 and 2010 is 2274.62 +/- 7.84 sq km.

High resolution global gridded data for use in population studies

NASA Astrophysics Data System (ADS)

Lloyd, Christopher T.; Sorichetta, Alessandro; Tatem, Andrew J.

2017-01-01

Recent years have seen substantial growth in openly available satellite and other geospatial data layers, which represent a range of metrics relevant to global human population mapping at fine spatial scales. The specifications of such data differ widely and therefore the harmonisation of data layers is a prerequisite to constructing detailed and contemporary spatial datasets which accurately describe population distributions. Such datasets are vital to measure impacts of population growth, monitor change, and plan interventions. To this end the WorldPop Project has produced an open access archive of 3 and 30 arc-second resolution gridded data. Four tiled raster datasets form the basis of the archive: (i) Viewfinder Panoramas topography clipped to Global ADMinistrative area (GADM) coastlines; (ii) a matching ISO 3166 country identification grid; (iii) country area; (iv) and slope layer. Further layers include transport networks, landcover, nightlights, precipitation, travel time to major cities, and waterways. Datasets and production methodology are here described. The archive can be downloaded both from the WorldPop Dataverse Repository and the WorldPop Project website.

High resolution global gridded data for use in population studies.

PubMed

Lloyd, Christopher T; Sorichetta, Alessandro; Tatem, Andrew J

2017-01-31

Recent years have seen substantial growth in openly available satellite and other geospatial data layers, which represent a range of metrics relevant to global human population mapping at fine spatial scales. The specifications of such data differ widely and therefore the harmonisation of data layers is a prerequisite to constructing detailed and contemporary spatial datasets which accurately describe population distributions. Such datasets are vital to measure impacts of population growth, monitor change, and plan interventions. To this end the WorldPop Project has produced an open access archive of 3 and 30 arc-second resolution gridded data. Four tiled raster datasets form the basis of the archive: (i) Viewfinder Panoramas topography clipped to Global ADMinistrative area (GADM) coastlines; (ii) a matching ISO 3166 country identification grid; (iii) country area; (iv) and slope layer. Further layers include transport networks, landcover, nightlights, precipitation, travel time to major cities, and waterways. Datasets and production methodology are here described. The archive can be downloaded both from the WorldPop Dataverse Repository and the WorldPop Project website.

Evaluation of CLM4 Solar Radiation Partitioning Scheme Using Remote Sensing and Site Level FPAR Datasets

DOE PAGES

Wang, Kai; Mao, Jiafu; Dickinson, Robert; ...

2013-06-05

This paper examines a land surface solar radiation partitioning scheme, i.e., that of the Community Land Model version 4 (CLM4) with coupled carbon and nitrogen cycles. Taking advantage of a unique 30-year fraction of absorbed photosynthetically active radiation (FPAR) dataset derived from the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) data set, multiple other remote sensing datasets, and site level observations, we evaluated the CLM4 FPAR ’s seasonal cycle, diurnal cycle, long-term trends and spatial patterns. These findings show that the model generally agrees with observations in the seasonal cycle, long-term trends, and spatial patterns,more » but does not reproduce the diurnal cycle. Discrepancies also exist in seasonality magnitudes, peak value months, and spatial heterogeneity. Here, we identify the discrepancy in the diurnal cycle as, due to, the absence of dependence on sun angle in the model. Implementation of sun angle dependence in a one-dimensional (1-D) model is proposed. The need for better relating of vegetation to climate in the model, indicated by long-term trends, is also noted. Evaluation of the CLM4 land surface solar radiation partitioning scheme using remote sensing and site level FPAR datasets provides targets for future development in its representation of this naturally complicated process.« less

Dataset of Passerine bird communities in a Mediterranean high mountain (Sierra Nevada, Spain).

PubMed

Pérez-Luque, Antonio Jesús; Barea-Azcón, José Miguel; Álvarez-Ruiz, Lola; Bonet-García, Francisco Javier; Zamora, Regino

2016-01-01

In this data paper, a dataset of passerine bird communities is described in Sierra Nevada, a Mediterranean high mountain located in southern Spain. The dataset includes occurrence data from bird surveys conducted in four representative ecosystem types of Sierra Nevada from 2008 to 2015. For each visit, bird species numbers as well as distance to the transect line were recorded. A total of 27847 occurrence records were compiled with accompanying measurements on distance to the transect and animal counts. All records are of species in the order Passeriformes. Records of 16 different families and 44 genera were collected. Some of the taxa in the dataset are included in the European Red List. This dataset belongs to the Sierra Nevada Global-Change Observatory (OBSNEV), a long-term research project designed to compile socio-ecological information on the major ecosystem types in order to identify the impacts of global change in this area.

Dataset of Passerine bird communities in a Mediterranean high mountain (Sierra Nevada, Spain)

PubMed Central

Pérez-Luque, Antonio Jesús; Barea-Azcón, José Miguel; Álvarez-Ruiz, Lola; Bonet-García, Francisco Javier; Zamora, Regino

2016-01-01

Abstract In this data paper, a dataset of passerine bird communities is described in Sierra Nevada, a Mediterranean high mountain located in southern Spain. The dataset includes occurrence data from bird surveys conducted in four representative ecosystem types of Sierra Nevada from 2008 to 2015. For each visit, bird species numbers as well as distance to the transect line were recorded. A total of 27847 occurrence records were compiled with accompanying measurements on distance to the transect and animal counts. All records are of species in the order Passeriformes. Records of 16 different families and 44 genera were collected. Some of the taxa in the dataset are included in the European Red List. This dataset belongs to the Sierra Nevada Global-Change Observatory (OBSNEV), a long-term research project designed to compile socio-ecological information on the major ecosystem types in order to identify the impacts of global change in this area. PMID:26865820

Access NASA Satellite Global Precipitation Data Visualization on YouTube

NASA Astrophysics Data System (ADS)

Liu, Z.; Su, J.; Acker, J. G.; Huffman, G. J.; Vollmer, B.; Wei, J.; Meyer, D. J.

2017-12-01

Since the satellite era began, NASA has collected a large volume of Earth science observations for research and applications around the world. Satellite data at 12 NASA data centers can also be used for STEM activities such as disaster events, climate change, etc. However, accessing satellite data can be a daunting task for non-professional users such as teachers and students because of unfamiliarity of terminology, disciplines, data formats, data structures, computing resources, processing software, programing languages, etc. Over the years, many efforts have been developed to improve satellite data access, but barriers still exist for non-professionals. In this presentation, we will present our latest activity that uses the popular online video sharing web site, YouTube, to access visualization of global precipitation datasets at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). With YouTube, users can access and visualize a large volume of satellite data without necessity to learn new software or download data. The dataset in this activity is the 3-hourly TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Analysis (TMPA). The video consists of over 50,000 data files collected since 1998 onwards, covering a zone between 50°N-S. The YouTube video will last 36 minutes for the entire dataset record (over 19 years). Since the time stamp is on each frame of the video, users can begin at any time by dragging the time progress bar. This precipitation animation will allow viewing precipitation events and processes (e.g., hurricanes, fronts, atmospheric rivers, etc.) on a global scale. The next plan is to develop a similar animation for the GPM (Global Precipitation Measurement) Integrated Multi-satellitE Retrievals for GPM (IMERG). The IMERG provides precipitation on a near-global (60°N-S) coverage at half-hourly time interval, showing more details on precipitation processes and development, compared to the 3-hourly TMPA product. The entire video will contain more than 330,000 files and will last 3.6 hours. Future plans include development of fly-over videos for orbital data for an entire satellite mission or project. All videos will be uploaded and available at the GES DISC site on YouTube (https://www.youtube.com/user/NASAGESDISC).

Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning.

PubMed

de Souza, Erico N; Boerder, Kristina; Matwin, Stan; Worm, Boris

2016-01-01

A key challenge in contemporary ecology and conservation is the accurate tracking of the spatial distribution of various human impacts, such as fishing. While coastal fisheries in national waters are closely monitored in some countries, existing maps of fishing effort elsewhere are fraught with uncertainty, especially in remote areas and the High Seas. Better understanding of the behavior of the global fishing fleets is required in order to prioritize and enforce fisheries management and conservation measures worldwide. Satellite-based Automatic Information Systems (S-AIS) are now commonly installed on most ocean-going vessels and have been proposed as a novel tool to explore the movements of fishing fleets in near real time. Here we present approaches to identify fishing activity from S-AIS data for three dominant fishing gear types: trawl, longline and purse seine. Using a large dataset containing worldwide fishing vessel tracks from 2011-2015, we developed three methods to detect and map fishing activities: for trawlers we produced a Hidden Markov Model (HMM) using vessel speed as observation variable. For longliners we have designed a Data Mining (DM) approach using an algorithm inspired from studies on animal movement. For purse seiners a multi-layered filtering strategy based on vessel speed and operation time was implemented. Validation against expert-labeled datasets showed average detection accuracies of 83% for trawler and longliner, and 97% for purse seiner. Our study represents the first comprehensive approach to detect and identify potential fishing behavior for three major gear types operating on a global scale. We hope that this work will enable new efforts to assess the spatial and temporal distribution of global fishing effort and make global fisheries activities transparent to ocean scientists, managers and the public.

Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning

PubMed Central

Matwin, Stan; Worm, Boris

2016-01-01

A key challenge in contemporary ecology and conservation is the accurate tracking of the spatial distribution of various human impacts, such as fishing. While coastal fisheries in national waters are closely monitored in some countries, existing maps of fishing effort elsewhere are fraught with uncertainty, especially in remote areas and the High Seas. Better understanding of the behavior of the global fishing fleets is required in order to prioritize and enforce fisheries management and conservation measures worldwide. Satellite-based Automatic Information Systems (S-AIS) are now commonly installed on most ocean-going vessels and have been proposed as a novel tool to explore the movements of fishing fleets in near real time. Here we present approaches to identify fishing activity from S-AIS data for three dominant fishing gear types: trawl, longline and purse seine. Using a large dataset containing worldwide fishing vessel tracks from 2011–2015, we developed three methods to detect and map fishing activities: for trawlers we produced a Hidden Markov Model (HMM) using vessel speed as observation variable. For longliners we have designed a Data Mining (DM) approach using an algorithm inspired from studies on animal movement. For purse seiners a multi-layered filtering strategy based on vessel speed and operation time was implemented. Validation against expert-labeled datasets showed average detection accuracies of 83% for trawler and longliner, and 97% for purse seiner. Our study represents the first comprehensive approach to detect and identify potential fishing behavior for three major gear types operating on a global scale. We hope that this work will enable new efforts to assess the spatial and temporal distribution of global fishing effort and make global fisheries activities transparent to ocean scientists, managers and the public. PMID:27367425

Displaying Planetary and Geophysical Datasets on NOAAs Science On a Sphere (TM)

NASA Astrophysics Data System (ADS)

Albers, S. C.; MacDonald, A. E.; Himes, D.

2005-12-01

NOAAs Science On a Sphere(TM)(SOS)was developed to educate current and future generations about the changing Earth and its processes. This system presents NOAAs global science through a 3D representation of our planet as if the viewer were looking at the Earth from outer space. In our presentation, we will describe the preparation of various global datasets for display on Science On a Sphere(TM), a 1.7-m diameter spherical projection system developed and patented at the Forecast Systems Laboratory (FSL) in Boulder, Colorado. Four projectors cast rotating images onto a spherical projection screen to create the effect of Earth, planet, or satellite floating in space. A static dataset can be prepared for display using popular image formats such as JPEG, usually sized at 1024x2048 or 2048x4096 pixels. A set of static images in a directory will comprise a movie. Imagery and data for SOS are obtained from a variety of government organizations, sometimes post-processed by various individuals, including the authors. Some datasets are already available in the required cylindrical projection. Readily available planetary maps can often be improved in coverage and/or appearance by reprojecting and combining additional images and mosaics obtained by various spacecraft, such as Voyager, Galileo, and Cassini. A map of Mercury was produced by blending some Mariner 10 photo-mosaics with a USGS shaded-relief map. An improved high-resolution map of Venus was produced by combining several Magellan mosaics, supplied by The Planetary Society, along with other spacecraft data. We now have a full set of Jupiter's Galilean satellite imagery that we can display on Science On a Sphere(TM). Photo-mosaics of several Saturnian satellites were updated by reprojecting and overlaying recently taken Cassini flyby images. Maps of imagery from five Uranian satellites were added, as well as one for Neptune. More image processing was needed to add a high-resolution Voyager mosaic to a pre-existing map of Neptune's moon Triton. A map of the cosmic background radiation was produced that shows the early universe from an external perspective. Full details and credits for these maps may be viewed online at http://laps.fsl.noaa.gov/albers/sos/sos.html. Geophysical imagery recently added to SOS includes a real-time global infrared weather satellite animation of Earth. This is a 15-minute, quality controlled animation spanning the most recent month, which draws on a number of geosynchronous and polar-orbiting weather satellites for data. Other meteorological and oceanographic datasets can be displayed, such as animations depicting the three-dimensional drifting of the ARGO buoy network through the oceans. Oceanic buoy observations were overlaid on the "Blue Marble" Earth imagery displayed on Science On a Sphere(TM). A static image shows locations for five different global buoy networks. We also produced two movies that show the drift of >1000 ARGO buoys over a period of several months. The first movie shows only the horizontal buoy drift, and the second modulates the intensities to represent the timing of each buoy dive cycle. Animations in real time are also being produced for sea surface temperatures (and anomalies). These analyses are obtained from web displays provided by the DOD Fleet Numerical Operations Center. With advanced technologies, the possibilities are limitless for displaying additional global datasets on Science On a Sphere(TM) and other spherical projection screens.

GTN-G, WGI, RGI, DCW, GLIMS, WGMS, GCOS - What's all this about? (Invited)

NASA Astrophysics Data System (ADS)

Paul, F.; Raup, B. H.; Zemp, M.

2013-12-01

In a large collaborative effort, the glaciological community has compiled a new and spa-tially complete global dataset of glacier outlines, the so-called Randolph Glacier Inventory or RGI. Despite its regional shortcomings in quality (e.g. in regard to geolocation, gener-alization, and interpretation), this dataset was heavily used for global-scale modelling ap-plications (e.g. determination of total glacier volume and glacier contribution to sea-level rise) in support of the forthcoming 5th Assessment Report (AR5) of Working Group I of the IPCC. The RGI is a merged dataset that is largely based on the GLIMS database and several new datasets provided by the community (both are mostly derived from satellite data), as well as the Digital Chart of the World (DCW) and glacier attribute information (location, size) from the World Glacier Inventory (WGI). There are now two key tasks to be performed, (1) improving the quality of the RGI in all regions where the outlines do not met the quality required for local scale applications, and (2) integrating the RGI in the GLIMS glacier database to improve its spatial completeness. While (1) requires again a huge effort but is already ongoing, (2) is mainly a technical issue that is nearly solved. Apart from this technical dimension, there is also a more political or structural one. While GLIMS is responsible for the remote sensing and glacier inventory part (Tier 5) of the Global Terrestrial Network for Glaciers (GTN-G) within the Global Climate Observing System (GCOS), the World Glacier Monitoring Service (WGMS) is collecting and dis-seminating the field observations. Along with new global products derived from satellite data (e.g. elevation changes and velocity fields) and the community wish to keep a snap-shot dataset such as the RGI available, how to make all these datasets available to the community without duplicating efforts and making best use of the very limited financial resources available must now be discussed. This overview presentation describes the cur-rently available datasets, clarifying the terminology and the international framework, and suggesting a way forward to serve the community at best.

GLEAM v3: updated land evaporation and root-zone soil moisture datasets

NASA Astrophysics Data System (ADS)

Martens, Brecht; Miralles, Diego; Lievens, Hans; van der Schalie, Robin; de Jeu, Richard; Fernández-Prieto, Diego; Verhoest, Niko

2016-04-01

Evaporation determines the availability of surface water resources and the requirements for irrigation. In addition, through its impacts on the water, carbon and energy budgets, evaporation influences the occurrence of rainfall and the dynamics of air temperature. Therefore, reliable estimates of this flux at regional to global scales are of major importance for water management and meteorological forecasting of extreme events. However, the global-scale magnitude and variability of the flux, and the sensitivity of the underlying physical process to changes in environmental factors, are still poorly understood due to the limited global coverage of in situ measurements. Remote sensing techniques can help to overcome the lack of ground data. However, evaporation is not directly observable from satellite systems. As a result, recent efforts have focussed on combining the observable drivers of evaporation within process-based models. The Global Land Evaporation Amsterdam Model (GLEAM, www.gleam.eu) estimates terrestrial evaporation based on daily satellite observations of meteorological drivers of terrestrial evaporation, vegetation characteristics and soil moisture. Since the publication of the first version of the model in 2011, GLEAM has been widely applied for the study of trends in the water cycle, interactions between land and atmosphere and hydrometeorological extreme events. A third version of the GLEAM global datasets will be available from the beginning of 2016 and will be distributed using www.gleam.eu as gateway. The updated datasets include separate estimates for the different components of the evaporative flux (i.e. transpiration, bare-soil evaporation, interception loss, open-water evaporation and snow sublimation), as well as variables like the evaporative stress, potential evaporation, root-zone soil moisture and surface soil moisture. A new dataset using SMOS-based input data of surface soil moisture and vegetation optical depth will also be distributed. The most important updates in GLEAM include the revision of the soil moisture data assimilation system, the evaporative stress functions and the infiltration of rainfall. In this presentation, we will highlight the changes of the methodology and present the new datasets, their validation against in situ observations and the comparisons against alternative datasets of terrestrial evaporation, such as GLDAS-Noah, ERA-Interim and previous GLEAM datasets. Preliminary results indicate that the magnitude and the spatio-temporal variability of the evaporation estimates have been slightly improved upon previous versions of the datasets.

Seasonal evaluation of evapotranspiration fluxes from MODIS satellite and mesoscale model downscaled global reanalysis datasets

NASA Astrophysics Data System (ADS)

Srivastava, Prashant K.; Han, Dawei; Islam, Tanvir; Petropoulos, George P.; Gupta, Manika; Dai, Qiang

2016-04-01

Reference evapotranspiration (ETo) is an important variable in hydrological modeling, which is not always available, especially for ungauged catchments. Satellite data, such as those available from the MODerate Resolution Imaging Spectroradiometer (MODIS), and global datasets via the European Centre for Medium Range Weather Forecasts (ECMWF) reanalysis (ERA) interim and National Centers for Environmental Prediction (NCEP) reanalysis are important sources of information for ETo. This study explored the seasonal performances of MODIS (MOD16) and Weather Research and Forecasting (WRF) model downscaled global reanalysis datasets, such as ERA interim and NCEP-derived ETo, against ground-based datasets. Overall, on the basis of the statistical metrics computed, ETo derived from ERA interim and MODIS were more accurate in comparison to the estimates from NCEP for all the seasons. The pooled datasets also revealed a similar performance to the seasonal assessment with higher agreement for the ERA interim (r = 0.96, RMSE = 2.76 mm/8 days; bias = 0.24 mm/8 days), followed by MODIS (r = 0.95, RMSE = 7.66 mm/8 days; bias = -7.17 mm/8 days) and NCEP (r = 0.76, RMSE = 11.81 mm/8 days; bias = -10.20 mm/8 days). The only limitation with downscaling ERA interim reanalysis datasets using WRF is that it is time-consuming in contrast to the readily available MODIS operational product for use in mesoscale studies and practical applications.

Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems

PubMed Central

Herrero, Mario; Havlík, Petr; Valin, Hugo; Notenbaert, An; Rufino, Mariana C.; Thornton, Philip K.; Blümmel, Michael; Weiss, Franz; Grace, Delia; Obersteiner, Michael

2013-01-01

We present a unique, biologically consistent, spatially disaggregated global livestock dataset containing information on biomass use, production, feed efficiency, excretion, and greenhouse gas emissions for 28 regions, 8 livestock production systems, 4 animal species (cattle, small ruminants, pigs, and poultry), and 3 livestock products (milk, meat, and eggs). The dataset contains over 50 new global maps containing high-resolution information for understanding the multiple roles (biophysical, economic, social) that livestock can play in different parts of the world. The dataset highlights: (i) feed efficiency as a key driver of productivity, resource use, and greenhouse gas emission intensities, with vast differences between production systems and animal products; (ii) the importance of grasslands as a global resource, supplying almost 50% of biomass for animals while continuing to be at the epicentre of land conversion processes; and (iii) the importance of mixed crop–livestock systems, producing the greater part of animal production (over 60%) in both the developed and the developing world. These data provide critical information for developing targeted, sustainable solutions for the livestock sector and its widely ranging contribution to the global food system. PMID:24344273

Exploring Global Exposure Factors Resources URLs

EPA Pesticide Factsheets

The dataset is a compilation of hyperlinks (URLs) for resources (databases, compendia, published articles, etc.) useful for exposure assessment specific to consumer product use.This dataset is associated with the following publication:Zaleski, R., P. Egeghy, and P. Hakkinen. Exploring Global Exposure Factors Resources for Use in Consumer Exposure Assessments. International Journal of Environmental Research and Public Health. Molecular Diversity Preservation International, Basel, SWITZERLAND, 13(7): 744, (2016).

Global evaluation of ammonia bidirectional exchange and livestock diurnal variation schemes

EPA Pesticide Factsheets

There is no EPA generated dataset in this study.This dataset is associated with the following publication:Zhu, L., D. Henze, J. Bash , G. Jeong, K. Cady-Pereira, M. Shephard, M. Luo, F. Poulot, and S. Capps. Global evaluation of ammonia bidirectional exchange and livestock diurnal variation schemes. Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau, GERMANY, 15: 12823-12843, (2015).

Global gridded crop specific agricultural areas from 1961-2014

NASA Astrophysics Data System (ADS)

Konar, M.; Jackson, N. D.

2017-12-01

Current global cropland datasets are limited in crop specificity and temporal resolution. Time series maps of crop specific agricultural areas would enable us to better understand the global agricultural geography of the 20th century. To this end, we develop a global gridded dataset of crop specific agricultural areas from 1961-2014. To do this, we downscale national cropland information using a probabilistic approach. Our method relies upon gridded Global Agro-Ecological Zones (GAEZ) maps, the History Database of the Global Environment (HYDE), and crop calendars from Sacks et al. (2010). We estimate crop-specific agricultural areas for a 0.25 degree spatial grid and annual time scale for all major crops. We validate our global estimates for the year 2000 with Monfreda et al. (2008) and our time series estimates within the United States using government data. This database will contribute to our understanding of global agricultural change of the past century.

HydroSHEDS: A global comprehensive hydrographic dataset

NASA Astrophysics Data System (ADS)

Wickel, B. A.; Lehner, B.; Sindorf, N.

2007-12-01

The Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS) is an innovative product that, for the first time, provides hydrographic information in a consistent and comprehensive format for regional and global-scale applications. HydroSHEDS offers a suite of geo-referenced data sets, including stream networks, watershed boundaries, drainage directions, and ancillary data layers such as flow accumulations, distances, and river topology information. The goal of developing HydroSHEDS was to generate key data layers to support regional and global watershed analyses, hydrological modeling, and freshwater conservation planning at a quality, resolution and extent that had previously been unachievable. Available resolutions range from 3 arc-second (approx. 90 meters at the equator) to 5 minute (approx. 10 km at the equator) with seamless near-global extent. HydroSHEDS is derived from elevation data of the Shuttle Radar Topography Mission (SRTM) at 3 arc-second resolution. The original SRTM data have been hydrologically conditioned using a sequence of automated procedures. Existing methods of data improvement and newly developed algorithms have been applied, including void filling, filtering, stream burning, and upscaling techniques. Manual corrections were made where necessary. Preliminary quality assessments indicate that the accuracy of HydroSHEDS significantly exceeds that of existing global watershed and river maps. HydroSHEDS was developed by the Conservation Science Program of the World Wildlife Fund (WWF) in partnership with the U.S. Geological Survey (USGS), the International Centre for Tropical Agriculture (CIAT), The Nature Conservancy (TNC), and the Center for Environmental Systems Research (CESR) of the University of Kassel, Germany.

Solar resource assessment in complex orography: a comparison of available datasets for the Trentino region

NASA Astrophysics Data System (ADS)

Laiti, Lavinia; Giovannini, Lorenzo; Zardi, Dino

2015-04-01

The accurate assessment of the solar radiation available at the Earth's surface is essential for a wide range of energy-related applications, such as the design of solar power plants, water heating systems and energy-efficient buildings, as well as in the fields of climatology, hydrology, ecology and agriculture. The characterization of solar radiation is particularly challenging in complex-orography areas, where topographic shadowing and altitude effects, together with local weather phenomena, greatly increase the spatial and temporal variability of such variable. At present, approaches ranging from surface measurements interpolation to orographic down-scaling of satellite data, to numerical model simulations are adopted for mapping solar radiation. In this contribution a high-resolution (200 m) solar atlas for the Trentino region (Italy) is presented, which was recently developed on the basis of hourly observations of global radiation collected from the local radiometric stations during the period 2004-2012. Monthly and annual climatological irradiation maps were obtained by the combined use of a GIS-based clear-sky model (r.sun module of GRASS GIS) and geostatistical interpolation techniques (kriging). Moreover, satellite radiation data derived by the MeteoSwiss HelioMont algorithm (2 km resolution) were used for missing-data reconstruction and for the final mapping, thus integrating ground-based and remote-sensing information. The results are compared with existing solar resource datasets, such as the PVGIS dataset, produced by the Joint Research Center Institute for Energy and Transport, and the HelioMont dataset, in order to evaluate the accuracy of the different datasets available for the region of interest.

MSWEP V2 global 3-hourly 0.1° precipitation: methodology and quantitative appraisal

NASA Astrophysics Data System (ADS)

Beck, H.; Yang, L.; Pan, M.; Wood, E. F.; William, L.

2017-12-01

Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) V2, the first fully global gridded precipitation (P) dataset with a 0.1° spatial resolution. The dataset covers the period 1979-2016, has a 3-hourly temporal resolution, and was derived by optimally merging a wide range of data sources based on gauges (WorldClim, GHCN-D, GSOD, and others), satellites (CMORPH, GridSat, GSMaP, and TMPA 3B42RT), and reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR). MSWEP V2 implements some major improvements over V1, such as (i) the correction of distributional P biases using cumulative distribution function matching, (ii) increasing the spatial resolution from 0.25° to 0.1°, (iii) the inclusion of ocean areas, (iv) the addition of NCEP-CFSR P estimates, (v) the addition of thermal infrared-based P estimates for the pre-TRMM era, (vi) the addition of 0.1° daily interpolated gauge data, (vii) the use of a daily gauge correction scheme that accounts for regional differences in the 24-hour accumulation period of gauges, and (viii) extension of the data record to 2016. The gauge-based assessment of the reanalysis and satellite P datasets, necessary for establishing the merging weights, revealed that the reanalysis datasets strongly overestimate the P frequency for the entire globe, and that the satellite (resp. reanalysis) datasets consistently performed better at low (high) latitudes. Compared to other state-of-the-art P datasets, MSWEP V2 exhibits more plausible global patterns in mean annual P, percentiles, and annual number of dry days, and better resolves the small-scale variability over topographically complex terrain. Other P datasets appear to consistently underestimate P amounts over mountainous regions. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 959, 796, and 1026 mm/yr, respectively, in close agreement with the best previous published estimates.

Where is my wine from? - A global exposure database for wineries and wine growing regions

NASA Astrophysics Data System (ADS)

Daniell, James E.; Daniell, Trevor M.; Wenzel, Friedemann; Schaefer, Andreas M.; Daniell, Katherine A.; Burford, Robert

2017-04-01

The production of a global winery and wine database is a great undertaking and was required for the evaluation of winery risk in various locations (see NH ECS Lecture MH42/NH). The following study detailed a country wide study of wineries in 15 major wine growing locations globally in order to evaluate the ability of using existing information to detail the risk properties of the wine growing regions. In addition parameters such as the winery types, grape types, slopes, buildings, hazard properties and land use were surveyed. In terms of the winery locations, point-based as well as spatial land-use disaggregated polygons were used. For grape production, national and winery region data was aggregated from existing sources in each country. The value and type were assessed. For the slopes, global and regional DEMs such as ALOS, SRTM and EU-DEM were examined and converted within GIS envrionments. Building level information was often difficult to establish where OSM data was lacking (OpenStreetMap). Hazard parameters such as earthquake ground motion probability, weather, wind speeds, changing grape types, seasonality as well as the variability within seasons were collected with the variability being key to showing an increase or decrease in quality. Tools that were used can be applied to other exposure datasets; and shows a methodology to aggregate exposure information with respect to industries as well as other sectors using open data.

A Review on Human Activity Recognition Using Vision-Based Method.

PubMed

Zhang, Shugang; Wei, Zhiqiang; Nie, Jie; Huang, Lei; Wang, Shuang; Li, Zhen

2017-01-01

Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research.

A Review on Human Activity Recognition Using Vision-Based Method

PubMed Central

Nie, Jie

2017-01-01

Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research. PMID:29065585

An Approach to Monitoring Mangrove Extents Through Time-Series Comparison of JERS-1 SAR and ALOS PALSAR Data

NASA Technical Reports Server (NTRS)

Thomas, Nathan; Lucas, Richard; Itoh, Takuya; Simard, Marc; Fatoyinbo, Lucas; Bunting, Peter; Rosenqvist, Ake

2014-01-01

Between 2007 and 2010, Japan's Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) captured dual polarization HH and HV data across the tropics and sub-tropics. A pan tropical dataset of Japanese Earth Resources Satellite (JERS-1) SAR (HH) data was also acquired between 1995 and 1998. The provision of these comparable cloud-free datasets provided an opportunity for observing changes in the extent of coastal mangroves over more than a decade. Focusing on nine sites distributed through the tropics, this paper demonstrates how these data can be used to backdate and update existing baseline maps of mangrove extent. The benefits of integrating dense timeseries of Landsat sensor data for both validating assessments of change and determining the causes of change are outlined. The approach is evaluated for wider application across the geographical range of mangroves in order to advance the development of JAXA's Global Mangrove Watch (GMW) program.

Estimating the volume of Alpine glacial lakes

NASA Astrophysics Data System (ADS)

Cook, S. J.; Quincey, D. J.

2015-09-01

Supraglacial, moraine-dammed and ice-dammed lakes represent a potential glacial lake outburst flood (GLOF) threat to downstream communities in many mountain regions. This has motivated the development of empirical relationships to predict lake volume given a measurement of lake surface area obtained from satellite imagery. Such relationships are based on the notion that lake depth, area and volume scale predictably. We critically evaluate the performance of these existing empirical relationships by examining a global database of measured glacial lake depths, areas and volumes. Results show that lake area and depth are not always well correlated (r2 = 0.38), and that although lake volume and area are well correlated (r2 = 0.91), there are distinct outliers in the dataset. These outliers represent situations where it may not be appropriate to apply existing empirical relationships to predict lake volume, and include growing supraglacial lakes, glaciers that recede into basins with complex overdeepened morphologies or that have been deepened by intense erosion, and lakes formed where glaciers advance across and block a main trunk valley. We use the compiled dataset to develop a conceptual model of how the volumes of supraglacial ponds and lakes, moraine-dammed lakes and ice-dammed lakes should be expected to evolve with increasing area. Although a large amount of bathymetric data exist for moraine-dammed and ice-dammed lakes, we suggest that further measurements of growing supraglacial ponds and lakes are needed to better understand their development.

GLEAM version 3: Global Land Evaporation Datasets and Model

NASA Astrophysics Data System (ADS)

Martens, B.; Miralles, D. G.; Lievens, H.; van der Schalie, R.; de Jeu, R.; Fernandez-Prieto, D.; Verhoest, N.

2015-12-01

Terrestrial evaporation links energy, water and carbon cycles over land and is therefore a key variable of the climate system. However, the global-scale magnitude and variability of the flux, and the sensitivity of the underlying physical process to changes in environmental factors, are still poorly understood due to limitations in in situ measurements. As a result, several methods have risen to estimate global patterns of land evaporation from satellite observations. However, these algorithms generally differ in their approach to model evaporation, resulting in large differences in their estimates. One of these methods is GLEAM, the Global Land Evaporation: the Amsterdam Methodology. GLEAM estimates terrestrial evaporation based on daily satellite observations of meteorological variables, vegetation characteristics and soil moisture. Since the publication of the first version of the algorithm (2011), the model has been widely applied to analyse trends in the water cycle and land-atmospheric feedbacks during extreme hydrometeorological events. A third version of the GLEAM global datasets is foreseen by the end of 2015. Given the relevance of having a continuous and reliable record of global-scale evaporation estimates for climate and hydrological research, the establishment of an online data portal to host these data to the public is also foreseen. In this new release of the GLEAM datasets, different components of the model have been updated, with the most significant change being the revision of the data assimilation algorithm. In this presentation, we will highlight the most important changes of the methodology and present three new GLEAM datasets and their validation against in situ observations and an alternative dataset of terrestrial evaporation (ERA-Land). Results of the validation exercise indicate that the magnitude and the spatiotemporal variability of the modelled evaporation agree reasonably well with the estimates of ERA-Land and the in situ observations. It is also shown that the performance of the revised model is higher compared to the original one.

High resolution global gridded data for use in population studies

PubMed Central

Lloyd, Christopher T.; Sorichetta, Alessandro; Tatem, Andrew J.

2017-01-01

Recent years have seen substantial growth in openly available satellite and other geospatial data layers, which represent a range of metrics relevant to global human population mapping at fine spatial scales. The specifications of such data differ widely and therefore the harmonisation of data layers is a prerequisite to constructing detailed and contemporary spatial datasets which accurately describe population distributions. Such datasets are vital to measure impacts of population growth, monitor change, and plan interventions. To this end the WorldPop Project has produced an open access archive of 3 and 30 arc-second resolution gridded data. Four tiled raster datasets form the basis of the archive: (i) Viewfinder Panoramas topography clipped to Global ADMinistrative area (GADM) coastlines; (ii) a matching ISO 3166 country identification grid; (iii) country area; (iv) and slope layer. Further layers include transport networks, landcover, nightlights, precipitation, travel time to major cities, and waterways. Datasets and production methodology are here described. The archive can be downloaded both from the WorldPop Dataverse Repository and the WorldPop Project website. PMID:28140386

Just the right age: well-clustered exposure ages from a global glacial 10Be compilation

NASA Astrophysics Data System (ADS)

Heyman, Jakob; Margold, Martin

2017-04-01

Cosmogenic exposure dating has been used extensively for defining glacial chronologies, both in ice sheet and alpine settings, and the global set of published ages today reaches well beyond 10,000 samples. Over the last few years, a number of important developments have improved the measurements (with well-defined AMS standards) and exposure age calculations (with updated data and methods for calculating production rates), in the best case enabling high precision dating of past glacial events. A remaining problem, however, is the fact that a large portion of all dated samples have been affected by prior and/or incomplete exposure, yielding erroneous exposure ages under the standard assumptions. One way to address this issue is to only use exposure ages that can be confidently considered as unaffected by prior/incomplete exposure, such as groups of samples with statistically identical ages. Here we use objective statistical criteria to identify groups of well-clustered exposure ages from the global glacial "expage" 10Be compilation. Out of ˜1700 groups with at least 3 individual samples ˜30% are well-clustered, increasing to ˜45% if allowing outlier rejection of a maximum of 1/3 of the samples (still requiring a minimum of 3 well-clustered ages). The dataset of well-clustered ages is heavily dominated by ages <30 ka, showing that well-defined cosmogenic chronologies primarily exist for the last glaciation. We observe a large-scale global synchronicity in the timing of the last deglaciation from ˜20 to 10 ka. There is also a general correlation between the timing of deglaciation and latitude (or size of the individual ice mass), with earlier deglaciation in lower latitudes and later deglaciation towards the poles. Grouping the data into regions and comparing with available paleoclimate data we can start to untangle regional differences in the last deglaciation and the climate events controlling the ice mass loss. The extensive dataset and the statistical analysis enables an unprecedented global view on the last deglaciation.

Assessing the Impact of Land Use and Land Cover Change on Global Water Resources

NASA Astrophysics Data System (ADS)

Batra, N.; Yang, Y. E.; Choi, H. I.; Islam, A.; Charlotte, D. F.; Cai, X.; Kumar, P.

2007-12-01

Land use and land cover changes (LULCC) significantly modify the hydrological regime of the watersheds, affecting water resources and environment from regional to global scale. This study seeks to advance and integrate water and energy cycle observation, scientific understanding, and human impacts to assess future water availability. To achieve the research objective, we integrate and interpret past and current space based and in situ observations into a global hydrologic model (GHM). GHM is developed with enhanced spatial and temporal resolution, physical complexity, hydrologic theory and processes to quantify the impact of LULCC on physical variables: surface runoff, subsurface flow, groundwater, infiltration, ET, soil moisture, etc. Coupled with the common land model (CLM), a 3-dimensional volume averaged soil-moisture transport (VAST) model is expanded to incorporate the lateral flow and subgrid heterogeneity. The model consists of 11 soil-hydrology layers to predict lateral as well as vertical moisture flux transport based on Richard's equations. The primary surface boundary conditions (SBCs) include surface elevation and its derivatives, land cover category, sand and clay fraction profiles, bedrock depth and fractional vegetation cover. A consistent global GIS-based dataset is constructed for the SBCs of the model from existing observational datasets comprising of various resolutions, map projections and data formats. Global ECMWF data at 6-hour time steps for the period 1971 through 2000 is processed to get the forcing data which includes incoming longwave and shortwave radiation, precipitation, air temperature, pressure, wind components, boundary layer height and specific humidity. Land use land cover data, generated using IPCC scenarios for every 10 years from 2000 to 2100 is used for future assessment on water resources. Alterations due to LULCC on surface water balance components: ET, groundwater recharge and runoff are then addressed in the study. Land use change disrupts the hydrological cycle through increasing the water yield at some places leading to floods while diminishing, or even eliminating the low flow at other places.

Connecting the Public to Scientific Research Data - Science On a Sphere°

NASA Astrophysics Data System (ADS)

Henderson, M. A.; Russell, E. L.; Science on a Sphere Datasets

2011-12-01

Connecting the Public to Scientific Research Data - Science On a Sphere° Maurice Henderson, NASA Goddard Space Flight Center Elizabeth Russell, NOAA Earth System Research Laboratory, University of Colorado Cooperative Institute for Research in Environmental Sciences Science On a Sphere° is a six foot animated globe developed by the National Ocean and Atmospheric Administration, NOAA, as a means to display global scientific research data in an intuitive, engaging format in public forums. With over 70 permanent installations of SOS around the world in science museums, visitor's centers and universities, the audience that enjoys SOS yearly is substantial, wide-ranging, and diverse. Through partnerships with the National Aeronautics and Space Administration, NASA, the SOS Data Catalog (http://sos.noaa.gov/datasets/) has grown to a collection of over 350 datasets from NOAA, NASA, and many others. Using an external projection system, these datasets are displayed onto the sphere creating a seamless global image. In a cross-site evaluation of Science On a Sphere°, 82% of participants said yes, seeing information displayed on a sphere changed their understanding of the information. This unique technology captivates viewers and exposes them to scientific research data in a way that is accessible, presentable, and understandable. The datasets that comprise the SOS Data Catalog are scientific research data that have been formatted for display on SOS. By formatting research data into visualizations that can be used on SOS, NOAA and NASA are able to turn research data into educational materials that are easily accessible for users. In many cases, visualizations do not need to be modified because SOS uses a common map projection. The SOS Data Catalog has become a "one-stop shop" for a broad range of global datasets from across NOAA and NASA, and as a result, the traffic on the site is more than just SOS users. While the target audience for this site is SOS users, many inquiries come from teachers, book editors, film producers and students interested in using the available datasets. The SOS Data Catalog online includes a written description of each dataset, rendered images of the data, animated movies of the data, links to more information, details on the data source and creator, and a link to a FTP server where each dataset can be downloaded. Many of the datasets are also displayed on the SOS YouTube Channel and Facebook page. In addition, NASA has developed NASA Earth Observations, NEO, which is a collection of global satellite datasets. The NEO website allows users to layer multiple datasets and perform basic analysis. Through a new iPad application, the NASA Earth Observations datasets can be exported to SOS and analyzed on the sphere. This new capability greatly expands the number of datasets that can be shown on SOS and adds a new element of interactivity with the datasets.

Co-variation of Temperature and Precipitation in CMIP5 Models and Satellite Observations

NASA Technical Reports Server (NTRS)

Liu, Chunlei; Allan, Richard P.; Huffman, George J.

2013-01-01

Current variability of precipitation (P) and its response to surface temperature (T) are analysed using coupled (CMIP5) and atmosphere-only (AMIP5) climate model simulations and compared with observational estimates.There is striking agreement between Global Precipitation Climatology Project (GPCP) observed and AMIP5)simulated P anomalies over land both globally and in the tropics suggesting that prescribed sea surface temperature and realistic radiative forcings are sufficient for simulating the interannual variability in continental P. Differences between the observed and simulated P variability over the ocean, originate primarily from the wet tropical regions, in particular the western Pacific, but are reduced slightly after 1995. All datasets show positive responses of P to T globally of around 2 % K for simulations and 3-4 % K in GPCP observations but model responses over the tropical oceans are around 3 times smaller than GPCP over the period 1988-2005. The observed anticorrelation between land and ocean P, linked with El Nio Southern Oscillation, is captured by the simulations. All data sets over the tropical ocean show a tendency for wet regions to become wetter and dry regions drier with warming. Over the wet region (greater than or equal to 75 precipitation percentile), the precipitation response is 13-15%K for GPCP and 5%K for models while trends in P are 2.4% decade for GPCP, 0.6% decade for CMIP5 and 0.9decade for AMIP5 suggesting that models are underestimating the precipitation responses or a deficiency exists in the satellite datasets.

Analysis of the uncertainty associated with national fossil fuel CO2 emissions datasets for use in the global Fossil Fuel Data Assimilation System (FFDAS) and carbon budgets

NASA Astrophysics Data System (ADS)

Song, Y.; Gurney, K. R.; Rayner, P. J.; Asefi-Najafabady, S.

2012-12-01

High resolution quantification of global fossil fuel CO2 emissions has become essential in research aimed at understanding the global carbon cycle and supporting the verification of international agreements on greenhouse gas emission reductions. The Fossil Fuel Data Assimilation System (FFDAS) was used to estimate global fossil fuel carbon emissions at 0.25 degree from 1992 to 2010. FFDAS quantifies CO2 emissions based on areal population density, per capita economic activity, energy intensity and carbon intensity. A critical constraint to this system is the estimation of national-scale fossil fuel CO2 emissions disaggregated into economic sectors. Furthermore, prior uncertainty estimation is an important aspect of the FFDAS. Objective techniques to quantify uncertainty for the national emissions are essential. There are several institutional datasets that quantify national carbon emissions, including British Petroleum (BP), the International Energy Agency (IEA), the Energy Information Administration (EIA), and the Carbon Dioxide Information and Analysis Center (CDIAC). These four datasets have been "harmonized" by Jordan Macknick for inter-comparison purposes (Macknick, Carbon Management, 2011). The harmonization attempted to generate consistency among the different institutional datasets via a variety of techniques such as reclassifying into consistent emitting categories, recalculating based on consistent emission factors, and converting into consistent units. These harmonized data form the basis of our uncertainty estimation. We summarized the maximum, minimum and mean national carbon emissions for all the datasets from 1992 to 2010. We calculated key statistics highlighting the remaining differences among the harmonized datasets. We combine the span (max - min) of datasets for each country and year with the standard deviation of the national spans over time. We utilize the economic sectoral definitions from IEA to disaggregate the national total emission into specific sectors required by FFDAS. Our results indicated that although the harmonization performed by Macknick generates better agreement among datasets, significant differences remain at national total level. For example, the CO2 emission span for most countries range from 10% to 12%; BP is generally the highest of the four datasets while IEA is typically the lowest; The US and China had the highest absolute span values but lower percentage span values compared to other countries. However, the US and China make up nearly one-half of the total global absolute span quantity. The absolute span value for the summation of national differences approaches 1 GtC/year in 2007, almost one-half of the biological "missing sink". The span value is used as a potential bias in a recalculation of global and regional carbon budgets to highlight the importance of fossil fuel CO2 emissions in calculating the missing sink. We conclude that if the harmonized span represents potential bias, calculations of the missing sink through forward budget or inverse approaches may be biased by nearly a factor of two.

Multi-model analysis of the Atlantic influence on Southern Amazon rainfall

DOE PAGES

Yoon, Jin -Ho

2015-12-07

Amazon rainfall is subject to year-to-year fluctuation resulting in drought and flood in various intensities. A major climatic driver of the interannual variation of the Amazon rainfall is El Niño/Southern Oscillation. Also, the Sea Surface Temperature over the Atlantic Ocean is identified as an important climatic driver on the Amazon water cycle. Previously, observational datasets were used to support the Atlantic influence on Amazon rainfall. Furthermore, it is found that multiple global climate models do reproduce the Atlantic-Amazon link robustly. However, there exist differences in rainfall response, which primarily depends on the climatological rainfall amount.

Production of a national 1:1,000,000-scale hydrography dataset for the United States: feature selection, simplification, and refinement

USGS Publications Warehouse

Gary, Robin H.; Wilson, Zachary D.; Archuleta, Christy-Ann M.; Thompson, Florence E.; Vrabel, Joseph

2009-01-01

During 2006-09, the U.S. Geological Survey, in cooperation with the National Atlas of the United States, produced a 1:1,000,000-scale (1:1M) hydrography dataset comprising streams and waterbodies for the entire United States, including Puerto Rico and the U.S. Virgin Islands, for inclusion in the recompiled National Atlas. This report documents the methods used to select, simplify, and refine features in the 1:100,000-scale (1:100K) (1:63,360-scale in Alaska) National Hydrography Dataset to create the national 1:1M hydrography dataset. Custom tools and semi-automated processes were created to facilitate generalization of the 1:100K National Hydrography Dataset (1:63,360-scale in Alaska) to 1:1M on the basis of existing small-scale hydrography datasets. The first step in creating the new 1:1M dataset was to address feature selection and optimal data density in the streams network. Several existing methods were evaluated. The production method that was established for selecting features for inclusion in the 1:1M dataset uses a combination of the existing attributes and network in the National Hydrography Dataset and several of the concepts from the methods evaluated. The process for creating the 1:1M waterbodies dataset required a similar approach to that used for the streams dataset. Geometric simplification of features was the next step. Stream reaches and waterbodies indicated in the feature selection process were exported as new feature classes and then simplified using a geographic information system tool. The final step was refinement of the 1:1M streams and waterbodies. Refinement was done through the use of additional geographic information system tools.

A nonparametric statistical technique for combining global precipitation datasets: development and hydrological evaluation over the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Abul Ehsan Bhuiyan, Md; Nikolopoulos, Efthymios I.; Anagnostou, Emmanouil N.; Quintana-Seguí, Pere; Barella-Ortiz, Anaïs

2018-02-01

This study investigates the use of a nonparametric, tree-based model, quantile regression forests (QRF), for combining multiple global precipitation datasets and characterizing the uncertainty of the combined product. We used the Iberian Peninsula as the study area, with a study period spanning 11 years (2000-2010). Inputs to the QRF model included three satellite precipitation products, CMORPH, PERSIANN, and 3B42 (V7); an atmospheric reanalysis precipitation and air temperature dataset; satellite-derived near-surface daily soil moisture data; and a terrain elevation dataset. We calibrated the QRF model for two seasons and two terrain elevation categories and used it to generate ensemble for these conditions. Evaluation of the combined product was based on a high-resolution, ground-reference precipitation dataset (SAFRAN) available at 5 km 1 h-1 resolution. Furthermore, to evaluate relative improvements and the overall impact of the combined product in hydrological response, we used the generated ensemble to force a distributed hydrological model (the SURFEX land surface model and the RAPID river routing scheme) and compared its streamflow simulation results with the corresponding simulations from the individual global precipitation and reference datasets. We concluded that the proposed technique could generate realizations that successfully encapsulate the reference precipitation and provide significant improvement in streamflow simulations, with reduction in systematic and random error on the order of 20-99 and 44-88 %, respectively, when considering the ensemble mean.

Effects of habitat disturbance on tropical forest biodiversity

PubMed Central

Alroy, John

2017-01-01

It is widely expected that habitat destruction in the tropics will cause a mass extinction in coming years, but the potential magnitude of the loss is unclear. Existing literature has focused on estimating global extinction rates indirectly or on quantifying effects only at local and regional scales. This paper directly predicts global losses in 11 groups of organisms that would ensue from disturbance of all remaining tropical forest habitats. The results are based on applying a highly accurate method of estimating species richness to 875 ecological samples. About 41% of the tree and animal species in this dataset are absent from disturbed habitats, even though most samples do still represent forests of some kind. The individual figures are 30% for trees and 8–65% for 10 animal groups. Local communities are more robust to disturbance because losses are partially balanced out by gains resulting from homogenization. PMID:28461482

Outlier identification in urban soils and its implications for identification of potential contaminated land

NASA Astrophysics Data System (ADS)

Zhang, Chaosheng

2010-05-01

Outliers in urban soil geochemical databases may imply potential contaminated land. Different methodologies which can be easily implemented for the identification of global and spatial outliers were applied for Pb concentrations in urban soils of Galway City in Ireland. Due to its strongly skewed probability feature, a Box-Cox transformation was performed prior to further analyses. The graphic methods of histogram and box-and-whisker plot were effective in identification of global outliers at the original scale of the dataset. Spatial outliers could be identified by a local indicator of spatial association of local Moran's I, cross-validation of kriging, and a geographically weighted regression. The spatial locations of outliers were visualised using a geographical information system. Different methods showed generally consistent results, but differences existed. It is suggested that outliers identified by statistical methods should be confirmed and justified using scientific knowledge before they are properly dealt with.

Mars Global Geologic Mapping Progress and Suggested Geographic-Based Hierarchal Systems for Unit Grouping and Naming

NASA Technical Reports Server (NTRS)

Tanaka, K. L.; Dohm, J. M.; Irwin, R.; Kolb, E. J.; Skinner, J. A., Jr.; Hare, T. M.

2010-01-01

We are in the fourth year of a fiveyear effort to map the global geology of Mars at 1:20M scale using mainly Mars Global Surveyor, Mars Express, and Mars Odyssey image and altimetry datasets. Previously, we reported on details of project management, mapping datasets (local and regional), initial and anticipated mapping approaches, and tactics of map unit delineation and description [1-2]. Last year, we described mapping and unit delineation results thus far, a new unit identified in the northern plains, and remaining steps to complete the map [3].

Efficient genotype compression and analysis of large genetic variation datasets

PubMed Central

Layer, Ryan M.; Kindlon, Neil; Karczewski, Konrad J.; Quinlan, Aaron R.

2015-01-01

Genotype Query Tools (GQT) is a new indexing strategy that expedites analyses of genome variation datasets in VCF format based on sample genotypes, phenotypes and relationships. GQT’s compressed genotype index minimizes decompression for analysis, and performance relative to existing methods improves with cohort size. We show substantial (up to 443 fold) performance gains over existing methods and demonstrate GQT’s utility for exploring massive datasets involving thousands to millions of genomes. PMID:26550772

Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades.

PubMed

Orchard, Garrick; Jayawant, Ajinkya; Cohen, Gregory K; Thakor, Nitish

2015-01-01

Creating datasets for Neuromorphic Vision is a challenging task. A lack of available recordings from Neuromorphic Vision sensors means that data must typically be recorded specifically for dataset creation rather than collecting and labeling existing data. The task is further complicated by a desire to simultaneously provide traditional frame-based recordings to allow for direct comparison with traditional Computer Vision algorithms. Here we propose a method for converting existing Computer Vision static image datasets into Neuromorphic Vision datasets using an actuated pan-tilt camera platform. Moving the sensor rather than the scene or image is a more biologically realistic approach to sensing and eliminates timing artifacts introduced by monitor updates when simulating motion on a computer monitor. We present conversion of two popular image datasets (MNIST and Caltech101) which have played important roles in the development of Computer Vision, and we provide performance metrics on these datasets using spike-based recognition algorithms. This work contributes datasets for future use in the field, as well as results from spike-based algorithms against which future works can compare. Furthermore, by converting datasets already popular in Computer Vision, we enable more direct comparison with frame-based approaches.

Water vapour correction of the daily 1 km AVHRR global land dataset: Part I validation and use of the Water Vapour input field

USGS Publications Warehouse

DeFelice, Thomas P.; Lloyd, D.; Meyer, D.J.; Baltzer, T. T.; Piraina, P.

2003-01-01

An atmospheric correction algorithm developed for the 1 km Advanced Very High Resolution Radiometer (AVHRR) global land dataset was modified to include a near real-time total column water vapour data input field to account for the natural variability of atmospheric water vapour. The real-time data input field used for this study is the Television and Infrared Observational Satellite (TIROS) Operational Vertical Sounder (TOVS) Pathfinder A global total column water vapour dataset. It was validated prior to its use in the AVHRR atmospheric correction process using two North American AVHRR scenes, namely 13 June and 28 November 1996. The validation results are consistent with those reported by others and entail a comparison between TOVS, radiosonde, experimental sounding, microwave radiometer, and data from a hand-held sunphotometer. The use of this data layer as input to the AVHRR atmospheric correction process is discussed.

A 7.5-Year Dataset of SSM/I-Derived Surface Turbulent Fluxes Over Global Oceans

NASA Technical Reports Server (NTRS)

Chou, Shu-Hsien; Shie, Chung-Lin; Atlas, Robert M.; Adizzone, Joe; Nelkin, Eric; Starr, David OC. (Technical Monitor)

2001-01-01

The global air-sea turbulent fluxes are needed for driving ocean models and validating coupled ocean-atmosphere global models. A method was developed to retrieve surface air humidity from the radiances measured by the Special Sensor Microwave/Imager (SSM/I) Using both SSM/I-retrieved surface wind and air humidity, they computed daily turbulent fluxes over global oceans with a stability-dependent bulk scheme. Based on this method, we have produced Version 1 of Goddard Satellite-Based Surface Turbulent Fluxes (GSSTF) dataset from the SSM/I data and other data. It provides daily- and monthly-mean surface turbulent fluxes and some relevant parameters over global oceans for individual F8, F10, and F11 satellites covering the period July 1987-December 1994. It also provides 1988-94 annual- and monthly-mean climatologies of the same variables, using only F8 and F1 1 satellite data. It has a spatial resolution of 2.0 degrees x 2.5 degrees lat-long and is archived at the NASA/GSFC DAAC. The purpose of this paper is to present an updated assessment of the GSSTF 1.0 dataset.

Comparison of GFED3, QFED2 and FEER1 Biomass Burning Emissions Datasets in a Global Model

NASA Technical Reports Server (NTRS)

Pan, Xiaohua; Ichoku, Charles; Bian, Huisheng; Chin, Mian; Ellison, Luke; da Silva, Arlindo; Darmenov, Anton

2015-01-01

Biomass burning contributes about 40% of the global loading of carbonaceous aerosols, significantly affecting air quality and the climate system by modulating solar radiation and cloud properties. However, fire emissions are poorly constrained in models on global and regional levels. In this study, we investigate 3 global biomass burning emission datasets in NASA GEOS5, namely: (1) GFEDv3.1 (Global Fire Emissions Database version 3.1); (2) QFEDv2.4 (Quick Fire Emissions Dataset version 2.4); (3) FEERv1 (Fire Energetics and Emissions Research version 1.0). The simulated aerosol optical depth (AOD), absorption AOD (AAOD), angstrom exponent and surface concentrations of aerosol plumes dominated by fire emissions are evaluated and compared to MODIS, OMI, AERONET, and IMPROVE data over different regions. In general, the spatial patterns of biomass burning emissions from these inventories are similar, although the strength of the emissions can be noticeably different. The emissions estimates from QFED are generally larger than those of FEER, which are in turn larger than those of GFED. AOD simulated with all these 3 databases are lower than the corresponding observations in Southern Africa and South America, two of the major biomass burning regions in the world.

Reconstruction of Arctic surface temperature in past 100 years using DINEOF

NASA Astrophysics Data System (ADS)

Zhang, Qiyi; Huang, Jianbin; Luo, Yong

2015-04-01

Global annual mean surface temperature has not risen apparently since 1998, which is described as global warming hiatus in recent years. However, measuring of temperature variability in Arctic is difficult because of large gaps in coverage of Arctic region in most observed gridded datasets. Since Arctic has experienced a rapid temperature change in recent years that called polar amplification, and temperature risen in Arctic is faster than global mean, the unobserved temperature in central Arctic will result in cold bias in both global and Arctic temperature measurement compared with model simulations and reanalysis datasets. Moreover, some datasets that have complete coverage in Arctic but short temporal scale cannot show Arctic temperature variability for long time. Data Interpolating Empirical Orthogonal Function (DINEOF) were applied to fill the coverage gap of NASA's Goddard Institute for Space Studies Surface Temperature Analysis (GISTEMP 250km smooth) product in Arctic with IABP dataset which covers entire Arctic region between 1979 and 1998, and to reconstruct Arctic temperature in 1900-2012. This method provided temperature reconstruction in central Arctic and precise estimation of both global and Arctic temperature variability with a long temporal scale. Results have been verified by extra independent station records in Arctic by statistical analysis, such as variance and standard deviation. The result of reconstruction shows significant warming trend in Arctic in recent 30 years, as the temperature trend in Arctic since 1997 is 0.76°C per decade, compared with 0.48°C and 0.67°C per decade from 250km smooth and 1200km smooth of GISTEMP. And global temperature trend is two times greater after using DINEOF. The discrepancies above stress the importance of fully consideration of temperature variance in Arctic because gaps of coverage in Arctic cause apparent cold bias in temperature estimation. The result of global surface temperature also proves that global warming in recent years is not as slow as thought.

MSWEP: 3-hourly 0.25° global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data

NASA Astrophysics Data System (ADS)

Beck, Hylke E.; van Dijk, Albert I. J. M.; Levizzani, Vincenzo; Schellekens, Jaap; Miralles, Diego G.; Martens, Brecht; de Roo, Ad

2017-01-01

Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979-2015 with a 3-hourly temporal and 0.25° spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite- and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0 % of the stations and a median R of 0.67 vs. 0.44-0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (< 50 000 km2) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) against daily Q observations with P from each of the different datasets. For the 1058 sparsely gauged catchments, representative of 83.9 % of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 vs. 0.29-0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org.

A hybrid Land Cover Dataset for Russia: a new methodology for merging statistics, remote sensing and in-situ information

NASA Astrophysics Data System (ADS)

Schepaschenko, D.; McCallum, I.; Shvidenko, A.; Kraxner, F.; Fritz, S.

2009-04-01

There is a critical need for accurate land cover information for resource assessment, biophysical modeling, greenhouse gas studies, and for estimating possible terrestrial responses and feedbacks to climate change. However, practically all existing land cover datasets have quite a high level of uncertainty and suffer from a lack of important details that does not allow for relevant parameterization, e.g., data derived from different forest inventories. The objective of this study is to develop a methodology in order to create a hybrid land cover dataset at the level which would satisfy requirements of the verified terrestrial biota full greenhouse gas account (Shvidenko et al., 2008) for large regions i.e. Russia. Such requirements necessitate a detailed quantification of land classes (e.g., for forests - dominant species, age, growing stock, net primary production, etc.) with additional information on uncertainties of the major biometric and ecological parameters in the range of 10-20% and a confidence interval of around 0.9. The approach taken here allows the integration of different datasets to explore synergies and in particular the merging and harmonization of land and forest inventories, ecological monitoring, remote sensing data and in-situ information. The following datasets have been integrated: Remote sensing: Global Land Cover 2000 (Fritz et al., 2003), Vegetation Continuous Fields (Hansen et al., 2002), Vegetation Fire (Sukhinin, 2007), Regional land cover (Schmullius et al., 2005); GIS: Soil 1:2.5 Mio (Dokuchaev Soil Science Institute, 1996), Administrative Regions 1:2.5 Mio, Vegetation 1:4 Mio, Bioclimatic Zones 1:4 Mio (Stolbovoi & McCallum, 2002), Forest Enterprises 1:2.5 Mio, Rivers/Lakes and Roads/Railways 1:1 Mio (IIASA's data base); Inventories and statistics: State Land Account (FARSC RF, 2006), State Forest Account - SFA (FFS RF, 2003), Disturbances in forests (FFS RF, 2006). The resulting hybrid land cover dataset at 1-km resolution comprises the following classes: Forest (each grid links to the SFA database, which contains 86,613 records); Agriculture (5 classes, parameterized by 89 administrative units); Wetlands (8 classes, parameterized by 83 zone/region units); Open Woodland, Burnt area; Shrub/grassland (50 classes, parameterized by 300 zone/region units); Water; Unproductive area. This study has demonstrated the ability to produce a highly detailed (both spatially and thematically) land cover dataset over Russia. Future efforts include further validation of the hybrid land cover dataset for Russia, and its use for assessment of the terrestrial biota full greenhouse gas budget across Russia. The methodology proposed in this study could be applied at the global level. Results of such an undertaking would however be highly dependent upon the quality of the available ground data. The implementation of the hybrid land cover dataset was undertaken in a way that it can be regularly updated based on new ground data and remote sensing products (ie. MODIS).

An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes.

PubMed

Booma, P M; Prabhakaran, S; Dhanalakshmi, R

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality.

An Improved Pearson's Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes

PubMed Central

Booma, P. M.; Prabhakaran, S.; Dhanalakshmi, R.

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality. PMID:25136661

Mixed Effectiveness of Africa's Tropical Protected Areas for Maintaining Forest Cover: Insights from a Global Forest Change Dataset

NASA Astrophysics Data System (ADS)

De Vos, A.; Bowker, J.; Ament, J.; Cumming, G.

2016-12-01

The effectiveness of parks for forest conservation is widely debated in Africa, where increasing human pressure, insufficient funding, and lack of management capacity frequently place significant demands on forest habitats. Tropical forests house a significant portion of the world's remaining biodiversity and are being heavily impacted by anthropogenic activity. We used Hansen et al.'s (2013) global forest change dataset to analyse park effectiveness at the individual (224 parks) and national (23 countries) level across Africa by comparing the extent of forest loss (as a proxy for deforestation) inside parks to matched unprotected control samples. We found that, although significant geographical variation exists between parks, the majority of African parks experienced significantly lower deforestation within their boundaries. Accessibility was a significant driver of deforestation, with less accessible areas having a higher probability of forest loss in ineffective parks and more accessible areas having a higher probability of forest loss in effective parks. Smaller parks were less effective at preventing forest loss inside park boundaries than larger parks, and older parks were less effective than younger parks. Our analysis, which is the first individual and national assessment of park effectiveness across Africa, demonstrates the complexity of factors influencing the ability of a park to curb deforestation within its boundaries and highlights the potential of web-based remote sensing technology in monitoring protected area effectiveness.

Globally-Gridded Interpolated Night-Time Marine Air Temperatures 1900-2014

NASA Astrophysics Data System (ADS)

Junod, R.; Christy, J. R.

2016-12-01

Over the past century, climate records have pointed to an increase in global near-surface average temperature. Near-surface air temperature over the oceans is a relatively unused parameter in understanding the current state of climate, but is useful as an independent temperature metric over the oceans and serves as a geographical and physical complement to near-surface air temperature over land. Though versions of this dataset exist (i.e. HadMAT1 and HadNMAT2), it has been strongly recommended that various groups generate climate records independently. This University of Alabama in Huntsville (UAH) study began with the construction of monthly night-time marine air temperature (UAHNMAT) values from the early-twentieth century through to the present era. Data from the International Comprehensive Ocean and Atmosphere Data Set (ICOADS) were used to compile a time series of gridded UAHNMAT, (20S-70N). This time series was homogenized to correct for the many biases such as increasing ship height, solar deck heating, etc. The time series of UAHNMAT, once adjusted to a standard reference height, is gridded to 1.25° pentad grid boxes and interpolated using the kriging interpolation technique. This study will present results which quantify the variability and trends and compare to current trends of other related datasets that include HadNMAT2 and sea-surface temperatures (HadISST & ERSSTv4).

HGDP and HapMap Analysis by Ancestry Mapper Reveals Local and Global Population Relationships

PubMed Central

Magalhães, Tiago R.; Casey, Jillian P.; Conroy, Judith; Regan, Regina; Fitzpatrick, Darren J.; Shah, Naisha; Sobral, João; Ennis, Sean

2012-01-01

Knowledge of human origins, migrations, and expansions is greatly enhanced by the availability of large datasets of genetic information from different populations and by the development of bioinformatic tools used to analyze the data. We present Ancestry Mapper, which we believe improves on existing methods, for the assignment of genetic ancestry to an individual and to study the relationships between local and global populations. The principle function of the method, named Ancestry Mapper, is to give each individual analyzed a genetic identifier, made up of just 51 genetic coordinates, that corresponds to its relationship to the HGDP reference population. As a consequence, the Ancestry Mapper Id (AMid) has intrinsic biological meaning and provides a tool to measure similarity between world populations. We applied Ancestry Mapper to a dataset comprised of the HGDP and HapMap data. The results show distinctions at the continental level, while simultaneously giving details at the population level. We clustered AMids of HGDP/HapMap and observe a recapitulation of human migrations: for a small number of clusters, individuals are grouped according to continental origins; for a larger number of clusters, regional and population distinctions are evident. Calculating distances between AMids allows us to infer ancestry. The number of coordinates is expandable, increasing the power of Ancestry Mapper. An R package called Ancestry Mapper is available to apply this method to any high density genomic data set. PMID:23189146

HGDP and HapMap analysis by Ancestry Mapper reveals local and global population relationships.

PubMed

Magalhães, Tiago R; Casey, Jillian P; Conroy, Judith; Regan, Regina; Fitzpatrick, Darren J; Shah, Naisha; Sobral, João; Ennis, Sean

2012-01-01

Knowledge of human origins, migrations, and expansions is greatly enhanced by the availability of large datasets of genetic information from different populations and by the development of bioinformatic tools used to analyze the data. We present Ancestry Mapper, which we believe improves on existing methods, for the assignment of genetic ancestry to an individual and to study the relationships between local and global populations. The principle function of the method, named Ancestry Mapper, is to give each individual analyzed a genetic identifier, made up of just 51 genetic coordinates, that corresponds to its relationship to the HGDP reference population. As a consequence, the Ancestry Mapper Id (AMid) has intrinsic biological meaning and provides a tool to measure similarity between world populations. We applied Ancestry Mapper to a dataset comprised of the HGDP and HapMap data. The results show distinctions at the continental level, while simultaneously giving details at the population level. We clustered AMids of HGDP/HapMap and observe a recapitulation of human migrations: for a small number of clusters, individuals are grouped according to continental origins; for a larger number of clusters, regional and population distinctions are evident. Calculating distances between AMids allows us to infer ancestry. The number of coordinates is expandable, increasing the power of Ancestry Mapper. An R package called Ancestry Mapper is available to apply this method to any high density genomic data set.

National Hydropower Plant Dataset, Version 1 (Update FY18Q2)

DOE Data Explorer

Samu, Nicole; Kao, Shih-Chieh; O'Connor, Patrick; Johnson, Megan; Uria-Martinez, Rocio; McManamay, Ryan

2016-09-30

The National Hydropower Plant Dataset, Version 1, Update FY18Q2, includes geospatial point-level locations and key characteristics of existing hydropower plants in the United States that are currently online. These data are a subset extracted from NHAAP’s Existing Hydropower Assets (EHA) dataset, which is a cornerstone of NHAAP’s EHA effort that has supported multiple U.S. hydropower R&D research initiatives related to market acceleration, environmental impact reduction, technology-to-market activities, and climate change impact assessment.

Can Dynamic Global Vegetation Models Reproduce Satellite Observed Extreme Browning and Greening Events in Vegetation Productivity?

NASA Astrophysics Data System (ADS)

van Eck, C. M.; Morfopoulos, C.; Betts, R. A.; Chang, J.; Ciais, P.; Friedlingstein, P.; Regnier, P. A. G.

2016-12-01

The frequency and severity of extreme climate events such as droughts, extreme precipitation and heatwaves are expected to increase in our changing climate. These extreme climate events will have an effect on vegetation either by enhanced or reduced productivity. Subsequently, this can have a substantial impact on the terrestrial carbon sink and thus the global carbon cycle, especially as extreme climate events are expected to increase in frequency and severity. Connecting observational datasets with modelling studies provides new insights into these climate-vegetation interactions. This study aims to compare extremes in vegetation productivity as derived from observations with that of Dynamic Global Vegetation Models (DGVMs). In this case GIMMS-NDVI 3g is selected as the observational dataset and both JULES (Joint UK Land Environment Simulator) and ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) as the DGVMs. Both models are forced with PGFv2 Global Meteorological Forcing Dataset according to the ISI-MIP2 protocol for historical runs. Extremes in vegetation productivity are the focal point, which are identified as NDVI anomalies below the 10th percentile or above the 90th percentile during the growing season, referred to as browning or greening events respectively. The monthly NDVI dataset GIMMS-NDVI 3g is used to obtain the location in time and space of the vegetation extremes. The global GIMMS-NDVI 3g dataset has been subdivided into IPCC's SREX-regions for which the NDVI anomalies are calculated and the extreme thresholds are determined. With this information we can identify the location in time and space of the browning and greening events in remotely-sensed vegetation productivity. The same procedure is applied to the modelled Gross Primary Productivity (GPP) allowing a comparison between the spatial and temporal occurrence of the browning and greening events in the observational dataset and the models' output. The capacity of the models to catch observed extremes in vegetation productivity is assessed and compared. Factors contributing to observed and modelled vegetation browning/greening extremes are analysed. The results of this study provide a stepping stone to modelling future extremes in vegetation productivity.

A global gas flaring black carbon emission rate dataset from 1994 to 2012

PubMed Central

Huang, Kan; Fu, Joshua S.

2016-01-01

Global flaring of associated petroleum gas is a potential emission source of particulate matters (PM) and could be notable in some specific regions that are in urgent need of mitigation. PM emitted from gas flaring is mainly in the form of black carbon (BC), which is a strong short-lived climate forcer. However, BC from gas flaring has been neglected in most global/regional emission inventories and is rarely considered in climate modeling. Here we present a global gas flaring BC emission rate dataset for the period 1994–2012 in a machine-readable format. We develop a region-dependent gas flaring BC emission factor database based on the chemical compositions of associated petroleum gas at various oil fields. Gas flaring BC emission rates are estimated using this emission factor database and flaring volumes retrieved from satellite imagery. Evaluation using a chemical transport model suggests that consideration of gas flaring emissions can improve model performance. This dataset will benefit and inform a broad range of research topics, e.g., carbon budget, air quality/climate modeling, and environmental/human exposure. PMID:27874852

A global gas flaring black carbon emission rate dataset from 1994 to 2012

NASA Astrophysics Data System (ADS)

Huang, Kan; Fu, Joshua S.

2016-11-01

Global flaring of associated petroleum gas is a potential emission source of particulate matters (PM) and could be notable in some specific regions that are in urgent need of mitigation. PM emitted from gas flaring is mainly in the form of black carbon (BC), which is a strong short-lived climate forcer. However, BC from gas flaring has been neglected in most global/regional emission inventories and is rarely considered in climate modeling. Here we present a global gas flaring BC emission rate dataset for the period 1994-2012 in a machine-readable format. We develop a region-dependent gas flaring BC emission factor database based on the chemical compositions of associated petroleum gas at various oil fields. Gas flaring BC emission rates are estimated using this emission factor database and flaring volumes retrieved from satellite imagery. Evaluation using a chemical transport model suggests that consideration of gas flaring emissions can improve model performance. This dataset will benefit and inform a broad range of research topics, e.g., carbon budget, air quality/climate modeling, and environmental/human exposure.

MEaSUReS Land Surface Temperature and Emissivity data records

NASA Astrophysics Data System (ADS)

Cawse-Nicholson, K.; Hook, S. J.; Gulley, G.; Borbas, E. E.; Knuteson, R. O.

2017-12-01

The NASA MEaSUReS program was put into place to produce long-term, well calibrated and validated data records for Earth Science research. As part of this program, we have developed three Earth System Data Records (ESDR) to measure Land Surface Temperature (LST) and emissivity: a high spatial resolution (1km) LST product using Low Earth Orbiting (LEO) satellites; a high temporal resolution (hourly over North America) LST product using Geostationary (GEO) satellites; and a Combined ASTER MODIS Emissivity for Land (CAMEL) ESDR. CAMEL was produced by merging two state-of-the-art emissivity datasets: the UW-Madison MODIS Infrared emissivity dataset (UWIREMIS), and the JPL ASTER Global Emissivity Dataset v4 (GEDv4). The CAMEL ESDR is currently available for download, and is being tested in sounder retrieval schemes (e.g. CrIS, IASI, AIRS) to reduce uncertainties in water vapor retrievals, and has already been implemented in the radiative transfer software RTTOV v12 for immediate use in numerical weather modeling and data assimilation systems. The LEO-LST product combines two existing MODIS products, using an uncertainty analysis approach to optimize accuracy over different landcover classes. Validation of these approaches for retrieving LST have shown that they are complementary, with the split-window approach (MxD11) being more stable over heavily vegetated regions and the physics-based approach (MxD21) demonstrating higher accuracy in semi-arid and arid regions where the largest variations in emissivity exist, both spatially and spectrally. The GEO LST-ESDR product uses CAMEL ESDR for improved temperature-emissivity separation, and the same atmospheric correction as the LEO LST product to ensure consistency across all three data records.

FLUXNET2015 Dataset: Batteries included

NASA Astrophysics Data System (ADS)

Pastorello, G.; Papale, D.; Agarwal, D.; Trotta, C.; Chu, H.; Canfora, E.; Torn, M. S.; Baldocchi, D. D.

2016-12-01

The synthesis datasets have become one of the signature products of the FLUXNET global network. They are composed from contributions of individual site teams to regional networks, being then compiled into uniform data products - now used in a wide variety of research efforts: from plant-scale microbiology to global-scale climate change. The FLUXNET Marconi Dataset in 2000 was the first in the series, followed by the FLUXNET LaThuile Dataset in 2007, with significant additions of data products and coverage, solidifying the adoption of the datasets as a research tool. The FLUXNET2015 Dataset counts with another round of substantial improvements, including extended quality control processes and checks, use of downscaled reanalysis data for filling long gaps in micrometeorological variables, multiple methods for USTAR threshold estimation and flux partitioning, and uncertainty estimates - all of which accompanied by auxiliary flags. This "batteries included" approach provides a lot of information for someone who wants to explore the data (and the processing methods) in detail. This inevitably leads to a large number of data variables. Although dealing with all these variables might seem overwhelming at first, especially to someone looking at eddy covariance data for the first time, there is method to our madness. In this work we describe the data products and variables that are part of the FLUXNET2015 Dataset, and the rationale behind the organization of the dataset, covering the simplified version (labeled SUBSET), the complete version (labeled FULLSET), and the auxiliary products in the dataset.

Methods to achieve accurate projection of regional and global raster databases

USGS Publications Warehouse

Usery, E. Lynn; Seong, Jeong Chang; Steinwand, Dan

2002-01-01

Modeling regional and global activities of climatic and human-induced change requires accurate geographic data from which we can develop mathematical and statistical tabulations of attributes and properties of the environment. Many of these models depend on data formatted as raster cells or matrices of pixel values. Recently, it has been demonstrated that regional and global raster datasets are subject to significant error from mathematical projection and that these errors are of such magnitude that model results may be jeopardized (Steinwand, et al., 1995; Yang, et al., 1996; Usery and Seong, 2001; Seong and Usery, 2001). There is a need to develop methods of projection that maintain the accuracy of these datasets to support regional and global analyses and modeling

Recommendations on Arresting Global Health Challenges Facing Adolescents and Young Adults.

PubMed

Lassi, Zohra S; Salam, Rehana A; Bhutta, Zulfiqar A

The health challenges faced by young people are more complex than adults and can compromise their full growth and development. Attention must be paid to the health of this age group, yet adolescents and youth remain largely invisible and often disappear from the major global datasets. The aim of this paper is to discuss the global health challenges faced by adolescents and youth, global legislations and guidelines pertaining to this particular age group, recommendations to arrest these challenges, and research priorities. Major direct and indirect global health risks faced by adolescents include early pregnancy and childbirth, femicide, honor killing, female genital mutilation, nutritional habits and choices, social media, and peer pressure. There are no standard legal age cut-offs for adulthood; rather, the age varies for different activities, such as age of consent or the minimum age that young people can legally work, leave school, drive, buy alcohol, marry, be held accountable for criminal action, and make medical decisions. This reflects the fact that the existing systems and structures are focused on either children or adults, with very few investments and interventions directed specifically to young people. Existing legislation and guidelines need transformation to bring about a specific focus on adolescents in the domains of substance use and sexual behaviors, and the capacity for adolescent learning should be exploited through graduated legal and policy frameworks. Sustainable development goals provide an opportunity to target this neglected and vulnerable age group. A multisectoral approach is needed to bring about healthy change and address the challenges faced by adolescents and youth, from modifications at a broader legislative and policy level to ground-level (community-level) implementations. Copyright © 2017 Icahn School of Medicine at Mount Sinai. Published by Elsevier Inc. All rights reserved.

Can we achieve Millennium Development Goal 4? New analysis of country trends and forecasts of under-5 mortality to 2015.

PubMed

Murray, Christopher J L; Laakso, Thomas; Shibuya, Kenji; Hill, Kenneth; Lopez, Alan D

2007-09-22

Global efforts have increased the accuracy and timeliness of estimates of under-5 mortality; however, these estimates fail to use all data available, do not use transparent and reproducible methods, do not distinguish predictions from measurements, and provide no indication of uncertainty around point estimates. We aimed to develop new reproducible methods and reanalyse existing data to elucidate detailed time trends. We merged available databases, added to them when possible, and then applied Loess regression to estimate past trends and forecast to 2015 for 172 countries. We developed uncertainty estimates based on different model specifications and estimated levels and trends in neonatal, post-neonatal, and childhood mortality. Global under-5 mortality has fallen from 110 (109-110) per 1000 in 1980 to 72 (70-74) per 1000 in 2005. Child deaths worldwide have decreased from 13.5 (13.4-13.6) million in 1980 to an estimated 9.7 (9.5-10.0) million in 2005. Global under-5 mortality is expected to decline by 27% from 1990 to 2015, substantially less than the target of Millennium Development Goal 4 (MDG4) of a 67% decrease. Several regions in Latin America, north Africa, the Middle East, Europe, and southeast Asia have had consistent annual rates of decline in excess of 4% over 35 years. Global progress on MDG4 is dominated by slow reductions in sub-Saharan Africa, which also has the slowest rates of decline in fertility. Globally, we are not doing a better job of reducing child mortality now than we were three decades ago. Further improvements in the quality and timeliness of child-mortality measurements should be possible by more fully using existing datasets and applying standard analytical strategies.

The Development of the Global Citizenship Inventory for Adolescents

ERIC Educational Resources Information Center

Van Gent, Marije; Carabain, Christine; De Goede, Irene; Boonstoppel, Evelien; Hogeling, Lette

2013-01-01

In this paper we report on the development of an inventory that measures global citizenship among adolescents. The methodology used consists of cognitive interviews for questionnaire design and explorative and confirmatory factor analyses among several datasets. The resulting Global Citizenship Inventory (GCI) includes a global citizenship…

A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

NASA Astrophysics Data System (ADS)

Sommer, Philipp S.; Kaplan, Jed O.

2017-10-01

While a wide range of Earth system processes occur at daily and even subdaily timescales, many global vegetation and other terrestrial dynamics models historically used monthly meteorological forcing both to reduce computational demand and because global datasets were lacking. Recently, dynamic land surface modeling has moved towards resolving daily and subdaily processes, and global datasets containing daily and subdaily meteorology have become available. These meteorological datasets, however, cover only the instrumental era of the last approximately 120 years at best, are subject to considerable uncertainty, and represent extremely large data files with associated computational costs of data input/output and file transfer. For periods before the recent past or in the future, global meteorological forcing can be provided by climate model output, but the quality of these data at high temporal resolution is low, particularly for daily precipitation frequency and amount. Here, we present GWGEN, a globally applicable statistical weather generator for the temporal downscaling of monthly climatology to daily meteorology. Our weather generator is parameterized using a global meteorological database and simulates daily values of five common variables: minimum and maximum temperature, precipitation, cloud cover, and wind speed. GWGEN is lightweight, modular, and requires a minimal set of monthly mean variables as input. The weather generator may be used in a range of applications, for example, in global vegetation, crop, soil erosion, or hydrological models. While GWGEN does not currently perform spatially autocorrelated multi-point downscaling of daily weather, this additional functionality could be implemented in future versions.

Sinfonevada: Dataset of Floristic diversity in Sierra Nevada forests (SE Spain)

PubMed Central

Pérez-Luque, Antonio Jesús; Bonet, Francisco Javier; Pérez-Pérez, Ramón; Rut Aspizua; Lorite, Juan; Zamora, Regino

2014-01-01

Abstract The Sinfonevada database is a forest inventory that contains information on the forest ecosystem in the Sierra Nevada mountains (SE Spain). The Sinfonevada dataset contains more than 7,500 occurrence records belonging to 270 taxa (24 of these threatened) from floristic inventories of the Sinfonevada Forest inventory. Expert field workers collected the information. The whole dataset underwent a quality control by botanists with broad expertise in Sierra Nevada flora. This floristic inventory was created to gather useful information for the proper management of Pinus plantations in Sierra Nevada. This is the only dataset that shows a comprehensive view of the forest flora in Sierra Nevada. This is the reason why it is being used to assess the biodiversity in the very dense pine plantations on this massif. With this dataset, managers have improved their ability to decide where to apply forest treatments in order to avoid biodiversity loss. The dataset forms part of the Sierra Nevada Global Change Observatory (OBSNEV), a long-term research project designed to compile socio-ecological information on the major ecosystem types in order to identify the impacts of global change in this area. PMID:24843285

How well can we measure Earth's Energy Imbalance?

NASA Astrophysics Data System (ADS)

Hakuba, M. Z.; Stephens, G. L.; Landerer, F. W.; Webb, F.; Bettadpur, S. V.; Tapley, B. D.; Christophe, B.; Foulon, B.

2017-12-01

The direct measurement of Earth's energy imbalance (EEI) is one of the greatest challenges in climate research. The global mean EEI is the integrated value of global warming, while its spatial and temporal variability can tell us about the strength and direction of heat transports and reflects internal climate modes such as ENSO. These heat flows ultimately control the circulation in the atmosphere and ocean, and henceforth the water cycle and habitability of our planet. Current space-born systems measure the radiative components of the global mean energy budget with unprecedented accuracy and stability, but the residual budget derived from them has errors too large to determine the absolute magnitude of EEI. Best estimates of EEI are currently derived from changes in ocean heat content, which are afflicted with horizontal and vertical sampling issues. Hence, we see the need to improve on current approaches in order to circumvent calibration issues that are inevitable in radiometry, and sampling issues that are inevitable when profiling the ocean. We will present alternative methods to estimate the EEI by 1) exploiting existing datasets of ocean mass and sea level height from remote sensing. A combination of such datasets, as for example provided by the GRACE and Jason missions, provides a way of estimating the thermo-steric sea level rise and therefore the thermal expansion of the ocean due to heat uptake. Recent studies suggest the retrieval of ocean heat uptake is possible within acceptable error bounds, although the magnitude and sources of error are yet to be comprehensively defined. 2) To monitor the integrated value of EEI from space, we propose a method that aims at measuring the non-gravitational force due to radiation pressure acting on Earth orbiting spacecrafts. This requires measurements of acceleration at high accuracy. The concept of deriving EEI from radiation pressure has been explored in the past and today's advanced capabilities suggest it is feasible to measure the EEI accurately enough to answer the question: At what rate is our planet warming? This method provides little information on spectral distribution and spatiotemporal resolution. However, by directly measuring EEI, it could complement existing efforts and improve our understanding of the climatic changes our planet is subjected to.

A new global 1-km dataset of percentage tree cover derived from remote sensing

USGS Publications Warehouse

DeFries, R.S.; Hansen, M.C.; Townshend, J.R.G.; Janetos, A.C.; Loveland, Thomas R.

2000-01-01

Accurate assessment of the spatial extent of forest cover is a crucial requirement for quantifying the sources and sinks of carbon from the terrestrial biosphere. In the more immediate context of the United Nations Framework Convention on Climate Change, implementation of the Kyoto Protocol calls for estimates of carbon stocks for a baseline year as well as for subsequent years. Data sources from country level statistics and other ground-based information are based on varying definitions of 'forest' and are consequently problematic for obtaining spatially and temporally consistent carbon stock estimates. By combining two datasets previously derived from the Advanced Very High Resolution Radiometer (AVHRR) at 1 km spatial resolution, we have generated a prototype global map depicting percentage tree cover and associated proportions of trees with different leaf longevity (evergreen and deciduous) and leaf type (broadleaf and needleleaf). The product is intended for use in terrestrial carbon cycle models, in conjunction with other spatial datasets such as climate and soil type, to obtain more consistent and reliable estimates of carbon stocks. The percentage tree cover dataset is available through the Global Land Cover Facility at the University of Maryland at http://glcf.umiacs.umd.edu.

Analysis of the global ISCCP TOVS water vapor climatology

NASA Technical Reports Server (NTRS)

Wittmeyer, Ian L.; Vonder Haar, Thomas H.

1994-01-01

A climatological examination of the global water vapor field based on a multiyear period of successfull satellite-based observations is presented. Results from the multiyear global ISCCP TIROS Operational Vertical Sounder (TOVS) water vapor dataset as operationally produced by NESDIS and ISCCP are shown. The methods employed for the retrieval of precipitable water content (PWC) utilize infrared measurements collected by the TOVS instrument package flown aboard the NOAA series of operational polar-orbiting satellites. Strengths of this dataset include the nearly global daily coverage, availability for a multiyear period, operational internal quality checks, and its description of important features in the mean state of the atmosphere. Weaknesses of this PWC dataset include that the infrared sensors are unable to collect data in cloudy regions, the retrievals are strongly biased toward a land-based radiosonde first-guess dataset, and the description of high spatial and temporal variability is inadequate. Primary consequences of these factors are seen in the underestimation of ITCZ water vapor maxima, and underestimation of midlatitude water vapor mean and standard deviation values where transient atmospheric phenomena contribute significantly toward time means. A comparison of TOVS analyses to SSM/I data over ocean for the month of July 1988 shows fair agreement in the magnitude and distribution of the monthly mean values, but the TOVS fields exhibit much less temporal and spatial variability on a daily basis in comparison to the SSM/I analyses. The emphasis of this paper is on the presentation and documentation of an early satellite-based water vapor climatology, and description of factors that prevent a more accurate representation of the global water vapor field.

On standardization of basic datasets of electronic medical records in traditional Chinese medicine.

PubMed

Zhang, Hong; Ni, Wandong; Li, Jing; Jiang, Youlin; Liu, Kunjing; Ma, Zhaohui

2017-12-24

Standardization of electronic medical record, so as to enable resource-sharing and information exchange among medical institutions has become inevitable in view of the ever increasing medical information. The current research is an effort towards the standardization of basic dataset of electronic medical records in traditional Chinese medicine. In this work, an outpatient clinical information model and an inpatient clinical information model are created to adequately depict the diagnosis processes and treatment procedures of traditional Chinese medicine. To be backward compatible with the existing dataset standard created for western medicine, the new standard shall be a superset of the existing standard. Thus, the two models are checked against the existing standard in conjunction with 170,000 medical record cases. If a case cannot be covered by the existing standard due to the particularity of Chinese medicine, then either an existing data element is expanded with some Chinese medicine contents or a new data element is created. Some dataset subsets are also created to group and record Chinese medicine special diagnoses and treatments such as acupuncture. The outcome of this research is a proposal of standardized traditional Chinese medicine medical records datasets. The proposal has been verified successfully in three medical institutions with hundreds of thousands of medical records. A new dataset standard for traditional Chinese medicine is proposed in this paper. The proposed standard, covering traditional Chinese medicine as well as western medicine, is expected to be soon approved by the authority. A widespread adoption of this proposal will enable traditional Chinese medicine hospitals and institutions to easily exchange information and share resources. Copyright © 2017. Published by Elsevier B.V.

Global retrieval of soil moisture and vegetation properties using data-driven methods

NASA Astrophysics Data System (ADS)

Rodriguez-Fernandez, Nemesio; Richaume, Philippe; Kerr, Yann

2017-04-01

Data-driven methods such as neural networks (NNs) are a powerful tool to retrieve soil moisture from multi-wavelength remote sensing observations at global scale. In this presentation we will review a number of recent results regarding the retrieval of soil moisture with the Soil Moisture and Ocean Salinity (SMOS) satellite, either using SMOS brightness temperatures as input data for the retrieval or using SMOS soil moisture retrievals as reference dataset for the training. The presentation will discuss several possibilities for both the input datasets and the datasets to be used as reference for the supervised learning phase. Regarding the input datasets, it will be shown that NNs take advantage of the synergy of SMOS data and data from other sensors such as the Advanced Scatterometer (ASCAT, active microwaves) and MODIS (visible and infra red). NNs have also been successfully used to construct long time series of soil moisture from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and SMOS. A NN with input data from ASMR-E observations and SMOS soil moisture as reference for the training was used to construct a dataset sharing a similar climatology and without a significant bias with respect to SMOS soil moisture. Regarding the reference data to train the data-driven retrievals, we will show different possibilities depending on the application. Using actual in situ measurements is challenging at global scale due to the scarce distribution of sensors. In contrast, in situ measurements have been successfully used to retrieve SM at continental scale in North America, where the density of in situ measurement stations is high. Using global land surface models to train the NN constitute an interesting alternative to implement new remote sensing surface datasets. In addition, these datasets can be used to perform data assimilation into the model used as reference for the training. This approach has recently been tested at the European Centre for Medium-Range Weather Forecasts (ECMWF). Finally, retrievals using radiative transfer models can also be used as a reference SM dataset for the training phase. This approach was used to retrieve soil moisture from ASMR-E, as mentioned above, and also to implement the official European Space Agency (ESA) SMOS soil moisture product in Near-Real-Time. We will finish with a discussion of the retrieval of vegetation parameters from SMOS observations using data-driven methods.

Recent Progresses in Incorporating Human Land-Water Management into Global Land Surface Models Toward Their Integration into Earth System Models

NASA Technical Reports Server (NTRS)

Pokhrel, Yadu N.; Hanasaki, Naota; Wada, Yoshihide; Kim, Hyungjun

2016-01-01

The global water cycle has been profoundly affected by human land-water management. As the changes in the water cycle on land can affect the functioning of a wide range of biophysical and biogeochemical processes of the Earth system, it is essential to represent human land-water management in Earth system models (ESMs). During the recent past, noteworthy progress has been made in large-scale modeling of human impacts on the water cycle but sufficient advancements have not yet been made in integrating the newly developed schemes into ESMs. This study reviews the progresses made in incorporating human factors in large-scale hydrological models and their integration into ESMs. The study focuses primarily on the recent advancements and existing challenges in incorporating human impacts in global land surface models (LSMs) as a way forward to the development of ESMs with humans as integral components, but a brief review of global hydrological models (GHMs) is also provided. The study begins with the general overview of human impacts on the water cycle. Then, the algorithms currently employed to represent irrigation, reservoir operation, and groundwater pumping are discussed. Next, methodological deficiencies in current modeling approaches and existing challenges are identified. Furthermore, light is shed on the sources of uncertainties associated with model parameterizations, grid resolution, and datasets used for forcing and validation. Finally, representing human land-water management in LSMs is highlighted as an important research direction toward developing integrated models using ESM frameworks for the holistic study of human-water interactions within the Earths system.

Trace Gas/Aerosol Interactions and GMI Modeling Support

NASA Technical Reports Server (NTRS)

Penner, Joyce E.; Liu, Xiaohong; Das, Bigyani; Bergmann, Dan; Rodriquez, Jose M.; Strahan, Susan; Wang, Minghuai; Feng, Yan

2005-01-01

Current global aerosol models use different physical and chemical schemes and parameters, different meteorological fields, and often different emission sources. Since the physical and chemical parameterization schemes are often tuned to obtain results that are consistent with observations, it is difficult to assess the true uncertainty due to meteorology alone. Under the framework of the NASA global modeling initiative (GMI), the differences and uncertainties in aerosol simulations (for sulfate, organic carbon, black carbon, dust and sea salt) solely due to different meteorological fields are analyzed and quantified. Three meteorological datasets available from the NASA DAO GCM, the GISS-II' GCM, and the NASA finite volume GCM (FVGCM) are used to drive the same aerosol model. The global sulfate and mineral dust burdens with FVGCM fields are 40% and 20% less than those with DAO and GISS fields, respectively due to its heavier rainfall. Meanwhile, the sea salt burden predicted with FVGCM fields is 56% and 43% higher than those with DAO and GISS, respectively, due to its stronger convection especially over the Southern Hemispheric Ocean. Sulfate concentrations at the surface in the Northern Hemisphere extratropics and in the middle to upper troposphere differ by more than a factor of 3 between the three meteorological datasets. The agreement between model calculated and observed aerosol concentrations in the industrial regions (e.g., North America and Europe) is quite similar for all three meteorological datasets. Away from the source regions, however, the comparisons with observations differ greatly for DAO, FVGCM and GISS, and the performance of the model using different datasets varies largely depending on sites and species. Global annual average aerosol optical depth at 550 nm is 0.120-0.131 for the three meteorological datasets.

How consistent are global long-term satellite LAI products in terms of interannual variability and trend?

NASA Astrophysics Data System (ADS)

Jiang, C.; Ryu, Y.; Fang, H.

2016-12-01

Proper usage of global satellite LAI products requires comprehensive evaluation. To address this issue, the Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) subgroup proposed a four-stage validation hierarchy. During the past decade, great efforts have been made following this validation framework, mainly focused on absolute magnitude, seasonal trajectory, and spatial pattern of those global satellite LAI products. However, interannual variability and trends of global satellite LAI products have been investigated marginally. Targeting on this gap, we made an intercomparison between seven global satellite LAI datasets, including four short-term ones: MODIS C5, MODIS C6, GEOV1, MERIS, and three long-term products ones: LAI3g, GLASS, and GLOBMAP. We calculated global annual LAI time series for each dataset, among which we found substantial differences. During the overlapped period (2003 - 2011), MODIS C5, GLASS and GLOBMAP have positive correlation (r > 0.6) between each other, while MODIS C6, GEOV1, MERIS, and LAI3g are highly consistent (r > 0.7) in interannual variations. However, the previous three datasets show negative trends, all of which use MODIS C5 reflectance data, whereas the latter four show positive trends, using MODIS C6, SPOT/VGT, ENVISAT/MERIS, and NOAA/AVHRR, respectively. During the pre-MODIS era (1982 - 1999), the three AVHRR-based datasets (LAI3g, GLASS and GLOBMAP) agree well (r > 0.7), yet all of them show oscillation related with NOAA platform changes. In addition, both GLASS and GLOBMAP show clear cut-points around 2000 when they move from AVHRR to MODIS. Such inconsistency is also visible for GEOV1, which uses SPOT-4 and SPOT-5 before and after 2002. We further investigate the map-to-map deviations among these products. This study highlights that continuous sensor calibration and cross calibration are essential to obtain reliable global LAI time series.

Applying Advances in GPM Radiometer Intercalibration and Algorithm Development to a Long-Term TRMM/GPM Global Precipitation Dataset

NASA Astrophysics Data System (ADS)

Berg, W. K.

2016-12-01

The Global Precipitation Mission (GPM) Core Observatory, which was launched in February of 2014, provides a number of advances for satellite monitoring of precipitation including a dual-frequency radar, high frequency channels on the GPM Microwave Imager (GMI), and coverage over middle and high latitudes. The GPM concept, however, is about producing unified precipitation retrievals from a constellation of microwave radiometers to provide approximately 3-hourly global sampling. This involves intercalibration of the input brightness temperatures from the constellation radiometers, development of an apriori precipitation database using observations from the state-of-the-art GPM radiometer and radars, and accounting for sensor differences in the retrieval algorithm in a physically-consistent way. Efforts by the GPM inter-satellite calibration working group, or XCAL team, and the radiometer algorithm team to create unified precipitation retrievals from the GPM radiometer constellation were fully implemented into the current version 4 GPM precipitation products. These include precipitation estimates from a total of seven conical-scanning and six cross-track scanning radiometers as well as high spatial and temporal resolution global level 3 gridded products. Work is now underway to extend this unified constellation-based approach to the combined TRMM/GPM data record starting in late 1997. The goal is to create a long-term global precipitation dataset employing these state-of-the-art calibration and retrieval algorithm approaches. This new long-term global precipitation dataset will incorporate the physics provided by the combined GPM GMI and DPR sensors into the apriori database, extend prior TRMM constellation observations to high latitudes, and expand the available TRMM precipitation data to the full constellation of available conical and cross-track scanning radiometers. This combined TRMM/GPM precipitation data record will thus provide a high-quality high-temporal resolution global dataset for use in a wide variety of weather and climate research applications.

The SeaFlux Turbulent Flux Dataset Version 1.0 Documentation

NASA Technical Reports Server (NTRS)

Clayson, Carol Anne; Roberts, J. Brent; Bogdanoff, Alec S.

2012-01-01

Under the auspices of the World Climate Research Programme (WCRP) Global Energy and Water cycle EXperiment (GEWEX) Data and Assessment Panel (GDAP), the SeaFlux Project was created to investigate producing a high-resolution satellite-based dataset of surface turbulent fluxes over the global oceans. The most current release of the SeaFlux product is Version 1.0; this represents the initial release of turbulent surface heat fluxes, associated near-surface variables including a diurnally varying sea surface temperature.

A global experimental dataset for assessing grain legume production

PubMed Central

Cernay, Charles; Pelzer, Elise; Makowski, David

2016-01-01

Grain legume crops are a significant component of the human diet and animal feed and have an important role in the environment, but the global diversity of agricultural legume species is currently underexploited. Experimental assessments of grain legume performances are required, to identify potential species with high yields. Here, we introduce a dataset including results of field experiments published in 173 articles. The selected experiments were carried out over five continents on 39 grain legume species. The dataset includes measurements of grain yield, aerial biomass, crop nitrogen content, residual soil nitrogen content and water use. When available, yields for cereals and oilseeds grown after grain legumes in the crop sequence are also included. The dataset is arranged into a relational database with nine structured tables and 198 standardized attributes. Tillage, fertilization, pest and irrigation management are systematically recorded for each of the 8,581 crop*field site*growing season*treatment combinations. The dataset is freely reusable and easy to update. We anticipate that it will provide valuable information for assessing grain legume production worldwide. PMID:27676125

Secondary analysis of national survey datasets.

PubMed

Boo, Sunjoo; Froelicher, Erika Sivarajan

2013-06-01

This paper describes the methodological issues associated with secondary analysis of large national survey datasets. Issues about survey sampling, data collection, and non-response and missing data in terms of methodological validity and reliability are discussed. Although reanalyzing large national survey datasets is an expedient and cost-efficient way of producing nursing knowledge, successful investigations require a methodological consideration of the intrinsic limitations of secondary survey analysis. Nursing researchers using existing national survey datasets should understand potential sources of error associated with survey sampling, data collection, and non-response and missing data. Although it is impossible to eliminate all potential errors, researchers using existing national survey datasets must be aware of the possible influence of errors on the results of the analyses. © 2012 The Authors. Japan Journal of Nursing Science © 2012 Japan Academy of Nursing Science.

Global Precipitation Measurement: Methods, Datasets and Applications

NASA Technical Reports Server (NTRS)

Tapiador, Francisco; Turk, Francis J.; Petersen, Walt; Hou, Arthur Y.; Garcia-Ortega, Eduardo; Machado, Luiz, A. T.; Angelis, Carlos F.; Salio, Paola; Kidd, Chris; Huffman, George J.;

Coordinating Earth and Environmental Cross-disciplinary projects to promote GEOSS: the EGIDA project

NASA Astrophysics Data System (ADS)

Nativi, S.

2011-12-01

Earth Observation System of Systems' (GEOSS) is completed in 2015, it will constitute a flexible network of global content providers allowing decision makers to access an extraordinary range of information, proactively linking existing and planned observing systems around the world. Where gaps exist, GEOSS will support the development of new systems and promote common technical standards, so that information from thousands of different instruments can be combined into coherent datasets. The basic need for open access to data across disciplines is still omnipresent in Europe and beyond. Available datasets are often not easy to find, or lack proper metadata, making them virtually useless, while data interoperability continues to be a key hurdle. 'Coordinating Earth and Environmental Cross-disciplinary projects to promote GEOSS' (EGIDA) is an initiative which prepares a sustainable process promoting coordination of activities carried out by the GEO Science & Technology (S&T) Committee, the S&T national and European initiatives, and other S&T communities. The project builds on existing national initiatives and European projects, facilitating the S&T Community contributions to, and interactions with, GEOSS, and will involve developing countries by transferring the EGIDA S&T methodology to them. EGIDA has established a stakeholder network across Europe, the U.S., Brazil, South Africa, Turkey, China, Japan and Australia. The network implements the links between EGIDA and the global programmes framework, facilitating S&T community contributions to GEOSS and disseminating project results to the S&T community. Several key organisations, representing the different regions involved in GEO/GEOSS, have joined the network, which also acts as a forum for refining the EGIDA Methodology, and will help ensure it is sustainable beyond the project. By utilising new and existing groups of stakeholders throughout the network, the project aims to enhance information exchange, knowledge creation and sharing of good practice. EGIDA also operates an Advisory Board comprising worldwide S&T leaders, which gives advice to EGIDA about broader collaboration and coordination implementation issues. The board aims to act as a mutual link between the project's consortium and the European and international research systems, and to provide guidance from the perspective of these systems. Members of the Advisory Board have played key roles in GEO/GEOSS committees for many years, and have been involved in the main European initiatives to build a geosciences system of systems and in international S&T groups and networks.

Can Global Weed Assemblages Be Used to Predict Future Weeds?

PubMed Central

Morin, Louise; Paini, Dean R.; Randall, Roderick P.

2013-01-01

Predicting which plant taxa are more likely to become weeds in a region presents significant challenges to both researchers and government agencies. Often it is done in a qualitative or semi-quantitative way. In this study, we explored the potential of using the quantitative self-organising map (SOM) approach to analyse global weed assemblages and estimate likelihoods of plant taxa becoming weeds before and after they have been moved to a new region. The SOM approach examines plant taxa associations by analysing where a taxon is recorded as a weed and what other taxa are recorded as weeds in those regions. The dataset analysed was extracted from a pre-existing, extensive worldwide database of plant taxa recorded as weeds or other related status and, following reformatting, included 187 regions and 6690 plant taxa. To assess the value of the SOM approach we selected Australia as a case study. We found that the key and most important limitation in using such analytical approach lies with the dataset used. The classification of a taxon as a weed in the literature is not often based on actual data that document the economic, environmental and/or social impact of the taxon, but mostly based on human perceptions that the taxon is troublesome or simply not wanted in a particular situation. The adoption of consistent and objective criteria that incorporate a standardized approach for impact assessment of plant taxa will be necessary to develop a new global database suitable to make predictions regarding weediness using methods like SOM. It may however, be more realistic to opt for a classification system that focuses on the invasive characteristics of plant taxa without any inference to impacts, which to be defined would require some level of research to avoid bias from human perceptions and value systems. PMID:23393591

Defining global neuroendocrine gene expression patterns associated with reproductive seasonality in fish.

PubMed

Zhang, Dapeng; Xiong, Huiling; Mennigen, Jan A; Popesku, Jason T; Marlatt, Vicki L; Martyniuk, Christopher J; Crump, Kate; Cossins, Andrew R; Xia, Xuhua; Trudeau, Vance L

2009-06-05

Many vertebrates, including the goldfish, exhibit seasonal reproductive rhythms, which are a result of interactions between external environmental stimuli and internal endocrine systems in the hypothalamo-pituitary-gonadal axis. While it is long believed that differential expression of neuroendocrine genes contributes to establishing seasonal reproductive rhythms, no systems-level investigation has yet been conducted. In the present study, by analyzing multiple female goldfish brain microarray datasets, we have characterized global gene expression patterns for a seasonal cycle. A core set of genes (873 genes) in the hypothalamus were identified to be differentially expressed between May, August and December, which correspond to physiologically distinct stages that are sexually mature (prespawning), sexual regression, and early gonadal redevelopment, respectively. Expression changes of these genes are also shared by another brain region, the telencephalon, as revealed by multivariate analysis. More importantly, by examining one dataset obtained from fish in October who were kept under long-daylength photoperiod (16 h) typical of the springtime breeding season (May), we observed that the expression of identified genes appears regulated by photoperiod, a major factor controlling vertebrate reproductive cyclicity. Gene ontology analysis revealed that hormone genes and genes functionally involved in G-protein coupled receptor signaling pathway and transmission of nerve impulses are significantly enriched in an expression pattern, whose transition is located between prespawning and sexually regressed stages. The existence of seasonal expression patterns was verified for several genes including isotocin, ependymin II, GABA(A) gamma2 receptor, calmodulin, and aromatase b by independent samplings of goldfish brains from six seasonal time points and real-time PCR assays. Using both theoretical and experimental strategies, we report for the first time global gene expression patterns throughout a breeding season which may account for dynamic neuroendocrine regulation of seasonal reproductive development.

Defining Global Neuroendocrine Gene Expression Patterns Associated with Reproductive Seasonality in Fish

PubMed Central

Mennigen, Jan A.; Popesku, Jason T.; Marlatt, Vicki L.; Martyniuk, Christopher J.; Crump, Kate; Cossins, Andrew R.; Xia, Xuhua; Trudeau, Vance L.

2009-01-01

Background Many vertebrates, including the goldfish, exhibit seasonal reproductive rhythms, which are a result of interactions between external environmental stimuli and internal endocrine systems in the hypothalamo-pituitary-gonadal axis. While it is long believed that differential expression of neuroendocrine genes contributes to establishing seasonal reproductive rhythms, no systems-level investigation has yet been conducted. Methodology/Principal Findings In the present study, by analyzing multiple female goldfish brain microarray datasets, we have characterized global gene expression patterns for a seasonal cycle. A core set of genes (873 genes) in the hypothalamus were identified to be differentially expressed between May, August and December, which correspond to physiologically distinct stages that are sexually mature (prespawning), sexual regression, and early gonadal redevelopment, respectively. Expression changes of these genes are also shared by another brain region, the telencephalon, as revealed by multivariate analysis. More importantly, by examining one dataset obtained from fish in October who were kept under long-daylength photoperiod (16 h) typical of the springtime breeding season (May), we observed that the expression of identified genes appears regulated by photoperiod, a major factor controlling vertebrate reproductive cyclicity. Gene ontology analysis revealed that hormone genes and genes functionally involved in G-protein coupled receptor signaling pathway and transmission of nerve impulses are significantly enriched in an expression pattern, whose transition is located between prespawning and sexually regressed stages. The existence of seasonal expression patterns was verified for several genes including isotocin, ependymin II, GABAA gamma2 receptor, calmodulin, and aromatase b by independent samplings of goldfish brains from six seasonal time points and real-time PCR assays. Conclusions/Significance Using both theoretical and experimental strategies, we report for the first time global gene expression patterns throughout a breeding season which may account for dynamic neuroendocrine regulation of seasonal reproductive development. PMID:19503831

Impacts of spatial resolution and representation of flow connectivity on large-scale simulation of floods

NASA Astrophysics Data System (ADS)

Mateo, Cherry May R.; Yamazaki, Dai; Kim, Hyungjun; Champathong, Adisorn; Vaze, Jai; Oki, Taikan

2017-10-01

Global-scale river models (GRMs) are core tools for providing consistent estimates of global flood hazard, especially in data-scarce regions. Due to former limitations in computational power and input datasets, most GRMs have been developed to use simplified representations of flow physics and run at coarse spatial resolutions. With increasing computational power and improved datasets, the application of GRMs to finer resolutions is becoming a reality. To support development in this direction, the suitability of GRMs for application to finer resolutions needs to be assessed. This study investigates the impacts of spatial resolution and flow connectivity representation on the predictive capability of a GRM, CaMa-Flood, in simulating the 2011 extreme flood in Thailand. Analyses show that when single downstream connectivity (SDC) is assumed, simulation results deteriorate with finer spatial resolution; Nash-Sutcliffe efficiency coefficients decreased by more than 50 % between simulation results at 10 km resolution and 1 km resolution. When multiple downstream connectivity (MDC) is represented, simulation results slightly improve with finer spatial resolution. The SDC simulations result in excessive backflows on very flat floodplains due to the restrictive flow directions at finer resolutions. MDC channels attenuated these effects by maintaining flow connectivity and flow capacity between floodplains in varying spatial resolutions. While a regional-scale flood was chosen as a test case, these findings should be universal and may have significant impacts on large- to global-scale simulations, especially in regions where mega deltas exist.These results demonstrate that a GRM can be used for higher resolution simulations of large-scale floods, provided that MDC in rivers and floodplains is adequately represented in the model structure.

Global terrestrial carbon and nitrogen cycling insensitive to estimates of biological N fixation

NASA Astrophysics Data System (ADS)

Steinkamp, J.; Weber, B.; Werner, C.; Hickler, T.

2015-12-01

Dinitrogen (N2) is the most abundant molecule in the atmosphere and incorporated in other molecules an essential nutrient for life on earth. However, only few natural processes can initiate a reaction of N2. These natural processes are fire, lightning and biological nitrogen fixation (BNF) with BNF being the largest source. In the course of the last century humans have outperformed the natural processes of nitrogen fixation by the production of fertilizer. Industrial and other human emission of reactive nitrogen, as well as fire and lightning lead to a deposition of 63 Tg (N) per year. This is twice the amount of BNF estimated by the default setup of the dynamic global vegetation model LPJ-GUESS (30 Tg), which is a conservative approach. We use different methods and parameterizations for BNF in LPJ-GUESS: 1.) varying total annual amount; 2.) annual evenly distributed and daily calculated fixation rates; 3.) an improved dataset of BNF by cryptogamic covers (free-living N-fixers). With this setup BNF is ranging from 30 Tg to 60 Tg. We assess the impact of BNF on carbon storage and grand primary production (GPP) of the natural vegetation. These results are compared to and evaluated against available independent datasets. We do not see major differences in the productivity and carbon stocks with these BNF estimates, suggesting that natural vegetation is insensitive to BNF on a global scale and the vegetation can compensate for the different nitrogen availabilities. Current deposition of nitrogen compounds and internal cycling through mineralization and uptake is sufficient for natural vegetation productivity. However, due to the coarse model grid and spatial heterogeneity in the real world this conclusion does not exclude the existence of habitats constrained by BNF.

Is the global mean temperature trend too low?

NASA Astrophysics Data System (ADS)

Venema, Victor; Lindau, Ralf

2015-04-01

The global mean temperature trend may be biased due to similar technological and economic developments worldwide. In this study we want to present a number of recent results that suggest that the global mean temperature trend might be steeper as generally thought. In the Global Historical Climate Network version 3 (GHCNv3) the global land surface temperature is estimated to have increased by about 0.8°C between 1880 and 2012. In the raw temperature record, the increase is 0.6°C; the 0.2°C difference is due to homogenization adjustments. Given that homogenization can only reduce biases, this 0.2°C stems from a partial correction of bias errors and it seems likely that the real non-climatic trend bias will be larger. Especially in regions with sparser networks, homogenization will not be able to improve the trend much. Thus if the trend bias in these regions is similar to the bias for more dense networks (industrialized countries), one would expect the real bias to be larger. Stations in sparse networks are representative for a larger region and are given more weight in the computation of the global mean temperature. If all stations are given equal weight, the homogenization adjustments of the GHCNv3 dataset are about 0.4°C per century. In the subdaily HadISH dataset one break with mean size 0.12°C is found every 15 years for the period 1973-2013. That would be a trend bias of 0.78°C per century on a station by station basis. Unfortunately, these estimates strongly focus on Western countries having more stations. It is known from the literature that rich countries have a (statistically insignificant) stronger trend in the global datasets. Regional datasets can be better homogenized than global ones, the main reason being that global datasets do not contain all stations known to the weather services. Furthermore, global datasets use automatic homogenization methods and have less or no metadata. Thus while regional data can be biased themselves, comparing them with global datasets can provide some indication on biases. Compared to the global BEST dataset for the same countries, the national datasets of Austria, Italy and Switzerland have a 0.36°C per century stronger trend since 1901. For the trend since 1960 we can also take Australia, France and Slovenia into account and find a trend bias of 0.40°C per century. Relative to CRUCY the trend biases are smaller and only statistically significant for the period since 1980. The most direct way to study biases in the temperature records is by making parallel measurements with historical measurement set-ups. Several recent parallel data studies for the transition to Stevenson screens suggest larger biases: Austria 0.2°C, Spain 0.5 & 0.6°C. As well as older tropical ones: India 0.42°C and Sri Lanka 0.37°C. The smaller values from the Parker (1994) review mainly stem from parallel measurements from North-West Europe, which may have less problems with exposure. Furthermore, the influence of many historical transitions, especially the ones that could cause an artificial smaller trend, have not been studied in detail yet. We urgently need to study improvements of exposure (especially in the (sub-)tropics), increases in watering and irrigation, mechanical ventilation, better paints, relocations to airports, and relocations to suburbs of stations that started in the cities and from village centers to pasture, for example. Our current understanding surprisingly suggests that the more recent period may have the largest biases, but it could also be that even the best datasets are unable to improve earlier data sufficiently. If the temperature trend were actually larger it would reduce discrepancies between studies for a number of problems in climatology. For example, the estimates of transient climate sensitivity using instrumental data are lower as the one using climate models, volcanic eruptions or paleo data. Furthermore, several changes observed in the climate system are larger than expected. On the other hand, a large trend in the land surface temperature would make the discrepancy with the tropospheric temperature even larger (radiosondes and satellites) and it would introduce a larger difference between land and sea temperature trends. Concluding, at the moment there is no strong evidence yet that the temperature trend is underestimated. However, we do have a considerable amount of evidence that suggests that there is a moderate, but climatologically important bias that we should study with urgency. As far as we know there are no estimates for the remaining uncertainty in the global mean trend after homogenization. Also studies into the causes of cooling biases are a pressing need. (Many have contributed to this study, but it is not clear at this moment who would be official collaborators; they will be added later.)

BABAR: an R package to simplify the normalisation of common reference design microarray-based transcriptomic datasets

PubMed Central

2010-01-01

Background The development of DNA microarrays has facilitated the generation of hundreds of thousands of transcriptomic datasets. The use of a common reference microarray design allows existing transcriptomic data to be readily compared and re-analysed in the light of new data, and the combination of this design with large datasets is ideal for 'systems'-level analyses. One issue is that these datasets are typically collected over many years and may be heterogeneous in nature, containing different microarray file formats and gene array layouts, dye-swaps, and showing varying scales of log2- ratios of expression between microarrays. Excellent software exists for the normalisation and analysis of microarray data but many data have yet to be analysed as existing methods struggle with heterogeneous datasets; options include normalising microarrays on an individual or experimental group basis. Our solution was to develop the Batch Anti-Banana Algorithm in R (BABAR) algorithm and software package which uses cyclic loess to normalise across the complete dataset. We have already used BABAR to analyse the function of Salmonella genes involved in the process of infection of mammalian cells. Results The only input required by BABAR is unprocessed GenePix or BlueFuse microarray data files. BABAR provides a combination of 'within' and 'between' microarray normalisation steps and diagnostic boxplots. When applied to a real heterogeneous dataset, BABAR normalised the dataset to produce a comparable scaling between the microarrays, with the microarray data in excellent agreement with RT-PCR analysis. When applied to a real non-heterogeneous dataset and a simulated dataset, BABAR's performance in identifying differentially expressed genes showed some benefits over standard techniques. Conclusions BABAR is an easy-to-use software tool, simplifying the simultaneous normalisation of heterogeneous two-colour common reference design cDNA microarray-based transcriptomic datasets. We show BABAR transforms real and simulated datasets to allow for the correct interpretation of these data, and is the ideal tool to facilitate the identification of differentially expressed genes or network inference analysis from transcriptomic datasets. PMID:20128918

BIDDSAT: visualizing the content of biodiversity data publishers in the Global Biodiversity Information Facility network.

PubMed

Otegui, Javier; Ariño, Arturo H

2012-08-15

In any data quality workflow, data publishers must become aware of issues in their data so these can be corrected. User feedback mechanisms provide one avenue, while global assessments of datasets provide another. To date, there is no publicly available tool to allow both biodiversity data institutions sharing their data through the Global Biodiversity Information Facility network and its potential users to assess datasets as a whole. Contributing to bridge this gap both for publishers and users, we introduce BIoDiversity DataSets Assessment Tool, an online tool that enables selected diagnostic visualizations on the content of data publishers and/or their individual collections. The online application is accessible at http://www.unav.es/unzyec/mzna/biddsat/ and is supported by all major browsers. The source code is licensed under the GNU GPLv3 license (http://www.gnu.org/licenses/gpl-3.0.txt) and is available at https://github.com/jotegui/BIDDSAT.

A global × global test for testing associations between two large sets of variables.

PubMed

Chaturvedi, Nimisha; de Menezes, Renée X; Goeman, Jelle J

2017-01-01

In high-dimensional omics studies where multiple molecular profiles are obtained for each set of patients, there is often interest in identifying complex multivariate associations, for example, copy number regulated expression levels in a certain pathway or in a genomic region. To detect such associations, we present a novel approach to test for association between two sets of variables. Our approach generalizes the global test, which tests for association between a group of covariates and a single univariate response, to allow high-dimensional multivariate response. We apply the method to several simulated datasets as well as two publicly available datasets, where we compare the performance of multivariate global test (G2) with univariate global test. The method is implemented in R and will be available as a part of the globaltest package in R. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Developing Novel Machine Learning Algorithms to Improve Sedentary Assessment for Youth Health Enhancement.

PubMed

Golla, Gowtham Kumar; Carlson, Jordan A; Huan, Jun; Kerr, Jacqueline; Mitchell, Tarrah; Borner, Kelsey

2016-10-01

Sedentary behavior of youth is an important determinant of health. However, better measures are needed to improve understanding of this relationship and the mechanisms at play, as well as to evaluate health promotion interventions. Wearable accelerometers are considered as the standard for assessing physical activity in research, but do not perform well for assessing posture (i.e., sitting vs. standing), a critical component of sedentary behavior. The machine learning algorithms that we propose for assessing sedentary behavior will allow us to re-examine existing accelerometer data to better understand the association between sedentary time and health in various populations. We collected two datasets, a laboratory-controlled dataset and a free-living dataset. We trained machine learning classifiers separately on each dataset and compared performance across datasets. The classifiers predict five postures: sit, stand, sit-stand, stand-sit, and stand\\walk. We compared a manually constructed Hidden Markov model (HMM) with an automated HMM from existing software. The manually constructed HMM gave more F1-Macro score on both datasets.

A New Global Group Velocity Dataset for Constraining Crust and Upper Mantle Properties

NASA Astrophysics Data System (ADS)

Ma, Z.; Masters, G.; Laske, G.; Pasyanos, M. E.

2010-12-01

We are improving our CRUST2.0 to a new LITHO1.0 model, refining the nominal resolution to 1 degree and including lithospheric structure. The new model is constrained by many datasets, including very large datasets of surface wave group velocity built using a new, efficient measurement technique. This technique starts in a similar fashion to the traditional frequency-time analysis, but instead of making measurements for all frequencies for a single source-station pair, we apply cluster analysis to make measurements for all recordings for a single event at a single target frequency. By changing the nominal frequencies of the bandpass filter, we filter each trace until the centroid frequency of the band-passed spectrum matches the target frequency. We have processed all the LH data from IRIS (and some of the BH data from PASSCAL experiments and the POLARIS network) from 1976 to 2007. The Rayleigh wave group velocity data set is complete from 10mHz to 40mHz at increments of 2.5mHz. The data set has about 330000 measurements for 10 and 20mHz, 200000 for 30mHz and 110000 for 40mHz. We are also building a similar dataset for Love waves, though its size will be about half that of the Rayleigh wave dataset. The SMAD of the group arrival time difference between our global dataset and other more regional datasets is about 12 seconds for 20mHz, 9 seconds for 30mHz, and 7 seconds for 40mHz. Though the discrepancies are about twice as big as our measurement precision (estimated by looking at group arrival time differences between closely-spaced stations), it is still much smaller than the signal in the data (e.g., the group arrival time for 20mHz can differ from the prediction of a 1D Earth by over 250 seconds). The fact that there is no systematic bias between the datasets encourages us to combine them to improve coverage of some key areas. Group velocity maps inverted from the combined datasets show many interesting signals though the dominant signal is related to variations in crustal thickness. For 20mHz, group velocity perturbations from the global mean range from -25% to 11%, with a standard deviation of 4%. We adjust the smoothing of lateral structure in the inversion so that the error of the inferred group velocity is nearly uniform globally. For 20mHz, a 0.1% error in group velocity leads to resolution of features of dimension 9 degrees or less everywhere. The resolution in Eurasia is 5.5 degrees and in N. America is 4.5-5 degrees. For 30mHz, for the same 0.1% error, we can resolve structure of 10 degrees globally, 6.5 degrees for Eurasia and 5.5 degree for N. America.

Understanding Achievement Differences between Schools in Ireland--Can Existing Data-Sets Help?

ERIC Educational Resources Information Center

Gilleece, Lorraine

2014-01-01

Recent years have seen an increased focus on school accountability in Ireland and calls for greater use to be made of student achievement data for monitoring student outcomes. In this paper, it is argued that existing data-sets in Ireland offer limited potential for the value-added modelling approaches used for accountability purposes in many…

Comparison of CORA and EN4 in-situ datasets validation methods, toward a better quality merged dataset.

NASA Astrophysics Data System (ADS)

Szekely, Tanguy; Killick, Rachel; Gourrion, Jerome; Reverdin, Gilles

2017-04-01

CORA and EN4 are both global delayed time mode validated in-situ ocean temperature and salinity datasets distributed by the Met Office (http://www.metoffice.gov.uk/) and Copernicus (www.marine.copernicus.eu). A large part of the profiles distributed by CORA and EN4 in recent years are Argo profiles from the ARGO DAC, but profiles are also extracted from the World Ocean Database and TESAC profiles from GTSPP. In the case of CORA, data coming from the EUROGOOS Regional operationnal oserving system( ROOS) operated by European institutes no managed by National Data Centres and other datasets of profiles povided by scientific sources can also be found (Sea mammals profiles from MEOP, XBT datasets from cruises ...). (EN4 also takes data from the ASBO dataset to supplement observations in the Arctic). First advantage of this new merge product is to enhance the space and time coverage at global and european scales for the period covering 1950 till a year before the current year. This product is updated once a year and T&S gridded fields are alos generated for the period 1990-year n-1. The enhancement compared to the revious CORA product will be presented Despite the fact that the profiles distributed by both datasets are mostly the same, the quality control procedures developed by the Met Office and Copernicus teams differ, sometimes leading to different quality control flags for the same profile. Started in 2016 a new study started that aims to compare both validation procedures to move towards a Copernicus Marine Service dataset with the best features of CORA and EN4 validation.A reference data set composed of the full set of in-situ temperature and salinity measurements collected by Coriolis during 2015 is used. These measurements have been made thanks to wide range of instruments (XBTs, CTDs, Argo floats, Instrumented sea mammals,...), covering the global ocean. The reference dataset has been validated simultaneously by both teams.An exhaustive comparison of the validation test results is now performed to find the best features of both datasets. The study shows the differences between the EN4 and CORA validation results. It highlights the complementarity between the EN4 and CORA higher order tests. The design of the CORA and EN4 validation charts is discussed to understand how a different approach on the dataset scope can lead to differences in data validation. The new validation chart of the Copernicus Marine Service dataset is presented.

Access NASA Satellite Global Precipitation Data Visualization on YouTube

NASA Technical Reports Server (NTRS)

Liu, Z.; Su, J.; Acker, J.; Huffman, G.; Vollmer, B.; Wei, J.; Meyer, D.

2017-01-01

Since the satellite era began, NASA has collected a large volume of Earth science observations for research and applications around the world. The collected and archived satellite data at 12 NASA data centers can also be used for STEM education and activities such as disaster events, climate change, etc. However, accessing satellite data can be a daunting task for non-professional users such as teachers and students because of unfamiliarity of terminology, disciplines, data formats, data structures, computing resources, processing software, programming languages, etc. Over the years, many efforts including tools, training classes, and tutorials have been developed to improve satellite data access for users, but barriers still exist for non-professionals. In this presentation, we will present our latest activity that uses a very popular online video sharing Web site, YouTube (https://www.youtube.com/), for accessing visualizations of our global precipitation datasets at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). With YouTube, users can access and visualize a large volume of satellite data without the necessity to learn new software or download data. The dataset in this activity is a one-month animation for the GPM (Global Precipitation Measurement) Integrated Multi-satellite Retrievals for GPM (IMERG). IMERG provides precipitation on a near-global (60 deg. N-S) coverage at half-hourly time interval, providing more details on precipitation processes and development compared to the 3-hourly TRMM (Tropical Rainfall Measuring Mission) Multisatellite Precipitation Analysis (TMPA, 3B42) product. When the retro-processing of IMERG during the TRMM era is finished in 2018, the entire video will contain more than 330,000 files and will last 3.6 hours. Future plans include development of flyover videos for orbital data for an entire satellite mission or project. All videos, including the one-month animation, will be uploaded and available at the GES DISC site on YouTube (https://www.youtube.com/user/NASAGESDISC).

Advance strategy for climate change adaptation and mitigation in cities

NASA Astrophysics Data System (ADS)

Varquez, A. C. G.; Kanda, M.; Darmanto, N. S.; Sueishi, T.; Kawano, N.

2017-12-01

An on-going 5-yr project financially supported by the Ministry of Environment, Japan, has been carried out to specifically address the issue of prescribing appropriate adaptation and mitigation measures to climate change in cities. Entitled "Case Study on Mitigation and Local Adaptation to Climate Change in an Asian Megacity, Jakarta", the project's relevant objectives is to develop a research framework that can consider both urbanization and climate change with the main advantage of being readily implementable for all cities around the world. The test location is the benchmark city, Jakarta, Indonesia, with the end focus of evaluating the benefits of various mitigation and adaptation strategies in Jakarta and other megacities. The framework was designed to improve representation of urban areas when conducting climate change investigations in cities; and to be able to quantify separately the impacts of urbanization and climate change to all cities globally. It is comprised of a sophisticated, top-down, multi-downscaling approach utilizing a regional model (numerical weather model) and a microscale model (energy balance model and CFD model), with global circulation models (GCM) as input. The models, except the GCM, were configured to reasonably consider land cover, urban morphology, and anthropogenic heating (AH). Equally as important, methodologies that can collect and estimate global distribution of urban parametric and AH datasets are continually being developed. Urban growth models, climate scenario matrices that match representative concentration pathways with shared socio-economic pathways, present distribution of socio-demographic indicators such as population and GDP, existing GIS datasets of urban parameters, are utilized. From these tools, future urbanization (urban morphological parameters and AH) can be introduced into the models. Sensitivity using various combinations of GCM and urbanization can be conducted. Furthermore, since the models utilize parameters that can be readily modified to suit certain countermeasures, adaptation and mitigation strategies can be evaluated using thermal comfort and other social indicators. With the approaches introduced through this project, a deeper understanding of urban-climate interactions in the changing global climate can be achieved.

Recommended GIS Analysis Methods for Global Gridded Population Data

NASA Astrophysics Data System (ADS)

Frye, C. E.; Sorichetta, A.; Rose, A.

2017-12-01

When using geographic information systems (GIS) to analyze gridded, i.e., raster, population data, analysts need a detailed understanding of several factors that affect raster data processing, and thus, the accuracy of the results. Global raster data is most often provided in an unprojected state, usually in the WGS 1984 geographic coordinate system. Most GIS functions and tools evaluate data based on overlay relationships (area) or proximity (distance). Area and distance for global raster data can be either calculated directly using the various earth ellipsoids or after transforming the data to equal-area/equidistant projected coordinate systems to analyze all locations equally. However, unlike when projecting vector data, not all projected coordinate systems can support such analyses equally, and the process of transforming raster data from one coordinate space to another often results unmanaged loss of data through a process called resampling. Resampling determines which values to use in the result dataset given an imperfect locational match in the input dataset(s). Cell size or resolution, registration, resampling method, statistical type, and whether the raster represents continuous or discreet information potentially influence the quality of the result. Gridded population data represent estimates of population in each raster cell, and this presentation will provide guidelines for accurately transforming population rasters for analysis in GIS. Resampling impacts the display of high resolution global gridded population data, and we will discuss how to properly handle pyramid creation using the Aggregate tool with the sum option to create overviews for mosaic datasets.

MLACP: machine-learning-based prediction of anticancer peptides

PubMed Central

Manavalan, Balachandran; Basith, Shaherin; Shin, Tae Hwan; Choi, Sun; Kim, Myeong Ok; Lee, Gwang

2017-01-01

Cancer is the second leading cause of death globally, and use of therapeutic peptides to target and kill cancer cells has received considerable attention in recent years. Identification of anticancer peptides (ACPs) through wet-lab experimentation is expensive and often time consuming; therefore, development of an efficient computational method is essential to identify potential ACP candidates prior to in vitro experimentation. In this study, we developed support vector machine- and random forest-based machine-learning methods for the prediction of ACPs using the features calculated from the amino acid sequence, including amino acid composition, dipeptide composition, atomic composition, and physicochemical properties. We trained our methods using the Tyagi-B dataset and determined the machine parameters by 10-fold cross-validation. Furthermore, we evaluated the performance of our methods on two benchmarking datasets, with our results showing that the random forest-based method outperformed the existing methods with an average accuracy and Matthews correlation coefficient value of 88.7% and 0.78, respectively. To assist the scientific community, we also developed a publicly accessible web server at www.thegleelab.org/MLACP.html. PMID:29100375

A Solar Data Model for Use in Virtual Observatories

NASA Astrophysics Data System (ADS)

Reardon, K. P.; Bentley, R. D.; Messerotti, M.; Giordano, S.

2004-05-01

The creation of a virtual solar observatories relies heavily on the merging of the metadata describing different datasets into a common form so that it can be handled in a standard way for all associated resources. In order to bring together the varied data descriptions that already exist, it is necessary to have a common framework on which all the different datasets can be represented. The definition of this framework is done through a data model which attempts to provide a simplified but realistic description of the various entities that make up a data set or solar resource. We present the solar data model which has been developed as part of the European Grid of Solar Observations (EGSO) project. This model attempts to include many of the different elements in the field of solar physics, including data producers, data sets, event lists, and data providers. This global picture can then be used to focus on the particular elements required for a specific implementation. We present the different aspects of the model and describe some systems in which portions of this model have been implemented.

A strategy to load balancing for non-connectivity MapReduce job

NASA Astrophysics Data System (ADS)

Zhou, Huaping; Liu, Guangzong; Gui, Haixia

2017-09-01

MapReduce has been widely used in large scale and complex datasets as a kind of distributed programming model. Original Hash partitioning function in MapReduce often results the problem of data skew when data distribution is uneven. To solve the imbalance of data partitioning, we proposes a strategy to change the remaining partitioning index when data is skewed. In Map phase, we count the amount of data which will be distributed to each reducer, then Job Tracker monitor the global partitioning information and dynamically modify the original partitioning function according to the data skew model, so the Partitioner can change the index of these partitioning which will cause data skew to the other reducer that has less load in the next partitioning process, and can eventually balance the load of each node. Finally, we experimentally compare our method with existing methods on both synthetic and real datasets, the experimental results show our strategy can solve the problem of data skew with better stability and efficiency than Hash method and Sampling method for non-connectivity MapReduce task.

Evaluation of the differences between the SRTM and satellite radar altimetry height measurements and the approach taken for the ACE2 GDEM in areas of large disagreement.

PubMed

Smith, Richard Gavin; Berry, Philippa A M

2011-06-01

The new ACE2 (Altimeter Corrected Elevations 2) Global Digital Elevation Model (GDEM) which has recently been released aims to provide the most accurate GDEM to date. ACE2 was created by synergistically merging the SRTM and altimetry datasets. The comprehensive comparison carried out between the two datasets yielded a myriad of information, with the areas of disagreement providing as much valuable information as the areas of agreement. Analysis of the comparison dataset revealed that certain topographic features displayed consistent differences between the two datasets. The largest differences globally are present over the rainforests, particularly the two largest, around the Amazon and the Congo. The differences range between 10 m and 40 m; these differences can be attributed to the height of the rainforest canopy, as the SRTM returned height values from somewhere within the uppermost reaches of the vegetation whereas the altimeter was able to penetrate through and return true ground heights. The second major class of terrain feature to demonstrate coherent differences are desert regions; here, different deserts present different characteristics. The final area of interest is that of Wetlands; these are areas of special significance because even a slight misrepresentation of the heights can have wide ranging effects in modelling wetland areas. These examples illustrate the valuable additional information content gleaned from the synergistic global combination of the two datasets.

Differential privacy based on importance weighting

PubMed Central

Ji, Zhanglong

2014-01-01

This paper analyzes a novel method for publishing data while still protecting privacy. The method is based on computing weights that make an existing dataset, for which there are no confidentiality issues, analogous to the dataset that must be kept private. The existing dataset may be genuine but public already, or it may be synthetic. The weights are importance sampling weights, but to protect privacy, they are regularized and have noise added. The weights allow statistical queries to be answered approximately while provably guaranteeing differential privacy. We derive an expression for the asymptotic variance of the approximate answers. Experiments show that the new mechanism performs well even when the privacy budget is small, and when the public and private datasets are drawn from different populations. PMID:24482559

Global heating distributions for January 1979 calculated from GLA assimilated and simulated model-based datasets

NASA Technical Reports Server (NTRS)

Schaack, Todd K.; Lenzen, Allen J.; Johnson, Donald R.

1991-01-01

This study surveys the large-scale distribution of heating for January 1979 obtained from five sources of information. Through intercomparison of these distributions, with emphasis on satellite-derived information, an investigation is conducted into the global distribution of atmospheric heating and the impact of observations on the diagnostic estimates of heating derived from assimilated datasets. The results indicate a substantial impact of satellite information on diagnostic estimates of heating in regions where there is a scarcity of conventional observations. The addition of satellite data provides information on the atmosphere's temperature and wind structure that is important for estimation of the global distribution of heating and energy exchange.

Cloud Compute for Global Climate Station Summaries

NASA Astrophysics Data System (ADS)

Baldwin, R.; May, B.; Cogbill, P.

2017-12-01

Global Climate Station Summaries are simple indicators of observational normals which include climatic data summarizations and frequency distributions. These typically are statistical analyses of station data over 5-, 10-, 20-, 30-year or longer time periods. The summaries are computed from the global surface hourly dataset. This dataset totaling over 500 gigabytes is comprised of 40 different types of weather observations with 20,000 stations worldwide. NCEI and the U.S. Navy developed these value added products in the form of hourly summaries from many of these observations. Enabling this compute functionality in the cloud is the focus of the project. An overview of approach and challenges associated with application transition to the cloud will be presented.

Global lake response to the recent warming hiatus

NASA Astrophysics Data System (ADS)

Winslow, Luke A.; Leach, Taylor H.; Rose, Kevin C.

2018-05-01

Understanding temporal variability in lake warming rates over decadal scales is important for understanding observed change in aquatic systems. We analyzed a global dataset of lake surface water temperature observations (1985‑2009) to examine how lake temperatures responded to a recent global air temperature warming hiatus (1998‑2012). Prior to the hiatus (1985‑1998), surface water temperatures significantly increased at an average rate of 0.532 °C decade‑1 (±0.214). In contrast, water temperatures did not change significantly during the hiatus (average rate ‑0.087 °C decade‑1 ±0.223). Overall, 83% of lakes in our dataset (129 of 155) had faster warming rates during the pre-hiatus period than during the hiatus period. These results demonstrate that lakes have exhibited decadal-scale variability in warming rates coherent with global air temperatures and represent an independent line of evidence for the recent warming hiatus. Our analyses provide evidence that lakes are sentinels of broader climatological processes and indicate that warming rates based on datasets where a large proportion of observations were collected during the hiatus period may underestimate longer-term trends.

History and Future for the Happy Marriage between the MODIS Land team and Fluxnet

NASA Astrophysics Data System (ADS)

Running, S. W.

2015-12-01

When I wrote the proposal to NASA in 1988 for daily global evapotranspiration and gross primary production algorithms for the MODIS sensor, I had no validation plan. Fluxnet probably saved my MODIS career by developing a global network of rigorously calibrated towers measuring water and carbon fluxes over a wide variety of ecosystems that I could not even envision at the time that first proposal was written. However my enthusiasm for Fluxnet was not reciprocated by the Fluxnet community until we began providing 7 x 7 pixel MODIS Land datasets exactly over each of their towers every 8 days, without them having to crawl thru the global datasets and make individual orders. This system, known informally as the MODIS ASCII cutouts, began in 2002 and operates at the Oak Ridge DAAC to this day, cementing a mutually beneficial data interchange between the Fluxnet and remote sensing communities. This talk will briefly discuss the history of MODIS validation with flux towers, and flux spatial scaling with MODIS data. More importantly I will detail the future continuity of global biophysical datasets in the post-MODIS era, and what next generation sensors will provide.

CometQuest: A Rosetta Adventure

NASA Technical Reports Server (NTRS)

Leon, Nancy J.; Fisher, Diane K.; Novati, Alexander; Chmielewski, Artur B.; Fitzpatrick, Austin J.; Angrum, Andrea

2012-01-01

This software is a higher-performance implementation of tiled WMS, with integral support for KML and time-varying data. This software is compliant with the Open Geospatial WMS standard, and supports KML natively as a WMS return type, including support for the time attribute. Regionated KML wrappers are generated that match the existing tiled WMS dataset. Ping and JPG formats are supported, and the software is implemented as an Apache 2.0 module that supports a threading execution model that is capable of supporting very high request rates. The module intercepts and responds to WMS requests that match certain patterns and returns the existing tiles. If a KML format that matches an existing pyramid and tile dataset is requested, regionated KML is generated and returned to the requesting application. In addition, KML requests that do not match the existing tile datasets generate a KML response that includes the corresponding JPG WMS request, effectively adding KML support to a backing WMS server.

Tiled WMS/KML Server V2

NASA Technical Reports Server (NTRS)

Plesea, Lucian

2012-01-01

This software is a higher-performance implementation of tiled WMS, with integral support for KML and time-varying data. This software is compliant with the Open Geospatial WMS standard, and supports KML natively as a WMS return type, including support for the time attribute. Regionated KML wrappers are generated that match the existing tiled WMS dataset. Ping and JPG formats are supported, and the software is implemented as an Apache 2.0 module that supports a threading execution model that is capable of supporting very high request rates. The module intercepts and responds to WMS requests that match certain patterns and returns the existing tiles. If a KML format that matches an existing pyramid and tile dataset is requested, regionated KML is generated and returned to the requesting application. In addition, KML requests that do not match the existing tile datasets generate a KML response that includes the corresponding JPG WMS request, effectively adding KML support to a backing WMS server.

Genome-Wide Methylation Analyses in Glioblastoma Multiforme

PubMed Central

Lai, Rose K.; Chen, Yanwen; Guan, Xiaowei; Nousome, Darryl; Sharma, Charu; Canoll, Peter; Bruce, Jeffrey; Sloan, Andrew E.; Cortes, Etty; Vonsattel, Jean-Paul; Su, Tao; Delgado-Cruzata, Lissette; Gurvich, Irina; Santella, Regina M.; Ostrom, Quinn; Lee, Annette; Gregersen, Peter; Barnholtz-Sloan, Jill

2014-01-01

Few studies had investigated genome-wide methylation in glioblastoma multiforme (GBM). Our goals were to study differential methylation across the genome in gene promoters using an array-based method, as well as repetitive elements using surrogate global methylation markers. The discovery sample set for this study consisted of 54 GBM from Columbia University and Case Western Reserve University, and 24 brain controls from the New York Brain Bank. We assembled a validation dataset using methylation data of 162 TCGA GBM and 140 brain controls from dbGAP. HumanMethylation27 Analysis Bead-Chips (Illumina) were used to interrogate 26,486 informative CpG sites in both the discovery and validation datasets. Global methylation levels were assessed by analysis of L1 retrotransposon (LINE1), 5 methyl-deoxycytidine (5m-dC) and 5 hydroxylmethyl-deoxycytidine (5hm-dC) in the discovery dataset. We validated a total of 1548 CpG sites (1307 genes) that were differentially methylated in GBM compared to controls. There were more than twice as many hypomethylated genes as hypermethylated ones. Both the discovery and validation datasets found 5 tumor methylation classes. Pathway analyses showed that the top ten pathways in hypomethylated genes were all related to functions of innate and acquired immunities. Among hypermethylated pathways, transcriptional regulatory network in embryonic stem cells was the most significant. In the study of global methylation markers, 5m-dC level was the best discriminant among methylation classes, whereas in survival analyses, high level of LINE1 methylation was an independent, favorable prognostic factor in the discovery dataset. Based on a pathway approach, hypermethylation in genes that control stem cell differentiation were significant, poor prognostic factors of overall survival in both the discovery and validation datasets. Approaches that targeted these methylated genes may be a future therapeutic goal. PMID:24586730

Extraction and Analysis of Regional Emission and Absorption Events of Greenhouse Gases with GOSAT and OCO-2

NASA Astrophysics Data System (ADS)

Kasai, K.; Shiomi, K.; Konno, A.; Tadono, T.; Hori, M.

2016-12-01

Global observation of greenhouse gases such as carbon dioxide (CO2) and methane (CH4) with high spatio-temporal resolution and accurate estimation of sources and sinks are important to understand greenhouse gases dynamics. Greenhouse Gases Observing Satellite (GOSAT) has observed column-averaged dry-air mole fractions of CO2 (XCO2) and CH4 (XCH4) over 7 years since January 2009 with wide swath but sparse pointing. Orbiting Carbon Observatory-2 (OCO-2) has observed XCO2 jointly on orbit since July 2014 with narrow swath but high resolution. We use two retrieved datasets as GOSAT observation data. One is ACOS GOSAT/TANSO-FTS Level 2 Full Product by NASA/JPL, and the other is NIES TANSO-FTS L2 column amount (SWIR). By using these GOSAT datasets and OCO-2 L2 Full Product, the biases among datasets, local sources and sinks, and temporal variability of greenhouse gases are clarified. In addition, CarbonTracker, which is a global model of atmospheric CO2 and CH4 developed by NOAA/ESRL, are also analyzed for comparing between satellite observation data and atmospheric model data. Before analyzing these datasets, outliers are screened by using quality flag, outcome flag, and warn level in land or sea parts. Time series data of XCO2 and XCH4 are obtained globally from satellite observation and atmospheric model datasets, and functions which express typical inter-annual and seasonal variation are fitted to each spatial grid. Consequently, anomalous events of XCO2 and XCH4 are extracted by the difference between each time series dataset and the fitted function. Regional emission and absorption events are analyzed by time series variation of satellite observation data and by comparing with atmospheric model data.

Development of an Open Global Oil and Gas Infrastructure Inventory and Geodatabase

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rose, Kelly

This submission contains a technical report describing the development process and visual graphics for the Global Oil and Gas Infrastructure database. Access the GOGI database using the following link: https://edx.netl.doe.gov/dataset/global-oil-gas-features-database

Internal Consistency of the NVAP Water Vapor Dataset

NASA Technical Reports Server (NTRS)

Suggs, Ronnie J.; Jedlovec, Gary J.; Arnold, James E. (Technical Monitor)

2001-01-01

The NVAP (NASA Water Vapor Project) dataset is a global dataset at 1 x 1 degree spatial resolution consisting of daily, pentad, and monthly atmospheric precipitable water (PW) products. The analysis blends measurements from the Television and Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS), the Special Sensor Microwave/Imager (SSM/I), and radiosonde observations into a daily collage of PW. The original dataset consisted of five years of data from 1988 to 1992. Recent updates have added three additional years (1993-1995) and incorporated procedural and algorithm changes from the original methodology. Since each of the PW sources (TOVS, SSM/I, and radiosonde) do not provide global coverage, each of these sources compliment one another by providing spatial coverage over regions and during times where the other is not available. For this type of spatial and temporal blending to be successful, each of the source components should have similar or compatible accuracies. If this is not the case, regional and time varying biases may be manifested in the NVAP dataset. This study examines the consistency of the NVAP source data by comparing daily collocated TOVS and SSM/I PW retrievals with collocated radiosonde PW observations. The daily PW intercomparisons are performed over the time period of the dataset and for various regions.

In Situ Global Sea Surface Salinity and Variability from the NCEI Global Thermosalinograph Database

NASA Astrophysics Data System (ADS)

Wang, Z.; Boyer, T.; Zhang, H. M.

2017-12-01

Sea surface salinity (SSS) plays an important role in the global ocean circulations. The variations of sea surface salinity are key indicators of changes in air-sea water fluxes. Using nearly 30 years of in situ measurements of sea surface salinity from thermosalinographs, we will evaluate the variations of the sea surface salinity in the global ocean. The sea surface salinity data used are from our newly-developed NCEI Global Thermosalinograph Database - NCEI-TSG. This database provides a comprehensive set of quality-controlled in-situ sea-surface salinity and temperature measurements collected from over 340 vessels during the period 1989 to the present. The NCEI-TSG is the world's most complete TSG dataset, containing all data from the different TSG data assembly centers, e.g. COAPS (SAMOS), IODE (GOSUD) and AOML, with more historical data from NCEI's archive to be added. Using this unique dataset, we will investigate the spatial variations of the global SSS and its variability. Annual and interannual variability will also be studied at selected regions.

Global seafloor geomorphic features map: applications for ocean conservation and management

NASA Astrophysics Data System (ADS)

Harris, P. T.; Macmillan-Lawler, M.; Rupp, J.; Baker, E.

2013-12-01

Seafloor geomorphology, mapped and measured by marine scientists, has proven to be a very useful physical attribute for ocean management because different geomorphic features (eg. submarine canyons, seamounts, spreading ridges, escarpments, plateaus, trenches etc.) are commonly associated with particular suites of habitats and biological communities. Although we now have better bathymetric datasets than ever before, there has been little effort to integrate these data to create an updated map of seabed geomorphic features or habitats. Currently the best available global seafloor geomorphic features map is over 30 years old. A new global seafloor geomorphic features map (GSGM) has been created based on the analysis and interpretation of the SRTM (Shuttle Radar Topography Mission) 30 arc-second (~1 km) global bathymetry grid. The new map includes global spatial data layers for 29 categories of geomorphic features, defined by the International Hydrographic Organisation. The new geomorphic features map will allow: 1) Characterization of bioregions in terms of their geomorphic content (eg. GOODS bioregions, Large Marine Ecosystems (LMEs), ecologically or biologically significant areas (EBSA)); 2) Prediction of the potential spatial distribution of vulnerable marine ecosystems (VME) and marine genetic resources (MGR; eg. associated with hydrothermal vent communities, shelf-incising submarine canyons and seamounts rising to a specified depth); and 3) Characterization of national marine jurisdictions in terms of their inventory of geomorphic features and their global representativeness of features. To demonstrate the utility of the GSGM, we have conducted an analysis of the geomorphic feature content of the current global inventory of marine protected areas (MPAs) to assess the extent to which features are currently represented. The analysis shows that many features have very low representation, for example fans and rises have less than 1 per cent of their total area inside existing protected areas. The ';best' represented features, trenches and troughs, have only 8.7 and 5.9 per cent respectively of their total area inside existing protected areas. Seamounts have only 2.8% of their area within existing MPAs. Diagram showing the hierarchy of geomorphic features mapped in the present study. Base layer features are the shelf, slope, abyss and hadal zones. The occurrence of some features is confined to one of the base layers, whereas the occurrence of other features is confined to two or more base layers, as illustrated by shading. Basins and sills are the only features that occur over all four base layers.

A method and software framework for enriching private biomedical sources with data from public online repositories.

PubMed

Anguita, Alberto; García-Remesal, Miguel; Graf, Norbert; Maojo, Victor

2016-04-01

Modern biomedical research relies on the semantic integration of heterogeneous data sources to find data correlations. Researchers access multiple datasets of disparate origin, and identify elements-e.g. genes, compounds, pathways-that lead to interesting correlations. Normally, they must refer to additional public databases in order to enrich the information about the identified entities-e.g. scientific literature, published clinical trial results, etc. While semantic integration techniques have traditionally focused on providing homogeneous access to private datasets-thus helping automate the first part of the research, and there exist different solutions for browsing public data, there is still a need for tools that facilitate merging public repositories with private datasets. This paper presents a framework that automatically locates public data of interest to the researcher and semantically integrates it with existing private datasets. The framework has been designed as an extension of traditional data integration systems, and has been validated with an existing data integration platform from a European research project by integrating a private biological dataset with data from the National Center for Biotechnology Information (NCBI). Copyright © 2016 Elsevier Inc. All rights reserved.

Pyrogenic Carbon in soils: a literature-based inventory and a global estimation of its content in soil organic carbon and stocks

NASA Astrophysics Data System (ADS)

Reisser, Moritz; Purves, Ross; Schmidt, Michael W. I.; Abiven, Samuel

2016-08-01

Pyrogenic carbon (PyC) is considered one of the most stable components in soil and can represent more than 30% of total soil organic carbon (SOC). However, few estimates of global PyC stock or distribution exist and thus PyC is not included in any global carbon cycle models, despite its potential major relevance for the soil pool. To obtain a global picture, we reviewed the literature for published PyC content in SOC data. We generated the first PyC database including more than 560 measurements from 55 studies. Despite limitations due to heterogeneous distribution of the studied locations and gaps in the database, we were able to produce a worldwide PyC inventory. We found that global PyC represent on average 13.7% of the SOC and can be even up to 60%, making it one of the largest groups of identifiable compounds in soil, together with polysaccharides. We observed a consistent range of PyC content in SOC, despite the diverse methods of quantification. We tested the PyC content against different environmental explanatory variables: fire and land use (fire characteristics, land use, net primary productivity), climate (temperature, precipitation, climatic zones, altitude) and pedogenic properties (clay content, pH, SOC content). Surprisingly, soil properties explain PyC content the most. Soils with clay content higher than 50% contain significantly more PyC (> 30% of the SOC) than with clay content lower than 5% (< 6% of the SOC). Alkaline soils contain at least 50% more PyC than acidic soils. Furthermore, climatic conditions, represented by climatic zone or mean temperature or precipitation, correlate significantly with the PyC content. By contrast, fire characteristics could only explain PyC content, if site-specific information was available. Datasets derived from remote sensing did not explain the PyC content. To show the potential of this database, we used it in combination with other global datasets to create a global worldwide PyC content and a stock estimation, which resulted in around 200Pg PyC for the uppermost 2 meters. These modelled estimates indicated a clear mismatch between the location of the current PyC studies and the geographical zones where we expect high PyC stocks.

Dataset of Phenology of Mediterranean high-mountain meadows flora (Sierra Nevada, Spain).

PubMed

Pérez-Luque, Antonio Jesús; Sánchez-Rojas, Cristina Patricia; Zamora, Regino; Pérez-Pérez, Ramón; Bonet, Francisco Javier

2015-01-01

Sierra Nevada mountain range (southern Spain) hosts a high number of endemic plant species, being one of the most important biodiversity hotspots in the Mediterranean basin. The high-mountain meadow ecosystems (borreguiles) harbour a large number of endemic and threatened plant species. In this data paper, we describe a dataset of the flora inhabiting this threatened ecosystem in this Mediterranean mountain. The dataset includes occurrence data for flora collected in those ecosystems in two periods: 1988-1990 and 2009-2013. A total of 11002 records of occurrences belonging to 19 orders, 28 families 52 genera were collected. 73 taxa were recorded with 29 threatened taxa. We also included data of cover-abundance and phenology attributes for the records. The dataset is included in the Sierra Nevada Global-Change Observatory (OBSNEV), a long-term research project designed to compile socio-ecological information on the major ecosystem types in order to identify the impacts of global change in this area.

Dataset of Phenology of Mediterranean high-mountain meadows flora (Sierra Nevada, Spain)

PubMed Central

Pérez-Luque, Antonio Jesús; Sánchez-Rojas, Cristina Patricia; Zamora, Regino; Pérez-Pérez, Ramón; Bonet, Francisco Javier

2015-01-01

Abstract Sierra Nevada mountain range (southern Spain) hosts a high number of endemic plant species, being one of the most important biodiversity hotspots in the Mediterranean basin. The high-mountain meadow ecosystems (borreguiles) harbour a large number of endemic and threatened plant species. In this data paper, we describe a dataset of the flora inhabiting this threatened ecosystem in this Mediterranean mountain. The dataset includes occurrence data for flora collected in those ecosystems in two periods: 1988–1990 and 2009–2013. A total of 11002 records of occurrences belonging to 19 orders, 28 families 52 genera were collected. 73 taxa were recorded with 29 threatened taxa. We also included data of cover-abundance and phenology attributes for the records. The dataset is included in the Sierra Nevada Global-Change Observatory (OBSNEV), a long-term research project designed to compile socio-ecological information on the major ecosystem types in order to identify the impacts of global change in this area. PMID:25878552

Comparing soil moisture anomalies from multiple independent sources over different regions across the globe

NASA Astrophysics Data System (ADS)

Cammalleri, Carmelo; Vogt, Jürgen V.; Bisselink, Bernard; de Roo, Ad

2017-12-01

Agricultural drought events can affect large regions across the world, implying the need for a suitable global tool for an accurate monitoring of this phenomenon. Soil moisture anomalies are considered a good metric to capture the occurrence of agricultural drought events, and they have become an important component of several operational drought monitoring systems. In the framework of the JRC Global Drought Observatory (GDO, http://edo.jrc.ec.europa.eu/gdo/), the suitability of three datasets as possible representations of root zone soil moisture anomalies has been evaluated: (1) the soil moisture from the Lisflood distributed hydrological model (namely LIS), (2) the remotely sensed Land Surface Temperature data from the MODIS satellite (namely LST), and (3) the ESA Climate Change Initiative combined passive/active microwave skin soil moisture dataset (namely CCI). Due to the independency of these three datasets, the triple collocation (TC) technique has been applied, aiming at quantifying the likely error associated with each dataset in comparison to the unknown true status of the system. TC analysis was performed on five macro-regions (namely North America, Europe, India, southern Africa and Australia) detected as suitable for the experiment, providing insight into the mutual relationship between these datasets as well as an assessment of the accuracy of each method. Even if no definitive statement on the spatial distribution of errors can be provided, a clear outcome of the TC analysis is the good performance of the remote sensing datasets, especially CCI, over dry regions such as Australia and southern Africa, whereas the outputs of LIS seem to be more reliable over areas that are well monitored through meteorological ground station networks, such as North America and Europe. In a global drought monitoring system, the results of the error analysis are used to design a weighted-average ensemble system that exploits the advantages of each dataset.

Discovering New Global Climate Patterns: Curating a 21-Year High Temporal (Hourly) and Spatial (40km) Resolution Reanalysis Dataset

NASA Astrophysics Data System (ADS)

Hou, C. Y.; Dattore, R.; Peng, G. S.

2014-12-01

The National Center for Atmospheric Research's Global Climate Four-Dimensional Data Assimilation (CFDDA) Hourly 40km Reanalysis dataset is a dynamically downscaled dataset with high temporal and spatial resolution. The dataset contains three-dimensional hourly analyses in netCDF format for the global atmospheric state from 1985 to 2005 on a 40km horizontal grid (0.4°grid increment) with 28 vertical levels, providing good representation of local forcing and diurnal variation of processes in the planetary boundary layer. This project aimed to make the dataset publicly available, accessible, and usable in order to provide a unique resource to allow and promote studies of new climate characteristics. When the curation project started, it had been five years since the data files were generated. Also, although the Principal Investigator (PI) had generated a user document at the end of the project in 2009, the document had not been maintained. Furthermore, the PI had moved to a new institution, and the remaining team members were reassigned to other projects. These factors made data curation in the areas of verifying data quality, harvest metadata descriptions, documenting provenance information especially challenging. As a result, the project's curation process found that: Data curator's skill and knowledge helped make decisions, such as file format and structure and workflow documentation, that had significant, positive impact on the ease of the dataset's management and long term preservation. Use of data curation tools, such as the Data Curation Profiles Toolkit's guidelines, revealed important information for promoting the data's usability and enhancing preservation planning. Involving data curators during each stage of the data curation life cycle instead of at the end could improve the curation process' efficiency. Overall, the project showed that proper resources invested in the curation process would give datasets the best chance to fulfill their potential to help with new climate pattern discovery.

Mapping the Urban Side of the Earth- the new GUF+ Layer

NASA Astrophysics Data System (ADS)

Gorelick, N.; Marconcini, M.; Üreyen, S.; Zeidler, J.; Svaton, V.; Esch, T.

2017-12-01

From the beginning of the years 2000, it is estimated that more than half of the global population is living in cities and the dynamic trend of urbanization is growing at an unprecedented speed. In such framework, how does expanding population affect the surrounding landscape? Are urban areas making good use of limited space or is rapid urbanization threatening the planet's sustainability? What is the impact of urbanization on vulnerability to natural disasters? To try answering these and other challenging questions, a key information is to reliably know the location and characteristics (e.g. shape, extent, greenness) of human settlements worldwide. In this context, yet from the last decade different global maps outlining urban areas have started being produced. Here, DLR's Global Urban Footprint (GUF) layer, generated on the basis of very high resolution radar imagery, represents one of the most accurate and largely employed datasets. However, in order to overcome still existing limitations of the GUF layer, often originating from specifics of the underlying radar imagery, DLR developed a novel methodology that for the first time exploits mass multitemporal collections of optical and radar satellite imagery. The new approach has been employed for generating the GUF+ 2015 layer, a global map of settlement areas derived at 10m spatial resolution based overall on a joint analysis of hundreds of thousands of Landsat and Sentinel-1 scenes (processed with the support of Google Earth Engine) collected in the years 2014-2015. The GUF+2015 outperforms all other existing global human settlements maps and allows - among others - to considerably improve the detection of very small settlements in rural regions and better outline scattered peri-urban areas. Nevertheless, this is not an arrival but rather a starting point for generating a suite of additional products (GUF+ suite) supposed to support a 360° analysis of global urbanization - e.g. with data on the imperviousness/greenness and the spatiotemporal development of the built-up area over the last decades.

The Increasing Availability of Official Datasets: Methods, Limitations and Opportunities for Studies of Education

ERIC Educational Resources Information Center

Gorard, Stephen

2012-01-01

The re-use of existing and official data has a very long and largely honourable history in education and social science. The principal change in the 60 years since the first issue of the "British Journal of Educational Studies" has been the increasing range, availability and quality of existing numeric datasets. New and valuable fields…

The Generation of a Stochastic Flood Event Catalogue for Continental USA

NASA Astrophysics Data System (ADS)

Quinn, N.; Wing, O.; Smith, A.; Sampson, C. C.; Neal, J. C.; Bates, P. D.

2017-12-01

Recent advances in the acquisition of spatiotemporal environmental data and improvements in computational capabilities has enabled the generation of large scale, even global, flood hazard layers which serve as a critical decision-making tool for a range of end users. However, these datasets are designed to indicate only the probability and depth of inundation at a given location and are unable to describe the likelihood of concurrent flooding across multiple sites.Recent research has highlighted that although the estimation of large, widespread flood events is of great value to flood mitigation and insurance industries, to date it has been difficult to deal with this spatial dependence structure in flood risk over relatively large scales. Many existing approaches have been restricted to empirical estimates of risk based on historic events, limiting their capability of assessing risk over the full range of plausible scenarios. Therefore, this research utilises a recently developed model-based approach to describe the multisite joint distribution of extreme river flows across continental USA river gauges. Given an extreme event at a site, the model characterises the likelihood neighbouring sites are also impacted. This information is used to simulate an ensemble of plausible synthetic extreme event footprints from which flood depths are extracted from an existing global flood hazard catalogue. Expected economic losses are then estimated by overlaying flood depths with national datasets defining asset locations, characteristics and depth damage functions. The ability of this approach to quantify probabilistic economic risk and rare threshold exceeding events is expected to be of value to those interested in the flood mitigation and insurance sectors.This work describes the methodological steps taken to create the flood loss catalogue over a national scale; highlights the uncertainty in the expected annual economic vulnerability within the USA from extreme river flows; and presents future developments to the modelling approach.

Attribution of trends in global vegetation greenness from 1982 to 2011

NASA Astrophysics Data System (ADS)

Zhu, Z.; Xu, L.; Bi, J.; Myneni, R.; Knyazikhin, Y.

2012-12-01

Time series of remotely sensed vegetation indices data provide evidence of changes in terrestrial vegetation activity over the past decades in the world. However, it is difficult to attribute cause-and-effect to vegetation trends because variations in vegetation productivity are driven by various factors. This study investigated changes in global vegetation productivity first, and then attributed the global natural vegetation with greening trend. Growing season integrated normalized difference vegetation index (GSI NDVI) derived from the new GIMMS NDVI3g dataset (1982-2011was analyzed. A combined time series analysis model, which was developed from simper linear trend model (SLT), autoregressive integrated moving average model (ARIMA) and Vogelsang's t-PST model shows that productivity of all vegetation types except deciduous broadleaf forest predominantly showed increasing trends through the 30-year period. The evolution of changes in productivity in the last decade was also investigated. Area of greening vegetation monotonically increased through the last decade, and both the browning and no change area monotonically decreased. To attribute the predominant increase trend of productivity of global natural vegetation, trends of eight climate time series datasets (three temperature, three precipitation and two radiation datasets) were analyzed. The attribution of trends in global vegetation greenness was summarized as relaxation of climatic constraints, fertilization and other unknown reasons. Result shows that nearly all the productivity increase of global natural vegetation was driven by relaxation of climatic constraints and fertilization, which play equally important role in driving global vegetation greenness.; Area fraction and productivity change fraction of IGBP vegetation land cover classes showing statistically significant (10% level) trend in GSI NDVIt;

Global phylogeography and genetic diversity of the zoonotic tapeworm Echinococcus granulosus sensu stricto genotype G1.

PubMed

Kinkar, Liina; Laurimäe, Teivi; Acosta-Jamett, Gerardo; Andresiuk, Vanessa; Balkaya, Ibrahim; Casulli, Adriano; Gasser, Robin B; van der Giessen, Joke; González, Luis Miguel; Haag, Karen L; Zait, Houria; Irshadullah, Malik; Jabbar, Abdul; Jenkins, David J; Kia, Eshrat Beigom; Manfredi, Maria Teresa; Mirhendi, Hossein; M'rad, Selim; Rostami-Nejad, Mohammad; Oudni-M'rad, Myriam; Pierangeli, Nora Beatriz; Ponce-Gordo, Francisco; Rehbein, Steffen; Sharbatkhori, Mitra; Simsek, Sami; Soriano, Silvia Viviana; Sprong, Hein; Šnábel, Viliam; Umhang, Gérald; Varcasia, Antonio; Saarma, Urmas

2018-05-19

Echinococcus granulosus sensu stricto (s.s.) is the major cause of human cystic echinococcosis worldwide and is listed among the most severe parasitic diseases of humans. To date, numerous studies have investigated the genetic diversity and population structure of E. granulosus s.s. in various geographic regions. However, there has been no global study. Recently, using mitochondrial DNA, it was shown that E. granulosus s.s. G1 and G3 are distinct genotypes, but a larger dataset is required to confirm the distinction of these genotypes. The objectives of this study were to: (i) investigate the distinction of genotypes G1 and G3 using a large global dataset; and (ii) analyse the genetic diversity and phylogeography of genotype G1 on a global scale using near-complete mitogenome sequences. For this study, 222 globally distributed E. granulosus s.s. samples were used, of which 212 belonged to genotype G1 and 10 to G3. Using a total sequence length of 11,682 bp, we inferred phylogenetic networks for three datasets: E. granulosus s.s. (n = 222), G1 (n = 212) and human G1 samples (n = 41). In addition, the Bayesian phylogenetic and phylogeographic analyses were performed. The latter yielded several strongly supported diffusion routes of genotype G1 originating from Turkey, Tunisia and Argentina. We conclude that: (i) using a considerably larger dataset than employed previously, E. granulosus s.s. G1 and G3 are indeed distinct mitochondrial genotypes; (ii) the genetic diversity of E. granulosus s.s. G1 is high globally, with lower values in South America; and (iii) the complex phylogeographic patterns emerging from the phylogenetic and geographic analyses suggest that the current distribution of genotype G1 has been shaped by intensive animal trade. Copyright © 2018 Australian Society for Parasitology. Published by Elsevier Ltd. All rights reserved.

The uncertainties and causes of the recent changes in global evapotranspiration from 1982 to 2010

NASA Astrophysics Data System (ADS)

Dong, Bo; Dai, Aiguo

2017-07-01

Recent studies have shown considerable changes in terrestrial evapotranspiration (ET) since the early 1980s, but the causes of these changes remain unclear. In this study, the relative contributions of external climate forcing and internal climate variability to the recent ET changes are examined. Three datasets of global terrestrial ET and the CMIP5 multi-model ensemble mean ET are analyzed, respectively, to quantify the apparent and externally-forced ET changes, while the unforced ET variations are estimated as the apparent ET minus the forced component. Large discrepancies of the ET estimates, in terms of their trend, variability, and temperature- and precipitation-dependence, are found among the three datasets. Results show that the forced global-mean ET exhibits an upward trend of 0.08 mm day-1 century-1 from 1982 to 2010. The forced ET also contains considerable multi-year to decadal variations during the latter half of the 20th century that are caused by volcanic aerosols. The spatial patterns and interannual variations of the forced ET are more closely linked to precipitation than temperature. After removing the forced component, the global-mean ET shows a trend ranging from -0.07 to 0.06 mm day-1 century-1 during 1982-2010 with varying spatial patterns among the three datasets. Furthermore, linkages between the unforced ET and internal climate modes are examined. Variations in Pacific sea surface temperatures (SSTs) are found to be consistently correlated with ET over many land areas among the ET datasets. The results suggest that there are large uncertainties in our current estimates of global terrestrial ET for the recent decades, and the greenhouse gas (GHG) and aerosol external forcings account for a large part of the apparent trend in global-mean terrestrial ET since 1982, but Pacific SST and other internal climate variability dominate recent ET variations and changes over most regions.

Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data

USGS Publications Warehouse

Yuan, W.; Liu, S.; Yu, G.; Bonnefond, J.-M.; Chen, J.; Davis, K.; Desai, A.R.; Goldstein, Allen H.; Gianelle, D.; Rossi, F.; Suyker, A.E.; Verma, S.B.

2010-01-01

The simulation of gross primary production (GPP) at various spatial and temporal scales remains a major challenge for quantifying the global carbon cycle. We developed a light use efficiency model, called EC-LUE, driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux. The EC-LUE model may have the most potential to adequately address the spatial and temporal dynamics of GPP because its parameters (i.e., the potential light use efficiency and optimal plant growth temperature) are invariant across the various land cover types. However, the application of the previous EC-LUE model was hampered by poor prediction of Bowen ratio at the large spatial scale. In this study, we substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET. Fifty-four eddy covariance towers, including various ecosystem types, were selected to calibrate and validate the revised RS-PM and EC-LUE models. The revised RS-PM model explained 82% and 68% of the observed variations of ET for all the calibration and validation sites, respectively. Using estimated ET as input, the EC-LUE model performed well in calibration and validation sites, explaining 75% and 61% of the observed GPP variation for calibration and validation sites respectively.Global patterns of ET and GPP at a spatial resolution of 0.5° latitude by 0.6° longitude during the years 2000–2003 were determined using the global MERRA dataset (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate Resolution Imaging Spectroradiometer). The global estimates of ET and GPP agreed well with the other global models from the literature, with the highest ET and GPP over tropical forests and the lowest values in dry and high latitude areas. However, comparisons with observed GPP at eddy flux towers showed significant underestimation of ET and GPP due to lower net radiation of MERRA dataset. Applying a procedure to correct the systematic errors of global meteorological data would improve global estimates of GPP and ET. The revised RS-PM and EC-LUE models will provide the alternative approaches making it possible to map ET and GPP over large areas because (1) the model parameters are invariant across various land cover types and (2) all driving forces of the models may be derived from remote sensing data or existing climate observation networks.

Outlier identification and visualization for Pb concentrations in urban soils and its implications for identification of potential contaminated land.

PubMed

Zhang, Chaosheng; Tang, Ya; Luo, Lin; Xu, Weilin

2009-11-01

Outliers in urban soil geochemical databases may imply potential contaminated land. Different methodologies which can be easily implemented for the identification of global and spatial outliers were applied for Pb concentrations in urban soils of Galway City in Ireland. Due to its strongly skewed probability feature, a Box-Cox transformation was performed prior to further analyses. The graphic methods of histogram and box-and-whisker plot were effective in identification of global outliers at the original scale of the dataset. Spatial outliers could be identified by a local indicator of spatial association of local Moran's I, cross-validation of kriging, and a geographically weighted regression. The spatial locations of outliers were visualised using a geographical information system. Different methods showed generally consistent results, but differences existed. It is suggested that outliers identified by statistical methods should be confirmed and justified using scientific knowledge before they are properly dealt with.

The Effects of Temperature on Political Violence: Global Evidence at the Subnational Level

PubMed Central

Bollfrass, Alexander; Shaver, Andrew

2015-01-01

A number of studies have demonstrated an empirical relationship between higher ambient temperatures and substate violence, which have been extrapolated to make predictions about the security implications of climate change. This literature rests on the untested assumption that the mechanism behind the temperature-conflict link is that disruption of agricultural production provokes local violence. Using a subnational-level dataset, this paper demonstrates that the relationship: (1) obtains globally, (2) exists at the substate level — provinces that experience positive temperature deviations see increased conflict; and (3) occurs even in regions without significant agricultural production. Diminished local farm output resulting from elevated temperatures is unlikely to account for the entire increase in substate violence. The findings encourage future research to identify additional mechanisms, including the possibility that a substantial portion of the variation is brought about by the well-documented direct effects of temperature on individuals' propensity for violence or through macroeconomic mechanisms such as food price shocks. PMID:25992616

Satellite orbit and data sampling requirements

NASA Technical Reports Server (NTRS)

Rossow, William

1993-01-01

Climate forcings and feedbacks vary over a wide range of time and space scales. The operation of non-linear feedbacks can couple variations at widely separated time and space scales and cause climatological phenomena to be intermittent. Consequently, monitoring of global, decadal changes in climate requires global observations that cover the whole range of space-time scales and are continuous over several decades. The sampling of smaller space-time scales must have sufficient statistical accuracy to measure the small changes in the forcings and feedbacks anticipated in the next few decades, while continuity of measurements is crucial for unambiguous interpretation of climate change. Shorter records of monthly and regional (500-1000 km) measurements with similar accuracies can also provide valuable information about climate processes, when 'natural experiments' such as large volcanic eruptions or El Ninos occur. In this section existing satellite datasets and climate model simulations are used to test the satellite orbits and sampling required to achieve accurate measurements of changes in forcings and feedbacks at monthly frequency and 1000 km (regional) scale.

Sea Surface Temperature for Climate Applications: A New Dataset from the European Space Agency Climate Change Initiative

NASA Astrophysics Data System (ADS)

Merchant, C. J.; Hulley, G. C.

2013-12-01

There are many datasets describing the evolution of global sea surface temperature (SST) over recent decades -- so why make another one? Answer: to provide observations of SST that have particular qualities relevant to climate applications: independence, accuracy and stability. This has been done within the European Space Agency (ESA) Climate Change Initative (CCI) project on SST. Independence refers to the fact that the new SST CCI dataset is not derived from or tuned to in situ observations. This matters for climate because the in situ observing network used to assess marine climate change (1) was not designed to monitor small changes over decadal timescales, and (2) has evolved significantly in its technology and mix of types of observation, even during the past 40 years. The potential for significant artefacts in our picture of global ocean surface warming is clear. Only by having an independent record can we confirm (or refute) that the work done to remove biases/trend artefacts in in-situ datasets has been successful. Accuracy is the degree to which SSTs are unbiased. For climate applications, a common accuracy target is 0.1 K for all regions of the ocean. Stability is the degree to which the bias, if any, in a dataset is constant over time. Long-term instability introduces trend artefacts. To observe trends of the magnitude of 'global warming', SST datasets need to be stable to <5 mK/year. The SST CCI project has produced a satellite-based dataset that addresses these characteristics relevant to climate applications. Satellite radiances (brightness temperatures) have been harmonised exploiting periods of overlapping observations between sensors. Less well-characterised sensors have had their calibration tuned to that of better characterised sensors (at radiance level). Non-conventional retrieval methods (optimal estimation) have been employed to reduce regional biases to the 0.1 K level, a target violated in most satellite SST datasets. Models for quantifying uncertainty have been developed to attach uncertainty to SST across a range of space-time scales. The stability of the data has been validated.

Overview on recent upper atmosphere atomic oxygen measurements

NASA Astrophysics Data System (ADS)

Zhu, Yajun; Kaufmann, Martin; Chen, Qiuyu; Martin, Riese

2017-04-01

In recent years, new global datasets of atomic oxygen in the upper mesosphere and lower thermosphere have been presented. They are based on airglow measurements from low earth satellites. Surprisingly, the atomic oxygen abundance differs by 30-50% for similar atmospheric conditions. This paper gives an overview on the various atomic oxygen datasets available so far and presents most recent results obtained from measurements on Envisat. Differences between the datasets are discussed.

The French Muséum national d'histoire naturelle vascular plant herbarium collection dataset

NASA Astrophysics Data System (ADS)

Le Bras, Gwenaël; Pignal, Marc; Jeanson, Marc L.; Muller, Serge; Aupic, Cécile; Carré, Benoît; Flament, Grégoire; Gaudeul, Myriam; Gonçalves, Claudia; Invernón, Vanessa R.; Jabbour, Florian; Lerat, Elodie; Lowry, Porter P.; Offroy, Bérangère; Pimparé, Eva Pérez; Poncy, Odile; Rouhan, Germinal; Haevermans, Thomas

2017-02-01

We provide a quantitative description of the French national herbarium vascular plants collection dataset. Held at the Muséum national d'histoire naturelle, Paris, it currently comprises records for 5,400,000 specimens, representing 90% of the estimated total of specimens. Ninety nine percent of the specimen entries are linked to one or more images and 16% have field-collecting information available. This major botanical collection represents the results of over three centuries of exploration and study. The sources of the collection are global, with a strong representation for France, including overseas territories, and former French colonies. The compilation of this dataset was made possible through numerous national and international projects, the most important of which was linked to the renovation of the herbarium building. The vascular plant collection is actively expanding today, hence the continuous growth exhibited by the dataset, which can be fully accessed through the GBIF portal or the MNHN database portal (available at: https://science.mnhn.fr/institution/mnhn/collection/p/item/search/form). This dataset is a major source of data for systematics, global plants macroecological studies or conservation assessments.

The French Muséum national d'histoire naturelle vascular plant herbarium collection dataset.

PubMed

Le Bras, Gwenaël; Pignal, Marc; Jeanson, Marc L; Muller, Serge; Aupic, Cécile; Carré, Benoît; Flament, Grégoire; Gaudeul, Myriam; Gonçalves, Claudia; Invernón, Vanessa R; Jabbour, Florian; Lerat, Elodie; Lowry, Porter P; Offroy, Bérangère; Pimparé, Eva Pérez; Poncy, Odile; Rouhan, Germinal; Haevermans, Thomas

2017-02-14

We provide a quantitative description of the French national herbarium vascular plants collection dataset. Held at the Muséum national d'histoire naturelle, Paris, it currently comprises records for 5,400,000 specimens, representing 90% of the estimated total of specimens. Ninety nine percent of the specimen entries are linked to one or more images and 16% have field-collecting information available. This major botanical collection represents the results of over three centuries of exploration and study. The sources of the collection are global, with a strong representation for France, including overseas territories, and former French colonies. The compilation of this dataset was made possible through numerous national and international projects, the most important of which was linked to the renovation of the herbarium building. The vascular plant collection is actively expanding today, hence the continuous growth exhibited by the dataset, which can be fully accessed through the GBIF portal or the MNHN database portal (available at: https://science.mnhn.fr/institution/mnhn/collection/p/item/search/form). This dataset is a major source of data for systematics, global plants macroecological studies or conservation assessments.

Global Data Spatially Interrelate System for Scientific Big Data Spatial-Seamless Sharing

NASA Astrophysics Data System (ADS)

Yu, J.; Wu, L.; Yang, Y.; Lei, X.; He, W.

2014-04-01

A good data sharing system with spatial-seamless services will prevent the scientists from tedious, boring, and time consuming work of spatial transformation, and hence encourage the usage of the scientific data, and increase the scientific innovation. Having been adopted as the framework of Earth datasets by Group on Earth Observation (GEO), Earth System Spatial Grid (ESSG) is potential to be the spatial reference of the Earth datasets. Based on the implementation of ESSG, SDOG-ESSG, a data sharing system named global data spatially interrelate system (GASE) was design to make the data sharing spatial-seamless. The architecture of GASE was introduced. The implementation of the two key components, V-Pools, and interrelating engine, and the prototype is presented. Any dataset is firstly resampled into SDOG-ESSG, and is divided into small blocks, and then are mapped into hierarchical system of the distributed file system in V-Pools, which together makes the data serving at a uniform spatial reference and at a high efficiency. Besides, the datasets from different data centres are interrelated by the interrelating engine at the uniform spatial reference of SDOGESSG, which enables the system to sharing the open datasets in the internet spatial-seamless.

The French Muséum national d’histoire naturelle vascular plant herbarium collection dataset

PubMed Central

Le Bras, Gwenaël; Pignal, Marc; Jeanson, Marc L.; Muller, Serge; Aupic, Cécile; Carré, Benoît; Flament, Grégoire; Gaudeul, Myriam; Gonçalves, Claudia; Invernón, Vanessa R.; Jabbour, Florian; Lerat, Elodie; Lowry, Porter P.; Offroy, Bérangère; Pimparé, Eva Pérez; Poncy, Odile; Rouhan, Germinal; Haevermans, Thomas

2017-01-01

We provide a quantitative description of the French national herbarium vascular plants collection dataset. Held at the Muséum national d’histoire naturelle, Paris, it currently comprises records for 5,400,000 specimens, representing 90% of the estimated total of specimens. Ninety nine percent of the specimen entries are linked to one or more images and 16% have field-collecting information available. This major botanical collection represents the results of over three centuries of exploration and study. The sources of the collection are global, with a strong representation for France, including overseas territories, and former French colonies. The compilation of this dataset was made possible through numerous national and international projects, the most important of which was linked to the renovation of the herbarium building. The vascular plant collection is actively expanding today, hence the continuous growth exhibited by the dataset, which can be fully accessed through the GBIF portal or the MNHN database portal (available at: https://science.mnhn.fr/institution/mnhn/collection/p/item/search/form). This dataset is a major source of data for systematics, global plants macroecological studies or conservation assessments. PMID:28195585

Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae

PubMed Central

Reguly, Teresa; Breitkreutz, Ashton; Boucher, Lorrie; Breitkreutz, Bobby-Joe; Hon, Gary C; Myers, Chad L; Parsons, Ainslie; Friesen, Helena; Oughtred, Rose; Tong, Amy; Stark, Chris; Ho, Yuen; Botstein, David; Andrews, Brenda; Boone, Charles; Troyanskya, Olga G; Ideker, Trey; Dolinski, Kara; Batada, Nizar N; Tyers, Mike

2006-01-01

Background The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference. Results We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC) dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID () and SGD () databases. Conclusion Comprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks. PMID:16762047

Quantification of effective plant rooting depth: advancing global hydrological modelling

NASA Astrophysics Data System (ADS)

Yang, Y.; Donohue, R. J.; McVicar, T.

2017-12-01

Plant rooting depth (Zr) is a key parameter in hydrological and biogeochemical models, yet the global spatial distribution of Zr is largely unknown due to the difficulties in its direct measurement. Moreover, Zr observations are usually only representative of a single plant or several plants, which can differ greatly from the effective Zr over a modelling unit (e.g., catchment or grid-box). Here, we provide a global parameterization of an analytical Zr model that balances the marginal carbon cost and benefit of deeper roots, and produce a climatological (i.e., 1982-2010 average) global Zr map. To test the Zr estimates, we apply the estimated Zr in a highly transparent hydrological model (i.e., the Budyko-Choudhury-Porporato (BCP) model) to estimate mean annual actual evapotranspiration (E) across the globe. We then compare the estimated E with both water balance-based E observations at 32 major catchments and satellite grid-box retrievals across the globe. Our results show that the BCP model, when implemented with Zr estimated herein, optimally reproduced the spatial pattern of E at both scales and provides improved model outputs when compared to BCP model results from two already existing global Zr datasets. These results suggest that our Zr estimates can be effectively used in state-of-the-art hydrological models, and potentially biogeochemical models, where the determination of Zr currently largely relies on biome type-based look-up tables.

Towards more Global Coordination of Atmospheric Electricity Measurements (GloCAEM)

NASA Astrophysics Data System (ADS)

Nicoll, Keri; Harrison, Giles

2017-04-01

Earth's atmospheric electrical environment has been studied since the 1750s but its more recent applications to science questions around clouds and climate highlight the incompleteness of our understanding, in part due to lack of suitable global measurements. The Global Electric Circuit (GEC) sustains the near-surface fair weather (FW) electric field, which is present globally in regions which are not strongly electrically disturbed by weather or pollution. It can be measured routinely at the surface using well established instrumentation such as electric field mills. Despite the central role of lightning as a weather hazard and the potentially widespread importance of charge for atmospheric processes, research is hampered by the fragmented nature of surface atmospheric electricity measurements. This makes anything other than local studies in fortuitous fair weather conditions difficult. In contrast to detection of global lightning using satellite measurements and ground-based radio networks, the FW electric field and GEC cannot be measured by remote sensing and no similar measurement networks exist for its study. This presents an opportunity as many researchers worldwide now make high temporal resolution measurements of the FW electric field routinely, which is neither coordinated nor exploited. The GLOCAEM (Global Coordination of Atmospheric Electricity Measurements) project is currently bringing some of these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring. A specific objective of the project is to establish the first modern archive of international FW atmospheric electric field data in close to real time to allow global studies of atmospheric electricity to be straightforwardly and robustly performed. Data will be archived through the UK Centre for Environmental Data Analysis (CEDA) and will be available for download by users from early 2018. Both 1 second and 1 minute electric field data will be archived, along with meteorological measurements (if available) for ease of interpretation of electrical measurements. Although the primary aim of the project is to provide a close to real time electric field database, archiving of existing historical electric field datasets is also planned to extend the range of studies possible. This presentation will provide a summary of progress with the GLOCAEM project.

Global Oil & Gas Features Database

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kelly Rose; Jennifer Bauer; Vic Baker

This submission contains a zip file with the developed Global Oil & Gas Features Database (as an ArcGIS geodatabase). Access the technical report describing how this database was produced using the following link: https://edx.netl.doe.gov/dataset/development-of-an-open-global-oil-and-gas-infrastructure-inventory-and-geodatabase

A Comparison of Latent Heat Fluxes over Global Oceans for Four Flux Products

NASA Technical Reports Server (NTRS)

Chou, Shu-Hsien; Nelkin, Eric; Ardizzone, Joe; Atlas, Robert M.

2003-01-01

To improve our understanding of global energy and water cycle variability, and to improve model simulations of climate variations, it is vital to have accurate latent heat fluxes (LHF) over global oceans. Monthly LHF, 10-m wind speed (U10m), 10-m specific humidity (Q10h), and sea-air humidity difference (Qs-Q10m) of GSSTF2 (version 2 Goddard Satellite-based Surface Turbulent Fluxes) over global Oceans during 1992-93 are compared with those of HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data), NCEP (NCEP/NCAR reanalysis). The mean differences, standard deviations of differences, and temporal correlation of these monthly variables over global Oceans during 1992-93 between GSSTF2 and each of the three datasets are analyzed. The large-scale patterns of the 2yr-mean fields for these variables are similar among these four datasets, but significant quantitative differences are found. The temporal correlation is higher in the northern extratropics than in the south for all variables, with the contrast being especially large for da Silva as a result of more missing ship data in the south. The da Silva has extremely low temporal correlation and large differences with GSSTF2 for all variables in the southern extratropics, indicating that da Silva hardly produces a realistic variability in these variables. The NCEP has extremely low temporal correlation (0.27) and large spatial variations of differences with GSSTF2 for Qs-Q10m in the tropics, which causes the low correlation for LHF. Over the tropics, the HOAPS LHF is significantly smaller than GSSTF2 by approx. 31% (37 W/sq m), whereas the other two datasets are comparable to GSSTF2. This is because the HOAPS has systematically smaller LHF than GSSTF2 in space, while the other two datasets have very large spatial variations of large positive and negative LHF differences with GSSTF2 to cancel and to produce smaller regional-mean differences. Our analyses suggest that the GSSTF2 latent heat flux, surface air humidity, and winds are likely to be more realistic than the other three flux datasets examined, although those of GSSTF2 are still subject to regional biases.

Towards systematic evaluation of crop model outputs for global land-use models

NASA Astrophysics Data System (ADS)

Leclere, David; Azevedo, Ligia B.; Skalský, Rastislav; Balkovič, Juraj; Havlík, Petr

2016-04-01

Land provides vital socioeconomic resources to the society, however at the cost of large environmental degradations. Global integrated models combining high resolution global gridded crop models (GGCMs) and global economic models (GEMs) are increasingly being used to inform sustainable solution for agricultural land-use. However, little effort has yet been done to evaluate and compare the accuracy of GGCM outputs. In addition, GGCM datasets require a large amount of parameters whose values and their variability across space are weakly constrained: increasing the accuracy of such dataset has a very high computing cost. Innovative evaluation methods are required both to ground credibility to the global integrated models, and to allow efficient parameter specification of GGCMs. We propose an evaluation strategy for GGCM datasets in the perspective of use in GEMs, illustrated with preliminary results from a novel dataset (the Hypercube) generated by the EPIC GGCM and used in the GLOBIOM land use GEM to inform on present-day crop yield, water and nutrient input needs for 16 crops x 15 management intensities, at a spatial resolution of 5 arc-minutes. We adopt the following principle: evaluation should provide a transparent diagnosis of model adequacy for its intended use. We briefly describe how the Hypercube data is generated and how it articulates with GLOBIOM in order to transparently identify the performances to be evaluated, as well as the main assumptions and data processing involved. Expected performances include adequately representing the sub-national heterogeneity in crop yield and input needs: i) in space, ii) across crop species, and iii) across management intensities. We will present and discuss measures of these expected performances and weight the relative contribution of crop model, input data and data processing steps in performances. We will also compare obtained yield gaps and main yield-limiting factors against the M3 dataset. Next steps include iterative improvement of parameter assumptions and evaluation of implications of GGCM performances for intended use in the IIASA EPIC-GLOBIOM model cluster. Our approach helps targeting future efforts at improving GGCM accuracy and would achieve highest efficiency if combined with traditional field-scale evaluation and sensitivity analysis.

A high resolution spatial population database of Somalia for disease risk mapping.

PubMed

Linard, Catherine; Alegana, Victor A; Noor, Abdisalan M; Snow, Robert W; Tatem, Andrew J

2010-09-14

Millions of Somali have been deprived of basic health services due to the unstable political situation of their country. Attempts are being made to reconstruct the health sector, in particular to estimate the extent of infectious disease burden. However, any approach that requires the use of modelled disease rates requires reasonable information on population distribution. In a low-income country such as Somalia, population data are lacking, are of poor quality, or become outdated rapidly. Modelling methods are therefore needed for the production of contemporary and spatially detailed population data. Here land cover information derived from satellite imagery and existing settlement point datasets were used for the spatial reallocation of populations within census units. We used simple and semi-automated methods that can be implemented with free image processing software to produce an easily updatable gridded population dataset at 100 × 100 meters spatial resolution. The 2010 population dataset was matched to administrative population totals projected by the UN. Comparison tests between the new dataset and existing population datasets revealed important differences in population size distributions, and in population at risk of malaria estimates. These differences are particularly important in more densely populated areas and strongly depend on the settlement data used in the modelling approach. The results show that it is possible to produce detailed, contemporary and easily updatable settlement and population distribution datasets of Somalia using existing data. The 2010 population dataset produced is freely available as a product of the AfriPop Project and can be downloaded from: http://www.afripop.org.

A high resolution spatial population database of Somalia for disease risk mapping

PubMed Central

2010-01-01

Background Millions of Somali have been deprived of basic health services due to the unstable political situation of their country. Attempts are being made to reconstruct the health sector, in particular to estimate the extent of infectious disease burden. However, any approach that requires the use of modelled disease rates requires reasonable information on population distribution. In a low-income country such as Somalia, population data are lacking, are of poor quality, or become outdated rapidly. Modelling methods are therefore needed for the production of contemporary and spatially detailed population data. Results Here land cover information derived from satellite imagery and existing settlement point datasets were used for the spatial reallocation of populations within census units. We used simple and semi-automated methods that can be implemented with free image processing software to produce an easily updatable gridded population dataset at 100 × 100 meters spatial resolution. The 2010 population dataset was matched to administrative population totals projected by the UN. Comparison tests between the new dataset and existing population datasets revealed important differences in population size distributions, and in population at risk of malaria estimates. These differences are particularly important in more densely populated areas and strongly depend on the settlement data used in the modelling approach. Conclusions The results show that it is possible to produce detailed, contemporary and easily updatable settlement and population distribution datasets of Somalia using existing data. The 2010 population dataset produced is freely available as a product of the AfriPop Project and can be downloaded from: http://www.afripop.org. PMID:20840751

Providing Geographic Datasets as Linked Data in Sdi

NASA Astrophysics Data System (ADS)

Hietanen, E.; Lehto, L.; Latvala, P.

2016-06-01

In this study, a prototype service to provide data from Web Feature Service (WFS) as linked data is implemented. At first, persistent and unique Uniform Resource Identifiers (URI) are created to all spatial objects in the dataset. The objects are available from those URIs in Resource Description Framework (RDF) data format. Next, a Web Ontology Language (OWL) ontology is created to describe the dataset information content using the Open Geospatial Consortium's (OGC) GeoSPARQL vocabulary. The existing data model is modified in order to take into account the linked data principles. The implemented service produces an HTTP response dynamically. The data for the response is first fetched from existing WFS. Then the Geographic Markup Language (GML) format output of the WFS is transformed on-the-fly to the RDF format. Content Negotiation is used to serve the data in different RDF serialization formats. This solution facilitates the use of a dataset in different applications without replicating the whole dataset. In addition, individual spatial objects in the dataset can be referred with URIs. Furthermore, the needed information content of the objects can be easily extracted from the RDF serializations available from those URIs. A solution for linking data objects to the dataset URI is also introduced by using the Vocabulary of Interlinked Datasets (VoID). The dataset is divided to the subsets and each subset is given its persistent and unique URI. This enables the whole dataset to be explored with a web browser and all individual objects to be indexed by search engines.

Leveling data in geochemical mapping: scope of application, pros and cons of existing methods

NASA Astrophysics Data System (ADS)

Pereira, Benoît; Vandeuren, Aubry; Sonnet, Philippe

2017-04-01

Geochemical mapping successfully met a range of needs from mineral exploration to environmental management. In Europe and around the world numerous geochemical datasets already exist. These datasets may originate from geochemical mapping projects or from the collection of sample analyses requested by environmental protection regulatory bodies. Combining datasets can be highly beneficial for establishing geochemical maps with increased resolution and/or coverage area. However this practice requires assessing the equivalence between datasets and, if needed, applying data leveling to remove possible biases between datasets. In the literature, several procedures for assessing dataset equivalence and leveling data are proposed. Daneshfar & Cameron (1998) proposed a method for the leveling of two adjacent datasets while Pereira et al. (2016) proposed two methods for the leveling of datasets that contain records located within the same geographical area. Each discussed method requires its own set of assumptions (underlying populations of data, spatial distribution of data, etc.). Here we propose to discuss the scope of application, pros, cons and practical recommendations for each method. This work is illustrated with several case studies in Wallonia (Southern Belgium) and in Europe involving trace element geochemical datasets. References: Daneshfar, B. & Cameron, E. (1998), Leveling geochemical data between map sheets, Journal of Geochemical Exploration 63(3), 189-201. Pereira, B.; Vandeuren, A.; Govaerts, B. B. & Sonnet, P. (2016), Assessing dataset equivalence and leveling data in geochemical mapping, Journal of Geochemical Exploration 168, 36-48.

Development of global sea ice 6.0 CICE configuration for the Met Office global coupled model

DOE PAGES

Rae, J. . G. L; Hewitt, H. T.; Keen, A. B.; ...

2015-03-05

The new sea ice configuration GSI6.0, used in the Met Office global coupled configuration GC2.0, is described and the sea ice extent, thickness and volume are compared with the previous configuration and with observationally-based datasets. In the Arctic, the sea ice is thicker in all seasons than in the previous configuration, and there is now better agreement of the modelled concentration and extent with the HadISST dataset. In the Antarctic, a warm bias in the ocean model has been exacerbated at the higher resolution of GC2.0, leading to a large reduction in ice extent and volume; further work is requiredmore » to rectify this in future configurations.« less

Multi-model Mean Nitrogen and Sulfur Deposition from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): Evaluation of Historical and Projected Future Changes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lamarque, Jean-Francois; Dentener, Frank; McConnell, J.R.

2013-08-20

We present multi-model global datasets of nitrogen and sulfate deposition covering time periods from 1850 to 2100, calculated within the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The computed deposition fluxes are compared to surface wet deposition and ice-core measurements. We use a new dataset of wet deposition for 2000-2002 based on critical assessment of the quality of existing regional network data. We show that for present-day (year 2000 ACCMIP time-slice), the ACCMIP results perform similarly to previously published multi-model assessments. The analysis of changes between 1980 and 2000 indicates significant differences between model and measurements over the Unitedmore » States, but less so over Europe. This difference points towards misrepresentation of 1980 NH3 emissions over North America. Based on ice-core records, the 1850 deposition fluxes agree well with Greenland ice cores but the change between 1850 and 2000 seems to be overestimated in the Northern Hemisphere for both nitrogen and sulfur species. Using the Representative Concentration Pathways to define the projected climate and atmospheric chemistry related emissions and concentrations, we find large regional nitrogen deposition increases in 2100 in Latin America, Africa and parts of Asia under some of the scenarios considered. Increases in South Asia are especially large, and are seen in all scenarios, with 2100 values more than double 2000 in some scenarios and reaching >1300 mgN/m2/yr averaged over regional to continental scale regions in RCP 2.6 and 8.5, ~30-50% larger than the values in any region currently (2000). Despite known issues, the new ACCMIP deposition dataset provides novel, consistent and evaluated global gridded deposition fields for use in a wide range of climate and ecological studies.« less

Multi-model Mean Nitrogen and Sulfur Deposition from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): Evaluation Historical and Projected Changes

NASA Technical Reports Server (NTRS)

Lamarque, J.-F.; Dentener, F.; McConnell, J.; Ro, C.-U.; Shaw, M.; Vet, R.; Bergmann, D.; Cameron-Smith, P.; Doherty, R.; Faluvegi, G.;

Comparison of present global reanalysis datasets in the context of a statistical downscaling method for precipitation prediction

NASA Astrophysics Data System (ADS)

Horton, Pascal; Weingartner, Rolf; Brönnimann, Stefan

2017-04-01

The analogue method is a statistical downscaling method for precipitation prediction. It uses similarity in terms of synoptic-scale predictors with situations in the past in order to provide a probabilistic prediction for the day of interest. It has been used for decades in a context of weather or flood forecasting, and is more recently also applied to climate studies, whether for reconstruction of past weather conditions or future climate impact studies. In order to evaluate the relationship between synoptic scale predictors and the local weather variable of interest, e.g. precipitation, reanalysis datasets are necessary. Nowadays, the number of available reanalysis datasets increases. These are generated by different atmospheric models with different assimilation techniques and offer various spatial and temporal resolutions. A major difference between these datasets is also the length of the archive they provide. While some datasets start at the beginning of the satellite era (1980) and assimilate these data, others aim at homogeneity on a longer period (e.g. 20th century) and only assimilate conventional observations. The context of the application of analogue methods might drive the choice of an appropriate dataset, for example when the archive length is a leading criterion. However, in many studies, a reanalysis dataset is subjectively chosen, according to the user's preferences or the ease of access. The impact of this choice on the results of the downscaling procedure is rarely considered and no comprehensive comparison has been undertaken so far. In order to fill this gap and to advise on the choice of appropriate datasets, nine different global reanalysis datasets were compared in seven distinct versions of analogue methods, over 300 precipitation stations in Switzerland. Significant differences in terms of prediction performance were identified. Although the impact of the reanalysis dataset on the skill score varies according to the chosen predictor, be it atmospheric circulation or thermodynamic variables, some hierarchy between the datasets is often preserved. This work can thus help choosing an appropriate dataset for the analogue method, or raise awareness of the consequences of using a certain dataset.

Global manure nitrogen production and application in cropland during 1860-2014: a 5 arcmin gridded global dataset for Earth system modeling

NASA Astrophysics Data System (ADS)

Zhang, Bowen; Tian, Hanqin; Lu, Chaoqun; Dangal, Shree R. S.; Yang, Jia; Pan, Shufen

2017-09-01

Given the important role of nitrogen input from livestock systems in terrestrial nutrient cycles and the atmospheric chemical composition, it is vital to have a robust estimation of the magnitude and spatiotemporal variation in manure nitrogen production and its application to cropland across the globe. In this study, we used the dataset from the Global Livestock Impact Mapping System (GLIMS) in conjunction with country-specific annual livestock populations to reconstruct the manure nitrogen production during 1860-2014. The estimated manure nitrogen production increased from 21.4 Tg N yr-1 in 1860 to 131.0 Tg N yr-1 in 2014 with a significant annual increasing trend (0.7 Tg N yr-1, p < 0.01). Changes in manure nitrogen production exhibited high spatial variability and concentrated in several hotspots (e.g., Western Europe, India, northeastern China, and southeastern Australia) across the globe over the study period. In the 1860s, the northern midlatitude region was the largest manure producer, accounting for ˜ 52 % of the global total, while low-latitude regions became the largest share (˜ 48 %) in the most recent 5 years (2010-2014). Among all the continents, Asia accounted for over one-fourth of the global manure production during 1860-2014. Cattle dominated the manure nitrogen production and contributed ˜ 44 % of the total manure nitrogen production in 2014, followed by goats, sheep, swine, and chickens. The manure nitrogen application to cropland accounts for less than one-fifth of the total manure nitrogen production over the study period. The 5 arcmin gridded global dataset of manure nitrogen production generated from this study could be used as an input for global or regional land surface and ecosystem models to evaluate the impacts of manure nitrogen on key biogeochemical processes and water quality. To ensure food security and environmental sustainability, it is necessary to implement proper manure management practices on cropland across the globe. Datasets are available at https://doi.org/10.1594/PANGAEA.871980 (Zhang et al., 2017).

Testing the Flat World Thesis: Using a Public Dataset to Engage Students in the Global Inequality Debate

ERIC Educational Resources Information Center

Arabandi, Bhavani; Sweet, Stephen; Swords, Alicia

2014-01-01

We present a learning module to engage students in the global inequality debate using Google Public Data World Development Indicators. Goals of this article are to articulate the importance and urgency of teaching global issues to American students; situate the central debate in the globalization literature, paying particular attention to global…

Global wetlands: Potential distribution, wetland loss, and status.

PubMed

Hu, Shengjie; Niu, Zhenguo; Chen, Yanfen; Li, Lifeng; Zhang, Haiying

2017-05-15

Even though researchers have paid a great deal of attention to wetland loss and status, the actual extent of wetland loss on a global scale, especially the loss caused directly by human activities, and the actual extent of currently surviving wetlands remains uncertain. This paper simulated the potential distribution of global wetlands by employing a new Precipitation Topographic Wetness Index (PTWI) and global remote sensing training samples. The results show earth would have approximately 29.83millionkm 2 of wetlands, if humans did not interfere with wetland ecosystems. By combining datasets related to global wetlands, we found that at least 33% of global wetlands had been lost as of 2009, including 4.58millionkm 2 of non-water wetlands and 2.64millionkm 2 of open water. The areal extent of wetland loss has been greatest in Asia, but Europe has experienced the most serious losses. Wetland-related datasets suffer from major inconsistencies, and estimates of the areal extent of the remaining global wetlands ranged from 1.53 to 14.86millionkm 2 . Therefore, although it is challenging, thematic mapping of global wetlands is necessary and urgently needed. Copyright © 2017 Elsevier B.V. All rights reserved.

Evaluating ALOS AW3D30 data

NASA Astrophysics Data System (ADS)

Nikolakopoulos, Konstantinos G.

2017-09-01

A global digital surface model dataset named ALOS Global Digital Surface Model (AW3D30) with a horizontal resolution of approx. 30-meter mesh (1 arcsec) has been released by the Japan Aerospace Exploration Agency (JAXA). The dataset has been compiled with images acquired by the Advanced Land Observing Satellite "DAICHI" (ALOS) and it is published based on the DSM dataset (5-meter mesh version) of the "World 3D Topographic Data", which is the most precise global-scale elevation data at this time, and its elevation precision is also at a world-leading level as a 30-meter mesh version. In this study the accuracy of ALOS AW3D30 was examined. For an area with complex geomorphologic characteristics DSM from ALOS stereo pairs were created with classical photogrammetric techniques. Those DSMs were compared with the ALOS AW3D30. Points of certified elevation collected with DGPS have been used to estimate the accuracy of the DSM. The elevation difference between the two DSMs was calculated. 2D RMSE, correlation and the percentile value were also computed and the results are presented.

Spatializing 6,000 years of global urbanization from 3700 BC to AD 2000

NASA Astrophysics Data System (ADS)

Reba, Meredith; Reitsma, Femke; Seto, Karen C.

2016-06-01

How were cities distributed globally in the past? How many people lived in these cities? How did cities influence their local and regional environments? In order to understand the current era of urbanization, we must understand long-term historical urbanization trends and patterns. However, to date there is no comprehensive record of spatially explicit, historic, city-level population data at the global scale. Here, we developed the first spatially explicit dataset of urban settlements from 3700 BC to AD 2000, by digitizing, transcribing, and geocoding historical, archaeological, and census-based urban population data previously published in tabular form by Chandler and Modelski. The dataset creation process also required data cleaning and harmonization procedures to make the data internally consistent. Additionally, we created a reliability ranking for each geocoded location to assess the geographic uncertainty of each data point. The dataset provides the first spatially explicit archive of the location and size of urban populations over the last 6,000 years and can contribute to an improved understanding of contemporary and historical urbanization trends.

Spatializing 6,000 years of global urbanization from 3700 BC to AD 2000

PubMed Central

Reba, Meredith; Reitsma, Femke; Seto, Karen C.

2016-01-01

How were cities distributed globally in the past? How many people lived in these cities? How did cities influence their local and regional environments? In order to understand the current era of urbanization, we must understand long-term historical urbanization trends and patterns. However, to date there is no comprehensive record of spatially explicit, historic, city-level population data at the global scale. Here, we developed the first spatially explicit dataset of urban settlements from 3700 BC to AD 2000, by digitizing, transcribing, and geocoding historical, archaeological, and census-based urban population data previously published in tabular form by Chandler and Modelski. The dataset creation process also required data cleaning and harmonization procedures to make the data internally consistent. Additionally, we created a reliability ranking for each geocoded location to assess the geographic uncertainty of each data point. The dataset provides the first spatially explicit archive of the location and size of urban populations over the last 6,000 years and can contribute to an improved understanding of contemporary and historical urbanization trends. PMID:27271481

Global surface displacement data for assessing variability of displacement at a point on a fault

USGS Publications Warehouse

Hecker, Suzanne; Sickler, Robert; Feigelson, Leah; Abrahamson, Norman; Hassett, Will; Rosa, Carla; Sanquini, Ann

2014-01-01

This report presents a global dataset of site-specific surface-displacement data on faults. We have compiled estimates of successive displacements attributed to individual earthquakes, mainly paleoearthquakes, at sites where two or more events have been documented, as a basis for analyzing inter-event variability in surface displacement on continental faults. An earlier version of this composite dataset was used in a recent study relating the variability of surface displacement at a point to the magnitude-frequency distribution of earthquakes on faults, and to hazard from fault rupture (Hecker and others, 2013). The purpose of this follow-on report is to provide potential data users with an updated comprehensive dataset, largely complete through 2010 for studies in English-language publications, as well as in some unpublished reports and abstract volumes.

Quantification and Visualization of Variation in Anatomical Trees

DOE Office of Scientific and Technical Information (OSTI.GOV)

Amenta, Nina; Datar, Manasi; Dirksen, Asger

This paper presents two approaches to quantifying and visualizing variation in datasets of trees. The first approach localizes subtrees in which significant population differences are found through hypothesis testing and sparse classifiers on subtree features. The second approach visualizes the global metric structure of datasets through low-distortion embedding into hyperbolic planes in the style of multidimensional scaling. A case study is made on a dataset of airway trees in relation to Chronic Obstructive Pulmonary Disease.

Secondary Analysis and Integration of Existing Data to Elucidate the Genetic Architecture of Cancer Risk and Related Outcomes, R21 | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

This funding opportunity announcement (FOA) encourages applications that propose to conduct secondary data analysis and integration of existing datasets and database resources, with the ultimate aim to elucidate the genetic architecture of cancer risk and related outcomes. The goal of this initiative is to address key scientific questions relevant to cancer epidemiology by supporting the analysis of existing genetic or genomic datasets, possibly in combination with environmental, outcomes, behavioral, lifestyle, and molecular profiles data.

Secondary Analysis and Integration of Existing Data to Elucidate the Genetic Architecture of Cancer Risk and Related Outcomes, R01 | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

This funding opportunity announcement (FOA) encourages applications that propose to conduct secondary data analysis and integration of existing datasets and database resources, with the ultimate aim to elucidate the genetic architecture of cancer risk and related outcomes. The goal of this initiative is to address key scientific questions relevant to cancer epidemiology by supporting the analysis of existing genetic or genomic datasets, possibly in combination with environmental, outcomes, behavioral, lifestyle, and molecular profiles data.

Managing uncertainty: a review of food system scenario analysis and modelling

PubMed Central

Reilly, Michael; Willenbockel, Dirk

2010-01-01

Complex socio-ecological systems like the food system are unpredictable, especially to long-term horizons such as 2050. In order to manage this uncertainty, scenario analysis has been used in conjunction with food system models to explore plausible future outcomes. Food system scenarios use a diversity of scenario types and modelling approaches determined by the purpose of the exercise and by technical, methodological and epistemological constraints. Our case studies do not suggest Malthusian futures for a projected global population of 9 billion in 2050; but international trade will be a crucial determinant of outcomes; and the concept of sustainability across the dimensions of the food system has been inadequately explored so far. The impact of scenario analysis at a global scale could be strengthened with participatory processes involving key actors at other geographical scales. Food system models are valuable in managing existing knowledge on system behaviour and ensuring the credibility of qualitative stories but they are limited by current datasets for global crop production and trade, land use and hydrology. Climate change is likely to challenge the adaptive capacity of agricultural production and there are important knowledge gaps for modelling research to address. PMID:20713402

The decomposition of fine and coarse roots: their global patterns and controlling factors

PubMed Central

Zhang, Xinyue; Wang, Wei

2015-01-01

Fine root decomposition represents a large carbon (C) cost to plants, and serves as a potential soil C source, as well as a substantial proportion of net primary productivity. Coarse roots differ markedly from fine roots in morphology, nutrient concentrations, functions, and decomposition mechanisms. Still poorly understood is whether a consistent global pattern exists between the decomposition of fine (<2 mm root diameter) and coarse (≥2 mm) roots. A comprehensive terrestrial root decomposition dataset, including 530 observations from 71 sampling sites, was thus used to compare global patterns of decomposition of fine and coarse roots. Fine roots decomposed significantly faster than coarse roots in middle latitude areas, but their decomposition in low latitude regions was not significantly different from that of coarse roots. Coarse root decomposition showed more dependence on climate, especially mean annual temperature (MAT), than did fine roots. Initial litter lignin content was the most important predictor of fine root decomposition, while lignin to nitrogen ratios, MAT, and mean annual precipitation were the most important predictors of coarse root decomposition. Our study emphasizes the necessity of separating fine roots and coarse roots when predicting the response of belowground C release to future climate changes. PMID:25942391

Pathogenic Influenza Viruses and Coronaviruses Utilize Similar and Contrasting Approaches To Control Interferon-Stimulated Gene Responses

PubMed Central

Menachery, Vineet D.; Eisfeld, Amie J.; Schäfer, Alexandra; Josset, Laurence; Sims, Amy C.; Proll, Sean; Fan, Shufang; Li, Chengjun; Neumann, Gabriele; Tilton, Susan C.; Chang, Jean; Gralinski, Lisa E.; Long, Casey; Green, Richard; Williams, Christopher M.; Weiss, Jeffrey; Matzke, Melissa M.; Webb-Robertson, Bobbie-Jo; Schepmoes, Athena A.; Shukla, Anil K.; Metz, Thomas O.; Smith, Richard D.; Waters, Katrina M.; Katze, Michael G.; Kawaoka, Yoshihiro

2014-01-01

ABSTRACT The broad range and diversity of interferon-stimulated genes (ISGs) function to induce an antiviral state within the host, impeding viral pathogenesis. While successful respiratory viruses overcome individual ISG effectors, analysis of the global ISG response and subsequent viral antagonism has yet to be examined. Employing models of the human airway, transcriptomics and proteomics datasets were used to compare ISG response patterns following highly pathogenic H5N1 avian influenza (HPAI) A virus, 2009 pandemic H1N1, severe acute respiratory syndrome coronavirus (SARS-CoV), and Middle East respiratory syndrome CoV (MERS-CoV) infection. The results illustrated distinct approaches utilized by each virus to antagonize the global ISG response. In addition, the data revealed that highly virulent HPAI virus and MERS-CoV induce repressive histone modifications, which downregulate expression of ISG subsets. Notably, influenza A virus NS1 appears to play a central role in this histone-mediated downregulation in highly pathogenic influenza strains. Together, the work demonstrates the existence of unique and common viral strategies for controlling the global ISG response and provides a novel avenue for viral antagonism via altered histone modifications. PMID:24846384

How do glacier inventory data aid global glacier assessments and projections?

NASA Astrophysics Data System (ADS)

Hock, R.

2017-12-01

Large-scale glacier modeling relies heavily on datasets that are collected by many individuals across the globe, but managed and maintained in a coordinated fashion by international data centers. The Global Terrestrial Network for Glaciers (GTN-G) provides the framework for coordinating and making available a suite of data sets such as the Randolph Glacier Inventory (RGI), the Glacier Thickness Dataset or the World Glacier Inventory (WGI). These datasets have greatly increased our ability to assess global-scale glacier mass changes. These data have also been vital for projecting the glacier mass changes of all mountain glaciers in the world outside the Greenland and Antarctic ice sheet, a total >200,000 glaciers covering an area of more than 700,000 km2. Using forcing from 8 to 15 GCMs and 4 different emission scenarios, global-scale glacier evolution models project multi-model mean net mass losses of all glaciers between 7 cm and 24 cm sea-level equivalent by the end of the 21st century. Projected mass losses vary greatly depending on the choice of the forcing climate and emission scenario. Insufficiently constrained model parameters likely are an important reason for large differences found among these studies even when forced by the same emission scenario, especially on regional scales.

Inverse modelling estimates of N2O surface emissions and stratospheric losses using a global dataset

NASA Astrophysics Data System (ADS)

Thompson, R. L.; Bousquet, P.; Chevallier, F.; Dlugokencky, E. J.; Vermeulen, A. T.; Aalto, T.; Haszpra, L.; Meinhardt, F.; O'Doherty, S.; Moncrieff, J. B.; Popa, M.; Steinbacher, M.; Jordan, A.; Schuck, T. J.; Brenninkmeijer, C. A.; Wofsy, S. C.; Kort, E. A.

2010-12-01

Nitrous oxide (N2O) levels have been steadily increasing in the atmosphere over the past few decades at a rate of approximately 0.3% per year. This trend is of major concern as N2O is both a long-lived Greenhouse Gas (GHG) and an Ozone Depleting Substance (ODS), as it is a precursor of NO and NO2, which catalytically destroy ozone in the stratosphere. Recently, N2O emissions have been recognised as the most important ODS emissions and are now of greater importance than emissions of CFC's. The growth in atmospheric N2O is predominantly due to the enhancement of surface emissions by human activities. Most notably, the intensification and proliferation of agriculture since the mid-19th century, which has been accompanied by the increased input of reactive nitrogen to soils and has resulted in significant perturbations to the natural N-cycle and emissions of N2O. There exist two approaches for estimating N2O emissions, the so-called 'bottom-up' and 'top-down' approaches. Top-down approaches, based on the inversion of atmospheric measurements, require an estimate of the loss of N2O via photolysis and oxidation in the stratosphere. Uncertainties in the loss magnitude contribute uncertainties of 15 to 20% to the global annual surface emissions, complicating direct comparisons between bottom-up and top-down estimates. In this study, we present a novel inversion framework for the simultaneous optimization of N2O surface emissions and the magnitude of the loss, which avoids errors in the emissions due to incorrect assumptions about the lifetime of N2O. We use a Bayesian inversion with a variational formulation (based on 4D-Var) in order to handle very large datasets. N2O fluxes are retrieved at 4-weekly resolution over a global domain with a spatial resolution of 3.75° x 2.5° longitude by latitude. The efficacy of the simultaneous optimization of emissions and losses is tested using a global synthetic dataset, which mimics the available atmospheric data. Lastly, using real atmospheric data from the networks of NOAA, AGAGE, and CHIOTTO, and additionally aircraft data from the CARIBIC and NOAA programmes and the START campaign, we infer N2O emissions for the years 2006 to 2008. We find large N2O emissions in the tropics, namely in tropical south-east Asia, America and Africa, with notable emissions also in Europe and south Asia.

JADDS - towards a tailored global atmospheric composition data service for CAMS forecasts and reanalysis

NASA Astrophysics Data System (ADS)

Stein, Olaf; Schultz, Martin G.; Rambadt, Michael; Saini, Rajveer; Hoffmann, Lars; Mallmann, Daniel

2017-04-01

Global model data of atmospheric composition produced by the Copernicus Atmospheric Monitoring Service (CAMS) is collected since 2010 at FZ Jülich and serves as boundary condition for use by Regional Air Quality (RAQ) modellers world-wide. RAQ models need time-resolved meteorological as well as chemical lateral boundary conditions for their individual model domains. While the meteorological data usually come from well-established global forecast systems, the chemical boundary conditions are not always well defined. In the past, many models used 'climatic' boundary conditions for the tracer concentrations, which can lead to significant concentration biases, particularly for tracers with longer lifetimes which can be transported over long distances (e.g. over the whole northern hemisphere) with the mean wind. The Copernicus approach utilizes extensive near-realtime data assimilation of atmospheric composition data observed from space which gives additional reliability to the global modelling data and is well received by the RAQ communities. An existing Web Coverage Service (WCS) for sharing these individually tailored model results is currently being re-engineered to make use of a modern, scalable database technology in order to improve performance, enhance flexibility, and allow the operation of catalogue services. The new Jülich Atmospheric Data Distributions Server (JADDS) adheres to the Web Coverage Service WCS2.0 standard as defined by the Open Geospatial Consortium OGC. This enables the user groups to flexibly define datasets they need by selecting a subset of chemical species or restricting geographical boundaries or the length of the time series. The data is made available in the form of different catalogues stored locally on our server. In addition, the Jülich OWS Interface (JOIN) provides interoperable web services allowing for easy download and visualization of datasets delivered from WCS servers via the internet. We will present the prototype JADDS server and address the major issues identified when relocating large four-dimensional datasets into a RASDAMAN raster array database. So far the RASDAMAN support for data available in netCDF format is limited with respect to metadata related to variables and axes. For community-wide accepted solutions, selected data coverages shall result in downloadable netCDF files including metadata complying with the netCDF CF Metadata Conventions standard (http://cfconventions.org/). This can be achieved by adding custom metadata elements for RASDAMAN bands (model levels) on data ingestion. Furthermore, an optimization strategy for ingestion of several TB of 4D model output data will be outlined.

Global Mapping Project - Applications and Development of Version 2 Dataset

NASA Astrophysics Data System (ADS)

Ubukawa, T.; Nakamura, T.; Otsuka, T.; Iimura, T.; Kishimoto, N.; Nakaminami, K.; Motojima, Y.; Suga, M.; Yatabe, Y.; Koarai, M.; Okatani, T.

2012-07-01

The Global Mapping Project aims to develop basic geospatial information of the whole land area of the globe, named Global Map, through the cooperation of National Mapping Organizations (NMOs) around the world. The Global Map data can be a base of global geospatial infrastructure and is composed of eight layers: Boundaries, Drainage, Transportation, Population Centers, Elevation, Land Use, Land Cover and Vegetation. The Global Map Version 1 was released in 2008, and the Version 2 will be released in 2013 as the data are to be updated every five years. In 2009, the International Steering Committee for Global Mapping (ISCGM) adopted new Specifications to develop the Global Map Version 2 with a change of its format so that it is compatible with the international standards, namely ISO 19136 and ISO 19115. With the support of the secretariat of ISCGM, the project participating countries are accelerating their data development toward the completion of the global coverage in 2013, while some countries have already released their Global Map version 2 datasets since 2010. Global Map data are available from the Internet free of charge for non-commercial purposes, which can be used to predict, assess, prepare for and cope with global issues by combining with other spatial data. There are a lot of Global Map applications in various fields, and further utilization of Global Map is expected. This paper summarises the activities toward the development of the Global Map Version 2 as well as some examples of the Global Map applications in various fields.

Application of a Global-to-Beam Irradiance Model to the NASA GEWEX SRB Dataset: An Extension of the NASA Surface Meteorology and Solar Energy Datasets

NASA Technical Reports Server (NTRS)

Zhang, Taiping; Stackhouse, Paul W., Jr.; Chandler, William S.; Westberg, David J.

2014-01-01

The DIRINDEX model was designed to estimate hourly solar beam irradiances from hourly global horizontal irradiances. This model was applied to the NASA GEWEX SRB(Rel. 3.0) 3-hourly global horizontal irradiance data to derive3-hourly global maps of beam, or direct normal, irradiance for the period from January 2000 to December 2005 at the 1 deg. x 1 deg. resolution. The DIRINDEX model is a combination of the DIRINT model, a quasi-physical global-to-beam irradiance model based on regression of hourly observed data, and a broadband simplified version of the SOLIS clear-sky beam irradiance model. In this study, the input variables of the DIRINDEX model are 3-hourly global horizontal irradiance, solar zenith angle, dew-point temperature, surface elevation, surface pressure, sea-level pressure, aerosol optical depth at 700 nm, and column water vapor. The resulting values of the 3-hourly direct normal irradiance are then used to compute daily and monthly means. The results are validated against the ground-based BSRN data. The monthly means show better agreement with the BSRN data than the results from an earlier endeavor which empirically derived the monthly mean direct normal irradiance from the GEWEX SRB monthly mean global horizontal irradiance. To assimilate the observed information into the final results, the direct normal fluxes from the DIRINDEX model are adjusted according to the comparison statistics in the latitude-longitude-cosine of solar zenith angle phase space, in which the inverse-distance interpolation is used for the adjustment. Since the NASA Surface meteorology and Solar Energy derives its data from the GEWEX SRB datasets, the results discussed herein will serve to extend the former.

The Problem with Big Data: Operating on Smaller Datasets to Bridge the Implementation Gap.

PubMed

Mann, Richard P; Mushtaq, Faisal; White, Alan D; Mata-Cervantes, Gabriel; Pike, Tom; Coker, Dalton; Murdoch, Stuart; Hiles, Tim; Smith, Clare; Berridge, David; Hinchliffe, Suzanne; Hall, Geoff; Smye, Stephen; Wilkie, Richard M; Lodge, J Peter A; Mon-Williams, Mark

2016-01-01

Big datasets have the potential to revolutionize public health. However, there is a mismatch between the political and scientific optimism surrounding big data and the public's perception of its benefit. We suggest a systematic and concerted emphasis on developing models derived from smaller datasets to illustrate to the public how big data can produce tangible benefits in the long term. In order to highlight the immediate value of a small data approach, we produced a proof-of-concept model predicting hospital length of stay. The results demonstrate that existing small datasets can be used to create models that generate a reasonable prediction, facilitating health-care delivery. We propose that greater attention (and funding) needs to be directed toward the utilization of existing information resources in parallel with current efforts to create and exploit "big data."

Topographic Science

USGS Publications Warehouse

Poppenga, Sandra K.; Evans, Gayla; Gesch, Dean; Stoker, Jason M.; Queija, Vivian R.; Worstell, Bruce; Tyler, Dean J.; Danielson, Jeff; Bliss, Norman; Greenlee, Susan

2010-01-01

The mission of U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center Topographic Science is to establish partnerships and conduct research and applications that facilitate the development and use of integrated national and global topographic datasets. Topographic Science includes a wide range of research and applications that result in improved seamless topographic datasets, advanced elevation technology, data integration and terrain visualization, new and improved elevation derivatives, and development of Web-based tools. In cooperation with our partners, Topographic Science is developing integrated-science applications for mapping, national natural resource initiatives, hazards, and global change science. http://topotools.cr.usgs.gov/.

Generation of openEHR Test Datasets for Benchmarking.

PubMed

El Helou, Samar; Karvonen, Tuukka; Yamamoto, Goshiro; Kume, Naoto; Kobayashi, Shinji; Kondo, Eiji; Hiragi, Shusuke; Okamoto, Kazuya; Tamura, Hiroshi; Kuroda, Tomohiro

2017-01-01

openEHR is a widely used EHR specification. Given its technology-independent nature, different approaches for implementing openEHR data repositories exist. Public openEHR datasets are needed to conduct benchmark analyses over different implementations. To address their current unavailability, we propose a method for generating openEHR test datasets that can be publicly shared and used.

A three-dimensional multivariate representation of atmospheric variability

NASA Astrophysics Data System (ADS)

Žagar, Nedjeljka; Jelić, Damjan; Blaauw, Marten; Jesenko, Blaž

2016-04-01

A recently developed MODES software has been applied to the ECMWF analyses and forecasts and to several reanalysis datasets to describe the global variability of the balanced and inertio-gravity (IG) circulation across many scales by considering both mass and wind field and the whole model depth. In particular, the IG spectrum, which has only recently become observable in global datasets, can be studied simultaneously in the mass field and wind field and considering the whole model depth. MODES is open-access software that performs the normal-mode function decomposition of the 3D global datasets. Its application to the ERA Interim dataset reveals several aspects of the large-scale circulation after it has been partitioned into the linearly balanced and IG components. The global energy distribution is dominated by the balanced energy while the IG modes contribute around 8% of the total wave energy. However, on subsynoptic scales IG energy dominates and it is associated with the main features of tropical variability on all scales. The presented energy distribution and features of the zonally-averaged and equatorial circulation provide a reference for the intercomparison of several reanalysis datasets and for the validation of climate models. Features of the global IG circulation are compared in ERA Interim, MERRA and JRA reanalysis datasets and in several CMIP5 models. Since October 2014 the operational medium-range forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) have been analyzed by MODES daily and an online archive of all the outputs is available at http://meteo.fmf.uni-lj.si/MODES. New outputs are made available daily based on the 00 UTC run and subsequent 12-hour forecasts up to 240-hour forecast. In addition to the energy spectra and horizontal circulation on selected levels for the balanced and IG components, the equatorial Kelvin waves are presented in time and space as the most energetic tropical IG modes propagating vertically and along the equator from its main generation regions in the upper troposphere over the Indian and Pacific region. The validation of the 10-day ECMWF forecasts with analyses in the modal space suggests a lack of variability in the tropics in the medium range. Reference: Žagar, N. et al., 2015: Normal-mode function representation of global 3-D data sets: open-access software for the atmospheric research community. Geosci. Model Dev., 8, 1169-1195, doi:10.5194/gmd-8-1169-2015 Žagar, N., R. Buizza, and J. Tribbia, 2015: A three-dimensional multivariate modal analysis of atmospheric predictability with application to the ECMWF ensemble. J. Atmos. Sci., 72, 4423-4444 The MODES software is available from http://meteo.fmf.uni-lj.si/MODES.

A new method to generate a high-resolution global distribution map of lake chlorophyll

USGS Publications Warehouse

Sayers, Michael J; Grimm, Amanda G.; Shuchman, Robert A.; Deines, Andrew M.; Bunnell, David B.; Raymer, Zachary B; Rogers, Mark W.; Woelmer, Whitney; Bennion, David; Brooks, Colin N.; Whitley, Matthew A.; Warner, David M.; Mychek-Londer, Justin G.

2015-01-01

A new method was developed, evaluated, and applied to generate a global dataset of growing-season chlorophyll-a (chl) concentrations in 2011 for freshwater lakes. Chl observations from freshwater lakes are valuable for estimating lake productivity as well as assessing the role that these lakes play in carbon budgets. The standard 4 km NASA OceanColor L3 chlorophyll concentration products generated from MODIS and MERIS sensor data are not sufficiently representative of global chl values because these can only resolve larger lakes, which generally have lower chl concentrations than lakes of smaller surface area. Our new methodology utilizes the 300 m-resolution MERIS full-resolution full-swath (FRS) global dataset as input and does not rely on the land mask used to generate standard NASA products, which masks many lakes that are otherwise resolvable in MERIS imagery. The new method produced chl concentration values for 78,938 and 1,074 lakes in the northern and southern hemispheres, respectively. The mean chl for lakes visible in the MERIS composite was 19.2 ± 19.2, the median was 13.3, and the interquartile range was 3.90–28.6 mg m−3. The accuracy of the MERIS-derived values was assessed by comparison with temporally near-coincident and globally distributed in situmeasurements from the literature (n = 185, RMSE = 9.39, R2 = 0.72). This represents the first global-scale dataset of satellite-derived chl estimates for medium to large lakes.

Spatial distribution, sampling precision and survey design optimisation with non-normal variables: The case of anchovy (Engraulis encrasicolus) recruitment in Spanish Mediterranean waters

NASA Astrophysics Data System (ADS)

Tugores, M. Pilar; Iglesias, Magdalena; Oñate, Dolores; Miquel, Joan

2016-02-01

In the Mediterranean Sea, the European anchovy (Engraulis encrasicolus) displays a key role in ecological and economical terms. Ensuring stock sustainability requires the provision of crucial information, such as species spatial distribution or unbiased abundance and precision estimates, so that management strategies can be defined (e.g. fishing quotas, temporal closure areas or marine protected areas MPA). Furthermore, the estimation of the precision of global abundance at different sampling intensities can be used for survey design optimisation. Geostatistics provide a priori unbiased estimations of the spatial structure, global abundance and precision for autocorrelated data. However, their application to non-Gaussian data introduces difficulties in the analysis in conjunction with low robustness or unbiasedness. The present study applied intrinsic geostatistics in two dimensions in order to (i) analyse the spatial distribution of anchovy in Spanish Western Mediterranean waters during the species' recruitment season, (ii) produce distribution maps, (iii) estimate global abundance and its precision, (iv) analyse the effect of changing the sampling intensity on the precision of global abundance estimates and, (v) evaluate the effects of several methodological options on the robustness of all the analysed parameters. The results suggested that while the spatial structure was usually non-robust to the tested methodological options when working with the original dataset, it became more robust for the transformed datasets (especially for the log-backtransformed dataset). The global abundance was always highly robust and the global precision was highly or moderately robust to most of the methodological options, except for data transformation.

Microclimate Data Improve Predictions of Insect Abundance Models Based on Calibrated Spatiotemporal Temperatures.

PubMed

Rebaudo, François; Faye, Emile; Dangles, Olivier

2016-01-01

A large body of literature has recently recognized the role of microclimates in controlling the physiology and ecology of species, yet the relevance of fine-scale climatic data for modeling species performance and distribution remains a matter of debate. Using a 6-year monitoring of three potato moth species, major crop pests in the tropical Andes, we asked whether the spatiotemporal resolution of temperature data affect the predictions of models of moth performance and distribution. For this, we used three different climatic data sets: (i) the WorldClim dataset (global dataset), (ii) air temperature recorded using data loggers (weather station dataset), and (iii) air crop canopy temperature (microclimate dataset). We developed a statistical procedure to calibrate all datasets to monthly and yearly variation in temperatures, while keeping both spatial and temporal variances (air monthly temperature at 1 km² for the WorldClim dataset, air hourly temperature for the weather station, and air minute temperature over 250 m radius disks for the microclimate dataset). Then, we computed pest performances based on these three datasets. Results for temperature ranging from 9 to 11°C revealed discrepancies in the simulation outputs in both survival and development rates depending on the spatiotemporal resolution of the temperature dataset. Temperature and simulated pest performances were then combined into multiple linear regression models to compare predicted vs. field data. We used an additional set of study sites to test the ability of the results of our model to be extrapolated over larger scales. Results showed that the model implemented with microclimatic data best predicted observed pest abundances for our study sites, but was less accurate than the global dataset model when performed at larger scales. Our simulations therefore stress the importance to consider different temperature datasets depending on the issue to be solved in order to accurately predict species abundances. In conclusion, keeping in mind that the mismatch between the size of organisms and the scale at which climate data are collected and modeled remains a key issue, temperature dataset selection should be balanced by the desired output spatiotemporal scale for better predicting pest dynamics and developing efficient pest management strategies.

Microclimate Data Improve Predictions of Insect Abundance Models Based on Calibrated Spatiotemporal Temperatures

PubMed Central

Rebaudo, François; Faye, Emile; Dangles, Olivier

2016-01-01

A large body of literature has recently recognized the role of microclimates in controlling the physiology and ecology of species, yet the relevance of fine-scale climatic data for modeling species performance and distribution remains a matter of debate. Using a 6-year monitoring of three potato moth species, major crop pests in the tropical Andes, we asked whether the spatiotemporal resolution of temperature data affect the predictions of models of moth performance and distribution. For this, we used three different climatic data sets: (i) the WorldClim dataset (global dataset), (ii) air temperature recorded using data loggers (weather station dataset), and (iii) air crop canopy temperature (microclimate dataset). We developed a statistical procedure to calibrate all datasets to monthly and yearly variation in temperatures, while keeping both spatial and temporal variances (air monthly temperature at 1 km² for the WorldClim dataset, air hourly temperature for the weather station, and air minute temperature over 250 m radius disks for the microclimate dataset). Then, we computed pest performances based on these three datasets. Results for temperature ranging from 9 to 11°C revealed discrepancies in the simulation outputs in both survival and development rates depending on the spatiotemporal resolution of the temperature dataset. Temperature and simulated pest performances were then combined into multiple linear regression models to compare predicted vs. field data. We used an additional set of study sites to test the ability of the results of our model to be extrapolated over larger scales. Results showed that the model implemented with microclimatic data best predicted observed pest abundances for our study sites, but was less accurate than the global dataset model when performed at larger scales. Our simulations therefore stress the importance to consider different temperature datasets depending on the issue to be solved in order to accurately predict species abundances. In conclusion, keeping in mind that the mismatch between the size of organisms and the scale at which climate data are collected and modeled remains a key issue, temperature dataset selection should be balanced by the desired output spatiotemporal scale for better predicting pest dynamics and developing efficient pest management strategies. PMID:27148077

A genome-wide association study of anorexia nervosa

PubMed Central

Boraska, Vesna; Franklin, Christopher S; Floyd, James AB; Thornton, Laura M; Huckins, Laura M; Southam, Lorraine; Rayner, N William; Tachmazidou, Ioanna; Klump, Kelly L; Treasure, Janet; Lewis, Cathryn M; Schmidt, Ulrike; Tozzi, Federica; Kiezebrink, Kirsty; Hebebrand, Johannes; Gorwood, Philip; Adan, Roger AH; Kas, Martien JH; Favaro, Angela; Santonastaso, Paolo; Fernández-Aranda, Fernando; Gratacos, Monica; Rybakowski, Filip; Dmitrzak-Weglarz, Monika; Kaprio, Jaakko; Keski-Rahkonen, Anna; Raevuori, Anu; Van Furth, Eric F; Landt, Margarita CT Slof-Op t; Hudson, James I; Reichborn-Kjennerud, Ted; Knudsen, Gun Peggy S; Monteleone, Palmiero; Kaplan, Allan S; Karwautz, Andreas; Hakonarson, Hakon; Berrettini, Wade H; Guo, Yiran; Li, Dong; Schork, Nicholas J.; Komaki, Gen; Ando, Tetsuya; Inoko, Hidetoshi; Esko, Tõnu; Fischer, Krista; Männik, Katrin; Metspalu, Andres; Baker, Jessica H; Cone, Roger D; Dackor, Jennifer; DeSocio, Janiece E; Hilliard, Christopher E; O'Toole, Julie K; Pantel, Jacques; Szatkiewicz, Jin P; Taico, Chrysecolla; Zerwas, Stephanie; Trace, Sara E; Davis, Oliver SP; Helder, Sietske; Bühren, Katharina; Burghardt, Roland; de Zwaan, Martina; Egberts, Karin; Ehrlich, Stefan; Herpertz-Dahlmann, Beate; Herzog, Wolfgang; Imgart, Hartmut; Scherag, André; Scherag, Susann; Zipfel, Stephan; Boni, Claudette; Ramoz, Nicolas; Versini, Audrey; Brandys, Marek K; Danner, Unna N; de Kovel, Carolien; Hendriks, Judith; Koeleman, Bobby PC; Ophoff, Roel A; Strengman, Eric; van Elburg, Annemarie A; Bruson, Alice; Clementi, Maurizio; Degortes, Daniela; Forzan, Monica; Tenconi, Elena; Docampo, Elisa; Escaramís, Geòrgia; Jiménez-Murcia, Susana; Lissowska, Jolanta; Rajewski, Andrzej; Szeszenia-Dabrowska, Neonila; Slopien, Agnieszka; Hauser, Joanna; Karhunen, Leila; Meulenbelt, Ingrid; Slagboom, P Eline; Tortorella, Alfonso; Maj, Mario; Dedoussis, George; Dikeos, Dimitris; Gonidakis, Fragiskos; Tziouvas, Konstantinos; Tsitsika, Artemis; Papezova, Hana; Slachtova, Lenka; Martaskova, Debora; Kennedy, James L.; Levitan, Robert D.; Yilmaz, Zeynep; Huemer, Julia; Koubek, Doris; Merl, Elisabeth; Wagner, Gudrun; Lichtenstein, Paul; Breen, Gerome; Cohen-Woods, Sarah; Farmer, Anne; McGuffin, Peter; Cichon, Sven; Giegling, Ina; Herms, Stefan; Rujescu, Dan; Schreiber, Stefan; Wichmann, H-Erich; Dina, Christian; Sladek, Rob; Gambaro, Giovanni; Soranzo, Nicole; Julia, Antonio; Marsal, Sara; Rabionet, Raquel; Gaborieau, Valerie; Dick, Danielle M; Palotie, Aarno; Ripatti, Samuli; Widén, Elisabeth; Andreassen, Ole A; Espeseth, Thomas; Lundervold, Astri; Reinvang, Ivar; Steen, Vidar M; Le Hellard, Stephanie; Mattingsdal, Morten; Ntalla, Ioanna; Bencko, Vladimir; Foretova, Lenka; Janout, Vladimir; Navratilova, Marie; Gallinger, Steven; Pinto, Dalila; Scherer, Stephen; Aschauer, Harald; Carlberg, Laura; Schosser, Alexandra; Alfredsson, Lars; Ding, Bo; Klareskog, Lars; Padyukov, Leonid; Finan, Chris; Kalsi, Gursharan; Roberts, Marion; Logan, Darren W; Peltonen, Leena; Ritchie, Graham RS; Barrett, Jeffrey C; Estivill, Xavier; Hinney, Anke; Sullivan, Patrick F; Collier, David A; Zeggini, Eleftheria; Bulik, Cynthia M

2015-01-01

Anorexia nervosa (AN) is a complex and heritable eating disorder characterized by dangerously low body weight. Neither candidate gene studies nor an initial genome wide association study (GWAS) have yielded significant and replicated results. We performed a GWAS in 2,907 cases with AN from 14 countries (15 sites) and 14,860 ancestrally matched controls as part of the Genetic Consortium for AN (GCAN) and the Wellcome Trust Case Control Consortium 3 (WTCCC3). Individual association analyses were conducted in each stratum and meta-analyzed across all 15 discovery datasets. Seventy-six (72 independent) SNPs were taken forward for in silico (two datasets) or de novo (13 datasets) replication genotyping in 2,677 independent AN cases and 8,629 European ancestry controls along with 458 AN cases and 421 controls from Japan. The final global meta-analysis across discovery and replication datasets comprised 5,551 AN cases and 21,080 controls. AN subtype analyses (1,606 AN restricting; 1,445 AN binge-purge) were performed. No findings reached genome-wide significance. Two intronic variants were suggestively associated: rs9839776 (P=3.01×10-7) in SOX2OT and rs17030795 (P=5.84×10-6) in PPP3CA. Two additional signals were specific to Europeans: rs1523921 (P=5.76×10-6) between CUL3 and FAM124B and rs1886797 (P=8.05×10-6) near SPATA13. Comparing discovery to replication results, 76% of the effects were in the same direction, an observation highly unlikely to be due to chance (P=4×10-6), strongly suggesting that true findings exist but that our sample, the largest yet reported, was underpowered for their detection. The accrual of large genotyped AN case-control samples should be an immediate priority for the field. PMID:24514567

Generation of a precise DEM by interactive synthesis of multi-temporal elevation datasets: a case study of Schirmacher Oasis, East Antarctica

NASA Astrophysics Data System (ADS)

Jawak, Shridhar D.; Luis, Alvarinho J.

2016-05-01

Digital elevation model (DEM) is indispensable for analysis such as topographic feature extraction, ice sheet melting, slope stability analysis, landscape analysis and so on. Such analysis requires a highly accurate DEM. Available DEMs of Antarctic region compiled by using radar altimetry and the Antarctic digital database indicate elevation variations of up to hundreds of meters, which necessitates the generation of local improved DEM. An improved DEM of the Schirmacher Oasis, East Antarctica has been generated by synergistically fusing satellite-derived laser altimetry data from Geoscience Laser Altimetry System (GLAS), Radarsat Antarctic Mapping Project (RAMP) elevation data and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global elevation data (GDEM). This is a characteristic attempt to generate a DEM of any part of Antarctica by fusing multiple elevation datasets, which is essential to model the ice elevation change and address the ice mass balance. We analyzed a suite of interpolation techniques for constructing a DEM from GLAS, RAMP and ASTER DEM-based point elevation datasets, in order to determine the level of confidence with which the interpolation techniques can generate a better interpolated continuous surface, and eventually improve the elevation accuracy of DEM from synergistically fused RAMP, GLAS and ASTER point elevation datasets. The DEM presented in this work has a vertical accuracy (≈ 23 m) better than RAMP DEM (≈ 57 m) and ASTER DEM (≈ 64 m) individually. The RAMP DEM and ASTER DEM elevations were corrected using differential GPS elevations as ground reference data, and the accuracy obtained after fusing multitemporal datasets is found to be improved than that of existing DEMs constructed by using RAMP or ASTER alone. This is our second attempt of fusing multitemporal, multisensory and multisource elevation data to generate a DEM of Antarctica, in order to address the ice elevation change and address the ice mass balance. Our approach focuses on the strengths of each elevation data source to produce an accurate elevation model.

Scaling up: What coupled land-atmosphere models can tell us about critical zone processes

NASA Astrophysics Data System (ADS)

FitzGerald, K. A.; Masarik, M. T.; Rudisill, W. J.; Gelb, L.; Flores, A. N.

2017-12-01

A significant limitation to extending our knowledge of critical zone (CZ) evolution and function is a lack of hydrometeorological information at sufficiently fine spatial and temporal resolutions to resolve topo-climatic gradients and adequate spatial and temporal extent to capture a range of climatic conditions across ecoregions. Research at critical zone observatories (CZOs) suggests hydrometeorological stores and fluxes exert key controls on processes such as hydrologic partitioning and runoff generation, landscape evolution, soil formation, biogeochemical cycling, and vegetation dynamics. However, advancing fundamental understanding of CZ processes necessitates understanding how hydrometeorological drivers vary across space and time. As a result of recent advances in computational capabilities it has become possible, although still computationally expensive, to simulate hydrometeorological conditions via high resolution coupled land-atmosphere models. Using the Weather Research and Forecasting (WRF) model, we developed a high spatiotemporal resolution dataset extending from water year 1987 to present for the Snake River Basin in the northwestern USA including the Reynolds Creek and Dry Creek Experimental Watersheds, both part of the Reynolds Creek CZO, as well as a range of other ecosystems including shrubland desert, montane forests, and alpine tundra. Drawing from hypotheses generated by work at these sites and across the CZO network, we use the resulting dataset in combination with CZO observations and publically available datasets to provide insights regarding hydrologic partitioning, vegetation distribution, and erosional processes. This dataset provides key context in interpreting and reconciling what observations obtained at particular sites reveal about underlying CZ structure and function. While this dataset does not extend to future climates, the same modeling framework can be used to dynamically downscale coarse global climate model output to scales relevant to CZ processes. This presents an opportunity to better characterize the impact of climate change on the CZ. We also argue that opportunities exist beyond the one way flow of information and that what we learn at CZOs has the potential to contribute significantly to improved Earth system models.

Open Data as Open Educational Resources: Towards Transversal Skills and Global Citizenship

ERIC Educational Resources Information Center

Atenas, Javiera; Havemann, Leo; Priego, Ernesto

2015-01-01

Open Data is the name given to datasets which have been generated by international organisations, governments, NGOs and academic researchers, and made freely available online and openly-licensed. These datasets can be used by educators as Open Educational Resources (OER) to support different teaching and learning activities, allowing students to…

Post-MBA Industry Shifts: An Investigation of Career, Educational and Demographic Factors

ERIC Educational Resources Information Center

Hwang, Alvin; Bento, Regina; Arbaugh, J. B.

2011-01-01

Purpose: The purpose of this study is to examine factors that predict industry-level career change among MBA graduates. Design/methodology/approach: The study analyzed longitudinal data from the Management Education Research Institute (MERI)'s Global MBA Graduate Survey Dataset and MBA Alumni Perspectives Survey Datasets, using principal component…

Data Sources for the Analyses

EPA Pesticide Factsheets

Links are provided for the National Wetlands Inventory, National Hydrography Dataset, and the WorldClim-Global Climate Data source data websitesThis dataset is associated with the following publication:Lane , C., and E. D'Amico. Identification of Putative Geographically Isolated Wetlands of the Conterminous United States. JAWRA. American Water Resources Association, Middleburg, VA, USA, online, (2016).

A Large-scale Benchmark Dataset for Event Recognition in Surveillance Video

DTIC Science & Technology

2011-06-01

orders of magnitude larger than existing datasets such CAVIAR [7]. TRECVID 2008 airport dataset [16] contains 100 hours of video, but, it provides only...entire human figure (e.g., above shoulder), amounting to 500% human to video 2Some statistics are approximate, obtained from the CAVIAR 1st scene and...and diversity in both col- lection sites and viewpoints. In comparison to surveillance datasets such as CAVIAR [7] and TRECVID [16] shown in Fig. 3

Sparse Group Penalized Integrative Analysis of Multiple Cancer Prognosis Datasets

PubMed Central

Liu, Jin; Huang, Jian; Xie, Yang; Ma, Shuangge

2014-01-01

SUMMARY In cancer research, high-throughput profiling studies have been extensively conducted, searching for markers associated with prognosis. Because of the “large d, small n” characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Recent studies have shown that integrative analysis, which simultaneously analyzes multiple datasets, can be more effective than single-dataset analysis and classic meta-analysis. In most of existing integrative analysis, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. Several approaches have been designed to reinforce this assumption. In practice, different datasets may differ in terms of patient selection criteria, profiling techniques, and many other aspects. Such differences may make the homogeneity model too restricted. In this study, we assume the heterogeneity model, under which different datasets are allowed to have different sets of markers. With multiple cancer prognosis datasets, we adopt the AFT (accelerated failure time) model to describe survival. This model may have the lowest computational cost among popular semiparametric survival models. For marker selection, we adopt a sparse group MCP (minimax concave penalty) approach. This approach has an intuitive formulation and can be computed using an effective group coordinate descent algorithm. Simulation study shows that it outperforms the existing approaches under both the homogeneity and heterogeneity models. Data analysis further demonstrates the merit of heterogeneity model and proposed approach. PMID:23938111

Comparison of Different Global Information Sources Used in Surface Radiative Flux Calculation: Radiative Properties of the Surface

NASA Technical Reports Server (NTRS)

Zhang, Yuanchong; Rossow, William B.; Stackhouse, Paul W., Jr.

2007-01-01

Direct estimates of surface radiative fluxes that resolve regional and weather-scale variabilty over the whole globe with reasonable accuracy have only become possible with the advent of extensive global, mostly satellite, datasets within the past couple of decades. The accuracy of these fluxes, estimated to be about 10-15 W per square meter is largely limited by the accuracy of the input datasets. The leading uncertainties in the surface fluxes are no longer predominantly induced by clouds but are now as much associated with uncertainties in the surface and near-surface atmospheric properties. This study presents a fuller, more quantitative evaluation of the uncertainties for the surface albedo and emissivity and surface skin temperatures by comparing the main available global datasets from the Moderate-Resolution Imaging Spectroradiometer product, the NASA Global Energy and Water Cycle Experiment Surface Radiation Budget project, the European Centre for Medium-Range Weather Forecasts, the National Aeronautics and Space Administration, the National Centers for Environmental Prediction, the International Satellite Cloud Climatology Project (ISCCP), the Laboratoire de Meteorologie Dynamique, NOAA/NASA Pathfinder Advanced Very High Resolution Radiometer project, NOAA Optimum Interpolation Sea Surface Temperature Analysis and the Tropical Rainfall Measuring Mission (TRMM) Microwave Image project. The datasets are, in practice, treated as an ensemble of realizations of the actual climate such that their differences represent an estimate of the uncertainty in their measurements because we do not possess global truth datasets for these quantities. The results are globally representative and may be taken as a generalization of our previous ISCCP-based uncertainty estimates for the input datasets. Surface properties have the primary role in determining the surface upward shortwave (SW) and longwave (LW) flux. From this study, the following conclusions are obtained. Although land surface albedos in the near near-infrared remain poorly constrained (highly uncertain), they do not cause too much error in total surface SW fluxes; the more subtle regional and seasonal variations associated with vegetation and snow are still on doubt. The uncertainty of the broadband black-sky SW albedo for land surface from this study is about 7%, which can easily induce 5-10 W per square meter uncertainty in (upwelling) surface SW flux estimates. Even though available surface (broadband) LW emissivity datasets differ significantly (3%-5% uncertainty), this disagreement is confined to wavelengths greater than 20 micrometers so that there is little practical effect (1-3 W per square meters) on the surface upwelling LW fluxes. The surface skin temperature is one of two leading factors that cause problems with surface LW fluxes. Even though the differences among the various datasets are generally only 2-4 K, this can easily cause 10-15 W per square meter uncertainty in calculated surface (upwelling) LW fluxes. Significant improvements could be obtained for surface LW flux calculations by improving the retrievals of (in order of decreasing importance): (1) surface skin temperature, (2) surface air and near-surface-layer temperature, (3) column precipitable water amount and (4) broadband emissivity. And for surface SW fluxes, improvements could be obtained (excluding improved cloud treatment) by improving the retrievals of (1) aerosols (from our sensitivity studies but not discussed in this work), and (2) surface (black-sky) albedo, of which, NIR part of the spectrum has much larger uncertainty.

The Greenwich Photo-heliographic Results (1874 - 1976): Summary of the Observations, Applications, Datasets, Definitions and Errors

NASA Astrophysics Data System (ADS)

Willis, D. M.; Coffey, H. E.; Henwood, R.; Erwin, E. H.; Hoyt, D. V.; Wild, M. N.; Denig, W. F.

2013-11-01

The measurements of sunspot positions and areas that were published initially by the Royal Observatory, Greenwich, and subsequently by the Royal Greenwich Observatory (RGO), as the Greenwich Photo-heliographic Results ( GPR), 1874 - 1976, exist in both printed and digital forms. These printed and digital sunspot datasets have been archived in various libraries and data centres. Unfortunately, however, typographic, systematic and isolated errors can be found in the various datasets. The purpose of the present paper is to begin the task of identifying and correcting these errors. In particular, the intention is to provide in one foundational paper all the necessary background information on the original solar observations, their various applications in scientific research, the format of the different digital datasets, the necessary definitions of the quantities measured, and the initial identification of errors in both the printed publications and the digital datasets. Two companion papers address the question of specific identifiable errors; namely, typographic errors in the printed publications, and both isolated and systematic errors in the digital datasets. The existence of two independently prepared digital datasets, which both contain information on sunspot positions and areas, makes it possible to outline a preliminary strategy for the development of an even more accurate digital dataset. Further work is in progress to generate an extremely reliable sunspot digital dataset, based on the programme of solar observations supported for more than a century by the Royal Observatory, Greenwich, and the Royal Greenwich Observatory. This improved dataset should be of value in many future scientific investigations.

Integrating genome-wide association studies and gene expression data highlights dysregulated multiple sclerosis risk pathways.

PubMed

Liu, Guiyou; Zhang, Fang; Jiang, Yongshuai; Hu, Yang; Gong, Zhongying; Liu, Shoufeng; Chen, Xiuju; Jiang, Qinghua; Hao, Junwei

2017-02-01

Much effort has been expended on identifying the genetic determinants of multiple sclerosis (MS). Existing large-scale genome-wide association study (GWAS) datasets provide strong support for using pathway and network-based analysis methods to investigate the mechanisms underlying MS. However, no shared genetic pathways have been identified to date. We hypothesize that shared genetic pathways may indeed exist in different MS-GWAS datasets. Here, we report results from a three-stage analysis of GWAS and expression datasets. In stage 1, we conducted multiple pathway analyses of two MS-GWAS datasets. In stage 2, we performed a candidate pathway analysis of the large-scale MS-GWAS dataset. In stage 3, we performed a pathway analysis using the dysregulated MS gene list from seven human MS case-control expression datasets. In stage 1, we identified 15 shared pathways. In stage 2, we successfully replicated 14 of these 15 significant pathways. In stage 3, we found that dysregulated MS genes were significantly enriched in 10 of 15 MS risk pathways identified in stages 1 and 2. We report shared genetic pathways in different MS-GWAS datasets and highlight some new MS risk pathways. Our findings provide new insights on the genetic determinants of MS.

Online 3D Ear Recognition by Combining Global and Local Features.

PubMed

Liu, Yahui; Zhang, Bob; Lu, Guangming; Zhang, David

2016-01-01

The three-dimensional shape of the ear has been proven to be a stable candidate for biometric authentication because of its desirable properties such as universality, uniqueness, and permanence. In this paper, a special laser scanner designed for online three-dimensional ear acquisition was described. Based on the dataset collected by our scanner, two novel feature classes were defined from a three-dimensional ear image: the global feature class (empty centers and angles) and local feature class (points, lines, and areas). These features are extracted and combined in an optimal way for three-dimensional ear recognition. Using a large dataset consisting of 2,000 samples, the experimental results illustrate the effectiveness of fusing global and local features, obtaining an equal error rate of 2.2%.

Online 3D Ear Recognition by Combining Global and Local Features

PubMed Central

Liu, Yahui; Zhang, Bob; Lu, Guangming; Zhang, David

2016-01-01

The three-dimensional shape of the ear has been proven to be a stable candidate for biometric authentication because of its desirable properties such as universality, uniqueness, and permanence. In this paper, a special laser scanner designed for online three-dimensional ear acquisition was described. Based on the dataset collected by our scanner, two novel feature classes were defined from a three-dimensional ear image: the global feature class (empty centers and angles) and local feature class (points, lines, and areas). These features are extracted and combined in an optimal way for three-dimensional ear recognition. Using a large dataset consisting of 2,000 samples, the experimental results illustrate the effectiveness of fusing global and local features, obtaining an equal error rate of 2.2%. PMID:27935955

Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project

NASA Astrophysics Data System (ADS)

Stengel, Martin; Stapelberg, Stefan; Sus, Oliver; Schlundt, Cornelia; Poulsen, Caroline; Thomas, Gareth; Christensen, Matthew; Carbajal Henken, Cintia; Preusker, Rene; Fischer, Jürgen; Devasthale, Abhay; Willén, Ulrika; Karlsson, Karl-Göran; McGarragh, Gregory R.; Proud, Simon; Povey, Adam C.; Grainger, Roy G.; Fokke Meirink, Jan; Feofilov, Artem; Bennartz, Ralf; Bojanowski, Jedrzej S.; Hollmann, Rainer

2017-11-01

New cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two retrieval systems were developed that include components for cloud detection and cloud typing followed by cloud property retrievals based on the optimal estimation (OE) technique. The OE-based retrievals are applied to simultaneously retrieve cloud-top pressure, cloud particle effective radius and cloud optical thickness using measurements at visible, near-infrared and thermal infrared wavelengths, which ensures spectral consistency. The retrieved cloud properties are further processed to derive cloud-top height, cloud-top temperature, cloud liquid water path, cloud ice water path and spectral cloud albedo. The Cloud_cci products are pixel-based retrievals, daily composites of those on a global equal-angle latitude-longitude grid, and monthly cloud properties such as averages, standard deviations and histograms, also on a global grid. All products include rigorous propagation of the retrieval and sampling uncertainties. Grouping the orbital properties of the sensor families, six datasets have been defined, which are named AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each comprising a specific subset of all available sensors. The individual characteristics of the datasets are presented together with a summary of the retrieval systems and measurement records on which the dataset generation were based. Example validation results are given, based on comparisons to well-established reference observations, which demonstrate the good quality of the data. In particular the ensured spectral consistency and the rigorous uncertainty propagation through all processing levels can be considered as new features of the Cloud_cci datasets compared to existing datasets. In addition, the consistency among the individual datasets allows for a potential combination of them as well as facilitates studies on the impact of temporal sampling and spatial resolution on cloud climatologies.

For each dataset a digital object identifier has been issued:

Cloud_cci AVHRR-AM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002

Cloud_cci AVHRR-PM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002

Cloud_cci MODIS-Terra: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Terra/V002

Cloud_cci MODIS-Aqua: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Aqua/V002

Cloud_cci ATSR2-AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V002

Cloud_cci MERIS+AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MERIS+AATSR/V002

Observed Budgets for the Global Climate

NASA Astrophysics Data System (ADS)

Kottek, M.; Haimberger, L.; Rubel, F.; Hantel, M.

2003-04-01

A global dataset for selected budget quantities specifying the present climate for the period 1991-1995 has been compiled. This dataset is an essential component of the new climate volume within the series Landolt Boernstein - Numerical Data and Functional Relationships in Science and Technology, to be published this year. Budget quantities are those that appear in a budget equation. Emphasis in this collection is placed on observational data of both in situ and remotely sensed quantities. The fields are presented as monthly means with a uniform space resolution of one degree. Main focus is on climatologically relevant state and flux quantities at the earth's surface and at the top of atmosphere. Some secondary and complex climate elements are also presented (e.g. tornadoe frequency). The progress of this collection as compared to other climate datasets is, apart from the quality of the input data, that all fields are presented in standardized form as far as possible. Further, visualization loops of the global fields in various projections will be available for the user in the eventual book. For some budget quantities, e.g. precipitation, it has been necessary to merge data from different sources; insufficiently observed parameters have been supplemented through the ECMWF ERA-40 reanalyses. If all quantities of a budget have been evaluated the gross residual represents an estimate of data quality. For example, the global water budget residual is found to be up to 30 % depending on the used data. This suggests that the observation of global climate parameters needs further improvement.

A Global View of Large-Scale Commercial Fishing

NASA Astrophysics Data System (ADS)

Kroodsma, D.

2016-12-01

Advances in big data processing and satellite technology, combined with the widespread adoption of Automatic Identification System (AIS) devices, now allow the monitoring of fishing activity at a global scale and in high resolution. We analyzed AIS data from more than 40,000 vessels from 2012-2015 to produce 0.1 degree global daily maps of apparent fishing effort. Vessels were matched to publically accessible fishing vessel registries and identified as fishing vessels through AIS Type 5 and Type 24 self-reported messages. Fishing vessels that broadcasted false locations in AIS data were excluded from the analysis. To model fishing pattern classification, a subset of fishing vessels were analyzed and specific movements were classified as "fishing" or "not fishing." A logistic regression model was fitted to these classifications using the following features: a vessel's average speed, the standard deviation of its speed, and the standard deviation of its course over a 12 hour time window. We then applied this model to the entire fishing vessel dataset and time normalized it to produce a global map of fishing hours. The resulting dataset allows for numerous new analyses. For instance, it can assist with monitoring apparent fishing activity in large pelagic marine protected areas and restricted gear use areas, or it can quantify how activity may be affected by seasonal or annual changes in biological productivity. This dataset is now published and freely available in Google's Earth Engine platform, available for researchers to answer a host of questions related to global fishing effort.

From Data to Knowledge: GEOSS experience and the GEOSS Knowledge Base contribution to the GCI

NASA Astrophysics Data System (ADS)

Santoro, M.; Nativi, S.; Mazzetti, P., Sr.; Plag, H. P.

2016-12-01

According to systems theory, data is raw, it simply exists and has no significance beyond its existence; while, information is data that has been given meaning by way of relational connection. The appropriate collection of information, such that it contributes to understanding, is a process of knowledge creation.The Global Earth Observation System of Systems (GEOSS) developed by the Group on Earth Observations (GEO) is a set of coordinated, independent Earth observation, information and processing systems that interact and provide access to diverse information for a broad range of users in both public and private sectors. GEOSS links these systems to strengthen the monitoring of the state of the Earth. In the past ten years, the development of GEOSS has taught several lessons dealing with the need to move from (open) data to information and knowledge sharing. Advanced user-focused services require to move from a data-driven framework to a knowledge sharing platform. Such a platform needs to manage information and knowledge, in addition to datasets linked to them. For this scope, GEO has launched a specific task called "GEOSS Knowledge Base", which deals with resources, like user requirements, Sustainable Development Goals (SDGs), observation and processing ontologies, publications, guidelines, best practices, business processes/algorithms, definition of advanced concepts like Essential Variables (EVs), indicators, strategic goals, etc. In turn, information and knowledge (e.g. guidelines, best practices, user requirements, business processes, algorithms, etc.) can be used to generate additional information and knowledge from shared datasets. To fully utilize and leverage the GEOSS Knowledge Base, the current GEOSS Common Infrastructure (GCI) model will be extended and advanced to consider important concepts and implementation artifacts, such as data processing services and environmental/economic models as well as EVs, Primary Indicators, and SDGs. The new GCI model will link these concepts to the present dataset, observation and sensor concepts, enabling a set of very important new capabilities to be offered to GEOSS users.

A global dataset of sub-daily rainfall indices

NASA Astrophysics Data System (ADS)

Fowler, H. J.; Lewis, E.; Blenkinsop, S.; Guerreiro, S.; Li, X.; Barbero, R.; Chan, S.; Lenderink, G.; Westra, S.

2017-12-01

It is still uncertain how hydrological extremes will change with global warming as we do not fully understand the processes that cause extreme precipitation under current climate variability. The INTENSE project is using a novel and fully-integrated data-modelling approach to provide a step-change in our understanding of the nature and drivers of global precipitation extremes and change on societally relevant timescales, leading to improved high-resolution climate model representation of extreme rainfall processes. The INTENSE project is in conjunction with the World Climate Research Programme (WCRP)'s Grand Challenge on 'Understanding and Predicting Weather and Climate Extremes' and the Global Water and Energy Exchanges Project (GEWEX) Science questions. A new global sub-daily precipitation dataset has been constructed (data collection is ongoing). Metadata for each station has been calculated, detailing record lengths, missing data, station locations. A set of global hydroclimatic indices have been produced based upon stakeholder recommendations including indices that describe maximum rainfall totals and timing, the intensity, duration and frequency of storms, frequency of storms above specific thresholds and information about the diurnal cycle. This will provide a unique global data resource on sub-daily precipitation whose derived indices will be freely available to the wider scientific community.

SWAT use of gridded observations for simulating runoff - a Vietnam river basin study

NASA Astrophysics Data System (ADS)

Vu, M. T.; Raghavan, S. V.; Liong, S. Y.

2011-12-01

Many research studies that focus on basin hydrology have used the SWAT model to simulate runoff. One common practice in calibrating the SWAT model is the application of station data rainfall to simulate runoff. But over regions lacking robust station data, there is a problem of applying the model to study the hydrological responses. For some countries and remote areas, the rainfall data availability might be a constraint due to many different reasons such as lacking of technology, war time and financial limitation that lead to difficulty in constructing the runoff data. To overcome such a limitation, this research study uses some of the available globally gridded high resolution precipitation datasets to simulate runoff. Five popular gridded observation precipitation datasets: (1) Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE), (2) Tropical Rainfall Measuring Mission (TRMM), (3) Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN), (4) Global Precipitation Climatology Project (GPCP), (5) modified Global Historical Climatology Network version 2 (GHCN2) and one reanalysis dataset National Centers for Environment Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used to simulate runoff over the Dakbla River (a small tributary of the Mekong River) in Vietnam. Wherever possible, available station data are also used for comparison. Bilinear interpolation of these gridded datasets is used to input the precipitation data at the closest grid points to the station locations. Sensitivity Analysis and Auto-calibration are performed for the SWAT model. The Nash-Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) indices are used to benchmark the model performance. This entails a good understanding of the response of the hydrological model to different datasets and a quantification of the uncertainties in these datasets. Such a methodology is also useful for planning on Rainfall-runoff and even reservoir/river management both at rural and urban scales.

Scaling of global input-output networks

NASA Astrophysics Data System (ADS)

Liang, Sai; Qi, Zhengling; Qu, Shen; Zhu, Ji; Chiu, Anthony S. F.; Jia, Xiaoping; Xu, Ming

2016-06-01

Examining scaling patterns of networks can help understand how structural features relate to the behavior of the networks. Input-output networks consist of industries as nodes and inter-industrial exchanges of products as links. Previous studies consider limited measures for node strengths and link weights, and also ignore the impact of dataset choice. We consider a comprehensive set of indicators in this study that are important in economic analysis, and also examine the impact of dataset choice, by studying input-output networks in individual countries and the entire world. Results show that Burr, Log-Logistic, Log-normal, and Weibull distributions can better describe scaling patterns of global input-output networks. We also find that dataset choice has limited impacts on the observed scaling patterns. Our findings can help examine the quality of economic statistics, estimate missing data in economic statistics, and identify key nodes and links in input-output networks to support economic policymaking.

High Spatial Resolution Forecasting of Long-Term Monthly Precipitation and Mean Temperature Trends in Data Scarce Regions

NASA Astrophysics Data System (ADS)

Mosier, T. M.; Hill, D. F.; Sharp, K. V.

2013-12-01

High spatial resolution time-series data are critical for many hydrological and earth science studies. Multiple groups have developed historical and forecast datasets of high-resolution monthly time-series for regions of the world such as the United States (e.g. PRISM for hindcast data and MACA for long-term forecasts); however, analogous datasets have not been available for most data scarce regions. The current work fills this data need by producing and freely distributing hindcast and forecast time-series datasets of monthly precipitation and mean temperature for all global land surfaces, gridded at a 30 arc-second resolution. The hindcast data are constructed through a Delta downscaling method, using as inputs 0.5 degree monthly time-series and 30 arc-second climatology global weather datasets developed by Willmott & Matsuura and WorldClim, respectively. The forecast data are formulated using a similar downscaling method, but with an additional step to remove bias from the climate variable's probability distribution over each region of interest. The downscaling package is designed to be compatible with a number of general circulation models (GCM) (e.g. with GCMs developed for the IPCC AR4 report and CMIP5), and is presently implemented using time-series data from the NCAR CESM1 model in conjunction with 30 arc-second future decadal climatologies distributed by the Consultative Group on International Agricultural Research. The resulting downscaled datasets are 30 arc-second time-series forecasts of monthly precipitation and mean temperature available for all global land areas. As an example of these data, historical and forecast 30 arc-second monthly time-series from 1950 through 2070 are created and analyzed for the region encompassing Pakistan. For this case study, forecast datasets corresponding to the future representative concentration pathways 45 and 85 scenarios developed by the IPCC are presented and compared. This exercise highlights a range of potential meteorological trends for the Pakistan region and more broadly serves to demonstrate the utility of the presented 30 arc-second monthly precipitation and mean temperature datasets for use in data scarce regions.

Uncertainty Propagation of Non-Parametric-Derived Precipitation Estimates into Multi-Hydrologic Model Simulations

NASA Astrophysics Data System (ADS)

Bhuiyan, M. A. E.; Nikolopoulos, E. I.; Anagnostou, E. N.

2017-12-01

Quantifying the uncertainty of global precipitation datasets is beneficial when using these precipitation products in hydrological applications, because precipitation uncertainty propagation through hydrologic modeling can significantly affect the accuracy of the simulated hydrologic variables. In this research the Iberian Peninsula has been used as the study area with a study period spanning eleven years (2000-2010). This study evaluates the performance of multiple hydrologic models forced with combined global rainfall estimates derived based on a Quantile Regression Forests (QRF) technique. In QRF technique three satellite precipitation products (CMORPH, PERSIANN, and 3B42 (V7)); an atmospheric reanalysis precipitation and air temperature dataset; satellite-derived near-surface daily soil moisture data; and a terrain elevation dataset are being utilized in this study. A high-resolution, ground-based observations driven precipitation dataset (named SAFRAN) available at 5 km/1 h resolution is used as reference. Through the QRF blending framework the stochastic error model produces error-adjusted ensemble precipitation realizations, which are used to force four global hydrological models (JULES (Joint UK Land Environment Simulator), WaterGAP3 (Water-Global Assessment and Prognosis), ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) and SURFEX (Stands for Surface Externalisée) ) to simulate three hydrologic variables (surface runoff, subsurface runoff and evapotranspiration). The models are forced with the reference precipitation to generate reference-based hydrologic simulations. This study presents a comparative analysis of multiple hydrologic model simulations for different hydrologic variables and the impact of the blending algorithm on the simulated hydrologic variables. Results show how precipitation uncertainty propagates through the different hydrologic model structures to manifest in reduction of error in hydrologic variables.

Developing a Resource for Implementing ArcSWAT Using Global Datasets

NASA Astrophysics Data System (ADS)

Taggart, M.; Caraballo Álvarez, I. O.; Mueller, C.; Palacios, S. L.; Schmidt, C.; Milesi, C.; Palmer-Moloney, L. J.

2015-12-01

This project developed a comprehensive user manual outlining methods for adapting and implementing global datasets for use within ArcSWAT for international and worldwide applications. The Soil and Water Assessment Tool (SWAT) is a hydrologic model that looks at a number of hydrologic variables including runoff and the chemical makeup of water at a given location on the Earth's surface using Digital Elevation Models (DEM), land cover, soil, and weather data. However, the application of ArcSWAT for projects outside of the United States is challenging as there is no standard framework for inputting global datasets into ArcSWAT. This project aims to remove this obstacle by outlining methods for adapting and implementing these global datasets via the user manual. The manual takes the user through the processes of data conditioning while providing solutions and suggestions for common errors. The efficacy of the manual was explored using examples from watersheds located in Puerto Rico, Mexico and Western Africa. Each run explored the various options for setting up a ArcSWAT project as well as a range of satellite data products and soil databases. Future work will incorporate in-situ data for validation and calibration of the model and outline additional resources to assist future users in efficiently implementing the model for worldwide applications. The capacity to manage and monitor freshwater availability is of critical importance in both developed and developing countries. As populations grow and climate changes, both the quality and quantity of freshwater are affected resulting in negative impacts on the health of the surrounding population. The use of hydrologic models such as ArcSWAT can help stakeholders and decision makers understand the future impacts of these changes enabling informed and substantiated decisions.

Virtual water flows and water-footprint of agricultural crop production, import and export: A case study for Israel.

PubMed

Shtull-Trauring, E; Bernstein, N

2018-05-01

Agriculture is the largest global consumer of freshwater. As the volume of international trade continues to rise, so does the understanding that trade of water-intensive crops from areas with high precipitation, to arid regions can help mitigate water scarcity, highlighting the importance of crop water accounting. Virtual-Water, or Water-Footprint [WF] of agricultural crops, is a powerful indicator for assessing the extent of water use by plants, contamination of water bodies by agricultural practices and trade between countries, which underlies any international trade of crops. Most available studies of virtual-water flows by import/export of agricultural commodities were based on global databases, which are considered to be of limited accuracy. The present study analyzes the WF of crop production, import, and export on a country level, using Israel as a case study, comparing data from two high-resolution local databases and two global datasets. Results for local datasets demonstrate a WF of ~1200Million Cubic Meters [MCM]/year) for total crop production, ~1000MCM/year for import and ~250MCM/year for export. Fruits and vegetables comprise ~80% of Export WF (~200MCM/year), ~50% of crop production and only ~20% of the imports. Economic Water Productivity [EWP] ($/m 3 ) for fruits and vegetables is 1.5 higher compared to other crops. Moreover, the results based on local and global datasets varied significantly, demonstrating the importance of developing high-resolution local datasets based on local crop coefficients. Performing high resolution WF analysis can help in developing agricultural policies that include support for low WF/high EWP and limit high WF/low EWP crop export, where water availability is limited. Copyright © 2017 Elsevier B.V. All rights reserved.

Detection of longitudinal visual field progression in glaucoma using machine learning.

PubMed

Yousefi, Siamak; Kiwaki, Taichi; Zheng, Yuhui; Suigara, Hiroki; Asaoka, Ryo; Murata, Hiroshi; Lemij, Hans; Yamanishi, Kenji

2018-06-16

Global indices of standard automated perimerty are insensitive to localized losses, while point-wise indices are sensitive but highly variable. Region-wise indices sit in between. This study introduces a machine-learning-based index for glaucoma progression detection that outperforms global, region-wise, and point-wise indices. Development and comparison of a prognostic index. Visual fields from 2085 eyes of 1214 subjects were used to identify glaucoma progression patterns using machine learning. Visual fields from 133 eyes of 71 glaucoma patients were collected 10 times over 10 weeks to provide a no-change, test-retest dataset. The parameters of all methods were identified using visual field sequences in the test-retest dataset to meet fixed 95% specificity. An independent dataset of 270 eyes of 136 glaucoma patients and survival analysis were utilized to compare methods. The time to detect progression in 25% of the eyes in the longitudinal dataset using global mean deviation (MD) was 5.2 years (95% confidence interval, 4.1 - 6.5 years); 4.5 years (4.0 - 5.5) using region-wise, 3.9 years (3.5 - 4.6) using point-wise, and 3.5 years (3.1 - 4.0) using machine learning analysis. The time until 25% of eyes showed subsequently confirmed progression after two additional visits were included were 6.6 years (5.6 - 7.4 years), 5.7 years (4.8 - 6.7), 5.6 years (4.7 - 6.5), and 5.1 years (4.5 - 6.0) for global, region-wise, point-wise, and machine learning analyses, respectively. Machine learning analysis detects progressing eyes earlier than other methods consistently, with or without confirmation visits. In particular, machine learning detects more slowly progressing eyes than other methods. Copyright © 2018 Elsevier Inc. All rights reserved.

Hierarchical Bayesian modelling of mobility metrics for hazard model input calibration

NASA Astrophysics Data System (ADS)

Calder, Eliza; Ogburn, Sarah; Spiller, Elaine; Rutarindwa, Regis; Berger, Jim

2015-04-01

In this work we present a method to constrain flow mobility input parameters for pyroclastic flow models using hierarchical Bayes modeling of standard mobility metrics such as H/L and flow volume etc. The advantage of hierarchical modeling is that it can leverage the information in global dataset for a particular mobility metric in order to reduce the uncertainty in modeling of an individual volcano, especially important where individual volcanoes have only sparse datasets. We use compiled pyroclastic flow runout data from Colima, Merapi, Soufriere Hills, Unzen and Semeru volcanoes, presented in an open-source database FlowDat (https://vhub.org/groups/massflowdatabase). While the exact relationship between flow volume and friction varies somewhat between volcanoes, dome collapse flows originating from the same volcano exhibit similar mobility relationships. Instead of fitting separate regression models for each volcano dataset, we use a variation of the hierarchical linear model (Kass and Steffey, 1989). The model presents a hierarchical structure with two levels; all dome collapse flows and dome collapse flows at specific volcanoes. The hierarchical model allows us to assume that the flows at specific volcanoes share a common distribution of regression slopes, then solves for that distribution. We present comparisons of the 95% confidence intervals on the individual regression lines for the data set from each volcano as well as those obtained from the hierarchical model. The results clearly demonstrate the advantage of considering global datasets using this technique. The technique developed is demonstrated here for mobility metrics, but can be applied to many other global datasets of volcanic parameters. In particular, such methods can provide a means to better contain parameters for volcanoes for which we only have sparse data, a ubiquitous problem in volcanology.

Water Balance in the Amazon Basin from a Land Surface Model Ensemble

NASA Technical Reports Server (NTRS)

Getirana, Augusto C. V.; Dutra, Emanuel; Guimberteau, Matthieu; Kam, Jonghun; Li, Hong-Yi; Decharme, Bertrand; Zhang, Zhengqiu; Ducharne, Agnes; Boone, Aaron; Balsamo, Gianpaolo;

A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset

NASA Astrophysics Data System (ADS)

Schellekens, Jaap; Dutra, Emanuel; Martínez-de la Torre, Alberto; Balsamo, Gianpaolo; van Dijk, Albert; Sperna Weiland, Frederiek; Minvielle, Marie; Calvet, Jean-Christophe; Decharme, Bertrand; Eisner, Stephanie; Fink, Gabriel; Flörke, Martina; Peßenteiner, Stefanie; van Beek, Rens; Polcher, Jan; Beck, Hylke; Orth, René; Calton, Ben; Burke, Sophia; Dorigo, Wouter; Weedon, Graham P.

2017-07-01

The dataset presented here consists of an ensemble of 10 global hydrological and land surface models for the period 1979-2012 using a reanalysis-based meteorological forcing dataset (0.5° resolution). The current dataset serves as a state of the art in current global hydrological modelling and as a benchmark for further improvements in the coming years. A signal-to-noise ratio analysis revealed low inter-model agreement over (i) snow-dominated regions and (ii) tropical rainforest and monsoon areas. The large uncertainty of precipitation in the tropics is not reflected in the ensemble runoff. Verification of the results against benchmark datasets for evapotranspiration, snow cover, snow water equivalent, soil moisture anomaly and total water storage anomaly using the tools from The International Land Model Benchmarking Project (ILAMB) showed overall useful model performance, while the ensemble mean generally outperformed the single model estimates. The results also show that there is currently no single best model for all variables and that model performance is spatially variable. In our unconstrained model runs the ensemble mean of total runoff into the ocean was 46 268 km3 yr-1 (334 kg m-2 yr-1), while the ensemble mean of total evaporation was 537 kg m-2 yr-1. All data are made available openly through a Water Cycle Integrator portal (WCI, wci.earth2observe.eu), and via a direct http and ftp download. The portal follows the protocols of the open geospatial consortium such as OPeNDAP, WCS and WMS. The DOI for the data is https://doi.org/10.1016/10.5281/zenodo.167070.

Global characterization and monitoring of forest cover using Landsat data: opportunities and challanges

USDA-ARS?s Scientific Manuscript database

The compilation of global Landsat data-sets and the ever-lowering costs of computing now make it feasible to monitor the Earth’s land cover at Landsat resolutions of 30 m. In this article, we describe the methods to create global products of forest cover and cover change at Landsat resolutions. Neve...

Isotopic composition (oxygen and hydrogen) of groundwater in Norway - a review of current knowledge and future perspectives

NASA Astrophysics Data System (ADS)

Kracht, O.

2012-04-01

A review of current knowledge and available data covering the stable isotope composition of groundwater in Norway is presented. Furthermore, the future challenge of obtaining systematic background datasets and of integrating isotopes into the mainstream of hydrogeological observation programmes is discussed. I will summarize our experiences gained from different preliminary studies and will try to identify relationships to existing datasets, historical registrations, and networks on precipitation data. The study of transient effects in hydrological cycles is highly topical as these are supposed to provide means for investigating the effects of climate change and increasing human activities. From a hydrogeological point of view, is critical to establish suitable tools for the large scale observation of changes in groundwater recharge and depletion, their likely controls, and the expected nature of responses to changing climate, urbanization and other human activities. In this context, stable isotopes (δ18O and δ2H of water) can provide an expedient instrument to investigate the general hydrological setting, connections, and pathways of various scale aquifer systems. However, we are up to now missing an expedient background dataset on hydrogeological and hydrological stable isotopes observations for mainland Norway. Against this background, during years 2010 and 2011 the Geological Survey of Norway (NGU) organized two nation-wide sampling campaigns on the stable isotope composition of modern groundwater. These pilot studies aimed to obtain a first overview about the data ranges and natural variations to be expected. We used stations from the existing Norwegian Groundwater Monitoring Network (Landsomfattende Grunnvannsnett, LGN) to collect samples of groundwater at 55 different locations throughout Norway. As a main characteristic of these two datasets, all δ18O and δ2H values of the "LGN series" were well correlated and plotted close to the global meteoric water line. This essentially documents the in principal exclusively meteoric origin of these waters, and indicates that the LGN groundwaters investigated shared the same type of origin: (i) evaporation from the ocean, and (ii) isotopic enrichment by rainout (continental effect). Conversely this also indicates that other processes (re-evaporation, admixture of water with a different genesis, etc.) did not have significant influence in this dataset. In parallel, two more detailed local application studies have been conducted in unconsolidated glaciofluvial aquifers in S-Norway (eastern part of the Gardermoen / Øvre Romerike Aquifer in Akershus county, and glaciofluvial deposits at the Granli waterworks of Kongsvinger in Hedmark county). In these investigations, detailed vertical profiles obtained with multi level sampling devices displayed systematic vertical evolution of groundwater isotopic composition, and it is demonstrated how an extended local dataset can enable to discuss the discrimination between different groundwater / surface water influences, and supports the planning of groundwater exploitations and groundwater water resources management.

Utilizing the Antarctic Master Directory to find orphan datasets

NASA Astrophysics Data System (ADS)

Bonczkowski, J.; Carbotte, S. M.; Arko, R. A.; Grebas, S. K.

2011-12-01

While most Antarctic data are housed at an established disciplinary-specific data repository, there are data types for which no suitable repository exists. In some cases, these "orphan" data, without an appropriate national archive, are served from local servers by the principal investigators who produced the data. There are many pitfalls with data served privately, including the frequent lack of adequate documentation to ensure the data can be understood by others for re-use and the impermanence of personal web sites. For example, if an investigator leaves an institution and the data moves, the link published is no longer accessible. To ensure continued availability of data, submission to long-term national data repositories is needed. As stated in the National Science Foundation Office of Polar Programs (NSF/OPP) Guidelines and Award Conditions for Scientific Data, investigators are obligated to submit their data for curation and long-term preservation; this includes the registration of a dataset description into the Antarctic Master Directory (AMD), http://gcmd.nasa.gov/Data/portals/amd/. The AMD is a Web-based, searchable directory of thousands of dataset descriptions, known as DIF records, submitted by scientists from over 20 countries. It serves as a node of the International Directory Network/Global Change Master Directory (IDN/GCMD). The US Antarctic Program Data Coordination Center (USAP-DCC), http://www.usap-data.org/, funded through NSF/OPP, was established in 2007 to help streamline the process of data submission and DIF record creation. When data does not quite fit within any existing disciplinary repository, it can be registered within the USAP-DCC as the fallback data repository. Within the scope of the USAP-DCC we undertook the challenge of discovering and "rescuing" orphan datasets currently registered within the AMD. In order to find which DIF records led to data served privately, all records relating to US data within the AMD were parsed. After identifying the records containing a URL leading to a national data center or other disciplinary data repository, the remaining records were individually inspected for data type, format, and quality of metadata and then assessed to determine how best to preserve. Of the records reviewed, those for which appropriate repositories could be identified were submitted. An additional 35 were deemed acceptable in quality of metadata to register in the USAP-DCC. The content of these datasets were varied in nature, ranging from penguin counts to paleo-geologic maps to results of meteorological models all of which are discoverable through our search interface, http://www.usap-data.org/search.php. The remaining 40 records linked to either no data or had inadequate documentation for preservation highlighting the danger of serving datasets on local servers where minimal metadata standards can not be enforced and long-term access can not be ensured.

Transverse spin-dependent azimuthal correlations of charged pion pairs measured in p↑ + p collisions at √{ s } = 500 GeV

NASA Astrophysics Data System (ADS)

Adamczyk, L.; Adams, J. R.; Adkins, J. K.; Agakishiev, G.; Aggarwal, M. M.; Ahammed, Z.; Ajitanand, N. N.; Alekseev, I.; Anderson, D. M.; Aoyama, R.; Aparin, A.; Arkhipkin, D.; Aschenauer, E. C.; Ashraf, M. U.; Attri, A.; Averichev, G. S.; Bairathi, V.; Barish, K.; Behera, A.; Bellwied, R.; Bhasin, A.; Bhati, A. K.; Bhattarai, P.; Bielcik, J.; Bielcikova, J.; Bland, L. C.; Bordyuzhin, I. G.; Bouchet, J.; Brandenburg, J. D.; Brandin, A. V.; Brown, D.; Bryslawskyj, J.; Bunzarov, I.; Butterworth, J.; Caines, H.; Calderón de la Barca Sánchez, M.; Campbell, J. M.; Cebra, D.; Chakaberia, I.; Chaloupka, P.; Chang, Z.; Chankova-Bunzarova, N.; Chatterjee, A.; Chattopadhyay, S.; Chen, J. H.; Chen, X.; Chen, X.; Cheng, J.; Cherney, M.; Christie, W.; Contin, G.; Crawford, H. J.; Dedovich, T. G.; Deng, J.; Deppner, I. M.; Derevschikov, A. A.; Didenko, L.; Dilks, C.; Dong, X.; Drachenberg, J. L.; Draper, J. E.; Dunlop, J. C.; Efimov, L. G.; Elsey, N.; Engelage, J.; Eppley, G.; Esha, R.; Esumi, S.; Evdokimov, O.; Ewigleben, J.; Eyser, O.; Fatemi, R.; Fazio, S.; Federic, P.; Federicova, P.; Fedorisin, J.; Feng, Z.; Filip, P.; Finch, E.; Fisyak, Y.; Flores, C. E.; Fujita, J.; Fulek, L.; Gagliardi, C. A.; Geurts, F.; Gibson, A.; Girard, M.; Grosnick, D.; Gunarathne, D. S.; Guo, Y.; Gupta, A.; Guryn, W.; Hamad, A. I.; Hamed, A.; Harlenderova, A.; Harris, J. W.; He, L.; Heppelmann, S.; Heppelmann, S.; Herrmann, N.; Hirsch, A.; Horvat, S.; Huang, X.; Huang, H. Z.; Huang, T.; Huang, B.; Humanic, T. J.; Huo, P.; Igo, G.; Jacobs, W. W.; Jentsch, A.; Jia, J.; Jiang, K.; Jowzaee, S.; Judd, E. G.; Kabana, S.; Kalinkin, D.; Kang, K.; Kapukchyan, D.; Kauder, K.; Ke, H. W.; Keane, D.; Kechechyan, A.; Khan, Z.; Kikoła, D. P.; Kim, C.; Kisel, I.; Kisiel, A.; Kochenda, L.; Kocmanek, M.; Kollegger, T.; Kosarzewski, L. K.; Kraishan, A. F.; Krauth, L.; Kravtsov, P.; Krueger, K.; Kulathunga, N.; Kumar, L.; Kvapil, J.; Kwasizur, J. H.; Lacey, R.; Landgraf, J. M.; Landry, K. D.; Lauret, J.; Lebedev, A.; Lednicky, R.; Lee, J. H.; Li, W.; Li, C.; Li, X.; Li, Y.; Lidrych, J.; Lin, T.; Lisa, M. A.; Liu, Y.; Liu, H.; Liu, F.; Liu, P.; Ljubicic, T.; Llope, W. J.; Lomnitz, M.; Longacre, R. S.; Luo, X.; Luo, S.; Ma, L.; Ma, Y. G.; Ma, G. L.; Ma, R.; Magdy, N.; Majka, R.; Mallick, D.; Margetis, S.; Markert, C.; Matis, H. S.; Mayes, D.; Meehan, K.; Mei, J. C.; Miller, Z. W.; Minaev, N. G.; Mioduszewski, S.; Mishra, D.; Mizuno, S.; Mohanty, B.; Mondal, M. M.; Morozov, D. A.; Mustafa, M. K.; Nasim, Md.; Nayak, T. K.; Nelson, J. M.; Nemes, D. B.; Nie, M.; Nigmatkulov, G.; Niida, T.; Nogach, L. V.; Nonaka, T.; Nurushev, S. B.; Odyniec, G.; Ogawa, A.; Oh, K.; Okorokov, V. A.; Olvitt, D.; Page, B. S.; Pak, R.; Pandit, Y.; Panebratsev, Y.; Pawlik, B.; Pei, H.; Perkins, C.; Pluta, J.; Poniatowska, K.; Porter, J.; Posik, M.; Pruthi, N. K.; Przybycien, M.; Putschke, J.; Quintero, A.; Ramachandran, S.; Ray, R. L.; Reed, R.; Rehbein, M. J.; Ritter, H. G.; Roberts, J. B.; Rogachevskiy, O. V.; Romero, J. L.; Roth, J. D.; Ruan, L.; Rusnak, J.; Rusnakova, O.; Sahoo, N. R.; Sahu, P. K.; Salur, S.; Sandweiss, J.; Saur, M.; Schambach, J.; Schmah, A. M.; Schmidke, W. B.; Schmitz, N.; Schweid, B. R.; Seger, J.; Sergeeva, M.; Seto, R.; Seyboth, P.; Shah, N.; Shahaliev, E.; Shanmuganathan, P. V.; Shao, M.; Shen, W. Q.; Shi, S. S.; Shi, Z.; Shou, Q. Y.; Sichtermann, E. P.; Sikora, R.; Simko, M.; Singha, S.; Skoby, M. J.; Smirnov, N.; Smirnov, D.; Solyst, W.; Sorensen, P.; Spinka, H. M.; Srivastava, B.; Stanislaus, T. D. S.; Stewart, D. J.; Strikhanov, M.; Stringfellow, B.; Suaide, A. A. P.; Sugiura, T.; Sumbera, M.; Summa, B.; Sun, X.; Sun, X. M.; Sun, Y.; Surrow, B.; Svirida, D. N.; Tang, Z.; Tang, A. H.; Taranenko, A.; Tarnowsky, T.; Tawfik, A.; Thäder, J.; Thomas, J. H.; Timmins, A. R.; Tlusty, D.; Todoroki, T.; Tokarev, M.; Trentalange, S.; Tribble, R. E.; Tribedy, P.; Tripathy, S. K.; Trzeciak, B. A.; Tsai, O. D.; Tu, B.; Ullrich, T.; Underwood, D. G.; Upsal, I.; Van Buren, G.; van Nieuwenhuizen, G.; Vasiliev, A. N.; Videbæk, F.; Vokal, S.; Voloshin, S. A.; Vossen, A.; Wang, G.; Wang, Y.; Wang, Y.; Wang, F.; Webb, G.; Webb, J. C.; Wen, L.; Westfall, G. D.; Wieman, H.; Wissink, S. W.; Witt, R.; Wu, Y.; Xiao, Z. G.; Xie, G.; Xie, W.; Xu, N.; Xu, Y. F.; Xu, Q. H.; Xu, Z.; Yang, Y.; Yang, C.; Yang, S.; Yang, Q.; Ye, Z.; Ye, Z.; Yi, L.; Yip, K.; Yoo, I.-K.; Zbroszczyk, H.; Zha, W.; Zhang, J. B.; Zhang, J.; Zhang, S.; Zhang, J.; Zhang, S.; Zhang, Z.; Zhang, Y.; Zhang, L.; Zhang, X. P.; Zhao, J.; Zhong, C.; Zhou, C.; Zhou, L.; Zhu, X.; Zhu, Z.; Zyzak, M.

2018-05-01

The transversity distribution, which describes transversely polarized quarks in transversely polarized nucleons, is a fundamental component of the spin structure of the nucleon, and is only loosely constrained by global fits to existing semi-inclusive deep inelastic scattering (SIDIS) data. In transversely polarized p↑ + p collisions it can be accessed using transverse polarization dependent fragmentation functions which give rise to azimuthal correlations between the polarization of the struck parton and the final state scalar mesons. This letter reports on spin dependent di-hadron correlations measured by the STAR experiment. The new dataset corresponds to 25 pb-1 integrated luminosity of p↑ + p collisions at √{ s } = 500 GeV, an increase of more than a factor of ten compared to our previous measurement at √{ s } = 200 GeV. Non-zero asymmetries sensitive to transversity are observed at a Q2 of several hundred GeV and are found to be consistent with the former measurement and a model calculation. We expect that these data will enable an extraction of transversity with comparable precision to current SIDIS datasets but at much higher momentum transfers where subleading effects are suppressed.

A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

NASA Technical Reports Server (NTRS)

Parinussa, Robert M.; de Jeu, Richard A. M.; van Der Schalie, Robin; Crow, Wade T.; Lei, Fangni; Holmes, Thomas R. H.

2016-01-01

Passive microwave observations from various spaceborne sensors have been linked to the soil moisture of the Earth's surface layer. A new generation of passive microwave sensors are dedicated to retrieving this variable and make observations in the single theoretically optimal L-band frequency (1-2 GHz). Previous generations of passive microwave sensors made observations in a range of higher frequencies, allowing for simultaneous estimation of additional variables required for solving the radiative transfer equation. One of these additional variables is land surface temperature, which plays a unique role in the radiative transfer equation and has an influence on the final quality of retrieved soil moisture anomalies. This study presents an optimization procedure for soil moisture retrievals through a quasi-global precipitation-based verification technique, the so-called Rvalue metric. Various land surface temperature scenarios were evaluated in which biases were added to an existing linear regression, specifically focusing on improving the skills to capture the temporal variability of soil moisture. We focus on the relative quality of the day-time (01:30 pm) observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), as these are theoretically most challenging due to the thermal equilibrium theory, and existing studies indicate that larger improvements are possible for these observations compared to their night-time (01:30 am) equivalent. Soil moisture data used in this study were retrieved through the Land Parameter Retrieval Model (LPRM), and in line with theory, both satellite paths show a unique and distinct degradation as a function of vegetation density. Both the ascending (01:30 pm) and descending (01:30 am) paths of the publicly available and widely used AMSR-E LPRM soil moisture products were used for benchmarking purposes. Several scenarios were employed in which the land surface temperature input for the radiative transfer was varied by imposing a bias on an existing regression. These scenarios were evaluated through the Rvalue technique, resulting in optimal bias values on top of this regression. In a next step, these optimal bias values were incorporated in order to re-calibrate the existing linear regression, resulting in a quasi-global uniform LST relation for day-time observations. In a final step, day-time soil moisture retrievals using the re-calibrated land surface temperature relation were again validated through the Rvalue technique. Results indicate an average increasing Rvalue of 16.5%, which indicates a better performance obtained through the re-calibration. This number was confirmed through an independent Triple Collocation verification over the same domain, demonstrating an average root mean square error reduction of 15.3%. Furthermore, a comparison against an extensive in situ database (679 stations) also indicates a generally higher quality for the re-calibrated dataset. Besides the improved day-time dataset, this study furthermore provides insights on the relative quality of soil moisture retrieved from AMSR-E's day- and night-time observations.

Carbon carry capacity and carbon sequestration potential in China based on an integrated analysis of mature forest biomass.

PubMed

Liu, YingChun; Yu, GuiRui; Wang, QiuFeng; Zhang, YangJian; Xu, ZeHong

2014-12-01

Forests play an important role in acting as a carbon sink of terrestrial ecosystem. Although global forests have huge carbon carrying capacity (CCC) and carbon sequestration potential (CSP), there were few quantification reports on Chinese forests. We collected and compiled a forest biomass dataset of China, a total of 5841 sites, based on forest inventory and literature search results. From the dataset we extracted 338 sites with forests aged over 80 years, a threshold for defining mature forest, to establish the mature forest biomass dataset. After analyzing the spatial pattern of the carbon density of Chinese mature forests and its controlling factors, we used carbon density of mature forests as the reference level, and conservatively estimated the CCC of the forests in China by interpolation methods of Regression Kriging, Inverse Distance Weighted and Partial Thin Plate Smoothing Spline. Combining with the sixth National Forest Resources Inventory, we also estimated the forest CSP. The results revealed positive relationships between carbon density of mature forests and temperature, precipitation and stand age, and the horizontal and elevational patterns of carbon density of mature forests can be well predicted by temperature and precipitation. The total CCC and CSP of the existing forests are 19.87 and 13.86 Pg C, respectively. Subtropical forests would have more CCC and CSP than other biomes. Consequently, relying on forests to uptake carbon by decreasing disturbance on forests would be an alternative approach for mitigating greenhouse gas concentration effects besides afforestation and reforestation.

A 24.5-Year Global Dataset of Direct Normal Irradiance: Result from the Application of a Global-to-Beam Model to the NASA GEWEX SRB Global Horizontal Irradiance

NASA Astrophysics Data System (ADS)

Zhang, T.; Stackhouse, P. W.; Chandler, W.; Hoell, J. M., Jr.; Westberg, D. J.

2015-12-01

The DIRINDEX model has previously been applied to the NASA GEWEX SRB Release 3.0 global horizontal irradiances (GHIs) to derive 3-hourly, daily and monthly mean direct normal irradiances (DNIs) for the period from 2000 to 2005 (http://dx.doi.org/10.1016/j.solener.2014.09.006), though the model was originally designed to estimate hourly DNIs from hourly GHIs. Input to the DIRINDEX model comprised 1.) the 3-hourly all-sky and clear-sky GHIs from the GEWEX SRB dataset; 2.) the surface pressure and the atmospheric column water vapor from the GEOS4 dataset; and 3.) daily mean aerosol optical depth at 700 nm derived from the daily mean aerosol data from the Model of Atmospheric Transport and CHemistry (MATCH). The GEWEX SRB data is spatially available on a quasi-equal-area global grid system consisting of 44016 boxes ranging from 1 degree latitude by 1 degree longitude around the Equator to 1 degree latitude by 120 degree longitude next to the poles. The derived DNIs were on the same grid system. Due to the limited availability of the MATCH aerosol data, the model was applied to the years from 2000 to 2005 only. The results were compared with ground-based measurements from 39 sites of the Baseline Surface Radiation Network (BSRN). The comparison statistics show that the results were in better agreement with their BSRN counterparts than the current Surface meteorology and Solar Energy (SSE) Release 6.0 data (https://eosweb.larc.nasa.gov/sse/). In this paper, we present results from the model over the entire time span of the GEWEX SRB Release 3.0 data (July 1983 to December2007) in which the MERRA atmospheric data were substituted for the GEOS4 data, and the Max-Planck Aerosol Climatology Version 1 (MAC-v1) data for the MATCH data. As a consequence, we derived a 24.5-year DNI dataset of global coverage continuous from July 1983 to December 2007. Comparisons with the BSRN data show that the results are comparable in quality with that from the earlier application.

Gridded climate data from 5 GCMs of the Last Glacial Maximum downscaled to 30 arc s for Europe

NASA Astrophysics Data System (ADS)

Schmatz, D. R.; Luterbacher, J.; Zimmermann, N. E.; Pearman, P. B.

2015-06-01

Studies of the impacts of historical, current and future global change require very high-resolution climate data (≤ 1 km) as a basis for modelled responses, meaning that data from digital climate models generally require substantial rescaling. Another shortcoming of available datasets on past climate is that the effects of sea level rise and fall are not considered. Without such information, the study of glacial refugia or early Holocene plant and animal migration are incomplete if not impossible. Sea level at the last glacial maximum (LGM) was approximately 125 m lower, creating substantial additional terrestrial area for which no current baseline data exist. Here, we introduce the development of a novel, gridded climate dataset for LGM that is both very high resolution (1 km) and extends to the LGM sea and land mask. We developed two methods to extend current terrestrial precipitation and temperature data to areas between the current and LGM coastlines. The absolute interpolation error is less than 1 and 0.5 °C for 98.9 and 87.8 %, respectively, of all pixels within two arc degrees of the current coastline. We use the change factor method with these newly assembled baseline data to downscale five global circulation models of LGM climate to a resolution of 1 km for Europe. As additional variables we calculate 19 "bioclimatic" variables, which are often used in climate change impact studies on biological diversity. The new LGM climate maps are well suited for analysing refugia and migration during Holocene warming following the LGM.

An empirical understanding of triple collocation evaluation measure

NASA Astrophysics Data System (ADS)

Scipal, Klaus; Doubkova, Marcela; Hegyova, Alena; Dorigo, Wouter; Wagner, Wolfgang

2013-04-01

Triple collocation method is an advanced evaluation method that has been used in the soil moisture field for only about half a decade. The method requires three datasets with an independent error structure that represent an identical phenomenon. The main advantages of the method are that it a) doesn't require a reference dataset that has to be considered to represent the truth, b) limits the effect of random and systematic errors of other two datasets, and c) simultaneously assesses the error of three datasets. The objective of this presentation is to assess the triple collocation error (Tc) of the ASAR Global Mode Surface Soil Moisture (GM SSM 1) km dataset and highlight problems of the method related to its ability to cancel the effect of error of ancillary datasets. In particular, the goal is to a) investigate trends in Tc related to the change in spatial resolution from 5 to 25 km, b) to investigate trends in Tc related to the choice of a hydrological model, and c) to study the relationship between Tc and other absolute evaluation methods (namely RMSE and Error Propagation EP). The triple collocation method is implemented using ASAR GM, AMSR-E, and a model (either AWRA-L, GLDAS-NOAH, or ERA-Interim). First, the significance of the relationship between the three soil moisture datasets was tested that is a prerequisite for the triple collocation method. Second, the trends in Tc related to the choice of the third reference dataset and scale were assessed. For this purpose the triple collocation is repeated replacing AWRA-L with two different globally available model reanalysis dataset operating at different spatial resolution (ERA-Interim and GLDAS-NOAH). Finally, the retrieved results were compared to the results of the RMSE and EP evaluation measures. Our results demonstrate that the Tc method does not eliminate the random and time-variant systematic errors of the second and the third dataset used in the Tc. The possible reasons include the fact a) that the TC method could not fully function with datasets acting at very different spatial resolutions, or b) that the errors were not fully independent as initially assumed.

GNSS climatology: A summary of findings from the COST Action ES1206 GNSS4SWEC

NASA Astrophysics Data System (ADS)

Bock, Olivier; Pacione, Rosa

2017-04-01

Working Group 3 of COST Action GNSS4SWEC promoted the coordinated development and assessment of GNSS tropospheric products for climate research. More than 50 researchers from 17 institutions participated in the discussions. The activities were organised in five main topics, each of which led to conclusions and recommendations for a proper production and use of GNSS tropospheric products for climate research. 1) GNSS data processing and validation: an inventory was established listing the main existing reprocessed datasets and one of them (IGS repro1) was more specifically assessed and used as a community dataset to demonstrate the capacity of GNSS to retrieve decadal trends and variability in zenith tropospheric delay (ZTD). Several groups performed also processing sensitivity studies producing long term (15 years or more) solutions and testing the impact of various processing parameters (tropospheric models, cutoff angle…) on the accuracy and stability of the retrieved ZTD estimates. 2) Standards and methods for post-processing: (i) elaborate screening methods have been developed and tested for the detection of outliers in ZTD data; (ii) ZTD to IWV conversion methods and auxiliary datasets have been reviewed and assessed; (iii) the homogeneity of long ZTD and IWV time series has been investigated. Standardised procedures were proposed for first two points. Inhomogeneities have been identified in all reprocessed GNSS datasets which are due to equipment changes or changes in the measurement conditions. Significant activity is on-going on the development of statistical homogenisation techniques that match the GNSS data characteristics. 3) IWV validations: new intercomparisons of GNSS IWV estimates to IWV retrieved from other observational techniques (radiosondes, microwave radiometers, VLBI, DORIS…) have been encouraged to enhance the results of the past and contribute to a better evaluation of inter-technique biases and absolute accuracy of the different IWV sensing techniques. 4) GNSS climatology: as a major goal of this working group, applications have been promoted in collaboration with the climate research community such as the analysis of global and regional trends and variability, the evaluation of global and regional climate model simulations (IPCC, EC-Earth, CORDEX…) and reanalysis products (ERA-Interim, ERA20C, 20CR…). 5) Databases and data formats: cooperation with IGS and EUREF fostered the specification and development of new database structures and updated SINEX format for a more efficient and enhanced exchange, use, and validation of GNSS tropospheric data.

Human neutral genetic variation and forensic STR data.

PubMed

Silva, Nuno M; Pereira, Luísa; Poloni, Estella S; Currat, Mathias

2012-01-01

The forensic genetics field is generating extensive population data on polymorphism of short tandem repeats (STR) markers in globally distributed samples. In this study we explored and quantified the informative power of these datasets to address issues related to human evolution and diversity, by using two online resources: an allele frequency dataset representing 141 populations summing up to almost 26 thousand individuals; a genotype dataset consisting of 42 populations and more than 11 thousand individuals. We show that the genetic relationships between populations based on forensic STRs are best explained by geography, as observed when analysing other worldwide datasets generated specifically to study human diversity. However, the global level of genetic differentiation between populations (as measured by a fixation index) is about half the value estimated with those other datasets, which contain a much higher number of markers but much less individuals. We suggest that the main factor explaining this difference is an ascertainment bias in forensics data resulting from the choice of markers for individual identification. We show that this choice results in average low variance of heterozygosity across world regions, and hence in low differentiation among populations. Thus, the forensic genetic markers currently produced for the purpose of individual assignment and identification allow the detection of the patterns of neutral genetic structure that characterize the human population but they do underestimate the levels of this genetic structure compared to the datasets of STRs (or other kinds of markers) generated specifically to study the diversity of human populations.

Persistent identifiers for CMIP6 data in the Earth System Grid Federation

NASA Astrophysics Data System (ADS)

Buurman, Merret; Weigel, Tobias; Juckes, Martin; Lautenschlager, Michael; Kindermann, Stephan

2016-04-01

The Earth System Grid Federation (ESGF) is a distributed data infrastructure that will provide access to the CMIP6 experiment data. The data consist of thousands of datasets composed of millions of files. Over the course of the CMIP6 operational phase, datasets may be retracted and replaced by newer versions that consist of completely or partly new files. Each dataset is hosted at a single data centre, but can have one or several backups (replicas) at other data centres. To keep track of the different data entities and relationships between them, to ensure their consistency and improve exchange of information about them, Persistent Identifiers (PIDs) are used. These are unique identifiers that are registered at a globally accessible server, along with some metadata (the PID record). While usually providing access to the data object they refer to, as long as it exists, the metadata record will remain available even beyond the object's lifetime. Besides providing access to data and metadata, PIDs will allow scientists to communicate effectively and on a fine granularity about CMIP6 data. The initiative to introduce PIDs in the ESGF infrastructure has been described and agreed upon through a series of white papers governed by the WGCM Infrastructure Panel (WIP). In CMIP6, each dataset and each file is assigned a PID that keeps track of the data object's physical copies throughout the object lifetime. In addition to this, its relationship with other data objects is stored in the PID recordA human-readable version of this information is available on an information page also linked in the PID record. A possible application that exploits the information available from the PID records is a smart information tool, which a scientific user can call to find out if his/her version was replaced by a new one, to view and browse the related datasets and files, and to get access to the various copies or to additional metadata on a dedicated website. The PID registration process is embedded in the ESGF data publication process. During their first publication, the PID records are populated with metadata including the parent dataset(s), other existing versions and physical location. Every subsequent publication, un-publication or replica publication of a dataset or file then updates the PID records to keep track of changing physical locations of the data (or lack thereof) and of reported errors in the data. Assembling the metadata records and registering the PIDs on a central server is a potential performance bottleneck as millions of data objects may be published in a short timeframe when the CMIP6 experiment phase begins. For this reason, the PID registration and metadata update tasks are pushed to a message queueing system facilitating high availability and scalability and then processed asynchronously. This will lead to a slight delay in PID registration but will avoid blocking resources at the data centres and slowing down the publication of the data so eagerly awaited by the scientists.

International Data | Geospatial Data Science | NREL

Science.gov Websites

International Data International Data These datasets detail solar and wind resources for select Annual.xml India 10-km Monthly Direct Normal and Global Horizontal Zip 4.68 MB 04/25/2013 Monthly.xml Wind Data 50-m Wind Data These 50-m hub-height datasets have been validated by NREL and wind energy

Offshore Wind Energy Climate Projection Using UPSCALE Climate Data under the RCP8.5 Emission Scenario

PubMed Central

Gross, Markus; Magar, Vanesa

2016-01-01

In previous work, the authors demonstrated how data from climate simulations can be utilized to estimate regional wind power densities. In particular, it was shown that the quality of wind power densities, estimated from the UPSCALE global dataset in offshore regions of Mexico, compared well with regional high resolution studies. Additionally, a link between surface temperature and moist air density in the estimates was presented. UPSCALE is an acronym for UK on PRACE (the Partnership for Advanced Computing in Europe)—weather-resolving Simulations of Climate for globAL Environmental risk. The UPSCALE experiment was performed in 2012 by NCAS (National Centre for Atmospheric Science)-Climate, at the University of Reading and the UK Met Office Hadley Centre. The study included a 25.6-year, five-member ensemble simulation of the HadGEM3 global atmosphere, at 25km resolution for present climate conditions. The initial conditions for the ensemble runs were taken from consecutive days of a test configuration. In the present paper, the emphasis is placed on the single climate run for a potential future climate scenario in the UPSCALE experiment dataset, using the Representation Concentrations Pathways (RCP) 8.5 climate change scenario. Firstly, some tests were performed to ensure that the results using only one instantiation of the current climate dataset are as robust as possible within the constraints of the available data. In order to achieve this, an artificial time series over a longer sampling period was created. Then, it was shown that these longer time series provided almost the same results than the short ones, thus leading to the argument that the short time series is sufficient to capture the climate. Finally, with the confidence that one instantiation is sufficient, the future climate dataset was analysed to provide, for the first time, a projection of future changes in wind power resources using the UPSCALE dataset. It is hoped that this, in turn, will provide some guidance for wind power developers and policy makers to prepare and adapt for climate change impacts on wind energy production. Although offshore locations around Mexico were used as a case study, the dataset is global and hence the methodology presented can be readily applied at any desired location. PMID:27788208

Offshore Wind Energy Climate Projection Using UPSCALE Climate Data under the RCP8.5 Emission Scenario.

PubMed

Gross, Markus; Magar, Vanesa

2016-01-01

In previous work, the authors demonstrated how data from climate simulations can be utilized to estimate regional wind power densities. In particular, it was shown that the quality of wind power densities, estimated from the UPSCALE global dataset in offshore regions of Mexico, compared well with regional high resolution studies. Additionally, a link between surface temperature and moist air density in the estimates was presented. UPSCALE is an acronym for UK on PRACE (the Partnership for Advanced Computing in Europe)-weather-resolving Simulations of Climate for globAL Environmental risk. The UPSCALE experiment was performed in 2012 by NCAS (National Centre for Atmospheric Science)-Climate, at the University of Reading and the UK Met Office Hadley Centre. The study included a 25.6-year, five-member ensemble simulation of the HadGEM3 global atmosphere, at 25km resolution for present climate conditions. The initial conditions for the ensemble runs were taken from consecutive days of a test configuration. In the present paper, the emphasis is placed on the single climate run for a potential future climate scenario in the UPSCALE experiment dataset, using the Representation Concentrations Pathways (RCP) 8.5 climate change scenario. Firstly, some tests were performed to ensure that the results using only one instantiation of the current climate dataset are as robust as possible within the constraints of the available data. In order to achieve this, an artificial time series over a longer sampling period was created. Then, it was shown that these longer time series provided almost the same results than the short ones, thus leading to the argument that the short time series is sufficient to capture the climate. Finally, with the confidence that one instantiation is sufficient, the future climate dataset was analysed to provide, for the first time, a projection of future changes in wind power resources using the UPSCALE dataset. It is hoped that this, in turn, will provide some guidance for wind power developers and policy makers to prepare and adapt for climate change impacts on wind energy production. Although offshore locations around Mexico were used as a case study, the dataset is global and hence the methodology presented can be readily applied at any desired location.

Historic AVHRR Processing in the Eumetsat Climate Monitoring Satellite Application Facility (cmsaf) (Invited)

NASA Astrophysics Data System (ADS)

Karlsson, K.

2010-12-01

The EUMETSAT CMSAF project (www.cmsaf.eu) compiles climatological datasets from various satellite sources with emphasis on the use of EUMETSAT-operated satellites. However, since climate monitoring primarily has a global scope, also datasets merging data from various satellites and satellite operators are prepared. One such dataset is the CMSAF historic GAC (Global Area Coverage) dataset which is based on AVHRR data from the full historic series of NOAA-satellites and the European METOP satellite in mid-morning orbit launched in October 2006. The CMSAF GAC dataset consists of three groups of products: Macroscopical cloud products (cloud amount, cloud type and cloud top), cloud physical products (cloud phase, cloud optical thickness and cloud liquid water path) and surface radiation products (including surface albedo). Results will be presented and discussed for all product groups, including some preliminary inter-comparisons with other datasets (e.g., PATMOS-X, MODIS and CloudSat/CALIPSO datasets). A background will also be given describing the basic methodology behind the derivation of all products. This will include a short historical review of AVHRR cloud processing and resulting AVHRR applications at SMHI. Historic GAC processing is one of five pilot projects selected by the SCOPE-CM (Sustained Co-Ordinated Processing of Environmental Satellite data for Climate Monitoring) project organised by the WMO Space programme. The pilot project is carried out jointly between CMSAF and NOAA with the purpose of finding an optimal GAC processing approach. The initial activity is to inter-compare results of the CMSAF GAC dataset and the NOAA PATMOS-X dataset for the case when both datasets have been derived using the same inter-calibrated AVHRR radiance dataset. The aim is to get further knowledge of e.g. most useful multispectral methods and the impact of ancillary datasets (for example from meteorological reanalysis datasets from NCEP and ECMWF). The CMSAF project is currently defining plans for another five years (2012-2017) of operations and development. New GAC reprocessing efforts are planned and new methodologies will be tested. Central questions here will be how to increase the quantitative use of the products through improving error and uncertainty estimates and how to compile the information in a way to allow meaningful and efficient ways of using the data for e.g. validation of climate model information.

Integration of Remotely Sensed Data Into Geospatial Reference Information Databases. Un-Ggim National Approach

NASA Astrophysics Data System (ADS)

Arozarena, A.; Villa, G.; Valcárcel, N.; Pérez, B.

2016-06-01

Remote sensing satellites, together with aerial and terrestrial platforms (mobile and fixed), produce nowadays huge amounts of data coming from a wide variety of sensors. These datasets serve as main data sources for the extraction of Geospatial Reference Information (GRI), constituting the "skeleton" of any Spatial Data Infrastructure (SDI). Since very different situations can be found around the world in terms of geographic information production and management, the generation of global GRI datasets seems extremely challenging. Remotely sensed data, due to its wide availability nowadays, is able to provide fundamental sources for any production or management system present in different countries. After several automatic and semiautomatic processes including ancillary data, the extracted geospatial information is ready to become part of the GRI databases. In order to optimize these data flows for the production of high quality geospatial information and to promote its use to address global challenges several initiatives at national, continental and global levels have been put in place, such as European INSPIRE initiative and Copernicus Programme, and global initiatives such as the Group on Earth Observation/Global Earth Observation System of Systems (GEO/GEOSS) and United Nations Global Geospatial Information Management (UN-GGIM). These workflows are established mainly by public organizations, with the adequate institutional arrangements at national, regional or global levels. Other initiatives, such as Volunteered Geographic Information (VGI), on the other hand may contribute to maintain the GRI databases updated. Remotely sensed data hence becomes one of the main pillars underpinning the establishment of a global SDI, as those datasets will be used by public agencies or institutions as well as by volunteers to extract the required spatial information that in turn will feed the GRI databases. This paper intends to provide an example of how institutional arrangements and cooperative production systems can be set up at any territorial level in order to exploit remotely sensed data in the most intensive manner, taking advantage of all its potential.

Integrating landslide and liquefaction hazard and loss estimates with existing USGS real-time earthquake information products

USGS Publications Warehouse

Allstadt, Kate E.; Thompson, Eric M.; Hearne, Mike; Nowicki Jessee, M. Anna; Zhu, J.; Wald, David J.; Tanyas, Hakan

2017-01-01

The U.S. Geological Survey (USGS) has made significant progress toward the rapid estimation of shaking and shakingrelated losses through their Did You Feel It? (DYFI), ShakeMap, ShakeCast, and PAGER products. However, quantitative estimates of the extent and severity of secondary hazards (e.g., landsliding, liquefaction) are not currently included in scenarios and real-time post-earthquake products despite their significant contributions to hazard and losses for many events worldwide. We are currently running parallel global statistical models for landslides and liquefaction developed with our collaborators in testing mode, but much work remains in order to operationalize these systems. We are expanding our efforts in this area by not only improving the existing statistical models, but also by (1) exploring more sophisticated, physics-based models where feasible; (2) incorporating uncertainties; and (3) identifying and undertaking research and product development to provide useful landslide and liquefaction estimates and their uncertainties. Although our existing models use standard predictor variables that are accessible globally or regionally, including peak ground motions, topographic slope, and distance to water bodies, we continue to explore readily available proxies for rock and soil strength as well as other susceptibility terms. This work is based on the foundation of an expanding, openly available, case-history database we are compiling along with historical ShakeMaps for each event. The expected outcome of our efforts is a robust set of real-time secondary hazards products that meet the needs of a wide variety of earthquake information users. We describe the available datasets and models, developments currently underway, and anticipated products. 

The Global Drivers of Photosynthesis and Light Use Efficiency Seasonality: A Granger Frequency Causality Analysis

NASA Astrophysics Data System (ADS)

Green, J.; Lee, J. E.; Gentine, P.; Berry, J. A.; Konings, A. G.

2015-12-01

hotosynthesis and light use efficiency (LUE) are major factors in the evolution of the continental carbon cycle due to their contribution to gross primary production (GPP). However, while the drivers of photosynthesis and LUE on a plant or canopy scale can often be identified, significant uncertainties exist when modeling these on a global scale. This is due to sparse observations in regions such as the tropics and the lack of a direct global observation dataset. Although others have attempted to address this issue using correlations (Beer, 2010) or calculating GPP from vegetation indices (Running, 2004), in this study we take a new approach. We combine the statistical method of Granger frequency causality and partial Granger frequency causality with remote sensing data products (including sun-induced fluorescence used as a proxy for GPP) to determine the main environmental drivers of GPP across the globe. References:Beer, C., M. Reichstein, E. Tomelleri, P. Ciais, M. Jung, N. Carvalhais, C. Ro¨denbeck, M. Altaf Arain, D. Baldocchi, G. B. Bonan, A. Bondeau, A. Cescatti, G. Lasslop, A. Lindroth, M. Lomas, S. Luyssaert, H. Margolis, K. W. Oleson, O. Roupsard, E. Veenendaal, N. Viovy, C. Williams, I. Woodward, and D. Papale, 2010: Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate. doi: 10.1126/science.1184984. Running, S.W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., Hashimoto, H., 2004. A Continuous Satellite Derived Measure of Global Terrestrial Primary Production. BioScience 54(6), 547-560.

AUI&GIV: Recommendation with Asymmetric User Influence and Global Importance Value.

PubMed

Zhao, Zhi-Lin; Wang, Chang-Dong; Lai, Jian-Huang

2016-01-01

The user-based collaborative filtering (CF) algorithm is one of the most popular approaches for making recommendation. Despite its success, the traditional user-based CF algorithm suffers one serious problem that it only measures the influence between two users based on their symmetric similarities calculated by their consumption histories. It means that, for a pair of users, the influences on each other are the same, which however may not be true. Intuitively, an expert may have an impact on a novice user but a novice user may not affect an expert at all. Besides, each user may possess a global importance factor that affects his/her influence to the remaining users. To this end, in this paper, we propose an asymmetric user influence model to measure the directed influence between two users and adopt the PageRank algorithm to calculate the global importance value of each user. And then the directed influence values and the global importance values are integrated to deduce the final influence values between two users. Finally, we use the final influence values to improve the performance of the traditional user-based CF algorithm. Extensive experiments have been conducted, the results of which have confirmed that both the asymmetric user influence model and global importance value play key roles in improving recommendation accuracy, and hence the proposed method significantly outperforms the existing recommendation algorithms, in particular the user-based CF algorithm on the datasets of high rating density.

AUI&GIV: Recommendation with Asymmetric User Influence and Global Importance Value

PubMed Central

Zhao, Zhi-Lin; Wang, Chang-Dong; Lai, Jian-Huang

2016-01-01

The user-based collaborative filtering (CF) algorithm is one of the most popular approaches for making recommendation. Despite its success, the traditional user-based CF algorithm suffers one serious problem that it only measures the influence between two users based on their symmetric similarities calculated by their consumption histories. It means that, for a pair of users, the influences on each other are the same, which however may not be true. Intuitively, an expert may have an impact on a novice user but a novice user may not affect an expert at all. Besides, each user may possess a global importance factor that affects his/her influence to the remaining users. To this end, in this paper, we propose an asymmetric user influence model to measure the directed influence between two users and adopt the PageRank algorithm to calculate the global importance value of each user. And then the directed influence values and the global importance values are integrated to deduce the final influence values between two users. Finally, we use the final influence values to improve the performance of the traditional user-based CF algorithm. Extensive experiments have been conducted, the results of which have confirmed that both the asymmetric user influence model and global importance value play key roles in improving recommendation accuracy, and hence the proposed method significantly outperforms the existing recommendation algorithms, in particular the user-based CF algorithm on the datasets of high rating density. PMID:26828803

Mapping the spatial distribution of global anthropogenic mercury atmospheric emission inventories

NASA Astrophysics Data System (ADS)

Wilson, Simon J.; Steenhuisen, Frits; Pacyna, Jozef M.; Pacyna, Elisabeth G.

This paper describes the procedures employed to spatially distribute global inventories of anthropogenic emissions of mercury to the atmosphere, prepared by Pacyna, E.G., Pacyna, J.M., Steenhuisen, F., Wilson, S. [2006. Global anthropogenic mercury emission inventory for 2000. Atmospheric Environment, this issue, doi:10.1016/j.atmosenv.2006.03.041], and briefly discusses the results of this work. A new spatially distributed global emission inventory for the (nominal) year 2000, and a revised version of the 1995 inventory are presented. Emissions estimates for total mercury and major species groups are distributed within latitude/longitude-based grids with a resolution of 1×1 and 0.5×0.5°. A key component in the spatial distribution procedure is the use of population distribution as a surrogate parameter to distribute emissions from sources that cannot be accurately geographically located. In this connection, new gridded population datasets were prepared, based on the CEISIN GPW3 datasets (CIESIN, 2004. Gridded Population of the World (GPW), Version 3. Center for International Earth Science Information Network (CIESIN), Columbia University and Centro Internacional de Agricultura Tropical (CIAT). GPW3 data are available at http://beta.sedac.ciesin.columbia.edu/gpw/index.jsp). The spatially distributed emissions inventories and population datasets prepared in the course of this work are available on the Internet at www.amap.no/Resources/HgEmissions/

Correlation Dimension Estimates of Global and Local Temperature Data.

NASA Astrophysics Data System (ADS)

Wang, Qiang

1995-11-01

The author has attempted to detect the presence of low-dimensional deterministic chaos in temperature data by estimating the correlation dimension with the Hill estimate that has been recently developed by Mikosch and Wang. There is no convincing evidence of low dimensionality with either global dataset (Southern Hemisphere monthly average temperatures from 1858 to 1984) or local temperature dataset (daily minimums at Auckland, New Zealand). Any apparent reduction in the dimension estimates appears to be due large1y, if not entirely, to effects of statistical bias, but neither is it a purely random stochastic process. The dimension of the climatic attractor may be significantly larger than 10.

Mapping Human-Dominated Landscapes: the Distribution and Yield of Major Crops of the World

NASA Astrophysics Data System (ADS)

Monfreda, C.; Ramankutty, N.; Foley, J. A.

2005-12-01

Croplands cover 18 million km2, an area the size of South America, and provide ecosystem goods and services essential to human well-being. Most global land-cover classifications group the diversity of croplands into a single or very few categories, thereby excluding critical information to answer key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information on land-use practices is even more limited. The relative lack of information about agricultural landscapes results partly from difficulties in using satellite data to identify individual crop types and land-use practices at a global scale. We address limitations common to remote-sensing classifications by distributing national, state, and county level statistics across a recently updated global dataset of cropland cover at 5 minute resolution. The resulting datasets depict the fractional harvested area and yield of twenty distinct crop types: maize, wheat, rice, sorghum, millet, barley, oats, soybeans, sunflower, rapeseed/canola, pulses, groundnuts/peanuts, oil palm, cassava, potatoes, sugar cane, sugar beets, tobacco, coffee, and cotton. These datasets represent the state of agriculture circa the year 2000 and will be made available for applications in ecological analysis, modeling, visualization, and education.

A Web-Based Validation Tool for GEWEX

NASA Astrophysics Data System (ADS)

Smith, R. A.; Gibson, S.; Heckert, E.; Minnis, P.; Sun-Mack, S.; Chen, Y.; Stubenrauch, C.; Kinne, S. A.; Ackerman, S. A.; Baum, B. A.; Chepfer, H.; Di Girolamo, L.; Heidinger, A. K.; Getzewich, B. J.; Guignard, A.; Maddux, B. C.; Menzel, W. P.; Platnick, S. E.; Poulsen, C.; Raschke, E. A.; Riedi, J.; Rossow, W. B.; Sayer, A. M.; Walther, A.; Winker, D. M.

2011-12-01

The Global Energy and Water Cycle Experiment (GEWEX) Cloud assessment was initiated by the GEWEX Radiation Panel (GRP) in 2005 to evaluate the variability of available, global, long-term cloud data products. Since then, eleven cloud data records have been established from various instruments, mostly onboard polar orbiting satellites. Cloud properties under study include cloud amount, cloud pressure, cloud temperature, cloud infrared (IR) emissivity and visible (VIS) optical thickness, cloud thermodynamic phase, as well as bulk microphysical properties. The volume of data and variations in parameters, spatial, and temporal resolution for the different datasets constitute a significant challenge for understanding the differences and the value of having more than one dataset. To address this issue, this paper presents a NASA Langley web-based tool to facilitate comparisons among the different cloud data sets. With this tool, the operator can choose to view numeric or graphic presentations to allow comparison between products. Multiple records are displayed in time series graphs, global maps, or zonal plots. The tool has been made flexible so that additional teams can easily add their data sets to the record selection list for use in their own analyses. This tool has possible applications to other climate and weather datasets.

Hurricane Data Analysis Tool

NASA Technical Reports Server (NTRS)

Liu, Zhong; Ostrenga, Dana; Leptoukh, Gregory

2011-01-01

In order to facilitate Earth science data access, the NASA Goddard Earth Sciences Data Information Services Center (GES DISC) has developed a web prototype, the Hurricane Data Analysis Tool (HDAT; URL: http://disc.gsfc.nasa.gov/HDAT), to allow users to conduct online visualization and analysis of several remote sensing and model datasets for educational activities and studies of tropical cyclones and other weather phenomena. With a web browser and few mouse clicks, users can have a full access to terabytes of data and generate 2-D or time-series plots and animation without downloading any software and data. HDAT includes data from the NASA Tropical Rainfall Measuring Mission (TRMM), the NASA Quick Scatterometer(QuikSCAT) and NECP Reanalysis, and the NCEP/CPC half-hourly, 4-km Global (60 N - 60 S) IR Dataset. The GES DISC archives TRMM data. The daily global rainfall product derived from the 3-hourly multi-satellite precipitation product (3B42 V6) is available in HDAT. The TRMM Microwave Imager (TMI) sea surface temperature from the Remote Sensing Systems is in HDAT as well. The NASA QuikSCAT ocean surface wind and the NCEP Reanalysis provide ocean surface and atmospheric conditions, respectively. The global merged IR product, also known as, the NCEP/CPC half-hourly, 4-km Global (60 N -60 S) IR Dataset, is one of TRMM ancillary datasets. They are globally-merged pixel-resolution IR brightness temperature data (equivalent blackbody temperatures), merged from all available geostationary satellites (GOES-8/10, METEOSAT-7/5 & GMS). The GES DISC has collected over 10 years of the data beginning from February of 2000. This high temporal resolution (every 30 minutes) dataset not only provides additional background information to TRMM and other satellite missions, but also allows observing a wide range of meteorological phenomena from space, such as, hurricanes, typhoons, tropical cyclones, mesoscale convection system, etc. Basic functions include selection of area of interest and time, single imagery, overlay of two different products, animation,a time skip capability and different image size outputs. Users can save an animation as a file (animated gif) and import it in other presentation software, such as, Microsoft PowerPoint. Since the tool can directly access the real data, more features and functionality can be added in the future.

Ten-year global distribution of downwelling longwave radiation

NASA Astrophysics Data System (ADS)

Pavlakis, K. G.; Hatzidimitriou, D.; Matsoukas, C.; Drakakis, E.; Hatzianastassiou, N.; Vardavas, I.

2003-10-01

Downwelling longwave fluxes, DLFs, have been derived for each month over a ten year period (1984-1993), on a global scale with a resolution of 2.5° × 2.5°. The fluxes were computed using a deterministic model for atmospheric radiation transfer, along with satellite and reanalysis data for the key atmospheric input parameters, i.e. cloud properties, and specific humidity and temperature profiles. The cloud climatologies were taken from the latest released and improved International Satellite Climatology Project D2 series. Specific humidity and temperature vertical profiles were taken from three different reanalysis datasets; NCEP/NCAR, GEOS, and ECMWF (acronyms explained in main text). DLFs were computed for each reanalysis dataset, with differences reaching values as high as 30 Wm-2 in specific regions, particularly over high altitude areas and deserts. However, globally, the agreement is good, with the rms of the difference between the DLFs derived from the different reanalysis datasets ranging from 5 to 7 Wm-2. The results are presented as geographical distributions and as time series of hemispheric and global averages. The DLF time series based on the different reanalysis datasets show similar seasonal and inter-annual variations, and similar anomalies related to the 86/87 El Niño and 89/90 La Niña events. The global ten-year average of the DLF was found to be between 342.2 Wm-2 and 344.3 Wm-2, depending on the dataset. We also conducted a detailed sensitivity analysis of the calculated DLFs to the key input data. Plots are given that can be used to obtain a quick assessment of the sensitivity of the DLF to each of the three key climatic quantities, for specific climatic conditions corresponding to different regions of the globe. Our model downwelling fluxes are validated against available data from ground-based stations distributed over the globe, as given by the Baseline Surface Radiation Network. There is a negative bias of the model fluxes when compared against BSRN fluxes, ranging from -7 to -9 Wm-2, mostly caused by low cloud amount differences between the station and satellite measurements, particularly in cold climates. Finally, we compare our model results with those of other deterministic models and general circulation models.

Ten-year global distribution of downwelling longwave radiation

NASA Astrophysics Data System (ADS)

Pavlakis, K. G.; Hatzidimitriou, D.; Matsoukas, C.; Drakakis, E.; Hatzianastassiou, N.; Vardavas, I.

2004-01-01

Downwelling longwave fluxes, DLFs, have been derived for each month over a ten year period (1984-1993), on a global scale with a spatial resolution of 2.5x2.5 degrees and a monthly temporal resolution. The fluxes were computed using a deterministic model for atmospheric radiation transfer, along with satellite and reanalysis data for the key atmospheric input parameters, i.e. cloud properties, and specific humidity and temperature profiles. The cloud climatologies were taken from the latest released and improved International Satellite Climatology Project D2 series. Specific humidity and temperature vertical profiles were taken from three different reanalysis datasets; NCEP/NCAR, GEOS, and ECMWF (acronyms explained in main text). DLFs were computed for each reanalysis dataset, with differences reaching values as high as 30 Wm-2 in specific regions, particularly over high altitude areas and deserts. However, globally, the agreement is good, with the rms of the difference between the DLFs derived from the different reanalysis datasets ranging from 5 to 7 Wm-2. The results are presented as geographical distributions and as time series of hemispheric and global averages. The DLF time series based on the different reanalysis datasets show similar seasonal and inter-annual variations, and similar anomalies related to the 86/87 El Niño and 89/90 La Niña events. The global ten-year average of the DLF was found to be between 342.2 Wm-2 and 344.3 Wm-2, depending on the dataset. We also conducted a detailed sensitivity analysis of the calculated DLFs to the key input data. Plots are given that can be used to obtain a quick assessment of the sensitivity of the DLF to each of the three key climatic quantities, for specific climatic conditions corresponding to different regions of the globe. Our model downwelling fluxes are validated against available data from ground-based stations distributed over the globe, as given by the Baseline Surface Radiation Network. There is a negative bias of the model fluxes when compared against BSRN fluxes, ranging from -7 to -9 Wm-2, mostly caused by low cloud amount differences between the station and satellite measurements, particularly in cold climates. Finally, we compare our model results with those of other deterministic models and general circulation models.

The Soil Atlas of Africa: raising awareness and educate to the importance of soil

NASA Astrophysics Data System (ADS)

Dewitte, Olivier; Jones, Arwyn; Bosco, Claudio; Spaargaren, Otto; Montanarella, Luca

2010-05-01

The richness of African soil resources need to be protected for future generations. A number of threats are affecting the functioning of African soils, not only for the purpose of agricultural production, but also for other important environmental services that soil delivers to all of us. This is of particular importance once we know that many health-related problems in Africa are indirectly related to the services of soils. To raise the awareness of the general public, policy makers and other scientists to the importance of soil in Africa, the Joint Research Centre of the European Commission is to produce the first ever Soil Atlas of Africa. This is in collaboration with the African Union Commission, the Food and Agriculture Organization of the United Nations (FAO), the Africa Soil Science Society, ISRIC - World Soil Information and scientists from both Europe and Africa. The Atlas compiles existing information on different soil types as easily understandable maps (both at regional and continental scale) covering the African continent. The Soil Atlas of Africa intends to produce derived maps at continental scale with descriptive text (e.g. vulnerability to desertification, soil nutrient status, carbon stocks and sequestration potential, irrigable areas and water resources) as well as specific maps to illustrate threats such as soil erosion for instance. For each regional overview, large scale examples of soil maps and derived products are presented too. The Atlas will be published as a hardcover book containing 174 A3 pages, which will allow soils maps to be displayed at the A2 scale. Both French and English versions of the Atlas will be edited. The Atlas will be sold at a low cost and will be for free for educational purpose (Schools and Universities). A digital version on CD and eventually freely downloadable on internet will also be available. Together with the publication of the Atlas, associated datasets on soil characteristics for Africa will be made available. These datasets will be useful for making broad distinction among soil types and provide general trends at the global and regional scales. The datasets will be made accessible for free downloading from the portals of the SOIL Action (http://eusoils.jrc.ec.europa.eu/) and the ACP Observatory for Sustainable Development (http://acpobservatory.jrc.ec.europa.eu). The Atlas links the theme of soil with rural development and, at the same time, supports the goals of the EU Thematic Strategy for Soil Protection in conserving a threatened natural resource that is vital to human existence. Not only climate change, but also desertification and loss of biodiversity are strongly affecting soils globally, making the "Soil Atlas of Africa" relevant to a much larger community of stakeholders involved in the implementation of the three "Rio-Conventions" and allowing to explore possible synergies among international multilateral agreements towards global soil protection.

Multi-Decadal Variation of Aerosols: Sources, Transport, and Climate Effects

NASA Technical Reports Server (NTRS)

Chin, Mian; Diehl, Thomas; Bian, Huisheng; Streets, David

2008-01-01

We present a global model study of multi-decadal changes of atmospheric aerosols and their climate effects using a global chemistry transport model along with the near-term to longterm data records. We focus on a 27-year time period of satellite era from 1980 to 2006, during which a suite of aerosol data from satellite observations, ground-based measurements, and intensive field experiments have become available. We will use the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model, which involves a time-varying, comprehensive global emission dataset that we put together in our previous investigations and will be improved/extended in this project. This global emission dataset includes emissions of aerosols and their precursors from fuel combustion, biomass burning, volcanic eruptions, and other sources from 1980 to the present. Using the model and satellite data, we will analyze (1) the long-term global and regional aerosol trends and their relationship to the changes of aerosol and precursor emissions from anthropogenic and natural sources, (2) the intercontinental source-receptor relationships controlled by emission, transport pathway, and climate variability.

A unified high-resolution wind and solar dataset from a rapidly updating numerical weather prediction model

DOE PAGES

James, Eric P.; Benjamin, Stanley G.; Marquis, Melinda

2016-10-28

A new gridded dataset for wind and solar resource estimation over the contiguous United States has been derived from hourly updated 1-h forecasts from the National Oceanic and Atmospheric Administration High-Resolution Rapid Refresh (HRRR) 3-km model composited over a three-year period (approximately 22 000 forecast model runs). The unique dataset features hourly data assimilation, and provides physically consistent wind and solar estimates for the renewable energy industry. The wind resource dataset shows strong similarity to that previously provided by a Department of Energy-funded study, and it includes estimates in southern Canada and northern Mexico. The solar resource dataset represents anmore » initial step towards application-specific fields such as global horizontal and direct normal irradiance. This combined dataset will continue to be augmented with new forecast data from the advanced HRRR atmospheric/land-surface model.« less

Theory of impossible worlds: Toward a physics of information.

PubMed

Buscema, Paolo Massimo; Sacco, Pier Luigi; Della Torre, Francesca; Massini, Giulia; Breda, Marco; Ferilli, Guido

2018-05-01

In this paper, we introduce an innovative approach to the fusion between datasets in terms of attributes and observations, even when they are not related at all. With our technique, starting from datasets representing independent worlds, it is possible to analyze a single global dataset, and transferring each dataset onto the others is always possible. This procedure allows a deeper perspective in the study of a problem, by offering the chance of looking into it from other, independent points of view. Even unrelated datasets create a metaphoric representation of the problem, useful in terms of speed of convergence and predictive results, preserving the fundamental relationships in the data. In order to extract such knowledge, we propose a new learning rule named double backpropagation, by which an auto-encoder concurrently codifies all the different worlds. We test our methodology on different datasets and different issues, to underline the power and flexibility of the Theory of Impossible Worlds.

Theory of impossible worlds: Toward a physics of information

NASA Astrophysics Data System (ADS)

Buscema, Paolo Massimo; Sacco, Pier Luigi; Della Torre, Francesca; Massini, Giulia; Breda, Marco; Ferilli, Guido

2018-05-01

In this paper, we introduce an innovative approach to the fusion between datasets in terms of attributes and observations, even when they are not related at all. With our technique, starting from datasets representing independent worlds, it is possible to analyze a single global dataset, and transferring each dataset onto the others is always possible. This procedure allows a deeper perspective in the study of a problem, by offering the chance of looking into it from other, independent points of view. Even unrelated datasets create a metaphoric representation of the problem, useful in terms of speed of convergence and predictive results, preserving the fundamental relationships in the data. In order to extract such knowledge, we propose a new learning rule named double backpropagation, by which an auto-encoder concurrently codifies all the different worlds. We test our methodology on different datasets and different issues, to underline the power and flexibility of the Theory of Impossible Worlds.

Advances in Remote Sensing for Vegetation Dynamics and Agricultural Management

NASA Technical Reports Server (NTRS)

Tucker, Compton; Puma, Michael

2015-01-01

Spaceborne remote sensing has led to great advances in the global monitoring of vegetation. For example, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) group has developed widely used datasets from the Advanced Very High Resolution Radiometer (AVHRR) sensors as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) map imagery and normalized difference vegetation index datasets. These data are valuable for analyzing vegetation trends and variability at the regional and global levels. Numerous studies have investigated such trends and variability for both natural vegetation (e.g., re-greening of the Sahel, shifts in the Eurasian boreal forest, Amazonian drought sensitivity) and crops (e.g., impacts of extremes on agricultural production). Here, a critical overview is presented on recent developments and opportunities in the use of remote sensing for monitoring vegetation and crop dynamics.

Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach

PubMed Central

Ding, Fangyu; Ge, Quansheng; Fu, Jingying; Hao, Mengmeng

2017-01-01

Terror events can cause profound consequences for the whole society. Finding out the regularity of terrorist attacks has important meaning for the global counter-terrorism strategy. In the present study, we demonstrate a novel method using relatively popular and robust machine learning methods to simulate the risk of terrorist attacks at a global scale based on multiple resources, long time series and globally distributed datasets. Historical data from 1970 to 2015 was adopted to train and evaluate machine learning models. The model performed fairly well in predicting the places where terror events might occur in 2015, with a success rate of 96.6%. Moreover, it is noteworthy that the model with optimized tuning parameter values successfully predicted 2,037 terrorism event locations where a terrorist attack had never happened before. PMID:28591138

Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach.

PubMed

Ding, Fangyu; Ge, Quansheng; Jiang, Dong; Fu, Jingying; Hao, Mengmeng

2017-01-01

Terror events can cause profound consequences for the whole society. Finding out the regularity of terrorist attacks has important meaning for the global counter-terrorism strategy. In the present study, we demonstrate a novel method using relatively popular and robust machine learning methods to simulate the risk of terrorist attacks at a global scale based on multiple resources, long time series and globally distributed datasets. Historical data from 1970 to 2015 was adopted to train and evaluate machine learning models. The model performed fairly well in predicting the places where terror events might occur in 2015, with a success rate of 96.6%. Moreover, it is noteworthy that the model with optimized tuning parameter values successfully predicted 2,037 terrorism event locations where a terrorist attack had never happened before.

A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska

USGS Publications Warehouse

Selkowitz, D.J.

2010-01-01

Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km2 study area in Arctic Alaska. The study area is centered at 69 ??N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10??, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000-2009.

Rainfall extremes from TRMM data and the Metastatistical Extreme Value Distribution

NASA Astrophysics Data System (ADS)

Zorzetto, Enrico; Marani, Marco

2017-04-01

A reliable quantification of the probability of weather extremes occurrence is essential for designing resilient water infrastructures and hazard mitigation measures. However, it is increasingly clear that the presence of inter-annual climatic fluctuations determines a substantial long-term variability in the frequency of occurrence of extreme events. This circumstance questions the foundation of the traditional extreme value theory, hinged on stationary Poisson processes or on asymptotic assumptions to derive the Generalized Extreme Value (GEV) distribution. We illustrate here, with application to daily rainfall, a new approach to extreme value analysis, the Metastatistical Extreme Value Distribution (MEVD). The MEVD relaxes the above assumptions and is based on the whole distribution of daily rainfall events, thus allowing optimal use of all available observations. Using a global dataset of rain gauge observations, we show that the MEVD significantly outperforms the Generalized Extreme Value distribution, particularly for long average recurrence intervals and when small samples are available. The latter property suggests MEVD to be particularly suited for applications to satellite rainfall estimates, which only cover two decades, thus making extreme value estimation extremely challenging. Here we apply MEVD to the TRMM TMPA 3B42 product, an 18-year dataset of remotely-sensed daily rainfall providing a quasi-global coverage. Our analyses yield a global scale mapping of daily rainfall extremes and of their distributional tail properties, bridging the existing large gaps in ground-based networks. Finally, we illustrate how our global-scale analysis can provide insight into how properties of local rainfall regimes affect tail estimation uncertainty when using the GEV or MEVD approach. We find a dependence of the estimation uncertainty, for both the GEV- and MEV-based approaches, on the average annual number and on the inter-annual variability of rainy days. In particular, estimation uncertainty decreases 1) as the mean annual number of wet days increases, and 2) as the variability in the number of rainy days, expressed by its coefficient of variation, decreases. We tentatively explain this behavior in terms of the assumptions underlying the two approaches.

Global assessment of human losses due to earthquakes

USGS Publications Warehouse

Silva, Vitor; Jaiswal, Kishor; Weatherill, Graeme; Crowley, Helen

2014-01-01

Current studies have demonstrated a sharp increase in human losses due to earthquakes. These alarming levels of casualties suggest the need for large-scale investment in seismic risk mitigation, which, in turn, requires an adequate understanding of the extent of the losses, and location of the most affected regions. Recent developments in global and uniform datasets such as instrumental and historical earthquake catalogues, population spatial distribution and country-based vulnerability functions, have opened an unprecedented possibility for a reliable assessment of earthquake consequences at a global scale. In this study, a uniform probabilistic seismic hazard assessment (PSHA) model was employed to derive a set of global seismic hazard curves, using the open-source software OpenQuake for seismic hazard and risk analysis. These results were combined with a collection of empirical fatality vulnerability functions and a population dataset to calculate average annual human losses at the country level. The results from this study highlight the regions/countries in the world with a higher seismic risk, and thus where risk reduction measures should be prioritized.

Status and interconnections of selected environmental issues in the global coastal zones

USGS Publications Warehouse

Shi, Hua; Singh, Ashbindu

2003-01-01

This study focuses on assessing the state of population distribution, land cover distribution, biodiversity hotspots, and protected areas in global coastal zones. The coastal zone is defined as land within 100 km of the coastline. This study attempts to answer such questions as: how crowded are the coastal zones, what is the pattern of land cover distribution in these areas, how much of these areas are designated as protected areas, what is the state of the biodiversity hotspots, and what are the interconnections between people and coastal environment. This study uses globally consistent and comprehensive geospatial datasets based on remote sensing and other sources. The application of Geographic Information System (GIS) layering methods and consistent datasets has made it possible to identify and quantify selected coastal zones environmental issues and their interconnections. It is expected that such information provide a scientific basis for global coastal zones management and assist in policy formulations at the national and international levels.

Development of a video tampering dataset for forensic investigation.

PubMed

Ismael Al-Sanjary, Omar; Ahmed, Ahmed Abdullah; Sulong, Ghazali

2016-09-01

Forgery is an act of modifying a document, product, image or video, among other media. Video tampering detection research requires an inclusive database of video modification. This paper aims to discuss a comprehensive proposal to create a dataset composed of modified videos for forensic investigation, in order to standardize existing techniques for detecting video tampering. The primary purpose of developing and designing this new video library is for usage in video forensics, which can be consciously associated with reliable verification using dynamic and static camera recognition. To the best of the author's knowledge, there exists no similar library among the research community. Videos were sourced from YouTube and by exploring social networking sites extensively by observing posted videos and rating their feedback. The video tampering dataset (VTD) comprises a total of 33 videos, divided among three categories in video tampering: (1) copy-move, (2) splicing, and (3) swapping-frames. Compared to existing datasets, this is a higher number of tampered videos, and with longer durations. The duration of every video is 16s, with a 1280×720 resolution, and a frame rate of 30 frames per second. Moreover, all videos possess the same formatting quality (720p(HD).avi). Both temporal and spatial video features were considered carefully during selection of the videos, and there exists complete information related to the doctored regions in every modified video in the VTD dataset. This database has been made publically available for research on splicing, Swapping frames, and copy-move tampering, and, as such, various video tampering detection issues with ground truth. The database has been utilised by many international researchers and groups of researchers. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

GIEMS-D3: A new long-term, dynamical, high-spatial resolution inundation extent dataset at global scale

NASA Astrophysics Data System (ADS)

Aires, Filipe; Miolane, Léo; Prigent, Catherine; Pham Duc, Binh; Papa, Fabrice; Fluet-Chouinard, Etienne; Lehner, Bernhard

2017-04-01

The Global Inundation Extent from Multi-Satellites (GIEMS) provides multi-year monthly variations of the global surface water extent at 25kmx25km resolution. It is derived from multiple satellite observations. Its spatial resolution is usually compatible with climate model outputs and with global land surface model grids but is clearly not adequate for local applications that require the characterization of small individual water bodies. There is today a strong demand for high-resolution inundation extent datasets, for a large variety of applications such as water management, regional hydrological modeling, or for the analysis of mosquitos-related diseases. A new procedure is introduced to downscale the GIEMS low spatial resolution inundations to a 3 arc second (90 m) dataset. The methodology is based on topography and hydrography information from the HydroSHEDS database. A new floodability index is adopted and an innovative smoothing procedure is developed to ensure the smooth transition, in the high-resolution maps, between the low-resolution boxes from GIEMS. Topography information is relevant for natural hydrology environments controlled by elevation, but is more limited in human-modified basins. However, the proposed downscaling approach is compatible with forthcoming fusion with other more pertinent satellite information in these difficult regions. The resulting GIEMS-D3 database is the only high spatial resolution inundation database available globally at the monthly time scale over the 1993-2007 period. GIEMS-D3 is assessed by analyzing its spatial and temporal variability, and evaluated by comparisons to other independent satellite observations from visible (Google Earth and Landsat), infrared (MODIS) and active microwave (SAR).

25m-resolution Global Mosaic and Forest/Non-Forest map using PALSAR-2 data set

NASA Astrophysics Data System (ADS)

Itoh, T.; Shimada, M.; Motooka, T.; Hayashi, M.; Tadono, T.; DAN, R.; Isoguchi, O.; Yamanokuchi, T.

2017-12-01

A continuous observation of forests is important as information necessary for monitoring deforestation, climate change and environmental changes i.e. Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD+). Japan Aerospace Exploration Agency (JAXA) is conducting research on forest monitoring using satellite-based L-Band Synthetic Aperture Radars (SARs) continuously. Using the FBD (Fine Beam Dual polarizations) data of the Phased Array type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS), JAXA created the global 25 m-resolution mosaic images and the Forest/Non-Forest (FNF) maps dataset for forest monitoring. SAR can monitor forest areas under all weather conditions, and L-band is highly sensitive to forests and their changes, therefore it is suitable for forest observation. JAXA also created the global 25 m mosaics and FNF maps using ALOS-2/PALSAR-2 launched on 2014 as a successor to ALOS. FNF dataset by PALSAR and PALSAR-2 covers from 2007 to 2010, and from 2015 to 2016, respectively. Therefore, it is possible to monitor forest changes during approx. 10 years. The classification method is combination of the object-based classification and the thresholding of HH and HV polarized images, and the result of FNF was compared with Forest Resource Assessment (FRA, developed by FAO) and their inconsistency is less than 10 %. Also, by comparing with the optical image of Google Earth, rate of coincidence was 80 % or more. We will create PALSAR-2 global mosaics and FNF dataset continuously to contribute global forest monitoring.

Aster Global dem Version 3, and New Aster Water Body Dataset

NASA Astrophysics Data System (ADS)

Abrams, M.

2016-06-01

In 2016, the US/Japan ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) project released Version 3 of the Global DEM (GDEM). This 30 m DEM covers the earth's surface from 82N to 82S, and improves on two earlier versions by correcting some artefacts and filling in areas of missing DEMs by the acquisition of additional data. The GDEM was produced by stereocorrelation of 2 million ASTER scenes and operation on a pixel-by-pixel basis: cloud screening; stacking data from overlapping scenes; removing outlier values, and averaging elevation values. As previously, the GDEM is packaged in ~ 23,000 1 x 1 degree tiles. Each tile has a DEM file, and a NUM file reporting the number of scenes used for each pixel, and identifying the source for fill-in data (where persistent clouds prevented computation of an elevation value). An additional data set was concurrently produced and released: the ASTER Water Body Dataset (AWBD). This is a 30 m raster product, which encodes every pixel as either lake, river, or ocean; thus providing a global inland and shore-line water body mask. Water was identified through spectral analysis algorithms and manual editing. This product was evaluated against the Shuttle Water Body Dataset (SWBD), and the Landsat-based Global Inland Water (GIW) product. The SWBD only covers the earth between about 60 degrees north and south, so it is not a global product. The GIW only delineates inland water bodies, and does not deal with ocean coastlines. All products are at 30 m postings.

A global wind resource atlas including high-resolution terrain effects

NASA Astrophysics Data System (ADS)

Hahmann, Andrea; Badger, Jake; Olsen, Bjarke; Davis, Neil; Larsen, Xiaoli; Badger, Merete

2015-04-01

Currently no accurate global wind resource dataset is available to fill the needs of policy makers and strategic energy planners. Evaluating wind resources directly from coarse resolution reanalysis datasets underestimate the true wind energy resource, as the small-scale spatial variability of winds is missing. This missing variability can account for a large part of the local wind resource. Crucially, it is the windiest sites that suffer the largest wind resource errors: in simple terrain the windiest sites may be underestimated by 25%, in complex terrain the underestimate can be as large as 100%. The small-scale spatial variability of winds can be modelled using novel statistical methods and by application of established microscale models within WAsP developed at DTU Wind Energy. We present the framework for a single global methodology, which is relative fast and economical to complete. The method employs reanalysis datasets, which are downscaled to high-resolution wind resource datasets via a so-called generalization step, and microscale modelling using WAsP. This method will create the first global wind atlas (GWA) that covers all land areas (except Antarctica) and 30 km coastal zone over water. Verification of the GWA estimates will be done at carefully selected test regions, against verified estimates from mesoscale modelling and satellite synthetic aperture radar (SAR). This verification exercise will also help in the estimation of the uncertainty of the new wind climate dataset. Uncertainty will be assessed as a function of spatial aggregation. It is expected that the uncertainty at verification sites will be larger than that of dedicated assessments, but the uncertainty will be reduced at levels of aggregation appropriate for energy planning, and importantly much improved relative to what is used today. In this presentation we discuss the methodology used, which includes the generalization of wind climatologies, and the differences in local and spatially aggregated wind resources that result from using different reanalyses in the various verification regions. A prototype web interface for the public access to the data will also be showcased.

Combining global land cover datasets to quantify agricultural expansion into forests in Latin America: Limitations and challenges

PubMed Central

Persson, U. Martin

2017-01-01

While we know that deforestation in the tropics is increasingly driven by commercial agriculture, most tropical countries still lack recent and spatially-explicit assessments of the relative importance of pasture and cropland expansion in causing forest loss. Here we present a spatially explicit quantification of the extent to which cultivated land and grassland expanded at the expense of forests across Latin America in 2001–2011, by combining two “state-of-the-art” global datasets (Global Forest Change forest loss and GlobeLand30-2010 land cover). We further evaluate some of the limitations and challenges in doing this. We find that this approach does capture some of the major patterns of land cover following deforestation, with GlobeLand30-2010’s Grassland class (which we interpret as pasture) being the most common land cover replacing forests across Latin America. However, our analysis also reveals some major limitations to combining these land cover datasets for quantifying pasture and cropland expansion into forest. First, a simple one-to-one translation between GlobeLand30-2010’s Cultivated land and Grassland classes into cropland and pasture respectively, should not be made without caution, as GlobeLand30-2010 defines its Cultivated land to include some pastures. Comparisons with the TerraClass dataset over the Brazilian Amazon and with previous literature indicates that Cultivated land in GlobeLand30-2010 includes notable amounts of pasture and other vegetation (e.g. in Paraguay and the Brazilian Amazon). This further suggests that the approach taken here generally leads to an underestimation (of up to ~60%) of the role of pasture in replacing forest. Second, a large share (~33%) of the Global Forest Change forest loss is found to still be forest according to GlobeLand30-2010 and our analysis suggests that the accuracy of the combined datasets, especially for areas with heterogeneous land cover and/or small-scale forest loss, is still too poor for deriving accurate quantifications of land cover following forest loss. PMID:28704510

Combining global land cover datasets to quantify agricultural expansion into forests in Latin America: Limitations and challenges.

PubMed

Pendrill, Florence; Persson, U Martin

2017-01-01

While we know that deforestation in the tropics is increasingly driven by commercial agriculture, most tropical countries still lack recent and spatially-explicit assessments of the relative importance of pasture and cropland expansion in causing forest loss. Here we present a spatially explicit quantification of the extent to which cultivated land and grassland expanded at the expense of forests across Latin America in 2001-2011, by combining two "state-of-the-art" global datasets (Global Forest Change forest loss and GlobeLand30-2010 land cover). We further evaluate some of the limitations and challenges in doing this. We find that this approach does capture some of the major patterns of land cover following deforestation, with GlobeLand30-2010's Grassland class (which we interpret as pasture) being the most common land cover replacing forests across Latin America. However, our analysis also reveals some major limitations to combining these land cover datasets for quantifying pasture and cropland expansion into forest. First, a simple one-to-one translation between GlobeLand30-2010's Cultivated land and Grassland classes into cropland and pasture respectively, should not be made without caution, as GlobeLand30-2010 defines its Cultivated land to include some pastures. Comparisons with the TerraClass dataset over the Brazilian Amazon and with previous literature indicates that Cultivated land in GlobeLand30-2010 includes notable amounts of pasture and other vegetation (e.g. in Paraguay and the Brazilian Amazon). This further suggests that the approach taken here generally leads to an underestimation (of up to ~60%) of the role of pasture in replacing forest. Second, a large share (~33%) of the Global Forest Change forest loss is found to still be forest according to GlobeLand30-2010 and our analysis suggests that the accuracy of the combined datasets, especially for areas with heterogeneous land cover and/or small-scale forest loss, is still too poor for deriving accurate quantifications of land cover following forest loss.

Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling

NASA Astrophysics Data System (ADS)

Beck, Hylke E.; Vergopolan, Noemi; Pan, Ming; Levizzani, Vincenzo; van Dijk, Albert I. J. M.; Weedon, Graham P.; Brocca, Luca; Pappenberger, Florian; Huffman, George J.; Wood, Eric F.

2017-12-01

We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000-2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( < 50 000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates.

A New High Resolution Tidal Model in the Arctic Ocean

NASA Astrophysics Data System (ADS)

Cancet, M.; Andersen, O.; Lyard, F.; Schulz, A.; Cotton, D.; Benveniste, J.

2016-08-01

The Arctic Ocean is a challenging region for tidal modelling. The accuracy of the global tidal models decreases by several centimeters in the Polar Regions, which has a large impact on the quality of the satellite altimeter sea surface heights and the altimetry-derived products.NOVELTIS and DTU Space have developed a regional, high-resolution tidal atlas in the Arctic Ocean, in the framework of an extension of the CryoSat Plus for Ocean (CP4O) ESA STSE (Support to Science Element) project. In particular, this atlas benefits from the assimilation of the most complete satellite altimetry dataset ever used in this region, including Envisat data up to 82°N and CryoSat-2 data between 82°N and 88°N. The combination of these satellite altimetry missions gives the best possible coverage of altimetry-derived tidal constituents. The available tide gauge data were also used for data assimilation and validation.This paper presents the implementation methodology and the performance of this new regional tidal model in the Arctic Ocean, compared to the existing global tidal models.

Potential effects of LNG trade shift on transfer of ballast water and biota by ships.

PubMed

Holzer, Kimberly K; Muirhead, Jim R; Minton, Mark S; Carney, Katharine J; Miller, A Whitman; Ruiz, Gregory M

2017-02-15

As the US natural gas surplus grows, so does the prospect of establishing new trade partnerships with buyers abroad, a process that has major consequences for global ship movement and ballast water delivery. Since US annual imports of liquefied natural gas (LNG) peaked in 2004-2007, the country is rapidly transitioning from net importer to net exporter of LNG. Combining multiple datasets, we estimated changes in the associated flux of ships' ballast water to the US during 2015-2040, using existing scenarios for projected exports of domestic LNG by ships. Our analysis of the current market (2015) scenario predicts an approximate 90-fold annual increase in LNG-related ballast water discharge to the US by 2040 (42millionm 3 ), with the potential to be even greater under high oil prices. We also described changes in geographic connectivity related to trade direction. These findings highlight how 21 st century global energy markets could dramatically alter opportunities for seaborne introductions and invasions by nonnative species. Copyright © 2016 Elsevier B.V. All rights reserved.

Enabling Research Tools for Sustained Climate Assessment

NASA Technical Reports Server (NTRS)

Leidner, Allison K.; Bosilovich, Michael G.; Jasinski, Michael F.; Nemani, Ramakrishna R.; Waliser, Duane Edward; Lee, Tsengdar J.

2016-01-01

The U.S. Global Change Research Program Sustained Assessment process benefits from long-term investments in Earth science research that enable the scientific community to conduct assessment-relevant science. To this end, NASA initiated several research programs over the past five years to support the Earth observation community in developing indicators, datasets, research products, and tools to support ongoing and future National Climate Assessments. These activities complement NASA's ongoing Earth science research programs. One aspect of the assessment portfolio funds four "enabling tools" projects at NASA research centers. Each tool leverages existing capacity within the center, but has developed tailored applications and products for National Climate Assessments. The four projects build on the capabilities of a global atmospheric reanalysis (MERRA-2), a continental U.S. land surface reanalysis (NCA-LDAS), the NASA Earth Exchange (NEX), and a Regional Climate Model Evaluation System (RCMES). Here, we provide a brief overview of each enabling tool, highlighting the ways in which it has advanced assessment science to date. We also discuss how the assessment community can access and utilize these tools for National Climate Assessments and other sustained assessment activities.

Big earth-observation data analytics for modelling pan-tropical land-use change trajectories for newly deforested areas

NASA Astrophysics Data System (ADS)

Coca Castro, Alejandro; Reymondin, Louis; Rebetez, Julien; Fabio Satizabal Mejia, Hector; Perez-Uribe, Andres; Mulligan, Mark; Smith, Thomas; Hyman, Glenn

2017-04-01

Global land use monitoring is important to the the Sustainable Development Goals (SDGs). The latest advances in storage and manipulation of big earth-observation data have been key to developing multiple operational forest monitoring initiatives such as FORMA, Terra-i and Global Forest Change. Although the data provided by these systems are useful for identifying and estimating newly deforested areas (from 2000), they do not provide details about the land use to which these deforested areas are transitioned. This information is critical to understand the biodiversity and ecosystem services impact of deforestation and the resulting impacts on human wellbeing, locally and downstream. With the aim of contributing to current forest monitoring initiatives, this research presents a set of experimental case studies in Latin America which integrate existing land-change information derived from remote sensing image and aerial photography/ground datasets, high-temporal resolution MODIS data, advanced machine learning (i.e deep learning) and big data technologies (i.e. Hadoop and Spark) to assess land-use change trajectories in newly deforested areas in near real time.

The Earth Observatory Natural Event Tracker (EONET): An API for Matching Natural Events to GIBS Imagery

NASA Astrophysics Data System (ADS)

Ward, K.

2015-12-01

Hidden within the terabytes of imagery in NASA's Global Imagery Browse Services (GIBS) collection are hundreds of daily natural events. Some events are newsworthy, devastating, and visibly obvious at a global scale, others are merely regional curiosities. Regardless of the scope and significance of any one event, it is likely that multiple GIBS layers can be viewed to provide a multispectral, dataset-based view of the event. To facilitate linking between the discrete event and the representative dataset imagery, NASA's Earth Observatory Group has developed a prototype application programming interface (API): the Earth Observatory Natural Event Tracker (EONET). EONET supports an API model that allows users to retrieve event-specific metadata--date/time, location, and type (wildfire, storm, etc.)--and web service layer-specific metadata which can be used to link to event-relevant dataset imagery in GIBS. GIBS' ability to ingest many near real time datasets, combined with its growing archive of past imagery, means that API users will be able to develop client applications that not only show ongoing events but can also look at imagery from before and after. In our poster, we will present the API and show examples of its use.

Variability of Upper-Tropospheric Precipitable from Satellite and Model Reanalysis Datasets

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Iwai, Hisaki

1999-01-01

Numerous datasets have been used to quantify water vapor and its variability in the upper-troposphere from satellite and model reanalysis data. These investigations have shown some usefulness in monitoring seasonal and inter-annual variations in moisture either globally, with polar orbiting satellite data or global model output analysis, or regionally, with the higher spatial and temporal resolution geostationary measurements. The datasets are not without limitations, however, due to coverage or limited temporal sampling, and may also contain bias in their representation of moisture processes. The research presented in this conference paper inter-compares the NVAP, NCEP/NCAR and DAO reanalysis models, and GOES satellite measurements of upper-tropospheric,precipitable water for the period from 1988-1994. This period captures several dramatic swings in climate events associated with ENSO events. The data are evaluated for temporal and spatial continuity, inter-compared to assess reliability and potential bias, and analyzed in light of expected trends due to changes in precipitation and synoptic-scale weather features. This work is the follow-on to previous research which evaluated total precipitable water over the same period. The relationship between total and upper-level precipitable water in the datasets will be discussed as well.

Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling

NASA Astrophysics Data System (ADS)

Beck, H.; Vergopolan, N.; Pan, M.; Levizzani, V.; van Dijk, A.; Weedon, G. P.; Brocca, L.; Huffman, G. J.; Wood, E. F.; William, L.

2017-12-01

We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000-2016. Twelve non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76,086 gauges worldwide. Another ten gauge-corrected ones were evaluated using hydrological modeling, by calibrating the conceptual model HBV against streamflow records for each of 9053 small to medium-sized (<50,000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR), the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed those indirectly incorporating gauge data through other multi-source datasets (PERSIANN-CDR V1R1 and PGF). Our results highlight large differences in estimation accuracy, and hence, the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite- and reanalysis-based P estimates.

NCAR's Research Data Archive: OPeNDAP Access for Complex Datasets

NASA Astrophysics Data System (ADS)

Dattore, R.; Worley, S. J.

2014-12-01

Many datasets have complex structures including hundreds of parameters and numerous vertical levels, grid resolutions, and temporal products. Making these data accessible is a challenge for a data provider. OPeNDAP is powerful protocol for delivering in real-time multi-file datasets that can be ingested by many analysis and visualization tools, but for these datasets there are too many choices about how to aggregate. Simple aggregation schemes can fail to support, or at least make it very challenging, for many potential studies based on complex datasets. We address this issue by using a rich file content metadata collection to create a real-time customized OPeNDAP service to match the full suite of access possibilities for complex datasets. The Climate Forecast System Reanalysis (CFSR) and it's extension, the Climate Forecast System Version 2 (CFSv2) datasets produced by the National Centers for Environmental Prediction (NCEP) and hosted by the Research Data Archive (RDA) at the Computational and Information Systems Laboratory (CISL) at NCAR are examples of complex datasets that are difficult to aggregate with existing data server software. CFSR and CFSv2 contain 141 distinct parameters on 152 vertical levels, six grid resolutions and 36 products (analyses, n-hour forecasts, multi-hour averages, etc.) where not all parameter/level combinations are available at all grid resolution/product combinations. These data are archived in the RDA with the data structure provided by the producer; no additional re-organization or aggregation have been applied. Since 2011, users have been able to request customized subsets (e.g. - temporal, parameter, spatial) from the CFSR/CFSv2, which are processed in delayed-mode and then downloaded to a user's system. Until now, the complexity has made it difficult to provide real-time OPeNDAP access to the data. We have developed a service that leverages the already-existing subsetting interface and allows users to create a virtual dataset with its own structure (das, dds). The user receives a URL to the customized dataset that can be used by existing tools to ingest, analyze, and visualize the data. This presentation will detail the metadata system and OPeNDAP server that enable user-customized real-time access and show an example of how a visualization tool can access the data.

Global negative emissions capacity of ocean macronutrient fertilization

NASA Astrophysics Data System (ADS)

Harrison, Daniel P.

2017-03-01

In order to meet the goal of limiting global average temperature increase to less than 2 °C, it is increasingly apparent that negative emissions technologies of up to 10 Pg C yr-1 will be needed before the end of the century. Recent research indicates that fertilization of the ocean with the macronutrients nitrogen and phosphorus where they limit primary production, may have sequestration advantages over fertilizing iron limited regions. Utilizing global datasets of oceanographic field measurements, and output from a high resolution global circulation model, the current study provides the first comprehensive assessment of the global potential for carbon sequestration from ocean macronutrient fertilization (OMF). Sufficient excess phosphate exists outside the iron limited surface ocean to support once-off sequestration of up to 3.6 Pg C by fertilization with nitrogen. Ongoing maximum capacity of nitrogen only fertilization is estimated at 0.7 ± 0.4 Pg C yr-1. Sequestration capacity is expected to decrease from the upper toward the lower bound over time under continued intense fertilization. If N and P were used in combination the capacity is ultimately limited by societies willingness to utilize phosphate resources. Doubling current phosphate production would allow an additional 0.9 Pg C yr-1 and consume 0.07% yr-1 of known global resources. Therefore offsetting up to around 15% (1.5 Pg C yr-1) of annual global CO2 emissions is assessed as being technically plausible. Environmental risks which to date have received little quantitative evaluation, could also limit the scale of implementation. These results reinforce the need to consider a multi-faceted approach to greenhouse gasses, including a reduction in emissions coupled with further research into negative emissions technologies.

Differentially Private Histogram Publication For Dynamic Datasets: An Adaptive Sampling Approach

PubMed Central

Li, Haoran; Jiang, Xiaoqian; Xiong, Li; Liu, Jinfei

2016-01-01

Differential privacy has recently become a de facto standard for private statistical data release. Many algorithms have been proposed to generate differentially private histograms or synthetic data. However, most of them focus on “one-time” release of a static dataset and do not adequately address the increasing need of releasing series of dynamic datasets in real time. A straightforward application of existing histogram methods on each snapshot of such dynamic datasets will incur high accumulated error due to the composibility of differential privacy and correlations or overlapping users between the snapshots. In this paper, we address the problem of releasing series of dynamic datasets in real time with differential privacy, using a novel adaptive distance-based sampling approach. Our first method, DSFT, uses a fixed distance threshold and releases a differentially private histogram only when the current snapshot is sufficiently different from the previous one, i.e., with a distance greater than a predefined threshold. Our second method, DSAT, further improves DSFT and uses a dynamic threshold adaptively adjusted by a feedback control mechanism to capture the data dynamics. Extensive experiments on real and synthetic datasets demonstrate that our approach achieves better utility than baseline methods and existing state-of-the-art methods. PMID:26973795

Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens

PubMed Central

Yin, Zheng; Zhou, Xiaobo; Bakal, Chris; Li, Fuhai; Sun, Youxian; Perrimon, Norbert; Wong, Stephen TC

2008-01-01

Background The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens. Results Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos [Additional files 1, 2], dataset for cell cycle phase identification using HeLa cells [Additional files 1, 3, 4] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%–90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms. Conclusion We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens. PMID:18534020

Geoscience Meets Social Science: A Flexible Data Driven Approach for Developing High Resolution Population Datasets at Global Scale

NASA Astrophysics Data System (ADS)

Rose, A.; McKee, J.; Weber, E.; Bhaduri, B. L.

2017-12-01

Leveraging decades of expertise in population modeling, and in response to growing demand for higher resolution population data, Oak Ridge National Laboratory is now generating LandScan HD at global scale. LandScan HD is conceived as a 90m resolution population distribution where modeling is tailored to the unique geography and data conditions of individual countries or regions by combining social, cultural, physiographic, and other information with novel geocomputation methods. Similarities among these areas are exploited in order to leverage existing training data and machine learning algorithms to rapidly scale development. Drawing on ORNL's unique set of capabilities, LandScan HD adapts highly mature population modeling methods developed for LandScan Global and LandScan USA, settlement mapping research and production in high-performance computing (HPC) environments, land use and neighborhood mapping through image segmentation, and facility-specific population density models. Adopting a flexible methodology to accommodate different geographic areas, LandScan HD accounts for the availability, completeness, and level of detail of relevant ancillary data. Beyond core population and mapped settlement inputs, these factors determine the model complexity for an area, requiring that for any given area, a data-driven model could support either a simple top-down approach, a more detailed bottom-up approach, or a hybrid approach.

Dynamic Non-Rigid Objects Reconstruction with a Single RGB-D Sensor

PubMed Central

Zuo, Xinxin; Du, Chao; Wang, Runxiao; Zheng, Jiangbin; Yang, Ruigang

2018-01-01

This paper deals with the 3D reconstruction problem for dynamic non-rigid objects with a single RGB-D sensor. It is a challenging task as we consider the almost inevitable accumulation error issue in some previous sequential fusion methods and also the possible failure of surface tracking in a long sequence. Therefore, we propose a global non-rigid registration framework and tackle the drifting problem via an explicit loop closure. Our novel scheme starts with a fusion step to get multiple partial scans from the input sequence, followed by a pairwise non-rigid registration and loop detection step to obtain correspondences between neighboring partial pieces and those pieces that form a loop. Then, we perform a global registration procedure to align all those pieces together into a consistent canonical space as guided by those matches that we have established. Finally, our proposed model-update step helps fixing potential misalignments that still exist after the global registration. Both geometric and appearance constraints are enforced during our alignment; therefore, we are able to get the recovered model with accurate geometry as well as high fidelity color maps for the mesh. Experiments on both synthetic and various real datasets have demonstrated the capability of our approach to reconstruct complete and watertight deformable objects. PMID:29547562

Astronaut Photography of the Earth: A Long-Term Dataset for Earth Systems Research, Applications, and Education

NASA Technical Reports Server (NTRS)

Stefanov, William L.

2017-01-01

The NASA Earth observations dataset obtained by humans in orbit using handheld film and digital cameras is freely accessible to the global community through the online searchable database at https://eol.jsc.nasa.gov, and offers a useful compliment to traditional ground-commanded sensor data. The dataset includes imagery from the NASA Mercury (1961) through present-day International Space Station (ISS) programs, and currently totals over 2.6 million individual frames. Geographic coverage of the dataset includes land and oceans areas between approximately 52 degrees North and South latitudes, but is spatially and temporally discontinuous. The photographic dataset includes some significant impediments for immediate research, applied, and educational use: commercial RGB films and camera systems with overlapping bandpasses; use of different focal length lenses, unconstrained look angles, and variable spacecraft altitudes; and no native geolocation information. Such factors led to this dataset being underutilized by the community but recent advances in automated and semi-automated image geolocation, image feature classification, and web-based services are adding new value to the astronaut-acquired imagery. A coupled ground software and on-orbit hardware system for the ISS is in development for planned deployment in mid-2017; this system will capture camera pose information for each astronaut photograph to allow automated, full georegistration of the data. The ground system component of the system is currently in use to fully georeference imagery collected in response to International Disaster Charter activations, and the auto-registration procedures are being applied to the extensive historical database of imagery to add value for research and educational purposes. In parallel, machine learning techniques are being applied to automate feature identification and classification throughout the dataset, in order to build descriptive metadata that will improve search capabilities. It is expected that these value additions will increase interest and use of the dataset by the global community.

ISRUC-Sleep: A comprehensive public dataset for sleep researchers.

PubMed

Khalighi, Sirvan; Sousa, Teresa; Santos, José Moutinho; Nunes, Urbano

2016-02-01

To facilitate the performance comparison of new methods for sleep patterns analysis, datasets with quality content, publicly-available, are very important and useful. We introduce an open-access comprehensive sleep dataset, called ISRUC-Sleep. The data were obtained from human adults, including healthy subjects, subjects with sleep disorders, and subjects under the effect of sleep medication. Each recording was randomly selected between PSG recordings that were acquired by the Sleep Medicine Centre of the Hospital of Coimbra University (CHUC). The dataset comprises three groups of data: (1) data concerning 100 subjects, with one recording session per subject; (2) data gathered from 8 subjects; two recording sessions were performed per subject, and (3) data collected from one recording session related to 10 healthy subjects. The polysomnography (PSG) recordings, associated with each subject, were visually scored by two human experts. Comparing the existing sleep-related public datasets, ISRUC-Sleep provides data of a reasonable number of subjects with different characteristics such as: data useful for studies involving changes in the PSG signals over time; and data of healthy subjects useful for studies involving comparison of healthy subjects with the patients, suffering from sleep disorders. This dataset was created aiming to complement existing datasets by providing easy-to-apply data collection with some characteristics not covered yet. ISRUC-Sleep can be useful for analysis of new contributions: (i) in biomedical signal processing; (ii) in development of ASSC methods; and (iii) on sleep physiology studies. To evaluate and compare new contributions, which use this dataset as a benchmark, results of applying a subject-independent automatic sleep stage classification (ASSC) method on ISRUC-Sleep dataset are presented. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Development of an internationally agreed minimal dataset for juvenile dermatomyositis (JDM) for clinical and research use.

PubMed

McCann, Liza J; Kirkham, Jamie J; Wedderburn, Lucy R; Pilkington, Clarissa; Huber, Adam M; Ravelli, Angelo; Appelbe, Duncan; Williamson, Paula R; Beresford, Michael W

2015-06-12

Juvenile dermatomyositis (JDM) is a rare autoimmune inflammatory disorder associated with significant morbidity and mortality. International collaboration is necessary to better understand the pathogenesis of the disease, response to treatment and long-term outcome. To aid international collaboration, it is essential to have a core set of data that all researchers and clinicians collect in a standardised way for clinical purposes and for research. This should include demographic details, diagnostic data and measures of disease activity, investigations and treatment. Variables in existing clinical registries have been compared to produce a provisional data set for JDM. We now aim to develop this into a consensus-approved minimum core dataset, tested in a wider setting, with the objective of achieving international agreement. A two-stage bespoke Delphi-process will engage the opinion of a large number of key stakeholders through Email distribution via established international paediatric rheumatology and myositis organisations. This, together with a formalised patient/parent participation process will help inform a consensus meeting of international experts that will utilise a nominal group technique (NGT). The resulting proposed minimal dataset will be tested for feasibility within existing database infrastructures. The developed minimal dataset will be sent to all internationally representative collaborators for final comment. The participants of the expert consensus group will be asked to draw together these comments, ratify and 'sign off' the final minimal dataset. An internationally agreed minimal dataset has the potential to significantly enhance collaboration, allow effective communication between groups, provide a minimal standard of care and enable analysis of the largest possible number of JDM patients to provide a greater understanding of this disease. The final approved minimum core dataset could be rapidly incorporated into national and international collaborative efforts, including existing prospective databases, and be available for use in randomised controlled trials and for treatment/protocol comparisons in cohort studies.

Reconciling controversies about the ‘global warming hiatus’

NASA Astrophysics Data System (ADS)

Medhaug, Iselin; Stolpe, Martin B.; Fischer, Erich M.; Knutti, Reto

2017-05-01

Between about 1998 and 2012, a time that coincided with political negotiations for preventing climate change, the surface of Earth seemed hardly to warm. This phenomenon, often termed the ‘global warming hiatus’, caused doubt in the public mind about how well anthropogenic climate change and natural variability are understood. Here we show that apparently contradictory conclusions stem from different definitions of ‘hiatus’ and from different datasets. A combination of changes in forcing, uptake of heat by the oceans, natural variability and incomplete observational coverage reconciles models and data. Combined with stronger recent warming trends in newer datasets, we are now more confident than ever that human influence is dominant in long-term warming.

Reconciling controversies about the 'global warming hiatus'.

PubMed

Medhaug, Iselin; Stolpe, Martin B; Fischer, Erich M; Knutti, Reto

2017-05-03

Between about 1998 and 2012, a time that coincided with political negotiations for preventing climate change, the surface of Earth seemed hardly to warm. This phenomenon, often termed the 'global warming hiatus', caused doubt in the public mind about how well anthropogenic climate change and natural variability are understood. Here we show that apparently contradictory conclusions stem from different definitions of 'hiatus' and from different datasets. A combination of changes in forcing, uptake of heat by the oceans, natural variability and incomplete observational coverage reconciles models and data. Combined with stronger recent warming trends in newer datasets, we are now more confident than ever that human influence is dominant in long-term warming.

Development of a consensus core dataset in juvenile dermatomyositis for clinical use to inform research

PubMed Central

McCann, Liza J; Pilkington, Clarissa A; Huber, Adam M; Ravelli, Angelo; Appelbe, Duncan; Kirkham, Jamie J; Williamson, Paula R; Aggarwal, Amita; Christopher-Stine, Lisa; Constantin, Tamas; Feldman, Brian M; Lundberg, Ingrid; Maillard, Sue; Mathiesen, Pernille; Murphy, Ruth; Pachman, Lauren M; Reed, Ann M; Rider, Lisa G; van Royen-Kerkof, Annet; Russo, Ricardo; Spinty, Stefan; Wedderburn, Lucy R

2018-01-01

Objectives This study aimed to develop consensus on an internationally agreed dataset for juvenile dermatomyositis (JDM), designed for clinical use, to enhance collaborative research and allow integration of data between centres. Methods A prototype dataset was developed through a formal process that included analysing items within existing databases of patients with idiopathic inflammatory myopathies. This template was used to aid a structured multistage consensus process. Exploiting Delphi methodology, two web-based questionnaires were distributed to healthcare professionals caring for patients with JDM identified through email distribution lists of international paediatric rheumatology and myositis research groups. A separate questionnaire was sent to parents of children with JDM and patients with JDM, identified through established research networks and patient support groups. The results of these parallel processes informed a face-to-face nominal group consensus meeting of international myositis experts, tasked with defining the content of the dataset. This developed dataset was tested in routine clinical practice before review and finalisation. Results A dataset containing 123 items was formulated with an accompanying glossary. Demographic and diagnostic data are contained within form A collected at baseline visit only, disease activity measures are included within form B collected at every visit and disease damage items within form C collected at baseline and annual visits thereafter. Conclusions Through a robust international process, a consensus dataset for JDM has been formulated that can capture disease activity and damage over time. This dataset can be incorporated into national and international collaborative efforts, including existing clinical research databases. PMID:29084729

Subnational Opposition to Globalization

ERIC Educational Resources Information Center

Almeida, Paul

2012-01-01

Using a unique dataset on the geographic distribution of reported protest events from local sources, the study explains the variation in community-level mobilization in response to neoliberal reforms in two countries in the global periphery. Building on insights from macro, cross-national studies of protests related to market reforms, this article…

Initial Development and Validation of the Global Citizenship Scale

ERIC Educational Resources Information Center

Morais, Duarte B.; Ogden, Anthony C.

2011-01-01

The purpose of this article is to report on the initial development of a theoretically grounded and empirically validated scale to measure global citizenship. The methodology employed is multi-faceted, including two expert face validity trials, extensive exploratory and confirmatory factor analyses with multiple datasets, and a series of three…

Radiative effects of global MODIS cloud regimes

PubMed Central

Oreopoulos, Lazaros; Cho, Nayeong; Lee, Dongmin; Kato, Seiji

2018-01-01

We update previously published MODIS global cloud regimes (CRs) using the latest MODIS cloud retrievals in the Collection 6 dataset. We implement a slightly different derivation method, investigate the composition of the regimes, and then proceed to examine several aspects of CR radiative appearance with the aid of various radiative flux datasets. Our results clearly show the CRs are radiatively distinct in terms of shortwave, longwave and their combined (total) cloud radiative effect. We show that we can clearly distinguish regimes based on whether they radiatively cool or warm the atmosphere, and thanks to radiative heating profiles to discern the vertical distribution of cooling and warming. Terra and Aqua comparisons provide information about the degree to which morning and afternoon occurrences of regimes affect the symmetry of CR radiative contribution. We examine how the radiative discrepancies among multiple irradiance datasets suffering from imperfect spatiotemporal matching depend on CR, and whether they are therefore related to the complexity of cloud structure, its interpretation by different observational systems, and its subsequent representation in radiative transfer calculations. PMID:29619289

Radiative effects of global MODIS cloud regimes.

PubMed

Oreopoulos, Lazaros; Cho, Nayeong; Lee, Dongmin; Kato, Seiji

2016-03-16

We update previously published MODIS global cloud regimes (CRs) using the latest MODIS cloud retrievals in the Collection 6 dataset. We implement a slightly different derivation method, investigate the composition of the regimes, and then proceed to examine several aspects of CR radiative appearance with the aid of various radiative flux datasets. Our results clearly show the CRs are radiatively distinct in terms of shortwave, longwave and their combined (total) cloud radiative effect. We show that we can clearly distinguish regimes based on whether they radiatively cool or warm the atmosphere, and thanks to radiative heating profiles to discern the vertical distribution of cooling and warming. Terra and Aqua comparisons provide information about the degree to which morning and afternoon occurrences of regimes affect the symmetry of CR radiative contribution. We examine how the radiative discrepancies among multiple irradiance datasets suffering from imperfect spatiotemporal matching depend on CR, and whether they are therefore related to the complexity of cloud structure, its interpretation by different observational systems, and its subsequent representation in radiative transfer calculations.

Radiative Effects of Global MODIS Cloud Regimes

NASA Technical Reports Server (NTRS)

Oraiopoulos, Lazaros; Cho, Nayeong; Lee, Dong Min; Kato, Seiji

2016-01-01

We update previously published MODIS global cloud regimes (CRs) using the latest MODIS cloud retrievals in the Collection 6 dataset. We implement a slightly different derivation method, investigate the composition of the regimes, and then proceed to examine several aspects of CR radiative appearance with the aid of various radiative flux datasets. Our results clearly show the CRs are radiatively distinct in terms of shortwave, longwave and their combined (total) cloud radiative effect. We show that we can clearly distinguish regimes based on whether they radiatively cool or warm the atmosphere, and thanks to radiative heating profiles to discern the vertical distribution of cooling and warming. Terra and Aqua comparisons provide information about the degree to which morning and afternoon occurrences of regimes affect the symmetry of CR radiative contribution. We examine how the radiative discrepancies among multiple irradiance datasets suffering from imperfect spatiotemporal matching depend on CR, and whether they are therefore related to the complexity of cloud structure, its interpretation by different observational systems, and its subsequent representation in radiative transfer calculations.

Large-scale Labeled Datasets to Fuel Earth Science Deep Learning Applications

NASA Astrophysics Data System (ADS)

Maskey, M.; Ramachandran, R.; Miller, J.

2017-12-01

Deep learning has revolutionized computer vision and natural language processing with various algorithms scaled using high-performance computing. However, generic large-scale labeled datasets such as the ImageNet are the fuel that drives the impressive accuracy of deep learning results. Large-scale labeled datasets already exist in domains such as medical science, but creating them in the Earth science domain is a challenge. While there are ways to apply deep learning using limited labeled datasets, there is a need in the Earth sciences for creating large-scale labeled datasets for benchmarking and scaling deep learning applications. At the NASA Marshall Space Flight Center, we are using deep learning for a variety of Earth science applications where we have encountered the need for large-scale labeled datasets. We will discuss our approaches for creating such datasets and why these datasets are just as valuable as deep learning algorithms. We will also describe successful usage of these large-scale labeled datasets with our deep learning based applications.

Finding relevant biomedical datasets: the UC San Diego solution for the bioCADDIE Retrieval Challenge

PubMed Central

Wei, Wei; Ji, Zhanglong; He, Yupeng; Zhang, Kai; Ha, Yuanchi; Li, Qi; Ohno-Machado, Lucila

2018-01-01

Abstract The number and diversity of biomedical datasets grew rapidly in the last decade. A large number of datasets are stored in various repositories, with different formats. Existing dataset retrieval systems lack the capability of cross-repository search. As a result, users spend time searching datasets in known repositories, and they typically do not find new repositories. The biomedical and healthcare data discovery index ecosystem (bioCADDIE) team organized a challenge to solicit new indexing and searching strategies for retrieving biomedical datasets across repositories. We describe the work of one team that built a retrieval pipeline and examined its performance. The pipeline used online resources to supplement dataset metadata, automatically generated queries from users’ free-text questions, produced high-quality retrieval results and achieved the highest inferred Normalized Discounted Cumulative Gain among competitors. The results showed that it is a promising solution for cross-database, cross-domain and cross-repository biomedical dataset retrieval. Database URL: https://github.com/w2wei/dataset_retrieval_pipeline PMID:29688374

A New Global Open Source Marine Hydrocarbon Emission Site Database

NASA Astrophysics Data System (ADS)

Onyia, E., Jr.; Wood, W. T.; Barnard, A.; Dada, T.; Qazzaz, M.; Lee, T. R.; Herrera, E.; Sager, W.

2017-12-01

Hydrocarbon emission sites (e.g. seeps) discharge large volumes of fluids and gases into the oceans that are not only important for biogeochemical budgets, but also support abundant chemosynthetic communities. Documenting the locations of modern emissions is a first step towards understanding and monitoring how they affect the global state of the seafloor and oceans. Currently, no global open source (i.e. non-proprietry) detailed maps of emissions sites are available. As a solution, we have created a database that is housed within an Excel spreadsheet and use the latest versions of Earthpoint and Google Earth for position coordinate conversions and data mapping, respectively. To date, approximately 1,000 data points have been collected from referenceable sources across the globe, and we are continualy expanding the dataset. Due to the variety of spatial extents encountered, to identify each site we used two different methods: 1) point (x, y, z) locations for individual sites and; 2) delineation of areas where sites are clustered. Certain well-known areas, such as the Gulf of Mexico and the Mediterranean Sea, have a greater abundance of information; whereas significantly less information is available in other regions due to the absence of emission sites, lack of data, or because the existing data is proprietary. Although the geographical extent of the data is currently restricted to regions where the most data is publicly available, as the database matures, we expect to have more complete coverage of the world's oceans. This database is an information resource that consolidates and organizes the existing literature on hydrocarbons released into the marine environment, thereby providing a comprehensive reference for future work. We expect that the availability of seafloor hydrocarbon emission maps will benefit scientific understanding of hydrocarbon rich areas as well as potentially aiding hydrocarbon exploration and environmental impact assessements.

Estimating Global Seafloor Total Organic Carbon Using a Machine Learning Technique and Its Relevance to Methane Hydrates

NASA Astrophysics Data System (ADS)

Lee, T. R.; Wood, W. T.; Dale, J.

2017-12-01

Empirical and theoretical models of sub-seafloor organic matter transformation, degradation and methanogenesis require estimates of initial seafloor total organic carbon (TOC). This subsurface methane, under the appropriate geophysical and geochemical conditions may manifest as methane hydrate deposits. Despite the importance of seafloor TOC, actual observations of TOC in the world's oceans are sparse and large regions of the seafloor yet remain unmeasured. To provide an estimate in areas where observations are limited or non-existent, we have implemented interpolation techniques that rely on existing data sets. Recent geospatial analyses have provided accurate accounts of global geophysical and geochemical properties (e.g. crustal heat flow, seafloor biomass, porosity) through machine learning interpolation techniques. These techniques find correlations between the desired quantity (in this case TOC) and other quantities (predictors, e.g. bathymetry, distance from coast, etc.) that are more widely known. Predictions (with uncertainties) of seafloor TOC in regions lacking direct observations are made based on the correlations. Global distribution of seafloor TOC at 1 x 1 arc-degree resolution was estimated from a dataset of seafloor TOC compiled by Seiter et al. [2004] and a non-parametric (i.e. data-driven) machine learning algorithm, specifically k-nearest neighbors (KNN). Built-in predictor selection and a ten-fold validation technique generated statistically optimal estimates of seafloor TOC and uncertainties. In addition, inexperience was estimated. Inexperience is effectively the distance in parameter space to the single nearest neighbor, and it indicates geographic locations where future data collection would most benefit prediction accuracy. These improved geospatial estimates of TOC in data deficient areas will provide new constraints on methane production and subsequent methane hydrate accumulation.

Recent Trends in Local-Scale Marine Biodiversity Reflect Community Structure and Human Impacts.

PubMed

Elahi, Robin; O'Connor, Mary I; Byrnes, Jarrett E K; Dunic, Jillian; Eriksson, Britas Klemens; Hensel, Marc J S; Kearns, Patrick J

2015-07-20

The modern biodiversity crisis reflects global extinctions and local introductions. Human activities have dramatically altered rates and scales of processes that regulate biodiversity at local scales. Reconciling the threat of global biodiversity loss with recent evidence of stability at fine spatial scales is a major challenge and requires a nuanced approach to biodiversity change that integrates ecological understanding. With a new dataset of 471 diversity time series spanning from 1962 to 2015 from marine coastal ecosystems, we tested (1) whether biodiversity changed at local scales in recent decades, and (2) whether we can ignore ecological context (e.g., proximate human impacts, trophic level, spatial scale) and still make informative inferences regarding local change. We detected a predominant signal of increasing species richness in coastal systems since 1962 in our dataset, though net species loss was associated with localized effects of anthropogenic impacts. Our geographically extensive dataset is unlikely to be a random sample of marine coastal habitats; impacted sites (3% of our time series) were underrepresented relative to their global presence. These local-scale patterns do not contradict the prospect of accelerating global extinctions but are consistent with local species loss in areas with direct human impacts and increases in diversity due to invasions and range expansions in lower impact areas. Attempts to detect and understand local biodiversity trends are incomplete without information on local human activities and ecological context. Copyright © 2015 Elsevier Ltd. All rights reserved.

Base flow calibration in a global hydrological model

NASA Astrophysics Data System (ADS)

van Beek, L. P.; Bierkens, M. F.

2006-12-01

Base flow constitutes an important water resource in many parts of the world. Its provenance and yield over time are governed by the storage capacity of local aquifers and the internal drainage paths, which are difficult to capture at the global scale. To represent the spatial and temporal variability in base flow adequately in a distributed global model at 0.5 degree resolution, we resorted to the conceptual model of aquifer storage of Kraaijenhoff- van de Leur (1958) that yields the reservoir coefficient for a linear groundwater store. This model was parameterised using global information on drainage density, climatology and lithology. Initial estimates of aquifer thickness, permeability and specific porosity from literature were linked to the latter two categories and calibrated to low flow data by means of simulated annealing so as to conserve the ordinal information contained by them. The observations used stem from the RivDis dataset of monthly discharge. From this dataset 324 stations were selected with at least 10 years of observations in the period 1958-1991 and an areal coverage of at least 10 cells of 0.5 degree. The dataset was split between basins into a calibration and validation set whilst preserving a representative distribution of lithology types and climate zones. Optimisation involved minimising the absolute differences between the simulated base flow and the lowest 10% of the observed monthly discharge. Subsequently, the reliability of the calibrated parameters was tested by reversing the calibration and validation sets.

A new climate dataset for systematic assessments of climate change impacts as a function of global warming

NASA Astrophysics Data System (ADS)

Heinke, J.; Ostberg, S.; Schaphoff, S.; Frieler, K.; Müller, C.; Gerten, D.; Meinshausen, M.; Lucht, W.

2013-10-01

In the ongoing political debate on climate change, global mean temperature change (ΔTglob) has become the yardstick by which mitigation costs, impacts from unavoided climate change, and adaptation requirements are discussed. For a scientifically informed discourse along these lines, systematic assessments of climate change impacts as a function of ΔTglob are required. The current availability of climate change scenarios constrains this type of assessment to a narrow range of temperature change and/or a reduced ensemble of climate models. Here, a newly composed dataset of climate change scenarios is presented that addresses the specific requirements for global assessments of climate change impacts as a function of ΔTglob. A pattern-scaling approach is applied to extract generalised patterns of spatially explicit change in temperature, precipitation and cloudiness from 19 Atmosphere-Ocean General Circulation Models (AOGCMs). The patterns are combined with scenarios of global mean temperature increase obtained from the reduced-complexity climate model MAGICC6 to create climate scenarios covering warming levels from 1.5 to 5 degrees above pre-industrial levels around the year 2100. The patterns are shown to sufficiently maintain the original AOGCMs' climate change properties, even though they, necessarily, utilise a simplified relationships between ΔTglob and changes in local climate properties. The dataset (made available online upon final publication of this paper) facilitates systematic analyses of climate change impacts as it covers a wider and finer-spaced range of climate change scenarios than the original AOGCM simulations.

A new dataset for systematic assessments of climate change impacts as a function of global warming

NASA Astrophysics Data System (ADS)

Heinke, J.; Ostberg, S.; Schaphoff, S.; Frieler, K.; M{ü}ller, C.; Gerten, D.; Meinshausen, M.; Lucht, W.

2012-11-01

In the ongoing political debate on climate change, global mean temperature change (ΔTglob) has become the yardstick by which mitigation costs, impacts from unavoided climate change, and adaptation requirements are discussed. For a scientifically informed discourse along these lines systematic assessments of climate change impacts as a function of ΔTglob are required. The current availability of climate change scenarios constrains this type of assessment to a~narrow range of temperature change and/or a reduced ensemble of climate models. Here, a newly composed dataset of climate change scenarios is presented that addresses the specific requirements for global assessments of climate change impacts as a function of ΔTglob. A pattern-scaling approach is applied to extract generalized patterns of spatially explicit change in temperature, precipitation and cloudiness from 19 AOGCMs. The patterns are combined with scenarios of global mean temperature increase obtained from the reduced-complexity climate model MAGICC6 to create climate scenarios covering warming levels from 1.5 to 5 degrees above pre-industrial levels around the year 2100. The patterns are shown to sufficiently maintain the original AOGCMs' climate change properties, even though they, necessarily, utilize a simplified relationships betweenΔTglob and changes in local climate properties. The dataset (made available online upon final publication of this paper) facilitates systematic analyses of climate change impacts as it covers a wider and finer-spaced range of climate change scenarios than the original AOGCM simulations.

Absolute Geostrophic Velocity Inverted from World Ocean Atlas 2013 (WOAV13) with the P-Vector Method

DTIC Science & Technology

2015-11-01

The WOAV13 dataset comprises 3D global gridded climatological fields of absolute geostrophic velocity inverted...from World Ocean Atlas-2013 (WOA13) temperature and salinity fields using the P-vector method. It provides a climatological velocity field that is... climatology Dataset Identifier: gov.noaa.nodc:0121576 Creator: NOAP Lab, Department of Oceanography, Naval Postgraduate School, Monterey, CA Title

[Comparison of GIMMS and MODIS normalized vegetation index composite data for Qing-Hai-Tibet Plateau].

PubMed

Du, Jia-Qiang; Shu, Jian-Min; Wang, Yue-Hui; Li, Ying-Chang; Zhang, Lin-Bo; Guo, Yang

2014-02-01

Consistent NDVI time series are basic and prerequisite in long-term monitoring of land surface properties. Advanced very high resolution radiometer (AVHRR) measurements provide the longest records of continuous global satellite measurements sensitive to live green vegetation, and moderate resolution imaging spectroradiometer (MODIS) is more recent typical with high spatial and temporal resolution. Understanding the relationship between the AVHRR-derived NDVI and MODIS NDVI is critical to continued long-term monitoring of ecological resources. NDVI time series acquired by the global inventory modeling and mapping studies (GIMMS) and Terra MODIS were compared over the same time periods from 2000 to 2006 at four scales of Qinghai-Tibet Plateau (whole region, sub-region, biome and pixel) to assess the level of agreement in terms of absolute values and dynamic change by independently assessing the performance of GIMMS and MODIS NDVI and using 495 Landsat samples of 20 km x20 km covering major land cover type. High correlations existed between the two datasets at the four scales, indicating their mostly equal capability of capturing seasonal and monthly phenological variations (mostly at 0. 001 significance level). Simi- larities of the two datasets differed significantly among different vegetation types. The relative low correlation coefficients and large difference of NDVI value between the two datasets were found among dense vegetation types including broadleaf forest and needleleaf forest, yet the correlations were strong and the deviations were small in more homogeneous vegetation types, such as meadow, steppe and crop. 82% of study area was characterized by strong consistency between GIMMS and MODIS NDVI at pixel scale. In the Landsat NDVI vs. GIMMS and MODIS NDVI comparison of absolute values, the MODIS NDVI performed slightly better than GIMMS NDVI, whereas in the comparison of temporal change values, the GIMMS data set performed best. Similar with comparison results of GIMMS and MODIS NDVI, the consistency across the three datasets was clearly different among various vegetation types. In dynamic changes, differences between Landsat and MODIS NDVI were smaller than Landsat NDVI vs. GIMMS NDVI for forest, but Landsat and GIMMS NDVI agreed better for grass and crop. The results suggested that spatial patterns and dynamic trends of GIMMS NDVI were found to be in overall acceptable agreement with MODIS NDVI. It might be feasible to successfully integrate historical GIMMS and more recent MODIS NDVI to provide continuity of NDVI products. The accuracy of merging AVHRR historical data recorded with more modern MODIS NDVI data strongly depends on vegetation type, season and phenological period, and spatial scale. The integration of the two datasets for needleleaf forest, broadleaf forest, and for all vegetation types in the phenological transition periods in spring and autumn should be treated with caution.

Data Recommender: An Alternative Way to Discover Open Scientific Datasets

NASA Astrophysics Data System (ADS)

Klump, J. F.; Devaraju, A.; Williams, G.; Hogan, D.; Davy, R.; Page, J.; Singh, D.; Peterson, N.

2017-12-01

Over the past few years, institutions and government agencies have adopted policies to openly release their data, which has resulted in huge amounts of open data becoming available on the web. When trying to discover the data, users face two challenges: an overload of choice and the limitations of the existing data search tools. On the one hand, there are too many datasets to choose from, and therefore, users need to spend considerable effort to find the datasets most relevant to their research. On the other hand, data portals commonly offer keyword and faceted search, which depend fully on the user queries to search and rank relevant datasets. Consequently, keyword and faceted search may return loosely related or irrelevant results, although the results may contain the same query. They may also return highly specific results that depend more on how well metadata was authored. They do not account well for variance in metadata due to variance in author styles and preferences. The top-ranked results may also come from the same data collection, and users are unlikely to discover new and interesting datasets. These search modes mainly suits users who can express their information needs in terms of the structure and terminology of the data portals, but may pose a challenge otherwise. The above challenges reflect that we need a solution that delivers the most relevant (i.e., similar and serendipitous) datasets to users, beyond the existing search functionalities on the portals. A recommender system is an information filtering system that presents users with relevant and interesting contents based on users' context and preferences. Delivering data recommendations to users can make data discovery easier, and as a result may enhance user engagement with the portal. We developed a hybrid data recommendation approach for the CSIRO Data Access Portal. The approach leverages existing recommendation techniques (e.g., content-based filtering and item co-occurrence) to produce similar and serendipitous data recommendations. It measures the relevance between datasets based on their properties, and search and download patterns. We evaluated the recommendation approach in a user study, and the obtained user judgments revealed the ability of the approach to accurately quantify the relevance of the datasets.

Transductive multi-view zero-shot learning.

PubMed

Fu, Yanwei; Hospedales, Timothy M; Xiang, Tao; Gong, Shaogang

2015-11-01

Most existing zero-shot learning approaches exploit transfer learning via an intermediate semantic representation shared between an annotated auxiliary dataset and a target dataset with different classes and no annotation. A projection from a low-level feature space to the semantic representation space is learned from the auxiliary dataset and applied without adaptation to the target dataset. In this paper we identify two inherent limitations with these approaches. First, due to having disjoint and potentially unrelated classes, the projection functions learned from the auxiliary dataset/domain are biased when applied directly to the target dataset/domain. We call this problem the projection domain shift problem and propose a novel framework, transductive multi-view embedding, to solve it. The second limitation is the prototype sparsity problem which refers to the fact that for each target class, only a single prototype is available for zero-shot learning given a semantic representation. To overcome this problem, a novel heterogeneous multi-view hypergraph label propagation method is formulated for zero-shot learning in the transductive embedding space. It effectively exploits the complementary information offered by different semantic representations and takes advantage of the manifold structures of multiple representation spaces in a coherent manner. We demonstrate through extensive experiments that the proposed approach (1) rectifies the projection shift between the auxiliary and target domains, (2) exploits the complementarity of multiple semantic representations, (3) significantly outperforms existing methods for both zero-shot and N-shot recognition on three image and video benchmark datasets, and (4) enables novel cross-view annotation tasks.

Content-level deduplication on mobile internet datasets

NASA Astrophysics Data System (ADS)

Hou, Ziyu; Chen, Xunxun; Wang, Yang

2017-06-01

Various systems and applications involve a large volume of duplicate items. Based on high data redundancy in real world datasets, data deduplication can reduce storage capacity and improve the utilization of network bandwidth. However, chunks of existing deduplications range in size from 4KB to over 16KB, existing systems are not applicable to the datasets consisting of short records. In this paper, we propose a new framework called SF-Dedup which is able to implement the deduplication process on a large set of Mobile Internet records, the size of records can be smaller than 100B, or even smaller than 10B. SF-Dedup is a short fingerprint, in-line, hash-collisions-resolved deduplication. Results of experimental applications illustrate that SH-Dedup is able to reduce storage capacity and shorten query time on relational database.

Controls on delta formation, area, and topset slope: New predictive relationships developed using a global delta dataset

NASA Astrophysics Data System (ADS)

Caldwell, R. L.; Edmonds, D. A.; Baumgardner, S. E.; Paola, C.; Roy, S.; Nienhuis, J.

2017-12-01

River deltas are irreplaceable natural and societal resources, though they are at risk of drowning due to sea-level rise and decreased sediment delivery. To enhance hazard mitigation efforts in the face of global environmental change, we must understand the controls on delta growth. Previous empirical studies of delta growth are based on small datasets and often biased towards large, river-dominated deltas. We are currently lacking relationships that predict delta formation, area, or topset slope across the full breadth of global deltas. To this end, we developed a global dataset of 5,229 rivers (with and without deltas) paired with nine upstream (e.g., sediment discharge) and four downstream (e.g., wave height) environmental variables. Using Google Earth imagery, we identify all coastal river mouths (≥ 50 m wide) connected to an upstream catchment, and define deltas as river mouths that split into two or more distributary channels, end in a depositional protrusion from the shoreline, or do both. Delta area is defined as the area of the polygon connecting the delta node, two lateral shoreline extent points, and the basinward-most extent of the delta. Topset slope is calculated as the average, linear slope from the delta node elevation (extracted from SRTM data) to the main channel mouth, and shoreline and basinward extent points. Of the 5,229 rivers in our dataset, 1,816 (35%) have a delta. Using 495 rivers (those with data available for all variables), we build an empirically-derived relationship that predicts delta formation with 76% success. Delta formation is controlled predominantly by upstream water and sediment discharge, with secondary control by downstream waves and tides that suppress delta formation. For those rivers that do form deltas, we show that delta area is best predicted by sediment discharge, bathymetric slope, and drainage basin area (R2 = 0.95, n = 170), and exhibits a negative power-law relationship with topset slope (R2 = 0.85, n = 1,342). Topset slope is best predicted by grain size and wave height (R2 = 0.50, n = 358). These empirical relationships can aid in forecasting delta response to continued global environmental change.

WikiPEATia - a web based platform for assembling peatland data through ‘crowd sourcing’

NASA Astrophysics Data System (ADS)

Wisser, D.; Glidden, S.; Fieseher, C.; Treat, C. C.; Routhier, M.; Frolking, S. E.

2009-12-01

The Earth System Science community is realizing that peatlands are an important and unique terrestrial ecosystem that has not yet been well-integrated into large-scale earth system analyses. A major hurdle is the lack of accessible, geospatial data of peatland distribution, coupled with data on peatland properties (e.g., vegetation composition, peat depth, basal dates, soil chemistry, peatland class) at the global scale. This data, however, is available at the local scale. Although a comprehensive global database on peatlands probably lags similar data on more economically important ecosystems such as forests, grasslands, croplands, a large amount of field data have been collected over the past several decades. A few efforts have been made to map peatlands at large scales but existing data have not been assembled into a single geospatial database that is publicly accessible or do not depict data with a level of detail that is needed in the Earth System Science Community. A global peatland database would contribute to advances in a number of research fields such as hydrology, vegetation and ecosystem modeling, permafrost modeling, and earth system modeling. We present a Web 2.0 approach that uses state-of-the-art webserver and innovative online mapping technologies and is designed to create such a global database through ‘crowd-sourcing’. Primary functions of the online system include form-driven textual user input of peatland research metadata, spatial data input of peatland areas via a mapping interface, database editing and querying editing capabilities, as well as advanced visualization and data analysis tools. WikiPEATia provides an integrated information technology platform for assembling, integrating, and posting peatland-related geospatial datasets facilitates and encourages research community involvement. A successful effort will make existing peatland data much more useful to the research community, and will help to identify significant data gaps.

Access to Emissions Distributions and Related Ancillary Data through the ECCAD database

NASA Astrophysics Data System (ADS)

Darras, Sabine; Granier, Claire; Liousse, Catherine; De Graaf, Erica; Enriquez, Edgar; Boulanger, Damien; Brissebrat, Guillaume

2017-04-01

The ECCAD database (Emissions of atmospheric Compounds and Compilation of Ancillary Data) provides a user-friendly access to global and regional surface emissions for a large set of chemical compounds and ancillary data (land use, active fires, burned areas, population,etc). The emissions inventories are time series gridded data at spatial resolution from 1x1 to 0.1x0.1 degrees. ECCAD is the emissions database of the GEIA (Global Emissions InitiAtive) project and a sub-project of the French Atmospheric Data Center AERIS (http://www.aeris-data.fr). ECCAD has currently more than 2200 users originating from more than 80 countries. The project benefits from this large international community of users to expand the number of emission datasets made available. ECCAD provides detailed metadata for each of the datasets and various tools for data visualization, for computing global and regional totals and for interactive spatial and temporal analysis. The data can be downloaded as interoperable NetCDF CF-compliant files, i.e. the data are compatible with many other client interfaces. The presentation will provide information on the datasets available within ECCAD, as well as examples of the analysis work that can be done online through the website: http://eccad.aeris-data.fr.

Access to Emissions Distributions and Related Ancillary Data through the ECCAD database

NASA Astrophysics Data System (ADS)

Darras, Sabine; Enriquez, Edgar; Granier, Claire; Liousse, Catherine; Boulanger, Damien; Fontaine, Alain

2016-04-01

The ECCAD database (Emissions of atmospheric Compounds and Compilation of Ancillary Data) provides a user-friendly access to global and regional surface emissions for a large set of chemical compounds and ancillary data (land use, active fires, burned areas, population,etc). The emissions inventories are time series gridded data at spatial resolution from 1x1 to 0.1x0.1 degrees. ECCAD is the emissions database of the GEIA (Global Emissions InitiAtive) project and a sub-project of the French Atmospheric Data Center AERIS (http://www.aeris-data.fr). ECCAD has currently more than 2200 users originating from more than 80 countries. The project benefits from this large international community of users to expand the number of emission datasets made available. ECCAD provides detailed metadata for each of the datasets and various tools for data visualization, for computing global and regional totals and for interactive spatial and temporal analysis. The data can be downloaded as interoperable NetCDF CF-compliant files, i.e. the data are compatible with many other client interfaces. The presentation will provide information on the datasets available within ECCAD, as well as examples of the analysis work that can be done online through the website: http://eccad.aeris-data.fr.

Validation of Long-Term Global Aerosol Climatology Project Optical Thickness Retrievals Using AERONET and MODIS Data

NASA Technical Reports Server (NTRS)

Geogdzhayev, Igor V.; Mishchenko, Michael I.

2015-01-01

A comprehensive set of monthly mean aerosol optical thickness (AOT) data from coastal and island AErosol RObotic NETwork (AERONET) stations is used to evaluate Global Aerosol Climatology Project (GACP) retrievals for the period 1995-2009 during which contemporaneous GACP and AERONET data were available. To put the GACP performance in broader perspective, we also compare AERONET and MODerate resolution Imaging Spectroradiometer (MODIS) Aqua level-2 data for 2003-2009 using the same methodology. We find that a large mismatch in geographic coverage exists between the satellite and ground-based datasets, with very limited AERONET coverage of open-ocean areas. This is especially true of GACP because of the smaller number of AERONET stations at the early stages of the network development. Monthly mean AOTs from the two over-the-ocean satellite datasets are well-correlated with the ground-based values, the correlation coefficients being 0.81-0.85 for GACP and 0.74-0.79 for MODIS. Regression analyses demonstrate that the GACP mean AOTs are approximately 17%-27% lower than the AERONET values on average, while the MODIS mean AOTs are 5%-25% higher. The regression coefficients are highly dependent on the weighting assumptions (e.g., on the measure of aerosol variability) as well as on the set of AERONET stations used for comparison. Comparison of over-the-land and over-the-ocean MODIS monthly mean AOTs in the vicinity of coastal AERONET stations reveals a significant bias. This may indicate that aerosol amounts in coastal locations can differ significantly from those in adjacent open-ocean areas. Furthermore, the color of coastal waters and peculiarities of coastline meteorological conditions may introduce biases in the GACP AOT retrievals. We conclude that the GACP and MODIS over-the-ocean retrieval algorithms show similar ranges of discrepancy when compared to available coastal and island AERONET stations. The factors mentioned above may limit the performance of the validation procedure and cause us to caution against a direct extrapolation of the presented validation results to the entirety of the GACP dataset.

Diviner lunar radiometer gridded brightness temperatures from geodesic binning of modeled fields of view

NASA Astrophysics Data System (ADS)

Sefton-Nash, E.; Williams, J.-P.; Greenhagen, B. T.; Aye, K.-M.; Paige, D. A.

2017-12-01

An approach is presented to efficiently produce high quality gridded data records from the large, global point-based dataset returned by the Diviner Lunar Radiometer Experiment aboard NASA's Lunar Reconnaissance Orbiter. The need to minimize data volume and processing time in production of science-ready map products is increasingly important with the growth in data volume of planetary datasets. Diviner makes on average >1400 observations per second of radiance that is reflected and emitted from the lunar surface, using 189 detectors divided into 9 spectral channels. Data management and processing bottlenecks are amplified by modeling every observation as a probability distribution function over the field of view, which can increase the required processing time by 2-3 orders of magnitude. Geometric corrections, such as projection of data points onto a digital elevation model, are numerically intensive and therefore it is desirable to perform them only once. Our approach reduces bottlenecks through parallel binning and efficient storage of a pre-processed database of observations. Database construction is via subdivision of a geodesic icosahedral grid, with a spatial resolution that can be tailored to suit the field of view of the observing instrument. Global geodesic grids with high spatial resolution are normally impractically memory intensive. We therefore demonstrate a minimum storage and highly parallel method to bin very large numbers of data points onto such a grid. A database of the pre-processed and binned points is then used for production of mapped data products that is significantly faster than if unprocessed points were used. We explore quality controls in the production of gridded data records by conditional interpolation, allowed only where data density is sufficient. The resultant effects on the spatial continuity and uncertainty in maps of lunar brightness temperatures is illustrated. We identify four binning regimes based on trades between the spatial resolution of the grid, the size of the FOV and the on-target spacing of observations. Our approach may be applicable and beneficial for many existing and future point-based planetary datasets.

Rolling Deck to Repository (R2R): Linking and Integrating Data for Oceanographic Research

NASA Astrophysics Data System (ADS)

Arko, R. A.; Chandler, C. L.; Clark, P. D.; Shepherd, A.; Moore, C.

2012-12-01

The Rolling Deck to Repository (R2R) program is developing infrastructure to ensure the underway sensor data from NSF-supported oceanographic research vessels are routinely and consistently documented, preserved in long-term archives, and disseminated to the science community. We have published the entire R2R Catalog as a Linked Data collection, making it easily accessible to encourage linking and integration with data at other repositories. We are developing the R2R Linked Data collection with specific goals in mind: 1.) We facilitate data access and reuse by providing the richest possible collection of resources to describe vessels, cruises, instruments, and datasets from the U.S. academic fleet, including data quality assessment results and clean trackline navigation. We are leveraging or adopting existing community-standard concepts and vocabularies, particularly concepts from the Biological and Chemical Oceanography Data Management Office (BCO-DMO) ontology and terms from the pan-European SeaDataNet vocabularies, and continually re-publish resources as new concepts and terms are mapped. 2.) We facilitate data citation through the entire data lifecycle from field acquisition to shoreside archiving to (ultimately) global syntheses and journal articles. We are implementing globally unique and persistent identifiers at the collection, dataset, and granule levels, and encoding these citable identifiers directly into the Linked Data resources. 3.) We facilitate linking and integration with other repositories that publish Linked Data collections for the U.S. academic fleet, such as BCO-DMO and the Index to Marine and Lacustrine Geological Samples (IMLGS). We are initially mapping datasets at the resource level, and plan to eventually implement rule-based mapping at the concept level. We work collaboratively with partner repositories to develop best practices for URI patterns and consensus on shared vocabularies. The R2R Linked Data collection is implemented as a lightweight "virtual RDF graph" generated on-the-fly from our SQL database using the D2RQ (http://d2rq.org) package. In addition to the default SPARQL endpoint for programmatic access, we are developing a Web-based interface from open-source software components that offers user-friendly browse and search.

Landfalling Tropical Cyclones: Forecast Problems and Associated Research Opportunities

USGS Publications Warehouse

Marks, F.D.; Shay, L.K.; Barnes, G.; Black, P.; Demaria, M.; McCaul, B.; Mounari, J.; Montgomery, M.; Powell, M.; Smith, J.D.; Tuleya, B.; Tripoli, G.; Xie, Lingtian; Zehr, R.

1998-01-01

The Fifth Prospectus Development Team of the U.S. Weather Research Program was charged to identify and delineate emerging research opportunities relevant to the prediction of local weather, flooding, and coastal ocean currents associated with landfalling U.S. hurricanes specifically, and tropical cyclones in general. Central to this theme are basic and applied research topics, including rapid intensity change, initialization of and parameterization in dynamical models, coupling of atmospheric and oceanic models, quantitative use of satellite information, and mobile observing strategies to acquire observations to evaluate and validate predictive models. To improve the necessary understanding of physical processes and provide the initial conditions for realistic predictions, a focused, comprehensive mobile observing system in a translating storm-coordinate system is required. Given the development of proven instrumentation and improvement of existing systems, three-dimensional atmospheric and oceanic datasets need to be acquired whenever major hurricanes threaten the United States. The spatial context of these focused three-dimensional datasets over the storm scales is provided by satellites, aircraft, expendable probes released from aircraft, and coastal (both fixed and mobile), moored, and drifting surface platforms. To take full advantage of these new observations, techniques need to be developed to objectively analyze these observations, and initialize models aimed at improving prediction of hurricane track and intensity from global-scale to mesoscale dynamical models. Multinested models allow prediction of all scales from the global, which determine long- term hurricane motion to the convective scale, which affect intensity. Development of an integrated analysis and model forecast system optimizing the use of three-dimensional observations and providing the necessary forecast skill on all relevant spatial scales is required. Detailed diagnostic analyses of these datasets will lead to improved understanding of the physical processes of hurricane motion, intensity change, the atmospheric and oceanic boundary layers, and the air- sea coupling mechanisms. The ultimate aim of this effort is the construction of real-time analyses of storm surge, winds, and rain, prior to and during landfall, to improve warnings and provide local officials with the comprehensive information required for recovery efforts in the hardest hit areas as quickly as possible.

Challenges in Collating Spirometry Reference Data for South-Asian Children: An Observational Study

PubMed Central

Lum, Sooky; Bountziouka, Vassiliki; Quanjer, Philip; Sonnappa, Samatha; Wade, Angela; Beardsmore, Caroline; Chhabra, Sunil K.; Chudasama, Rajesh K.; Cook, Derek G.; Harding, Seeromanie; Kuehni, Claudia E.; Prasad, K. V. V.; Whincup, Peter H.; Lee, Simon; Stocks, Janet

2016-01-01

Availability of sophisticated statistical modelling for developing robust reference equations has improved interpretation of lung function results. In 2012, the Global Lung function Initiative(GLI) published the first global all-age, multi-ethnic reference equations for spirometry but these lacked equations for those originating from the Indian subcontinent (South-Asians). The aims of this study were to assess the extent to which existing GLI-ethnic adjustments might fit South-Asian paediatric spirometry data, assess any similarities and discrepancies between South-Asian datasets and explore the feasibility of deriving a suitable South-Asian GLI-adjustment. Methods Spirometry datasets from South-Asian children were collated from four centres in India and five within the UK. Records with transcription errors, missing values for height or spirometry, and implausible values were excluded(n = 110). Results Following exclusions, cross-sectional data were available from 8,124 children (56.3% male; 5–17 years). When compared with GLI-predicted values from White Europeans, forced expired volume in 1s (FEV1) and forced vital capacity (FVC) in South-Asian children were on average 15% lower, ranging from 4–19% between centres. By contrast, proportional reductions in FEV1 and FVC within all but two datasets meant that the FEV1/FVC ratio remained independent of ethnicity. The ‘GLI-Other’ equation fitted data from North India reasonably well while ‘GLI-Black’ equations provided a better approximation for South-Asian data than the ‘GLI-White’ equation. However, marked discrepancies in the mean lung function z-scores between centres especially when examined according to socio-economic conditions precluded derivation of a single South-Asian GLI-adjustment. Conclusion Until improved and more robust prediction equations can be derived, we recommend the use of ‘GLI-Black’ equations for interpreting most South-Asian data, although ‘GLI-Other’ may be more appropriate for North Indian data. Prospective data collection using standardised protocols to explore potential sources of variation due to socio-economic circumstances, secular changes in growth/predictors of lung function and ethnicities within the South-Asian classification are urgently required. PMID:27119342

The Benefits and Complexities of Operating Geographic Information Systems (GIS) in a High Performance Computing (HPC) Environment

NASA Astrophysics Data System (ADS)

Shute, J.; Carriere, L.; Duffy, D.; Hoy, E.; Peters, J.; Shen, Y.; Kirschbaum, D.

2017-12-01

The NASA Center for Climate Simulation (NCCS) at the Goddard Space Flight Center is building and maintaining an Enterprise GIS capability for its stakeholders, to include NASA scientists, industry partners, and the public. This platform is powered by three GIS subsystems operating in a highly-available, virtualized environment: 1) the Spatial Analytics Platform is the primary NCCS GIS and provides users discoverability of the vast DigitalGlobe/NGA raster assets within the NCCS environment; 2) the Disaster Mapping Platform provides mapping and analytics services to NASA's Disaster Response Group; and 3) the internal (Advanced Data Analytics Platform/ADAPT) enterprise GIS provides users with the full suite of Esri and open source GIS software applications and services. All systems benefit from NCCS's cutting edge infrastructure, to include an InfiniBand network for high speed data transfers; a mixed/heterogeneous environment featuring seamless sharing of information between Linux and Windows subsystems; and in-depth system monitoring and warning systems. Due to its co-location with the NCCS Discover High Performance Computing (HPC) environment and the Advanced Data Analytics Platform (ADAPT), the GIS platform has direct access to several large NCCS datasets including DigitalGlobe/NGA, Landsat, MERRA, and MERRA2. Additionally, the NCCS ArcGIS Desktop Windows virtual machines utilize existing NetCDF and OPeNDAP assets for visualization, modelling, and analysis - thus eliminating the need for data duplication. With the advent of this platform, Earth scientists have full access to vast data repositories and the industry-leading tools required for successful management and analysis of these multi-petabyte, global datasets. The full system architecture and integration with scientific datasets will be presented. Additionally, key applications and scientific analyses will be explained, to include the NASA Global Landslide Catalog (GLC) Reporter crowdsourcing application, the NASA GLC Viewer discovery and analysis tool, the DigitalGlobe/NGA Data Discovery Tool, the NASA Disaster Response Group Mapping Platform (https://maps.disasters.nasa.gov), and support for NASA's Arctic - Boreal Vulnerability Experiment (ABoVE).

Harvard Aging Brain Study: Dataset and accessibility.

PubMed

Dagley, Alexander; LaPoint, Molly; Huijbers, Willem; Hedden, Trey; McLaren, Donald G; Chatwal, Jasmeer P; Papp, Kathryn V; Amariglio, Rebecca E; Blacker, Deborah; Rentz, Dorene M; Johnson, Keith A; Sperling, Reisa A; Schultz, Aaron P

2017-01-01

The Harvard Aging Brain Study is sharing its data with the global research community. The longitudinal dataset consists of a 284-subject cohort with the following modalities acquired: demographics, clinical assessment, comprehensive neuropsychological testing, clinical biomarkers, and neuroimaging. To promote more extensive analyses, imaging data was designed to be compatible with other publicly available datasets. A cloud-based system enables access to interested researchers with blinded data available contingent upon completion of a data usage agreement and administrative approval. Data collection is ongoing and currently in its fifth year. Copyright © 2015 Elsevier Inc. All rights reserved.

SWAT use of gridded observations for simulating runoff - a Vietnam river basin study

NASA Astrophysics Data System (ADS)

Vu, M. T.; Raghavan, S. V.; Liong, S. Y.

2012-08-01

Many research studies that focus on basin hydrology have applied the SWAT model using station data to simulate runoff. But over regions lacking robust station data, there is a problem of applying the model to study the hydrological responses. For some countries and remote areas, the rainfall data availability might be a constraint due to many different reasons such as lacking of technology, war time and financial limitation that lead to difficulty in constructing the runoff data. To overcome such a limitation, this research study uses some of the available globally gridded high resolution precipitation datasets to simulate runoff. Five popular gridded observation precipitation datasets: (1) Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE), (2) Tropical Rainfall Measuring Mission (TRMM), (3) Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN), (4) Global Precipitation Climatology Project (GPCP), (5) a modified version of Global Historical Climatology Network (GHCN2) and one reanalysis dataset, National Centers for Environment Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used to simulate runoff over the Dak Bla river (a small tributary of the Mekong River) in Vietnam. Wherever possible, available station data are also used for comparison. Bilinear interpolation of these gridded datasets is used to input the precipitation data at the closest grid points to the station locations. Sensitivity Analysis and Auto-calibration are performed for the SWAT model. The Nash-Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) indices are used to benchmark the model performance. Results indicate that the APHRODITE dataset performed very well on a daily scale simulation of discharge having a good NSE of 0.54 and R2 of 0.55, when compared to the discharge simulation using station data (0.68 and 0.71). The GPCP proved to be the next best dataset that was applied to the runoff modelling, with NSE and R2 of 0.46 and 0.51, respectively. The PERSIANN and TRMM rainfall data driven runoff did not show good agreement compared to the station data as both the NSE and R2 indices showed a low value of 0.3. GHCN2 and NCEP also did not show good correlations. The varied results by using these datasets indicate that although the gauge based and satellite-gauge merged products use some ground truth data, the different interpolation techniques and merging algorithms could also be a source of uncertainties. This entails a good understanding of the response of the hydrological model to different datasets and a quantification of the uncertainties in these datasets. Such a methodology is also useful for planning on Rainfall-runoff and even reservoir/river management both at rural and urban scales.

Determination of the absorption coefficient of chromophoric dissolved organic matter from underway spectrophotometry.

PubMed

Dall'Olmo, Giorgio; Brewin, Robert J W; Nencioli, Francesco; Organelli, Emanuele; Lefering, Ina; McKee, David; Röttgers, Rüdiger; Mitchell, Catherine; Boss, Emmanuel; Bricaud, Annick; Tilstone, Gavin

2017-11-27

Measurements of the absorption coefficient of chromophoric dissolved organic matter (ay) are needed to validate existing ocean-color algorithms. In the surface open ocean, these measurements are challenging because of low ay values. Yet, existing global datasets demonstrate that ay could contribute between 30% to 50% of the total absorption budget in the 400-450 nm spectral range, thus making accurate measurement of ay essential to constrain these uncertainties. In this study, we present a simple way of determining ay using a commercially-available in-situ spectrophotometer operated in underway mode. The obtained ay values were validated using independent collocated measurements. The method is simple to implement, can provide measurements with very high spatio-temporal resolution, and has an accuracy of about 0.0004 m -1 and a precision of about 0.0025 m -1 when compared to independent data (at 440 nm). The only limitation for using this method at sea is that it relies on the availability of relatively large volumes of ultrapure water. Despite this limitation, the method can deliver the ay data needed for validating and assessing uncertainties in ocean-colour algorithms.

The European Glaucoma Society Glaucocard project: improved digital documentation of medical data for glaucoma patients based on standardized structured international datasets.

PubMed

Schargus, Marc; Grehn, Franz; Glaucocard Workgroup

2008-12-01

To evaluate existing international IT-based ophthalmological medical data projects, and to define a glaucoma data set based on existing international standards of medical and ophthalmological documentation. To develop the technical environment for easy data mining and data exchange in different countries in Europe. Existing clinical and IT-based projects for documentation of medical data in general medicine and ophthalmology were analyzed to create new data sets for medical documentation in glaucoma patients. Different types of data transfer methods were evaluated to find the best method of data exchange between ophthalmologists in different European countries. Data sets from existing IT projects showed a wide variability in specifications, use of codes, terms and graphical data (perimetry, optic nerve analysis etc.) in glaucoma patients. New standardized digital datasets for glaucoma patients were defined, based on existing standards, which can be used by general ophthalmologists for follow-up examinations and for glaucoma specialists to perform teleconsultation, also across country borders. Datasets are available in different languages. Different types of data exchange methods using secure medical data transfer by internet, USB stick and smartcard were tested for different countries with regard to legal acceptance, practicability and technical realization (e.g. compatibility with EMR systems). By creating new standardized glaucoma specific cross-national datasets, it is now possible to develop an electronic glaucoma patient record system for data storage and transfer based on internet, smartcard or USB stick. The digital data can be used for referrals and for teleconsultation of glaucoma specialists in order to optimize glaucoma treatment. This should lead to an increase of quality in glaucoma care, and prevent expenses in health care costs by unnecessary repeated examinations.

How much do different global GPP products agree in distribution and magnitude of GPP extremes?

NASA Astrophysics Data System (ADS)

Kim, S.; Ryu, Y.; Jiang, C.

2016-12-01

To evaluate uncertainty of global Gross Primary Productivity (GPP) extremes, we compare three global GPP datasets derived from different data processing methods (e.g. MPI-BGC: machine-learning, MODIS GPP (MOD17): semi-empirical, Breathing Earth System Simulator (BESS): process based). We preprocess the datasets following the method from Zscheischler et al., (2012) to detect GPP extremes which occur in less than 1% of the number of whole pixels, and to identify 3D-connected spatiotemporal GPP extremes. We firstly analyze global patterns and the magnitude of GPP extremes with MPI-BGC, MOD17, and BESS over 2001-2011. For consistent analysis in the three products, spatial and temporal resolution were set at 50 km and a monthly scale, respectively. Our results indicated that the global patterns of GPP extremes derived from MPI-BGC and BESS agreed with each other by showing hotspots in Northeastern Brazil and Eastern Texas. However, the extreme events detected from MOD17 were concentrated in tropical forests (e.g. Southeast Asia and South America). The amount of GPP reduction caused by climate extremes considerably differed across the products. For example, Russian heatwave in 2010 led to 100 Tg C uncertainty (198.7 Tg C in MPI-BGC, 305.6 Tg C in MOD17, and 237.8 Tg C in BESS). Moreover, the duration of extreme events differ among the three GPP datasets for the Russian heatwave (MPI-BGC: May-Sep, MOD17: Jun-Aug, and BESS: May-Aug). To test whether Sun induced Fluorescence (SiF), a proxy of GPP, can capture GPP extremes, we investigate global distribution of GPP extreme events in BESS, MOD17 and GOME-2 SiF between 2008 and 2014 when SiF data is available. We found that extreme GPP events in GOME-2 SiF and MOD17 appear in tropical forests whereas those in BESS emerged in Northeastern Brazil and Eastern Texas. The GPP extremes by severe 2011 US drought were detected by BESS and MODIS, but not by SiF. Our findings highlight that different GPP datasets could result in varying duration and intensity of GPP extremes and distribution of hotspots, and this study could contribute to quantifying uncertainties in GPP extremes.

Comparative Evaluation of Five Fire Emissions Datasets Using the GEOS-5 Model

NASA Astrophysics Data System (ADS)

Ichoku, C. M.; Pan, X.; Chin, M.; Bian, H.; Darmenov, A.; Ellison, L.; Kucsera, T. L.; da Silva, A. M., Jr.; Petrenko, M. M.; Wang, J.; Ge, C.; Wiedinmyer, C.

2017-12-01

Wildfires and other types of biomass burning affect most vegetated parts of the globe, contributing 40% of the annual global atmospheric loading of carbonaceous aerosols, as well as significant amounts of numerous trace gases, such as carbon dioxide, carbon monoxide, and methane. Many of these smoke constituents affect the air quality and/or the climate system directly or through their interactions with solar radiation and cloud properties. However, fire emissions are poorly constrained in global and regional models, resulting in high levels of uncertainty in understanding their real impacts. With the advent of satellite remote sensing of fires and burned areas in the last couple of decades, a number of fire emissions products have become available for use in relevant research and applications. In this study, we evaluated five global biomass burning emissions datasets, namely: (1) GFEDv3.1 (Global Fire Emissions Database version 3.1); (2) GFEDv4s (Global Fire Emissions Database version 4 with small fires); (3) FEERv1 (Fire Energetics and Emissions Research version 1.0); (4) QFEDv2.4 (Quick Fire Emissions Dataset version 2.4); and (5) Fire INventory from NCAR (FINN) version 1.5. Overall, the spatial patterns of biomass burning emissions from these inventories are similar, although the magnitudes of the emissions can be noticeably different. The inventories derived using top-down approaches (QFEDv2.4 and FEERv1) are larger than those based on bottom-up approaches. For example, global organic carbon (OC) emissions in 2008 are: QFEDv2.4 (51.93 Tg), FEERv1 (28.48 Tg), FINN v1.5 (19.48 Tg), GFEDv3.1 (15.65 Tg) and GFEDv4s (13.76 Tg); representing a factor of 3.7 difference between the largest and the least. We also used all five biomass-burning emissions datasets to conduct aerosol simulations using the NASA Goddard Earth Observing System Model, Version 5 (GEOS-5), and compared the resulting aerosol optical depth (AOD) output to the corresponding retrievals from MODIS and AERONET. Simulated AOD based on all five emissions inventories show significant underestimation in biomass burning dominated regions. Attributions of possible factors responsible for the differences among the inventories were further explored in Southern Africa and South America, two of the major biomass burning regions of the world.

Modeling extreme sea levels due to tropical and extra-tropical cyclones at the global-scale

NASA Astrophysics Data System (ADS)

Muis, S.; Lin, N.; Verlaan, M.; Winsemius, H.; Ward, P.; Aerts, J.

2017-12-01

Extreme sea levels, a combination of storm surges and astronomical tides, can cause catastrophic floods. Due to their intense wind speeds and low pressure, tropical cyclones (TCs) typically cause higher storm surges than extra-tropical cyclones (ETCs), but ETCs may still contribute significantly to the overall flood risk. In this contribution, we show a novel approach to model extreme sea levels due to both tropical and extra-tropical cyclones at the global-scale. Using a global hydrodynamic model we have developed the Global Tide and Surge Reanalysis (GTSR) dataset (Muis et al., 2016), which provides daily maximum timeseries of storm tide from 1979 to 2014. GTSR is based on wind and pressure fields from the ERA-Interim climate reanalysis (Dee at al., 2011). A severe limitation of the GTSR dataset is the underrepresentation of TCs. This is due to the relatively coarse grid resolution of ERA-Interim, which means that the strong intensities of TCs are not fully included. Furthermore, the length of ERA-Interim is too short to estimate the probabilities of extreme TCs in a reliable way. We will discuss potential ways to address this limitation, and demonstrate how to improve the global GTSR framework. We will apply the improved framework to the east coast of the United States. First, we improve our meteorological forcing by applying a parametric hurricane model (Holland 1980), and we improve the tide and surge reanalysis dataset (Muis et al., 2016) by explicitly modeling the historical TCs in the Extended Best Track dataset (Demuth et al., 2006). Second, we improve our sampling by statistically extending the observed TC record to many thousands of years (Emanuel et al., 2006). The improved framework allows for the mapping of probabilities of extreme sea levels, including extremes TC events, for the east coast of the United States. ReferencesDee et al (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553-97. Emanuel et al (2006). A Statistical Deterministic Approach to Hurricane Risk Assessment/ Bull. Am. Meteorol. Soc. 87, 299-314. Holland (1980). An analytic model of the wind and pressure profiles in hurricanes. Mon. Weather Rev. 108, 1212-1218. Muis et al (2016). A global reanalysis of storm surge and extreme sea levels. Nat. Commun. 7, 1-11

Hard exudates segmentation based on learned initial seeds and iterative graph cut.

PubMed

Kusakunniran, Worapan; Wu, Qiang; Ritthipravat, Panrasee; Zhang, Jian

2018-05-01

(Background and Objective): The occurrence of hard exudates is one of the early signs of diabetic retinopathy which is one of the leading causes of the blindness. Many patients with diabetic retinopathy lose their vision because of the late detection of the disease. Thus, this paper is to propose a novel method of hard exudates segmentation in retinal images in an automatic way. (Methods): The existing methods are based on either supervised or unsupervised learning techniques. In addition, the learned segmentation models may often cause miss-detection and/or fault-detection of hard exudates, due to the lack of rich characteristics, the intra-variations, and the similarity with other components in the retinal image. Thus, in this paper, the supervised learning based on the multilayer perceptron (MLP) is only used to identify initial seeds with high confidences to be hard exudates. Then, the segmentation is finalized by unsupervised learning based on the iterative graph cut (GC) using clusters of initial seeds. Also, in order to reduce color intra-variations of hard exudates in different retinal images, the color transfer (CT) is applied to normalize their color information, in the pre-processing step. (Results): The experiments and comparisons with the other existing methods are based on the two well-known datasets, e_ophtha EX and DIARETDB1. It can be seen that the proposed method outperforms the other existing methods in the literature, with the sensitivity in the pixel-level of 0.891 for the DIARETDB1 dataset and 0.564 for the e_ophtha EX dataset. The cross datasets validation where the training process is performed on one dataset and the testing process is performed on another dataset is also evaluated in this paper, in order to illustrate the robustness of the proposed method. (Conclusions): This newly proposed method integrates the supervised learning and unsupervised learning based techniques. It achieves the improved performance, when compared with the existing methods in the literature. The robustness of the proposed method for the scenario of cross datasets could enhance its practical usage. That is, the trained model could be more practical for unseen data in the real-world situation, especially when the capturing environments of training and testing images are not the same. Copyright © 2018 Elsevier B.V. All rights reserved.

An Intercomparison of Large-Extent Tree Canopy Cover Geospatial Datasets

NASA Astrophysics Data System (ADS)

Bender, S.; Liknes, G.; Ruefenacht, B.; Reynolds, J.; Miller, W. P.

2017-12-01

As a member of the Multi-Resolution Land Characteristics Consortium (MRLC), the U.S. Forest Service (USFS) is responsible for producing and maintaining the tree canopy cover (TCC) component of the National Land Cover Database (NLCD). The NLCD-TCC data are available for the conterminous United States (CONUS), coastal Alaska, Hawai'i, Puerto Rico, and the U.S. Virgin Islands. The most recent official version of the NLCD-TCC data is based primarily on reference data from 2010-2011 and is part of the multi-component 2011 version of the NLCD. NLCD data are updated on a five-year cycle. The USFS is currently producing the next official version (2016) of the NLCD-TCC data for the United States, and it will be made publicly-available in early 2018. In this presentation, we describe the model inputs, modeling methods, and tools used to produce the 30-m NLCD-TCC data. Several tree cover datasets at 30-m, as well as datasets at finer resolution, have become available in recent years due to advancements in earth observation data and their availability, computing, and sensors. We compare multiple tree cover datasets that have similar resolution to the NLCD-TCC data. We also aggregate the tree class from fine-resolution land cover datasets to a percent canopy value on a 30-m pixel, in order to compare the fine-resolution datasets to the datasets created directly from 30-m Landsat data. The extent of the tree canopy cover datasets included in the study ranges from global and national to the state level. Preliminary investigation of multiple tree cover datasets over the CONUS indicates a high amount of spatial variability. For example, in a comparison of the NLCD-TCC and the Global Land Cover Facility's Landsat Tree Cover Continuous Fields (2010) data by MRLC mapping zones, the zone-level root mean-square deviation ranges from 2% to 39% (mean=17%, median=15%). The analysis outcomes are expected to inform USFS decisions with regard to the next cycle (2021) of NLCD-TCC production.

The Effect of Spatial and Spectral Resolution in Determining NDVI

NASA Astrophysics Data System (ADS)

Boelman, N. T.

2003-12-01

We explore the impact that varying spatial and spectral resolutions of several sensors (a field portable spectroradiometer, Landsat, MODIS and AVHRR) has in determining the average Normalized Difference Vegetation Index (NDVI) at Imnavait Creek, a small arctic tundra watershed located on the north slope of Alaska. We found that at the field-of-views (FOVs) of less than 20 m2 that were sampled, the average NDVI value for this watershed is 0.65, compared to 0.77 at FOVs equal to and greater than 20 m2. In addition, we found that at FOVs less than 20 m2, the average NDVI value calculated according to each of Landsat, MODIS and AVHRR band definitions (controlled by spectral resolution) was similar. However, at FOVs equal to and greater than 20 m2, the average NDVI value calculated according to AVHRR's broad-band definitions was significantly and consistently higher than that from both Landsat and MODIS's narrow-band NDVI values. We speculate that these differences in NDVI exist because high leaf-area-index vegetation communities associated with watertracks are commonly spaced between 10 and 20 m apart in arctic tundra landscapes and are often only included when spectral sampling is conducted at FOVs greater than tens of square meters. These results suggest that both spatial resolution alone and its interaction with spectral resolution have to be considered when interpreting commonly used global-scale NDVI datasets. This is because traditionally, the fundamental relationships established between NDVI and ecosystem parameters, such as CO2 fluxes, aboveground biomass and net primary productivity, have been established at scales less than 20 m2. Other ecosystems, such as landscapes with isolated tree islands in boreal forest-tundra ecotones, may exhibit similar scaling patterns that need to be considered when interpreting global-scale NDVI datasets.

Remote Sensing of Martian Terrain Hazards via Visually Salient Feature Detection

NASA Astrophysics Data System (ADS)

Al-Milli, S.; Shaukat, A.; Spiteri, C.; Gao, Y.

2014-04-01

The main objective of the FASTER remote sensing system is the detection of rocks on planetary surfaces by employing models that can efficiently characterise rocks in terms of semantic descriptions. The proposed technique abates some of the algorithmic limitations of existing methods with no training requirements, lower computational complexity and greater robustness towards visual tracking applications over long-distance planetary terrains. Visual saliency models inspired from biological systems help to identify important regions (such as rocks) in the visual scene. Surface rocks are therefore completely described in terms of their local or global conspicuity pop-out characteristics. These local and global pop-out cues are (but not limited to); colour, depth, orientation, curvature, size, luminance intensity, shape, topology etc. The currently applied methods follow a purely bottom-up strategy of visual attention for selection of conspicuous regions in the visual scene without any topdown control. Furthermore the choice of models used (tested and evaluated) are relatively fast among the state-of-the-art and have very low computational load. Quantitative evaluation of these state-ofthe- art models was carried out using benchmark datasets including the Surrey Space Centre Lab Testbed, Pangu generated images, RAL Space SEEKER and CNES Mars Yard datasets. The analysis indicates that models based on visually salient information in the frequency domain (SRA, SDSR, PQFT) are the best performing ones for detecting rocks in an extra-terrestrial setting. In particular the SRA model seems to be the most optimum of the lot especially that it requires the least computational time while keeping errors competitively low. The salient objects extracted using these models can then be merged with the Digital Elevation Models (DEMs) generated from the same navigation cameras in order to be fused to the navigation map thus giving a clear indication of the rock locations.

Comparison of ASTER Global Emissivity Database (ASTER-GED) With In-Situ Measurement In Italian Vulcanic Areas

NASA Astrophysics Data System (ADS)

Silvestri, M.; Musacchio, M.; Buongiorno, M. F.; Amici, S.; Piscini, A.

2015-12-01

LP DAAC released the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Database (GED) datasets on April 2, 2014. The database was developed by the National Aeronautics and Space Administration's (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology. The database includes land surface emissivities derived from ASTER data acquired over the contiguous United States, Africa, Arabian Peninsula, Australia, Europe, and China. In this work we compare ground measurements of emissivity acquired by means of Micro-FTIR (Fourier Thermal Infrared spectrometer) instrument with the ASTER emissivity map extract from ASTER-GED and the emissivity obtained by using single ASTER data. Through this analysis we want to investigate differences existing between the ASTER-GED dataset (average from 2000 to 2008 seasoning independent) and fall in-situ emissivity measurement. Moreover the role of different spatial resolution characterizing ASTER and MODIS, 90mt and 1km respectively, by comparing them with in situ measurements. Possible differences can be due also to the different algorithms used for the emissivity estimation, Temperature and Emissivity Separation algorithm for ASTER TIR band( Gillespie et al, 1998) and the classification-based emissivity method (Snyder and al, 1998) for MODIS. In-situ emissivity measurements have been collected during dedicated fields campaign on Mt. Etna vulcano and Solfatara of Pozzuoli. Gillespie, A. R., Matsunaga, T., Rokugawa, S., & Hook, S. J. (1998). Temperature and emissivity separation from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. IEEE Transactions on Geoscience and Remote Sensing, 36, 1113-1125. Snyder, W.C., Wan, Z., Zhang, Y., & Feng, Y.-Z. (1998). Classification-based emissivity for land surface temperature measurement from space. International Journal of Remote Sensing, 19, 2753-2574.

Detecting climate variations and change: New challenges for observing and data management systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karl, T.R.; Quayle, R.G.; Groisman, P.Ya.

1993-08-01

Several essential aspects of weather observing and the management of weather data related to improving knowledge of climate variations and change in the surface boundary layer and the consequences for socioeconomic and biogeophysical systems, are discussed. The issues include long-term homogeneous time series of routine weather observations; time- and space-scale resolution of datasets derived from the observations; information about observing systems, data collection systems, and data reduction algorithms; and the enhancement of weather observing systems to serve as climate observing systems. Although much has been learned from existing weather networks and methods of data management, the system is far frommore » perfect. Several vital areas have not received adequate attention. Particular improvements are needed in the interaction between network designers and climatologists; operational analyses that focus on detecting and documenting outliers and time-dependent biases within datasets; developing the means to cope with and minimize potential inhomogeneities in weather observing systems; and authoritative documentation of how various aspects of climate have or have not changed. In this last area, close attention must be given to time and space resolution of the data. In many instances the time and space resolution requirements for understanding why the climate changes are not synonymous with understanding how it has changed or varied. This is particularly true within the surface boundary layer. A standard global daily/monthly climate message should also be introduced to supplement current Global Telecommunication System's CLIMAT data. Overall, a call is made for improvements in routine weather observing, data management, and analysis systems. Routine observations have provided (and will continue to provide) most of the information regarding how the climate has changed during the last 100 years affecting where we live, work, and grow our food. 58 refs., 8 figs., 1 tab.« less

RIMS: An Integrated Mapping and Analysis System with Applications to Earth Sciences and Hydrology

NASA Astrophysics Data System (ADS)

Proussevitch, A. A.; Glidden, S.; Shiklomanov, A. I.; Lammers, R. B.

2011-12-01

A web-based information and computational system for analysis of spatially distributed Earth system, climate, and hydrologic data have been developed. The System allows visualization, data exploration, querying, manipulation and arbitrary calculations with any loaded gridded or vector polygon dataset. The system's acronym, RIMS, stands for its core functionality as a Rapid Integrated Mapping System. The system can be deployed for a Global scale projects as well as for regional hydrology and climatology studies. In particular, the Water Systems Analysis Group of the University of New Hampshire developed the global and regional (Northern Eurasia, pan-Arctic) versions of the system with different map projections and specific data. The system has demonstrated its potential for applications in other fields of Earth sciences and education. The key Web server/client components of the framework include (a) a visualization engine built on Open Source libraries (GDAL, PROJ.4, etc.) that are utilized in a MapServer; (b) multi-level data querying tools built on XML server-client communication protocols that allow downloading map data on-the-fly to a client web browser; and (c) data manipulation and grid cell level calculation tools that mimic desktop GIS software functionality via a web interface. Server side data management of the system is designed around a simple database of dataset metadata facilitating mounting of new data to the system and maintaining existing data in an easy manner. RIMS contains "built-in" river network data that allows for query of upstream areas on-demand which can be used for spatial data aggregation and analysis of sub-basin areas. RIMS is an ongoing effort and currently being used to serve a number of websites hosting a suite of hydrologic, environmental and other GIS data.

An observational and modeling study of the August 2017 Florida climate extreme event.

NASA Astrophysics Data System (ADS)

Konduru, R.; Singh, V.; Routray, A.

2017-12-01

A special report on the climate extremes by the Intergovernmental Panel on Climate Change (IPCC) elucidates that the sole cause of disasters is due to the exposure and vulnerability of the human and natural system to the climate extremes. The cause of such a climate extreme could be anthropogenic or non-anthropogenic. Therefore, it is challenging to discern the critical factor of influence for a particular climate extreme. Such kind of perceptive study with reasonable confidence on climate extreme events is possible only if there exist any past case studies. A similar rarest climate extreme problem encountered in the case of Houston floods and extreme rainfall over Florida in August 2017. A continuum of hurricanes like Harvey and Irma targeted the Florida region and caused catastrophe. Due to the rarity of August 2017 Florida climate extreme event, it requires the in-depth study on this case. To understand the multi-faceted nature of the event, a study on the development of the Harvey hurricane and its progression and dynamics is significant. Current article focus on the observational and modeling study on the Harvey hurricane. A global model named as NCUM (The global UK Met office Unified Model (UM) operational at National Center for Medium Range Weather Forecasting, India, was utilized to simulate the Harvey hurricane. The simulated rainfall and wind fields were compared with the observational datasets like Tropical Rainfall Measuring Mission rainfall datasets and Era-Interim wind fields. The National Centre for Environmental Prediction (NCEP) automated tracking system was utilized to track the Harvey hurricane, and the tracks were analyzed statistically for different forecasts concerning the Harvey hurricane track of Joint Typhon Warning Centre. Further, the current study will be continued to investigate the atmospheric processes involved in the August 2017 Florida climate extreme event.

Variability of hydrological extreme events in East Asia and their dynamical control: a comparison between observations and two high-resolution global climate models

NASA Astrophysics Data System (ADS)

Freychet, N.; Duchez, A.; Wu, C.-H.; Chen, C.-A.; Hsu, H.-H.; Hirschi, J.; Forryan, A.; Sinha, B.; New, A. L.; Graham, T.; Andrews, M. B.; Tu, C.-Y.; Lin, S.-J.

2017-02-01

This work investigates the variability of extreme weather events (drought spells, DS15, and daily heavy rainfall, PR99) over East Asia. It particularly focuses on the large scale atmospheric circulation associated with high levels of the occurrence of these extreme events. Two observational datasets (APHRODITE and PERSIANN) are compared with two high-resolution global climate models (HiRAM and HadGEM3-GC2) and an ensemble of other lower resolution climate models from CMIP5. We first evaluate the performance of the high resolution models. They both exhibit good skill in reproducing extreme events, especially when compared with CMIP5 results. Significant differences exist between the two observational datasets, highlighting the difficulty of having a clear estimate of extreme events. The link between the variability of the extremes and the large scale circulation is investigated, on monthly and interannual timescales, using composite and correlation analyses. Both extreme indices DS15 and PR99 are significantly linked to the low level wind intensity over East Asia, i.e. the monsoon circulation. It is also found that DS15 events are strongly linked to the surface temperature over the Siberian region and to the land-sea pressure contrast, while PR99 events are linked to the sea surface temperature anomalies over the West North Pacific. These results illustrate the importance of the monsoon circulation on extremes over East Asia. The dependencies on of the surface temperature over the continent and the sea surface temperature raise the question as to what extent they could affect the occurrence of extremes over tropical regions in future projections.

The resolution sensitivity of the South Asian monsoon and Indo-Pacific in a global 0.35° AGCM

NASA Astrophysics Data System (ADS)

Johnson, Stephanie J.; Levine, Richard C.; Turner, Andrew G.; Martin, Gill M.; Woolnough, Steven J.; Schiemann, Reinhard; Mizielinski, Matthew S.; Roberts, Malcolm J.; Vidale, Pier Luigi; Demory, Marie-Estelle; Strachan, Jane

2016-02-01

The South Asian monsoon is one of the most significant manifestations of the seasonal cycle. It directly impacts nearly one third of the world's population and also has substantial global influence. Using 27-year integrations of a high-resolution atmospheric general circulation model (Met Office Unified Model), we study changes in South Asian monsoon precipitation and circulation when horizontal resolution is increased from approximately 200-40 km at the equator (N96-N512, 1.9°-0.35°). The high resolution, integration length and ensemble size of the dataset make this the most extensive dataset used to evaluate the resolution sensitivity of the South Asian monsoon to date. We find a consistent pattern of JJAS precipitation and circulation changes as resolution increases, which include a slight increase in precipitation over peninsular India, changes in Indian and Indochinese orographic rain bands, increasing wind speeds in the Somali Jet, increasing precipitation over the Maritime Continent islands and decreasing precipitation over the northern Maritime Continent seas. To diagnose which resolution-related processes cause these changes, we compare them to published sensitivity experiments that change regional orography and coastlines. Our analysis indicates that improved resolution of the East African Highlands results in the improved representation of the Somali Jet and further suggests that improved resolution of orography over Indochina and the Maritime Continent results in more precipitation over the Maritime Continent islands at the expense of reduced precipitation further north. We also evaluate the resolution sensitivity of monsoon depressions and lows, which contribute more precipitation over northeast India at higher resolution. We conclude that while increasing resolution at these scales does not solve the many monsoon biases that exist in GCMs, it has a number of small, beneficial impacts.

An Alternative Default Soil Organic Carbon Method for National GHG Inventory Reporting to the UNFCCC

NASA Astrophysics Data System (ADS)

Ogle, S. M.; Gurung, R.; Klepfer, A.; Spencer, S.; Breidt, J.

2016-12-01

Estimating soil organic C stocks is challenging because of the large amount of data needed to evaluate the impact of land use and management on this terrestrial C pool. Moreover, some of the required data are rarely collected by governments through surveys programs, and are not typically available in remote sensing products. Examples include data on organic amendments, cover crops, crop rotation sequences, vegetated fallows, and fertilization practices. Due to these difficulties, only about 20% of the countries report soil organic C stock changes in their national communications to the UNFCCC. Yet, C sequestration in soils represents one of the least expensive options for reducing greenhouse gas emissions, and has the largest potential for mitigation in the agricultural sector. In order to facilitate reporting, we developed an alternative approach to the current default method provided by the Intergovernmental Panel on Climate Change (IPCC) for estimating soil organic C stock changes in mineral soils. The alternative method estimates the steady-state C stocks for a three pool model given annual crop yields or net primary production as the main input, along with monthly average temperature, total precipitation and soil texture data. Yield data are commonly available in a national agricultural census, and global datasets exists with adequate data for weather and soil texture if national datasets are not available. Tillage and irrigation data are also needed to address the impact of these practices on decomposition rates. The change in steady-state stocks is assumed to occur over a few decades. A Bayesian analysis framework has been developed to derive probability distribution functions for the parameters, and the method is being applied in a global analysis of soil organic carbon stock changes.

Global multi-layer network of human mobility

PubMed Central

Belyi, Alexander; Bojic, Iva; Sobolevsky, Stanislav; Sitko, Izabela; Hawelka, Bartosz; Rudikova, Lada; Kurbatski, Alexander; Ratti, Carlo

2017-01-01

ABSTRACT Recent availability of geo-localized data capturing individual human activity together with the statistical data on international migration opened up unprecedented opportunities for a study on global mobility. In this paper, we consider it from the perspective of a multi-layer complex network, built using a combination of three datasets: Twitter, Flickr and official migration data. Those datasets provide different, but equally important insights on the global mobility – while the first two highlight short-term visits of people from one country to another, the last one – migration – shows the long-term mobility perspective, when people relocate for good. The main purpose of the paper is to emphasize importance of this multi-layer approach capturing both aspects of human mobility at the same time. On the one hand, we show that although the general properties of different layers of the global mobility network are similar, there are important quantitative differences among them. On the other hand, we demonstrate that consideration of mobility from a multi-layer perspective can reveal important global spatial patterns in a way more consistent with those observed in other available relevant sources of international connections, in comparison to the spatial structure inferred from each network layer taken separately. PMID:28553155

A global inventory of small floating plastic debris

NASA Astrophysics Data System (ADS)

van Sebille, Erik; Wilcox, Chris; Lebreton, Laurent; Maximenko, Nikolai; Hardesty, Britta Denise; van Franeker, Jan A.; Eriksen, Marcus; Siegel, David; Galgani, Francois; Lavender Law, Kara

2015-12-01

Microplastic debris floating at the ocean surface can harm marine life. Understanding the severity of this harm requires knowledge of plastic abundance and distributions. Dozens of expeditions measuring microplastics have been carried out since the 1970s, but they have primarily focused on the North Atlantic and North Pacific accumulation zones, with much sparser coverage elsewhere. Here, we use the largest dataset of microplastic measurements assembled to date to assess the confidence we can have in global estimates of microplastic abundance and mass. We use a rigorous statistical framework to standardize a global dataset of plastic marine debris measured using surface-trawling plankton nets and coupled this with three different ocean circulation models to spatially interpolate the observations. Our estimates show that the accumulated number of microplastic particles in 2014 ranges from 15 to 51 trillion particles, weighing between 93 and 236 thousand metric tons, which is only approximately 1% of global plastic waste estimated to enter the ocean in the year 2010. These estimates are larger than previous global estimates, but vary widely because the scarcity of data in most of the world ocean, differences in model formulations, and fundamental knowledge gaps in the sources, transformations and fates of microplastics in the ocean.

Rapid Global Fitting of Large Fluorescence Lifetime Imaging Microscopy Datasets

PubMed Central

Warren, Sean C.; Margineanu, Anca; Alibhai, Dominic; Kelly, Douglas J.; Talbot, Clifford; Alexandrov, Yuriy; Munro, Ian; Katan, Matilda

2013-01-01

Fluorescence lifetime imaging (FLIM) is widely applied to obtain quantitative information from fluorescence signals, particularly using Förster Resonant Energy Transfer (FRET) measurements to map, for example, protein-protein interactions. Extracting FRET efficiencies or population fractions typically entails fitting data to complex fluorescence decay models but such experiments are frequently photon constrained, particularly for live cell or in vivo imaging, and this leads to unacceptable errors when analysing data on a pixel-wise basis. Lifetimes and population fractions may, however, be more robustly extracted using global analysis to simultaneously fit the fluorescence decay data of all pixels in an image or dataset to a multi-exponential model under the assumption that the lifetime components are invariant across the image (dataset). This approach is often considered to be prohibitively slow and/or computationally expensive but we present here a computationally efficient global analysis algorithm for the analysis of time-correlated single photon counting (TCSPC) or time-gated FLIM data based on variable projection. It makes efficient use of both computer processor and memory resources, requiring less than a minute to analyse time series and multiwell plate datasets with hundreds of FLIM images on standard personal computers. This lifetime analysis takes account of repetitive excitation, including fluorescence photons excited by earlier pulses contributing to the fit, and is able to accommodate time-varying backgrounds and instrument response functions. We demonstrate that this global approach allows us to readily fit time-resolved fluorescence data to complex models including a four-exponential model of a FRET system, for which the FRET efficiencies of the two species of a bi-exponential donor are linked, and polarisation-resolved lifetime data, where a fluorescence intensity and bi-exponential anisotropy decay model is applied to the analysis of live cell homo-FRET data. A software package implementing this algorithm, FLIMfit, is available under an open source licence through the Open Microscopy Environment. PMID:23940626

Analysis of Multiple Precipitation Products and Preliminary Assessment of Their Impact on Global Land Data Assimilation System (GLDAS) Land Surface States

NASA Technical Reports Server (NTRS)

Gottschalck, Jon; Meng, Jesse; Rodel, Matt; Houser, paul

2005-01-01

Land surface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the stocks and fluxes of water (including soil moisture, snow, evaporation, and runoff) and energy (including the temperature of and sensible heat released from the soil) after they arrive on the land surface as precipitation and sunlight. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy and space-time resolution. Hence LSMs have been developed to integrate the available observations with our understanding of the physical processes involved, using powerful computers, in order to map these stocks and fluxes as they change in time. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth's water cycle and climate variability. NASA's Global Land Data Assimilation System (GLDAS) project facilitates testing of several different LSMs with a variety of input datasets (e.g., precipitation, plant type). Precipitation is arguably the most important input to LSMs. Many precipitation datasets have been produced using satellite and rain gauge observations and weather forecast models. In this study, seven different global precipitation datasets were evaluated over the United States, where dense rain gauge networks contribute to reliable precipitation maps. We then used the seven datasets as inputs to GLDAS simulations, so that we could diagnose their impacts on output stocks and fluxes of water. In terms of totals, the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) had the closest agreement with the US rain gauge dataset for all seasons except winter. The CMAP precipitation was also the most closely correlated in time with the rain gauge data during spring, fall, and winter, while the satellitebased estimates performed best in summer. The GLDAS simulations revealed that modeled soil moisture is highly sensitive to precipitation, with differences in spring and summer as large as 45% depending on the choice of precipitation input.

Data Mining Approaches for Genomic Biomarker Development: Applications Using Drug Screening Data from the Cancer Genome Project and the Cancer Cell Line Encyclopedia.

PubMed

Covell, David G

2015-01-01

Developing reliable biomarkers of tumor cell drug sensitivity and resistance can guide hypothesis-driven basic science research and influence pre-therapy clinical decisions. A popular strategy for developing biomarkers uses characterizations of human tumor samples against a range of cancer drug responses that correlate with genomic change; developed largely from the efforts of the Cancer Cell Line Encyclopedia (CCLE) and Sanger Cancer Genome Project (CGP). The purpose of this study is to provide an independent analysis of this data that aims to vet existing and add novel perspectives to biomarker discoveries and applications. Existing and alternative data mining and statistical methods will be used to a) evaluate drug responses of compounds with similar mechanism of action (MOA), b) examine measures of gene expression (GE), copy number (CN) and mutation status (MUT) biomarkers, combined with gene set enrichment analysis (GSEA), for hypothesizing biological processes important for drug response, c) conduct global comparisons of GE, CN and MUT as biomarkers across all drugs screened in the CGP dataset, and d) assess the positive predictive power of CGP-derived GE biomarkers as predictors of drug response in CCLE tumor cells. The perspectives derived from individual and global examinations of GEs, MUTs and CNs confirm existing and reveal unique and shared roles for these biomarkers in tumor cell drug sensitivity and resistance. Applications of CGP-derived genomic biomarkers to predict the drug response of CCLE tumor cells finds a highly significant ROC, with a positive predictive power of 0.78. The results of this study expand the available data mining and analysis methods for genomic biomarker development and provide additional support for using biomarkers to guide hypothesis-driven basic science research and pre-therapy clinical decisions.

Towards data-driven decision-making for sustainable diets in Vietnam: Identifying priority indicators through stakeholder engagement

NASA Astrophysics Data System (ADS)

Mayton, H.; Beal, T.; Rubin, J.; Sanchez, A.; Heller, M.; Hoey, L.; Khoury, C. K.; Jones, A.

2017-12-01

Globally, food systems impact and are impacted by the sustainability of environmental, societal, political, and public health factors. At the center of these systems are human diets, which vary substantially by culture and region, and have significant influence on human health, community livelihoods, climate change, and natural resources. However, rapidly growing and highly diverse lower middle-income countries like Vietnam face challenges in gathering data and defining clear policy intervention points and approaches that will provide a net-positive systemic influence across sectors. A new collaboration, Entry points to Advance Transitions towards Sustainable diets (EATS), between the University of Michigan and the International Center for Tropical Agriculture (CIAT) aims to identify ways that existing data and insights into the policy process can be leveraged to inform decision-making on where and how to intervene to effectively shift multiple axes of food systems to enhance the sustainability of diets. As a first step towards developing a model that other policy communities could follow, researchers aggregated and characterized approximately 50 major existing datasets on food, agriculture, and nutrition in Vietnam. They also created a conceptual framework for evaluating the sustainability of diets and for characterizing existing datasets, including eight domains and over 200 unique, measurable indicators. Figure 1 summarizes these domains and their key relationships, which forms a foundation for identifying leverage points that can positively impact multiple aspects of sustainable diets. Researchers then engaged food system stakeholders through informal interviews, surveys, and collaborative workshops to prioritize indicators and identify additional relevant data sources. Stakeholders included national government, research, NGO, and private sector representatives from across the range of identified domains. The key indicators identified by stakeholders will ultimately be used to create food system data profiles for policymakers, in order to enable more evidence-based decision-making to advance transitions toward sustainable diets.

Sulfur dioxide retrievals from OMI and GOME-2 in preparation of TROPOMI

NASA Astrophysics Data System (ADS)

Theys, Nicolas; De Smedt, Isabelle; Danckaert, Thomas; Yu, Huan; van Gent, Jeroen; Van Roozendael, Michel

2016-04-01

The TROPOspheric Monitoring Instrument (TROPOMI) will be launched in 2016 onboard the ESA Sentinel-5 Precursor (S5P) platform and will provide global observations of atmospheric trace gases, with unprecedented spatial resolution. Sulfur dioxide (SO2) measurements from S5P will significantly improve the current capabilities for anthropogenic and volcanic emissions monitoring, and will extend the long-term datasets from past and existing UV sensors (TOMS, GOME, SCIAMACHY, OMI, GOME-2, OMPS). This work presents the SO2 retrieval schemes performed at BIRA-IASB as part of level-2 algorithm prototyping activities for S5P and tested on OMI and GOME-2. With a focus on anthropogenic sources, we show comparisons between OMI and GOME-2 as well as ground-based measurements, and discuss the possible reasons for the differences.

Retinal artery-vein classification via topology estimation

PubMed Central

Estrada, Rolando; Allingham, Michael J.; Mettu, Priyatham S.; Cousins, Scott W.; Tomasi, Carlo; Farsiu, Sina

2015-01-01

We propose a novel, graph-theoretic framework for distinguishing arteries from veins in a fundus image. We make use of the underlying vessel topology to better classify small and midsized vessels. We extend our previously proposed tree topology estimation framework by incorporating expert, domain-specific features to construct a simple, yet powerful global likelihood model. We efficiently maximize this model by iteratively exploring the space of possible solutions consistent with the projected vessels. We tested our method on four retinal datasets and achieved classification accuracies of 91.0%, 93.5%, 91.7%, and 90.9%, outperforming existing methods. Our results show the effectiveness of our approach, which is capable of analyzing the entire vasculature, including peripheral vessels, in wide field-of-view fundus photographs. This topology-based method is a potentially important tool for diagnosing diseases with retinal vascular manifestation. PMID:26068204

Realistic computer network simulation for network intrusion detection dataset generation

NASA Astrophysics Data System (ADS)

Payer, Garrett

2015-05-01

The KDD-99 Cup dataset is dead. While it can continue to be used as a toy example, the age of this dataset makes it all but useless for intrusion detection research and data mining. Many of the attacks used within the dataset are obsolete and do not reflect the features important for intrusion detection in today's networks. Creating a new dataset encompassing a large cross section of the attacks found on the Internet today could be useful, but would eventually fall to the same problem as the KDD-99 Cup; its usefulness would diminish after a period of time. To continue research into intrusion detection, the generation of new datasets needs to be as dynamic and as quick as the attacker. Simply examining existing network traffic and using domain experts such as intrusion analysts to label traffic is inefficient, expensive, and not scalable. The only viable methodology is simulation using technologies including virtualization, attack-toolsets such as Metasploit and Armitage, and sophisticated emulation of threat and user behavior. Simulating actual user behavior and network intrusion events dynamically not only allows researchers to vary scenarios quickly, but enables online testing of intrusion detection mechanisms by interacting with data as it is generated. As new threat behaviors are identified, they can be added to the simulation to make quicker determinations as to the effectiveness of existing and ongoing network intrusion technology, methodology and models.

Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks.

PubMed

Pan, Xiaoyong; Shen, Hong-Bin

2018-05-02

RNA-binding proteins (RBPs) take over 5∼10% of the eukaryotic proteome and play key roles in many biological processes, e.g. gene regulation. Experimental detection of RBP binding sites is still time-intensive and high-costly. Instead, computational prediction of the RBP binding sites using pattern learned from existing annotation knowledge is a fast approach. From the biological point of view, the local structure context derived from local sequences will be recognized by specific RBPs. However, in computational modeling using deep learning, to our best knowledge, only global representations of entire RNA sequences are employed. So far, the local sequence information is ignored in the deep model construction process. In this study, we present a computational method iDeepE to predict RNA-protein binding sites from RNA sequences by combining global and local convolutional neural networks (CNNs). For the global CNN, we pad the RNA sequences into the same length. For the local CNN, we split a RNA sequence into multiple overlapping fixed-length subsequences, where each subsequence is a signal channel of the whole sequence. Next, we train deep CNNs for multiple subsequences and the padded sequences to learn high-level features, respectively. Finally, the outputs from local and global CNNs are combined to improve the prediction. iDeepE demonstrates a better performance over state-of-the-art methods on two large-scale datasets derived from CLIP-seq. We also find that the local CNN run 1.8 times faster than the global CNN with comparable performance when using GPUs. Our results show that iDeepE has captured experimentally verified binding motifs. https://github.com/xypan1232/iDeepE. xypan172436@gmail.com or hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online.

Influence of El Niño Southern Oscillation on global hydropower production

NASA Astrophysics Data System (ADS)

Ng, Jia Yi; Turner, Sean; Galelli, Stefano

2016-04-01

Hydropower contributes significantly to meeting the world's energy demand, accounting for at least 16% of total electrical output. Its role as a mature and cost competitive renewable energy source is expected to become increasingly important as the world transits to a low-carbon economy. A key component of hydropower production is runoff, which is highly dependent on precipitation and other climate variables. As such, it becomes critical to understand how the drivers of climate variability impact hydropower production. One globally-important driver is the El Niño Southern Oscillation (ENSO). While it is known that ENSO influences hydrological processes, the potential value of its associated teleconnection in design related tasks has yet to be explored at the global scale. Our work seeks to characterize the impact of ENSO on global hydropower production so as to quantify the potential for increased production brought about by incorporating climate information within reservoir operating models. We study over 1,500 hydropower reservoirs - representing more than half the world's hydropower capacity. A historical monthly reservoir inflow time series is assigned to each reservoir from a 0.5 degree gridded global runoff dataset. Reservoir operating rules are designed using stochastic dynamic programming, and storage dynamics are simulated to assess performance under the climate conditions of the 20th century. Results show that hydropower reservoirs in the United States, Brazil, Argentina, Australia, and Eastern China are strongly influenced by ENSO episodes. Statistically significant lag correlations between ENSO indicators and hydropower production demonstrate predictive skill with lead times up to several months. Our work highlights the potential for using these indicators to increase the contribution of existing hydropower plants to global energy supplies.