The first ISLSCP field experiment (FIFE). [International Satellite Land Surface Climatology Project

NASA Technical Reports Server (NTRS)

Sellers, P. J.; Hall, F. G.; Asrar, G.; Strebel, D. E.; Murphy, R. E.

1988-01-01

The background and planning of the first International Satellite Land Surface Climatology Project (ISLSCP) field experiment (FIFE) are discussed. In FIFE, the NOAA series of satellites and GOES will be used to provide a moderate-temporal resolution coarse-spatial resolution data set, with SPOT and aircraft data providing the high-spatial resolution pointable-instrument capability. The paper describes the experiment design, the measurement strategy, the configuration of the site of the experiment (which will be at and around the Konza prairie near Manhattan, Kansas), and the experiment's operations and execution.

Extended-range high-resolution dynamical downscaling over a continental-scale spatial domain with atmospheric and surface nudging

NASA Astrophysics Data System (ADS)

Husain, S. Z.; Separovic, L.; Yu, W.; Fernig, D.

2014-12-01

Extended-range high-resolution mesoscale simulations with limited-area atmospheric models when applied to downscale regional analysis fields over large spatial domains can provide valuable information for many applications including the weather-dependent renewable energy industry. Long-term simulations over a continental-scale spatial domain, however, require mechanisms to control the large-scale deviations in the high-resolution simulated fields from the coarse-resolution driving fields. As enforcement of the lateral boundary conditions is insufficient to restrict such deviations, large scales in the simulated high-resolution meteorological fields are therefore spectrally nudged toward the driving fields. Different spectral nudging approaches, including the appropriate nudging length scales as well as the vertical profiles and temporal relaxations for nudging, have been investigated to propose an optimal nudging strategy. Impacts of time-varying nudging and generation of hourly analysis estimates are explored to circumvent problems arising from the coarse temporal resolution of the regional analysis fields. Although controlling the evolution of the atmospheric large scales generally improves the outputs of high-resolution mesoscale simulations within the surface layer, the prognostically evolving surface fields can nevertheless deviate from their expected values leading to significant inaccuracies in the predicted surface layer meteorology. A forcing strategy based on grid nudging of the different surface fields, including surface temperature, soil moisture, and snow conditions, toward their expected values obtained from a high-resolution offline surface scheme is therefore proposed to limit any considerable deviation. Finally, wind speed and temperature at wind turbine hub height predicted by different spectrally nudged extended-range simulations are compared against observations to demonstrate possible improvements achievable using higher spatiotemporal resolution.

Generating Daily Synthetic Landsat Imagery by Combining Landsat and MODIS Data

PubMed Central

Wu, Mingquan; Huang, Wenjiang; Niu, Zheng; Wang, Changyao

2015-01-01

Owing to low temporal resolution and cloud interference, there is a shortage of high spatial resolution remote sensing data. To address this problem, this study introduces a modified spatial and temporal data fusion approach (MSTDFA) to generate daily synthetic Landsat imagery. This algorithm was designed to avoid the limitations of the conditional spatial temporal data fusion approach (STDFA) including the constant window for disaggregation and the sensor difference. An adaptive window size selection method is proposed in this study to select the best window size and moving steps for the disaggregation of coarse pixels. The linear regression method is used to remove the influence of differences in sensor systems using disaggregated mean coarse reflectance by testing and validation in two study areas located in Xinjiang Province, China. The results show that the MSTDFA algorithm can generate daily synthetic Landsat imagery with a high correlation coefficient (R) ranged from 0.646 to 0.986 between synthetic images and the actual observations. We further show that MSTDFA can be applied to 250 m 16-day MODIS MOD13Q1 products and the Landsat Normalized Different Vegetation Index (NDVI) data by generating a synthetic NDVI image highly similar to actual Landsat NDVI observation with a high R of 0.97. PMID:26393607

Generating Daily Synthetic Landsat Imagery by Combining Landsat and MODIS Data.

PubMed

Wu, Mingquan; Huang, Wenjiang; Niu, Zheng; Wang, Changyao

2015-09-18

Owing to low temporal resolution and cloud interference, there is a shortage of high spatial resolution remote sensing data. To address this problem, this study introduces a modified spatial and temporal data fusion approach (MSTDFA) to generate daily synthetic Landsat imagery. This algorithm was designed to avoid the limitations of the conditional spatial temporal data fusion approach (STDFA) including the constant window for disaggregation and the sensor difference. An adaptive window size selection method is proposed in this study to select the best window size and moving steps for the disaggregation of coarse pixels. The linear regression method is used to remove the influence of differences in sensor systems using disaggregated mean coarse reflectance by testing and validation in two study areas located in Xinjiang Province, China. The results show that the MSTDFA algorithm can generate daily synthetic Landsat imagery with a high correlation coefficient (R) ranged from 0.646 to 0.986 between synthetic images and the actual observations. We further show that MSTDFA can be applied to 250 m 16-day MODIS MOD13Q1 products and the Landsat Normalized Different Vegetation Index (NDVI) data by generating a synthetic NDVI image highly similar to actual Landsat NDVI observation with a high R of 0.97.

Encoding of Natural Sounds at Multiple Spectral and Temporal Resolutions in the Human Auditory Cortex

PubMed Central

Santoro, Roberta; Moerel, Michelle; De Martino, Federico; Goebel, Rainer; Ugurbil, Kamil; Yacoub, Essa; Formisano, Elia

2014-01-01

Functional neuroimaging research provides detailed observations of the response patterns that natural sounds (e.g. human voices and speech, animal cries, environmental sounds) evoke in the human brain. The computational and representational mechanisms underlying these observations, however, remain largely unknown. Here we combine high spatial resolution (3 and 7 Tesla) functional magnetic resonance imaging (fMRI) with computational modeling to reveal how natural sounds are represented in the human brain. We compare competing models of sound representations and select the model that most accurately predicts fMRI response patterns to natural sounds. Our results show that the cortical encoding of natural sounds entails the formation of multiple representations of sound spectrograms with different degrees of spectral and temporal resolution. The cortex derives these multi-resolution representations through frequency-specific neural processing channels and through the combined analysis of the spectral and temporal modulations in the spectrogram. Furthermore, our findings suggest that a spectral-temporal resolution trade-off may govern the modulation tuning of neuronal populations throughout the auditory cortex. Specifically, our fMRI results suggest that neuronal populations in posterior/dorsal auditory regions preferably encode coarse spectral information with high temporal precision. Vice-versa, neuronal populations in anterior/ventral auditory regions preferably encode fine-grained spectral information with low temporal precision. We propose that such a multi-resolution analysis may be crucially relevant for flexible and behaviorally-relevant sound processing and may constitute one of the computational underpinnings of functional specialization in auditory cortex. PMID:24391486

Time Relevance of Convective Weather Forecast for Air Traffic Automation

NASA Technical Reports Server (NTRS)

Chan, William N.

2006-01-01

The Federal Aviation Administration (FAA) is handling nearly 120,000 flights a day through its Air Traffic Management (ATM) system and air traffic congestion is expected to increse substantially over the next 20 years. Weather-induced impacts to throughput and efficiency are the leading cause of flight delays accounting for 70% of all delays with convective weather accounting for 60% of all weather related delays. To support the Next Generation Air Traffic System goal of operating at 3X current capacity in the NAS, ATC decision support tools are being developed to create advisories to assist controllers in all weather constraints. Initial development of these decision support tools did not integrate information regarding weather constraints such as thunderstorms and relied on an additional system to provide that information. Future Decision Support Tools should move towards an integrated system where weather constraints are factored into the advisory of a Decision Support Tool (DST). Several groups such at NASA-Ames, Lincoln Laboratories, and MITRE are integrating convective weather data with DSTs. A survey of current convective weather forecast and observation data show they span a wide range of temporal and spatial resolutions. Short range convective observations can be obtained every 5 mins with longer range forecasts out to several days updated every 6 hrs. Today, the short range forecasts of less than 2 hours have a temporal resolution of 5 mins. Beyond 2 hours, forecasts have much lower temporal. resolution of typically 1 hour. Spatial resolutions vary from 1km for short range to 40km for longer range forecasts. Improving the accuracy of long range convective forecasts is a major challenge. A report published by the National Research Council states improvements for convective forecasts for the 2 to 6 hour time frame will only be achieved for a limited set of convective phenomena in the next 5 to 10 years. Improved longer range forecasts will be probabilistic as opposed to the deterministic shorter range forecasts. Despite the known low level of confidence with respect to long range convective forecasts, these data are still useful to a DST routing algorithm. It is better to develop an aircraft route using the best information available than no information. The temporally coarse long range forecast data needs to be interpolated to be useful to a DST. A DST uses aircraft trajectory predictions that need to be evaluated for impacts by convective storms. Each time-step of a trajectory prediction n&s to be checked against weather data. For the case of coarse temporal data, there needs to be a method fill in weather data where there is none. Simply using the coarse weather data without any interpolation can result in DST routes that are impacted by regions of strong convection. Increasing the temporal resolution of these data can be achieved but result in a large dataset that may prove to be an operational challenge in transmission and loading by a DST. Currently, it takes about 7mins retrieve a 7mb RUC2 forecast file from NOAA at NASA-Ames Research Center. A prototype NCWF6 1 hour forecast is about 3mb in size. A Six hour NCWFG forecast with a 1hr forecast time-step will be about l8mb (6 x 3mb). A 6 hour NCWF6 forecast with a l5min forecast time-step will be about 7mb (24 x 3mb). Based on the time it takes to retrieve a 7mb RUC2 forecast, it will take approximately 70mins to retrieve a 6 hour NCWF forecast with 15min time steps. Until those issues are addressed, there is a need to develop an algorithm that interpolates between these temporally coarse long range forecasts. This paper describes a method of how to use low temporal resolution probabilistic weather forecasts in a DST. The beginning of this paper is a description of some convective weather forecast and observation products followed by an example of how weather data are used by a DST. The subsequent sections will describe probabilistic forecasts followed by a descrtion of a method to use low temporal resolution probabilistic weather forecasts by providing a relevance value to these data outside of their valid times.

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

Pau, G. S. H.; Bisht, G.; Riley, W. J.

Existing land surface models (LSMs) describe physical and biological processes that occur over a wide range of spatial and temporal scales. For example, biogeochemical and hydrological processes responsible for carbon (CO 2, CH 4) exchanges with the atmosphere range from the molecular scale (pore-scale O 2 consumption) to tens of kilometers (vegetation distribution, river networks). Additionally, many processes within LSMs are nonlinearly coupled (e.g., methane production and soil moisture dynamics), and therefore simple linear upscaling techniques can result in large prediction error. In this paper we applied a reduced-order modeling (ROM) technique known as "proper orthogonal decomposition mapping method" thatmore » reconstructs temporally resolved fine-resolution solutions based on coarse-resolution solutions. We developed four different methods and applied them to four study sites in a polygonal tundra landscape near Barrow, Alaska. Coupled surface–subsurface isothermal simulations were performed for summer months (June–September) at fine (0.25 m) and coarse (8 m) horizontal resolutions. We used simulation results from three summer seasons (1998–2000) to build ROMs of the 4-D soil moisture field for the study sites individually (single-site) and aggregated (multi-site). The results indicate that the ROM produced a significant computational speedup (> 10 3) with very small relative approximation error (< 0.1%) for 2 validation years not used in training the ROM. We also demonstrate that our approach: (1) efficiently corrects for coarse-resolution model bias and (2) can be used for polygonal tundra sites not included in the training data set with relatively good accuracy (< 1.7% relative error), thereby allowing for the possibility of applying these ROMs across a much larger landscape. By coupling the ROMs constructed at different scales together hierarchically, this method has the potential to efficiently increase the resolution of land models for coupled climate simulations to spatial scales consistent with mechanistic physical process representation.« less

A reduced-order modeling approach to represent subgrid-scale hydrological dynamics for land-surface simulations: application in a polygonal tundra landscape

DOE PAGES

Pau, G. S. H.; Bisht, G.; Riley, W. J.

2014-09-17

Existing land surface models (LSMs) describe physical and biological processes that occur over a wide range of spatial and temporal scales. For example, biogeochemical and hydrological processes responsible for carbon (CO 2, CH 4) exchanges with the atmosphere range from the molecular scale (pore-scale O 2 consumption) to tens of kilometers (vegetation distribution, river networks). Additionally, many processes within LSMs are nonlinearly coupled (e.g., methane production and soil moisture dynamics), and therefore simple linear upscaling techniques can result in large prediction error. In this paper we applied a reduced-order modeling (ROM) technique known as "proper orthogonal decomposition mapping method" thatmore » reconstructs temporally resolved fine-resolution solutions based on coarse-resolution solutions. We developed four different methods and applied them to four study sites in a polygonal tundra landscape near Barrow, Alaska. Coupled surface–subsurface isothermal simulations were performed for summer months (June–September) at fine (0.25 m) and coarse (8 m) horizontal resolutions. We used simulation results from three summer seasons (1998–2000) to build ROMs of the 4-D soil moisture field for the study sites individually (single-site) and aggregated (multi-site). The results indicate that the ROM produced a significant computational speedup (> 10 3) with very small relative approximation error (< 0.1%) for 2 validation years not used in training the ROM. We also demonstrate that our approach: (1) efficiently corrects for coarse-resolution model bias and (2) can be used for polygonal tundra sites not included in the training data set with relatively good accuracy (< 1.7% relative error), thereby allowing for the possibility of applying these ROMs across a much larger landscape. By coupling the ROMs constructed at different scales together hierarchically, this method has the potential to efficiently increase the resolution of land models for coupled climate simulations to spatial scales consistent with mechanistic physical process representation.« less

Palm Swamp Wetland Ecosystems of the Upper Amazon: Characterizing their Distribution and Inundation State Using Multiple Resolution Microwave Remote Sensing

NASA Astrophysics Data System (ADS)

Podest, E.; McDonald, K. C.; Schröder, R.; Pinto, N.; Zimmermann, R.; Horna, V.

2011-12-01

Palm swamp wetlands are prevalent in the Amazon basin, including extensive regions in northern Peru. These ecosystems are characterized by constant surface inundation and moderate seasonal water level variation. The combination of constantly saturated soils, giving rise to low oxygen conditions, and warm temperatures year-round can lead to considerable methane release to the atmosphere. Because of the widespread occurrence and expected sensitivity of these ecosystems to climate change, knowledge of their spatial extent and inundation state is crucial for assessing the associated land-atmosphere carbon exchange. Precise spatio-temporal information on palm swamps is difficult to gather because of their remoteness and difficult accessibility. Spaceborne microwave remote sensing is an effective tool for characterizing these ecosystems since it is sensitive to surface water and vegetation structure and allows monitoring large inaccessible areas on a temporal basis regardless of atmospheric conditions or solar illumination. We are developing a remote sensing methodology using multiple resolution microwave remote sensing data to determine palm swamp distribution and inundation state over focus regions in the Amazon basin in northern Peru. For this purpose, two types of multi-temporal microwave data are used: 1) high-resolution (100 m) data from the Advanced Land Observing Satellite (ALOS) Phased Array L-Band Synthetic Aperture Radar (PALSAR) to derive maps of palm swamp extent and inundation from dual-polarization fine-beam and multi-temporal HH-polarized ScanSAR, and 2) coarse resolution (25 km) combined active and passive microwave data from QuikSCAT and AMSR-E to derive inundated area fraction on a weekly basis. We compare information content and accuracy of the coarse resolution products to the PALSAR-based datasets to ensure information harmonization. The synergistic combination of high and low resolution datasets will allow for characterization of palm swamps and assessment of their flooding status. This work has been undertaken partly within the framework of the JAXA ALOS Kyoto & Carbon Initiative. PALSAR data have been provided by JAXA/EORC. Portions of this work were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Spatial Modeling and Uncertainty Assessment of Fine Scale Surface Processes Based on Coarse Terrain Elevation Data

NASA Astrophysics Data System (ADS)

Rasera, L. G.; Mariethoz, G.; Lane, S. N.

2017-12-01

Frequent acquisition of high-resolution digital elevation models (HR-DEMs) over large areas is expensive and difficult. Satellite-derived low-resolution digital elevation models (LR-DEMs) provide extensive coverage of Earth's surface but at coarser spatial and temporal resolutions. Although useful for large scale problems, LR-DEMs are not suitable for modeling hydrologic and geomorphic processes at scales smaller than their spatial resolution. In this work, we present a multiple-point geostatistical approach for downscaling a target LR-DEM based on available high-resolution training data and recurrent high-resolution remote sensing images. The method aims at generating several equiprobable HR-DEMs conditioned to a given target LR-DEM by borrowing small scale topographic patterns from an analogue containing data at both coarse and fine scales. An application of the methodology is demonstrated by using an ensemble of simulated HR-DEMs as input to a flow-routing algorithm. The proposed framework enables a probabilistic assessment of the spatial structures generated by natural phenomena operating at scales finer than the available terrain elevation measurements. A case study in the Swiss Alps is provided to illustrate the methodology.

Multi-scale approaches for high-speed imaging and analysis of large neural populations

PubMed Central

Ahrens, Misha B.; Yuste, Rafael; Peterka, Darcy S.; Paninski, Liam

2017-01-01

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution. Here we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques based on simple local averaging provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified at fine scale (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover denoised fluorescence traces and deconvolved neural activity of each individual neuron from coarse scale data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to “zoom out” by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution. PMID:28771570

Scaling properties of Arctic sea ice deformation in high-resolution viscous-plastic sea ice models and satellite observations

NASA Astrophysics Data System (ADS)

Hutter, Nils; Losch, Martin; Menemenlis, Dimitris

2017-04-01

Sea ice models with the traditional viscous-plastic (VP) rheology and very high grid resolution can resolve leads and deformation rates that are localised along Linear Kinematic Features (LKF). In a 1-km pan-Arctic sea ice-ocean simulation, the small scale sea-ice deformations in the Central Arctic are evaluated with a scaling analysis in relation to satellite observations of the Envisat Geophysical Processor System (EGPS). A new coupled scaling analysis for data on Eulerian grids determines the spatial and the temporal scaling as well as the coupling between temporal and spatial scales. The spatial scaling of the modelled sea ice deformation implies multi-fractality. The spatial scaling is also coupled to temporal scales and varies realistically by region and season. The agreement of the spatial scaling and its coupling to temporal scales with satellite observations and models with the modern elasto-brittle rheology challenges previous results with VP models at coarse resolution where no such scaling was found. The temporal scaling analysis, however, shows that the VP model does not fully resolve the intermittency of sea ice deformation that is observed in satellite data.

Development of a spatio-temporal disaggregation method (DisNDVI) for generating a time series of fine resolution NDVI images

NASA Astrophysics Data System (ADS)

Bindhu, V. M.; Narasimhan, B.

2015-03-01

Normalized Difference Vegetation Index (NDVI), a key parameter in understanding the vegetation dynamics, has high spatial and temporal variability. However, continuous monitoring of NDVI is not feasible at fine spatial resolution (<60 m) owing to the long revisit time needed by the satellites to acquire the fine spatial resolution data. Further, the study attains significance in the case of humid tropical regions of the earth, where the prevailing atmospheric conditions restrict availability of fine resolution cloud free images at a high temporal frequency. As an alternative to the lack of high resolution images, the current study demonstrates a novel disaggregation method (DisNDVI) which integrates the spatial information from a single fine resolution image and temporal information in terms of crop phenology from time series of coarse resolution images to generate estimates of NDVI at fine spatial and temporal resolution. The phenological variation of the pixels captured at the coarser scale provides the basis for relating the temporal variability of the pixel with the NDVI available at fine resolution. The proposed methodology was tested over a 30 km × 25 km spatially heterogeneous study area located in the south of Tamil Nadu, India. The robustness of the algorithm was assessed by an independent comparison of the disaggregated NDVI and observed NDVI obtained from concurrent Landsat ETM+ imagery. The results showed good spatial agreement across the study area dominated with agriculture and forest pixels, with a root mean square error of 0.05. The validation done at the coarser scale showed that disaggregated NDVI spatially averaged to 240 m compared well with concurrent MODIS NDVI at 240 m (R2 > 0.8). The validation results demonstrate the effectiveness of DisNDVI in improving the spatial and temporal resolution of NDVI images for utility in fine scale hydrological applications such as crop growth monitoring and estimation of evapotranspiration.

Genetic particle filter application to land surface temperature downscaling

NASA Astrophysics Data System (ADS)

Mechri, Rihab; Ottlé, Catherine; Pannekoucke, Olivier; Kallel, Abdelaziz

2014-03-01

Thermal infrared data are widely used for surface flux estimation giving the possibility to assess water and energy budgets through land surface temperature (LST). Many applications require both high spatial resolution (HSR) and high temporal resolution (HTR), which are not presently available from space. It is therefore necessary to develop methodologies to use the coarse spatial/high temporal resolutions LST remote-sensing products for a better monitoring of fluxes at appropriate scales. For that purpose, a data assimilation method was developed to downscale LST based on particle filtering. The basic tenet of our approach is to constrain LST dynamics simulated at both HSR and HTR, through the optimization of aggregated temperatures at the coarse observation scale. Thus, a genetic particle filter (GPF) data assimilation scheme was implemented and applied to a land surface model which simulates prior subpixel temperatures. First, the GPF downscaling scheme was tested on pseudoobservations generated in the framework of the study area landscape (Crau-Camargue, France) and climate for the year 2006. The GPF performances were evaluated against observation errors and temporal sampling. Results show that GPF outperforms prior model estimations. Finally, the GPF method was applied on Spinning Enhanced Visible and InfraRed Imager time series and evaluated against HSR data provided by an Advanced Spaceborne Thermal Emission and Reflection Radiometer image acquired on 26 July 2006. The temperatures of seven land cover classes present in the study area were estimated with root-mean-square errors less than 2.4 K which is a very promising result for downscaling LST satellite products.

Estimation of Actual Crop ET of Paddy Using the Energy Balance Model SMARET and Validation with Field Water Balance Measurements and a Crop Growth Model (ORYZA)

NASA Astrophysics Data System (ADS)

Nallasamy, N. D.; Muraleedharan, B. V.; Kathirvel, K.; Narasimhan, B.

2014-12-01

Sustainable management of water resources requires reliable estimates of actual evapotranspiration (ET) at fine spatial and temporal resolution. This is significant in the case of rice based irrigation systems, one of the major consumers of surface water resources and where ET forms a major component of water consumption. However huge tradeoff in the spatial and temporal resolution of satellite images coupled with lack of adequate number of cloud free images within a growing season act as major constraints in deriving ET at fine spatial and temporal resolution using remote sensing based energy balance models. The scale at which ET is determined is decided by the spatial and temporal scale of Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), which form inputs to energy balance models. In this context, the current study employed disaggregation algorithms (NL-DisTrad and DisNDVI) to generate time series of LST and NDVI images at fine resolution. The disaggregation algorithms aimed at generating LST and NDVI at finer scale by integrating temporal information from concurrent coarse resolution data and spatial information from a single fine resolution image. The temporal frequency of the disaggregated images is further improved by employing composite images of NDVI and LST in the spatio-temporal disaggregation method. The study further employed half-hourly incoming surface insolation and outgoing long wave radiation obtained from the Indian geostationary satellite (Kalpana-1) to convert the instantaneous ET into daily ET and subsequently to the seasonal ET, thereby improving the accuracy of ET estimates. The estimates of ET were validated with field based water balance measurements carried out in Gadana, a subbasin predominated by rice paddy fields, located in Tamil Nadu, India.

Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty

Treesearch

Katharine White; Jennifer Pontius; Paul Schaberg

2014-01-01

Current remote sensing studies of phenology have been limited to coarse spatial or temporal resolution and often lack a direct link to field measurements. To address this gap, we compared remote sensing methodologies using Landsat Thematic Mapper (TM) imagery to extensive field measurements in a mixed northern hardwood forest. Five vegetation indices, five mathematical...

Sensitivity of chemical transport model simulations to the duration of chemical and transport operators: a case study with GEOS-Chem v10-01

NASA Astrophysics Data System (ADS)

Philip, S.; Martin, R. V.; Keller, C. A.

2015-11-01

Chemical transport models involve considerable computational expense. Fine temporal resolution offers accuracy at the expense of computation time. Assessment is needed of the sensitivity of simulation accuracy to the duration of chemical and transport operators. We conduct a series of simulations with the GEOS-Chem chemical transport model at different temporal and spatial resolutions to examine the sensitivity of simulated atmospheric composition to temporal resolution. Subsequently, we compare the tracers simulated with operator durations from 10 to 60 min as typically used by global chemical transport models, and identify the timesteps that optimize both computational expense and simulation accuracy. We found that longer transport timesteps increase concentrations of emitted species such as nitrogen oxides and carbon monoxide since a more homogeneous distribution reduces loss through chemical reactions and dry deposition. The increased concentrations of ozone precursors increase ozone production at longer transport timesteps. Longer chemical timesteps decrease sulfate and ammonium but increase nitrate due to feedbacks with in-cloud sulfur dioxide oxidation and aerosol thermodynamics. The simulation duration decreases by an order of magnitude from fine (5 min) to coarse (60 min) temporal resolution. We assess the change in simulation accuracy with resolution by comparing the root mean square difference in ground-level concentrations of nitrogen oxides, ozone, carbon monoxide and secondary inorganic aerosols with a finer temporal or spatial resolution taken as truth. Simulation error for these species increases by more than a factor of 5 from the shortest (5 min) to longest (60 min) temporal resolution. Chemical timesteps twice that of the transport timestep offer more simulation accuracy per unit computation. However, simulation error from coarser spatial resolution generally exceeds that from longer timesteps; e.g. degrading from 2° × 2.5° to 4° × 5° increases error by an order of magnitude. We recommend prioritizing fine spatial resolution before considering different temporal resolutions in offline chemical transport models. We encourage the chemical transport model users to specify in publications the durations of operators due to their effects on simulation accuracy.

Added-values of high spatiotemporal remote sensing data in crop yield estimation

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.

2017-12-01

Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing derived parameters have been used for estimating crop yield by using either empirical or crop growth models. The uses of remote sensing vegetation index (VI) in crop yield modeling have been typically evaluated at regional and country scales using coarse spatial resolution (a few hundred to kilo-meters) data or assessed over a small region at field level using moderate resolution spatial resolution data (10-100m). Both data sources have shown great potential in capturing spatial and temporal variability in crop yield. However, the added value of data with both high spatial and temporal resolution data has not been evaluated due to the lack of such data source with routine, global coverage. In recent years, more moderate resolution data have become freely available and data fusion approaches that combine data acquired from different spatial and temporal resolutions have been developed. These make the monitoring crop condition and estimating crop yield at field scale become possible. Here we investigate the added value of the high spatial and temporal VI for describing variability of crop yield. The explanatory ability of crop yield based on high spatial and temporal resolution remote sensing data was evaluated in a rain-fed agricultural area in the U.S. Corn Belt. Results show that the fused Landsat-MODIS (high spatial and temporal) VI explains yield variability better than single data source (Landsat or MODIS alone), with EVI2 performing slightly better than NDVI. The maximum VI describes yield variability better than cumulative VI. Even though VI is effective in explaining yield variability within season, the inter-annual variability is more complex and need additional information (e.g. weather, water use and management). Our findings augment the importance of high spatiotemporal remote sensing data and supports new moderate resolution satellite missions for agricultural applications.

[Air pollution in an urban area nearby the Rome-Ciampino city airport].

PubMed

Di Menno di Bucchianico, Alessandro; Cattani, Giorgio; Gaeta, Alessandra; Caricchia, Anna Maria; Troiano, Francesco; Sozzi, Roberto; Bolignano, Andrea; Sacco, Fabrizio; Damizia, Sesto; Barberini, Silvia; Caleprico, Roberta; Fabozzi, Tina; Ancona, Carla; Ancona, Laura; Cesaroni, Giulia; Forastiere, Francesco; Gobbi, Gian Paolo; Costabile, Francesca; Angelini, Federico; Barnaba, Francesca; Inglessis, Marco; Tancredi, Francesco; Palumbo, Lorenzo; Fontana, Luca; Bergamaschi, Antonio; Iavicoli, Ivo

2014-01-01

to assess air pollution spatial and temporal variability in the urban area nearby the Ciampino International Airport (Rome) and to investigate the airport-related emissions contribute. the study domain was a 64 km2 area around the airport. Two fifteen-day monitoring campaigns (late spring, winter) were carried out. Results were evaluated using several runs outputs of an airport-related sources Lagrangian particle model and a photochemical model (the Flexible Air quality Regional Model, FARM). both standard and high time resolution air pollutant concentrations measurements: CO, NO, NO2, C6H6, mass and number concentration of several PM fractions. 46 fixed points (spread over the study area) of NO2 and volatile organic compounds concentrations (fifteen days averages); deterministic models outputs. standard time resolution measurements, as well as model outputs, showed the airport contribution to air pollution levels being little compared to the main source in the area (i.e. vehicular traffic). However, using high time resolution measurements, peaks of particles associated with aircraft takeoff (total number concentration and soot mass concentration), and landing (coarse mass concentration) were observed, when the site measurement was downwind to the runway. the frequently observed transient spikes associated with aircraft movements could lead to a not negligible contribute to ultrafine, soot and coarse particles exposure of people living around the airport. Such contribute and its spatial and temporal variability should be investigated when assessing the airports air quality impact.

Examining fire-induced forest changes using novel remote sensing technique: a case study in a mixed pine-oak forest

NASA Astrophysics Data System (ADS)

Meng, R.; Wu, J.; Zhao, F. R.; Cook, B.; Hanavan, R. P.; Serbin, S.

2017-12-01

Fire-induced forest changes has long been a central focus for forest ecology and global carbon cycling studies, and is becoming a pressing issue for global change biologists particularly with the projected increases in the frequency and intensity of fire with a warmer and drier climate. Compared with time-consuming and labor intensive field-based approaches, remote sensing offers a promising way to efficiently assess fire effects and monitor post-fire forest responses across a range of spatial and temporal scales. However, traditional remote sensing studies relying on simple optical spectral indices or coarse resolution imagery still face a number of technical challenges, including confusion or contamination of the signal by understory dynamics and mixed pixels with moderate to coarse resolution data (>= 30 m). As such, traditional remote sensing may not meet the increasing demand for more ecologically-meaningful monitoring and quantitation of fire-induced forest changes. Here we examined the use of novel remote sensing technique (i.e. airborne imaging spectroscopy and LiDAR measurement, very high spatial resolution (VHR) space-borne multi-spectral measurement, and high temporal-spatial resolution UAS-based (Unmanned Aerial System) imagery), in combination with field and phenocam measurements to map forest burn severity across spatial scales, quantify crown-scale post-fire forest recovery rate, and track fire-induced phenology changes in the burned areas. We focused on a mixed pine-oak forest undergoing multiple fire disturbances for the past several years in Long Island, NY as a case study. We demonstrate that (1) forest burn severity mapping from VHR remote sensing measurement can capture crown-scale heterogeneous fire patterns over large-scale; (2) the combination of VHR optical and structural measurements provides an efficient means to remotely sense species-level post-fire forest responses; (3) the UAS-based remote sensing enables monitoring of fire-induced forest phenology changes at unprecedented temporal and spatial resolutions. This work provides the methodological approach monitor fire-induced forest changes in a spatially explicit manner across scales, with important implications for fire-related forest management and for constraining/benchmarking process models.

Modeling diurnal land temperature cycles over Los Angeles using downscaled GOES imagery

NASA Astrophysics Data System (ADS)

Weng, Qihao; Fu, Peng

2014-11-01

Land surface temperature is a key parameter for monitoring urban heat islands, assessing heat related risks, and estimating building energy consumption. These environmental issues are characterized by high temporal variability. A possible solution from the remote sensing perspective is to utilize geostationary satellites images, for instance, images from Geostationary Operational Environmental System (GOES) and Meteosat Second Generation (MSG). These satellite systems, however, with coarse spatial but high temporal resolution (sub-hourly imagery at 3-10 km resolution), often limit their usage to meteorological forecasting and global climate modeling. Therefore, how to develop efficient and effective methods to disaggregate these coarse resolution images to a proper scale suitable for regional and local studies need be explored. In this study, we propose a least square support vector machine (LSSVM) method to achieve the goal of downscaling of GOES image data to half-hourly 1-km LSTs by fusing it with MODIS data products and Shuttle Radar Topography Mission (SRTM) digital elevation data. The result of downscaling suggests that the proposed method successfully disaggregated GOES images to half-hourly 1-km LSTs with accuracy of approximately 2.5 K when validated against with MODIS LSTs at the same over-passing time. The synthetic LST datasets were further explored for monitoring of surface urban heat island (UHI) in the Los Angeles region by extracting key diurnal temperature cycle (DTC) parameters. It is found that the datasets and DTC derived parameters were more suitable for monitoring of daytime- other than nighttime-UHI. With the downscaled GOES 1-km LSTs, the diurnal temperature variations can well be characterized. An accuracy of about 2.5 K was achieved in terms of the fitted results at both 1 km and 5 km resolutions.

Extended-Range High-Resolution Dynamical Downscaling over a Continental-Scale Domain

NASA Astrophysics Data System (ADS)

Husain, S. Z.; Separovic, L.; Yu, W.; Fernig, D.

2014-12-01

High-resolution mesoscale simulations, when applied for downscaling meteorological fields over large spatial domains and for extended time periods, can provide valuable information for many practical application scenarios including the weather-dependent renewable energy industry. In the present study, a strategy has been proposed to dynamically downscale coarse-resolution meteorological fields from Environment Canada's regional analyses for a period of multiple years over the entire Canadian territory. The study demonstrates that a continuous mesoscale simulation over the entire domain is the most suitable approach in this regard. Large-scale deviations in the different meteorological fields pose the biggest challenge for extended-range simulations over continental scale domains, and the enforcement of the lateral boundary conditions is not sufficient to restrict such deviations. A scheme has therefore been developed to spectrally nudge the simulated high-resolution meteorological fields at the different model vertical levels towards those embedded in the coarse-resolution driving fields derived from the regional analyses. A series of experiments were carried out to determine the optimal nudging strategy including the appropriate nudging length scales, nudging vertical profile and temporal relaxation. A forcing strategy based on grid nudging of the different surface fields, including surface temperature, soil-moisture, and snow conditions, towards their expected values obtained from a high-resolution offline surface scheme was also devised to limit any considerable deviation in the evolving surface fields due to extended-range temporal integrations. The study shows that ensuring large-scale atmospheric similarities helps to deliver near-surface statistical scores for temperature, dew point temperature and horizontal wind speed that are better or comparable to the operational regional forecasts issued by Environment Canada. Furthermore, the meteorological fields resulting from the proposed downscaling strategy have significantly improved spatiotemporal variance compared to those from the operational forecasts, and any time series generated from the downscaled fields do not suffer from discontinuities due to switching between the consecutive forecasts.

Using High Resolution Model Data to Improve Lightning Forecasts across Southern California

NASA Astrophysics Data System (ADS)

Capps, S. B.; Rolinski, T.

2014-12-01

Dry lightning often results in a significant amount of fire starts in areas where the vegetation is dry and continuous. Meteorologists from the USDA Forest Service Predictive Services' program in Riverside, California are tasked to provide southern and central California's fire agencies with fire potential outlooks. Logistic regression equations were developed by these meteorologists several years ago, which forecast probabilities of lightning as well as lightning amounts, out to seven days across southern California. These regression equations were developed using ten years of historical gridded data from the Global Forecast System (GFS) model on a coarse scale (0.5 degree resolution), correlated with historical lightning strike data. These equations do a reasonably good job of capturing a lightning episode (3-5 consecutive days or greater of lightning), but perform poorly regarding more detailed information such as exact location and amounts. It is postulated that the inadequacies in resolving the finer details of episodic lightning events is due to the coarse resolution of the GFS data, along with limited predictors. Stability parameters, such as the Lifted Index (LI), the Total Totals index (TT), Convective Available Potential Energy (CAPE), along with Precipitable Water (PW) are the only parameters being considered as predictors. It is hypothesized that the statistical forecasts will benefit from higher resolution data both in training and implementing the statistical model. We have dynamically downscaled NCEP FNL (Final) reanalysis data using the Weather Research and Forecasting model (WRF) to 3km spatial and hourly temporal resolution across a decade. This dataset will be used to evaluate the contribution to the success of the statistical model of additional predictors in higher vertical, spatial and temporal resolution. If successful, we will implement an operational dynamically downscaled GFS forecast product to generate predictors for the resulting statistical lightning model. This data will help fire agencies be better prepared to pre-deploy resources in advance of these events. Specific information regarding duration, amount, and location will be especially valuable.

An Improved GRACE Terrestrial Water Storage Assimilation System For Estimating Large-Scale Soil Moisture and Shallow Groundwater

NASA Astrophysics Data System (ADS)

Girotto, M.; De Lannoy, G. J. M.; Reichle, R. H.; Rodell, M.

2015-12-01

The Gravity Recovery And Climate Experiment (GRACE) mission is unique because it provides highly accurate column integrated estimates of terrestrial water storage (TWS) variations. Major limitations of GRACE-based TWS observations are related to their monthly temporal and coarse spatial resolution (around 330 km at the equator), and to the vertical integration of the water storage components. These challenges can be addressed through data assimilation. To date, it is still not obvious how best to assimilate GRACE-TWS observations into a land surface model, in order to improve hydrological variables, and many details have yet to be worked out. This presentation discusses specific recent features of the assimilation of gridded GRACE-TWS data into the NASA Goddard Earth Observing System (GEOS-5) Catchment land surface model to improve soil moisture and shallow groundwater estimates at the continental scale. The major recent advancements introduced by the presented work with respect to earlier systems include: 1) the assimilation of gridded GRACE-TWS data product with scaling factors that are specifically derived for data assimilation purposes only; 2) the assimilation is performed through a 3D assimilation scheme, in which reasonable spatial and temporal error standard deviations and correlations are exploited; 3) the analysis step uses an optimized calculation and application of the analysis increments; 4) a poor-man's adaptive estimation of a spatially variable measurement error. This work shows that even if they are characterized by a coarse spatial and temporal resolution, the observed column integrated GRACE-TWS data have potential for improving our understanding of soil moisture and shallow groundwater variations.

Validation and Temporal Analysis of Lai and Fapar Products Derived from Medium Resolution Sensor

NASA Astrophysics Data System (ADS)

Claverie, M.; Vermote, E. F.; Baret, F.; Weiss, M.; Hagolle, O.; Demarez, V.

2012-12-01

Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) have been defined as Essential Climate Variables. Many Earth surface monitoring applications are based on global estimation combined with a relatively high frequency. The medium spatial resolution sensors (MRS), such as SPOT-VGT, MODIS or MERIS, have been widely used to provide land surface products (mainly LAI and FAPAR) to the scientific community. These products require quality assessment and consistency. However, due to consistency of the ground measurements spatial sampling, the medium resolution is not appropriate for direct validation with in situ measurements sampling. It is thus more adequate to use high spatial resolution sensors which can integrate the spatial variability. The recent availability of combined high spatial (8 m) and temporal resolutions (daily) Formosat-2 data allows to evaluate the accuracy and the temporal consistency of medium resolution sensors products. In this study, we proposed to validate MRS products over a cropland area and to analyze their spatial and temporal consistency. As a matter of fact, this study belongs to the Stage 2 of the validation, as defined by the Land Product Validation sub-group of the Earth Observation Satellites. Reference maps, derived from the aggregation of Formosat-2 data (acquired during the 2006-2010 period over croplands in southwest of France), were compared with (i) two existing global biophysical variables products (GEOV1/VGT and MODIS-15 coll. 5), and (ii) a new product (MODdaily) derived from the inversion of PROSAIL radiative transfer model (EMMAH, INRA Avignon) applied on MODIS BRDF-corrected daily reflectance. Their uncertainty was calculated with 105 LAI and FAPAR reference maps, which uncertainties (22 % for LAI and 12% for FAPAR) were evaluated with in situ measurements performed over maize, sunflower and soybean. Inter-comparison of coarse resolution (0.05°) products showed that LAI and FAPAR have consistent phenology (Figure). The GEOLAND-2 showed the smoothest time series due to a 30-day composite, while MODdaily noise was satisfactory (<12%). The RMSE of LAI calculated for the period 2006-2010 were 0.46 for GEOV1/VGT, 0.19 for MODIS-15 and 0.16 for MODdaily. A significant overestimation (bias=0.43) of the LAI peak were observed for GEOV1/VGT products, while MOD-15 showed a small underestimation (bias=-0.14) of highest LAI. Finally, over a larger area (a quarter of France) covered by cropland, grassland and forest, the products displayed a good spatial consistency.; LAI 2006-2010 time-series of a coarse resolution pixel of cropland (extent in upper-left corner). Products are compared to Formosat-2 reference maps.

Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings.

PubMed

Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P; Nair, Vimala D

2017-09-11

Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (K ex ) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.

A multi-temporal fusion-based approach for land cover mapping in support of nuclear incident response

NASA Astrophysics Data System (ADS)

Sah, Shagan

An increasingly important application of remote sensing is to provide decision support during emergency response and disaster management efforts. Land cover maps constitute one such useful application product during disaster events; if generated rapidly after any disaster, such map products can contribute to the efficacy of the response effort. In light of recent nuclear incidents, e.g., after the earthquake/tsunami in Japan (2011), our research focuses on constructing rapid and accurate land cover maps of the impacted area in case of an accidental nuclear release. The methodology involves integration of results from two different approaches, namely coarse spatial resolution multi-temporal and fine spatial resolution imagery, to increase classification accuracy. Although advanced methods have been developed for classification using high spatial or temporal resolution imagery, only a limited amount of work has been done on fusion of these two remote sensing approaches. The presented methodology thus involves integration of classification results from two different remote sensing modalities in order to improve classification accuracy. The data used included RapidEye and MODIS scenes over the Nine Mile Point Nuclear Power Station in Oswego (New York, USA). The first step in the process was the construction of land cover maps from freely available, high temporal resolution, low spatial resolution MODIS imagery using a time-series approach. We used the variability in the temporal signatures among different land cover classes for classification. The time series-specific features were defined by various physical properties of a pixel, such as variation in vegetation cover and water content over time. The pixels were classified into four land cover classes - forest, urban, water, and vegetation - using Euclidean and Mahalanobis distance metrics. On the other hand, a high spatial resolution commercial satellite, such as RapidEye, can be tasked to capture images over the affected area in the case of a nuclear event. This imagery served as a second source of data to augment results from the time series approach. The classifications from the two approaches were integrated using an a posteriori probability-based fusion approach. This was done by establishing a relationship between the classes, obtained after classification of the two data sources. Despite the coarse spatial resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion-based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. This fusion thus contributed to classification accuracy refinement, with a few additional advantages, such as correction for cloud cover and providing for an approach that is robust against point-in-time seasonal anomalies, due to the inclusion of multi-temporal data. We concluded that this approach is capable of generating land cover maps of acceptable accuracy and rapid turnaround, which in turn can yield reliable estimates of crop acreage of a region. The final algorithm is part of an automated software tool, which can be used by emergency response personnel to generate a nuclear ingestion pathway information product within a few hours of data collection.

Impacts of precipitation and potential evapotranspiration patterns on downscaling soil moisture in regions with large topographic relief

NASA Astrophysics Data System (ADS)

Cowley, Garret S.; Niemann, Jeffrey D.; Green, Timothy R.; Seyfried, Mark S.; Jones, Andrew S.; Grazaitis, Peter J.

2017-02-01

Soil moisture can be estimated at coarse resolutions (>1 km) using satellite remote sensing, but that resolution is poorly suited for many applications. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales coarse-resolution soil moisture using fine-resolution topographic, vegetation, and soil data to produce fine-resolution (10-30 m) estimates of soil moisture. The EMT+VS model performs well at catchments with low topographic relief (≤124 m), but it has not been applied to regions with larger ranges of elevation. Large relief can produce substantial variations in precipitation and potential evapotranspiration (PET), which might affect the fine-resolution patterns of soil moisture. In this research, simple methods to downscale temporal average precipitation and PET are developed and included in the EMT+VS model, and the effects of spatial variations in these variables on the surface soil moisture estimates are investigated. The methods are tested against ground truth data at the 239 km2 Reynolds Creek watershed in southern Idaho, which has 1145 m of relief. The precipitation and PET downscaling methods are able to capture the main features in the spatial patterns of both variables. The space-time Nash-Sutcliffe coefficients of efficiency of the fine-resolution soil moisture estimates improve from 0.33 to 0.36 and 0.41 when the precipitation and PET downscaling methods are included, respectively. PET downscaling provides a larger improvement in the soil moisture estimates than precipitation downscaling likely because the PET pattern is more persistent through time, and thus more predictable, than the precipitation pattern.

Deriving temporally continuous soil moisture estimations at fine resolution by downscaling remotely sensed product

NASA Astrophysics Data System (ADS)

Jin, Yan; Ge, Yong; Wang, Jianghao; Heuvelink, Gerard B. M.

2018-06-01

Land surface soil moisture (SSM) has important roles in the energy balance of the land surface and in the water cycle. Downscaling of coarse-resolution SSM remote sensing products is an efficient way for producing fine-resolution data. However, the downscaling methods used most widely require full-coverage visible/infrared satellite data as ancillary information. These methods are restricted to cloud-free days, making them unsuitable for continuous monitoring. The purpose of this study is to overcome this limitation to obtain temporally continuous fine-resolution SSM estimations. The local spatial heterogeneities of SSM and multiscale ancillary variables were considered in the downscaling process both to solve the problem of the strong variability of SSM and to benefit from the fusion of ancillary information. The generation of continuous downscaled remote sensing data was achieved via two principal steps. For cloud-free days, a stepwise hybrid geostatistical downscaling approach, based on geographically weighted area-to-area regression kriging (GWATARK), was employed by combining multiscale ancillary variables with passive microwave remote sensing data. Then, the GWATARK-estimated SSM and China Soil Moisture Dataset from Microwave Data Assimilation SSM data were combined to estimate fine-resolution data for cloudy days. The developed methodology was validated by application to the 25-km resolution daily AMSR-E SSM product to produce continuous SSM estimations at 1-km resolution over the Tibetan Plateau. In comparison with ground-based observations, the downscaled estimations showed correlation (R ≥ 0.7) for both ascending and descending overpasses. The analysis indicated the high potential of the proposed approach for producing a temporally continuous SSM product at fine spatial resolution.

Back to the future: using historical climate variation to project near-term shifts in habitat suitable for coast redwood.

PubMed

Fernández, Miguel; Hamilton, Healy H; Kueppers, Lara M

2015-11-01

Studies that model the effect of climate change on terrestrial ecosystems often use climate projections from downscaled global climate models (GCMs). These simulations are generally too coarse to capture patterns of fine-scale climate variation, such as the sharp coastal energy and moisture gradients associated with wind-driven upwelling of cold water. Coastal upwelling may limit future increases in coastal temperatures, compromising GCMs' ability to provide realistic scenarios of future climate in these coastal ecosystems. Taking advantage of naturally occurring variability in the high-resolution historic climatic record, we developed multiple fine-scale scenarios of California climate that maintain coherent relationships between regional climate and coastal upwelling. We compared these scenarios against coarse resolution GCM projections at a regional scale to evaluate their temporal equivalency. We used these historically based scenarios to estimate potential suitable habitat for coast redwood (Sequoia sempervirens D. Don) under 'normal' combinations of temperature and precipitation, and under anomalous combinations representative of potential future climates. We found that a scenario of warmer temperature with historically normal precipitation is equivalent to climate projected by GCMs for California by 2020-2030 and that under these conditions, climatically suitable habitat for coast redwood significantly contracts at the southern end of its current range. Our results suggest that historical climate data provide a high-resolution alternative to downscaled GCM outputs for near-term ecological forecasts. This method may be particularly useful in other regions where local climate is strongly influenced by ocean-atmosphere dynamics that are not represented by coarse-scale GCMs. © 2015 John Wiley & Sons Ltd.

Estimation of Geotropic Currents in the Bay of Bengal using In-situ Observations.

NASA Astrophysics Data System (ADS)

T, V. R.

2014-12-01

Geostraphic Currents (GCs) can be estimated from temperature and salinity observations. In this study an attempt has been made to compute GC using temperature and salinity observations from Expendable Bathy Thermograph (XBT) and CTD over Bay of Bengal (BoB). Although in recent time we have Argo observations but it is for a limited period and coarse temporal resolutions. In BoB Bengal, where not enough simultaneous hydrographic temperature and salinity data are available with reasonable spatial resolution (~one degree spatial resolution) and for a longer period. To overcome the limitations of GC computed from XBT profiles, temperature-salinity relationships were used from simultaneous temperature and salinity observations. We have demonstrated that GCs can be computed with an accuracy of less than 8.5 cm/s (root mean square error) at the surface with respect to temperature from XBT and salinity from climatological record. This error reduces with increasing depth. Finally, we demonstrated the application of this approach to study the temporal variation of the GCs during 1992 to 2012 along an XBT transect.

Downscaling of land surface temperatures from SEVIRI

NASA Astrophysics Data System (ADS)

Bechtel, B.; Zaksek, K.

2013-12-01

Land surface temperature (LST) determines the radiance emitted by the surface and hence is an important boundary condition of the energy balance. In urban areas, detailed knowledge about the diurnal cycle in LST can contribute to understand the urban heat island (UHI). Although the increased surface temperatures compared to the surrounding rural areas (surface urban heat island, SUHI) have been measured by satellites and analysed for several decades, an operational SUHI monitoring is still not available due to the lack of sensors with appropriate spatiotemporal resolution. While sensors on polar orbiting satellites are still restricted to approx. 100 m spatial resolution and coarse temporal coverage (about 1-2 weeks), sensors on geostationary platforms have high temporal (several times per hour) and poor spatial resolution (>3 km). Further, all polar orbiting satellites have a similar equator crossing time and hence the SUHI can at best be observed at two times a day. A downscaling DS scheme for LST from the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) sensor onboard the geostationary meteorological Meteosat 8 to spatial resolutions between 100 and 1000 m was developed and tested for Hamburg. Various data were tested as predictors, including multispectral data and derived indices, morphological parameters from interferometric SAR and multitemporal thermal data. All predictors were upscaled to the coarse resolution approximating the point spread function of SEVIRI. Then empirical relationships between the predictors and LST were derived which are then transferred to the high resolution domain, assuming they are scale invariant. For validation LST data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Enhanced Thematic Mapper Plus (ETM+) for two dates were used. Aggregated parameters from multi-temporal thermal data (in particular annual cycle parameters and principal components) proved particularly suitable. The results for the highest resolution of 100 m showed a high explained variance (R^2 = 0.71) and relatively low root mean square errors (RMSE = 2.2 K) for the ASTER scene and slightly higher errors (R^2 = 0.73, RMSE = 2.53) for the ETM+ scene. A considerable percentage of the error was systematic due to the different viewing geometry of the sensors (the high resolution LST was overestimated about 1.3 K for ASTER and 0.66 K for ETM+). This shows that DS of SEVIRI LST is possible up to a resolution of 100 m for urban areas and that multitemporal thermal data are particularly suitable as predictors. Further, the scheme was used to produce an entire diurnal cycle in high resolution. While essential characteristics of the diurnal cycle were well reproduced, certain artefacts resulting from the multitemporal predictors from different seasons (like phenology and different water surface temperatures) were generated. Eventually, the bias and its dependence on the viewing geometry and topography are currently investigated.

Vertical accretion sand proxies of gaged floods along the upper Little Tennessee River, Blue Ridge Mountains, USA

NASA Astrophysics Data System (ADS)

Leigh, David S.

2018-02-01

Understanding environmental hazards presented by river flooding has been enhanced by paleoflood analysis, which uses sedimentary records to document floods beyond historical records. Bottomland overbank deposits (e.g., natural levees, floodbasins, meander scars, low terraces) have the potential as continuous paleoflood archives of flood frequency and magnitude, but they have been under-utilized because of uncertainty about their ability to derive flood magnitude estimates. The purpose of this paper is to provide a case study that illuminates tremendous potential of bottomland overbank sediments as reliable proxies of both flood frequency and magnitude. Methods involve correlation of particle-size measurements of the coarse tail of overbank deposits (> 0.25 mm sand) from three separate sites with historical flood discharge records for the upper Little Tennessee River in the Blue Ridge Mountains of the southeastern United States. Results show that essentially all floods larger than a 20% probability event can be detected by the coarse tail of particle-size distributions, especially if the temporal resolution of sampling is annual or sub-annual. Coarser temporal resolution (1.0 to 2.5 year sample intervals) provides an adequate record of large floods, but is unable to discriminate individual floods separated by only one to three years. Measurements of > 0.25 mm sand that are normalized against a smoothed trend line through the down-column data produce highly significant correlations (R2 values of 0.50 to 0.60 with p-values of 0.004 to < 0.001) between sand peak values and flood peak discharges, indicating that flood magnitude can be reliably estimated. In summary, bottomland overbank deposits can provide excellent continuous records of paleofloods when the following conditions are met: 1) Stable depositional sites should be chosen; 2) Analysis should concentrate on the coarse tails of particle-size distributions; 3) Sampling of sediment intervals should achieve annual or better resolution; 4) Time-series data of particle-size should be detrended to minimize variation from dynamic aspects of fluvial sedimentation that are not related to flood magnitude; and 5) Multiple sites should be chosen to allow for replication of findings.

Downscaling of Seasonal Landsat-8 and MODIS Land Surface Temperature (LST) in Kolkata, India

NASA Astrophysics Data System (ADS)

Garg, R. D.; Guha, S.; Mondal, A.; Lakshmi, V.; Kundu, S.

2017-12-01

The quality of life of urban people is affected by urban heat environment. The urban heat studies can be carried out using remotely sensed thermal infrared imagery for retrieving Land Surface Temperature (LST). Currently, high spatial resolution (<200 m) thermal images are limited and their temporal resolution is low (e.g., 17 days of Landsat-8). Coarse spatial resolution (1000 m) and high temporal resolution (daily) thermal images of MODIS (Moderate Resolution Imaging Spectroradiometer) are frequently available. The present study is to downscale spatially coarser resolution of the thermal image to fine resolution thermal image using regression based downscaling technique. This method is based on the relationship between (LST) and vegetation indices (e.g., Normalized Difference Vegetation Index or NDVI) over a heterogeneous landscape. The Kolkata metropolitan city, which experiences a tropical wet-and-dry type of climate has been selected for the study. This study applied different seasonal open source satellite images viz., Landsat-8 and Terra MODIS. The Landsat-8 images are aggregated at 960 m resolution and downscaled into 480, 240 120 and 60 m. Optical and thermal resolution of Landsat-8 and MODIS are 30 m and 60 m; 250 m and 1000 m respectively. The homogeneous land cover areas have shown better accuracy than heterogeneous land cover areas. The downscaling method plays a crucial role while the spatial resolution of thermal band renders it unable for advanced study. Key words: Land Surface Temperature (LST), Downscale, MODIS, Landsat, Kolkata

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.

Tracking four-decade inundation changes with multi-temporal satellite images in China's largest freshwater lake

NASA Astrophysics Data System (ADS)

Wu, Guiping

2017-04-01

Poyang Lake is the largest freshwater lake in China. The lake has undergone remarkable spatio-temporal changes in both short- and long-term scales since 1970s, resulting in significant hydrological, ecological and economic consequences. Remote sensing techniques have advantages for large-scale studies, by offering images at different spatial and spectral resolutions. However, due to technical difficulties, no single satellite sensor can meet the needs for high spatio-temporal resolution required for such monitoring. In this study, using Landsat Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) images collected between 1973 and 2012, we documented and investigated the short- and long-term characteristics of lake inundation based on Normalized Difference Water Index (NDWI). First, we presented a novel downscaling method based on the NDWI statistical regression algorithm to generate small-scale resolution inundation map (30m) from coarse MODIS data (500m). The downscaling is a linear calibration of the NDWI index from MODIS imagery to Landsat imagery, which is based on the assumption that the relationships between fine resolution and coarse resolution are invariable. Second, Tupu analysis method was further performed to explore the spatial-temporal distribution and changing processes of lake inundation based on downscaling inundation maps. Then, a defined water variation rate (WVR) and inundation frequency (IF) indicator was used to reveal seasonal water surface submersion/exposure processes of lake expansion and shrinkage in different zones. Finally, mathematical statistics methods were utilized to explore the possible driving mechanisms of the revealed change patterns with meteorological data and hydrological data. The results show that, there is a high correlation (mean absolute error of 3.95% and an R2 of 0.97) between the MODIS- and Landsat-derived water surface areas in Poyang Lake. Over the past 40 years, a declining trend to a certain extent for the Poyang Lake's area could be detected. The lake surface displayed comparatively low values ( 2000 km2) in wet periods of 1980, 2006, 2009 and 2011, corresponding to severe hydrological droughts in the lake. In addition, the water surface variation in Poyang Lake had a typical seasonal behavior. It mostly followed a unimodal cycle with area peaks appeared in the wet season. The earliest beginning of the inundation cycle was emerged in 2000 and the latest in 2006. In general, the change of lake area is a synthetic result of climate change, land-cover change and construction of dykes. Our findings should be valuable to a comprehensive understanding of Poyang Lake's decadal and seasonal variation, which is critical for flood/drought prevention, land use planning and lake ecological conservation.

Spatial consistency of chinook salmon redd distribution within and among years in the Cowlitz River, Washington

USGS Publications Warehouse

Klett, Katherine J.C.; Torgersen, Christian E.; Henning, Julie A.; Murray, Christopher J.

2013-01-01

We investigated the spawning patterns of Chinook Salmon Oncorhynchus tshawytscha on the lower Cowlitz River, Washington, using a unique set of fine- and coarse-scale temporal and spatial data collected during biweekly aerial surveys conducted in 1991–2009 (500 m to 28 km resolution) and 2008–2009 (100–500 m resolution). Redd locations were mapped from a helicopter during 2008 and 2009 with a hand-held GPS synchronized with in-flight audio recordings. We examined spatial patterns of Chinook Salmon redd reoccupation among and within years in relation to segment-scale geomorphic features. Chinook Salmon spawned in the same sections each year with little variation among years. On a coarse scale, 5 years (1993, 1998, 2000, 2002, and 2009) were compared for reoccupation. Redd locations were highly correlated among years. Comparisons on a fine scale (500 m) between 2008 and 2009 also revealed a high degree of consistency among redd locations. On a finer temporal scale, we observed that Chinook Salmon spawned in the same sections during the first and last week. Redds were clustered in both 2008 and 2009. Regression analysis with a generalized linear model at the 500-m scale indicated that river kilometer and channel bifurcation were positively associated with redd density, whereas sinuosity was negatively associated with redd density. Collecting data on specific redd locations with a GPS during aerial surveys was logistically feasible and cost effective and greatly enhanced the spatial precision of Chinook Salmon spawning surveys.

A generalized formulation for downscaling data based on Fourier Transform and inversion: Mathematical rationale and application to the Max-Planck-Institute aerosol climatology data

NASA Astrophysics Data System (ADS)

Zhang, Taiping; Stackhouse, Paul W.; Gupta, Shashi K.; Cox, Stephen J.; Mikovitz, J. Colleen

2017-02-01

Occasionally, a need arises to downscale a time series of data from a coarse temporal resolution to a finer one, a typical example being from monthly means to daily means. For this case, daily means derived as such are used as inputs of climatic or atmospheric models so that the model results may exhibit variance on the daily time scale and retain the monthly mean of the original data set without an abrupt change from the end of one month to the beginning of the next. Different methods have been developed which often need assumptions, free parameters and the solution of simultaneous equations. Here we derive a generalized formulation by means of Fourier transform and inversion so that it can be used to directly compute daily means from a series of an arbitrary number of monthly means. The formulation can be used to transform any coarse temporal resolution to a finer one. From the derived results, the original data can be recovered almost identically. As a real application, we use this method to derive the daily counterpart of the MAC-v1 aerosol climatology that provides monthly mean aerosol properties for 18 shortwave bands and 12 longwave bands for the years from 1860 to 2100. The derived daily means are to be used as inputs of the shortwave and longwave algorithms of the NASA GEWEX SRB project.

Lagrangian Timescales of Southern Ocean Upwelling in a Hierarchy of Model Resolutions

NASA Astrophysics Data System (ADS)

Drake, Henri F.; Morrison, Adele K.; Griffies, Stephen M.; Sarmiento, Jorge L.; Weijer, Wilbert; Gray, Alison R.

2018-01-01

In this paper we study upwelling pathways and timescales of Circumpolar Deep Water (CDW) in a hierarchy of models using a Lagrangian particle tracking method. Lagrangian timescales of CDW upwelling decrease from 87 years to 31 years to 17 years as the ocean resolution is refined from 1° to 0.25° to 0.1°. We attribute some of the differences in timescale to the strength of the eddy fields, as demonstrated by temporally degrading high-resolution model velocity fields. Consistent with the timescale dependence, we find that an average Lagrangian particle completes 3.2 circumpolar loops in the 1° model in comparison to 0.9 loops in the 0.1° model. These differences suggest that advective timescales and thus interbasin merging of upwelling CDW may be overestimated by coarse-resolution models, potentially affecting the skill of centennial scale climate change projections.

Estimating Soil Moisture at High Spatial Resolution with Three Radiometric Satellite Products: A Study from a South-Eastern Australian Catchment

NASA Astrophysics Data System (ADS)

Senanayake, I. P.; Yeo, I. Y.; Tangdamrongsub, N.; Willgoose, G. R.; Hancock, G. R.; Wells, T.; Fang, B.; Lakshmi, V.

2017-12-01

Long-term soil moisture datasets at high spatial resolution are important in agricultural, hydrological, and climatic applications. The soil moisture estimates can be achieved using satellite remote sensing observations. However, the satellite soil moisture data are typically available at coarse spatial resolutions ( several tens of km), therefore require further downscaling. Different satellite soil moisture products have to be conjointly employed in developing a consistent time-series of high resolution soil moisture, while the discrepancies amongst different satellite retrievals need to be resolved. This study aims to downscale three different satellite soil moisture products, the Soil Moisture and Ocean Salinity (SMOS, 25 km), the Soil Moisture Active Passive (SMAP, 36 km) and the SMAP-Enhanced (9 km), and to conduct an inter-comparison of the downscaled results. The downscaling approach is developed based on the relationship between the diurnal temperature difference and the daily mean soil moisture content. The approach is applied to two sub-catchments (Krui and Merriwa River) of the Goulburn River catchment in the Upper Hunter region (NSW, Australia) to estimate soil moisture at 1 km resolution for 2015. The three coarse spatial resolution soil moisture products and their downscaled results will be validated with the in-situ observations obtained from the Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS) network. The spatial and temporal patterns of the downscaled results will also be analysed. This study will provide the necessary insights for data selection and bias corrections to maintain the consistency of a long-term high resolution soil moisture dataset. The results will assist in developing a time-series of high resolution soil moisture data over the south-eastern Australia.

Errors and improvements in the use of archived meteorological data for chemical transport modeling: an analysis using GEOS-Chem v11-01 driven by GEOS-5 meteorology

NASA Astrophysics Data System (ADS)

Yu, Karen; Keller, Christoph A.; Jacob, Daniel J.; Molod, Andrea M.; Eastham, Sebastian D.; Long, Michael S.

2018-01-01

Global simulations of atmospheric chemistry are commonly conducted with off-line chemical transport models (CTMs) driven by archived meteorological data from general circulation models (GCMs). The off-line approach has the advantages of simplicity and expediency, but it incurs errors due to temporal averaging in the meteorological archive and the inability to reproduce the GCM transport algorithms exactly. The CTM simulation is also often conducted at coarser grid resolution than the parent GCM. Here we investigate this cascade of CTM errors by using 222Rn-210Pb-7Be chemical tracer simulations off-line in the GEOS-Chem CTM at rectilinear 0.25° × 0.3125° (≈ 25 km) and 2° × 2.5° (≈ 200 km) resolutions and online in the parent GEOS-5 GCM at cubed-sphere c360 (≈ 25 km) and c48 (≈ 200 km) horizontal resolutions. The c360 GEOS-5 GCM meteorological archive, updated every 3 h and remapped to 0.25° × 0.3125°, is the standard operational product generated by the NASA Global Modeling and Assimilation Office (GMAO) and used as input by GEOS-Chem. We find that the GEOS-Chem 222Rn simulation at native 0.25° × 0.3125° resolution is affected by vertical transport errors of up to 20 % relative to the GEOS-5 c360 online simulation, in part due to loss of transient organized vertical motions in the GCM (resolved convection) that are temporally averaged out in the 3 h meteorological archive. There is also significant error caused by operational remapping of the meteorological archive from a cubed-sphere to a rectilinear grid. Decreasing the GEOS-Chem resolution from 0.25° × 0.3125° to 2° × 2.5° induces further weakening of vertical transport as transient vertical motions are averaged out spatially and temporally. The resulting 222Rn concentrations simulated by the coarse-resolution GEOS-Chem are overestimated by up to 40 % in surface air relative to the online c360 simulations and underestimated by up to 40 % in the upper troposphere, while the tropospheric lifetimes of 210Pb and 7Be against aerosol deposition are affected by 5-10 %. The lost vertical transport in the coarse-resolution GEOS-Chem simulation can be partly restored by recomputing the convective mass fluxes at the appropriate resolution to replace the archived convective mass fluxes and by correcting for bias in the spatial averaging of boundary layer mixing depths.

Synergistic Use of SMOS Measurements with SMAP Derived and In-situ Data over Valencia Anchor Station by Using Downscaling Technique

NASA Astrophysics Data System (ADS)

Ansari Amoli, Abdolreza; Lopez-Baeza, Ernesto; Mahmoudi, Ali; Mahmoodi, Ali

2016-07-01

Synergistic Use of SMOS Measurements with SMAP Derived and In-situ Data over the Valencia Anchor Station by Using a Downscaling Technique Ansari Amoli, A.(1),Mahmoodi, A.(2) and Lopez-Baeza, E.(3) (1) Department of Earth Physics and Thermodynamics, University of Valencia, Spain (2) Centre d'Etudes Spatiales de la BIOsphère (CESBIO), France (3) Department of Earth Physics and Thermodynamics, University of Valencia, Spain Soil moisture products from active sensors are not operationally available. Passive remote sensors return more accurate estimates, but their resolution is much coarser. One solution to overcome this problem is the synergy between radar and radiometric data by using disaggregation (downscaling) techniques. Few studies have been conducted to merge high resolution radar and coarse resolution radiometer measurements in order to obtain an intermediate resolution product. In this paper we present an algorithm using combined available SMAP (Soil Moisture Active and Passive) radar and SMOS (Soil Moisture and Ocean Salinity) radiometer measurements to estimate surface soil moisture over the Valencia Anchor Station (VAS), Valencia, Spain. The goal is to combine the respective attributes of the radar and radiometer observations to estimate soil moisture at a resolution of 3 km. The algorithm disaggregates the coarse resolution SMOS (15 km) radiometer brightness temperature product based on the spatial variation of the high resolution SMAP (3 km) radar backscatter. The disaggregation of the radiometer brightness temperature uses the radar backscatter spatial patterns within the radiometer footprint that are inferred from the radar measurements. For this reason the radar measurements within the radiometer footprint are scaled by parameters that are derived from the temporal fluctuations in the radar and radiometer measurements.

A New Approach in Downscaling Microwave Soil Moisture Product using Machine Learning

NASA Astrophysics Data System (ADS)

Abbaszadeh, Peyman; Yan, Hongxiang; Moradkhani, Hamid

2016-04-01

Understating the soil moisture pattern has significant impact on flood modeling, drought monitoring, and irrigation management. Although satellite retrievals can provide an unprecedented spatial and temporal resolution of soil moisture at a global-scale, their soil moisture products (with a spatial resolution of 25-50 km) are inadequate for regional study, where a resolution of 1-10 km is needed. In this study, a downscaling approach using Genetic Programming (GP), a specialized version of Genetic Algorithm (GA), is proposed to improve the spatial resolution of satellite soil moisture products. The GP approach was applied over a test watershed in United States using the coarse resolution satellite data (25 km) from Advanced Microwave Scanning Radiometer - EOS (AMSR-E) soil moisture products, the fine resolution data (1 km) from Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index, and ground based data including land surface temperature, vegetation and other potential physical variables. The results indicated the great potential of this approach to derive the fine resolution soil moisture information applicable for data assimilation and other regional studies.

Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale

PubMed Central

Skakun, Sergii; Vermote, Eric; Roger, Jean-Claude; Franch, Belen

2018-01-01

Timely and accurate information on crop yield is critical to many applications within agriculture monitoring. Thanks to its coverage and temporal resolution, coarse spatial resolution satellite imagery has always been a source of valuable information for yield forecasting and assessment at national and regional scales. With availability of free images acquired by Landsat-8 and Sentinel-2 remote sensing satellites, it becomes possible to enable temporal resolution of an image every 3–5 days, and therefore, to develop next generation agriculture products at higher spatial resolution (30 m). This paper explores the combined use of Landsat-8 and Sentinel-2A for winter crop mapping and winter wheat assessment at regional scale. For the former, we adapt a previously developed approach for Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m resolution that allows automatic mapping of winter crops taking into account knowledge on crop calendar and without ground truth data. For the latter, we use a generalized winter wheat yield model that is based on NDVI-peak estimation and MODIS data, and further downscaled to be applicable at 30 m resolution. We show that integration of Landsat-8 and Sentinel-2A has a positive impact both for winter crop mapping and winter wheat yield assessment. In particular, the error of winter wheat yield estimates can be reduced up to 1.8 times comparing to the single satellite usage. PMID:29888751

Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale.

PubMed

Skakun, Sergii; Vermote, Eric; Roger, Jean-Claude; Franch, Belen

2017-01-01

Timely and accurate information on crop yield is critical to many applications within agriculture monitoring. Thanks to its coverage and temporal resolution, coarse spatial resolution satellite imagery has always been a source of valuable information for yield forecasting and assessment at national and regional scales. With availability of free images acquired by Landsat-8 and Sentinel-2 remote sensing satellites, it becomes possible to enable temporal resolution of an image every 3-5 days, and therefore, to develop next generation agriculture products at higher spatial resolution (30 m). This paper explores the combined use of Landsat-8 and Sentinel-2A for winter crop mapping and winter wheat assessment at regional scale. For the former, we adapt a previously developed approach for Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m resolution that allows automatic mapping of winter crops taking into account knowledge on crop calendar and without ground truth data. For the latter, we use a generalized winter wheat yield model that is based on NDVI-peak estimation and MODIS data, and further downscaled to be applicable at 30 m resolution. We show that integration of Landsat-8 and Sentinel-2A has a positive impact both for winter crop mapping and winter wheat yield assessment. In particular, the error of winter wheat yield estimates can be reduced up to 1.8 times comparing to the single satellite usage.

Combined Use of Landsat-8 and Sentinel-2A Images for Winter Crop Mapping and Winter Wheat Yield Assessment at Regional Scale

NASA Technical Reports Server (NTRS)

Skakun, Sergii; Vermote, Eric; Roger, Jean-Claude; Franch, Belen

2017-01-01

Timely and accurate information on crop yield and production is critical to many applications within agriculture monitoring. Thanks to its coverage and temporal resolution, coarse spatial resolution satellite imagery has always been a source of valuable information for yield forecasting and assessment at national and regional scales. With availability of free images acquired by Landsat-8 and Sentinel-2 remote sensing satellites, it becomes possible to provide temporal resolution of an image every 3-5 days, and therefore, to develop next generation agriculture products at higher spatial resolution (10-30 m). This paper explores the combined use of Landsat-8 and Sentinel-2A for winter crop mapping and winter wheat yield assessment at regional scale. For the former, we adapt a previously developed approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument at 250 m resolution that allows automatic mapping of winter crops taking into account a priori knowledge on crop calendar. For the latter, we use a generalized winter wheat yield forecasting model that is based on estimation of the peak Normalized Difference Vegetation Index (NDVI) from MODIS image time-series, and further downscaled to be applicable at 30 m resolution. We show that integration of Landsat-8 and Sentinel-2A improves both winter crop mapping and winter wheat yield assessment. In particular, the error of winter wheat yield estimates can be reduced up to 1.8 times compared to using a single satellite.

A Simple Downscaling Algorithm for Remotely Sensed Land Surface Temperature

NASA Astrophysics Data System (ADS)

Sandholt, I.; Nielsen, C.; Stisen, S.

2009-05-01

The method is illustrated using a combination of MODIS NDVI data with a spatial resolution of 250m and 3 Km Meteosat Second Generation SEVIRI LST data. Geostationary Earth Observation data carry a large potential for assessment of surface state variables. Not the least the European Meteosat Second Generation platform with its SEVIRI sensor is well suited for studies of the dynamics of land surfaces due to its high temporal frequency (15 minutes) and its red, Near Infrared (NIR) channels that provides vegetation indices, and its two split window channels in the thermal infrared for assessment of Land Surface Temperature (LST). For some applications the spatial resolution in geostationary data is too coarse. Due to the low statial resolution of 4.8 km at nadir for the SEVIRI sensor, a means of providing sub pixel information is sought for. By combining and properly scaling two types of satellite images, namely data from the MODIS sensor onboard the polar orbiting platforms TERRA and AQUA and the coarse resolution MSG-SEVIRI, we exploit the best from two worlds. The vegetation index/surface temperature space has been used in a vast number of studies for assessment of air temperature, soil moisture, dryness indices, evapotranspiration and for studies of land use change. In this paper, we present an improved method to derive a finer resolution Land Surface Temperature (LST). A new, deterministic scaling method has been applied, and is compared to existing deterministic downscaling methods based on LST and NDVI. We also compare our results from in situ measurements of LST from the Dahra test site in West Africa.

Mapping Rice Cropping Patterns Using Multi-temporal Sentinel-1A Data

NASA Astrophysics Data System (ADS)

Nguyen, S. T.; Chen, C. F.; Chen, C. R.; Chiang, S. H.; Khin, L. V.

2016-12-01

Rice is the world's third largest crop behind maize and wheat, providing food for more than half of the world's population. Rice agriculture has been a key driver of socioeconomic development in Vietnam as it provides food for more than 90 million people and is considered as a main source of income for the majority of rural populations. Vietnam has approximately 7.5 million ha, annually producing roughly 39 million tons of grain rice making this nation become one of the largest rice suppliers on earth with approximately 7.4 million tons of grain rice exported annually. Thus, monitoring rice-growing areas to meet people's food needs while safeguarding the environment is important to developing strategies for national food security and rice grain exports. Previous studies of rice crop monitoring are often carried using coarse resolution optical satellite data such as MODIS data. Because rice fields in Vietnam are generally small and fragmental, the use of coarse resolution optical satellite data reveals disadvantages due to mixed-pixel issues and data contamination caused by cloud cover. The Sentinel-1A satellite launched on 3 April 2014 provides opportunities to collectively map small patches of rice fields at different scales owing to its high spatial resolution of 10 m and temporal resolution of 12 days. The main objective of this study is to develop an approach to map rice-cropping systems in An Giang and Dong Thap provinces, South Vietnam using multi-temporal Sentinel-1A VH data. We processed the data following four main steps: (1) data pre-processing, (2) constructing smooth time-series VH backscatter data, (3) rice crop classification using the support vector machines (SVM), and (4) accuracy assessment. The mapping results validated with the ground the ground reference data indicated that the overall accuracy and Kappa coefficient were 83.4% and 0.7, respectively. The mapping results also compared with the government's rice area statistics at the district level reaffirmed the consistency between these two datasets with the correlation coefficient (R2) of 0.93 and the relative error in area of 2.2%. This study demonstrates the potential application of time-series Sentinel-1A data for rice crop mapping and the methods are thus proposed for large-scale rice crop monitoring in the country.

Continuous data assimilation for downscaling large-footprint soil moisture retrievals

NASA Astrophysics Data System (ADS)

Altaf, Muhammad U.; Jana, Raghavendra B.; Hoteit, Ibrahim; McCabe, Matthew F.

2016-10-01

Soil moisture is a key component of the hydrologic cycle, influencing processes leading to runoff generation, infiltration and groundwater recharge, evaporation and transpiration. Generally, the measurement scale for soil moisture is found to be different from the modeling scales for these processes. Reducing this mismatch between observation and model scales in necessary for improved hydrological modeling. An innovative approach to downscaling coarse resolution soil moisture data by combining continuous data assimilation and physically based modeling is presented. In this approach, we exploit the features of Continuous Data Assimilation (CDA) which was initially designed for general dissipative dynamical systems and later tested numerically on the incompressible Navier-Stokes equation, and the Benard equation. A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model's large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (HYDRUS) are subjected to data assimilation conditioned upon coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. Results show that the approach is feasible to generate fine scale soil moisture fields across large extents, based on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometerbased, coarse resolution products from remote sensing satellites.

Enhanced Deforestation Mapping in North Korea using Spatial-temporal Image Fusion Method and Phenology-based Index

NASA Astrophysics Data System (ADS)

Jin, Y.; Lee, D.

2017-12-01

North Korea (the Democratic People's Republic of Korea, DPRK) is known to have some of the most degraded forest in the world. The characteristics of forest landscape in North Korea is complex and heterogeneous, the major vegetation cover types in the forest are hillside farm, unstocked forest, natural forest, and plateau vegetation. Better classification of types in high spatial resolution of deforested areas could provide essential information for decisions about forest management priorities and restoration of deforested areas. For mapping heterogeneous vegetation covers, the phenology-based indices are helpful to overcome the reflectance value confusion that occurs when using one season images. Coarse spatial resolution images may be acquired with a high repetition rate and it is useful for analyzing phenology characteristics, but may not capture the spatial detail of the land cover mosaic of the region of interest. Previous spatial-temporal fusion methods were only capture the temporal change, or focused on both temporal change and spatial change but with low accuracy in heterogeneous landscapes and small patches. In this study, a new concept for spatial-temporal image fusion method focus on heterogeneous landscape was proposed to produce fine resolution images at both fine spatial and temporal resolution. We classified the three types of pixels between the base image and target image, the first type is only reflectance changed caused by phenology, this type of pixels supply the reflectance, shape and texture information; the second type is both reflectance and spectrum changed in some bands caused by phenology like rice paddy or farmland, this type of pixels only supply shape and texture information; the third type is reflectance and spectrum changed caused by land cover type change, this type of pixels don't provide any information because we can't know how land cover changed in target image; and each type of pixels were applied different prediction methods. Results show that both STARFM and FSDAF predicted in low accuracy in second type pixels and small patches. Classification results used spatial-temporal image fusion method proposed in this study showed overall classification accuracy of 89.38%, with corresponding kappa coefficients of 0.87.

Highly Coarse-Grained Representations of Transmembrane Proteins

PubMed Central

2017-01-01

Numerous biomolecules and biomolecular complexes, including transmembrane proteins (TMPs), are symmetric or at least have approximate symmetries. Highly coarse-grained models of such biomolecules, aiming at capturing the essential structural and dynamical properties on resolution levels coarser than the residue scale, must preserve the underlying symmetry. However, making these models obey the correct physics is in general not straightforward, especially at the highly coarse-grained resolution where multiple (∼3–30 in the current study) amino acid residues are represented by a single coarse-grained site. In this paper, we propose a simple and fast method of coarse-graining TMPs obeying this condition. The procedure involves partitioning transmembrane domains into contiguous segments of equal length along the primary sequence. For the coarsest (lowest-resolution) mappings, it turns out to be most important to satisfy the symmetry in a coarse-grained model. As the resolution is increased to capture more detail, however, it becomes gradually more important to match modular repeats in the secondary structure (such as helix-loop repeats) instead. A set of eight TMPs of various complexity, functionality, structural topology, and internal symmetry, representing different classes of TMPs (ion channels, transporters, receptors, adhesion, and invasion proteins), has been examined. The present approach can be generalized to other systems possessing exact or approximate symmetry, allowing for reliable and fast creation of multiscale, highly coarse-grained mappings of large biomolecular assemblies. PMID:28043122

Mesosacle eddies in a high resolution OGCM and coupled ocean-atmosphere GCM

NASA Astrophysics Data System (ADS)

Yu, Y.; Liu, H.; Lin, P.

2017-12-01

The present study described high-resolution climate modeling efforts including oceanic, atmospheric and coupled general circulation model (GCM) at the state key laboratory of numerical modeling for atmospheric sciences and geophysical fluid dynamics (LASG), Institute of Atmospheric Physics (IAP). The high-resolution OGCM is established based on the latest version of the LASG/IAP Climate system Ocean Model (LICOM2.1), but its horizontal resolution and vertical resolution are increased to 1/10° and 55 layers, respectively. Forced by the surface fluxes from the reanalysis and observed data, the model has been integrated for approximately more than 80 model years. Compared with the simulation of the coarse-resolution OGCM, the eddy-resolving OGCM not only better simulates the spatial-temporal features of mesoscale eddies and the paths and positions of western boundary currents but also reproduces the large meander of the Kuroshio Current and its interannual variability. Another aspect, namely, the complex structures of equatorial Pacific currents and currents in the coastal ocean of China, are better captured due to the increased horizontal and vertical resolution. Then we coupled the high resolution OGCM to NCAR CAM4 with 25km resolution, in which the mesoscale air-sea interaction processes are better captured.

Downscaling soil moisture over regions that include multiple coarse-resolution grid cells

USDA-ARS?s Scientific Manuscript database

Many applications require soil moisture estimates over large spatial extents (30-300 km) and at fine-resolutions (10-30 m). Remote-sensing methods can provide soil moisture estimates over very large spatial extents (continental to global) at coarse resolutions (10-40 km), but their output must be d...

Source identification of coarse particles in the Desert Southwest, USA using Positive Matrix Factorization

EPA Science Inventory

The Desert Southwest Coarse Particulate Matter Study was undertaken to further our understanding of the spatial and temporal variability and sources of fine and coarse particulate matter (PM) in rural, arid, desert environments. Sampling was conducted between February 2009 and Fe...

Development of Spatiotemporal Bias-Correction Techniques for Downscaling GCM Predictions

NASA Astrophysics Data System (ADS)

Hwang, S.; Graham, W. D.; Geurink, J.; Adams, A.; Martinez, C. J.

2010-12-01

Accurately representing the spatial variability of precipitation is an important factor for predicting watershed response to climatic forcing, particularly in small, low-relief watersheds affected by convective storm systems. Although Global Circulation Models (GCMs) generally preserve spatial relationships between large-scale and local-scale mean precipitation trends, most GCM downscaling techniques focus on preserving only observed temporal variability on point by point basis, not spatial patterns of events. Downscaled GCM results (e.g., CMIP3 ensembles) have been widely used to predict hydrologic implications of climate variability and climate change in large snow-dominated river basins in the western United States (Diffenbaugh et al., 2008; Adam et al., 2009). However fewer applications to smaller rain-driven river basins in the southeastern US (where preserving spatial variability of rainfall patterns may be more important) have been reported. In this study a new method was developed to bias-correct GCMs to preserve both the long term temporal mean and variance of the precipitation data, and the spatial structure of daily precipitation fields. Forty-year retrospective simulations (1960-1999) from 16 GCMs were collected (IPCC, 2007; WCRP CMIP3 multi-model database: https://esg.llnl.gov:8443/), and the daily precipitation data at coarse resolution (i.e., 280km) were interpolated to 12km spatial resolution and bias corrected using gridded observations over the state of Florida (Maurer et al., 2002; Wood et al, 2002; Wood et al, 2004). In this method spatial random fields which preserved the observed spatial correlation structure of the historic gridded observations and the spatial mean corresponding to the coarse scale GCM daily rainfall were generated. The spatiotemporal variability of the spatio-temporally bias-corrected GCMs were evaluated against gridded observations, and compared to the original temporally bias-corrected and downscaled CMIP3 data for the central Florida. The hydrologic response of two southwest Florida watersheds to the gridded observation data, the original bias corrected CMIP3 data, and the new spatiotemporally corrected CMIP3 predictions was compared using an integrated surface-subsurface hydrologic model developed by Tampa Bay Water.

High-Resolution Coarse-Grained Modeling Using Oriented Coarse-Grained Sites.

PubMed

Haxton, Thomas K

2015-03-10

We introduce a method to bring nearly atomistic resolution to coarse-grained models, and we apply the method to proteins. Using a small number of coarse-grained sites (about one per eight atoms) but assigning an independent three-dimensional orientation to each site, we preferentially integrate out stiff degrees of freedom (bond lengths and angles, as well as dihedral angles in rings) that are accurately approximated by their average values, while retaining soft degrees of freedom (unconstrained dihedral angles) mostly responsible for conformational variability. We demonstrate that our scheme retains nearly atomistic resolution by mapping all experimental protein configurations in the Protein Data Bank onto coarse-grained configurations and then analytically backmapping those configurations back to all-atom configurations. This roundtrip mapping throws away all information associated with the eliminated (stiff) degrees of freedom except for their average values, which we use to construct optimal backmapping functions. Despite the 4:1 reduction in the number of degrees of freedom, we find that heavy atoms move only 0.051 Å on average during the roundtrip mapping, while hydrogens move 0.179 Å on average, an unprecedented combination of efficiency and accuracy among coarse-grained protein models. We discuss the advantages of such a high-resolution model for parametrizing effective interactions and accurately calculating observables through direct or multiscale simulations.

Fine-temporal forecasting of outbreak probability and severity: Ross River virus in Western Australia.

PubMed

Koolhof, I S; Bettiol, S; Carver, S

2017-10-01

Health warnings of mosquito-borne disease risk require forecasts that are accurate at fine-temporal resolutions (weekly scales); however, most forecasting is coarse (monthly). We use environmental and Ross River virus (RRV) surveillance to predict weekly outbreak probabilities and incidence spanning tropical, semi-arid, and Mediterranean regions of Western Australia (1991-2014). Hurdle and linear models were used to predict outbreak probabilities and incidence respectively, using time-lagged environmental variables. Forecast accuracy was assessed by model fit and cross-validation. Residual RRV notification data were also examined against mitigation expenditure for one site, Mandurah 2007-2014. Models were predictive of RRV activity, except at one site (Capel). Minimum temperature was an important predictor of RRV outbreaks and incidence at all predicted sites. Precipitation was more likely to cause outbreaks and greater incidence among tropical and semi-arid sites. While variable, mitigation expenditure coincided positively with increased RRV incidence (r 2 = 0·21). Our research demonstrates capacity to accurately predict mosquito-borne disease outbreaks and incidence at fine-temporal resolutions. We apply our findings, developing a user-friendly tool enabling managers to easily adopt this research to forecast region-specific RRV outbreaks and incidence. Approaches here may be of value to fine-scale forecasting of RRV in other areas of Australia, and other mosquito-borne diseases.

Non-Markovian closure models for large eddy simulations using the Mori-Zwanzig formalism

NASA Astrophysics Data System (ADS)

Parish, Eric J.; Duraisamy, Karthik

2017-01-01

This work uses the Mori-Zwanzig (M-Z) formalism, a concept originating from nonequilibrium statistical mechanics, as a basis for the development of coarse-grained models of turbulence. The mechanics of the generalized Langevin equation (GLE) are considered, and insight gained from the orthogonal dynamics equation is used as a starting point for model development. A class of subgrid models is considered which represent nonlocal behavior via a finite memory approximation [Stinis, arXiv:1211.4285 (2012)], the length of which is determined using a heuristic that is related to the spectral radius of the Jacobian of the resolved variables. The resulting models are intimately tied to the underlying numerical resolution and are capable of approximating non-Markovian effects. Numerical experiments on the Burgers equation demonstrate that the M-Z-based models can accurately predict the temporal evolution of the total kinetic energy and the total dissipation rate at varying mesh resolutions. The trajectory of each resolved mode in phase space is accurately predicted for cases where the coarse graining is moderate. Large eddy simulations (LESs) of homogeneous isotropic turbulence and the Taylor-Green Vortex show that the M-Z-based models are able to provide excellent predictions, accurately capturing the subgrid contribution to energy transfer. Last, LESs of fully developed channel flow demonstrate the applicability of M-Z-based models to nondecaying problems. It is notable that the form of the closure is not imposed by the modeler, but is rather derived from the mathematics of the coarse graining, highlighting the potential of M-Z-based techniques to define LES closures.

Quantifying the Uncertainty in High Spatial and Temporal Resolution Synthetic Land Surface Reflectance at Pixel Level Using Ground-Based Measurements

NASA Astrophysics Data System (ADS)

Kong, J.; Ryu, Y.

2017-12-01

Algorithms for fusing high temporal frequency and high spatial resolution satellite images are widely used to develop dense time-series land surface observations. While many studies have revealed that the synthesized frequent high spatial resolution images could be successfully applied in vegetation mapping and monitoring, validation and correction of fused images have not been focused than its importance. To evaluate the precision of fused image in pixel level, in-situ reflectance measurements which could account for the pixel-level heterogeneity are necessary. In this study, the synthetic images of land surface reflectance were predicted by the coarse high-frequency images acquired from MODIS and high spatial resolution images from Landsat-8 OLI using the Flexible Spatiotemporal Data Fusion (FSDAF). Ground-based reflectance was measured by JAZ Spectrometer (Ocean Optics, Dunedin, FL, USA) on rice paddy during five main growth stages in Cheorwon-gun, Republic of Korea, where the landscape heterogeneity changes through the growing season. After analyzing the spatial heterogeneity and seasonal variation of land surface reflectance based on the ground measurements, the uncertainties of the fused images were quantified at pixel level. Finally, this relationship was applied to correct the fused reflectance images and build the seasonal time series of rice paddy surface reflectance. This dataset could be significant for rice planting area extraction, phenological stages detection, and variables estimation.

Modeling Future Fire danger over North America in a Changing Climate

NASA Astrophysics Data System (ADS)

Jain, P.; Paimazumder, D.; Done, J.; Flannigan, M.

2016-12-01

Fire danger ratings are used to determine wildfire potential due to weather and climate factors. The Fire Weather Index (FWI), part of the Canadian Forest Fire Danger Rating System (CFFDRS), incorporates temperature, relative humidity, windspeed and precipitation to give a daily fire danger rating that is used by wildfire management agencies in an operational context. Studies using GCM output have shown that future wildfire danger will increase in a warming climate. However, these studies are somewhat limited by the coarse spatial resolution (typically 100-400km) and temporal resolution (typically 6-hourly to monthly) of the model output. Future wildfire potential over North America based on FWI is calculated using output from the Weather, Research and Forecasting (WRF) model, which is used to downscale future climate scenarios from the bias-corrected Community Climate System Model (CCSM) under RCP8.5 scenarios at a spatial resolution of 36km. We consider five eleven year time slices: 1990-2000, 2020-2030, 2030-2040, 2050-2060 and 2080-2090. The dynamically downscaled simulation improves determination of future extreme weather by improving both spatial and temporal resolution over most GCM models. To characterize extreme fire weather we calculate annual numbers of spread days (days for which FWI > 19) and annual 99th percentile of FWI. Additionally, an extreme value analysis based on the peaks-over-threshold method allows us to calculate the return values for extreme FWI values.

Tabulations of ambient ozone data obtained by GASP (Global Air Sampling Program) airliners, March 1975 to July 1979

NASA Technical Reports Server (NTRS)

Jasperson, W. H.; Holdeman, J. D.

1984-01-01

Tabulations are given of GASP ambient ozone mean, standard deviation, median, 84th percentile, and 98th percentile values, by month, flight level, and geographical region. These data are tabulated to conform to the temporal and spatial resolution required by FAA Advisory Circular 120-38 (monthly by 2000 ft in altitude by 5 deg in latitude) for climatological data used to show compliance with cabin ozone regulations. In addition seasonal x 10 deg latitude tabulations are included which are directly comparable to and supersede the interim GASP ambient ozone tabulations given in appendix B of FAA-EE-80-43 (NASA TM-81528). Selected probability variations are highlighted to illustrate the spatial and temporal variability of ambient ozone and to compare results from the coarse and fine grid analyses.

A functional model for characterizing long-distance movement behaviour

USGS Publications Warehouse

Buderman, Frances E.; Hooten, Mevin B.; Ivan, Jacob S.; Shenk, Tanya M.

2016-01-01

Advancements in wildlife telemetry techniques have made it possible to collect large data sets of highly accurate animal locations at a fine temporal resolution. These data sets have prompted the development of a number of statistical methodologies for modelling animal movement.Telemetry data sets are often collected for purposes other than fine-scale movement analysis. These data sets may differ substantially from those that are collected with technologies suitable for fine-scale movement modelling and may consist of locations that are irregular in time, are temporally coarse or have large measurement error. These data sets are time-consuming and costly to collect but may still provide valuable information about movement behaviour.We developed a Bayesian movement model that accounts for error from multiple data sources as well as movement behaviour at different temporal scales. The Bayesian framework allows us to calculate derived quantities that describe temporally varying movement behaviour, such as residence time, speed and persistence in direction. The model is flexible, easy to implement and computationally efficient.We apply this model to data from Colorado Canada lynx (Lynx canadensis) and use derived quantities to identify changes in movement behaviour.

A high resolution InSAR topographic reconstruction research in urban area based on TerraSAR-X data

NASA Astrophysics Data System (ADS)

Qu, Feifei; Qin, Zhang; Zhao, Chaoying; Zhu, Wu

2011-10-01

Aiming at the problems of difficult unwrapping and phase noise in InSAR DEM reconstruction, especially for the high-resolution TerraSAR-X data, this paper improved the height reconstruction algorithm in view of "remove-restore" based on external coarse DEM and multi-interferogram processing, proposed a height calibration method based on CR+GPS data. Several measures have been taken for urban high resolution DEM reconstruction with TerraSAR data. The SAR interferometric pairs with long spatial and short temporal baselines are served for the DEM. The external low resolution and low accuracy DEM is applied for the "remove-restore" concept to ease the phase unwrapping. The stochastic errors including atmospheric effects and phase noise are suppressed by weighted averaging of DEM phases. Six TerraSAR-X data are applied to create the twelve-meter's resolution DEM over Xian, China with the newly-proposed method. The heights in discrete GPS benchmarks are used to calibrate the result, and the RMS of 3.29 meter is achieved by comparing with 1:50000 DEM.

Quantifying Information Gain from Dynamic Downscaling Experiments

NASA Astrophysics Data System (ADS)

Tian, Y.; Peters-Lidard, C. D.

2015-12-01

Dynamic climate downscaling experiments are designed to produce information at higher spatial and temporal resolutions. Such additional information is generated from the low-resolution initial and boundary conditions via the predictive power of the physical laws. However, errors and uncertainties in the initial and boundary conditions can be propagated and even amplified to the downscaled simulations. Additionally, the limit of predictability in nonlinear dynamical systems will also damper the information gain, even if the initial and boundary conditions were error-free. Thus it is critical to quantitatively define and measure the amount of information increase from dynamic downscaling experiments, to better understand and appreciate their potentials and limitations. We present a scheme to objectively measure the information gain from such experiments. The scheme is based on information theory, and we argue that if a downscaling experiment is to exhibit value, it has to produce more information than what can be simply inferred from information sources already available. These information sources include the initial and boundary conditions, the coarse resolution model in which the higher-resolution models are embedded, and the same set of physical laws. These existing information sources define an "information threshold" as a function of the spatial and temporal resolution, and this threshold serves as a benchmark to quantify the information gain from the downscaling experiments, or any other approaches. For a downscaling experiment to shown any value, the information has to be above this threshold. A recent NASA-supported downscaling experiment is used as an example to illustrate the application of this scheme.

Exploration of scaling effects on coarse resolution land surface phenology

USDA-ARS?s Scientific Manuscript database

A great number of land surface phenoloy (LSP) data have been produced from various coarse resolution satellite datasets and detection algorithms across regional and global scales. Unlike field- measured phenological events which are quantitatively defined with clear biophysical meaning, current LSP ...

Spatial consistency of Chinook salmon redd distribution within and among years in the Cowlitz River, Washington

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

Klett, Katherine J.; Torgersen, Christian; Henning, Julie

2013-04-28

We investigated the spawning patterns of Chinook salmon Oncorhynchus tshawytscha on the lower Cowlitz River, Washington (USA) using a unique set of fine- and coarse-scale 35 temporal and spatial data collected during bi-weekly aerial surveys conducted in 1991-2009 (500 m to 28 km resolution) and 2008-2009 (100-500 m resolution). Redd locations were mapped from a helicopter during 2008 and 2009 with a hand-held global positioning system (GPS) synchronized with in-flight audio recordings. We examined spatial patterns of Chinook salmon redd reoccupation among and within years in relation to segment-scale geomorphic features. Chinook salmon spawned in the same sections each yearmore » with little variation among years. On a coarse scale, five years (1993, 1998, 2000, 2002, and 2009) were compared for reoccupation. Redd locations were highly correlated among years resulting in a minimum correlation coefficient of 0.90 (adjusted P = 0.002). Comparisons on a fine scale (500 m) between 2008 and 2009 also revealed a high degree of consistency among redd locations (P < 0.001). On a finer temporal scale, we observed that salmon spawned in the same sections during the first and last week (2008: P < 0.02; and 2009: P < 0.001). Redds were clustered in both 2008 and 2009 (P < 0.001). Regression analysis with a generalized linear model at the 500-m scale indicated that river kilometer and channel bifurcation were positively associated with redd density, whereas sinuosity was negatively associated with redd density. Collecting data on specific redd locations with a GPS during aerial surveys was logistically feasible and cost effective and greatly enhanced the spatial precision of Chinook salmon spawning surveys.« less

Adaptive resolution simulation of an atomistic protein in MARTINI water

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

Zavadlav, Julija; Melo, Manuel Nuno; Marrink, Siewert J., E-mail: s.j.marrink@rug.nl

2014-02-07

We present an adaptive resolution simulation of protein G in multiscale water. We couple atomistic water around the protein with mesoscopic water, where four water molecules are represented with one coarse-grained bead, farther away. We circumvent the difficulties that arise from coupling to the coarse-grained model via a 4-to-1 molecule coarse-grain mapping by using bundled water models, i.e., we restrict the relative movement of water molecules that are mapped to the same coarse-grained bead employing harmonic springs. The water molecules change their resolution from four molecules to one coarse-grained particle and vice versa adaptively on-the-fly. Having performed 15 ns long molecularmore » dynamics simulations, we observe within our error bars no differences between structural (e.g., root-mean-squared deviation and fluctuations of backbone atoms, radius of gyration, the stability of native contacts and secondary structure, and the solvent accessible surface area) and dynamical properties of the protein in the adaptive resolution approach compared to the fully atomistically solvated model. Our multiscale model is compatible with the widely used MARTINI force field and will therefore significantly enhance the scope of biomolecular simulations.« less

Adaptive resolution simulation of an atomistic protein in MARTINI water.

PubMed

Zavadlav, Julija; Melo, Manuel Nuno; Marrink, Siewert J; Praprotnik, Matej

2014-02-07

We present an adaptive resolution simulation of protein G in multiscale water. We couple atomistic water around the protein with mesoscopic water, where four water molecules are represented with one coarse-grained bead, farther away. We circumvent the difficulties that arise from coupling to the coarse-grained model via a 4-to-1 molecule coarse-grain mapping by using bundled water models, i.e., we restrict the relative movement of water molecules that are mapped to the same coarse-grained bead employing harmonic springs. The water molecules change their resolution from four molecules to one coarse-grained particle and vice versa adaptively on-the-fly. Having performed 15 ns long molecular dynamics simulations, we observe within our error bars no differences between structural (e.g., root-mean-squared deviation and fluctuations of backbone atoms, radius of gyration, the stability of native contacts and secondary structure, and the solvent accessible surface area) and dynamical properties of the protein in the adaptive resolution approach compared to the fully atomistically solvated model. Our multiscale model is compatible with the widely used MARTINI force field and will therefore significantly enhance the scope of biomolecular simulations.

Linear mixing model applied to coarse spatial resolution data from multispectral satellite sensors

NASA Technical Reports Server (NTRS)

Holben, Brent N.; Shimabukuro, Yosio E.

1993-01-01

A linear mixing model was applied to coarse spatial resolution data from the NOAA Advanced Very High Resolution Radiometer. The reflective component of the 3.55-3.95 micron channel was used with the two reflective channels 0.58-0.68 micron and 0.725-1.1 micron to run a constrained least squares model to generate fraction images for an area in the west central region of Brazil. The fraction images were compared with an unsupervised classification derived from Landsat TM data acquired on the same day. The relationship between the fraction images and normalized difference vegetation index images show the potential of the unmixing techniques when using coarse spatial resolution data for global studies.

Temporal acceleration of spatially distributed kinetic Monte Carlo simulations

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

Chatterjee, Abhijit; Vlachos, Dionisios G.

The computational intensity of kinetic Monte Carlo (KMC) simulation is a major impediment in simulating large length and time scales. In recent work, an approximate method for KMC simulation of spatially uniform systems, termed the binomial {tau}-leap method, was introduced [A. Chatterjee, D.G. Vlachos, M.A. Katsoulakis, Binomial distribution based {tau}-leap accelerated stochastic simulation, J. Chem. Phys. 122 (2005) 024112], where molecular bundles instead of individual processes are executed over coarse-grained time increments. This temporal coarse-graining can lead to significant computational savings but its generalization to spatially lattice KMC simulation has not been realized yet. Here we extend the binomial {tau}-leapmore » method to lattice KMC simulations by combining it with spatially adaptive coarse-graining. Absolute stability and computational speed-up analyses for spatial systems along with simulations provide insights into the conditions where accuracy and substantial acceleration of the new spatio-temporal coarse-graining method are ensured. Model systems demonstrate that the r-time increment criterion of Chatterjee et al. obeys the absolute stability limit for values of r up to near 1.« less

Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery

NASA Astrophysics Data System (ADS)

Qin, Yuanwei; Xiao, Xiangming; Dong, Jinwei; Zhou, Yuting; Zhu, Zhe; Zhang, Geli; Du, Guoming; Jin, Cui; Kou, Weili; Wang, Jie; Li, Xiangping

2015-07-01

Accurate and timely rice paddy field maps with a fine spatial resolution would greatly improve our understanding of the effects of paddy rice agriculture on greenhouse gases emissions, food and water security, and human health. Rice paddy field maps were developed using optical images with high temporal resolution and coarse spatial resolution (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) or low temporal resolution and high spatial resolution (e.g., Landsat TM/ETM+). In the past, the accuracy and efficiency for rice paddy field mapping at fine spatial resolutions were limited by the poor data availability and image-based algorithms. In this paper, time series MODIS and Landsat ETM+/OLI images, and the pixel- and phenology-based algorithm are used to map paddy rice planting area. The unique physical features of rice paddy fields during the flooding/open-canopy period are captured with the dynamics of vegetation indices, which are then used to identify rice paddy fields. The algorithm is tested in the Sanjiang Plain (path/row 114/27) in China in 2013. The overall accuracy of the resulted map of paddy rice planting area generated by both Landsat ETM+ and OLI is 97.3%, when evaluated with areas of interest (AOIs) derived from geo-referenced field photos. The paddy rice planting area map also agrees reasonably well with the official statistics at the level of state farms (R2 = 0.94). These results demonstrate that the combination of fine spatial resolution images and the phenology-based algorithm can provide a simple, robust, and automated approach to map the distribution of paddy rice agriculture in a year.

Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery.

PubMed

Qin, Yuanwei; Xiao, Xiangming; Dong, Jinwei; Zhou, Yuting; Zhu, Zhe; Zhang, Geli; Du, Guoming; Jin, Cui; Kou, Weili; Wang, Jie; Li, Xiangping

2015-07-01

Accurate and timely rice paddy field maps with a fine spatial resolution would greatly improve our understanding of the effects of paddy rice agriculture on greenhouse gases emissions, food and water security, and human health. Rice paddy field maps were developed using optical images with high temporal resolution and coarse spatial resolution (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) or low temporal resolution and high spatial resolution (e.g., Landsat TM/ETM+). In the past, the accuracy and efficiency for rice paddy field mapping at fine spatial resolutions were limited by the poor data availability and image-based algorithms. In this paper, time series MODIS and Landsat ETM+/OLI images, and the pixel- and phenology-based algorithm are used to map paddy rice planting area. The unique physical features of rice paddy fields during the flooding/open-canopy period are captured with the dynamics of vegetation indices, which are then used to identify rice paddy fields. The algorithm is tested in the Sanjiang Plain (path/row 114/27) in China in 2013. The overall accuracy of the resulted map of paddy rice planting area generated by both Landsat ETM+ and OLI is 97.3%, when evaluated with areas of interest (AOIs) derived from geo-referenced field photos. The paddy rice planting area map also agrees reasonably well with the official statistics at the level of state farms ( R 2 = 0.94). These results demonstrate that the combination of fine spatial resolution images and the phenology-based algorithm can provide a simple, robust, and automated approach to map the distribution of paddy rice agriculture in a year.

Spatially enhanced passive microwave derived soil moisture: capabilities and opportunities

USDA-ARS?s Scientific Manuscript database

Low frequency passive microwave remote sensing is a proven technique for soil moisture retrieval, but its coarse resolution restricts the range of applications. Downscaling, otherwise known as disaggregation, has been proposed as the solution to spatially enhance these coarse resolution soil moistur...

Evaluation of coarse scale land surface remote sensing albedo product over rugged terrain

NASA Astrophysics Data System (ADS)

Wen, J.; Xinwen, L.; You, D.; Dou, B.

2017-12-01

Satellite derived Land surface albedo is an essential climate variable which controls the earth energy budget and it can be used in applications such as climate change, hydrology, and numerical weather prediction. The accuracy and uncertainty of surface albedo products should be evaluated with a reliable reference truth data prior to applications. And more literatures investigated the validation methods about the albedo validation in a flat or homogenous surface. However, the albedo performance over rugged terrain is still unknow due to the validation method limited. A multi-validation strategy is implemented to give a comprehensive albedo validation, which will involve the high resolution albedo processing, high resolution albedo validation based on in situ albedo, and the method to upscale the high resolution albedo to a coarse scale albedo. Among them, the high resolution albedo generation and the upscale method is the core step for the coarse scale albedo validation. In this paper, the high resolution albedo is generated by Angular Bin algorithm. And a albedo upscale method over rugged terrain is developed to obtain the coarse scale albedo truth. The in situ albedo located 40 sites in mountain area are selected globally to validate the high resolution albedo, and then upscaled to the coarse scale albedo by the upscale method. This paper takes MODIS and GLASS albedo product as a example, and the prelimarily results show the RMSE of MODIS and GLASS albedo product over rugged terrain are 0.047 and 0.057, respectively under the RMSE with 0.036 of high resolution albedo.

Detecting population-environmental interactions with mismatched time series data.

PubMed

Ferguson, Jake M; Reichert, Brian E; Fletcher, Robert J; Jager, Henriëtte I

2017-11-01

Time series analysis is an essential method for decomposing the influences of density and exogenous factors such as weather and climate on population regulation. However, there has been little work focused on understanding how well commonly collected data can reconstruct the effects of environmental factors on population dynamics. We show that, analogous to similar scale issues in spatial data analysis, coarsely sampled temporal data can fail to detect covariate effects when interactions occur on timescales that are fast relative to the survey period. We propose a method for modeling mismatched time series data that couples high-resolution environmental data to low-resolution abundance data. We illustrate our approach with simulations and by applying it to Florida's southern Snail kite population. Our simulation results show that our method can reliably detect linear environmental effects and that detecting nonlinear effects requires high-resolution covariate data even when the population turnover rate is slow. In the Snail kite analysis, our approach performed among the best in a suite of previously used environmental covariates explaining Snail kite dynamics and was able to detect a potential phenological shift in the environmental dependence of Snail kites. Our work provides a statistical framework for reliably detecting population-environment interactions from coarsely surveyed time series. An important implication of this work is that the low predictability of animal population growth by weather variables found in previous studies may be due, in part, to how these data are utilized as covariates. © 2017 by the Ecological Society of America.

Detecting population–environmental interactions with mismatched time series data

PubMed Central

Ferguson, Jake M.; Reichert, Brian E.; Fletcher, Robert J.; Jager, Henriëtte I.

2017-01-01

Time series analysis is an essential method for decomposing the influences of density and exogenous factors such as weather and climate on population regulation. However, there has been little work focused on understanding how well commonly collected data can reconstruct the effects of environmental factors on population dynamics. We show that, analogous to similar scale issues in spatial data analysis, coarsely sampled temporal data can fail to detect covariate effects when interactions occur on timescales that are fast relative to the survey period. We propose a method for modeling mismatched time series data that couples high-resolution environmental data to low-resolution abundance data. We illustrate our approach with simulations and by applying it to Florida’s southern Snail kite population. Our simulation results show that our method can reliably detect linear environmental effects and that detecting nonlinear effects requires high-resolution covariate data even when the population turnover rate is slow. In the Snail kite analysis, our approach performed among the best in a suite of previously used environmental covariates explaining Snail kite dynamics and was able to detect a potential phenological shift in the environmental dependence of Snail kites. Our work provides a statistical framework for reliably detecting population–environment interactions from coarsely surveyed time series. An important implication of this work is that the low predictability of animal population growth by weather variables found in previous studies may be due, in part, to how these data are utilized as covariates. PMID:28759123

The influence of spatially and temporally high-resolution wind forcing on the power input to near-inertial waves in the ocean

NASA Astrophysics Data System (ADS)

Rimac, A.; Eden, C.; von Storch, J.

2012-12-01

Coexistence of stable stratification, the meridional overturning circulation and meso-scale eddies and their influence on the ocean's circulation still raise complex questions concerning the ocean energetics. Oceanic general circulation is mainly forced by the wind field and deep water tides. Its essential energetics are the conversion of kinetic energy of the winds and tides into oceanic potential and kinetic energy. Energy needed for the circulation is bound to internal wave fields. Direct internal wave generation by the wind at the sea surface is one of the sources of this energy. Previous studies using mixed-layer type of models and low frequency wind forcings (six-hourly and daily) left room for improvement. Using mixed-layer models it is not possible to assess the distribution of near-inertial energy into the deep ocean. Also, coarse temporal resolution of wind forcing strongly underestimates the near-inertial wave energy. To overcome this difficulty we use a high resolution ocean model with high frequency wind forcings. We establish the following model setup: We use the Max Planck Institute Ocean Model (MPIOM) on a tripolar grid with 45km horizontal resolution and 40 vertical levels. We run the model with wind forcings that vary in horizontal (250km versus 40km) and temporal resolution (six versus one-hourly). In our study we answer the following questions: How big is the wind kinetic energy input to the near-inertial waves? Is the kinetic energy of the near-inertial waves enhanced when high-frequency wind forcings are used? If so, by how much and why, due to higher level of temporal wind variability or due to better spatial representation of the near-inertial waves? How big is the total power of near-inertial waves generated by the wind at the surface of the ocean? We run the model for one year. Our model results show that the near-inertial waves are excited both using wind forcings of high and low horizontal and temporal resolution. Near-inertial energy is almost two times higher when we force the model with high frequency wind forcings. The influence on the energy mostly depends on the time difference between two forcing fields while the spatial difference has little influence.

A Modeling Approach to Global Land Surface Monitoring with Low Resolution Satellite Imaging

NASA Technical Reports Server (NTRS)

Hlavka, Christine A.; Dungan, Jennifer; Livingston, Gerry P.; Gore, Warren J. (Technical Monitor)

1998-01-01

The effects of changing land use/land cover on global climate and ecosystems due to greenhouse gas emissions and changing energy and nutrient exchange rates are being addressed by federal programs such as NASA's Mission to Planet Earth (MTPE) and by international efforts such as the International Geosphere-Biosphere Program (IGBP). The quantification of these effects depends on accurate estimates of the global extent of critical land cover types such as fire scars in tropical savannas and ponds in Arctic tundra. To address the requirement for accurate areal estimates, methods for producing regional to global maps with satellite imagery are being developed. The only practical way to produce maps over large regions of the globe is with data of coarse spatial resolution, such as Advanced Very High Resolution Radiometer (AVHRR) weather satellite imagery at 1.1 km resolution or European Remote-Sensing Satellite (ERS) radar imagery at 100 m resolution. The accuracy of pixel counts as areal estimates is in doubt, especially for highly fragmented cover types such as fire scars and ponds. Efforts to improve areal estimates from coarse resolution maps have involved regression of apparent area from coarse data versus that from fine resolution in sample areas, but it has proven difficult to acquire sufficient fine scale data to develop the regression. A method for computing accurate estimates from coarse resolution maps using little or no fine data is therefore needed.

Identifying grain-size dependent errors on global forest area estimates and carbon studies

Treesearch

Daolan Zheng; Linda S. Heath; Mark J. Ducey

2008-01-01

Satellite-derived coarse-resolution data are typically used for conducting global analyses. But the forest areas estimated from coarse-resolution maps (e.g., 1 km) inevitably differ from a corresponding fine-resolution map (such as a 30-m map) that would be closer to ground truth. A better understanding of changes in grain size on area estimation will improve our...

Phenological monitoring of Acadia National Park using Landsat, MODIS and VIIRS observations and fused data

NASA Astrophysics Data System (ADS)

Liu, Y.; McDonough MacKenzie, C.; Primack, R.; Zhang, X.; Schaaf, C.; Sun, Q.; Wang, Z.

2015-12-01

Monitoring phenology with remotely sensed data has become standard practice in large-plot agriculture but remains an area of research in complex terrain. Landsat data (30m) provides a more appropriate spatial resolution to describe such regions but may only capture a few cloud-free images over a growing period. Daily data from the MODerate resolution Imaging Spectroradiometer(MODIS) and Visible Infrared Imaging Radiometer Suite(VIIRS) offer better temporal acquisitions but at coarse spatial resolutions of 250m to 1km. Thus fused data sets are being employed to provide the temporal and spatial resolutions necessary to accurately monitor vegetation phenology. This study focused on Acadia National Park, Maine, attempts to compare green-up from remote sensing and ground observations over varying topography. Three north-south field transects were established in 2013 on parallel mountains. Along these transects, researchers record the leaf out and flowering phenology for thirty plant species biweekly. These in situ spring phenological observations are compared with the dates detected by Landsat 7, Landsat 8, MODIS, and VIIRS observations, both separately and as fused data, to explore the ability of remotely sensed data to capture the subtle variations due to elevation. Daily Nadir BRDF Adjusted Reflectances(NBAR) from MODIS and VIIRS are fused with Landsat imagery to simulate 30m daily data via the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model(ESTARFM) algorithm. Piecewise logistic functions are fit to the time series to establish spring leaf-out dates. Acadia National Park, a region frequently affected by coastal clouds, is a particularly useful study area as it falls in a Landsat overlap region and thus offers the possibility of acquiring as many as 4 Landsat observations in a 16 day period. With the recent launch of Sentinel 2A, the community will have routine access to such high spatial and temporal data for phenological monitoring.

High-Resolution Hydrological Sub-Seasonal Forecasting for Water Resources Management Over Europe

NASA Astrophysics Data System (ADS)

Wood, E. F.; Wanders, N.; Pan, M.; Sheffield, J.; Samaniego, L. E.; Thober, S.; Kumar, R.; Prudhomme, C.; Houghton-Carr, H.

2017-12-01

For decision-making at the sub-seasonal and seasonal time scale, hydrological forecasts with a high temporal and spatial resolution are required by water managers. So far such forecasts have been unavailable due to 1) lack of availability of meteorological seasonal forecasts, 2) coarse temporal resolution of meteorological seasonal forecasts, requiring temporal downscaling, 3) lack of consistency between observations and seasonal forecasts, requiring bias-correction. The EDgE (End-to-end Demonstrator for improved decision making in the water sector in Europe) project commissioned by the ECMWF (C3S) created a unique dataset of hydrological seasonal forecasts derived from four global climate models (CanCM4, FLOR-B01, ECMF, LFPW) in combination with four global hydrological models (PCR-GLOBWB, VIC, mHM, Noah-MP), resulting in 208 forecasts for any given day. The forecasts provide a daily temporal and 5-km spatial resolution, and are bias corrected against E-OBS meteorological observations. The forecasts are communicated to stakeholders via Sectoral Climate Impact Indicators (SCIIs), created in collaboration with the end-user community of the EDgE project (e.g. the percentage of ensemble realizations above the 10th percentile of monthly river flow, or below the 90th). Results show skillful forecasts for discharge from 3 months to 6 months (latter for N Europe due to snow); for soil moisture up to three months due precipitation forecast skill and short initial condition memory; and for groundwater greater than 6 months (lowest skill in western Europe.) The SCIIs are effective in communicating both forecast skill and uncertainty. Overall the new system provides an unprecedented ensemble for seasonal forecasts with significant skill over Europe to support water management. The consistency in both the GCM forecasts and the LSM parameterization ensures a stable and reliable forecast framework and methodology, even if additional GCMs or LSMs are added in the future.

Spatial, Temporal and Spectral Satellite Image Fusion via Sparse Representation

NASA Astrophysics Data System (ADS)

Song, Huihui

Remote sensing provides good measurements for monitoring and further analyzing the climate change, dynamics of ecosystem, and human activities in global or regional scales. Over the past two decades, the number of launched satellite sensors has been increasing with the development of aerospace technologies and the growing requirements on remote sensing data in a vast amount of application fields. However, a key technological challenge confronting these sensors is that they tradeoff between spatial resolution and other properties, including temporal resolution, spectral resolution, swath width, etc., due to the limitations of hardware technology and budget constraints. To increase the spatial resolution of data with other good properties, one possible cost-effective solution is to explore data integration methods that can fuse multi-resolution data from multiple sensors, thereby enhancing the application capabilities of available remote sensing data. In this thesis, we propose to fuse the spatial resolution with temporal resolution and spectral resolution, respectively, based on sparse representation theory. Taking the study case of Landsat ETM+ (with spatial resolution of 30m and temporal resolution of 16 days) and MODIS (with spatial resolution of 250m ~ 1km and daily temporal resolution) reflectance, we propose two spatial-temporal fusion methods to combine the fine spatial information of Landsat image and the daily temporal resolution of MODIS image. Motivated by that the images from these two sensors are comparable on corresponding bands, we propose to link their spatial information on available Landsat- MODIS image pair (captured on prior date) and then predict the Landsat image from the MODIS counterpart on prediction date. To well-learn the spatial details from the prior images, we use a redundant dictionary to extract the basic representation atoms for both Landsat and MODIS images based on sparse representation. Under the scenario of two prior Landsat-MODIS image pairs, we build the corresponding relationship between the difference images of MODIS and ETM+ by training a low- and high-resolution dictionary pair from the given prior image pairs. In the second scenario, i.e., only one Landsat- MODIS image pair being available, we directly correlate MODIS and ETM+ data through an image degradation model. Then, the fusion stage is achieved by super-resolving the MODIS image combining the high-pass modulation in a two-layer fusion framework. Remarkably, the proposed spatial-temporal fusion methods form a unified framework for blending remote sensing images with phenology change or land-cover-type change. Based on the proposed spatial-temporal fusion models, we propose to monitor the land use/land cover changes in Shenzhen, China. As a fast-growing city, Shenzhen faces the problem of detecting the rapid changes for both rational city planning and sustainable development. However, the cloudy and rainy weather in region Shenzhen located makes the capturing circle of high-quality satellite images longer than their normal revisit periods. Spatial-temporal fusion methods are capable to tackle this problem by improving the spatial resolution of images with coarse spatial resolution but frequent temporal coverage, thereby making the detection of rapid changes possible. On two Landsat-MODIS datasets with annual and monthly changes, respectively, we apply the proposed spatial-temporal fusion methods to the task of multiple change detection. Afterward, we propose a novel spatial and spectral fusion method for satellite multispectral and hyperspectral (or high-spectral) images based on dictionary-pair learning and sparse non-negative matrix factorization. By combining the spectral information from hyperspectral image, which is characterized by low spatial resolution but high spectral resolution and abbreviated as LSHS, and the spatial information from multispectral image, which is featured by high spatial resolution but low spectral resolution and abbreviated as HSLS, this method aims to generate the fused data with both high spatial and high spectral resolutions. Motivated by the observation that each hyperspectral pixel can be represented by a linear combination of a few endmembers, this method first extracts the spectral bases of LSHS and HSLS images by making full use of the rich spectral information in LSHS data. The spectral bases of these two categories data then formulate a dictionary-pair due to their correspondence in representing each pixel spectra of LSHS data and HSLS data, respectively. Subsequently, the LSHS image is spatially unmixed by representing the HSLS image with respect to the corresponding learned dictionary to derive its representation coefficients. Combining the spectral bases of LSHS data and the representation coefficients of HSLS data, we finally derive the fused data characterized by the spectral resolution of LSHS data and the spatial resolution of HSLS data.

Linear mixing model applied to coarse resolution satellite data

NASA Technical Reports Server (NTRS)

Holben, Brent N.; Shimabukuro, Yosio E.

1992-01-01

A linear mixing model typically applied to high resolution data such as Airborne Visible/Infrared Imaging Spectrometer, Thematic Mapper, and Multispectral Scanner System is applied to the NOAA Advanced Very High Resolution Radiometer coarse resolution satellite data. The reflective portion extracted from the middle IR channel 3 (3.55 - 3.93 microns) is used with channels 1 (0.58 - 0.68 microns) and 2 (0.725 - 1.1 microns) to run the Constrained Least Squares model to generate fraction images for an area in the west central region of Brazil. The derived fraction images are compared with an unsupervised classification and the fraction images derived from Landsat TM data acquired in the same day. In addition, the relationship betweeen these fraction images and the well known NDVI images are presented. The results show the great potential of the unmixing techniques for applying to coarse resolution data for global studies.

Subranging technique using superconducting technology

DOEpatents

Gupta, Deepnarayan

2003-01-01

Subranging techniques using "digital SQUIDs" are used to design systems with large dynamic range, high resolution and large bandwidth. Analog-to-digital converters (ADCs) embodying the invention include a first SQUID based "coarse" resolution circuit and a second SQUID based "fine" resolution circuit to convert an analog input signal into "coarse" and "fine" digital signals for subsequent processing. In one embodiment, an ADC includes circuitry for supplying an analog input signal to an input coil having at least a first inductive section and a second inductive section. A first superconducting quantum interference device (SQUID) is coupled to the first inductive section and a second SQUID is coupled to the second inductive section. The first SQUID is designed to produce "coarse" (large amplitude, low resolution) output signals and the second SQUID is designed to produce "fine" (low amplitude, high resolution) output signals in response to the analog input signals.

Coarse-Grained Models Reveal Functional Dynamics – II. Molecular Dynamics Simulation at the Coarse-Grained Level – Theories and Biological Applications

PubMed Central

Chng, Choon-Peng; Yang, Lee-Wei

2008-01-01

Molecular dynamics (MD) simulation has remained the most indispensable tool in studying equilibrium/non-equilibrium conformational dynamics since its advent 30 years ago. With advances in spectroscopy accompanying solved biocomplexes in growing sizes, sampling their dynamics that occur at biologically interesting spatial/temporal scales becomes computationally intractable; this motivated the use of coarse-grained (CG) approaches. CG-MD models are used to study folding and conformational transitions in reduced resolution and can employ enlarged time steps due to the absence of some of the fastest motions in the system. The Boltzmann-Inversion technique, heavily used in parameterizing these models, provides a smoothed-out effective potential on which molecular conformation evolves at a faster pace thus stretching simulations into tens of microseconds. As a result, a complete catalytic cycle of HIV-1 protease or the assembly of lipid-protein mixtures could be investigated by CG-MD to gain biological insights. In this review, we survey the theories developed in recent years, which are categorized into Folding-based and Molecular-Mechanics-based. In addition, physical bases in the selection of CG beads/time-step, the choice of effective potentials, representation of solvent, and restoration of molecular representations back to their atomic details are systematically discussed. PMID:19812774

Spatial and Temporal Variability of Outdoor Coarse Particulate Matter Mass Concentrations Measured with a New Coarse Particulate Sampler during the Detroit Exposure and Aerosol Research Study

EPA Science Inventory

The Detroit Exposure and Aerosol Research Study (DEARS) provided data to compare outdoor residential coarse particulate matter (PM_10-2.5) concentrations in six different areas of Detroit with data from a central monitoring site. Daily and seasonal influences on the spa...

Coarse-to-fine construction for high-resolution representation in visual working memory.

PubMed

Gao, Zaifeng; Ding, Xiaowei; Yang, Tong; Liang, Junying; Shui, Rende

2013-01-01

This study explored whether the high-resolution representations created by visual working memory (VWM) are constructed in a coarse-to-fine or all-or-none manner. The coarse-to-fine hypothesis suggests that coarse information precedes detailed information in entering VWM and that its resolution increases along with the processing time of the memory array, whereas the all-or-none hypothesis claims that either both enter into VWM simultaneously, or neither does. We tested the two hypotheses by asking participants to remember two or four complex objects. An ERP component, contralateral delay activity (CDA), was used as the neural marker. CDA is higher for four objects than for two objects when coarse information is primarily extracted; yet, this CDA difference vanishes when detailed information is encoded. Experiment 1 manipulated the comparison difficulty of the task under a 500-ms exposure time to determine a condition in which the detailed information was maintained. No CDA difference was found between two and four objects, even in an easy-comparison condition. Thus, Experiment 2 manipulated the memory array's exposure time under the easy-comparison condition and found a significant CDA difference at 100 ms while replicating Experiment 1's results at 500 ms. In Experiment 3, the 500-ms memory array was blurred to block the detailed information; this manipulation reestablished a significant CDA difference. These findings suggest that the creation of high-resolution representations in VWM is a coarse-to-fine process.

Voxel-Based Lesion Symptom Mapping of Coarse Coding and Suppression Deficits in Patients With Right Hemisphere Damage

PubMed Central

Tompkins, Connie A.; Meigh, Kimberly M.; Prat, Chantel S.

2015-01-01

Purpose This study examined right hemisphere (RH) neuroanatomical correlates of lexical–semantic deficits that predict narrative comprehension in adults with RH brain damage. Coarse semantic coding and suppression deficits were related to lesions by voxel-based lesion symptom mapping. Method Participants were 20 adults with RH cerebrovascular accidents. Measures of coarse coding and suppression deficits were computed from lexical decision reaction times at short (175 ms) and long (1000 ms) prime-target intervals. Lesions were drawn on magnetic resonance imaging images and through normalization were registered on an age-matched brain template. Voxel-based lesion symptom mapping analysis was applied to build a general linear model at each voxel. Z score maps were generated for each deficit, and results were interpreted using automated anatomical labeling procedures. Results A deficit in coarse semantic activation was associated with lesions to the RH posterior middle temporal gyrus, dorsolateral prefrontal cortex, and lenticular nuclei. A maintenance deficit for coarsely coded representations involved the RH temporal pole and dorsolateral prefrontal cortex more medially. Ineffective suppression implicated lesions to the RH inferior frontal gyrus and subcortical regions, as hypothesized, along with the rostral temporal pole. Conclusion Beyond their scientific implications, these lesion–deficit correspondences may help inform the clinical diagnosis and enhance decisions about candidacy for deficit-focused treatment to improve narrative comprehension in individuals with RH damage. PMID:26425785

Voxel-Based Lesion Symptom Mapping of Coarse Coding and Suppression Deficits in Patients With Right Hemisphere Damage.

PubMed

Yang, Ying; Tompkins, Connie A; Meigh, Kimberly M; Prat, Chantel S

2015-11-01

This study examined right hemisphere (RH) neuroanatomical correlates of lexical-semantic deficits that predict narrative comprehension in adults with RH brain damage. Coarse semantic coding and suppression deficits were related to lesions by voxel-based lesion symptom mapping. Participants were 20 adults with RH cerebrovascular accidents. Measures of coarse coding and suppression deficits were computed from lexical decision reaction times at short (175 ms) and long (1000 ms) prime-target intervals. Lesions were drawn on magnetic resonance imaging images and through normalization were registered on an age-matched brain template. Voxel-based lesion symptom mapping analysis was applied to build a general linear model at each voxel. Z score maps were generated for each deficit, and results were interpreted using automated anatomical labeling procedures. A deficit in coarse semantic activation was associated with lesions to the RH posterior middle temporal gyrus, dorsolateral prefrontal cortex, and lenticular nuclei. A maintenance deficit for coarsely coded representations involved the RH temporal pole and dorsolateral prefrontal cortex more medially. Ineffective suppression implicated lesions to the RH inferior frontal gyrus and subcortical regions, as hypothesized, along with the rostral temporal pole. Beyond their scientific implications, these lesion-deficit correspondences may help inform the clinical diagnosis and enhance decisions about candidacy for deficit-focused treatment to improve narrative comprehension in individuals with RH damage.

Characterization of Inundated Wetlands with Microwave Remote Sensing: Cross-Product Comparison for Uncertainty Assessment in Tropical Wetlands

NASA Astrophysics Data System (ADS)

McDonald, K. C.; Jensen, K.; Alvarez, J.; Azarderakhsh, M.; Schroeder, R.; Podest, E.; Chapman, B. D.; Zimmermann, R.

2015-12-01

We have been assembling a global-scale Earth System Data Record (ESDR) of natural Inundated Wetlands to facilitate investigations on their role in climate, biogeochemistry, hydrology, and biodiversity. The ESDR comprises (1) Fine-resolution (100 meter) maps, delineating wetland extent, vegetation type, and seasonal inundation dynamics for regional to continental-scale areas, and (2) global coarse-resolution (~25 km), multi-temporal mappings of inundated area fraction (Fw) across multiple years. During March 2013, the NASA/JPL L-band polarimetric airborne imaging radar, UAVSAR, conducted airborne studies over regions of South America including portions of the western Amazon basin. We collected UAVSAR datasets over regions of the Amazon basin during that time to support systematic analyses of error sources related to the Inundated Wetlands ESDR. UAVSAR datasets were collected over Pacaya Samiria, Peru, Madre de Dios, Peru, and the Napo River in Ecuador. We derive landcover classifications from the UAVSAR datasets emphasizing wetlands regions, identifying regions of open water and inundated vegetation. We compare the UAVSAR-based datasets with those comprising the ESDR to assess uncertainty associated with the high resolution and the coarse resolution ESDR components. Our goal is to create an enhanced ESDR of inundated wetlands with statistically robust uncertainty estimates. The ESDR documentation will include a detailed breakdown of error sources and associated uncertainties within the data record. This work was carried out in part within the framework of the ALOS Kyoto & Carbon Initiative. PALSAR data were provided by JAXA/EORC and the Alaska Satellite Facility. Portions of this work were conducted at the Jet Propulsion Laboratory, California Institute of Technology under contract to the National Aeronautics and Space Administration.

Modeling Spatial and Temporal Variability in Ammonia Emissions from Agricultural Fertilization

NASA Astrophysics Data System (ADS)

Balasubramanian, S.; Koloutsou-Vakakis, S.; Rood, M. J.

2013-12-01

Ammonia (NH3), is an important component of the reactive nitrogen cycle and a precursor to formation of atmospheric particulate matter (PM). Predicting regional PM concentrations and deposition of nitrogen species to ecosystems requires representative emission inventories. Emission inventories have traditionally been developed using top down approaches and more recently from data assimilation based on satellite and ground based ambient concentrations and wet deposition data. The National Emission Inventory (NEI) indicates agricultural fertilization as the predominant contributor (56%) to NH3 emissions in Midwest USA, in 2002. However, due to limited understanding of the complex interactions between fertilizer usage, farm practices, soil and meteorological conditions and absence of detailed statistical data, such emission estimates are currently based on generic emission factors, time-averaged temporal factors and coarse spatial resolution. Given the significance of this source, our study focuses on developing an improved NH3 emission inventory for agricultural fertilization at finer spatial and temporal scales for air quality modeling studies. Firstly, a high-spatial resolution 4 km x 4 km NH3 emission inventory for agricultural fertilization has been developed for Illinois by modifying spatial allocation of emissions based on combining crop-specific fertilization rates with cropland distribution in the Sparse Matrix Operator Kernel Emissions model. Net emission estimates of our method are within 2% of NEI, since both methods are constrained by fertilizer sales data. However, we identified localized crop-specific NH3 emission hotspots at sub-county resolutions absent in NEI. Secondly, we have adopted the use of the DeNitrification-DeComposition (DNDC) Biogeochemistry model to simulate the physical and chemical processes that control volatilization of nitrogen as NH3 to the atmosphere after fertilizer application and resolve the variability at the hourly scale. Representative temporal factors are being developed to capture crop-specific NH3 emission variability by combining knowledge of local crop management practices with high resolution cropland and soil maps. This improved spatially and temporally dependent NH3 emission inventory for agricultural fertilization is being prepared as a direct input to a state of the art air quality model to evaluate the effects of agricultural fertilization on regional air quality and atmospheric deposition of reactive nitrogen species.

STOCK: Structure mapper and online coarse-graining kit for molecular simulations

DOE PAGES

Bevc, Staš; Junghans, Christoph; Praprotnik, Matej

2015-03-15

We present a web toolkit STructure mapper and Online Coarse-graining Kit for setting up coarse-grained molecular simulations. The kit consists of two tools: structure mapping and Boltzmann inversion tools. The aim of the first tool is to define a molecular mapping from high, e.g. all-atom, to low, i.e. coarse-grained, resolution. Using a graphical user interface it generates input files, which are compatible with standard coarse-graining packages, e.g. VOTCA and DL_CGMAP. Our second tool generates effective potentials for coarse-grained simulations preserving the structural properties, e.g. radial distribution functions, of the underlying higher resolution model. The required distribution functions can be providedmore » by any simulation package. Simulations are performed on a local machine and only the distributions are uploaded to the server. The applicability of the toolkit is validated by mapping atomistic pentane and polyalanine molecules to a coarse-grained representation. Effective potentials are derived for systems of TIP3P (transferable intermolecular potential 3 point) water molecules and salt solution. The presented coarse-graining web toolkit is available at http://stock.cmm.ki.si.« less

The influence of model spatial resolution on simulated ozone and fine particulate matter for Europe: implications for health impact assessments

NASA Astrophysics Data System (ADS)

Fenech, Sara; Doherty, Ruth M.; Heaviside, Clare; Vardoulakis, Sotiris; Macintyre, Helen L.; O'Connor, Fiona M.

2018-04-01

We examine the impact of model horizontal resolution on simulated concentrations of surface ozone (O3) and particulate matter less than 2.5 µm in diameter (PM2.5), and the associated health impacts over Europe, using the HadGEM3-UKCA chemistry-climate model to simulate pollutant concentrations at a coarse (˜ 140 km) and a finer (˜ 50 km) resolution. The attributable fraction (AF) of total mortality due to long-term exposure to warm season daily maximum 8 h running mean (MDA8) O3 and annual-average PM2.5 concentrations is then calculated for each European country using pollutant concentrations simulated at each resolution. Our results highlight a seasonal variation in simulated O3 and PM2.5 differences between the two model resolutions in Europe. Compared to the finer resolution results, simulated European O3 concentrations at the coarse resolution are higher on average in winter and spring (˜ 10 and ˜ 6 %, respectively). In contrast, simulated O3 concentrations at the coarse resolution are lower in summer and autumn (˜ -1 and ˜ -4 %, respectively). These differences may be partly explained by differences in nitrogen dioxide (NO2) concentrations simulated at the two resolutions. Compared to O3, we find the opposite seasonality in simulated PM2.5 differences between the two resolutions. In winter and spring, simulated PM2.5 concentrations are lower at the coarse compared to the finer resolution (˜ -8 and ˜ -6 %, respectively) but higher in summer and autumn (˜ 29 and ˜ 8 %, respectively). Simulated PM2.5 values are also mostly related to differences in convective rainfall between the two resolutions for all seasons. These differences between the two resolutions exhibit clear spatial patterns for both pollutants that vary by season, and exert a strong influence on country to country variations in estimated AF for the two resolutions. Warm season MDA8 O3 levels are higher in most of southern Europe, but lower in areas of northern and eastern Europe when simulated at the coarse resolution compared to the finer resolution. Annual-average PM2.5 concentrations are higher across most of northern and eastern Europe but lower over parts of southwest Europe at the coarse compared to the finer resolution. Across Europe, differences in the AF associated with long-term exposure to population-weighted MDA8 O3 range between -0.9 and +2.6 % (largest positive differences in southern Europe), while differences in the AF associated with long-term exposure to population-weighted annual mean PM2.5 range from -4.7 to +2.8 % (largest positive differences in eastern Europe) of the total mortality. Therefore this study, with its unique focus on Europe, demonstrates that health impact assessments calculated using modelled pollutant concentrations, are sensitive to a change in model resolution by up to ˜ ±5 % of the total mortality across Europe.

Coarse Scale In Situ Albedo Observations over Heterogeneous Land Surfaces and Validation Strategy

NASA Astrophysics Data System (ADS)

Xiao, Q.; Wu, X.; Wen, J.; BAI, J., Sr.

2017-12-01

To evaluate and improve the quality of coarse-pixel land surface albedo products, validation with ground measurements of albedo is crucial over the spatially and temporally heterogeneous land surface. The performance of albedo validation depends on the quality of ground-based albedo measurements at a corresponding coarse-pixel scale, which can be conceptualized as the "truth" value of albedo at coarse-pixel scale. The wireless sensor network (WSN) technology provides access to continuously observe on the large pixel scale. Taking the albedo products as an example, this paper was dedicated to the validation of coarse-scale albedo products over heterogeneous surfaces based on the WSN observed data, which is aiming at narrowing down the uncertainty of results caused by the spatial scaling mismatch between satellite and ground measurements over heterogeneous surfaces. The reference value of albedo at coarse-pixel scale can be obtained through an upscaling transform function based on all of the observations for that pixel. We will devote to further improve and develop new method that that are better able to account for the spatio-temporal characteristic of surface albedo in the future. Additionally, how to use the widely distributed single site measurements over the heterogeneous surfaces is also a question to be answered. Keywords: Remote sensing; Albedo; Validation; Wireless sensor network (WSN); Upscaling; Heterogeneous land surface; Albedo truth at coarse-pixel scale

Downscaling Coarse Actual ET Data Using Land Surface Resistance

NASA Astrophysics Data System (ADS)

Shen, T.

2017-12-01

This study proposed a new approach of downscaling ETWATCH 1km actual evapotranspiration (ET) product to a spatial resolution of 30m using land surface resistance that simulated mainly from monthly Landsat8 data and Jarvis method, which combined the benefits of both high temporal resolution of ETWATCH product and fine spatial resolution of Landsat8. The driving factor, surface resistance (Rs), was chosen for the reason that could reflect the transfer ability of vapor flow over canopy. Combined resistance Rs both upon canopy conditions, atmospheric factors and available water content of soil, which remains stable inside one ETWATCH pixel (1km). In this research, we used ETWATCH 1km ten-day actual ET product from April to October in a total of twenty-one images and monthly 30 meters cloud-free NDVI of 2013 (two images from HJ as a substitute due to cloud contamination) combined meteorological indicators for downscaling. A good agreement and correlation were obtained between the downscaled data and three flux sites observation in the middle reach of Heihe basin. The downscaling results show good consistency with the original ETWATCH 1km data both temporal and spatial scale over different land cover types with R2 ranged from 0.8 to 0.98. Besides, downscaled result captured the progression of vegetation transpiration well. This study proved the practicability of new downscaling method in the water resource management.

The direct influence of ship traffic on atmospheric PM2.5, PM10 and PAH in Venice.

PubMed

Contini, D; Gambaro, A; Belosi, F; De Pieri, S; Cairns, W R L; Donateo, A; Zanotto, E; Citron, M

2011-09-01

The direct influence of ship traffic on atmospheric levels of coarse and fine particulate matter (PM(2.5), PM(10)) and fifteen polycyclic aromatic hydrocarbons (PAHs) has been estimated in the urban area of Venice. Data analysis has been performed on results collected at three sites over the summer, when ship traffic is at a maximum. Results indicate that monitoring of the PM daily concentrations is not sufficiently detailed for the evaluation of this contribution, even though it could be useful for specific markers such as PAHs. Therefore a new methodology, based on high temporal resolution measurements coupled with wind direction information and the database of ship passages of the Harbour Authority of Venice has been developed. The sampling sites were monitored with optical detectors (DustTrack(®) and Mie pDR-1200) operating at a high temporal resolution (20s and 1s respectively) for PM(2.5) and PM(10). PAH in the particulate and gas phases were recovered from quartz fibre filters and polyurethane foam plugs using pressurised solvent extraction, the extracts were then analysed by gas chromatography- high-resolution mass spectrometry. Our results shows that the direct contribution of ships traffic to PAHs in the gas phase is 10% while the contribution to PM(2.5) and to PM(10) is from 1% up to 8%. Copyright © 2011 Elsevier Ltd. All rights reserved.

Spatio-temporal Granger causality: a new framework

PubMed Central

Luo, Qiang; Lu, Wenlian; Cheng, Wei; Valdes-Sosa, Pedro A.; Wen, Xiaotong; Ding, Mingzhou; Feng, Jianfeng

2015-01-01

That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framework, Granger causality is redefined as a global index measuring the directed information flow between two time series with time-varying properties. Both theoretical analyses and numerical examples demonstrate that Granger causality is a monotonically increasing function of the temporal resolution used in the estimation. This is consistent with the general principle of coarse graining, which causes information loss by smoothing out very fine-scale details in time and space. Our results confirm that the Granger causality at the finer spatio-temporal scales considerably outperforms the traditional approach in terms of an improved consistency between two resting-state scans of the same subject. To optimally estimate the Granger causality, the proposed theoretical framework is implemented through a combination of several approaches, such as dividing the optimal time window and estimating the parameters at the fine temporal and spatial scales. Taken together, our approach provides a novel and robust framework for estimating the Granger causality from fMRI, EEG, and other related data. PMID:23643924

The relative entropy is fundamental to adaptive resolution simulations

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

Kreis, Karsten; Graduate School Materials Science in Mainz, Staudingerweg 9, 55128 Mainz; Potestio, Raffaello, E-mail: potestio@mpip-mainz.mpg.de

Adaptive resolution techniques are powerful methods for the efficient simulation of soft matter systems in which they simultaneously employ atomistic and coarse-grained (CG) force fields. In such simulations, two regions with different resolutions are coupled with each other via a hybrid transition region, and particles change their description on the fly when crossing this boundary. Here we show that the relative entropy, which provides a fundamental basis for many approaches in systematic coarse-graining, is also an effective instrument for the understanding of adaptive resolution simulation methodologies. We demonstrate that the use of coarse-grained potentials which minimize the relative entropy withmore » respect to the atomistic system can help achieve a smoother transition between the different regions within the adaptive setup. Furthermore, we derive a quantitative relation between the width of the hybrid region and the seamlessness of the coupling. Our results do not only shed light on the what and how of adaptive resolution techniques but will also help setting up such simulations in an optimal manner.« less

The relative entropy is fundamental to adaptive resolution simulations

NASA Astrophysics Data System (ADS)

Kreis, Karsten; Potestio, Raffaello

2016-07-01

Adaptive resolution techniques are powerful methods for the efficient simulation of soft matter systems in which they simultaneously employ atomistic and coarse-grained (CG) force fields. In such simulations, two regions with different resolutions are coupled with each other via a hybrid transition region, and particles change their description on the fly when crossing this boundary. Here we show that the relative entropy, which provides a fundamental basis for many approaches in systematic coarse-graining, is also an effective instrument for the understanding of adaptive resolution simulation methodologies. We demonstrate that the use of coarse-grained potentials which minimize the relative entropy with respect to the atomistic system can help achieve a smoother transition between the different regions within the adaptive setup. Furthermore, we derive a quantitative relation between the width of the hybrid region and the seamlessness of the coupling. Our results do not only shed light on the what and how of adaptive resolution techniques but will also help setting up such simulations in an optimal manner.

Temporal pattern and memory in sediment transport in an experimental step-pool channel

NASA Astrophysics Data System (ADS)

Saletti, Matteo; Molnar, Peter; Zimmermann, André; Hassan, Marwan A.; Church, Michael; Burlando, Paolo

2015-04-01

In this work we study the complex dynamics of sediment transport and bed morphology in steep streams, using a dataset of experiments performed in a steep flume with natural sediment. High-resolution (1 sec) time series of sediment transport were measured for individual size classes at the outlet of the flume for different combinations of sediment input rates, discharges, and flume slopes. The data show that the relation between instantaneous discharge and sediment transport exhibits large variability on different levels. After dividing the time series into segments of constant water discharge, we quantify the statistical properties of transport rates by fitting the data with a Generalized Extreme Value distribution, whose 3 parameters are related to the average sediment flux. We analyze separately extreme events of transport rate in terms of their fractional composition; if only events of high magnitude are considered, coarse grains become the predominant component of the total sediment yield. We quantify the memory in grain size dependent sediment transport with variance scaling and autocorrelation analyses; more specifically, we study how the variance changes with different aggregation scales and how the autocorrelation coefficient changes with different time lags. Our results show that there is a tendency to an infinite memory regime in transport rate signals, which is limited by the intermittency of the largest fractions. Moreover, the structure of memory is both grain size-dependent and magnitude-dependent: temporal autocorrelation is stronger for small grain size fractions and when the average sediment transport rate is large. The short-term memory in coarse grain transport increases with temporal aggregation and this reveals the importance of the sampling frequency of bedload transport rates in natural streams, especially for large fractions.

Probing electronic binding potentials with attosecond photoelectron wavepackets

NASA Astrophysics Data System (ADS)

Kiesewetter, D.; Jones, R. R.; Camper, A.; Schoun, S. B.; Agostini, P.; Dimauro, L. F.

2018-01-01

The central goal of attosecond science is to visualize, understand and ultimately control electron dynamics in matter over the fastest relevant timescales. To date, numerous schemes have demonstrated exquisite temporal resolution, on the order of ten attoseconds, in measurements of the response of photo-excited electrons to time-delayed probes. However, attributing this response to specific dynamical mechanisms is difficult, requiring guidance from advanced calculations. Here we show that energy transfer between an oscillating field and low-energy attosecond photoelectron wavepackets directly provides coarse-grained information on the effective binding potential from which the electrons are liberated. We employ a dense extreme ultraviolet (XUV) harmonic comb to photoionize He, Ne and Ar atoms and record the electron spectra as a function of the phase of a mid-infrared dressing field. The amplitude and phase of the resulting interference modulations in the electron spectra reveal the average momentum and change in momentum of the electron wavepackets during the first quarter-period of the dressing field after their creation, reflecting the corresponding coarse characteristics of the binding potential.

Sorted bedform pattern evolution: Persistence, destruction and self-organized intermittency

NASA Astrophysics Data System (ADS)

Goldstein, Evan B.; Murray, A. Brad; Coco, Giovanni

2011-12-01

We investigate the long-term evolution of inner continental shelf sorted bedform patterns. Numerical modeling suggests that a range of behaviors are possible, from pattern persistence to spatial-temporal intermittency. Sorted bedform persistence results from a robust sorting feedback that operates when the seabed features a sufficient concentration of coarse material. In the absence of storm events, pattern maturation processes such as defect dynamics and pattern migration tend to cause the burial of coarse material and excavation of fine material, leading to the fining of the active layer. Vertical sorting occurs until a critical state of active layer coarseness is reached. This critical state results in the local cessation of the sorting feedback, leading to a self-organized spatially intermittent pattern, a hallmark of observed sorted bedforms. Bedforms in shallow conditions and those subject to high wave climates may be temporally intermittent features as a result of increased wave orbital velocity during storms. Erosion, or deposition of bimodal sediment, similarly leads to a spatially intermittent pattern, with individual coarse domains exhibiting temporal intermittence. Recurring storm events cause coarsening of the seabed (strengthening the sorting feedback) and the development of large wavelength patterns. Cessation of storm events leads to the superposition of storm (large wavelength) and inter-storm (small wavelength) patterns and spatial heterogeneity of pattern modes.

Quantum Mechanics/Molecular Mechanics Method Combined with Hybrid All-Atom and Coarse-Grained Model: Theory and Application on Redox Potential Calculations.

PubMed

Shen, Lin; Yang, Weitao

2016-04-12

We developed a new multiresolution method that spans three levels of resolution with quantum mechanical, atomistic molecular mechanical, and coarse-grained models. The resolution-adapted all-atom and coarse-grained water model, in which an all-atom structural description of the entire system is maintained during the simulations, is combined with the ab initio quantum mechanics and molecular mechanics method. We apply this model to calculate the redox potentials of the aqueous ruthenium and iron complexes by using the fractional number of electrons approach and thermodynamic integration simulations. The redox potentials are recovered in excellent accordance with the experimental data. The speed-up of the hybrid all-atom and coarse-grained water model renders it computationally more attractive. The accuracy depends on the hybrid all-atom and coarse-grained water model used in the combined quantum mechanical and molecular mechanical method. We have used another multiresolution model, in which an atomic-level layer of water molecules around redox center is solvated in supramolecular coarse-grained waters for the redox potential calculations. Compared with the experimental data, this alternative multilayer model leads to less accurate results when used with the coarse-grained polarizable MARTINI water or big multipole water model for the coarse-grained layer.

Brain activity related to working memory for temporal order and object information.

PubMed

Roberts, Brooke M; Libby, Laura A; Inhoff, Marika C; Ranganath, Charan

2017-06-08

Maintaining items in an appropriate sequence is important for many daily activities; however, remarkably little is known about the neural basis of human temporal working memory. Prior work suggests that the prefrontal cortex (PFC) and medial temporal lobe (MTL), including the hippocampus, play a role in representing information about temporal order. The involvement of these areas in successful temporal working memory, however, is less clear. Additionally, it is unknown whether regions in the PFC and MTL support temporal working memory across different timescales, or at coarse or fine levels of temporal detail. To address these questions, participants were scanned while completing 3 working memory task conditions (Group, Position and Item) that were matched in terms of difficulty and the number of items to be actively maintained. Group and Position trials probed temporal working memory processes, requiring the maintenance of hierarchically organized coarse and fine temporal information, respectively. To isolate activation related to temporal working memory, Group and Position trials were contrasted against Item trials, which required detailed working memory maintenance of visual objects. Results revealed that working memory encoding and maintenance of temporal information relative to visual information was associated with increased activation in dorsolateral PFC (DLPFC), and perirhinal cortex (PRC). In contrast, maintenance of visual details relative to temporal information was characterized by greater activation of parahippocampal cortex (PHC), medial and anterior PFC, and retrosplenial cortex. In the hippocampus, a dissociation along the longitudinal axis was observed such that the anterior hippocampus was more active for working memory encoding and maintenance of visual detail information relative to temporal information, whereas the posterior hippocampus displayed the opposite effect. Posterior parietal cortex was the only region to show sensitivity to temporal working memory across timescales, and was particularly involved in the encoding and maintenance of fine temporal information relative to maintenance of temporal information at more coarse timescales. Collectively, these results highlight the involvement of PFC and MTL in temporal working memory processes, and suggest a dissociation in the type of working memory information represented along the longitudinal axis of the hippocampus. Copyright © 2017 Elsevier B.V. All rights reserved.

Sensitivity of chemistry-transport model simulations to the duration of chemical and transport operators: a case study with GEOS-Chem v10-01

NASA Astrophysics Data System (ADS)

Philip, Sajeev; Martin, Randall V.; Keller, Christoph A.

2016-05-01

Chemistry-transport models involve considerable computational expense. Fine temporal resolution offers accuracy at the expense of computation time. Assessment is needed of the sensitivity of simulation accuracy to the duration of chemical and transport operators. We conduct a series of simulations with the GEOS-Chem chemistry-transport model at different temporal and spatial resolutions to examine the sensitivity of simulated atmospheric composition to operator duration. Subsequently, we compare the species simulated with operator durations from 10 to 60 min as typically used by global chemistry-transport models, and identify the operator durations that optimize both computational expense and simulation accuracy. We find that longer continuous transport operator duration increases concentrations of emitted species such as nitrogen oxides and carbon monoxide since a more homogeneous distribution reduces loss through chemical reactions and dry deposition. The increased concentrations of ozone precursors increase ozone production with longer transport operator duration. Longer chemical operator duration decreases sulfate and ammonium but increases nitrate due to feedbacks with in-cloud sulfur dioxide oxidation and aerosol thermodynamics. The simulation duration decreases by up to a factor of 5 from fine (5 min) to coarse (60 min) operator duration. We assess the change in simulation accuracy with resolution by comparing the root mean square difference in ground-level concentrations of nitrogen oxides, secondary inorganic aerosols, ozone and carbon monoxide with a finer temporal or spatial resolution taken as "truth". Relative simulation error for these species increases by more than a factor of 5 from the shortest (5 min) to longest (60 min) operator duration. Chemical operator duration twice that of the transport operator duration offers more simulation accuracy per unit computation. However, the relative simulation error from coarser spatial resolution generally exceeds that from longer operator duration; e.g., degrading from 2° × 2.5° to 4° × 5° increases error by an order of magnitude. We recommend prioritizing fine spatial resolution before considering different operator durations in offline chemistry-transport models. We encourage chemistry-transport model users to specify in publications the durations of operators due to their effects on simulation accuracy.

Bridging the scales in atmospheric composition simulations using a nudging technique

NASA Astrophysics Data System (ADS)

D'Isidoro, Massimo; Maurizi, Alberto; Russo, Felicita; Tampieri, Francesco

2010-05-01

Studying the interaction between climate and anthropogenic activities, specifically those concentrated in megacities/hot spots, requires the description of processes in a very wide range of scales from local, where anthropogenic emissions are concentrated to global where we are interested to study the impact of these sources. The description of all the processes at all scales within the same numerical implementation is not feasible because of limited computer resources. Therefore, different phenomena are studied by means of different numerical models that can cover different range of scales. The exchange of information from small to large scale is highly non-trivial though of high interest. In fact uncertainties in large scale simulations are expected to receive large contribution from the most polluted areas where the highly inhomogeneous distribution of sources connected to the intrinsic non-linearity of the processes involved can generate non negligible departures between coarse and fine scale simulations. In this work a new method is proposed and investigated in a case study (August 2009) using the BOLCHEM model. Monthly simulations at coarse (0.5° European domain, run A) and fine (0.1° Central Mediterranean domain, run B) horizontal resolution are performed using the coarse resolution as boundary condition for the fine one. Then another coarse resolution run (run C) is performed, in which the high resolution fields remapped on to the coarse grid are used to nudge the concentrations on the Po Valley area. The nudging is applied to all gas and aerosol species of BOLCHEM. Averaged concentrations and variances over Po Valley and other selected areas for O3 and PM are computed. It is observed that although the variance of run B is markedly larger than that of run A, the variance of run C is smaller because the remapping procedure removes large portion of variance from run B fields. Mean concentrations show some differences depending on species: in general mean values of run C lie between run A and run B. A propagation of the signal outside the nudging region is observed, and is evaluated in terms of differences between coarse resolution (with and without nudging) and fine resolution simulations.

Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations

NASA Astrophysics Data System (ADS)

Zhang, Xiaoyang; Zhang, Qingyuan

2016-04-01

Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data have been extensively applied for crop yield prediction because of their daily temporal resolution and a global coverage. This study investigated global crop yield using daily two band Enhanced Vegetation Index (EVI2) derived from AVHRR (1981-1999) and MODIS (2000-2013) observations at a spatial resolution of 0.05° (∼5 km). Specifically, EVI2 temporal trajectory of crop growth was simulated using a hybrid piecewise logistic model (HPLM) for individual pixels, which was used to detect crop phenological metrics. The derived crop phenology was then applied to calculate crop greenness defined as EVI2 amplitude and EVI2 integration during annual crop growing seasons, which was further aggregated for croplands in each country, respectively. The interannual variations in EVI2 amplitude and EVI2 integration were combined to correlate to the variation in cereal yield from 1982-2012 for individual countries using a stepwise regression model, respectively. The results show that the confidence level of the established regression models was higher than 90% (P value < 0.1) in most countries in the northern hemisphere although it was relatively poor in the southern hemisphere (mainly in Africa). The error in the yield predication was relatively smaller in America, Europe and East Asia than that in Africa. In the 10 countries with largest cereal production across the world, the prediction error was less than 9% during past three decades. This suggests that crop phenology-controlled greenness from coarse resolution satellite data has the capability of predicting national crop yield across the world, which could provide timely and reliable crop information for global agricultural trade and policymakers.

Efficient super-resolution image reconstruction applied to surveillance video captured by small unmanned aircraft systems

NASA Astrophysics Data System (ADS)

He, Qiang; Schultz, Richard R.; Chu, Chee-Hung Henry

2008-04-01

The concept surrounding super-resolution image reconstruction is to recover a highly-resolved image from a series of low-resolution images via between-frame subpixel image registration. In this paper, we propose a novel and efficient super-resolution algorithm, and then apply it to the reconstruction of real video data captured by a small Unmanned Aircraft System (UAS). Small UAS aircraft generally have a wingspan of less than four meters, so that these vehicles and their payloads can be buffeted by even light winds, resulting in potentially unstable video. This algorithm is based on a coarse-to-fine strategy, in which a coarsely super-resolved image sequence is first built from the original video data by image registration and bi-cubic interpolation between a fixed reference frame and every additional frame. It is well known that the median filter is robust to outliers. If we calculate pixel-wise medians in the coarsely super-resolved image sequence, we can restore a refined super-resolved image. The primary advantage is that this is a noniterative algorithm, unlike traditional approaches based on highly-computational iterative algorithms. Experimental results show that our coarse-to-fine super-resolution algorithm is not only robust, but also very efficient. In comparison with five well-known super-resolution algorithms, namely the robust super-resolution algorithm, bi-cubic interpolation, projection onto convex sets (POCS), the Papoulis-Gerchberg algorithm, and the iterated back projection algorithm, our proposed algorithm gives both strong efficiency and robustness, as well as good visual performance. This is particularly useful for the application of super-resolution to UAS surveillance video, where real-time processing is highly desired.

Single Particulate SEM-EDX Analysis of Iron-Containing Coarse Particulate Matter in an Urban Environment: Sources and Distribution of Iron within Cleveland, Ohio

EPA Science Inventory

The physicochemical properties of coarse-mode, iron-containing particles, and their temporal and spatial distributions are poorly understood. Single particle analysis combining x-ray elemental mapping and computer-controlled scanning electron microscopy (CCSEM-EDX) of passively ...

Lava flows and cinder cones at Barren Island volcano, India (2005-2017): a spatio-temporal analysis using satellite images

NASA Astrophysics Data System (ADS)

Martha, Tapas R.; Roy, Priyom; Vinod Kumar, K.

2018-02-01

Barren Island volcano erupted during January-February 2017. Located near the Andaman trench and over a subduction zone, it is the only active volcano in India. It comprises a prominent caldera within which there is a polygenetic intra-caldera cinder cone system, with a record of eruptive events which date back to eighteenth century (1787-1832). Major eruptions occurred in 1991, 1994-1995, 2005 and, since 2008, the volcano has been showing near continuous activity with periodic eruptions. We used coarse spatial resolution "fire" products (Band I4) from Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite to detect days of eruption during the January-February 2017 period. Moderate spatial resolution (23.5 m) short-wavelength infrared (SWIR) data of Resourcesat-2 Linear Imaging Self Scanning Sensor-III available for specific days during this period were used to verify signatures of volcanic eruption. Thermal infrared band data from the Landsat series over the 2005-2017 periods were used to estimate the brightness temperature and location of the active vent within the polygenetic cinder cone field. High-spatial resolution images (1-5.8 m) in the visible bands (Resourcesat-2 LISS-IV, Cartosat-1 and 2) were used to delineate the changes in overall morphology of the volcano and to identify an inner crater ring fault, new paths of lava flow and the formation of a new cinder cone on the old crater. These multi-temporal data sets show significant changes in the paths of lava flows from 2005 to 2017. The observations also document periodic shifts in the location of effusive vents. Morphogenetic changes in recent eruptive phases of the Barren Island volcano were successfully delineated using a combination of multi-temporal and multi-resolution satellite images in visible, SWIR and thermal infrared regions of the electromagnetic spectrum.

Hybrid Multiscale Finite Volume method for multiresolution simulations of flow and reactive transport in porous media

NASA Astrophysics Data System (ADS)

Barajas-Solano, D. A.; Tartakovsky, A. M.

2017-12-01

We present a multiresolution method for the numerical simulation of flow and reactive transport in porous, heterogeneous media, based on the hybrid Multiscale Finite Volume (h-MsFV) algorithm. The h-MsFV algorithm allows us to couple high-resolution (fine scale) flow and transport models with lower resolution (coarse) models to locally refine both spatial resolution and transport models. The fine scale problem is decomposed into various "local'' problems solved independently in parallel and coordinated via a "global'' problem. This global problem is then coupled with the coarse model to strictly ensure domain-wide coarse-scale mass conservation. The proposed method provides an alternative to adaptive mesh refinement (AMR), due to its capacity to rapidly refine spatial resolution beyond what's possible with state-of-the-art AMR techniques, and the capability to locally swap transport models. We illustrate our method by applying it to groundwater flow and reactive transport of multiple species.

Application of GRACE for Monitoring Groundwater in Data Scarce Regions

NASA Technical Reports Server (NTRS)

Rodell, Matt; Li, Bailing; Famiglietti, Jay; Zaitchik, Ben

2012-01-01

In the United States, groundwater storage is somewhat well monitored (spatial and temporal data gaps notwithstanding) and abundant data are freely and easily accessible. Outside of the U.S., groundwater often is not monitored systematically and where it is the data are rarely centralized and made available. Since 2002 the Gravity Recovery and Climate Experiment (GRACE) satellite mission has delivered gravity field observations which have been used to infer variations in total terrestrial water storage, including groundwater, at regional to continental scales. Challenges to using GRACE for groundwater monitoring include its relatively coarse spatial and temporal resolutions, its inability to differentiate groundwater from other types of water on and under the land surface, and typical 2-3 month data latency. Data assimilation can be used to overcome these challenges, but uncertainty in the results remains and is difficult to quantify without independent observations. Nevertheless, the results are preferable to the alternative - no data at all- and GRACE has already revealed groundwater variability and trends in regions where only anecdotal evidence existed previously.

Multi-Decadal Pathfinder Data Sets of Global Land Biophysical Variables from AVHRR and MODIS and their Use in GCM Studies of Biogeophysics and Biogeochemistry

NASA Technical Reports Server (NTRS)

Myneni, Ranga

2003-01-01

The problem of how the scale, or spatial resolution, of reflectance data impacts retrievals of vegetation leaf area index (LAI) and fraction absorbed photosynthetically active radiation (PAR) has been investigated. We define the goal of scaling as the process by which it is established that LAI and FPAR values derived from coarse resolution sensor data equal the arithmetic average of values derived independently from fine resolution sensor data. The increasing probability of land cover mixtures with decreasing resolution is defined as heterogeneity, which is a key concept in scaling studies. The effect of pixel heterogeneity on spectral reflectances and LAI/FPAR retrievals is investigated with 1 km Advanced Very High Resolution Radiometer (AVHRR) data aggregated to different coarse spatial resolutions. It is shown that LAI retrieval errors at coarse resolution are inversely related to the proportion of the dominant land cover in such pixel. Further, large errors in LAI retrievals are incurred when forests are minority biomes in non-forest pixels compared to when forest biomes are mixed with one another, and vice-versa. A physically based technique for scaling with explicit spatial resolution dependent radiative transfer formulation is developed. The successful application of this theory to scaling LAI retrievals from AVHRR data of different resolutions is demonstrated

A downscaling scheme for atmospheric variables to drive soil-vegetation-atmosphere transfer models

NASA Astrophysics Data System (ADS)

Schomburg, A.; Venema, V.; Lindau, R.; Ament, F.; Simmer, C.

2010-09-01

For driving soil-vegetation-transfer models or hydrological models, high-resolution atmospheric forcing data is needed. For most applications the resolution of atmospheric model output is too coarse. To avoid biases due to the non-linear processes, a downscaling system should predict the unresolved variability of the atmospheric forcing. For this purpose we derived a disaggregation system consisting of three steps: (1) a bi-quadratic spline-interpolation of the low-resolution data, (2) a so-called `deterministic' part, based on statistical rules between high-resolution surface variables and the desired atmospheric near-surface variables and (3) an autoregressive noise-generation step. The disaggregation system has been developed and tested based on high-resolution model output (400m horizontal grid spacing). A novel automatic search-algorithm has been developed for deriving the deterministic downscaling rules of step 2. When applied to the atmospheric variables of the lowest layer of the atmospheric COSMO-model, the disaggregation is able to adequately reconstruct the reference fields. Applying downscaling step 1 and 2, root mean square errors are decreased. Step 3 finally leads to a close match of the subgrid variability and temporal autocorrelation with the reference fields. The scheme can be applied to the output of atmospheric models, both for stand-alone offline simulations, and a fully coupled model system.

Toward Automatic Georeferencing of Archival Aerial Photogrammetric Surveys

NASA Astrophysics Data System (ADS)

Giordano, S.; Le Bris, A.; Mallet, C.

2018-05-01

Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes over the past 100 years. They provide a relatively dense temporal sampling of the territories with very high spatial resolution. Such time series image analysis is a mandatory baseline for a large variety of long-term environmental monitoring studies. The current bottleneck for accurate comparison between epochs is their fine georeferencing step. No fully automatic method has been proposed yet and existing studies are rather limited in terms of area and number of dates. State-of-the art shows that the major challenge is the identification of ground references: cartographic coordinates and their position in the archival images. This task is manually performed, and extremely time-consuming. This paper proposes to use a photogrammetric approach, and states that the 3D information that can be computed is the key to full automation. Its original idea lies in a 2-step approach: (i) the computation of a coarse absolute image orientation; (ii) the use of the coarse Digital Surface Model (DSM) information for automatic absolute image orientation. It only relies on a recent orthoimage+DSM, used as master reference for all epochs. The coarse orthoimage, compared with such a reference, allows the identification of dense ground references and the coarse DSM provides their position in the archival images. Results on two areas and 5 dates show that this method is compatible with long and dense archival aerial image series. Satisfactory planimetric and altimetric accuracies are reported, with variations depending on the ground sampling distance of the images and the location of the Ground Control Points.

Revisiting surface albedo changes over Greenland since 1980s using satellite data from GLASS, CLARA, MODIS, and Landsat

NASA Astrophysics Data System (ADS)

He, T.; Liang, S.; Zhang, Y.

2017-12-01

Massive melting events over Greenland have been observed over the past few decades. Accompanying the melting events are the surface albedo changes, which had temporal and spatial variations. Albedo changes over Greenland during the past few decades have been reported in previous studies with the help of satellite observations; however, magnitudes and timing in albedo trends differ greatly in those studies. This has limited our understanding of albedo change mechanisms over Greenland. In this study, we present an analysis of surface albedo change over Greenland since 1980s combining four satellite albedo datasets, namely MODIS, GLASS, CLARA, and Landsat. MODIS, GLASS, and CLARA albedo data are publicly available and Landsat albedos were derived in our earlier study trying to bridge the scale difference between coarse resolution data and ground measurements available from early 1980s. Inter-comparisons were made among the satellite albedos and against ground measurements. We have several new findings. First, trends in surface albedo change among the satellite albedo datasets generally agree with each other and with ground measurements. Second, all datasets showed negative albedo trends after 2000, but magnitudes differ greatly. Third, trends before 2000 from coarse resolution data are not significant but Landsat data observed positive albedo changes. Fourth, the turning point of albedo trend was found to be earlier than 2000. Those findings may bring new research topics on timing and magnitude, and an improved understanding mechanisms of the albedo changes over Greenland during the past few decades.

Agent Based Modeling: Fine-Scale Spatio-Temporal Analysis of Pertussis

NASA Astrophysics Data System (ADS)

Mills, D. A.

2017-10-01

In epidemiology, spatial and temporal variables are used to compute vaccination efficacy and effectiveness. The chosen resolution and scale of a spatial or spatio-temporal analysis will affect the results. When calculating vaccination efficacy, for example, a simple environment that offers various ideal outcomes is often modeled using coarse scale data aggregated on an annual basis. In contrast to the inadequacy of this aggregated method, this research uses agent based modeling of fine-scale neighborhood data centered around the interactions of infants in daycare and their families to demonstrate an accurate reflection of vaccination capabilities. Despite being able to prevent major symptoms, recent studies suggest that acellular Pertussis does not prevent the colonization and transmission of Bordetella Pertussis bacteria. After vaccination, a treated individual becomes a potential asymptomatic carrier of the Pertussis bacteria, rather than an immune individual. Agent based modeling enables the measurable depiction of asymptomatic carriers that are otherwise unaccounted for when calculating vaccination efficacy and effectiveness. Using empirical data from a Florida Pertussis outbreak case study, the results of this model demonstrate that asymptomatic carriers bias the calculated vaccination efficacy and reveal a need for reconsidering current methods that are widely used for calculating vaccination efficacy and effectiveness.

Regional forest land cover characterisation using medium spatial resolution satellite data

USGS Publications Warehouse

Huang, Chengquan; Homer, Collin G.; Yang, Limin; Wulder, Michael A.; Franklin, Steven E.

2003-01-01

Increasing demands on forest resources require comprehensive, consistent and up-to-date information on those resources at spatial scales appropriate for management decision-making and for scientific analysis. While such information can be derived using coarse spatial resolution satellite data (e.g. Tucker et al. 1984; Zhu and Evans 1994; Cihlar et al. 1996; Cihlar et al., Chapter 12), many regional applications require more spatial and thematic details than can be derived by using coarse resolution imagery. High spatial resolution satellite data such as IKONOS and Quick Bird images (Aplin et al. 1997), though usable for deriving detailed forest information (Culvenor, Chapter 9), are currently not feasible for wall-to-wall regional applications because of extremely high data cost, huge data volume, and lack of contiguous coverage over large areas. Forest studies over large areas have often been accomplished using data acquired by intermediate spatial resolution sensor systems, including the Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and the Enhanced Thematic Mapper Plus (ETM+) of Landsat, the High Resolution Visible (HRV) of the Systeme Pour l'Observation de la Terre (SPOT), and the Linear Image Self-Scanner (LISS) of the Indian Remote Sensing satellite. These sensor systems are more appropriate for regional applications because they can routinely produce spatially contiguous data over large areas at relatively low cost, and can be used to derive a host of forest attributes (e.g. Cohen et al. 1995; Kimes et al. 1999; Cohen et al. 2001; Huang et al. 2001; Sugumaran 2001). Of the above intermediate spatial resolution satellites, Landsat is perhaps the most widely used in various types of land remote sensing applications, in part because it has provided more extensive spatial and temporal coverage of the globe than any other intermediate resolution satellite. Spatially contiguous Landsat data have been developed for many regions of the globe (e.g. Lunetta and Sturdevant 1993; Fuller et al. 1994b; Skole et al. 1997), and a circa 1990 Landsat image data set covering the entire land area of the globe has also been developed recently (Jones and Smith 2001). An acquisition strategy aimed at acquiring at least one cloud free image per year for the entire land area of the globe has been initiated for Landsat-7 (Arvidson et al. 2001). This will probably ensure the continued dominance of Landsat in the near future.

Policy applications of a highly resolved spatial and temporal onroad carbon dioxide emissions data product for the U.S.: Analyses and their implications for mitigation

NASA Astrophysics Data System (ADS)

Mendoza Lebrun, Daniel

Onroad CO2 emissions were analyzed as part of overall GHG emissions, but those studies have suffered from one or more of these five shortcomings: 1) the spatial resolution was coarse, usually encompassing a region, or the entire U.S.; 2) the temporal resolution was coarse (annual or monthly); 3) the study region was limited, usually a metropolitan planning organization (MPO) or state; 4) fuel sales were used as a proxy to quantify fuel consumption instead of focusing on travel; 5) the spatial heterogeneity of fleet and road network composition was not considered and instead national averages are used. Normalized vehicle-type state-level spatial biases range from 2.6% to 8.1%, while the road type classification biases range from -6.3% to 16.8%. These biases are found to cause errors in reduction estimates as large as ±60%, corresponding to ±0.2 MtC, for a national-average emissions mitigation strategy focused on a 10% emissions reduction from a single vehicle class. Temporal analysis shows distinct emissions seasonality that is particularly visible in the northernmost latitudes, demonstrating peak-to-peak deviations from the annual mean of up to 50%. The hourly structure shows peak-to-peak deviation from a weekly average of up to 200% for heavy-duty (HD) vehicles and 140% for light-duty (LD) vehicles. The present study focuses on reduction of travel and fuel economy improvements by putting forth several mitigation scenarios aimed at reducing VMT and increasing vehicle fuel efficiency. It was found that the most effective independent reduction strategies are those that increase fuel efficiency by extending standards proposed by the corporate average fuel economy (CAFE) or reduction of fuel consumption due to price increases. These two strategies show cumulative emissions reductions of approximately 11% and 12%, respectively, from a business as usual (BAU) approach over the 2000-2050 period. The U.S. onroad transportation sector is long overdue a comprehensive study of CO2 emissions at a highly resolved level. Such a study would improve fossil fuel flux products by enhancing measurement accuracy and prompt location-specific mitigation policy. The carbon cycle science and policymaking communities are both poised to benefit greatly from the development of a highly resolved spatiotemporal emissions product.

Satellite-based high-resolution PM2.5 estimation over the Beijing-Tianjin-Hebei region of China using an improved geographically and temporally weighted regression model.

PubMed

He, Qingqing; Huang, Bo

2018-05-01

Ground fine particulate matter (PM2.5) concentrations at high spatial resolution are substantially required for determining the population exposure to PM2.5 over densely populated urban areas. However, most studies for China have generated PM2.5 estimations at a coarse resolution (≥10 km) due to the limitation of satellite aerosol optical depth (AOD) product in spatial resolution. In this study, the 3 km AOD data fused using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 AOD products were employed to estimate the ground PM2.5 concentrations over the Beijing-Tianjin-Hebei (BTH) region of China from January 2013 to December 2015. An improved geographically and temporally weighted regression (iGTWR) model incorporating seasonal characteristics within the data was developed, which achieved comparable performance to the standard GTWR model for the days with paired PM 2.5 - AOD samples (Cross-validation (CV) R 2  = 0.82) and showed better predictive power for the days without PM 2.5 - AOD pairs (the R 2 increased from 0.24 to 0.46 in CV). Both iGTWR and GTWR (CV R 2  = 0.84) significantly outperformed the daily geographically weighted regression model (CV R 2  = 0.66). Also, the fused 3 km AODs improved data availability and presented more spatial gradients, thereby enhancing model performance compared with the MODIS original 3/10 km AOD product. As a result, ground PM2.5 concentrations at higher resolution were well represented, allowing, e.g., short-term pollution events and long-term PM2.5 trend to be identified, which, in turn, indicated that concerns about air pollution in the BTH region are justified despite its decreasing trend from 2013 to 2015. Copyright © 2018 Elsevier Ltd. All rights reserved.

Satellite-Scale Snow Water Equivalent Assimilation into a High-Resolution Land Surface Model

NASA Technical Reports Server (NTRS)

De Lannoy, Gabrielle J.M.; Reichle, Rolf H.; Houser, Paul R.; Arsenault, Kristi R.; Verhoest, Niko E.C.; Paulwels, Valentijn R.N.

2009-01-01

An ensemble Kalman filter (EnKF) is used in a suite of synthetic experiments to assimilate coarse-scale (25 km) snow water equivalent (SWE) observations (typical of satellite retrievals) into fine-scale (1 km) model simulations. Coarse-scale observations are assimilated directly using an observation operator for mapping between the coarse and fine scales or, alternatively, after disaggregation (re-gridding) to the fine-scale model resolution prior to data assimilation. In either case observations are assimilated either simultaneously or independently for each location. Results indicate that assimilating disaggregated fine-scale observations independently (method 1D-F1) is less efficient than assimilating a collection of neighboring disaggregated observations (method 3D-Fm). Direct assimilation of coarse-scale observations is superior to a priori disaggregation. Independent assimilation of individual coarse-scale observations (method 3D-C1) can bring the overall mean analyzed field close to the truth, but does not necessarily improve estimates of the fine-scale structure. There is a clear benefit to simultaneously assimilating multiple coarse-scale observations (method 3D-Cm) even as the entire domain is observed, indicating that underlying spatial error correlations can be exploited to improve SWE estimates. Method 3D-Cm avoids artificial transitions at the coarse observation pixel boundaries and can reduce the RMSE by 60% when compared to the open loop in this study.

Hyper-Resolution Groundwater Modeling using MODFLOW 6

NASA Astrophysics Data System (ADS)

Hughes, J. D.; Langevin, C.

2017-12-01

MODFLOW 6 is the latest version of the U.S. Geological Survey's modular hydrologic model. MODFLOW 6 was developed to synthesize many of the recent versions of MODFLOW into a single program, improve the way different process models are coupled, and to provide an object-oriented framework for adding new types of models and packages. The object-oriented framework and underlying numerical solver make it possible to tightly couple any number of hyper-resolution models within coarser regional models. The hyper-resolution models can be used to evaluate local-scale groundwater issues that may be affected by regional-scale forcings. In MODFLOW 6, hyper-resolution meshes can be maintained as separate model datasets, similar to MODFLOW-LGR, which simplifies the development of a coarse regional model with imbedded hyper-resolution models from a coarse regional model. For example, the South Atlantic Coastal Plain regional water availability model was converted from a MODFLOW-2000 model to a MODFLOW 6 model. The horizontal discretization of the original model is approximately 3,218 m x 3,218 m. Hyper-resolution models of the Aiken and Sumter County water budget areas in South Carolina with a horizontal discretization of approximately 322 m x 322 m were developed and were tightly coupled to a modified version of the original coarse regional model that excluded these areas. Hydraulic property and aquifer geometry data from the coarse model were mapped to the hyper-resolution models. The discretization of the hyper-resolution models is fine enough to make detailed analyses of the effect that changes in groundwater withdrawals in the production aquifers have on the water table and surface-water/groundwater interactions. The approach used in this analysis could be applied to other regional water availability models that have been developed by the U.S. Geological Survey to evaluate local scale groundwater issues.

Geospatial assessment of ecological functions and flood-related risks on floodplains along major rivers in the Puget Sound Basin, Washington

USGS Publications Warehouse

Konrad, Christopher P.

2015-01-01

Ecological functions and flood-related risks were assessed for floodplains along the 17 major rivers flowing into Puget Sound Basin, Washington. The assessment addresses five ecological functions, five components of flood-related risks at two spatial resolutions—fine and coarse. The fine-resolution assessment compiled spatial attributes of floodplains from existing, publically available sources and integrated the attributes into 10-meter rasters for each function, hazard, or exposure. The raster values generally represent different types of floodplains with regard to each function, hazard, or exposure rather than the degree of function, hazard, or exposure. The coarse-resolution assessment tabulates attributes from the fine-resolution assessment for larger floodplain units, which are floodplains associated with 0.1 to 21-kilometer long segments of major rivers. The coarse-resolution assessment also derives indices that can be used to compare function or risk among different floodplain units and to develop normative (based on observed distributions) standards. The products of the assessment are available online as geospatial datasets (Konrad, 2015; http://dx.doi.org/10.5066/F7DR2SJC).

Source identification of coarse particles in the Desert ...

EPA Pesticide Factsheets

The Desert Southwest Coarse Particulate Matter Study was undertaken to further our understanding of the spatial and temporal variability and sources of fine and coarse particulate matter (PM) in rural, arid, desert environments. Sampling was conducted between February 2009 and February 2010 in Pinal County, AZ near the town of Casa Grande where PM concentrations routinely exceed the U.S. National Ambient Air Quality Standards (NAAQS) for both PM10 and PM2.5. In this desert region, exceedances of the PM10 NAAQS are dominated by high coarse particle concentrations, a common occurrence in this region of the United States. This work expands on previously published measurements of PM mass and chemistry by examining the sources of fine and coarse particles and the relative contribution of each to ambient PM mass concentrations using the Positive Matrix Factorization receptor model (Clements et al., 2014). Highlights • Isolation of coarse particles from fine particle sources. • Unique chemical composition of coarse particles. • Role of primary biological particles on aerosol loadings.

Spatial and temporal dynamics of commercial reef-fish fisheries on the West Florida Shelf: Understanding drivers of fleet behavior and the implications for future management

NASA Astrophysics Data System (ADS)

Cockrell, M.; Murawski, S. A.; Sanchirico, J. N.; O'Farrell, S.; Strelcheck, A.

2016-02-01

Spatial and temporal patterns of fishing activity have historically been described over relatively coarse scales or with limited datasets. However, new and innovative approaches for fisheries management will require an understanding of both species population dynamics and fleet behavior at finer spatial and temporal resolution. In this study we describe the spatial and temporal patterns of commercial reef-fish fisheries on the West Florida Shelf (WFS) from 2006-14, using a combination of on-board observer, catch logbook, and vessel satellite tracking data. The satellite tracking data is both high resolution (ie, records from each vessel at least once every hour for the duration of a trip), and required of all federally-permitted reef fish vessels in the Gulf of Mexico, making this a uniquely rich and powerful dataset. Along with spatial and temporal fishery dynamics, we quantified concomitant patterns in fishery economics and catch metrics, such as total landings and catch composition. Fishery patterns were correlated to a number of variables across the vessel, trip, and whole fleet scales, including vessel size, distance from home port, number of days at sea, and days available to fish. Notably, changes in management structure during the years examined (eg, establishment of a seasonal closed area in 2009 and implementation of an individual fishing quota system for Grouper-Tilefish in 2010), as well as emergency spatial closures during the Deepwater Horizon oil spill in 2010, enabled us to examine the impacts of specific management frameworks on the WFS reef-fish fishery. This research highlights the need to better understand the biological, economic, and social impacts within fisheries when managing for conservation and fisheries sustainability. We discuss our results in the context of a changing policy and management landscape for marine and coastal resources in the Gulf of Mexico.

Framework for Detection and Localization of Extreme Climate Event with Pixel Recursive Super Resolution

NASA Astrophysics Data System (ADS)

Kim, S. K.; Lee, J.; Zhang, C.; Ames, S.; Williams, D. N.

2017-12-01

Deep learning techniques have been successfully applied to solve many problems in climate and geoscience using massive-scaled observed and modeled data. For extreme climate event detections, several models based on deep neural networks have been recently proposed and attend superior performance that overshadows all previous handcrafted expert based method. The issue arising, though, is that accurate localization of events requires high quality of climate data. In this work, we propose framework capable of detecting and localizing extreme climate events in very coarse climate data. Our framework is based on two models using deep neural networks, (1) Convolutional Neural Networks (CNNs) to detect and localize extreme climate events, and (2) Pixel recursive recursive super resolution model to reconstruct high resolution climate data from low resolution climate data. Based on our preliminary work, we have presented two CNNs in our framework for different purposes, detection and localization. Our results using CNNs for extreme climate events detection shows that simple neural nets can capture the pattern of extreme climate events with high accuracy from very coarse reanalysis data. However, localization accuracy is relatively low due to the coarse resolution. To resolve this issue, the pixel recursive super resolution model reconstructs the resolution of input of localization CNNs. We present a best networks using pixel recursive super resolution model that synthesizes details of tropical cyclone in ground truth data while enhancing their resolution. Therefore, this approach not only dramat- ically reduces the human effort, but also suggests possibility to reduce computing cost required for downscaling process to increase resolution of data.

Resolution, the key to unlocking granite petrogenesis using zircon U-Pb - Lu-Hf studies

NASA Astrophysics Data System (ADS)

Tapster, Simon; Horstwood, Matthew; Roberts, Nick M. W.; Deady, Eimear; Shail, Robin

2017-04-01

Coarse-scale understanding of crustal evolution and source contributions to igneous systems has been drastically enhanced by coupled zircon U-Pb and Lu-Hf data sets. These are now common place and potentially offer advantages over whole-rock analyses by resolving heterogeneous source components in the complex crystal cargos of single hand-samples. However, the application of coupled zircon U-Pb and Lu-Hf studies to address detailed petrogenetic questions faces a crisis of resolution - On the one hand, micro-beam analytical techniques have high spatial resolution, capable of interrogating crystals with complex growth histories. Yet, the >1-2% temporal resolution of these techniques places a fundamental limitation on their utility for developing petrogenetic models. This limitation in data interpretation arises from timescales of crystal recycling or changes in source evolution that are often shorter than the U-Pb analytical precision. Conversely, high-precision CA-ID-TIMS U-Pb analysis of single whole zircons and solution MC-ICP-MS Lu-Hf isotopes of column washes (Hf masses equating to ca. 10-50 ng) have much greater temporal resolution (<0.1%), yet lack the spatial resolution to deal with complex crystal growth. Analyses homogenize any heterogeneity within the zircon and convolute the petrogenetic model. A balance must be struck between spatial and temporal resolution to address petrogenetic issues. Here, we demonstrate that micro-sampling of complex xenocryst-rich zircon crystals (e.g. <40 µm zircon tips) from the granitic post-Variscan Cornubian Batholith (SW England), in tandem with low-common Pb blank CA-ID-TIMS U-Pb chemistry, permits the analysis of zircon volumes that approach those of LA-ICPMS analyses, whilst simultaneously retaining the majority of the temporal resolution associated with the CA-ID-TIMS U-Pb technique. The low volume of zircon within these analyses may only provide <5 ng Hf, and therefore gaining useful precision from Lu-Hf isotopes is beyond the scope of typical solution MC-ICP-MS techniques. However, we demonstrate that an uncertainty level of ca. 1 ɛHf can be achieved with as little as 0.4 ng Hf through the use of low-volume solution introduction methods - thus bridging the gap in resolving power between in-situ and isotope dilution coupled zircon U-Pb - Lu-Hf studies. We demonstrate the potential of this approach to unravel intra- and inter-sample heterogeneity and address models for granite genesis using a new regional data set for 21 samples encompassing all major granite types within the Early Permian Cornubian Batholith (SW England). The data provide a refined chronological framework for magma source evolution over 20 Myrs of crust-mantle melt extraction and upper crustal batholith construction. The resulting petrogenetic model will also be evaluated through the lens of low- temporal resolution commonly employed in granitic zircon U-Pb - Lu-Hf studies in order to highlight the enhanced insights into geological processes gained though our approach. The current limitations to data interpretation and directions of future research will be discussed.

Modeling Above-Ground Biomass Across Multiple Circum-Arctic Tundra Sites Using High Spatial Resolution Remote Sensing

NASA Astrophysics Data System (ADS)

Räsänen, Aleksi; Juutinen, Sari; Aurela, Mika; Virtanen, Tarmo

2017-04-01

Biomass is one of the central bio-geophysical variables in Earth observation for tracking plant productivity, and flow of carbon, nutrients, and water. Most of the satellite based biomass mapping exercises in Arctic environments have been performed by using rather coarse spatial resolution data, e.g. Landsat and AVHRR which have spatial resolutions of 30 m and >1 km, respectively. While the coarse resolution images have high temporal resolution, they are incapable of capturing the fragmented nature of tundra environment and fine-scale changes in vegetation and carbon exchange patterns. Very high spatial resolution (VHSR, spatial resolution 0.5-2 m) satellite images have the potential to detect environmental variables with an ecologically sound spatial resolution. The usage of VHSR images has, nevertheless, been modest so far in biomass modeling in the Arctic. Our objectives were to use VHSR for predicting above ground biomass in tundra landscapes, evaluate whether a common predictive model can be applied across circum-Arctic tundra and peatland sites having different types of vegetation, and produce knowledge on distribution of plant functional types (PFT) in these sites. Such model development is dependent on ground-based surveys of vegetation with the same spatial resolution and extent with the VHSR images. In this study, we conducted ground-based surveys of vegetation composition and biomass in four different arctic tundra or peatland areas located in Russia, Canada, and Finland. First, we sorted species into PFTs and developed PFT-specific models to predict biomass on the basis of non-destructive measurements (cover, height). Second, we predicted overall biomass on landscape scale by combinations of single bands and vegetation indices of very high resolution satellite images (QuickBird or WorldView-2 images of the eight sites). We compared area-specific empirical regression models and common models that were applied across all sites. We found that NDVI was usually the highest scoring spectral indices in explaining biomass distribution with good explanatory power. Furthermore, models which had more than one explanatory variable had higher explanatory power than models with a single index. The dissimilarity between common and site-specific model estimates was, however, high and data indicates that variation in vegetation properties and its impact on spectral reflectance needs to be acknowledged. Our work produced knowledge on above-ground biomass distribution and contribution of PFTs across circum-Arctic low-growth landscapes and will contribute to developing space-borne vegetation monitoring schemes utilizing VHSR satellite images.

Dual-resolution dose assessments for proton beamlet using MCNPX 2.6.0

NASA Astrophysics Data System (ADS)

Chao, T. C.; Wei, S. C.; Wu, S. W.; Tung, C. J.; Tu, S. J.; Cheng, H. W.; Lee, C. C.

2015-11-01

The purpose of this study is to access proton dose distribution in dual resolution phantoms using MCNPX 2.6.0. The dual resolution phantom uses higher resolution in Bragg peak, area near large dose gradient, or heterogeneous interface and lower resolution in the rest. MCNPX 2.6.0 was installed in Ubuntu 10.04 with MPI for parallel computing. FMesh1 tallies were utilized to record the energy deposition which is a special designed tally for voxel phantoms that converts dose deposition from fluence. 60 and 120 MeV narrow proton beam were incident into Coarse, Dual and Fine resolution phantoms with pure water, water-bone-water and water-air-water setups. The doses in coarse resolution phantoms are underestimated owing to partial volume effect. The dose distributions in dual or high resolution phantoms agreed well with each other and dual resolution phantoms were at least 10 times more efficient than fine resolution one. Because the secondary particle range is much longer in air than in water, the dose of low density region may be under-estimated if the resolution or calculation grid is not small enough.

Temporal resolution requirements of satellite constellations for 30 m global burned area mapping

NASA Astrophysics Data System (ADS)

Melchiorre, A.; Boschetti, L.

2017-12-01

Global burned area maps have been generated systematically with daily, coarse resolution satellite data (Giglio et al. 2013). The production of moderate resolution (10 - 30 m) global burned area products would meet the needs of several user communities: improved carbon emission estimations due to heterogeneous landscapes and for local scale air quality and fire management applications (Mouillot et al. 2014; van der Werf et al. 2010). While the increased spatial resolution reduces the influence of mixed burnt/unburnt pixels and it would increase the spectral separation of burned areas, moderate resolution satellites have reduced temporal resolution (10 - 16 days). Fire causes a land-cover change spectrally visible for a period ranging from a few weeks in savannas to over a year in forested ecosystems (Roy et al. 2010); because clouds, smoke, and other optically thick aerosols limit the number of available observations (Roy et al. 2008; Smith and Wooster 2005), burned areas might disappear before they are observed by moderate resolution sensors. Data fusion from a constellation of different sensors has been proposed to overcome these limits (Boschetti et al. 2015; Roy 2015). In this study, we estimated the probability of moderate resolution satellites and virtual constellations (including Landsat-8/9, Sentinel-2A/B) to provide sufficient observations for burned area mapping globally, and by ecosystem. First, we estimated the duration of the persistence of the signal associated with burned areas by combining the MODIS Global Burned Area and the Nadir BRDF-Adjusted Reflectance Product by characterizing the post-fire trends in reflectance to determine the length of the period in which the burn class is spectrally distinct from the unburned and, therefore, detectable. The MODIS-Terra daily cloud data were then used to estimate the probability of cloud cover. The cloud probability was used at each location to estimate the minimum revisit time needed to obtain at least one cloud-free observation within the duration of the persistence of burned areas. As complementary results, the expected omission error due to insufficient observations was estimated for each of the satellite combination considered making use of the calendar and geometry of acquisition for each of the sensor included in the virtual constellation.

Challenges of Representing Sub-Grid Physics in an Adaptive Mesh Refinement Atmospheric Model

NASA Astrophysics Data System (ADS)

O'Brien, T. A.; Johansen, H.; Johnson, J. N.; Rosa, D.; Benedict, J. J.; Keen, N. D.; Collins, W.; Goodfriend, E.

2015-12-01

Some of the greatest potential impacts from future climate change are tied to extreme atmospheric phenomena that are inherently multiscale, including tropical cyclones and atmospheric rivers. Extremes are challenging to simulate in conventional climate models due to existing models' coarse resolutions relative to the native length-scales of these phenomena. Studying the weather systems of interest requires an atmospheric model with sufficient local resolution, and sufficient performance for long-duration climate-change simulations. To this end, we have developed a new global climate code with adaptive spatial and temporal resolution. The dynamics are formulated using a block-structured conservative finite volume approach suitable for moist non-hydrostatic atmospheric dynamics. By using both space- and time-adaptive mesh refinement, the solver focuses computational resources only where greater accuracy is needed to resolve critical phenomena. We explore different methods for parameterizing sub-grid physics, such as microphysics, macrophysics, turbulence, and radiative transfer. In particular, we contrast the simplified physics representation of Reed and Jablonowski (2012) with the more complex physics representation used in the System for Atmospheric Modeling of Khairoutdinov and Randall (2003). We also explore the use of a novel macrophysics parameterization that is designed to be explicitly scale-aware.

Crop Surveillance Demonstration Using a Near-Daily MODIS Derived Vegetation Index Time Series

NASA Technical Reports Server (NTRS)

McKellip, Rodney; Ryan, Robert E.; Blonski, Slawomir; Prados, Don

2005-01-01

Effective response to crop disease outbreaks requires rapid identification and diagnosis of an event. A near-daily vegetation index product, such as a Normalized Difference Vegetation Index (NDVI), at moderate spatial resolution may serve as a good method for monitoring quick-acting diseases. NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument flown on the Terra and Aqua satellites has the temporal, spatial, and spectral properties to make it an excellent coarse-resolution data source for rapid, comprehensive surveillance of agricultural areas. A proof-of-concept wide area crop surveillance system using daily MODIS imagery was developed and tested on a set of San Joaquin cotton fields over a growing season. This area was chosen in part because excellent ground truth data were readily available. Preliminary results indicate that, at least in the southwestern part of the United States, near-daily NDVI products can be generated that show the natural variations in the crops as well as specific crop practices. Various filtering methods were evaluated and compared with standard MOD13 NDVI MODIS products. We observed that specific chemical applications that produce defoliation, which would have been missed using the standard 16-day product, were easily detectable with the filtered daily NDVI products.

A Review of Wetland Remote Sensing.

PubMed

Guo, Meng; Li, Jing; Sheng, Chunlei; Xu, Jiawei; Wu, Li

2017-04-05

Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, seven types of sensors were reviewed: aerial photos coarse-resolution, medium-resolution, high-resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. This study also discusses the advantage of each sensor for wetland research. Wetland research themes reviewed in this paper include wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest, and sea level rise. This study also gives an overview of the methods used in wetland research such as supervised and unsupervised classification and decision tree and object-based classification. Finally, this paper provides some advice on future wetland remote sensing. To our knowledge, this paper is the most comprehensive and detailed review of wetland remote sensing and it will be a good reference for wetland researchers.

A Review of Wetland Remote Sensing

PubMed Central

Guo, Meng; Li, Jing; Sheng, Chunlei; Xu, Jiawei; Wu, Li

2017-01-01

Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, seven types of sensors were reviewed: aerial photos coarse-resolution, medium-resolution, high-resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. This study also discusses the advantage of each sensor for wetland research. Wetland research themes reviewed in this paper include wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest, and sea level rise. This study also gives an overview of the methods used in wetland research such as supervised and unsupervised classification and decision tree and object-based classification. Finally, this paper provides some advice on future wetland remote sensing. To our knowledge, this paper is the most comprehensive and detailed review of wetland remote sensing and it will be a good reference for wetland researchers. PMID:28379174

Urban structure and dengue fever in Puntarenas, Costa Rica

PubMed Central

Troyo, Adriana; Fuller, Douglas O.; Calderón-Arguedas, Olger; Solano, Mayra E.; Beier, John C.

2009-01-01

Dengue is currently the most important arboviral disease globally and is usually associated with built environments in tropical areas. Remotely sensed information can facilitate the study of urban mosquito-borne diseases by providing multiple temporal and spatial resolutions appropriate to investigate urban structure and ecological characteristics associated with infectious disease. In this study, coarse, medium and fine resolution satellite imagery (Moderate Resolution Imaging Spectrometer, Advanced Spaceborne Thermal Emission and Reflection Radiometer and QuickBird respectively) and ground-based data were analyzed for the Greater Puntarenas area, Costa Rica for the years 2002–04. The results showed that the mean normalized difference vegetation index (NDVI) was generally higher in the localities with lower incidence of dengue fever during 2002, although the correlation was statistically significant only in the dry season (r=−0.40; p=0.03). Dengue incidence was inversely correlated to built area and directly correlated with tree cover (r=0.75, p=0.01). Overall, the significant correlations between dengue incidence and urban structural variables (tree cover and building density) suggest that properties of urban structure may be associated with dengue incidence in tropical urban settings. PMID:20161131

Sediment cores as archives of historical changes in floodplain lake hydrology.

PubMed

Lintern, Anna; Leahy, Paul J; Zawadzki, Atun; Gadd, Patricia; Heijnis, Henk; Jacobsen, Geraldine; Connor, Simon; Deletic, Ana; McCarthy, David T

2016-02-15

Anthropogenic activities are contributing to the changing hydrology of rivers, often resulting in their degradation. Understanding the drivers and nature of these changes is critical for the design and implementation of effective mitigation strategies for these systems. However, this can be hindered by gaps in historical measured flow data. This study therefore aims to use sediment cores to identify historical hydrological changes within a river catchment. Sediment cores from two floodplain lakes (billabongs) in the urbanised Yarra River catchment (Melbourne, South-East Australia) were collected and high resolution images, trends in magnetic susceptibility and trends in elemental composition through the sedimentary records were obtained. These were used to infer historical changes in river hydrology to determine both average trends in hydrology (i.e., coarse temporal resolution) as well as discrete flood layers in the sediment cores (i.e., fine temporal resolution). Through the 20th century, both billabongs became increasingly disconnected from the river, as demonstrated by the decreasing trends in magnetic susceptibility, particle size and inorganic matter in the cores. Additionally the number of discrete flood layers decreased up the cores. These reconstructed trends correlate with measured flow records of the river through the 20th century, which validates the methodology that has been used in this study. Not only does this study provide evidence on how natural catchments can be affected by land-use intensification and urbanisation, but it also introduces a general analytical framework that could be applied to other river systems to assist in the design of hydrological management strategies. Copyright © 2015 Elsevier B.V. All rights reserved.

The multiscale coarse-graining method. XI. Accurate interactions based on the centers of charge of coarse-grained sites

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

Cao, Zhen; Voth, Gregory A., E-mail: gavoth@uchicago.edu

It is essential to be able to systematically construct coarse-grained (CG) models that can efficiently and accurately reproduce key properties of higher-resolution models such as all-atom. To fulfill this goal, a mapping operator is needed to transform the higher-resolution configuration to a CG configuration. Certain mapping operators, however, may lose information related to the underlying electrostatic properties. In this paper, a new mapping operator based on the centers of charge of CG sites is proposed to address this issue. Four example systems are chosen to demonstrate this concept. Within the multiscale coarse-graining framework, CG models that use this mapping operatormore » are found to better reproduce the structural correlations of atomistic models. The present work also demonstrates the flexibility of the mapping operator and the robustness of the force matching method. For instance, important functional groups can be isolated and emphasized in the CG model.« less

Stochastic Ocean Eddy Perturbations in a Coupled General Circulation Model.

NASA Astrophysics Data System (ADS)

Howe, N.; Williams, P. D.; Gregory, J. M.; Smith, R. S.

2014-12-01

High-resolution ocean models, which are eddy permitting and resolving, require large computing resources to produce centuries worth of data. Also, some previous studies have suggested that increasing resolution does not necessarily solve the problem of unresolved scales, because it simply introduces a new set of unresolved scales. Applying stochastic parameterisations to ocean models is one solution that is expected to improve the representation of small-scale (eddy) effects without increasing run-time. Stochastic parameterisation has been shown to have an impact in atmosphere-only models and idealised ocean models, but has not previously been studied in ocean general circulation models. Here we apply simple stochastic perturbations to the ocean temperature and salinity tendencies in the low-resolution coupled climate model, FAMOUS. The stochastic perturbations are implemented according to T(t) = T(t-1) + (ΔT(t) + ξ(t)), where T is temperature or salinity, ΔT is the corresponding deterministic increment in one time step, and ξ(t) is Gaussian noise. We use high-resolution HiGEM data coarse-grained to the FAMOUS grid to provide information about the magnitude and spatio-temporal correlation structure of the noise to be added to the lower resolution model. Here we present results of adding white and red noise, showing the impacts of an additive stochastic perturbation on mean climate state and variability in an AOGCM.

Airborne Sea-Surface Topography in an Absolute Reference Frame

NASA Astrophysics Data System (ADS)

Brozena, J. M.; Childers, V. A.; Jacobs, G.; Blaha, J.

2003-12-01

Highly dynamic coastal ocean processes occur at temporal and spatial scales that cannot be captured by the present generation of satellite altimeters. Space-borne gravity missions such as GRACE also provide time-varying gravity and a geoidal msl reference surface at resolution that is too coarse for many coastal applications. The Naval Research Laboratory and the Naval Oceanographic Office have been testing the application of airborne measurement techniques, gravity and altimetry, to determine sea-surface height and height anomaly at the short scales required for littoral regions. We have developed a precise local gravimetric geoid over a test region in the northern Gulf of Mexico from historical gravity data and recent airborne gravity surveys. The local geoid provides a msl reference surface with a resolution of about 10-15 km and provides a means to connect airborne, satellite and tide-gage observations in an absolute (WGS-84) framework. A series of altimetry reflights over the region with time scales of 1 day to 1 year reveal a highly dynamic environment with coherent and rapidly varying sea-surface height anomalies. AXBT data collected at the same time show apparent correlation with wave-like temperature anomalies propagating up the continental slope of the Desoto Canyon. We present animations of the temporal evolution of the surface topography and water column temperature structure down to the 800 m depth of the AXBT sensors.

Hierarchical stochastic modeling of large river ecosystems and fish growth across spatio-temporal scales and climate models: the Missouri River endangered pallid sturgeon example

USGS Publications Warehouse

Wildhaber, Mark L.; Wikle, Christopher K.; Moran, Edward H.; Anderson, Christopher J.; Franz, Kristie J.; Dey, Rima

2017-01-01

We present a hierarchical series of spatially decreasing and temporally increasing models to evaluate the uncertainty in the atmosphere – ocean global climate model (AOGCM) and the regional climate model (RCM) relative to the uncertainty in the somatic growth of the endangered pallid sturgeon (Scaphirhynchus albus). For effects on fish populations of riverine ecosystems, cli- mate output simulated by coarse-resolution AOGCMs and RCMs must be downscaled to basins to river hydrology to population response. One needs to transfer the information from these climate simulations down to the individual scale in a way that minimizes extrapolation and can account for spatio-temporal variability in the intervening stages. The goal is a framework to determine whether, given uncertainties in the climate models and the biological response, meaningful inference can still be made. The non-linear downscaling of climate information to the river scale requires that one realistically account for spatial and temporal variability across scale. Our down- scaling procedure includes the use of fixed/calibrated hydrological flow and temperature models coupled with a stochastically parameterized sturgeon bioenergetics model. We show that, although there is a large amount of uncertainty associated with both the climate model output and the fish growth process, one can establish significant differences in fish growth distributions between models, and between future and current climates for a given model.

Temporal Stability of Soil Moisture and Radar Backscatter Observed by the Advanced Synthetic Aperture Radar (ASAR)

PubMed Central

Wagner, Wolfgang; Pathe, Carsten; Doubkova, Marcela; Sabel, Daniel; Bartsch, Annett; Hasenauer, Stefan; Blöschl, Günter; Scipal, Klaus; Martínez-Fernández, José; Löw, Alexander

2008-01-01

The high spatio-temporal variability of soil moisture is the result of atmospheric forcing and redistribution processes related to terrain, soil, and vegetation characteristics. Despite this high variability, many field studies have shown that in the temporal domain soil moisture measured at specific locations is correlated to the mean soil moisture content over an area. Since the measurements taken by Synthetic Aperture Radar (SAR) instruments are very sensitive to soil moisture it is hypothesized that the temporally stable soil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT Advanced Synthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located in the Duero basin, Spain. It is found that a time-invariant linear relationship is well suited for relating local scale (pixel) and regional scale (50 km) backscatter. The observed linear model coefficients can be estimated by considering the scattering properties of the terrain and vegetation and the soil moisture scaling properties. For both linear model coefficients, the relative error between observed and modelled values is less than 5 % and the coefficient of determination (R2) is 86 %. The results are of relevance for interpreting and downscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT) and passive (SMOS, AMSR-E) instruments. PMID:27879759

Impairments in Precision, Rather than Spatial Strategy, Characterize Performance on the Virtual Morris Water Maze: A Case Study

PubMed Central

Kolarik, Branden S.; Shahlaie, Kiarash; Hassan, Abdul; Borders, Alyssa A.; Kaufman, Kyle C.; Gurkoff, Gene; Yonelinas, Andy P.; Ekstrom, Arne D.

2015-01-01

Damage to the medial temporal lobes produces profound amnesia, greatly impairing the ability of patients to learn about new associations and events. While studies in rodents suggest a strong link between damage to the hippocampus and the ability to navigate using distal landmarks in a spatial environment, the connection between navigation and memory in humans remains less clear. Past studies on human navigation have provided mixed findings about whether patients with damage to the medial temporal lobes can successfully acquire and navigate new spatial environments, possibly due, in part, to issues related to patient demographics and characterization of medial temporal lobe damage. Here, we report findings from a young, high functioning patient who suffered severe medial temporal lobe damage. Although the patient is densely amnestic, her ability to acquire and utilize new, but coarse, spatial “maps” appears largely intact. Specifically, a novel computational analysis focused on the precision of her spatial search revealed a significant deficit in spatial precision rather than spatial search strategy. These findings argue that an intact hippocampus in humans is not necessary for representing multiple external landmarks during spatial navigation of new environments. We suggest instead that the human hippocampus may store and represent complex high-resolution bindings of features in the environment as part of a larger role in perception, memory, and navigation. PMID:26593960

Implementation of a generalized actuator line model for wind turbine parameterization in the Weather Research and Forecasting model

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

Marjanovic, Nikola; Mirocha, Jeffrey D.; Kosović, Branko

A generalized actuator line (GAL) wind turbine parameterization is implemented within the Weather Research and Forecasting model to enable high-fidelity large-eddy simulations of wind turbine interactions with boundary layer flows under realistic atmospheric forcing conditions. Numerical simulations using the GAL parameterization are evaluated against both an already implemented generalized actuator disk (GAD) wind turbine parameterization and two field campaigns that measured the inflow and near-wake regions of a single turbine. The representation of wake wind speed, variance, and vorticity distributions is examined by comparing fine-resolution GAL and GAD simulations and GAD simulations at both fine and coarse-resolutions. The higher-resolution simulationsmore » show slightly larger and more persistent velocity deficits in the wake and substantially increased variance and vorticity when compared to the coarse-resolution GAD. The GAL generates distinct tip and root vortices that maintain coherence as helical tubes for approximately one rotor diameter downstream. Coarse-resolution simulations using the GAD produce similar aggregated wake characteristics to both fine-scale GAD and GAL simulations at a fraction of the computational cost. The GAL parameterization provides the capability to resolve near wake physics, including vorticity shedding and wake expansion.« less

Quantifying Diurnal and Spatial Variations in CO2 Concentrations and Partial Columns using High-Resolution Global Model Simulations

NASA Astrophysics Data System (ADS)

Pawson, S.; Nielsen, J.; Ott, L. E.; Darmenov, A.; Putman, W.

2015-12-01

Model-data fusion approaches, such as global inverse modeling for surface flux estimation, have traditionally been performed at spatial resolutions of several tens to a few hundreds of kilometers. Use of such coarse scales presents a fundamental limitation in reconciling the modeled field with both the atmospheric observations and the distribution of surface emissions and uptake. Emissions typically occur on small scales, including point sources (e.g. power plants, forest fires) or with inhomegeneous structure. Biological uptake can have spatial variations related to complex, diverse vegetation, etc. Atmospheric observations of CO2 are either surface based, providing information at a single point, or space based with a finite-sized footprint. For instance, GOSAT and OCO-2 have footprint sizes of around 10km and proposed active sensors (such as ASCENDS) will likely have even finer footprints. One important aspect of reconciling models to measurements is the representativeness of the observation for the model field, and this depends on the generally unknown spatio-temporal variations of the CO2 field around the measurement location and time. This work presents an assessment of the global spatio-temporal variations of the CO2 field using the "7km GEOS-5 Nature Run" (7km-G5NR), which includes CO2 emissions and uptake mapped to the finest possible resolution. Results are shown for surface CO2 concentrations, total-column CO2, and separate upper and lower tropospheric columns. Spatial variability is shown to be largest in regions with strong point sources and at night in regions with complex terrain, especially where biological processes dominate the local CO2 fluxes, where the day-night differences are also most marked. The spatio-temporal variations are strongest for surface concentrations and for lower tropospheric CO2. While these results are largely anticipated, these high resolution simulations provide quantitative estimates of the global nature of spatio-temporal CO2 variability. Implications for characterizing representativeness of passive CO2 observations will be discussed. Differences between daytime and nighttime structures will be considered in light of active CO2 sensors. Finally, some possible limitations of the model will be highlighted, using some global 3-km simulations.

Predicting Near Real-Time Inundation Occurrence from Complimentary Satellite Microwave Brightness Temperature Observations

NASA Astrophysics Data System (ADS)

Fisher, C. K.; Pan, M.; Wood, E. F.

2017-12-01

Throughout the world, there is an increasing need for new methods and data that can aid decision makers, emergency responders and scientists in the monitoring of flood events as they happen. In many regions, it is possible to examine the extent of historical and real-time inundation occurrence from visible and infrared imagery provided by sensors such as MODIS or the Landsat TM; however, this is not possible in regions that are densely vegetated or are under persistent cloud cover. In addition, there is often a temporal mismatch between the sampling of a particular sensor and a given flood event, leading to limited observations in near real-time. As a result, there is a need for alternative methods that take full advantage of complimentary remotely sensed data sources, such as available microwave brightness temperature observations (e.g., SMAP, SMOS, AMSR2, AMSR-E, and GMI), to aid in the estimation of global flooding. The objective of this work was to develop a high-resolution mapping of inundated areas derived from multiple satellite microwave sensor observations with a daily temporal resolution. This system consists of first retrieving water fractions from complimentary microwave sensors (AMSR-2 and SMAP) which may spatially and temporally overlap in the region of interest. Using additional information in a Random Forest classifier, including high resolution topography and multiple datasets of inundated area (both historical and empirical), the resulting retrievals are spatially downscaled to derive estimates of the extent of inundation at a scale relevant to management and flood response activities ( 90m or better) instead of the relatively coarse resolution water fractions, which are limited by the microwave sensor footprints ( 5-50km). Here we present the training and validation of this method for the 2015 floods that occurred in Houston, Texas. Comparing the predicted inundation against historical occurrence maps derived from the Landsat TM record and MODIS imagery, we find good agreement for most areas and are able to provide a daily mapping given the increased temporal coverage. These results illustrate the feasibility of a near real-time inundation prediction system driven by multi-sensor satellite microwave observations, which can be extended to provide a daily estimate of global flooding.

Predicting Vulnerability of the Integrity and Connectivity Associated with Culverts in Low Order Streams of Northern Michigan

NASA Astrophysics Data System (ADS)

King, C. H.; Wagenbrenner, J.; Fedora, M.; Watkins, D.; Watkins, M. K.; Huckins, C.

2017-12-01

The Great Lakes Region of North America has experienced more frequent extreme precipitation events in recent decades, resulting in a large number of stream crossing failures. While there are accepted methods for designing stream crossings to accommodate peak storm discharges, less attention has been paid to assessing the risk of failure. To evaluate failure risk and potential impacts, coarse-resolution stream crossing surveys were completed on 51 stream crossings and dams in the North Branch Paint River watershed in Michigan's Upper Peninsula. These inventories determined stream crossing dimensions along with stream and watershed characteristics. Eleven culverts were selected from the coarse surveys for high resolution hydraulic analysis to estimate discharge conditions expected at crossing failure. Watershed attributes upstream of the crossing, including area, slope, and storage, were acquired. Sediment discharge and the economic impact associated with a failure event were also estimated for each stream crossing. Impacts to stream connectivity and fish passability were assessed from the coarse-level surveys. Using information from both the coarse and high-resolution surveys, we also developed indicators to predict failure risk without the need for complex hydraulic modeling. These passability scores and failure risk indicators will help to prioritize infrastructure replacement and improve the overall connectivity of river systems throughout the upper Great Lakes Region.

Spatio-temporal variation of coarse woody debris input in woodland key habitats in central Sweden

Treesearch

Mari Jonsson; Shawn Fraver; Bengt Gunnar. Jonsson

2011-01-01

The persistence of many saproxylic (wood-living) species depends on a readily available supply of coarse woody debris (CWD). Most studies of CWD inputs address stand-level patterns, despite the fact that many saproxylic species depend on landscape-level supplies of CWD. In the present study we used dated CWD inputs (tree mortality events) at each of 14 Norway spruce (...

Temporal trends and sources of variation in carbon flux from coarse woody debris in experimental forest canopy openings

Treesearch

J. A. Forrester; D. J. Mladenoff; A. W. D' Amato; S. Fraver; Daniel Lindner; N. J. Brazee; M. K. Clayton; S. T. Gower

2015-01-01

Pulses of respiration from coarse woody debris (CWD) have been observed immediately following canopy disturbances, but it is unclear how long these pulses are sustained. Several factors are known to influence carbon flux rates from CWD, but few studies have evaluated more than temperature and moisture. We experimentally manipulated forest structure in a second-growth...

Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network

NASA Astrophysics Data System (ADS)

Fang, K.; Shen, C.; Kifer, D.; Yang, X.

2017-12-01

The Soil Moisture Active Passive (SMAP) mission has delivered high-quality and valuable sensing of surface soil moisture since 2015. However, its short time span, coarse resolution, and irregular revisit schedule have limited its use. Utilizing a state-of-the-art deep-in-time neural network, Long Short-Term Memory (LSTM), we created a system that predicts SMAP level-3 soil moisture data using climate forcing, model-simulated moisture, and static physical attributes as inputs. The system removes most of the bias with model simulations and also improves predicted moisture climatology, achieving a testing accuracy of 0.025 to 0.03 in most parts of Continental United States (CONUS). As the first application of LSTM in hydrology, we show that it is more robust than simpler methods in either temporal or spatial extrapolation tests. We also discuss roles of different predictors, the effectiveness of regularization algorithms and impacts of training strategies. With high fidelity to SMAP products, our data can aid various applications including data assimilation, weather forecasting, and soil moisture hindcasting.

Spatial analysis and statistical modelling of snow cover dynamics in the Central Himalayas, Nepal

NASA Astrophysics Data System (ADS)

Weidinger, Johannes; Gerlitz, Lars; Böhner, Jürgen

2017-04-01

General circulation models are able to predict large scale climate variations in global dimensions, however small scale dynamic characteristics, such as snow cover and its temporal variations in high mountain regions, are not represented sufficiently. Detailed knowledge about shifts in seasonal ablation times and spatial distribution of snow cover are crucial for various research interests. Since high mountain areas, for instance the Central Himalayas in Nepal, are generally remote, it is difficult to obtain data in high spatio-temporal resolutions. Regional climate models and downscaling techniques are implemented to compensate coarse resolution. Furthermore earth observation systems, such as MODIS, also permit bridging this gap to a certain extent. They offer snow (cover) data in daily temporal and medium spatial resolution of around 500 m, which can be applied as evaluation and training data for dynamical hydrological and statistical analyses. Within this approach two snow distribution models (binary snow cover and fractional snow cover) as well as one snow recession model were implemented for a research domain in the Rolwaling Himal in Nepal, employing the random forest technique, which represents a state of the art machine learning algorithm. Both bottom-up strategies provide inductive reasoning to derive rules for snow related processes out of climate (temperature, precipitation and irradiance) and climate-related topographic data sets (elevation, aspect and convergence index) obtained by meteorological network stations, remote sensing products (snow cover - MOD10-A1 and land surface temperatures - MOD11-A1) along with GIS. Snow distribution is predicted reliably on a daily basis in the research area, whereas further effort is necessary for predicting daily snow cover recession processes adequately. Swift changes induced by clear sky conditions with high insolation rates are well represented, whereas steady snow loss still needs continuing effort. All approaches underline the technical difficulties of snow cover modelling during the monsoon season, in accordance with previous studies. The developed methods in combination with continuous in situ measurements provide a basis for further downscaling approaches.

Social sensing of urban land use based on analysis of Twitter users’ mobility patterns

PubMed Central

Soltani, Kiumars; Yin, Junjun; Padmanabhan, Anand; Wang, Shaowen

2017-01-01

A number of recent studies showed that digital footprints around built environments, such as geo-located tweets, are promising data sources for characterizing urban land use. However, challenges for achieving this purpose exist due to the volume and unstructured nature of geo-located social media. Previous studies focused on analyzing Twitter data collectively resulting in coarse resolution maps of urban land use. We argue that the complex spatial structure of a large collection of tweets, when viewed through the lens of individual-level human mobility patterns, can be simplified to a series of key locations for each user, which could be used to characterize urban land use at a higher spatial resolution. Contingent issues that could affect our approach, such as Twitter users’ biases and tendencies at locations where they tweet the most, were systematically investigated using 39 million geo-located Tweets and two independent datasets of the City of Chicago: 1) travel survey and 2) parcel-level land use map. Our results support that the majority of Twitter users show a preferential return, where their digital traces are clustered around a few key locations. However, we did not find a general relation among users between the ranks of locations for an individual—based on the density of tweets—and their land use types. On the contrary, temporal patterns of tweeting at key locations were found to be coherent among the majority of users and significantly associated with land use types of these locations. Furthermore, we used these temporal patterns to classify key locations into generic land use types with an overall classification accuracy of 0.78. The contribution of our research is twofold: a novel approach to resolving land use types at a higher resolution, and in-depth understanding of Twitter users’ location-related and temporal biases, promising to benefit human mobility and urban studies in general. PMID:28723936

Large-scale assessment of benthic communities across multiple marine protected areas using an autonomous underwater vehicle.

PubMed

Ferrari, Renata; Marzinelli, Ezequiel M; Ayroza, Camila Rezende; Jordan, Alan; Figueira, Will F; Byrne, Maria; Malcolm, Hamish A; Williams, Stefan B; Steinberg, Peter D

2018-01-01

Marine protected areas (MPAs) are designed to reduce threats to biodiversity and ecosystem functioning from anthropogenic activities. Assessment of MPAs effectiveness requires synchronous sampling of protected and non-protected areas at multiple spatial and temporal scales. We used an autonomous underwater vehicle to map benthic communities in replicate 'no-take' and 'general-use' (fishing allowed) zones within three MPAs along 7o of latitude. We recorded 92 taxa and 38 morpho-groups across three large MPAs. We found that important habitat-forming biota (e.g. massive sponges) were more prevalent and abundant in no-take zones, while short ephemeral algae were more abundant in general-use zones, suggesting potential short-term effects of zoning (5-10 years). Yet, short-term effects of zoning were not detected at the community level (community structure or composition), while community structure varied significantly among MPAs. We conclude that by allowing rapid, simultaneous assessments at multiple spatial scales, autonomous underwater vehicles are useful to document changes in marine communities and identify adequate scales to manage them. This study advanced knowledge of marine benthic communities and their conservation in three ways. First, we quantified benthic biodiversity and abundance, generating the first baseline of these benthic communities against which the effectiveness of three large MPAs can be assessed. Second, we identified the taxonomic resolution necessary to assess both short and long-term effects of MPAs, concluding that coarse taxonomic resolution is sufficient given that analyses of community structure at different taxonomic levels were generally consistent. Yet, observed differences were taxa-specific and may have not been evident using our broader taxonomic classifications, a classification of mid to high taxonomic resolution may be necessary to determine zoning effects on key taxa. Third, we provide an example of statistical analyses and sampling design that once temporal sampling is incorporated will be useful to detect changes of marine benthic communities across multiple spatial and temporal scales.

Large-scale assessment of benthic communities across multiple marine protected areas using an autonomous underwater vehicle

PubMed Central

Ayroza, Camila Rezende; Jordan, Alan; Figueira, Will F.; Byrne, Maria; Malcolm, Hamish A.; Williams, Stefan B.; Steinberg, Peter D.

2018-01-01

Marine protected areas (MPAs) are designed to reduce threats to biodiversity and ecosystem functioning from anthropogenic activities. Assessment of MPAs effectiveness requires synchronous sampling of protected and non-protected areas at multiple spatial and temporal scales. We used an autonomous underwater vehicle to map benthic communities in replicate ‘no-take’ and ‘general-use’ (fishing allowed) zones within three MPAs along 7o of latitude. We recorded 92 taxa and 38 morpho-groups across three large MPAs. We found that important habitat-forming biota (e.g. massive sponges) were more prevalent and abundant in no-take zones, while short ephemeral algae were more abundant in general-use zones, suggesting potential short-term effects of zoning (5–10 years). Yet, short-term effects of zoning were not detected at the community level (community structure or composition), while community structure varied significantly among MPAs. We conclude that by allowing rapid, simultaneous assessments at multiple spatial scales, autonomous underwater vehicles are useful to document changes in marine communities and identify adequate scales to manage them. This study advanced knowledge of marine benthic communities and their conservation in three ways. First, we quantified benthic biodiversity and abundance, generating the first baseline of these benthic communities against which the effectiveness of three large MPAs can be assessed. Second, we identified the taxonomic resolution necessary to assess both short and long-term effects of MPAs, concluding that coarse taxonomic resolution is sufficient given that analyses of community structure at different taxonomic levels were generally consistent. Yet, observed differences were taxa-specific and may have not been evident using our broader taxonomic classifications, a classification of mid to high taxonomic resolution may be necessary to determine zoning effects on key taxa. Third, we provide an example of statistical analyses and sampling design that once temporal sampling is incorporated will be useful to detect changes of marine benthic communities across multiple spatial and temporal scales. PMID:29547656

Social sensing of urban land use based on analysis of Twitter users' mobility patterns.

PubMed

Soliman, Aiman; Soltani, Kiumars; Yin, Junjun; Padmanabhan, Anand; Wang, Shaowen

2017-01-01

A number of recent studies showed that digital footprints around built environments, such as geo-located tweets, are promising data sources for characterizing urban land use. However, challenges for achieving this purpose exist due to the volume and unstructured nature of geo-located social media. Previous studies focused on analyzing Twitter data collectively resulting in coarse resolution maps of urban land use. We argue that the complex spatial structure of a large collection of tweets, when viewed through the lens of individual-level human mobility patterns, can be simplified to a series of key locations for each user, which could be used to characterize urban land use at a higher spatial resolution. Contingent issues that could affect our approach, such as Twitter users' biases and tendencies at locations where they tweet the most, were systematically investigated using 39 million geo-located Tweets and two independent datasets of the City of Chicago: 1) travel survey and 2) parcel-level land use map. Our results support that the majority of Twitter users show a preferential return, where their digital traces are clustered around a few key locations. However, we did not find a general relation among users between the ranks of locations for an individual-based on the density of tweets-and their land use types. On the contrary, temporal patterns of tweeting at key locations were found to be coherent among the majority of users and significantly associated with land use types of these locations. Furthermore, we used these temporal patterns to classify key locations into generic land use types with an overall classification accuracy of 0.78. The contribution of our research is twofold: a novel approach to resolving land use types at a higher resolution, and in-depth understanding of Twitter users' location-related and temporal biases, promising to benefit human mobility and urban studies in general.

Mapping Palm Swamp Wetland Ecosystems in the Peruvian Amazon: a Multi-Sensor Remote Sensing Approach

NASA Astrophysics Data System (ADS)

Podest, E.; McDonald, K. C.; Schroeder, R.; Pinto, N.; Zimmerman, R.; Horna, V.

2012-12-01

Wetland ecosystems are prevalent in the Amazon basin, especially in northern Peru. Of specific interest are palm swamp wetlands because they are characterized by constant surface inundation and moderate seasonal water level variation. This combination of constantly saturated soils and warm temperatures year-round can lead to considerable methane release to the atmosphere. Because of the widespread occurrence and expected sensitivity of these ecosystems to climate change, it is critical to develop methods to quantify their spatial extent and inundation state in order to assess their carbon dynamics. Spatio-temporal information on palm swamps is difficult to gather because of their remoteness and difficult accessibility. Spaceborne microwave remote sensing is an effective tool for characterizing these ecosystems since it is sensitive to surface water and vegetation structure and allows monitoring large inaccessible areas on a temporal basis regardless of atmospheric conditions or solar illumination. We developed a remote sensing methodology using multi-sensor remote sensing data from the Advanced Land Observing Satellite (ALOS) Phased Array L-Band Synthetic Aperture Radar (PALSAR), Shuttle Radar Topography Mission (SRTM) DEM, and Landsat to derive maps at 100 meter resolution of palm swamp extent and inundation based on ground data collections; and combined active and passive microwave data from AMSR-E and QuikSCAT to derive inundation extent at 25 kilometer resolution on a weekly basis. We then compared information content and accuracy of the coarse resolution products relative to the high-resolution datasets. The synergistic combination of high and low resolution datasets allowed for characterization of palm swamps and assessment of their flooding status. This work has been undertaken partly within the framework of the JAXA ALOS Kyoto & Carbon Initiative. PALSAR data have been provided by JAXA. Portions of this work were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Automated Generation of the Alaska Coastline Using High-Resolution Satellite Imagery

NASA Astrophysics Data System (ADS)

Roth, G.; Porter, C. C.; Cloutier, M. D.; Clementz, M. E.; Reim, C.; Morin, P. J.

2015-12-01

Previous campaigns to map Alaska's coast at high resolution have relied on airborne, marine, or ground-based surveying and manual digitization. The coarse temporal resolution, inability to scale geographically, and high cost of field data acquisition in these campaigns is inadequate for the scale and speed of recent coastal change in Alaska. Here, we leverage the Polar Geospatial Center (PGC) archive of DigitalGlobe, Inc. satellite imagery to produce a state-wide coastline at 2 meter resolution. We first select multispectral imagery based on time and quality criteria. We then extract the near-infrared (NIR) band from each processed image, and classify each pixel as water or land with a pre-determined NIR threshold value. Processing continues with vectorizing the water-land boundary, removing extraneous data, and attaching metadata. Final coastline raster and vector products maintain the original accuracy of the orthorectified satellite data, which is often within the local tidal range. The repeat frequency of coastline production can range from 1 month to 3 years, depending on factors such as satellite capacity, cloud cover, and floating ice. Shadows from trees or structures complicate the output and merit further data cleaning. The PGC's imagery archive, unique expertise, and computing resources enabled us to map the Alaskan coastline in a few months. The DigitalGlobe archive allows us to update this coastline as new imagery is acquired, and facilitates baseline data for studies of coastal change and improvement of topographic datasets. Our results are not simply a one-time coastline, but rather a system for producing multi-temporal, automated coastlines. Workflows and tools produced with this project can be freely distributed and utilized globally. Researchers and government agencies must now consider how they can incorporate and quality-control this high-frequency, high-resolution data to meet their mapping standards and research objectives.

Effect of spatial averaging on multifractal properties of meteorological time series

NASA Astrophysics Data System (ADS)

Hoffmann, Holger; Baranowski, Piotr; Krzyszczak, Jaromir; Zubik, Monika

2016-04-01

Introduction The process-based models for large-scale simulations require input of agro-meteorological quantities that are often in the form of time series of coarse spatial resolution. Therefore, the knowledge about their scaling properties is fundamental for transferring locally measured fluctuations to larger scales and vice-versa. However, the scaling analysis of these quantities is complicated due to the presence of localized trends and non-stationarities. Here we assess how spatially aggregating meteorological data to coarser resolutions affects the data's temporal scaling properties. While it is known that spatial aggregation may affect spatial data properties (Hoffmann et al., 2015), it is unknown how it affects temporal data properties. Therefore, the objective of this study was to characterize the aggregation effect (AE) with regard to both temporal and spatial input data properties considering scaling properties (i.e. statistical self-similarity) of the chosen agro-meteorological time series through multifractal detrended fluctuation analysis (MFDFA). Materials and Methods Time series coming from years 1982-2011 were spatially averaged from 1 to 10, 25, 50 and 100 km resolution to assess the impact of spatial aggregation. Daily minimum, mean and maximum air temperature (2 m), precipitation, global radiation, wind speed and relative humidity (Zhao et al., 2015) were used. To reveal the multifractal structure of the time series, we used the procedure described in Baranowski et al. (2015). The diversity of the studied multifractals was evaluated by the parameters of time series spectra. In order to analyse differences in multifractal properties to 1 km resolution grids, data of coarser resolutions was disaggregated to 1 km. Results and Conclusions Analysing the spatial averaging on multifractal properties we observed that spatial patterns of the multifractal spectrum (MS) of all meteorological variables differed from 1 km grids and MS-parameters were biased by -29.1 % (precipitation; width of MS) up to >4 % (min. Temperature, Radiation; asymmetry of MS). Also, the spatial variability of MS parameters was strongly affected at the highest aggregation (100 km). Obtained results confirm that spatial data aggregation may strongly affect temporal scaling properties. This should be taken into account when upscaling for large-scale studies. Acknowledgements The study was conducted within FACCE MACSUR. Please see Baranowski et al. (2015) for details on funding. References Baranowski, P., Krzyszczak, J., Sławiński, C. et al. (2015). Climate Research 65, 39-52. Hoffman, H., G. Zhao, L.G.J. Van Bussel et al. (2015). Climate Research 65, 53-69. Zhao, G., Siebert, S., Rezaei E. et al. (2015). Agricultural and Forest Meteorology 200, 156-171.

Applying Reduced Generator Models in the Coarse Solver of Parareal in Time Parallel Power System Simulation

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

Duan, Nan; Dimitrovski, Aleksandar D; Simunovic, Srdjan

2016-01-01

The development of high-performance computing techniques and platforms has provided many opportunities for real-time or even faster-than-real-time implementation of power system simulations. One approach uses the Parareal in time framework. The Parareal algorithm has shown promising theoretical simulation speedups by temporal decomposing a simulation run into a coarse simulation on the entire simulation interval and fine simulations on sequential sub-intervals linked through the coarse simulation. However, it has been found that the time cost of the coarse solver needs to be reduced to fully exploit the potentials of the Parareal algorithm. This paper studies a Parareal implementation using reduced generatormore » models for the coarse solver and reports the testing results on the IEEE 39-bus system and a 327-generator 2383-bus Polish system model.« less

Towards global Landsat burned area mapping: revisit time and availability of cloud free observations

NASA Astrophysics Data System (ADS)

Melchiorre, A.; Boschetti, L.

2016-12-01

Global, daily coarse resolution satellite data have been extensively used for systematic burned area mapping (Giglio et al. 2013; Mouillot et al. 2014). The adoption of similar approaches for producing moderate resolution (10 - 30 m) global burned area products would lead to very significant improvements for the wide variety of fire information users. It would meet a demand for accurate burned area perimeters needed for fire management, post-fire assessment and environmental restoration, and would lead to more accurate and precise atmospheric emission estimations, especially over heterogeneous areas (Mouillot et al. 2014; Randerson et al. 2012; van der Werf et al. 2010). The increased spatial resolution clearly benefits mapping accuracy: the reduction of mixed pixels directly translates in increased spectral separation compared to coarse resolution data. As a tradeoff, the lower temporal resolution (e.g. 16 days for Landsat), could potentially cause large omission errors in ecosystems with fast post-fire recovery. The spectral signal due to the fire effects is non-permanent, can be detected for a period ranging from a few weeks in savannas and grasslands, to over a year in forest ecosystems (Roy et al. 2010). Additionally, clouds, smoke, and other optically thick aerosols limit the number of available observations (Roy et al. 2008; Smith and Wooster 2005), exacerbating the issues related to mapping burned areas globally with moderate resolution sensors. This study presents a global analysis of the effect of cloud cover on Landsat data availability over burned areas, by analyzing the MODIS data record of burned area (MCD45) and cloud detections (MOD35), and combining it with the Landsat acquisition calendar and viewing geometry. For each pixel classified as burned in the MCD45 product, the MOD35 data are used to determine how many cloud free observations would have been available on Landsat overpass days, within the period of observability of the burned area spectral signal in the specific ecosystem. If a burned area pixel is covered by clouds on all the post-fire Landsat overpass days, we assume that it would not be detected in a hypothetical Landsat global burned area product. The resulting maps of expected omission errors are combined for the full 15-year MODIS dataset, and summarized by ecoregion and landcover class.

Deriving flow directions for coarse-resolution (1-4 km) gridded hydrologic modeling

NASA Astrophysics Data System (ADS)

Reed, Seann M.

2003-09-01

The National Weather Service Hydrology Laboratory (NWS-HL) is currently testing a grid-based distributed hydrologic model at a resolution (4 km) commensurate with operational, radar-based precipitation products. To implement distributed routing algorithms in this framework, a flow direction must be assigned to each model cell. A new algorithm, referred to as cell outlet tracing with an area threshold (COTAT) has been developed to automatically, accurately, and efficiently assign flow directions to any coarse-resolution grid cells using information from any higher-resolution digital elevation model. Although similar to previously published algorithms, this approach offers some advantages. Use of an area threshold allows more control over the tendency for producing diagonal flow directions. Analyses of results at different output resolutions ranging from 300 m to 4000 m indicate that it is possible to choose an area threshold that will produce minimal differences in average network flow lengths across this range of scales. Flow direction grids at a 4 km resolution have been produced for the conterminous United States.

Quantifying the imprint of mesoscale and synoptic-scale atmospheric transport on total column carbon dioxide measurements

NASA Astrophysics Data System (ADS)

Torres, A. D.; Keppel-Aleks, G.; Doney, S. C.; Feng, S.; Lauvaux, T.; Fendrock, M. A.; Rheuben, J.

2017-12-01

Remote sensing instruments provide an unprecedented density of observations of the atmospheric CO2 column average mole fraction (denoted as XCO2), which can be used to constrain regional scale carbon fluxes. Inferring fluxes from XCO2 observations is challenging, as measurements and inversion methods are sensitive to not only the imprint local and large-scale fluxes, but also mesoscale and synoptic-scale atmospheric transport. Quantifying the fine-scale variability in XCO2 from mesoscale and synoptic-scale atmospheric transport will likely improve overall error estimates from flux inversions by improving estimates of representation errors that occur when XCO2 observations are compared to modeled XCO2 in relatively coarse transport models. Here, we utilize various statistical methods to quantify the imprint of atmospheric transport on XCO2 observations. We compare spatial variations along Orbiting Carbon Observatory (OCO-2) satellite tracks to temporal variations observed by the Total Column Carbon Observing Network (TCCON). We observe a coherent seasonal cycle of both within-day temporal and fine-scale spatial variability (of order 10 km) of XCO2 from these two datasets, suggestive of the imprint of mesoscale systems. To account for other potential sources of error in XCO2 retrieval, we compare observed temporal and spatial variations of XCO2 to high-resolution output from the Weather Research and Forecasting (WRF) model run at 9 km resolution. In both simulations and observations, the Northern hemisphere mid-latitude XCO2 showed peak variability during the growing season when atmospheric gradients are largest. These results are qualitatively consistent with our expectations of seasonal variations of the imprint of synoptic and mesoscale atmospheric transport on XCO2 observations; suggesting that these statistical methods could be sensitive to the imprint of atmospheric transport on XCO2 observations.

Measuring water level in rivers and lakes from lightweight Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Bandini, Filippo; Jakobsen, Jakob; Olesen, Daniel; Reyna-Gutierrez, Jose Antonio; Bauer-Gottwein, Peter

2017-05-01

The assessment of hydrologic dynamics in rivers, lakes, reservoirs and wetlands requires measurements of water level, its temporal and spatial derivatives, and the extent and dynamics of open water surfaces. Motivated by the declining number of ground-based measurement stations, research efforts have been devoted to the retrieval of these hydraulic properties from spaceborne platforms in the past few decades. However, due to coarse spatial and temporal resolutions, spaceborne missions have several limitations when assessing the water level of terrestrial surface water bodies and determining complex water dynamics. Unmanned Aerial Vehicles (UAVs) can fill the gap between spaceborne and ground-based observations, and provide high spatial resolution and dense temporal coverage data, in quick turn-around time, using flexible payload design. This study focused on categorizing and testing sensors, which comply with the weight constraint of small UAVs (around 1.5 kg), capable of measuring the range to water surface. Subtracting the measured range from the vertical position retrieved by the onboard Global Navigation Satellite System (GNSS) receiver, we can determine the water level (orthometric height). Three different ranging payloads, which consisted of a radar, a sonar and an in-house developed camera-based laser distance sensor (CLDS), have been evaluated in terms of accuracy, precision, maximum ranging distance and beam divergence. After numerous flights, the relative accuracy of the overall system was estimated. A ranging accuracy better than 0.5% of the range and a maximum ranging distance of 60 m were achieved with the radar. The CLDS showed the lowest beam divergence, which is required to avoid contamination of the signal from interfering surroundings for narrow fields of view. With the GNSS system delivering a relative vertical accuracy better than 3-5 cm, water level can be retrieved with an overall accuracy better than 5-7 cm.

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

Nyhan, Marguerite; Sobolevsky, Stanislav; Kang, Chaogui

Air pollution related to traffic emissions pose an especially significant problem in cities; this is due to its adverse impact on human health and well-being. Previous studies which have aimed to quantify emissions from the transportation sector have been limited by either simulated or coarsely resolved traffic volume data. Emissions inventories form the basis of urban pollution models, therefore in this study, Global Positioning System (GPS) trajectory data from a taxi fleet of over 15,000 vehicles were analyzed with the aim of predicting air pollution emissions for Singapore. This novel approach enabled the quantification of instantaneous drive cycle parameters inmore » high spatio-temporal resolution, which provided the basis for a microscopic emissions model. Carbon dioxide (CO2), nitrogen oxides (NOx), volatile organic compounds (VOCs) and particulate matter (PM) emissions were thus estimated. Highly localized areas of elevated emissions levels were identified, with a spatio-temporal precision not possible with previously used methods for estimating emissions. Relatively higher emissions areas were mainly concentrated in a few districts that were the Singapore Downtown Core area, to the north of the central urban region and to the east of it. Daily emissions quantified for the total motor vehicle population of Singapore were found to be comparable to another emissions dataset Results demonstrated that high resolution spatio-temporal vehicle traces detected using GPS in large taxi fleets could be used to infer highly localized areas of elevated acceleration and air pollution emissions in cities, and may become a complement to traditional emission estimates, especially in emerging cities and countries where reliable fine-grained urban air quality data is not easily available. This is the first study of its kind to investigate measured microscopic vehicle movement in tandem with microscopic emissions modeling for a substantial study domain.« less

The Effects of Dissipation and Coarse Grid Resolution for Multigrid in Flow Problems

NASA Technical Reports Server (NTRS)

Eliasson, Peter; Engquist, Bjoern

1996-01-01

The objective of this paper is to investigate the effects of the numerical dissipation and the resolution of the solution on coarser grids for multigrid with the Euler equation approximations. The convergence is accomplished by multi-stage explicit time-stepping to steady state accelerated by FAS multigrid. A theoretical investigation is carried out for linear hyperbolic equations in one and two dimensions. The spectra reveals that for stability and hence robustness of spatial discretizations with a small amount of numerical dissipation the grid transfer operators have to be accurate enough and the smoother of low temporal accuracy. Numerical results give grid independent convergence in one dimension. For two-dimensional problems with a small amount of numerical dissipation, however, only a few grid levels contribute to an increased speed of convergence. This is explained by the small numerical dissipation leading to dispersion. Increasing the mesh density and hence making the problem over resolved increases the number of mesh levels contributing to an increased speed of convergence. If the steady state equations are elliptic, all grid levels contribute to the convergence regardless of the mesh density.

Cloud optical properties from satellites over Europe: CM SAF vs CERES

NASA Astrophysics Data System (ADS)

Konstantinou, Athanasia; Alexandri, Georgia; Balis, Dimitris

2017-04-01

In this work, the macro and micro physical properties of liquid and ice clouds over Europe are examined for the 8-year period 2004-2011. For the scopes of this research, high resolution (0.05x0.05 degree) satellite-based observations from CM SAF (Satellite Application Facility on Climate Monitoring) and coarse resolution (1x1 degree) data from CERES (Clouds and the Earth's Radiant Energy System) are utilized. The spatial and temporal patterns of the bias between the two products are examined. It is found that the difference between CM SAF and CERES cloud fractional cover (CFC) is 10% while cloud optical thickness (COT) from CM SAF is generally lower than CERES by 10 %. The effective radius of liquid (Rel) and ice (Rei) clouds is also examined. For the region of interest, CM SAF Rel is 12% higher while CM SAF Rei is lower by 20% than that of CERES. Intercomparison studies like the one presented here help us to get an insight into the capabilities and limitation of the cloud satellite products which are currently in use by the scientific community.

It's time for a crisper image of the Face of the Earth: Landsat and climate time series for massive land cover & climate change mapping at detailed resolution.

NASA Astrophysics Data System (ADS)

Pons, Xavier; Miquel, Ninyerola; Oscar, González-Guerrero; Cristina, Cea; Pere, Serra; Alaitz, Zabala; Lluís, Pesquer; Ivette, Serral; Joan, Masó; Cristina, Domingo; Maria, Serra Josep; Jordi, Cristóbal; Chris, Hain; Martha, Anderson; Juanjo, Vidal

2014-05-01

Combining climate dynamics and land cover at a relative coarse resolution allows a very interesting approach to global studies, because in many cases these studies are based on a quite high temporal resolution, but they may be limited in large areas like the Mediterranean. However, the current availability of long time series of Landsat imagery and spatially detailed surface climate models allow thinking on global databases improving the results of mapping in areas with a complex history of landscape dynamics, characterized by fragmentation, or areas where relief creates intricate climate patterns that can be hardly monitored or modeled at coarse spatial resolutions. DinaCliVe (supported by the Spanish Government and ERDF, and by the Catalan Government, under grants CGL2012-33927 and SGR2009-1511) is the name of the project that aims analyzing land cover and land use dynamics as well as vegetation stress, with a particular emphasis on droughts, and the role that climate variation may have had in such phenomena. To meet this objective is proposed to design a massive database from long time series of Landsat land cover products (grouped in quinquennia) and monthly climate records (in situ climate data) for the Iberian Peninsula (582,000 km2). The whole area encompasses 47 Landsat WRS2 scenes (Landsat 4 to 8 missions, from path 197 to 202 and from rows 30 to 34), and 52 Landsat WRS1 scenes (for the previous Landsat missions, 212 to 221 and 30 to 34). Therefore, a mean of 49.5 Landsat scenes, 8 quinquennia per scene and a about 6 dates per quinquennium , from 1975 to present, produces around 2376 sets resulting in 30 m x 30 m spatial resolution maps. Each set is composed by highly coherent geometric and radiometric multispectral and multitemporal (to account for phenology) imagery as well as vegetation and wetness indexes, and several derived topographic information (about 10 Tbyte of data). Furthermore, on the basis on a previous work: the Digital Climatic Atlas of the Iberian Peninsula, spatio-temporal surface climate data has been generated with a monthly resolution (from January 1950 to December 2010) through a multiple regression model and residuals spatial interpolation using geographic variables (altitude, latitude and continentality) and solar radiation (only in the case of temperatures). This database includes precipitation, mean minimum and mean maximum air temperature and mean air temperature, improving the previous one by using the ASTER GDEM at 30 m spatial resolution, by deepening to a monthly resolution and by increasing the number of meteorological stations used, representing a total amount of 0.7 Tbyte of data. An initial validation shows accuracies higher than 85 % for land cover maps and an RMS of 1.2 ºC, 1.6 ºC and 22 mm for mean and extreme temperatures, and for precipitation, respectively. This amount of new detailed data for the Iberian Peninsula framework will be used to study the spatial direction, velocity and acceleration of the tendencies related to climate change, land cover and tree line dynamics. A global analysis using all these datasets will try to discriminate the climatic signal when interpreted together with anthropogenic driving forces. Ultimately, getting ready for massive database computation and analysis will improve predictions for global models that will require of the growing high-resolution information available.

Temporal Variability in Vertical Groundwater Fluxes and the Effect of Solar Radiation on Streambed Temperatures Based on Vertical High Resolution Distributed Temperature Sensing

NASA Astrophysics Data System (ADS)

Sebok, E.; Karan, S.; Engesgaard, P. K.; Duque, C.

2013-12-01

Due to its large spatial and temporal variability, groundwater discharge to streams is difficult to quantify. Methods using vertical streambed temperature profiles to estimate vertical fluxes are often of coarse vertical spatial resolution and neglect to account for the natural heterogeneity in thermal conductivity of streambed sediments. Here we report on a field investigation in a stream, where air, stream water and streambed sediment temperatures were measured by Distributed Temperature Sensing (DTS) with high spatial resolution to; (i) detect spatial and temporal variability in groundwater discharge based on vertical streambed temperature profiles, (ii) study the thermal regime of streambed sediments exposed to different solar radiation influence, (iii) describe the effect of solar radiation on the measured streambed temperatures. The study was carried out at a field site located along Holtum stream, in Western Denmark. The 3 m wide stream has a sandy streambed with a cobbled armour layer, a mean discharge of 200 l/s and a mean depth of 0.3 m. Streambed temperatures were measured with a high-resolution DTS system (HR-DTS). By helically wrapping the fiber optic cable around two PVC pipes of 0.05 m and 0.075 m outer diameter over 1.5 m length, temperature measurements were recorded with 5.7 mm and 3.8 mm vertical spacing, respectively. The HR-DTS systems were installed 0.7 m deep in the streambed sediments, crossing both the sediment-water and the water-air interface, thus yielding high resolution water and air temperature data as well. One of the HR-DTS systems was installed in the open stream channel with only topographical shading, while the other HR-DTS system was placed 7 m upstream, under the canopy of a tree, thus representing the shaded conditions with reduced influence of solar radiation. Temperature measurements were taken with 30 min intervals between 16 April and 25 June 2013. The thermal conductivity of streambed sediments was calibrated in a 1D flow and heat transport model (HydroGeoSphere). Subsequently, time series of vertical groundwater fluxes were computed based on the high-resolution vertical streambed sediment temperature profiles by coupling the model with PEST. The calculated vertical flux time series show spatial differences in discharge between the two HR-DTS sites. A similar temporal variability in vertical fluxes at the two test sites can also be observed, most likely linked to rainfall-runoff processes. The effect of solar radiation as streambed conduction is visible both at the exposed and shaded test site in form of increased diel temperature oscillations up to 14 cm depth from the streambed surface, with the test site exposed to solar radiation showing larger diel temperature oscillations.

Constraining Stochastic Parametrisation Schemes Using High-Resolution Model Simulations

NASA Astrophysics Data System (ADS)

Christensen, H. M.; Dawson, A.; Palmer, T.

2017-12-01

Stochastic parametrisations are used in weather and climate models as a physically motivated way to represent model error due to unresolved processes. Designing new stochastic schemes has been the target of much innovative research over the last decade. While a focus has been on developing physically motivated approaches, many successful stochastic parametrisation schemes are very simple, such as the European Centre for Medium-Range Weather Forecasts (ECMWF) multiplicative scheme `Stochastically Perturbed Parametrisation Tendencies' (SPPT). The SPPT scheme improves the skill of probabilistic weather and seasonal forecasts, and so is widely used. However, little work has focused on assessing the physical basis of the SPPT scheme. We address this matter by using high-resolution model simulations to explicitly measure the `error' in the parametrised tendency that SPPT seeks to represent. The high resolution simulations are first coarse-grained to the desired forecast model resolution before they are used to produce initial conditions and forcing data needed to drive the ECMWF Single Column Model (SCM). By comparing SCM forecast tendencies with the evolution of the high resolution model, we can measure the `error' in the forecast tendencies. In this way, we provide justification for the multiplicative nature of SPPT, and for the temporal and spatial scales of the stochastic perturbations. However, we also identify issues with the SPPT scheme. It is therefore hoped these measurements will improve both holistic and process based approaches to stochastic parametrisation. Figure caption: Instantaneous snapshot of the optimal SPPT stochastic perturbation, derived by comparing high-resolution simulations with a low resolution forecast model.

Accurate registration of temporal CT images for pulmonary nodules detection

NASA Astrophysics Data System (ADS)

Yan, Jichao; Jiang, Luan; Li, Qiang

2017-02-01

Interpretation of temporal CT images could help the radiologists to detect some subtle interval changes in the sequential examinations. The purpose of this study was to develop a fully automated scheme for accurate registration of temporal CT images for pulmonary nodule detection. Our method consisted of three major registration steps. Firstly, affine transformation was applied in the segmented lung region to obtain global coarse registration images. Secondly, B-splines based free-form deformation (FFD) was used to refine the coarse registration images. Thirdly, Demons algorithm was performed to align the feature points extracted from the registered images in the second step and the reference images. Our database consisted of 91 temporal CT cases obtained from Beijing 301 Hospital and Shanghai Changzheng Hospital. The preliminary results showed that approximately 96.7% cases could obtain accurate registration based on subjective observation. The subtraction images of the reference images and the rigid and non-rigid registered images could effectively remove the normal structures (i.e. blood vessels) and retain the abnormalities (i.e. pulmonary nodules). This would be useful for the screening of lung cancer in our future study.

Spatial scaling of net primary productivity using subpixel landcover information

NASA Astrophysics Data System (ADS)

Chen, X. F.; Chen, Jing M.; Ju, Wei M.; Ren, L. L.

2008-10-01

Gridding the land surface into coarse homogeneous pixels may cause important biases on ecosystem model estimations of carbon budget components at local, regional and global scales. These biases result from overlooking subpixel variability of land surface characteristics. Vegetation heterogeneity is an important factor introducing biases in regional ecological modeling, especially when the modeling is made on large grids. This study suggests a simple algorithm that uses subpixel information on the spatial variability of land cover type to correct net primary productivity (NPP) estimates, made at coarse spatial resolutions where the land surface is considered as homogeneous within each pixel. The algorithm operates in such a way that NPP obtained from calculations made at coarse spatial resolutions are multiplied by simple functions that attempt to reproduce the effects of subpixel variability of land cover type on NPP. Its application to a carbon-hydrology coupled model(BEPS-TerrainLab model) estimates made at a 1-km resolution over a watershed (named Baohe River Basin) located in the southwestern part of Qinling Mountains, Shaanxi Province, China, improved estimates of average NPP as well as its spatial variability.

Requirements for a reliable millennium temperature reconstruction

NASA Astrophysics Data System (ADS)

Christiansen, Bo; Ljungqvist, Fredrik

2014-05-01

Quantitative temperature reconstructions are hampered by several problems. Proxy records are sparse which is witnessed by the fact that roughly half of all available high-resolution millennia-long proxy data have been published in the last five years. Moreover, proxies are inhomogeneously distributed around the globe and they often have coarse temporal resolution. The period of overlap between proxies and instrumental observations - the calibration period - is brief and dominated by a strong warming trend. Furthermore, proxies are often only weakly correlated to temperature and it is common that some form of screening procedure is applied to select only informative proxies. We study the influence of these limitations on the reliability of temperature reconstructions for the previous millennium. This influence depends on the spatial and temporal correlation structure of the surface temperature field. It also depends on the reconstruction methodology. We use gridded surface temperature data from GISTEMP and HadCRUT4 to investigate the geographical distribution of the spatial decorrelation length and of the temporal decorrelation time. The spatial decorrelation length varies with more than a factor of 5 with the largest values in the region dominated by the El Nino-Southern Oscillation. The temporal decorrelation time varies less with typical values of 1-2 years over land and 2-5 years over ocean. We also investigate the correlations between proxies and local temperatures (using the 91 proxies from Christiansen and Ljungqvist 2012) and between local temperatures and the NH mean temperature. These correlations have typical values around 0.3 but cover a wide range from weakly negative to larger than 0.8. The results outlined above allow us to identify regions where the effect of the lack of proxies is most important. They also inform us on the consequences of the short calibration period and on the influence of the recent trend. Finally, we demonstrate the effect of a weak proxy/temperature relationship on three different simple reconstruction methodologies. We show that the size and strength of this effect depends strongly on the chosen methodology.

Soil moisture downscaling using a simple thermal based proxy

NASA Astrophysics Data System (ADS)

Peng, Jian; Loew, Alexander; Niesel, Jonathan

2016-04-01

Microwave remote sensing has been largely applied to retrieve soil moisture (SM) from active and passive sensors. The obvious advantage of microwave sensor is that SM can be obtained regardless of atmospheric conditions. However, existing global SM products only provide observations at coarse spatial resolutions, which often hamper their applications in regional hydrological studies. Therefore, various downscaling methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to investigate the validity and robustness of a simple Vegetation Temperature Condition Index (VTCI) downscaling scheme over different climates and regions. Both polar orbiting (MODIS) and geostationary (MSG SEVIRI) satellite data are used to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture in-situ measurements, spatial pattern comparison, as well as seasonal and land use analyses show that the downscaling method can significantly improve the spatial details of CCI soil moisture while maintain the accuracy of CCI soil moisture. The application of the scheme with different satellite platforms and over different regions further demonstrate the robustness and effectiveness of the proposed method. Therefore, the VTCI downscaling method has the potential to facilitate relevant hydrological applications that require high spatial and temporal resolution soil moisture.

Spatial and temporal patterns of beetles associated with coarse woody debris in managed bottomland hardwood forests

Treesearch

Michael D. Ulyshen; James L. Hanula; Scott Horn; John C. Kilgo; Christopher E. Moorman

2004-01-01

Malaise traps were used to sample beetles in artificial canopy gaps of different size (0.13 ha, 0.26 ha, and 0.50 ha) and age in a South Carolina bottomland hardwood forest. Traps were placed at the center, edge, and in the surrounding forest of each gap. Young gaps (~1 year) had large amounts of coarse woody debris compared to the surrounding forest, while older gaps...

Towards a More Biologically-meaningful Climate Characterization: Variability in Space and Time at Multiple Scales

NASA Astrophysics Data System (ADS)

Christianson, D. S.; Kaufman, C. G.; Kueppers, L. M.; Harte, J.

2013-12-01

Sampling limitations and current modeling capacity justify the common use of mean temperature values in summaries of historical climate and future projections. However, a monthly mean temperature representing a 1-km2 area on the landscape is often unable to capture the climate complexity driving organismal and ecological processes. Estimates of variability in addition to mean values are more biologically meaningful and have been shown to improve projections of range shifts for certain species. Historical analyses of variance and extreme events at coarse spatial scales, as well as coarse-scale projections, show increasing temporal variability in temperature with warmer means. Few studies have considered how spatial variance changes with warming, and analysis for both temporal and spatial variability across scales is lacking. It is unclear how the spatial variability of fine-scale conditions relevant to plant and animal individuals may change given warmer coarse-scale mean values. A change in spatial variability will affect the availability of suitable habitat on the landscape and thus, will influence future species ranges. By characterizing variability across both temporal and spatial scales, we can account for potential bias in species range projections that use coarse climate data and enable improvements to current models. In this study, we use temperature data at multiple spatial and temporal scales to characterize spatial and temporal variability under a warmer climate, i.e., increased mean temperatures. Observational data from the Sierra Nevada (California, USA), experimental climate manipulation data from the eastern and western slopes of the Rocky Mountains (Colorado, USA), projected CMIP5 data for California (USA) and observed PRISM data (USA) allow us to compare characteristics of a mean-variance relationship across spatial scales ranging from sub-meter2 to 10,000 km2 and across temporal scales ranging from hours to decades. Preliminary spatial analysis at fine-spatial scales (sub-meter to 10-meter) shows greater temperature variability with warmer mean temperatures. This is inconsistent with the inherent assumption made in current species distribution models that fine-scale variability is static, implying that current projections of future species ranges may be biased -- the direction and magnitude requiring further study. While we focus our findings on the cross-scaling characteristics of temporal and spatial variability, we also compare the mean-variance relationship between 1) experimental climate manipulations and observed conditions and 2) temporal versus spatial variance, i.e., variability in a time-series at one location vs. variability across a landscape at a single time. The former informs the rich debate concerning the ability to experimentally mimic a warmer future. The latter informs space-for-time study design and analyses, as well as species persistence via a combined spatiotemporal probability of suitable future habitat.

Spatio-temporal reconstruction of air temperature maps and their application to estimate rice growing season heat accumulation using multi-temporal MODIS data*

PubMed Central

Zhang, Li-wen; Huang, Jing-feng; Guo, Rui-fang; Li, Xin-xing; Sun, Wen-bo; Wang, Xiu-zhen

2013-01-01

The accumulation of thermal time usually represents the local heat resources to drive crop growth. Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data collected from meteorological stations with coarse geographic continuity. To solve the critical problems of estimating air temperature (T a) and filling in missing pixels due to cloudy and low-quality images in growing degree days (GDDs) calculation from remotely sensed data, a novel spatio-temporal algorithm for T a estimation from Terra and Aqua moderate resolution imaging spectroradiometer (MODIS) data was proposed. This is a preliminary study to calculate heat accumulation, expressed in accumulative growing degree days (AGDDs) above 10 °C, from reconstructed T a based on MODIS land surface temperature (LST) data. The verification results of maximum T a, minimum T a, GDD, and AGDD from MODIS-derived data to meteorological calculation were all satisfied with high correlations over 0.01 significant levels. Overall, MODIS-derived AGDD was slightly underestimated with almost 10% relative error. However, the feasibility of employing AGDD anomaly maps to characterize the 2001–2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only MODIS data was finally demonstrated in the current paper. Our study may supply a novel way to calculate AGDD in heat-related study concerning crop growth monitoring, agricultural climatic regionalization, and agro-meteorological disaster detection at the regional scale. PMID:23365013

Spatio-temporal reconstruction of air temperature maps and their application to estimate rice growing season heat accumulation using multi-temporal MODIS data.

PubMed

Zhang, Li-wen; Huang, Jing-feng; Guo, Rui-fang; Li, Xin-xing; Sun, Wen-bo; Wang, Xiu-zhen

2013-02-01

The accumulation of thermal time usually represents the local heat resources to drive crop growth. Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data collected from meteorological stations with coarse geographic continuity. To solve the critical problems of estimating air temperature (T(a)) and filling in missing pixels due to cloudy and low-quality images in growing degree days (GDDs) calculation from remotely sensed data, a novel spatio-temporal algorithm for T(a) estimation from Terra and Aqua moderate resolution imaging spectroradiometer (MODIS) data was proposed. This is a preliminary study to calculate heat accumulation, expressed in accumulative growing degree days (AGDDs) above 10 °C, from reconstructed T(a) based on MODIS land surface temperature (LST) data. The verification results of maximum T(a), minimum T(a), GDD, and AGDD from MODIS-derived data to meteorological calculation were all satisfied with high correlations over 0.01 significant levels. Overall, MODIS-derived AGDD was slightly underestimated with almost 10% relative error. However, the feasibility of employing AGDD anomaly maps to characterize the 2001-2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only MODIS data was finally demonstrated in the current paper. Our study may supply a novel way to calculate AGDD in heat-related study concerning crop growth monitoring, agricultural climatic regionalization, and agro-meteorological disaster detection at the regional scale.

Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations.

NASA Astrophysics Data System (ADS)

López López, Patricia; Wanders, Niko; Sutanudjaja, Edwin; Renzullo, Luigi; Sterk, Geert; Schellekens, Jaap; Bierkens, Marc

2015-04-01

The coarse spatial resolution of global hydrological models (typically > 0.25o) often limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally-tunes river models. A possible solution to the problem may be to drive the coarse resolution models with high-resolution meteorological data as well as to assimilate ground-based and remotely-sensed observations of key water cycle variables. While this would improve the modelling resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study we investigated the impact that assimilating streamflow and satellite soil moisture observations have on global hydrological model estimation, driven by coarse- and high-resolution meteorological observations, for the Murrumbidgee river basin in Australia. The PCR-GLOBWB global hydrological model is forced with downscaled global climatological data (from 0.5o downscaled to 0.1o resolution) obtained from the WATCH Forcing Data (WFDEI) and local high resolution gauging station based gridded datasets (0.05o), sourced from the Australian Bureau of Meteorology. Downscaled satellite derived soil moisture (from 0.5o downscaled to 0.1o resolution) from AMSR-E and streamflow observations collected from 25 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global climatological data. Results show that the assimilation of streamflow observations result in the largest improvement of the model estimates. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improved in streamflow simulations (10% reduction in RMSE), mainly in the headwater catchments (up to 10,000 km2). Results also show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. This study demonstrates that it is possible to improve hydrological simulations forced by coarse resolution meteorological data with downscaled satellite soil moisture and streamflow observations and bring them closer to a hydrological model forced with local climatological data. These findings are important in light of the efforts that are currently done to go to global hyper-resolution modelling and can significantly help to advance this research.

Niche partitioning and biogeography of high light adapted Prochlorococcus across taxonomic ranks in the North Pacific

PubMed Central

Larkin, Alyse A; Blinebry, Sara K; Howes, Caroline; Lin, Yajuan; Loftus, Sarah E; Schmaus, Carrie A; Zinser, Erik R; Johnson, Zackary I

2016-01-01

The distribution of major clades of Prochlorococcus tracks light, temperature and other environmental variables; yet, the drivers of genomic diversity within these ecotypes and the net effect on biodiversity of the larger community are poorly understood. We examined high light (HL) adapted Prochlorococcus communities across spatial and temporal environmental gradients in the Pacific Ocean to determine the ecological drivers of population structure and diversity across taxonomic ranks. We show that the Prochlorococcus community has the highest diversity at low latitudes, but seasonality driven by temperature, day length and nutrients adds complexity. At finer taxonomic resolution, some ‘sub-ecotype' clades have unique, cohesive responses to environmental variables and distinct biogeographies, suggesting that presently defined ecotypes can be further partitioned into ecologically meaningful units. Intriguingly, biogeographies of the HL-I sub-ecotypes are driven by unique combinations of environmental traits, rather than through trait hierarchy, while the HL-II sub-ecotypes appear ecologically similar, thus demonstrating differences among these dominant HL ecotypes. Examining biodiversity across taxonomic ranks reveals high-resolution dynamics of Prochlorococcus evolution and ecology that are masked at phylogenetically coarse resolution. Spatial and seasonal trends of Prochlorococcus communities suggest that the future ocean may be comprised of different populations, with implications for ecosystem structure and function. PMID:26800235

Development of coarse-scale spatial data for wildland fire and fuel management

Treesearch

Kirsten M. Schmidt; James P. Menakis; Colin C. Hardy; Wendall J. Hann; David L. Bunnell

2002-01-01

We produced seven coarse-scale, 1-km2 resolution, spatial data layers for the conterminous United States to support national-level fire planning and risk assessments. Four of these layers were developed to evaluate ecological conditions and risk to ecosystem components: Potential Natural Vegetation Groups, a layer of climax vegetation types representing site...

Design and testing of a novel multi-stroke micropositioning system with variable resolutions.

PubMed

Xu, Qingsong

2014-02-01

Multi-stroke stages are demanded in micro-/nanopositioning applications which require smaller and larger motion strokes with fine and coarse resolutions, respectively. This paper presents the conceptual design of a novel multi-stroke, multi-resolution micropositioning stage driven by a single actuator for each working axis. It eliminates the issue of the interference among different drives, which resides in conventional multi-actuation stages. The stage is devised based on a fully compliant variable stiffness mechanism, which exhibits unequal stiffnesses in different strokes. Resistive strain sensors are employed to offer variable position resolutions in the different strokes. To quantify the design of the motion strokes and coarse/fine resolution ratio, analytical models are established. These models are verified through finite-element analysis simulations. A proof-of-concept prototype XY stage is designed, fabricated, and tested to demonstrate the feasibility of the presented ideas. Experimental results of static and dynamic testing validate the effectiveness of the proposed design.

Coupling Poisson rectangular pulse and multiplicative microcanonical random cascade models to generate sub-daily precipitation timeseries

NASA Astrophysics Data System (ADS)

Pohle, Ina; Niebisch, Michael; Müller, Hannes; Schümberg, Sabine; Zha, Tingting; Maurer, Thomas; Hinz, Christoph

2018-07-01

To simulate the impacts of within-storm rainfall variabilities on fast hydrological processes, long precipitation time series with high temporal resolution are required. Due to limited availability of observed data such time series are typically obtained from stochastic models. However, most existing rainfall models are limited in their ability to conserve rainfall event statistics which are relevant for hydrological processes. Poisson rectangular pulse models are widely applied to generate long time series of alternating precipitation events durations and mean intensities as well as interstorm period durations. Multiplicative microcanonical random cascade (MRC) models are used to disaggregate precipitation time series from coarse to fine temporal resolution. To overcome the inconsistencies between the temporal structure of the Poisson rectangular pulse model and the MRC model, we developed a new coupling approach by introducing two modifications to the MRC model. These modifications comprise (a) a modified cascade model ("constrained cascade") which preserves the event durations generated by the Poisson rectangular model by constraining the first and last interval of a precipitation event to contain precipitation and (b) continuous sigmoid functions of the multiplicative weights to consider the scale-dependency in the disaggregation of precipitation events of different durations. The constrained cascade model was evaluated in its ability to disaggregate observed precipitation events in comparison to existing MRC models. For that, we used a 20-year record of hourly precipitation at six stations across Germany. The constrained cascade model showed a pronounced better agreement with the observed data in terms of both the temporal pattern of the precipitation time series (e.g. the dry and wet spell durations and autocorrelations) and event characteristics (e.g. intra-event intermittency and intensity fluctuation within events). The constrained cascade model also slightly outperformed the other MRC models with respect to the intensity-frequency relationship. To assess the performance of the coupled Poisson rectangular pulse and constrained cascade model, precipitation events were stochastically generated by the Poisson rectangular pulse model and then disaggregated by the constrained cascade model. We found that the coupled model performs satisfactorily in terms of the temporal pattern of the precipitation time series, event characteristics and the intensity-frequency relationship.

Effects of Model Resolution and Ocean Mixing on Forced Ice-Ocean Physical and Biogeochemical Simulations Using Global and Regional System Models

NASA Astrophysics Data System (ADS)

Jin, Meibing; Deal, Clara; Maslowski, Wieslaw; Matrai, Patricia; Roberts, Andrew; Osinski, Robert; Lee, Younjoo J.; Frants, Marina; Elliott, Scott; Jeffery, Nicole; Hunke, Elizabeth; Wang, Shanlin

2018-01-01

The current coarse-resolution global Community Earth System Model (CESM) can reproduce major and large-scale patterns but is still missing some key biogeochemical features in the Arctic Ocean, e.g., low surface nutrients in the Canada Basin. We incorporated the CESM Version 1 ocean biogeochemical code into the Regional Arctic System Model (RASM) and coupled it with a sea-ice algal module to investigate model limitations. Four ice-ocean hindcast cases are compared with various observations: two in a global 1° (40˜60 km in the Arctic) grid: G1deg and G1deg-OLD with/without new sea-ice processes incorporated; two on RASM's 1/12° (˜9 km) grid R9km and R9km-NB with/without a subgrid scale brine rejection parameterization which improves ocean vertical mixing under sea ice. Higher-resolution and new sea-ice processes contributed to lower model errors in sea-ice extent, ice thickness, and ice algae. In the Bering Sea shelf, only higher resolution contributed to lower model errors in salinity, nitrate (NO3), and chlorophyll-a (Chl-a). In the Arctic Basin, model errors in mixed layer depth (MLD) were reduced 36% by brine rejection parameterization, 20% by new sea-ice processes, and 6% by higher resolution. The NO3 concentration biases were caused by both MLD bias and coarse resolution, because of excessive horizontal mixing of high NO3 from the Chukchi Sea into the Canada Basin in coarse resolution models. R9km showed improvements over G1deg on NO3, but not on Chl-a, likely due to light limitation under snow and ice cover in the Arctic Basin.

Spatial downscaling of SMAP soil moisture using MODIS land surface temperature and NDVI during SMAPVEX15

USDA-ARS?s Scientific Manuscript database

The SMAP (Soil Moisture Active Passive) mission provides global surface soil moisture product at 36 km resolution from its L-band radiometer. While the coarse resolution is satisfactory to many applications there are also a lot of applications which would benefit from a higher resolution soil moistu...

Study of key factors influencing dust emission: An assessment of GEOS-Chem and DEAD simulations with observations

NASA Astrophysics Data System (ADS)

Bartlett, Kevin S.

Mineral dust aerosols can impact air quality, climate change, biological cycles, tropical cyclone development and flight operations due to reduced visibility. Dust emissions are primarily limited to the extensive arid regions of the world, yet can negatively impact local to global scales, and are extremely complex to model accurately. Within this dissertation, the Dust Entrainment And Deposition (DEAD) model was adapted to run, for the first known time, using high temporal (hourly) and spatial (0.3°x0.3°) resolution data to methodically interrogate the key parameters and factors influencing global dust emissions. The dependence of dust emissions on key parameters under various conditions has been quantified and it has been shown that dust emissions within DEAD are largely determined by wind speeds, vegetation extent, soil moisture and topographic depressions. Important findings were that grid degradation from 0.3ºx0.3º to 1ºx1º, 2ºx2.5º, and 4°x5° of key meteorological, soil, and surface input parameters greatly reduced emissions approximately 13% and 29% and 64% respectively, as a result of the loss of sub grid detail within these key parameters at coarse grids. After running high resolution DEAD emissions globally for 2 years, two severe dust emission cases were chosen for an in-depth investigation of the root causes of the events and evaluation of the 2°x2.5° Goddard Earth Observing System (GEOS)-Chem and 0.3°x0.3° DEAD model capabilities to simulate the events: one over South West Asia (SWA) in June 2008 and the other over the Middle East in July 2009. The 2 year lack of rain over SWA preceding June 2008 with a 43% decrease in mean rainfall, yielded less than normal plant growth, a 28% increase in Aerosol Optical Depth (AOD), and a 24% decrease in Meteorological Aerodrome Report (METAR) observed visibility (VSBY) compared to average years. GEOS-Chem captured the observed higher AOD over SWA in June 2008. More detailed comparisons of GEOS-Chem predicted AOD and visibility over SWA with those observed at surface stations and from satellites revealed overall success of the model, although substantial regional differences exist. Within the extended drought, the study area was zoomed into the Middle East (ME) for July 2009 where multi-grid DEAD dust emissions using hourly CFSR meteorological input were compared with observations. The high resolution input yielded the best spatial and temporal dust patterns compared with Defense Meteorological Satellite Program (DMSP), Moderate Resolution Imaging Spectroradiometer (MODIS) and METAR VSBY observations and definitively revealed Syria as a major dust source for the region. The coarse resolution dust emissions degraded or missed daily dust emissions entirely. This readily showed that the spatial scale degradation of the input data can significantly impair DEAD dust emissions and offers a strong argument for adapting higher resolution dust emission schemes into future global models for improvements of dust simulations.

Temporal variability and memory in sediment transport in an experimental step-pool channel

NASA Astrophysics Data System (ADS)

Saletti, Matteo; Molnar, Peter; Zimmermann, André; Hassan, Marwan A.; Church, Michael

2015-11-01

Temporal dynamics of sediment transport in steep channels using two experiments performed in a steep flume (8%) with natural sediment composed of 12 grain sizes are studied. High-resolution (1 s) time series of sediment transport were measured for individual grain-size classes at the outlet of the flume for different combinations of sediment input rates and flow discharges. Our aim in this paper is to quantify (a) the relation of discharge and sediment transport and (b) the nature and strength of memory in grain-size-dependent transport. None of the simple statistical descriptors of sediment transport (mean, extreme values, and quantiles) display a clear relation with water discharge, in fact a large variability between discharge and sediment transport is observed. Instantaneous transport rates have probability density functions with heavy tails. Bed load bursts have a coarser grain-size distribution than that of the entire experiment. We quantify the strength and nature of memory in sediment transport rates by estimating the Hurst exponent and the autocorrelation coefficient of the time series for different grain sizes. Our results show the presence of the Hurst phenomenon in transport rates, indicating long-term memory which is grain-size dependent. The short-term memory in coarse grain transport increases with temporal aggregation and this reveals the importance of the sampling duration of bed load transport rates in natural streams, especially for large fractions.

A model of regional primary production for use with coarse resolution satellite data

NASA Technical Reports Server (NTRS)

Prince, S. D.

1991-01-01

A model of crop primary production, which was originally developed to relate the amount of absorbed photosynthetically active radiation (APAR) to net production in field studies, is discussed in the context of coarse resolution regional remote sensing of primary production. The model depends on an approximately linear relationship between APAR and the normalized difference vegetation index. A more comprehensive form of the conventional model is shown to be necessary when different physiological types of plants or heterogeneous vegetation types occur within the study area. The predicted variable in the new model is total assimilation (net production plus respiration) rather than net production alone or harvest yield.

A Coarse-to-Fine Geometric Scale-Invariant Feature Transform for Large Size High Resolution Satellite Image Registration

PubMed Central

Chang, Xueli; Du, Siliang; Li, Yingying; Fang, Shenghui

2018-01-01

Large size high resolution (HR) satellite image matching is a challenging task due to local distortion, repetitive structures, intensity changes and low efficiency. In this paper, a novel matching approach is proposed for the large size HR satellite image registration, which is based on coarse-to-fine strategy and geometric scale-invariant feature transform (SIFT). In the coarse matching step, a robust matching method scale restrict (SR) SIFT is implemented at low resolution level. The matching results provide geometric constraints which are then used to guide block division and geometric SIFT in the fine matching step. The block matching method can overcome the memory problem. In geometric SIFT, with area constraints, it is beneficial for validating the candidate matches and decreasing searching complexity. To further improve the matching efficiency, the proposed matching method is parallelized using OpenMP. Finally, the sensing image is rectified to the coordinate of reference image via Triangulated Irregular Network (TIN) transformation. Experiments are designed to test the performance of the proposed matching method. The experimental results show that the proposed method can decrease the matching time and increase the number of matching points while maintaining high registration accuracy. PMID:29702589

On the Role of Urban and Vegetative Land Cover in the Identification of Tornado Damage Using Dual-Resolution Multispectral Satellite Imagery

NASA Astrophysics Data System (ADS)

Kingfield, D.; de Beurs, K.

2014-12-01

It has been demonstrated through various case studies that multispectral satellite imagery can be utilized in the identification of damage caused by a tornado through the change detection process. This process involves the difference in returned surface reflectance between two images and is often summarized through a variety of ratio-based vegetation indices (VIs). Land cover type plays a large contributing role in the change detection process as the reflectance properties of vegetation can vary based on several factors (e.g. species, greenness, density). Consequently, this provides the possibility for a variable magnitude of loss, making certain land cover regimes less reliable in the damage identification process. Furthermore, the tradeoff between sensor resolution and orbital return period may also play a role in the ability to detect catastrophic loss. Moderate resolution imagery (e.g. Moderate Resolution Imaging Spectroradiometer (MODIS)) provides relatively coarse surface detail with a higher update rate which could hinder the identification of small regions that underwent a dynamic change. Alternatively, imagery with higher spatial resolution (e.g. Landsat) have a longer temporal return period between successive images which could result in natural recovery underestimating the absolute magnitude of damage incurred. This study evaluates the role of land cover type and sensor resolution on four high-end (EF3+) tornado events occurring in four different land cover groups (agriculture, forest, grassland, urban) in the spring season. The closest successive clear images from both Landsat 5 and MODIS are quality controlled for each case. Transacts of surface reflectance across a homogenous land cover type both inside and outside the damage swath are extracted. These metrics are synthesized through the calculation of six different VIs to rank the calculated change metrics by land cover type, sensor resolution and VI.

Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings.

PubMed

Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P

2017-09-15

Major end users of Digital Soil Mapping (DSM) such as policy makers and agricultural extension workers are faced with choosing the appropriate remote sensing data. The objective of this research is to analyze the spatial resolution effects of different remote sensing images on soil prediction models in two smallholder farms in Southern India called Kothapally (Telangana State), and Masuti (Karnataka State), and provide empirical guidelines to choose the appropriate remote sensing images in DSM. Bayesian kriging (BK) was utilized to characterize the spatial pattern of exchangeable potassium (K ex ) in the topsoil (0-15 cm) at different spatial resolutions by incorporating spectral indices from Landsat 8 (30 m), RapidEye (5 m), and WorldView-2/GeoEye-1/Pleiades-1A images (2 m). Some spectral indices such as band reflectances, band ratios, Crust Index and Atmospherically Resistant Vegetation Index from multiple images showed relatively strong correlations with soil K ex in two study areas. The research also suggested that fine spatial resolution WorldView-2/GeoEye-1/Pleiades-1A-based and RapidEye-based soil prediction models would not necessarily have higher prediction performance than coarse spatial resolution Landsat 8-based soil prediction models. The end users of DSM in smallholder farm settings need select the appropriate spectral indices and consider different factors such as the spatial resolution, band width, spectral resolution, temporal frequency, cost, and processing time of different remote sensing images. Overall, remote sensing-based Digital Soil Mapping has potential to be promoted to smallholder farm settings all over the world and help smallholder farmers implement sustainable and field-specific soil nutrient management scheme. Copyright © 2017 Elsevier Ltd. All rights reserved.

Classification and Accuracy Assessment for Coarse Resolution Mapping within the Great Lakes Basin, USA

EPA Science Inventory

This study applied a phenology-based land-cover classification approach across the Laurentian Great Lakes Basin (GLB) using time-series data consisting of 23 Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) composite images (250 ...

Detecting spatio-temporal changes in agricultural land use in Heilongjiang province, China using MODIS time-series data and a random forest regression model

NASA Astrophysics Data System (ADS)

Hu, Q.; Friedl, M. A.; Wu, W.

2017-12-01

Accurate and timely information regarding the spatial distribution of crop types and their changes is essential for acreage surveys, yield estimation, water management, and agricultural production decision-making. In recent years, increasing population, dietary shifts and climate change have driven drastic changes in China's agricultural land use. However, no maps are currently available that document the spatial and temporal patterns of these agricultural land use changes. Because of its short revisit period, rich spectral bands and global coverage, MODIS time series data has been shown to have great potential for detecting the seasonal dynamics of different crop types. However, its inherently coarse spatial resolution limits the accuracy with which crops can be identified from MODIS in regions with small fields or complex agricultural landscapes. To evaluate this more carefully and specifically understand the strengths and weaknesses of MODIS data for crop-type mapping, we used MODIS time-series imagery to map the sub-pixel fractional crop area for four major crop types (rice, corn, soybean and wheat) at 500-m spatial resolution for Heilongjiang province, one of the most important grain-production regions in China where recent agricultural land use change has been rapid and pronounced. To do this, a random forest regression (RF-g) model was constructed to estimate the percentage of each sub-pixel crop type in 2006, 2011 and 2016. Crop type maps generated through expert visual interpretation of high spatial resolution images (i.e., Landsat and SPOT data) were used to calibrate the regression model. Five different time series of vegetation indices (155 features) derived from different spectral channels of MODIS land surface reflectance (MOD09A1) data were used as candidate features for the RF-g model. An out-of-bag strategy and backward elimination approach was applied to select the optimal spectra-temporal feature subset for each crop type. The resulting crop maps were assessed in two ways: (1) wall-to-wall pixel comparison with corresponding high spatial resolution reference maps; and (2) county-level comparison with census data. Based on these derived maps, changes in crop type, total area, and spatial patterns of change in Heilongjiang province during 2006-2016 were analyzed.

Monitoring the dynamics of surface water fraction from MODIS time series in a Mediterranean environment

NASA Astrophysics Data System (ADS)

Li, Linlin; Vrieling, Anton; Skidmore, Andrew; Wang, Tiejun; Turak, Eren

2018-04-01

Detailed spatial information of changes in surface water extent is needed for water management and biodiversity conservation, particularly in drier parts of the globe where small, temporally-variant wetlands prevail. Although global surface water histories are now generated from 30 m Landsat data, for many locations they contain large temporal gaps particularly for longer periods (>10 years) due to revisit intervals and cloud cover. Daily Moderate Resolution Imaging Spectrometer (MODIS) imagery has potential to fill such gaps, but its relatively coarse spatial resolution may not detect small water bodies, which can be of great ecological importance. To address this problem, this study proposes and tests options for estimating the surface water fraction from MODIS 16-day 500 m Bidirectional Reflectance Distribution Function (BRDF) corrected surface reflectance image composites. The spatial extent of two Landsat tiles over Spain were selected as test areas. We obtained a 500 m reference dataset on surface water fraction by spatially aggregating 30 m binary water masks obtained from the Landsat-derived C-version of Function of Mask (CFmask), which themselves were evaluated against high-resolution Google Earth imagery. Twelve regression tree models were developed with two approaches, Random Forest and Cubist, using spectral metrics derived from MODIS data and topographic parameters generated from a 30 m spatial resolution digital elevation model. Results showed that accuracies were higher when we included annual summary statistics of the spectral metrics as predictor variables. Models trained on a single Landsat tile were ineffective in mapping surface water in the other tile, but global models trained with environmental conditions from both tiles can provide accurate results for both study areas. We achieved the highest accuracy with Cubist global model (R2 = 0.91, RMSE = 11.05%, MAE = 7.67%). Our method was not only effective for mapping permanent water fraction, but also in accurately capturing temporal fluctuations of surface water. Based on this good performance, we produced surface water fraction maps at 16-day interval for the 2000-2015 MODIS archive. Our approach is promising for monitoring surface water fraction at high frequency time intervals over much larger regions provided that training data are collected across the spatial domain for which the model will be applied.

The optical properties, physical properties and direct radiative forcing of urban columnar aerosols in the Yangtze River Delta, China

NASA Astrophysics Data System (ADS)

Zhuang, Bingliang; Wang, Tijian; Liu, Jane; Che, Huizheng; Han, Yong; Fu, Yu; Li, Shu; Xie, Min; Li, Mengmeng; Chen, Pulong; Chen, Huimin; Yang, Xiu-qun; Sun, Jianning

2018-02-01

The optical and physical properties as well as the direct radiative forcings (DRFs) of fractionated aerosols in the urban area of the western Yangtze River Delta (YRD) are investigated with measurements from a Cimel sun photometer combined with a radiation transfer model. Ground-based observations of aerosols have much higher temporal resolutions than satellite retrievals. An initial analysis reveals the characteristics of the optical properties of different types of fractionated aerosols in the western YRD. The total aerosols, mostly composed of scattering components (93.8 %), have mean optical depths of 0.65 at 550 nm and refractive index of 1.44 + 0.0084i at 440 nm. The fine aerosols are approximately four times more abundant and have very different compositions from coarse aerosols. The absorbing components account for only ˜ 4.6 % of fine aerosols and 15.5 % of coarse aerosols and have smaller sizes than the scattering aerosols within the same mode. Therefore, fine particles have stronger scattering than coarse ones, simultaneously reflecting the different size distributions between the absorbing and scattering aerosols. The relationships among the optical properties quantify the aerosol mixing and imply that approximately 15 and 27.5 % of the total occurrences result in dust- and black-carbon-dominating mixing aerosols, respectively, in the western YRD. Unlike the optical properties, the size distributions of aerosols in the western YRD are similar to those found at other sites over eastern China on a climatological scale, peaking at radii of 0.148 and 2.94 µm. However, further analysis reveals that the coarse-dominated particles can also lead to severe haze pollution over the YRD. Observation-based estimations indicate that both fine and coarse aerosols in the western YRD exert negative DRFs, and this is especially true for fine aerosols (-11.17 W m-2 at the top of atmosphere, TOA). A higher absorption fraction leads directly to the negative DRF being further offset for coarse aerosols (-0.33 W m-2) at the TOA. Similarly, the coarse-mode DRF contributes to only 13.3 % of the total scattering aerosols but > 33.7 % to the total absorbing aerosols. A sensitivity analysis states that aerosol DRFs are not highly sensitive to their profiles in clear-sky conditions. Most of the aerosol properties and DRFs have substantial seasonality in the western YRD. The results further reveal the contributions of each component of the different size particles to the total aerosol optical depths (AODs) and DRFs. Additionally, these results can be used to improve aerosol modelling performance and the modelling of aerosol effects in the eastern regions of China.

Tapping the full potential of geodetic glacier change assessment with air and space borne sensors

NASA Astrophysics Data System (ADS)

Zemp, M.; Paul, F.; Machguth, H.; Fischer, M.

2016-12-01

Glacier changes are recognized as independent and high-confidence natural indicators of climate change. Past, current, and future glacier changes impact on global sea level, the regional water cycle, and local hazard situations. In the 5th Assessment Report of the IPCC, glacier mass budgets were reconciled by combining traditional observations (i.e. results from glaciological and geodetic measurements) with satellite altimetry and gravimetry to fill regional gaps and obtain global coverage. However, this approach is challenged by the relatively small number and inhomogeneous distribution of in-situ measurement series and their often unknown representativeness for the respective mountain range as well as by scale issues of current satellite altimetry (only point data) and gravimetry (coarse resolution) missions. In this presentation, we highlight the potential of air and space borne sensors for (i) validation and calibration of direct measurements using the glaciological method, (ii) assessing glacier volume changes over entire mountain ranges, and for (iii) determination of the representativeness of the field measurements for respective mountain ranges. Whereas long-term in-situ measurements provide the temporal variability of glacier mass changes with annual or seasonal resolution, differencing of high-resolution digital elevation models, such as from airborne (national) surveys or TanDEM-X, bear the potential to assess thickness and volume changes for thousands of individual glaciers over entire mountain ranges on a decadal time scale. In combination, the calibrated field measurements can be used to determine volume and mass changes over entire mountain ranges at high confidence. The spatial-temporal extrapolation can be supported using dense temporal series of snow cover evolution derived from optical satellite data such as Sentinel 2. Finally, these results can be used to reconcile satellite altimetry and gravimetry products. Provided that resources for corresponding glacier monitoring activities are made available (within or outside the scientific funding system), the combination of in-situ with air and space borne measurements will boost the scientific capacity to address the grand challenges from climate-induced glacier changes and related societal impacts.

Spatio-temporal dynamics and laterality effects of face inversion, feature presence and configuration, and face outline

PubMed Central

Marinkovic, Ksenija; Courtney, Maureen G.; Witzel, Thomas; Dale, Anders M.; Halgren, Eric

2014-01-01

Although a crucial role of the fusiform gyrus (FG) in face processing has been demonstrated with a variety of methods, converging evidence suggests that face processing involves an interactive and overlapping processing cascade in distributed brain areas. Here we examine the spatio-temporal stages and their functional tuning to face inversion, presence and configuration of inner features, and face contour in healthy subjects during passive viewing. Anatomically-constrained magnetoencephalography (aMEG) combines high-density whole-head MEG recordings and distributed source modeling with high-resolution structural MRI. Each person's reconstructed cortical surface served to constrain noise-normalized minimum norm inverse source estimates. The earliest activity was estimated to the occipital cortex at ~100 ms after stimulus onset and was sensitive to an initial coarse level visual analysis. Activity in the right-lateralized ventral temporal area (inclusive of the FG) peaked at ~160 ms and was largest to inverted faces. Images containing facial features in the veridical and rearranged configuration irrespective of the facial outline elicited intermediate level activity. The M160 stage may provide structural representations necessary for downstream distributed areas to process identity and emotional expression. However, inverted faces additionally engaged the left ventral temporal area at ~180 ms and were uniquely subserved by bilateral processing. This observation is consistent with the dual route model and spared processing of inverted faces in prosopagnosia. The subsequent deflection, peaking at ~240 ms in the anterior temporal areas bilaterally, was largest to normal, upright faces. It may reflect initial engagement of the distributed network subserving individuation and familiarity. These results support dynamic models suggesting that processing of unfamiliar faces in the absence of a cognitive task is subserved by a distributed and interactive neural circuit. PMID:25426044

Packing of sidechains in low-resolution models for proteins.

PubMed

Keskin, O; Bahar, I

1998-01-01

Atomic level rotamer libraries for sidechains in proteins have been proposed by several groups. Conformations of side groups in coarse-grained models, on the other hand, have not yet been analyzed, although low resolution approaches are the only efficient way to explore global structural features. A residue-specific backbone-dependent library for sidechain isomers, compatible with a coarse-grained model, is proposed. The isomeric states are utilized in packing sidechains of known backbone structures. Sidechain positions are predicted with a root-mean-square deviation (r.m.s.d.) of 2.40 A with respect to crystal structure for 50 test proteins. The rmsd for core residues is 1.60 A and decreases to 1.35 A when conformational correlations and directional effects in inter-residue couplings are considered. An automated method for assigning sidechain positions in coarse-grained model proteins is proposed and made available on the internet; the method accounts satisfactorily for sidechain packing, particularly in the core.

Fine resolution mapping of population age-structures for health and development applications.

PubMed

Alegana, V A; Atkinson, P M; Pezzulo, C; Sorichetta, A; Weiss, D; Bird, T; Erbach-Schoenberg, E; Tatem, A J

2015-04-06

The age-group composition of populations varies considerably across the world, and obtaining accurate, spatially detailed estimates of numbers of children under 5 years is important in designing vaccination strategies, educational planning or maternal healthcare delivery. Traditionally, such estimates are derived from population censuses, but these can often be unreliable, outdated and of coarse resolution for resource-poor settings. Focusing on Nigeria, we use nationally representative household surveys and their cluster locations to predict the proportion of the under-five population in 1 × 1 km using a Bayesian hierarchical spatio-temporal model. Results showed that land cover, travel time to major settlements, night-time lights and vegetation index were good predictors and that accounting for fine-scale variation, rather than assuming a uniform proportion of under 5 year olds can result in significant differences in health metrics. The largest gaps in estimated bednet and vaccination coverage were in Kano, Katsina and Jigawa. Geolocated household surveys are a valuable resource for providing detailed, contemporary and regularly updated population age-structure data in the absence of recent census data. By combining these with covariate layers, age-structure maps of unprecedented detail can be produced to guide the targeting of interventions in resource-poor settings.

A PIXEL COMPOSITION-BASED REFERENCE DATA SET FOR THEMATIC ACCURACY ASSESSMENT

EPA Science Inventory

Developing reference data sets for accuracy assessment of land-cover classifications derived from coarse spatial resolution sensors such as MODIS can be difficult due to the large resolution differences between the image data and available reference data sources. Ideally, the spa...

An attempt at estimating Paris area CO2 emissions from atmospheric concentration measurements

NASA Astrophysics Data System (ADS)

Bréon, F. M.; Broquet, G.; Puygrenier, V.; Chevallier, F.; Xueref-Rémy, I.; Ramonet, M.; Dieudonné, E.; Lopez, M.; Schmidt, M.; Perrussel, O.; Ciais, P.

2014-04-01

Atmospheric concentration measurements are used to adjust the daily to monthly budget of CO2 emissions from the AirParif inventory of the Paris agglomeration. We use 5 atmospheric monitoring sites including one at the top of the Eiffel tower. The atmospheric inversion is based on a Bayesian approach, and relies on an atmospheric transport model with a spatial resolution of 2 km with boundary conditions from a global coarse grid transport model. The inversion tool adjusts the CO2 fluxes (anthropogenic and biogenic) with a temporal resolution of 6 h, assuming temporal correlation of emissions uncertainties within the daily cycle and from day to day, while keeping the a priori spatial distribution from the emission inventory. The inversion significantly improves the agreement between measured and modelled concentrations. However, the amplitude of the atmospheric transport errors is often large compared to the CO2 gradients between the sites that are used to estimate the fluxes, in particular for the Eiffel tower station. In addition, we sometime observe large model-measurement differences upwind from the Paris agglomeration, which confirms the large and poorly constrained contribution from distant sources and sinks included in the prescribed CO2 boundary conditions These results suggest that (i) the Eiffel measurements at 300 m above ground cannot be used with the current system and (ii) the inversion shall rely on the measured upwind-downwind gradients rather than the raw mole fraction measurements. With such setup, realistic emissions are retrieved for two 30 day periods. Similar inversions over longer periods are necessary for a proper evaluation of the results.

Estimating Vegetation Rainfall Interception Using Remote Sensing Observations at Very High Resolution

NASA Astrophysics Data System (ADS)

Cui, Y.; Zhao, P.; Hong, Y.; Fan, W.; Yan, B.; Xie, H.

2017-12-01

Abstract: As an important compont of evapotranspiration, vegetation rainfall interception is the proportion of gross rainfall that is intercepted, stored and subsequently evaporated from all parts of vegetation during or following rainfall. Accurately quantifying the vegetation rainfall interception at a high resolution is critical for rainfall-runoff modeling and flood forecasting, and is also essential for understanding its further impact on local, regional, and even global water cycle dynamics. In this study, the Remote Sensing-based Gash model (RS-Gash model) is developed based on a modified Gash model for interception loss estimation using remote sensing observations at the regional scale, and has been applied and validated in the upper reach of the Heihe River Basin of China for different types of vegetation. To eliminate the scale error and the effect of mixed pixels, the RS-Gash model is applied at a fine scale of 30 m with the high resolution vegetation area index retrieved by using the unified model of bidirectional reflectance distribution function (BRDF-U) for the vegetation canopy. Field validation shows that the RMSE and R2 of the interception ratio are 3.7% and 0.9, respectively, indicating the model's strong stability and reliability at fine scale. The temporal variation of vegetation rainfall interception loss and its relationship with precipitation are further investigated. In summary, the RS-Gash model has demonstrated its effectiveness and reliability in estimating vegetation rainfall interception. When compared to the coarse resolution results, the application of this model at 30-m fine resolution is necessary to resolve the scaling issues as shown in this study. Keywords: rainfall interception; remote sensing; RS-Gash analytical model; high resolution

Improved wetland classification using eight-band high-resolution satellite imagery and a hybrid approach

EPA Science Inventory

Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of derived wetland maps were limited or often unsatisfactory largely due to the relatively coarse spatial resolution of conventional satellite imagery. This re...

Hydrologic downscaling of soil moisture using global data without site-specific calibration

USDA-ARS?s Scientific Manuscript database

Numerous applications require fine-resolution (10-30 m) soil moisture patterns, but most satellite remote sensing and land-surface models provide coarse-resolution (9-60 km) soil moisture estimates. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales soil moistu...

Mapping Forest Fuels through Vegetation Phenology: The Role of Coarse-Resolution Satellite Time-Series

PubMed Central

Bajocco, Sofia; Dragoz, Eleni; Gitas, Ioannis; Smiraglia, Daniela; Salvati, Luca; Ricotta, Carlo

2015-01-01

Traditionally fuel maps are built in terms of ‘fuel types’, thus considering the structural characteristics of vegetation only. The aim of this work is to derive a phenological fuel map based on the functional attributes of coarse-scale vegetation phenology, such as seasonality and productivity. MODIS NDVI 250m images of Sardinia (Italy), a large Mediterranean island with high frequency of fire incidence, were acquired for the period 2000–2012 to construct a mean annual NDVI profile of the vegetation at the pixel-level. Next, the following procedure was used to develop the phenological fuel map: (i) image segmentation on the Fourier components of the NDVI profiles to identify phenologically homogeneous landscape units, (ii) cluster analysis of the phenological units and post-hoc analysis of the fire-proneness of the phenological fuel classes (PFCs) obtained, (iii) environmental characterization (in terms of land cover and climate) of the PFCs. Our results showed the ability of coarse-resolution satellite time-series to characterize the fire-proneness of Sardinia with an adequate level of accuracy. The remotely sensed phenological framework presented may represent a suitable basis for the development of fire distribution prediction models, coarse-scale fuel maps and for various biogeographic studies. PMID:25822505

Linear mixing model applied to AVHRR LAC data

NASA Technical Reports Server (NTRS)

Holben, Brent N.; Shimabukuro, Yosio E.

1993-01-01

A linear mixing model was applied to coarse spatial resolution data from the NOAA Advanced Very High Resolution Radiometer. The reflective component of the 3.55 - 3.93 microns channel was extracted and used with the two reflective channels 0.58 - 0.68 microns and 0.725 - 1.1 microns to run a Constraine Least Squares model to generate vegetation, soil, and shade fraction images for an area in the Western region of Brazil. The Landsat Thematic Mapper data covering the Emas National park region was used for estimating the spectral response of the mixture components and for evaluating the mixing model results. The fraction images were compared with an unsupervised classification derived from Landsat TM data acquired on the same day. The relationship between the fraction images and normalized difference vegetation index images show the potential of the unmixing techniques when using coarse resolution data for global studies.

Dynamical Downscaling of Typhoon Vera (1959) and related Storm Surge based on JRA-55 Reanalysis

NASA Astrophysics Data System (ADS)

Ninomiya, J.; Takemi, T.; Mori, N.; Shibutani, Y.; Kim, S.

2015-12-01

Typhoon Vera in 1959 is historical extreme typhoon that caused severest typhoon damage mainly due to the storm surge up to 389 cm in Japan. Vera developed 895 hPa on offshore and landed with 929.2 hPa. There are many studies of the dynamical downscaling of Vera but it is difficult to simulate accurately because of the lack of the accuracy of global reanalysis data. This study carried out dynamical downscaling experiment of Vera using WRF downscaling forced by JRA-55 that are latest atmospheric model and reanalysis data. In this study, the reproducibility of five global reanalysis data for Typhoon Vera were compered. Comparison shows that reanalysis data doesn't have strong typhoon information except for JRA-55, so that downscaling with conventional reanalysis data goes wrong. The dynamical downscaling method for storm surge is studied very much (e.g. choice of physical model, nudging, 4D-VAR, bogus and so on). In this study, domain size and resolution of the coarse domain were considered. The coarse domain size influences the typhoon route and central pressure, and larger domain restrains the typhoon strength. The results of simulations with different domain size show that the threshold of developing restrain is whether the coarse domain fully includes the area of wind speed more than 15 m/s around the typhoon. The results of simulations with different resolution show that the resolution doesn't affect the typhoon route, and higher resolution gives stronger typhoon simulation.

Spatial heterogeneity of leaf area index across scales from simulation and remote sensing

NASA Astrophysics Data System (ADS)

Reichenau, Tim G.; Korres, Wolfgang; Montzka, Carsten; Schneider, Karl

2016-04-01

Leaf area index (LAI, single sided leaf area per ground area) influences mass and energy exchange of vegetated surfaces. Therefore LAI is an input variable for many land surface schemes of coupled large scale models, which do not simulate LAI. Since these models typically run on rather coarse resolution grids, LAI is often inferred from coarse resolution remote sensing. However, especially in agriculturally used areas, a grid cell of these products often covers more than a single land-use. In that case, the given LAI does not apply to any single land-use. Therefore, the overall spatial heterogeneity in these datasets differs from that on resolutions high enough to distinguish areas with differing land-use. Detailed process-based plant growth models simulate LAI for separate plant functional types or specific species. However, limited availability of observations causes reduced spatial heterogeneity of model input data (soil, weather, land-use). Since LAI is strongly heterogeneous in space and time and since processes depend on LAI in a nonlinear way, a correct representation of LAI spatial heterogeneity is also desirable on coarse resolutions. The current study assesses this issue by comparing the spatial heterogeneity of LAI from remote sensing (RapidEye) and process-based simulations (DANUBIA simulation system) across scales. Spatial heterogeneity is assessed by analyzing LAI frequency distributions (spatial variability) and semivariograms (spatial structure). Test case is the arable land in the fertile loess plain of the Rur catchment near the Germany-Netherlands border.

Multiresolution Iterative Reconstruction in High-Resolution Extremity Cone-Beam CT

PubMed Central

Cao, Qian; Zbijewski, Wojciech; Sisniega, Alejandro; Yorkston, John; Siewerdsen, Jeffrey H; Stayman, J Webster

2016-01-01

Application of model-based iterative reconstruction (MBIR) to high resolution cone-beam CT (CBCT) is computationally challenging because of the very fine discretization (voxel size <100 µm) of the reconstructed volume. Moreover, standard MBIR techniques require that the complete transaxial support for the acquired projections is reconstructed, thus precluding acceleration by restricting the reconstruction to a region-of-interest. To reduce the computational burden of high resolution MBIR, we propose a multiresolution Penalized-Weighted Least Squares (PWLS) algorithm, where the volume is parameterized as a union of fine and coarse voxel grids as well as selective binning of detector pixels. We introduce a penalty function designed to regularize across the boundaries between the two grids. The algorithm was evaluated in simulation studies emulating an extremity CBCT system and in a physical study on a test-bench. Artifacts arising from the mismatched discretization of the fine and coarse sub-volumes were investigated. The fine grid region was parameterized using 0.15 mm voxels and the voxel size in the coarse grid region was varied by changing a downsampling factor. No significant artifacts were found in either of the regions for downsampling factors of up to 4×. For a typical extremities CBCT volume size, this downsampling corresponds to an acceleration of the reconstruction that is more than five times faster than a brute force solution that applies fine voxel parameterization to the entire volume. For certain configurations of the coarse and fine grid regions, in particular when the boundary between the regions does not cross high attenuation gradients, downsampling factors as high as 10× can be used without introducing artifacts, yielding a ~50× speedup in PWLS. The proposed multiresolution algorithm significantly reduces the computational burden of high resolution iterative CBCT reconstruction and can be extended to other applications of MBIR where computationally expensive, high-fidelity forward models are applied only to a sub-region of the field-of-view. PMID:27694701

Improving crop condition monitoring at field scale by using optimal Landsat and MODIS images

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing data at coarse resolution (kilometers) have been widely used in monitoring crop condition for decades. However, crop condition monitoring at field scale requires high resolution data in both time and space. Although a large number of remote sensing instruments with different...

Daily monitoring of vegetation conditions and evapotranspiration at field scale by fusing multi-satellite images

USDA-ARS?s Scientific Manuscript database

Vegetation monitoring requires frequent remote sensing observations. While imagery from coarse resolution sensors such as MODIS/VIIRS can provide daily observations, they lack spatial detail to capture surface features for vegetation monitoring. The medium spatial resolution (10-100m) sensors are su...

Reconstruction of a Three Hourly 1-km Land Surface Air Temperature Dataset in the Qinghai-Tibet Plateau

NASA Astrophysics Data System (ADS)

Zhou, J.; Ding, L.

2017-12-01

Land surface air temperature (SAT) is an important parameter in the modeling of radiation balance and energy budget of the earth surface. Generally, SAT is measured at ground meteorological stations; then SAT mapping is possible though a spatial interpolation process. The interpolated SAT map relies on the spatial distribution of ground stations, the terrain, and many other factors; thus, it has great uncertainties in regions with complicated terrain. Instead, SAT map can also be obtained through physical modeling of interactions between the land surface and the atmosphere. Such dataset generally has coarse spatial resolution (e.g. coarser than 0.1°) and cannot satisfy the applications at fine scales, e.g. 1 km. This presentation reports the reconstruction of a three hourly 1-km SAT dataset from 2001 to 2015 over the Qinghai-Tibet Plateau. The terrain in the Qinghai-Tibet Plateau, especially in the eastern part, is extremely complicated. Two SAT datasets with good qualities are used in this study. The first one is from the 3h China Meteorological Forcing Dataset with a 0.1° resolution released by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (Yang et al., 2010); the second one is from the ERA-Interim product with the same temporal resolution and a 0.125° resolution. A statistical approach is developed to downscale the spatial resolution of the derived SAT to 1-km. The elevation and the normalized difference vegetation index (NDVI) are selected as two scaling factors in the downscaling approach. Results demonstrate there is significantly negative correlation between the SAT and elevation in all seasons; there is also significantly negative correlation between the SAT and NDVI in the vegetation growth seasons, while the correlation decreases in the other seasons. Therefore, a temporally dynamic downscaling approach is feasible to enhance the spatial resolution of the SAT. Compared with the SAT at the 0.1° or 0.125°, the reconstructed 1-km SAT can provide much more spatial details in areas with complicated terrain. Additionally, the 1-km SAT agrees well with the ground measured air temperatures as well as the SAT before downscaling. The reconstructed SAT will be beneficial for the modeling of surface radiation balance and energy budget over the Qinghai-Tibet Plateau.

Coarse-grained sediment delivery and distribution in the Holocene Santa Monica Basin, California: Implications for evaluating source-to-sink flux at millennial time scales

USGS Publications Warehouse

Romans, B.W.; Normark, W.R.; McGann, M.M.; Covault, J.A.; Graham, S.A.

2009-01-01

Utilizing accumulations of coarse-grained terrigenous sediment from deep-marine basins to evaluate the relative contributions of and history of controls on sediment flux through a source-to-sink system has been difficult as a result of limited knowledge of event timing. In this study, six new radiocarbon (14C) dates are integrated with five previously published dates that have been recalibrated from a 12.5-m-thick turbidite section from Ocean Drilling Program (ODP) Site 1015 in Santa Monica Basin, offshore California. This borehole is tied to high-resolution seismic-reflection profiles that cover an 1100 km2 area of the middle and lower Hueneme submarine fan and most of the basin plain. The resulting stratigraphic framework provides the highest temporal resolution for a thick-bedded Holocene turbidite succession to date, permitting an evaluation of source-to-sink controls at millennial (1000 yr) scales. The depositional history from 7 ka to present indicates that the recurrence interval for large turbidity-current events is relatively constant (300-360 yr), but the volume of sediment deposited on the fan and in the basin plain has increased by a factor of 2 over this period. Moreover, the amount of sand per event on the basin plain during the same interval has increased by a factor of 7. Maps of sediment distribution derived from correlation of seismic-reflection profiles indicate that this trend cannot be attributed exclusively to autogenic processes (e.g., progradation of depocenters). The observed variability in sediment accumulation rates is thus largely controlled by allogenic factors, including: (1) increased discharge of Santa Clara River as a result of increased magnitude and frequency of El Ni??o-Southern Oscillation (ENSO) events from ca. 2 ka to present, (2) an apparent change in routing of coarse-grained sediment within the staging area at ca. 3 ka (i.e., from direct river input to indirect, littoral cell input into Hueneme submarine canyon), and (3) decreasing rates of sea-level rise (i.e., rate of rise slowed considerably by ca. 3 ka). The Holocene history of the Santa Clara River-Santa Monica Basin source-to-sink system demonstrates the ways in which varying sediment flux and changes in dispersal pathways affect the basinal stratigraphic record. ?? 2009 Geological Society of America.

Validation of the CHIRPS Satellite Rainfall Estimates over Eastern of Africa

NASA Astrophysics Data System (ADS)

Dinku, T.; Funk, C. C.; Tadesse, T.; Ceccato, P.

2017-12-01

Long and temporally consistent rainfall time series are essential in climate analyses and applications. Rainfall data from station observations are inadequate over many parts of the world due to sparse or non-existent observation networks, or limited reporting of gauge observations. As a result, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. However, many satellite-based rainfall products with long time series suffer from coarse spatial and temporal resolutions and inhomogeneities caused by variations in satellite inputs. There are some satellite rainfall products with reasonably consistent time series, but they are often limited to specific geographic areas. The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are recently produced satellite-based rainfall products with relatively high spatial and temporal resolutions and quasi-global coverage. In this study, CHIRP and CHIRPS were evaluated over East Africa at daily, dekadal (10-day) and monthly time scales. The evaluation was done by comparing the satellite products with rain gauge data from about 1200 stations. The is unprecedented number of validation stations for this region covering. The results provide a unique region-wide understanding of how satellite products perform over different climatic/geographic (low lands, mountainous regions, and coastal) regions. The CHIRP and CHIRPS products were also compared with two similar satellite rainfall products: the African Rainfall Climatology version 2 (ARC2) and the latest release of the Tropical Applications of Meteorology using Satellite data (TAMSAT). The results show that both CHIRP and CHIRPS products are significantly better than ARC2 with higher skill and low or no bias. These products were also found to be slightly better than the latest version of the TAMSAT product. A comparison was also done between the latest release of the TAMSAT product (TAMSAT3) and the earlier version(TAMSAT2), which has shown that the latest version is a substantial improvement over the previous one, particularly with regards to the bias statistics.

Spatiotemporal modelling of groundwater extraction in semi-arid central Queensland, Australia

NASA Astrophysics Data System (ADS)

Keir, Greg; Bulovic, Nevenka; McIntyre, Neil

2016-04-01

The semi-arid Surat Basin in central Queensland, Australia, forms part of the Great Artesian Basin, a groundwater resource of national significance. While this area relies heavily on groundwater supply bores to sustain agricultural industries and rural life in general, measurement of groundwater extraction rates is very limited. Consequently, regional groundwater extraction rates are not well known, which may have implications for regional numerical groundwater modelling. However, flows from a small number of bores are metered, and less precise anecdotal estimates of extraction are increasingly available. There is also an increasing number of other spatiotemporal datasets which may help predict extraction rates (e.g. rainfall, temperature, soils, stocking rates etc.). These can be used to construct spatial multivariate regression models to estimate extraction. The data exhibit complicated statistical features, such as zero-valued observations, non-Gaussianity, and non-stationarity, which limit the use of many classical estimation techniques, such as kriging. As well, water extraction histories may exhibit temporal autocorrelation. To account for these features, we employ a separable space-time model to predict bore extraction rates using the R-INLA package for computationally efficient Bayesian inference. A joint approach is used to model both the probability (using a binomial likelihood) and magnitude (using a gamma likelihood) of extraction. The correlation between extraction rates in space and time is modelled using a Gaussian Markov Random Field (GMRF) with a Matérn spatial covariance function which can evolve over time according to an autoregressive model. To reduce computational burden, we allow the GMRF to be evaluated at a relatively coarse temporal resolution, while still allowing predictions to be made at arbitrarily small time scales. We describe the process of model selection and inference using an information criterion approach, and present some preliminary results from the study area. We conclude by discussing issues related with upscaling of the modelling approach to the entire basin, including merging of extraction rate observations with different precision, temporal resolution, and even potentially different likelihoods.

The Impact of Horizontal and Temporal Resolution on Convection and Precipitation with High-Resolution GEOS-5

NASA Technical Reports Server (NTRS)

Putman, William P.

2012-01-01

Using a high-resolution non-hydrostatic version of GEOS-5 with the cubed-sphere finite-volume dynamical core, the impact of spatial and temporal resolution on cloud properties will be evaluated. There are indications from examining convective cluster development in high resolution GEOS-5 forecasts that the temporal resolution within the model may playas significant a role as horizontal resolution. Comparing modeled convective cloud clusters versus satellite observations of brightness temperature, we have found that improved. temporal resolution in GEOS-S accounts for a significant portion of the improvements in the statistical distribution of convective cloud clusters. Using satellite simulators in GEOS-S we will compare the cloud optical properties of GEOS-S at various spatial and temporal resolutions with those observed from MODIS. The potential impact of these results on tropical cyclone formation and intensity will be examined as well.

Temporal Resolution Needed for Auditory Communication: Measurement With Mosaic Speech

PubMed Central

Nakajima, Yoshitaka; Matsuda, Mizuki; Ueda, Kazuo; Remijn, Gerard B.

2018-01-01

Temporal resolution needed for Japanese speech communication was measured. A new experimental paradigm that can reflect the spectro-temporal resolution necessary for healthy listeners to perceive speech is introduced. As a first step, we report listeners' intelligibility scores of Japanese speech with a systematically degraded temporal resolution, so-called “mosaic speech”: speech mosaicized in the coordinates of time and frequency. The results of two experiments show that mosaic speech cut into short static segments was almost perfectly intelligible with a temporal resolution of 40 ms or finer. Intelligibility dropped for a temporal resolution of 80 ms, but was still around 50%-correct level. The data are in line with previous results showing that speech signals separated into short temporal segments of <100 ms can be remarkably robust in terms of linguistic-content perception against drastic manipulations in each segment, such as partial signal omission or temporal reversal. The human perceptual system thus can extract meaning from unexpectedly rough temporal information in speech. The process resembles that of the visual system stringing together static movie frames of ~40 ms into vivid motion. PMID:29740295

Bottom-up coarse-grained models that accurately describe the structure, pressure, and compressibility of molecular liquids

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

Dunn, Nicholas J. H.; Noid, W. G., E-mail: wnoid@chem.psu.edu

2015-12-28

The present work investigates the capability of bottom-up coarse-graining (CG) methods for accurately modeling both structural and thermodynamic properties of all-atom (AA) models for molecular liquids. In particular, we consider 1, 2, and 3-site CG models for heptane, as well as 1 and 3-site CG models for toluene. For each model, we employ the multiscale coarse-graining method to determine interaction potentials that optimally approximate the configuration dependence of the many-body potential of mean force (PMF). We employ a previously developed “pressure-matching” variational principle to determine a volume-dependent contribution to the potential, U{sub V}(V), that approximates the volume-dependence of the PMF.more » We demonstrate that the resulting CG models describe AA density fluctuations with qualitative, but not quantitative, accuracy. Accordingly, we develop a self-consistent approach for further optimizing U{sub V}, such that the CG models accurately reproduce the equilibrium density, compressibility, and average pressure of the AA models, although the CG models still significantly underestimate the atomic pressure fluctuations. Additionally, by comparing this array of models that accurately describe the structure and thermodynamic pressure of heptane and toluene at a range of different resolutions, we investigate the impact of bottom-up coarse-graining upon thermodynamic properties. In particular, we demonstrate that U{sub V} accounts for the reduced cohesion in the CG models. Finally, we observe that bottom-up coarse-graining introduces subtle correlations between the resolution, the cohesive energy density, and the “simplicity” of the model.« less

Monitoring forest dynamics with multi-scale and time series imagery.

PubMed

Huang, Chunbo; Zhou, Zhixiang; Wang, Di; Dian, Yuanyong

2016-05-01

To learn the forest dynamics and evaluate the ecosystem services of forest effectively, a timely acquisition of spatial and quantitative information of forestland is very necessary. Here, a new method was proposed for mapping forest cover changes by combining multi-scale satellite remote-sensing imagery with time series data. Using time series Normalized Difference Vegetation Index products derived from the Moderate Resolution Imaging Spectroradiometer images (MODIS-NDVI) and Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images as data source, a hierarchy stepwise analysis from coarse scale to fine scale was developed for detecting the forest change area. At the coarse scale, MODIS-NDVI data with 1-km resolution were used to detect the changes in land cover types and a land cover change map was constructed using NDVI values at vegetation growing seasons. At the fine scale, based on the results at the coarse scale, Landsat TM/ETM+ data with 30-m resolution were used to precisely detect the forest change location and forest change trend by analyzing time series forest vegetation indices (IFZ). The method was tested using the data for Hubei Province, China. The MODIS-NDVI data from 2001 to 2012 were used to detect the land cover changes, and the overall accuracy was 94.02 % at the coarse scale. At the fine scale, the available TM/ETM+ images at vegetation growing seasons between 2001 and 2012 were used to locate and verify forest changes in the Three Gorges Reservoir Area, and the overall accuracy was 94.53 %. The accuracy of the two layer hierarchical monitoring results indicated that the multi-scale monitoring method is feasible and reliable.

Review of surface particulate monitoring of dust events using geostationary satellite remote sensing

NASA Astrophysics Data System (ADS)

Sowden, M.; Mueller, U.; Blake, D.

2018-06-01

The accurate measurements of natural and anthropogenic aerosol particulate matter (PM) is important in managing both environmental and health risks; however, limited monitoring in regional areas hinders accurate quantification. This article provides an overview of the ability of recently launched geostationary earth orbit (GEO) satellites, such as GOES-R (North America) and HIMAWARI (Asia and Oceania), to provide near real-time ground-level PM concentrations (GLCs). The review examines the literature relating to the spatial and temporal resolution required by air quality studies, the removal of cloud and surface effects, the aerosol inversion problem, and the computation of ground-level concentrations rather than columnar aerosol optical depth (AOD). Determining surface PM concentrations using remote sensing is complicated by differentiating intrinsic aerosol properties (size, shape, composition, and quantity) from extrinsic signal intensities, particularly as the number of unknown intrinsic parameters exceeds the number of known extrinsic measurements. The review confirms that development of GEO satellite products has led to improvements in the use of coupled products such as GEOS-CHEM, aerosol types have consolidated on model species rather than prior descriptive classifications, and forward radiative transfer models have led to a better understanding of predictive spectra interdependencies across different aerosol types, despite fewer wavelength bands. However, it is apparent that the aerosol inversion problem remains challenging because there are limited wavelength bands for characterising localised mineralogy. The review finds that the frequency of GEO satellite data exceeds the temporal resolution required for air quality studies, but the spatial resolution is too coarse for localised air quality studies. Continual monitoring necessitates using the less sensitive thermal infra-red bands, which also reduce surface absorption effects. However, given the challenges of the aerosol inversion problem and difficulties in converting columnar AOD to surface concentrations, the review identifies coupled GEO-neural networks as potentially the most viable option for improving quantification.

Validation of satellite-based operational flood monitoring in Southern Queensland, Australia

NASA Astrophysics Data System (ADS)

Gouweleeuw, Ben; Ticehurst, Catherine; Lerat, Julien; Thew, Peter

2010-05-01

The integration of remote sensing observations with stage data and flood modeling has the potential to provide improved support to a number of disciplines, such as flood warning emergency response and operational water resources management. The ability of remote sensing technology to monitor the dynamics of hydrological events lies in its capacity to map surface water. For flood monitoring, remote sensing imagery needs to be available sufficiently frequently to capture subsequent inundation stages. MODIS optical data are available at a moderately high spatial and temporal resolution (250m-1km, twice daily), but are affected by cloud cover. AMSR-E passive microwave observations are available at comparable temporal resolution, but coarse spatial resolution (5-70km), where the smaller footprints corresponds with the higher frequency bands, which are affected by precipitating clouds. A novel operational technique to monitor flood extent combines MODIS reflectance and AMSR-E passive microwave imagery to optimize data continuity. Flood extent is subsequently combined with a DEM to obtain total flood water volume. The flood extent and volume product is operational for the lower-Balonne floodplain in Southern Queensland, Australia. For validation purposes, two moderate flood events coinciding with the MODIS and AMSR-E sensor lifetime are evaluated. The flood volume estimated from MODIS/AMSR-E images gives an accurate indication of both the timing and the magnitude of the flood peak compared to the net volume from recorded flow. In the flood recession, however, satellite-derived water volume declines rapidly, while the net flow volume remains level. This may be explained by a combination of ungauged outflows, soil infiltration, evaporation and diversion of flood water into many large open reservoirs for irrigation purposes. The open water storage extent unchanged, the water volume product is not sensitive enough to capture the change in storage water level. Additional information on the latter, e.g. via telemetered buoys, may circumvent this limitation.

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.

Application of Geostatistical Simulation to Enhance Satellite Image Products

NASA Technical Reports Server (NTRS)

Hlavka, Christine A.; Dungan, Jennifer L.; Thirulanambi, Rajkumar; Roy, David

2004-01-01

With the deployment of Earth Observing System (EOS) satellites that provide daily, global imagery, there is increasing interest in defining the limitations of the data and derived products due to its coarse spatial resolution. Much of the detail, i.e. small fragments and notches in boundaries, is lost with coarse resolution imagery such as the EOS MODerate-Resolution Imaging Spectroradiometer (MODIS) data. Higher spatial resolution data such as the EOS Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER), Landsat and airborne sensor imagery provide more detailed information but are less frequently available. There are, however, both theoretical and analytical evidence that burn scars and other fragmented types of land covers form self-similar or self-affine patterns, that is, patterns that look similar when viewed at widely differing spatial scales. Therefore small features of the patterns should be predictable, at least in a statistical sense, with knowledge about the large features. Recent developments in fractal modeling for characterizing the spatial distribution of undiscovered petroleum deposits are thus applicable to generating simulations of finer resolution satellite image products. We will present example EOS products, analysis to investigate self-similarity, and simulation results.

Impacts of precipitation and potential evapotranspiration patterns on downscaling soil moisture in regions with large topographic relief

USDA-ARS?s Scientific Manuscript database

Mapping of soil moisture is important for many applications such as flood forecasting, soil protection, and crop management. Soil moisture can be estimated at coarse resolutions (>1 km) using satellite remote sensing, but that resolution is poorly suited for many applications. The Equilibrium Mois...

Analyzing the Effects of Horizontal Resolution on Long-Term Coupled WRF-CMAQ Simulations

EPA Science Inventory

The objective of this study is to determine the adequacy of using a relatively coarse horizontal resolution (i.e. 36 km) to simulate long-term trends of pollutant concentrations and radiation variables with the coupled WRF-CMAQ model. To this end, WRF-CMAQ simulations over the co...

Assessment of the scale effect on statistical downscaling quality at a station scale using a weather generator-based model

USDA-ARS?s Scientific Manuscript database

The resolution of General Circulation Models (GCMs) is too coarse to assess the fine scale or site-specific impacts of climate change. Downscaling approaches including dynamical and statistical downscaling have been developed to meet this requirement. As the resolution of climate model increases, it...

Dynamically downscaling predictions for deciduous tree leaf emergence in California under current and future climate.

PubMed

Medvigy, David; Kim, Seung Hee; Kim, Jinwon; Kafatos, Menas C

2016-07-01

Models that predict the timing of deciduous tree leaf emergence are typically very sensitive to temperature. However, many temperature data products, including those from climate models, have been developed at a very coarse spatial resolution. Such coarse-resolution temperature products can lead to highly biased predictions of leaf emergence. This study investigates how dynamical downscaling of climate models impacts simulations of deciduous tree leaf emergence in California. Models for leaf emergence are forced with temperatures simulated by a general circulation model (GCM) at ~200-km resolution for 1981-2000 and 2031-2050 conditions. GCM simulations are then dynamically downscaled to 32- and 8-km resolution, and leaf emergence is again simulated. For 1981-2000, the regional average leaf emergence date is 30.8 days earlier in 32-km simulations than in ~200-km simulations. Differences between the 32 and 8 km simulations are small and mostly local. The impact of downscaling from 200 to 8 km is ~15 % smaller in 2031-2050 than in 1981-2000, indicating that the impacts of downscaling are unlikely to be stationary.

On neutral metacommunity patterns of river basins at different scales of aggregation

NASA Astrophysics Data System (ADS)

Convertino, Matteo; Muneepeerakul, Rachata; Azaele, Sandro; Bertuzzo, Enrico; Rinaldo, Andrea; Rodriguez-Iturbe, Ignacio

2009-08-01

Neutral metacommunity models for spatial biodiversity patterns are implemented on river networks acting as ecological corridors at different resolution. Coarse-graining elevation fields (under the constraint of preserving the basin mean elevation) produce a set of reconfigured drainage networks. The hydrologic assumption made implies uniform runoff production such that each link has the same habitat capacity. Despite the universal scaling properties shown by river basins regardless of size, climate, vegetation, or exposed lithology, we find that species richness at local and regional scales exhibits resolution-dependent behavior. In addition, we investigate species-area relationships and rank-abundance patterns. The slopes of the species-area relationships, which are consistent over coarse-graining resolutions, match those found in real landscapes in the case of long-distance dispersal. The rank-abundance patterns are independent of the resolution over a broad range of dispersal length. Our results confirm that strong interactions occur between network structure and the dispersal of species and that under the assumption of neutral dynamics, these interactions produce resolution-dependent biodiversity patterns that diverge from expectations following from universal geomorphic scaling laws. Both in theoretical and in applied ecology studying how patterns change in resolution is relevant for understanding how ecological dynamics work in fragmented landscape and for sampling and biodiversity management campaigns, especially in consideration of climate change.

Practical Application and Obstacles of AVHRR Thermal Data for Estimation of Effusion Rates at Tolbachik Volcano, Kamchatka Peninsula, Russian Federation

NASA Astrophysics Data System (ADS)

McAlpin, D. B.; Meyer, F. J.; Webley, P. W.

2017-12-01

Using thermal data from Advanced Very High Resolution Radiometer (AVHRR) sensors, we investigated algorithms to estimate the effusive volume of lava flows from the 2012-13 eruption of Tolbachik Volcano with high temporal resolution. AVHRR are polar orbiting, radiation detection instruments that provide reflectance and radiance data in six spectral bands with a ground resolution of 1.1 km². During the Tolbachik eruption of 2012-13, active AVHRR instruments were available aboard four polar orbiting platforms. Although the primary purpose of the instruments is climate and ocean studies, their multiple platforms provide global coverage at least twice daily, with data for all regions of the earth no older than six hours. This frequency makes the AVHRR instruments particularly suitable for the study of volcanic activity. While methods for deriving effusion rates from thermal observations have been previously published, a number of topics complicate their practical application. In particular, these include (1) unknown material parameters used in the estimation process; (2) relatively coarse resolution of thermal sensors; (3) optimizing a model to describe the number of thermal regimes within each pixel and (4) frequent saturation issues in thermal channels. We present ongoing investigations into effusion rate estimation from AVHRR data using the 2012-13 eruption of Tolbachik Volcano as a test event. For this eruption we studied approaches for coping with issues (1) - (4) to pave the way to a more operational implementation of published techniques. To address (1), we used Monte Carlo simulations to understand the sensitivity of effusion rate estimates to changes in material parameters. To study (2) and (3) we compared typical two-component (exposed lava on ambient background) and three-component models (exposed lava, cooled crust, ambient background) for their relative performance. To study issue (4), we compared AVHRR-derived effusion rates to reference data derived from multi-temporal digital elevation models. In our workflow, we correct for scan angle of the sensor and the transmissivity of the atmosphere before including include corrected temperatures in heat equations to determine the effusion volume necessary to satisfy the equations.

Multidecadal Rates of Disturbance- and Climate Change-Induced Land Cover Change in Arctic and Boreal Ecosystems over Western Canada and Alaska Inferred from Dense Landsat Time Series

NASA Astrophysics Data System (ADS)

Wang, J.; Sulla-menashe, D. J.; Woodcock, C. E.; Sonnentag, O.; Friedl, M. A.

2017-12-01

Rapid climate change in arctic and boreal ecosystems is driving changes to land cover composition, including woody expansion in the arctic tundra, successional shifts following boreal fires, and thaw-induced wetland expansion and forest collapse along the southern limit of permafrost. The impacts of these land cover transformations on the physical climate and the carbon cycle are increasingly well-documented from field and model studies, but there have been few attempts to empirically estimate rates of land cover change at decadal time scale and continental spatial scale. Previous studies have used too coarse spatial resolution or have been too limited in temporal range to enable broad multi-decadal assessment of land cover change. As part of NASA's Arctic Boreal Vulnerability Experiment (ABoVE), we are using dense time series of Landsat remote sensing data to map disturbances and classify land cover types across the ABoVE extended domain (spanning western Canada and Alaska) over the last three decades (1982-2014) at 30 m resolution. We utilize regionally-complete and repeated acquisition high-resolution (<2 m) DigitalGlobe imagery to generate training data from across the region that follows a nested, hierarchical classification scheme encompassing plant functional type and cover density, understory type, wetland status, and land use. Additionally, we crosswalk plot-level field data into our scheme for additional high quality training sites. We use the Continuous Change Detection and Classification algorithm to estimate land cover change dates and temporal-spectral features in the Landsat data. These features are used to train random forest classification models and map land cover and analyze land cover change processes, focusing primarily on tundra "shrubification", post-fire succession, and boreal wetland expansion. We will analyze the high resolution data based on stratified random sampling of our change maps to validate and assess the accuracy of our model predictions. In this paper, we present initial results from this effort, including sub-regional analyses focused on several key areas, such as the Taiga Plains and the Southern Arctic ecozones, to calibrate our random forest models and assess results.

The Canadian Hydrological Model (CHM): A multi-scale, variable-complexity hydrological model for cold regions

NASA Astrophysics Data System (ADS)

Marsh, C.; Pomeroy, J. W.; Wheater, H. S.

2016-12-01

There is a need for hydrological land surface schemes that can link to atmospheric models, provide hydrological prediction at multiple scales and guide the development of multiple objective water predictive systems. Distributed raster-based models suffer from an overrepresentation of topography, leading to wasted computational effort that increases uncertainty due to greater numbers of parameters and initial conditions. The Canadian Hydrological Model (CHM) is a modular, multiphysics, spatially distributed modelling framework designed for representing hydrological processes, including those that operate in cold-regions. Unstructured meshes permit variable spatial resolution, allowing coarse resolutions at low spatial variability and fine resolutions as required. Model uncertainty is reduced by lessening the necessary computational elements relative to high-resolution rasters. CHM uses a novel multi-objective approach for unstructured triangular mesh generation that fulfills hydrologically important constraints (e.g., basin boundaries, water bodies, soil classification, land cover, elevation, and slope/aspect). This provides an efficient spatial representation of parameters and initial conditions, as well as well-formed and well-graded triangles that are suitable for numerical discretization. CHM uses high-quality open source libraries and high performance computing paradigms to provide a framework that allows for integrating current state-of-the-art process algorithms. The impact of changes to model structure, including individual algorithms, parameters, initial conditions, driving meteorology, and spatial/temporal discretization can be easily tested. Initial testing of CHM compared spatial scales and model complexity for a spring melt period at a sub-arctic mountain basin. The meshing algorithm reduced the total number of computational elements and preserved the spatial heterogeneity of predictions.

From Global Climate Model Projections to Local Impacts Assessments: Analyses in Support of Planning for Climate Change

NASA Astrophysics Data System (ADS)

Snover, A. K.; Littell, J. S.; Mantua, N. J.; Salathe, E. P.; Hamlet, A. F.; McGuire Elsner, M.; Tohver, I.; Lee, S.

2010-12-01

Assessing and planning for the impacts of climate change require regionally-specific information. Information is required not only about projected changes in climate but also the resultant changes in natural and human systems at the temporal and spatial scales of management and decision making. Therefore, climate impacts assessment typically results in a series of analyses, in which relatively coarse-resolution global climate model projections of changes in regional climate are downscaled to provide appropriate input to local impacts models. This talk will describe recent examples in which coarse-resolution (~150 to 300km) GCM output was “translated” into information requested by decision makers at relatively small (watershed) and large (multi-state) scales using regional climate modeling, statistical downscaling, hydrologic modeling, and sector-specific impacts modeling. Projected changes in local air temperature, precipitation, streamflow, and stream temperature were developed to support Seattle City Light’s assessment of climate change impacts on hydroelectric operations, future electricity load, and resident fish populations. A state-wide assessment of climate impacts on eight sectors (agriculture, coasts, energy, forests, human health, hydrology and water resources, salmon, and urban stormwater infrastructure) was developed for Washington State to aid adaptation planning. Hydro-climate change scenarios for approximately 300 streamflow locations in the Columbia River basin and selected coastal drainages west of the Cascades were developed in partnership with major water management agencies in the Pacific Northwest to allow planners to consider how hydrologic changes may affect management objectives. Treatment of uncertainty in these assessments included: using “bracketing” scenarios to describe a range of impacts, using ensemble averages to characterize the central estimate of future conditions (given an emissions scenario), and explicitly assessing the impacts of multiple GCM ensemble members. The implications of various approaches to dealing with uncertainty, such as these, must be carefully communicated to decision makers in order for projected climate impacts to be viewed as credible and used appropriately.

Merging fine and coarse resolution remotely sensed data with household-level survey data to evaluate small-scale vulnerability to climate change in West Africa

NASA Astrophysics Data System (ADS)

Grace, K.; Husak, G. J.

2016-12-01

Climate change, in the form of increasingly variable temperatures and rainfall, is anticipated to have potentially dramatic impacts on subsistence agricultural communities throughout the world. Poor people who depend on rainfall to produce food or to produce products to sell to buy food are expected to be particularly vulnerable to the negative impacts associated with climate change. Poor people have extremely limited resources that can be used to cope with weather events and these resources are even more strained when the individuals live in poor countries. While poor and rural producers are most likely to face high levels of vulnerability to food insecurity due to their dependence on rainfall for their agricultural production, annual agricultural censuses are virtually non-existent. Surveying all of the producers in a country each year is extremely costly owing to difficulties in accessing farmers and the costs associated with extensive surveys. The result, however, is very limited information on the spatial and temporal variation in production and the resulting impacts on micro-scale food insecurity and livelihood stability. In this project we use a combination of fine and coarse resolution remotely sensed data ( 1m data, 250m NDVI data and 10km rainfall data, and others) and recently collected survey data from the World Bank to estimate agricultural and land use characteristics at a fine spatial scale in Burkina Faso, Mali and Niger. The analysis will produce estimates of cultivated area that incorporate spatially dynamic climate and vegetation data but that also account for the variation in agricultural practices associated with the different ethnic and religious groups within each country. The survey data will help to calibrate the models and will also serve as a way to validate the statistical models used to estimate on the ground agricultural practices. The models will then be used to evaluate fine-scale agricultural response to climate change in the form of drying and warming.

Topographic controls on overland flow generation in a forest - An ensemble tree approach

NASA Astrophysics Data System (ADS)

Loos, Martin; Elsenbeer, Helmut

2011-10-01

SummaryOverland flow is an important hydrological pathway in many forests of the humid tropics. Its generation is subject to topographic controls at differing spatial scales. Our objective was to identify such controls on the occurrence of overland flow in a lowland tropical rainforest. To this end, we installed 95 overland flow detectors (OFDs) in four nested subcatchments of the Lutzito catchment on Barro Colorado Island, Panama, and monitored the frequency of overland flow occurrence during 18 rainfall events at each OFD location temporal frequency. For each such location, we derived three non-digital terrain attributes and 17 digital ones, of which 15 were based on Digital Elevation Models (DEMs) of three different resolutions. These attributes then served as input into a Random Forest ensemble tree model to elucidate the importance and partial and joint dependencies of topographic controls for overland flow occurrence. Lutzito features a high median temporal frequency in overland flow occurrence of 0.421 among OFD locations. However, spatial temporal frequencies of overland flow occurrence vary strongly among these locations and the subcatchments of Lutzito catchment. This variability is best explained by (1) microtopography, (2) coarse terrain sloping and (3) various measures of distance-to-channel, with the contribution of all other terrain attributes being small. Microtopographic features such as concentrated flowlines and wash areas produce highest temporal frequencies, whereas the occurrence of overland flow drops sharply for flow distances and terrain sloping beyond certain threshold values. Our study contributes to understanding both the spatial controls on overland flow generation and the limitations of terrain attributes for the spatially explicit prediction of overland flow frequencies.

Resolution-Adapted All-Atomic and Coarse-Grained Model for Biomolecular Simulations.

PubMed

Shen, Lin; Hu, Hao

2014-06-10

We develop here an adaptive multiresolution method for the simulation of complex heterogeneous systems such as the protein molecules. The target molecular system is described with the atomistic structure while maintaining concurrently a mapping to the coarse-grained models. The theoretical model, or force field, used to describe the interactions between two sites is automatically adjusted in the simulation processes according to the interaction distance/strength. Therefore, all-atomic, coarse-grained, or mixed all-atomic and coarse-grained models would be used together to describe the interactions between a group of atoms and its surroundings. Because the choice of theory is made on the force field level while the sampling is always carried out in the atomic space, the new adaptive method preserves naturally the atomic structure and thermodynamic properties of the entire system throughout the simulation processes. The new method will be very useful in many biomolecular simulations where atomistic details are critically needed.

Optimal Design of Experiments by Combining Coarse and Fine Measurements

NASA Astrophysics Data System (ADS)

Lee, Alpha A.; Brenner, Michael P.; Colwell, Lucy J.

2017-11-01

In many contexts, it is extremely costly to perform enough high-quality experimental measurements to accurately parametrize a predictive quantitative model. However, it is often much easier to carry out large numbers of experiments that indicate whether each sample is above or below a given threshold. Can many such categorical or "coarse" measurements be combined with a much smaller number of high-resolution or "fine" measurements to yield accurate models? Here, we demonstrate an intuitive strategy, inspired by statistical physics, wherein the coarse measurements are used to identify the salient features of the data, while the fine measurements determine the relative importance of these features. A linear model is inferred from the fine measurements, augmented by a quadratic term that captures the correlation structure of the coarse data. We illustrate our strategy by considering the problems of predicting the antimalarial potency and aqueous solubility of small organic molecules from their 2D molecular structure.

Proposed Standard For Variable Format Picture Processing And A Codec Approach To Match Diverse Imaging Devices

NASA Astrophysics Data System (ADS)

Wendler, Th.; Meyer-Ebrecht, D.

1982-01-01

Picture archiving and communication systems, especially those for medical applications, will offer the potential to integrate the various image sources of different nature. A major problem, however, is the incompatibility of the different matrix sizes and data formats. This may be overcome by a novel hierarchical coding process, which could lead to a unified picture format standard. A picture coding scheme is described, which decomposites a given (2n)2 picture matrix into a basic (2m)2 coarse information matrix (representing lower spatial frequencies) and a set of n-m detail matrices, containing information of increasing spatial resolution. Thus, the picture is described by an ordered set of data blocks rather than by a full resolution matrix of pixels. The blocks of data are transferred and stored using data formats, which have to be standardized throughout the system. Picture sources, which produce pictures of different resolution, will provide the coarse-matrix datablock and additionally only those detail matrices that correspond to their required resolution. Correspondingly, only those detail-matrix blocks need to be retrieved from the picture base, that are actually required for softcopy or hardcopy output. Thus, picture sources and retrieval terminals of diverse nature and retrieval processes for diverse purposes are easily made compatible. Furthermore this approach will yield an economic use of storage space and transmission capacity: In contrast to fixed formats, redundand data blocks are always skipped. The user will get a coarse representation even of a high-resolution picture almost instantaneously with gradually added details, and may abort transmission at any desired detail level. The coding scheme applies the S-transform, which is a simple add/substract algorithm basically derived from the Hadamard Transform. Thus, an additional data compression can easily be achieved especially for high-resolution pictures by applying appropriate non-linear and/or adaptive quantizing.

Quantum mechanics/coarse-grained molecular mechanics (QM/CG-MM)

NASA Astrophysics Data System (ADS)

Sinitskiy, Anton V.; Voth, Gregory A.

2018-01-01

Numerous molecular systems, including solutions, proteins, and composite materials, can be modeled using mixed-resolution representations, of which the quantum mechanics/molecular mechanics (QM/MM) approach has become the most widely used. However, the QM/MM approach often faces a number of challenges, including the high cost of repetitive QM computations, the slow sampling even for the MM part in those cases where a system under investigation has a complex dynamics, and a difficulty in providing a simple, qualitative interpretation of numerical results in terms of the influence of the molecular environment upon the active QM region. In this paper, we address these issues by combining QM/MM modeling with the methodology of "bottom-up" coarse-graining (CG) to provide the theoretical basis for a systematic quantum-mechanical/coarse-grained molecular mechanics (QM/CG-MM) mixed resolution approach. A derivation of the method is presented based on a combination of statistical mechanics and quantum mechanics, leading to an equation for the effective Hamiltonian of the QM part, a central concept in the QM/CG-MM theory. A detailed analysis of different contributions to the effective Hamiltonian from electrostatic, induction, dispersion, and exchange interactions between the QM part and the surroundings is provided, serving as a foundation for a potential hierarchy of QM/CG-MM methods varying in their accuracy and computational cost. A relationship of the QM/CG-MM methodology to other mixed resolution approaches is also discussed.

A novel capacitive absolute positioning sensor based on time grating with nanometer resolution

NASA Astrophysics Data System (ADS)

Pu, Hongji; Liu, Hongzhong; Liu, Xiaokang; Peng, Kai; Yu, Zhicheng

2018-05-01

The present work proposes a novel capacitive absolute positioning sensor based on time grating. The sensor includes a fine incremental-displacement measurement component combined with a coarse absolute-position measurement component to obtain high-resolution absolute positioning measurements. A single row type sensor was proposed to achieve fine displacement measurement, which combines the two electrode rows of a previously proposed double-row type capacitive displacement sensor based on time grating into a single row. To achieve absolute positioning measurement, the coarse measurement component is designed as a single-row type displacement sensor employing a single spatial period over the entire measurement range. In addition, this component employs a rectangular induction electrode and four groups of orthogonal discrete excitation electrodes with half-sinusoidal envelope shapes, which were formed by alternately extending the rectangular electrodes of the fine measurement component. The fine and coarse measurement components are tightly integrated to form a compact absolute positioning sensor. A prototype sensor was manufactured using printed circuit board technology for testing and optimization of the design in conjunction with simulations. Experimental results show that the prototype sensor achieves a ±300 nm measurement accuracy with a 1 nm resolution over a displacement range of 200 mm when employing error compensation. The proposed sensor is an excellent alternative to presently available long-range absolute nanometrology sensors owing to its low cost, simple structure, and ease of manufacturing.

Quantum mechanics/coarse-grained molecular mechanics (QM/CG-MM).

PubMed

Sinitskiy, Anton V; Voth, Gregory A

2018-01-07

Numerous molecular systems, including solutions, proteins, and composite materials, can be modeled using mixed-resolution representations, of which the quantum mechanics/molecular mechanics (QM/MM) approach has become the most widely used. However, the QM/MM approach often faces a number of challenges, including the high cost of repetitive QM computations, the slow sampling even for the MM part in those cases where a system under investigation has a complex dynamics, and a difficulty in providing a simple, qualitative interpretation of numerical results in terms of the influence of the molecular environment upon the active QM region. In this paper, we address these issues by combining QM/MM modeling with the methodology of "bottom-up" coarse-graining (CG) to provide the theoretical basis for a systematic quantum-mechanical/coarse-grained molecular mechanics (QM/CG-MM) mixed resolution approach. A derivation of the method is presented based on a combination of statistical mechanics and quantum mechanics, leading to an equation for the effective Hamiltonian of the QM part, a central concept in the QM/CG-MM theory. A detailed analysis of different contributions to the effective Hamiltonian from electrostatic, induction, dispersion, and exchange interactions between the QM part and the surroundings is provided, serving as a foundation for a potential hierarchy of QM/CG-MM methods varying in their accuracy and computational cost. A relationship of the QM/CG-MM methodology to other mixed resolution approaches is also discussed.

Evaluating MODIS snow products for modelling snowmelt runoff: Case study of the Rio Grande headwaters

NASA Astrophysics Data System (ADS)

Steele, Caitriana; Dialesandro, John; James, Darren; Elias, Emile; Rango, Albert; Bleiweiss, Max

2017-12-01

Snow-covered area (SCA) is a key variable in the Snowmelt-Runoff Model (SRM) and in other models for simulating discharge from snowmelt. Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM +) or Operational Land Imager (OLI) provide remotely sensed data at an appropriate spatial resolution for mapping SCA in small headwater basins, but the temporal resolution of the data is low and may not always provide sufficient cloud-free dates. The coarser spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) offers better temporal resolution and in cloudy years, MODIS data offer the best alternative for mapping snow cover when finer spatial resolution data are unavailable. However, MODIS' coarse spatial resolution (500 m) can obscure fine spatial patterning in snow cover and some MODIS products are not sensitive to end-of-season snow cover. In this study, we aimed to test MODIS snow products for use in simulating snowmelt runoff from smaller headwater basins by a) comparing maps of TM and MODIS-based SCA and b) determining how SRM streamflow simulations are changed by the different estimates of seasonal snow depletion. We compared gridded MODIS snow products (Collection 5 MOD10A1 fractional and binary SCA; SCA derived from Collection 6 MOD10A1 Normalised Difference Snow Index (NDSI) Snow Cover), and the MODIS Snow Covered-Area and Grain size retrieval (MODSCAG) canopy-corrected fractional SCA (SCAMG), with reference SCA maps (SCAREF) generated from binary classification of TM imagery. SCAMG showed strong agreement with SCAREF; excluding true negatives (where both methods agreed no snow was present) the median percent difference between SCAREF and SCAMG ranged between -2.4% and 4.7%. We simulated runoff for each of the four study years using SRM populated with and calibrated for snow depletion curves derived from SCAREF. We then substituted in each of the MODIS-derived depletion curves. With efficiency coefficients ranging between 0.73 and 0.93, SRM simulation results from the SCAMG runs yielded the best results of all the MODIS products and only slightly underestimated discharge volume (between 7 and 11% of measured annual discharge). SRM simulations that used SCA derived from Collection 6 NDSI Snow Cover also yielded promising results, with efficiency coefficients ranging between 0.73 and 0.91. In conclusion, we recommend that when simulating snowmelt runoff from small basins (<4000 km2) with SRM, we recommend that users select either canopy-corrected MODSCAG or create their own site-specific products from the Collection 6 MOD10A1 NDSI.

Worldwide impact of aerosol's time scale on the predicted long-term concentrating solar power potential.

PubMed

Ruiz-Arias, Jose A; Gueymard, Christian A; Santos-Alamillos, Francisco J; Pozo-Vázquez, David

2016-08-10

Concentrating solar technologies, which are fuelled by the direct normal component of solar irradiance (DNI), are among the most promising solar technologies. Currently, the state-of the-art methods for DNI evaluation use datasets of aerosol optical depth (AOD) with only coarse (typically monthly) temporal resolution. Using daily AOD data from both site-specific observations at ground stations as well as gridded model estimates, a methodology is developed to evaluate how the calculated long-term DNI resource is affected by using AOD data averaged over periods from 1 to 30 days. It is demonstrated here that the use of monthly representations of AOD leads to systematic underestimations of the predicted long-term DNI up to 10% in some areas with high solar resource, which may result in detrimental consequences for the bankability of concentrating solar power projects. Recommendations for the use of either daily or monthly AOD data are provided on a geographical basis.

Worldwide impact of aerosol’s time scale on the predicted long-term concentrating solar power potential

PubMed Central

Ruiz-Arias, Jose A.; Gueymard, Christian A.; Santos-Alamillos, Francisco J.; Pozo-Vázquez, David

2016-01-01

Concentrating solar technologies, which are fuelled by the direct normal component of solar irradiance (DNI), are among the most promising solar technologies. Currently, the state-of the-art methods for DNI evaluation use datasets of aerosol optical depth (AOD) with only coarse (typically monthly) temporal resolution. Using daily AOD data from both site-specific observations at ground stations as well as gridded model estimates, a methodology is developed to evaluate how the calculated long-term DNI resource is affected by using AOD data averaged over periods from 1 to 30 days. It is demonstrated here that the use of monthly representations of AOD leads to systematic underestimations of the predicted long-term DNI up to 10% in some areas with high solar resource, which may result in detrimental consequences for the bankability of concentrating solar power projects. Recommendations for the use of either daily or monthly AOD data are provided on a geographical basis. PMID:27507711

Spatio-temporal Root Zone Soil Moisture Estimation for Indo - Gangetic Basin from Satellite Derived (AMSR-2 and SMOS) Surface Soil Moisture

NASA Astrophysics Data System (ADS)

Sure, A.; Dikshit, O.

2017-12-01

Root zone soil moisture (RZSM) is an important element in hydrology and agriculture. The estimation of RZSM provides insight in selecting the appropriate crops for specific soil conditions (soil type, bulk density, etc.). RZSM governs various vadose zone phenomena and subsequently affects the groundwater processes. With various satellite sensors dedicated to estimating surface soil moisture at different spatial and temporal resolutions, estimation of soil moisture at root zone level for Indo - Gangetic basin which inherits complex heterogeneous environment, is quite challenging. This study aims at estimating RZSM and understand its variation at the level of Indo - Gangetic basin with changing land use/land cover, topography, crop cycles, soil properties, temperature and precipitation patterns using two satellite derived soil moisture datasets operating at distinct frequencies with different principles of acquisition. Two surface soil moisture datasets are derived from AMSR-2 (6.9 GHz - `C' Band) and SMOS (1.4 GHz - `L' band) passive microwave sensors with coarse spatial resolution. The Soil Water Index (SWI), accounting for soil moisture from the surface, is derived by considering a theoretical two-layered water balance model and contributes in ascertaining soil moisture at the vadose zone. This index is evaluated against the widely used modelled soil moisture dataset of GLDAS - NOAH, version 2.1. This research enhances the domain of utilising the modelled soil moisture dataset, wherever the ground dataset is unavailable. The coupling between the surface soil moisture and RZSM is analysed for two years (2015-16), by defining a parameter T, the characteristic time length. The study demonstrates that deriving an optimal value of T for estimating SWI at a certain location is a function of various factors such as land, meteorological, and agricultural characteristics.

Evolution of regional to global paddy rice mapping methods: A review

NASA Astrophysics Data System (ADS)

Dong, Jinwei; Xiao, Xiangming

2016-09-01

Paddy rice agriculture plays an important role in various environmental issues including food security, water use, climate change, and disease transmission. However, regional and global paddy rice maps are surprisingly scarce and sporadic despite numerous efforts in paddy rice mapping algorithms and applications. With the increasing need for regional to global paddy rice maps, this paper reviewed the existing paddy rice mapping methods from the literatures ranging from the 1980s to 2015. In particular, we illustrated the evolution of these paddy rice mapping efforts, looking specifically at the future trajectory of paddy rice mapping methodologies. The biophysical features and growth phases of paddy rice were analyzed first, and feature selections for paddy rice mapping were analyzed from spectral, polarimetric, temporal, spatial, and textural aspects. We sorted out paddy rice mapping algorithms into four categories: (1) Reflectance data and image statistic-based approaches, (2) vegetation index (VI) data and enhanced image statistic-based approaches, (3) VI or RADAR backscatter-based temporal analysis approaches, and (4) phenology-based approaches through remote sensing recognition of key growth phases. The phenology-based approaches using unique features of paddy rice (e.g., transplanting) for mapping have been increasingly used in paddy rice mapping. Current applications of these phenology-based approaches generally use coarse resolution MODIS data, which involves mixed pixel issues in Asia where smallholders comprise the majority of paddy rice agriculture. The free release of Landsat archive data and the launch of Landsat 8 and Sentinel-2 are providing unprecedented opportunities to map paddy rice in fragmented landscapes with higher spatial resolution. Based on the literature review, we discussed a series of issues for large scale operational paddy rice mapping.

Timeseries Signal Processing for Enhancing Mobile Surveys: Learning from Field Studies

NASA Astrophysics Data System (ADS)

Risk, D. A.; Lavoie, M.; Marshall, A. D.; Baillie, J.; Atherton, E. E.; Laybolt, W. D.

2015-12-01

Vehicle-based surveys using laser and other analyzers are now commonplace in research and industry. In many cases when these studies target biologically-relevant gases like methane and carbon dioxide, the minimum detection limits are often coarse (ppm) relative to the analyzer's capabilities (ppb), because of the inherent variability in the ambient background concentrations across the landscape that creates noise and uncertainty. This variation arises from localized biological sinks and sources, but also atmospheric turbulence, air pooling, and other factors. Computational processing routines are widely used in many fields to increase resolution of a target signal in temporally dense data, and offer promise for enhancing mobile surveying techniques. Signal processing routines can both help identify anomalies at very low levels, or can be used inversely to remove localized industrially-emitted anomalies from ecological data. This presentation integrates learnings from various studies in which simple signal processing routines were used successfully to isolate different temporally-varying components of 1 Hz timeseries measured with laser- and UV fluorescence-based analyzers. As illustrative datasets, we present results from industrial fugitive emission studies from across Canada's western provinces and other locations, and also an ecological study that aimed to model near-surface concentration variability across different biomes within eastern Canada. In these cases, signal processing algorithms contributed significantly to the clarity of both industrial, and ecological processes. In some instances, signal processing was too computationally intensive for real-time in-vehicle processing, but we identified workarounds for analyzer-embedded software that contributed to an improvement in real-time resolution of small anomalies. Signal processing is a natural accompaniment to these datasets, and many avenues are open to researchers who wish to enhance existing, and future datasets.

The DRAGON scale concept and results for remote sensing of aerosol properties

NASA Astrophysics Data System (ADS)

Holben, B. N.; Eck, T. F.; Schafer, J.; Giles, D. M.; Kim, J.; Sano, I.; Mukai, S.; Kim, Y. J.; Reid, J. S.; Pickering, K. E.; Crawford, J. H.; Smirnov, A.; Sinyuk, A.; Slutsker, I.; Sorokin, M.; Rodriguez, J.; Liew, S.; Trevino, N.; Lim, H.; Lefer, B. L.; Nadkarni, R.; Macke, A.; Kinne, S. A.; Anderson, B. E.; Russell, P. B.; Maring, H. B.; Welton, E. J.; da Silva, A.; Toon, O. B.; Redemann, J.

2013-12-01

Aerosol processes occur at microscales but are typically observed and reported at continental to global scales. Often observable aerosol processes that have significant anthropogenic impact occur on spatial scales of tens to a few hundred km, representative of convective cloud processing, urban/megacity sources, anthropogenic burning and natural wildfires, dry lakebed dust sources etc. Historically remote sensing of aerosols has relied on relatively coarse temporal and spatial resolution satellite observations or high temporal resolution point observations from ground-based monitoring sites from networks such as AERONET, SKYNET, MPLNET and many other surface observation platforms. Airborne remote and in situ observations combined with assimilation models were/are to be the mesoscale link between the ground- and space-based RS scales. However clearly the in situ and ground-based RS characterizations of aerosols require a convergence of thought, parameterization and actual scale measurements in order to advance this goal. This has been served by periodic multidisciplinary field campaigns yet only recently has a concerted effort been made to establish these ground-based networks in an effort to capture the mesoscale processes through measurement programs such as DISCOVER AQ and NASA AERONET's effort to foster such measurements and analysis through the Distributed Regional Aerosol Gridded Observation Networks (DRAGON), short term meso-networks, with partners in Asia and Europe and N. America. This talk will review the historical need for such networks and discuss some of the results and in some cases unexpected findings from the eight DRAGON campaigns conducted the last several years. Emphasis will be placed on the most recent DISCOVER AQ campaign conducted in Houston TX and the synergism with a regional to global network plan through the SEAC4RS US campaign.

Towards a consistent framework to oversample multi-sensors, multi-species satellite data into a common grid

NASA Astrophysics Data System (ADS)

Sun, K.; Zhu, L.; Gonzalez Abad, G.; Nowlan, C. R.; Miller, C. E.; Huang, G.; Liu, X.; Chance, K.; Yang, K.

2017-12-01

It has been well demonstrated that regridding Level 2 products (satellite observations from individual footprints, or pixels) from multiple sensors/species onto regular spatial and temporal grids makes the data more accessible for scientific studies and can even lead to additional discoveries. However, synergizing multiple species retrieved from multiple satellite sensors faces many challenges, including differences in spatial coverage, viewing geometry, and data filtering criteria. These differences will lead to errors and biases if not treated carefully. Operational gridded products are often at 0.25°×0.25° resolution with a global scale, which is too coarse for local heterogeneous emission sources (e.g., urban areas), and at fixed temporal intervals (e.g., daily or monthly). We propose a consistent framework to fully use and properly weight the information of all possible individual satellite observations. A key aspect of this work is an accurate knowledge of the spatial response function (SRF) of the satellite Level 2 pixels. We found that the conventional overlap-area-weighting method (tessellation) is accurate only when the SRF is homogeneous within the parameterized pixel boundary and zero outside the boundary. There will be a tessellation error if the SRF is a smooth distribution, and if this distribution is not properly considered. On the other hand, discretizing the SRF at the destination grid will also induce errors. By balancing these error sources, we found that the SRF should be used in gridding OMI data to 0.2° for fine resolutions. Case studies by merging multiple species and wind data into 0.01° grid will be shown in the presentation.

Spatio-temporal conditional inference and hypothesis tests for neural ensemble spiking precision

PubMed Central

Harrison, Matthew T.; Amarasingham, Asohan; Truccolo, Wilson

2014-01-01

The collective dynamics of neural ensembles create complex spike patterns with many spatial and temporal scales. Understanding the statistical structure of these patterns can help resolve fundamental questions about neural computation and neural dynamics. Spatio-temporal conditional inference (STCI) is introduced here as a semiparametric statistical framework for investigating the nature of precise spiking patterns from collections of neurons that is robust to arbitrarily complex and nonstationary coarse spiking dynamics. The main idea is to focus statistical modeling and inference, not on the full distribution of the data, but rather on families of conditional distributions of precise spiking given different types of coarse spiking. The framework is then used to develop families of hypothesis tests for probing the spatio-temporal precision of spiking patterns. Relationships among different conditional distributions are used to improve multiple hypothesis testing adjustments and to design novel Monte Carlo spike resampling algorithms. Of special note are algorithms that can locally jitter spike times while still preserving the instantaneous peri-stimulus time histogram (PSTH) or the instantaneous total spike count from a group of recorded neurons. The framework can also be used to test whether first-order maximum entropy models with possibly random and time-varying parameters can account for observed patterns of spiking. STCI provides a detailed example of the generic principle of conditional inference, which may be applicable in other areas of neurostatistical analysis. PMID:25380339

Understanding Multifunctional Agricultural Land by Using Low Cost and Open Source Solutions to Quantify Ecosystem Function and Services

NASA Astrophysics Data System (ADS)

Forsmoo, Joel; Anderson, Karen; Brazier, Richard; Macleod, Kit; Wilkinson, Mark

2016-04-01

There is a need to advance our understanding of how the spatial structure of farmed landscapes contributes to the provision of functions and services. Agricultural land is of critical importance in NW Europe, covering large parts of NW Europe's temperate land. Moreover, these agricultural areas are primarily intensively managed, with a focus on maximizing food and fibre production. Such landscapes therefore can provide a wealth of ecosystem goods and services (ESs) including regulation of climate, erosion regulation, hydrology, water quality, nutrient cycling and biodiversity conservation. However, it has been shown they are key sources of sediment, phosphorous, nitrogen and storm runoff contributing to flooding, and therefore it is likely that most agricultural landscapes do not maximize the services or benefits that they might provide. The focus of this study is the spatio-temporal assessment of carbon sequestration (particularly through proxies such as above-ground biomass) and hydrological processes on agricultural land. Understanding and quantifying both of these is important to (a) inform payments for ecosystem services frameworks, (b) evaluate and improve carbon sequestration models, (c) manage the flood risk, (d) downstream water security and (e) water quality. Quantifying both of these ESs is dependent on data describing the fine spatial and temporal structure and function of the landscape. Common practice has been to use remote sensing techniques, e.g. satellites, providing coarse spatial resolution (around 30cm at 20° off nadir) and/or temporal resolution (around 5 days revisit time at <20° off nadir). In this paper we will explain how imaging data from lightweight and easily deployed unmanned aerial vehicles (UAVs) can be used to generate structure from motion (SFM) products describing the very fine detailed (<3 cm pixel resolution) structure of the agricultural environment. We will demonstrate how these products can be delivered using advanced free and open source post-processing alternatives and low cost sensors (digital cameras) and platforms (UAVs). We furthermore draw attention to the influence post-processing solutions have on the accuracy of the final product, the digital surface model (DSM), by using recently acquired data. Specifically, when applied in a structurally complex field site with irregular surface roughness patterns, over a land use gradient, from livestock grazing to agricultural crops. We will demonstrate the added value of using very fine detail data, highlighting important structural properties and patterns overlooked with coarser spatial resolution data.

A boundary condition for layer to level ocean model interaction

NASA Astrophysics Data System (ADS)

Mask, A.; O'Brien, J.; Preller, R.

2003-04-01

A radiation boundary condition based on vertical normal modes is introduced to allow a physical transition between nested/coupled ocean models that are of differing vertical structure and/or differing physics. In this particular study, a fine resolution regional/coastal sigma-coordinate Naval Coastal Ocean Model (NCOM) has been successfully nested to a coarse resolution (in the horizontal and vertical) basin scale NCOM and a coarse resolution basin scale Navy Layered Ocean Model (NLOM). Both of these models were developed at the Naval Research Laboratory (NRL) at Stennis Space Center, Mississippi, USA. This new method, which decomposes the vertical structure of the models into barotropic and baroclinic modes, gives improved results in the coastal domain over Orlanski radiation boundary conditions for the test cases. The principle reason for the improvement is that each mode has the radiation boundary condition applied individually; therefore, the packet of information passing through the boundary is allowed to have multiple phase speeds instead of a single-phase speed. Allowing multiple phase speeds reduces boundary reflections, thus improving results.

Downscaling Satellite Land Surface Temperatures in Urban Regions for Surface Energy Balance Study and Heat Index Development

NASA Astrophysics Data System (ADS)

Norouzi, H.; Bah, A.; Prakash, S.; Nouri, N.; Blake, R.

2017-12-01

A great percentage of the world's population reside in urban areas that are exposed to the threats of global and regional climate changes and associated extreme weather events. Among them, urban heat islands have significant health and economic impacts due to higher thermal gradients of impermeable surfaces in urban regions compared to their surrounding rural areas. Therefore, accurate characterization of the surface energy balance in urban regions are required to predict these extreme events. High spatial resolution Land surface temperature (LST) in the scale of street level in the cities can provide wealth of information to study surface energy balance and eventually providing a reliable heat index. In this study, we estimate high-resolution LST maps using combination of LandSat 8 and infrared based satellite products such as Moderate Resolution Imaging Spectroradiometer (MODIS) and newly launched Geostationary Operational Environmental Satellite-R Series (GOES-R). Landsat 8 provides higher spatial resolution (30 m) estimates of skin temperature every 16 days. However, MODIS and GOES-R have lower spatial resolution (1km and 4km respectively) with much higher temporal resolution. Several statistical downscaling methods were investigated to provide high spatiotemporal LST maps in urban regions. The results reveal that statistical methods such as Principal Component Analysis (PCA) can provide reliable estimations of LST downscaling with 2K accuracy. Other methods also were tried including aggregating (up-scaling) the high-resolution data to a coarse one to examine the limitations and to build the model. Additionally, we deployed flux towers over distinct materials such as concrete, asphalt, and rooftops in New York City to monitor the sensible and latent heat fluxes through eddy covariance method. To account for the incoming and outgoing radiation, a 4-component radiometer is used that can observe both incoming and outgoing longwave and shortwave radiation. This enables us to accurately build the relationship between LST, air temperature, and the heat index in the future.

High resolution simulations of aerosol microphysics in a global and regionally nested chemical transport model

NASA Astrophysics Data System (ADS)

Adams, P. J.; Marks, M.

2015-12-01

The aerosol indirect effect is the largest source of forcing uncertainty in current climate models. This effect arises from the influence of aerosols on the reflective properties and lifetimes of clouds, and its magnitude depends on how many particles can serve as cloud droplet formation sites. Assessing levels of this subset of particles (cloud condensation nuclei, or CCN) requires knowledge of aerosol levels and their global distribution, size distributions, and composition. A key tool necessary to advance our understanding of CCN is the use of global aerosol microphysical models, which simulate the processes that control aerosol size distributions: nucleation, condensation/evaporation, and coagulation. Previous studies have found important differences in CO (Chen, D. et al., 2009) and ozone (Jang, J., 1995) modeled at different spatial resolutions, and it is reasonable to believe that short-lived, spatially-variable aerosol species will be similarly - or more - susceptible to model resolution effects. The goal of this study is to determine how CCN levels and spatial distributions change as simulations are run at higher spatial resolution - specifically, to evaluate how sensitive the model is to grid size, and how this affects comparisons against observations. Higher resolution simulations are necessary supports for model/measurement synergy. Simulations were performed using the global chemical transport model GEOS-Chem (v9-02). The years 2008 and 2009 were simulated at 4ox5o and 2ox2.5o globally and at 0.5ox0.667o over Europe and North America. Results were evaluated against surface-based particle size distribution measurements from the European Supersites for Atmospheric Aerosol Research project. The fine-resolution model simulates more spatial and temporal variability in ultrafine levels, and better resolves topography. Results suggest that the coarse model predicts systematically lower ultrafine levels than does the fine-resolution model. Significant differences are also evident with respect to model-measurement comparisons, and will be discussed.

Fine resolution mapping of population age-structures for health and development applications

PubMed Central

Alegana, V. A.; Atkinson, P. M.; Pezzulo, C.; Sorichetta, A.; Weiss, D.; Bird, T.; Erbach-Schoenberg, E.; Tatem, A. J.

2015-01-01

The age-group composition of populations varies considerably across the world, and obtaining accurate, spatially detailed estimates of numbers of children under 5 years is important in designing vaccination strategies, educational planning or maternal healthcare delivery. Traditionally, such estimates are derived from population censuses, but these can often be unreliable, outdated and of coarse resolution for resource-poor settings. Focusing on Nigeria, we use nationally representative household surveys and their cluster locations to predict the proportion of the under-five population in 1 × 1 km using a Bayesian hierarchical spatio-temporal model. Results showed that land cover, travel time to major settlements, night-time lights and vegetation index were good predictors and that accounting for fine-scale variation, rather than assuming a uniform proportion of under 5 year olds can result in significant differences in health metrics. The largest gaps in estimated bednet and vaccination coverage were in Kano, Katsina and Jigawa. Geolocated household surveys are a valuable resource for providing detailed, contemporary and regularly updated population age-structure data in the absence of recent census data. By combining these with covariate layers, age-structure maps of unprecedented detail can be produced to guide the targeting of interventions in resource-poor settings. PMID:25788540

Linking Satellite Derived Land Surface Temperature with Cholera: A Case Study for South Sudan

NASA Astrophysics Data System (ADS)

Aldaach, H. S. V.; Jutla, A.; Akanda, A. S.; Colwell, R. R.

2014-12-01

A sudden onset of cholera in South Sudan, in April 2014 in Northern Bari in Juba town resulted in more than 400 cholera cases after four weeks of initial outbreak with a case of fatality rate of CFR 5.4%. The total number of reported cholera cases for the period of April to July, 2014 were 5,141 including 114 deaths. With the limited efficacy of cholera vaccines, it is necessary to develop mechanisms to predict cholera occurrence and thereafter devise intervention strategies for mitigating impacts of the disease. Hydroclimatic processes, primarily precipitation and air temperature are related to epidemic and episodic outbreak of cholera. However, due to coarse resolution of both datasets, it is not possible to precisely locate the geographical location of disease. Here, using Land Surface Temperature (LST) from MODIS sensors, we have developed an algorithm to identify regions susceptible for cholera. Conditions for occurrence of cholera were detectable at least one month in advance in South Sudan and were statistically sensitive to hydroclimatic anomalies of land surface and air temperature, and precipitation. Our results indicate significant spatial and temporal averaging required to infer usable information from LST over South Sudan. Preliminary results that geographically location of cholera outbreak was identifiable within 1km resolution of the LST data.

A Field Guide to Extra-Tropical Cyclones: Comparing Models to Observations

NASA Astrophysics Data System (ADS)

Bauer, M.

2008-12-01

Climate it is said is the accumulation of weather. And weather is not the concern of climate models. Justification for this latter sentiment has long hidden behind coarse model resolutions and blunt validation tools based on climatological maps and the like. The spatial-temporal resolutions of today's models and observations are converging onto meteorological scales however, which means that with the correct tools we can test the largely unproven assumption that climate model weather is correct enough, or at least lacks perverting biases, such that its accumulation does in fact result in a robust climate prediction. Towards this effort we introduce a new tool for extracting detailed cyclone statistics from climate model output. These include the usual cyclone distribution statistics (maps, histograms), but also adaptive cyclone- centric composites. We have also created a complementary dataset, The MAP Climatology of Mid-latitude Storminess (MCMS), which provides a detailed 6 hourly assessment of the areas under the influence of mid- latitude cyclones based on Reanalysis products. Using this we then extract complimentary composites from sources such as ISCCP and GPCP to create a large comparative dataset for climate model validation. A demonstration of the potential usefulness of these tools will be shown. dime.giss.nasa.gov/mcms/mcms.html

Extra-Tropical Cyclones at Climate Scales: Comparing Models to Observations

NASA Astrophysics Data System (ADS)

Tselioudis, G.; Bauer, M.; Rossow, W.

2009-04-01

Climate is often defined as the accumulation of weather, and weather is not the concern of climate models. Justification for this latter sentiment has long been hidden behind coarse model resolutions and blunt validation tools based on climatological maps. The spatial-temporal resolutions of today's climate models and observations are converging onto meteorological scales, however, which means that with the correct tools we can test the largely unproven assumption that climate model weather is correct enough that its accumulation results in a robust climate simulation. Towards this effort we introduce a new tool for extracting detailed cyclone statistics from observations and climate model output. These include the usual cyclone characteristics (centers, tracks), but also adaptive cyclone-centric composites. We have created a novel dataset, the MAP Climatology of Mid-latitude Storminess (MCMS), which provides a detailed 6 hourly assessment of the areas under the influence of mid-latitude cyclones, using a search algorithm that delimits the boundaries of each system from the outer-most closed SLP contour. Using this we then extract composites of cloud, radiation, and precipitation properties from sources such as ISCCP and GPCP to create a large comparative dataset for climate model validation. A demonstration of the potential usefulness of these tools in process-based climate model evaluation studies will be shown.

High-resolution radiography by means of a hodoscope

DOEpatents

De Volpi, Alexander

1978-01-01

The fast neutron hodoscope, a device that produces neutron radiographs with coarse space resolution in a short time, is modified to produce neutron or gamma radiographs of relatively thick samples and with high space resolution. The modification comprises motorizing a neutron and gamma collimator to permit a controlled scanning pattern, simultaneous collection of data in a number of hodoscope channels over a period of time, and computerized image reconstruction of the data thus gathered.

Fine resolution probabilistic land cover classification of landscapes in the southeastern United States

Treesearch

Joseph St. Peter; John Hogland; Nathaniel Anderson; Jason Drake; Paul Medley

2018-01-01

Land cover classification provides valuable information for prioritizing management and conservation operations across large landscapes. Current regional scale land cover geospatial products within the United States have a spatial resolution that is too coarse to provide the necessary information for operations at the local and project scales. This paper describes a...

Deriving a global land surface albedo product from Landsat MSS, TM, ETM+, and OLI data based on the unified direct estimation approach

USDA-ARS?s Scientific Manuscript database

Surface albedo is widely used in climate and environment applications as an important parameter for controlling the surface energy budget. There is an increasing need for fine resolution (< 100 m) albedo data for use in small scale applications and for validating coarse-resolution datasets; however,...

Assessment of the effects of horizontal grid resolution on long-term air quality trends using coupled WRF-CMAQ simulations

EPA Science Inventory

The objective of this study is to determine the adequacy of using a relatively coarse horizontal resolution (i.e. 36 km) to simulate long-term trends of pollutant concentrations and radiation variables with the coupled WRF-CMAQ model. WRF-CMAQ simulations over the continental Uni...

Validation of the USGS Landsat Burned Area Essential Climate Variable (BAECV) across the conterminous United States

USGS Publications Warehouse

Vanderhoof, Melanie; Fairaux, Nicole; Beal, Yen-Ju G.; Hawbaker, Todd J.

2017-01-01

The Landsat Burned Area Essential Climate Variable (BAECV), developed by the U.S. Geological Survey (USGS), capitalizes on the long temporal availability of Landsat imagery to identify burned areas across the conterminous United States (CONUS) (1984–2015). Adequate validation of such products is critical for their proper usage and interpretation. Validation of coarse-resolution products often relies on independent data derived from moderate-resolution sensors (e.g., Landsat). Validation of Landsat products, in turn, is challenging because there is no corresponding source of high-resolution, multispectral imagery that has been systematically collected in space and time over the entire temporal extent of the Landsat archive. Because of this, comparison between high-resolution images and Landsat science products can help increase user's confidence in the Landsat science products, but may not, alone, be adequate. In this paper, we demonstrate an approach to systematically validate the Landsat-derived BAECV product. Burned area extent was mapped for Landsat image pairs using a manually trained semi-automated algorithm that was manually edited across 28 path/rows and five different years (1988, 1993, 1998, 2003, 2008). Three datasets were independently developed by three analysts and the datasets were integrated on a pixel by pixel basis in which at least one to all three analysts were required to agree a pixel was burned. We found that errors within our Landsat reference dataset could be minimized by using the rendition of the dataset in which pixels were mapped as burned if at least two of the three analysts agreed. BAECV errors of omission and commission for the detection of burned pixels averaged 42% and 33%, respectively for CONUS across all five validation years. Errors of omission and commission were lowest across the western CONUS, for example in the shrub and scrublands of the Arid West (31% and 24%, respectively), and highest in the grasslands and agricultural lands of the Great Plains in central CONUS (62% and 57%, respectively). The BAECV product detected most (> 65%) fire events > 10 ha across the western CONUS (Arid and Mountain West ecoregions). Our approach and results demonstrate that a thorough validation of Landsat science products can be completed with independent Landsat-derived reference data, but could be strengthened by the use of complementary sources of high-resolution data.

Seismo-acoustic imaging of marine hard substrate habitats: a case study from the German Bight (SE North Sea)

NASA Astrophysics Data System (ADS)

Papenmeier, Svenja; Hass, H. Christian

2016-04-01

The detection of hard substrate habitats in sublittoral environments is a considerable challenge in spite of modern high resolution hydroacoustic techniques. In offshore areas those habitats are mainly represented by either cobbles and boulders (stones) often located in wide areas of soft sediments or by glacial relict sediments (heterogeneous mixture of medium sand to gravel size with cobbles and boulders). Sediment classification and object detection is commonly done on the basis of hydroacoustic backscatter intensities recorded with e.g. sidescan sonar (SSS) and multibeam echo sounder (MBES). Single objects lying on the sediment such as stones can generally be recognized by the acoustic shadow behind the object. However, objects close to the sonar's nadir may remain undetected because their shadows are below the data resolution. Further limitation in the detection of objects is caused by sessile communities that thrive on the objects. The bio-cover tends to absorb most of the acoustic signal. Automated identification based on the backscatter signal is often not satisfactory, especially when stones are present in a setting with glacial deposits. Areas characterized by glacial relict sediments are hardly differentiable in their backscatter characteristics from rippled coarse sand and fine gravel (rippled coarse sediments) without an intensive ground-truthing program. From the ecological point of view the relict and rippled coarse sediments are completely different habitats and need to be distinguished. The case study represents a seismo-acoustic approach in which SSS and nonlinear sediment echo sounder (SES) data are combined to enable a reliable and reproducible differentiation between relict sediments (with stones and coarse gravels) and rippled coarse sediments. Elevated objects produce hyperbola signatures at the sediment surface in the echo data which can be used to complement the SSS data. The nonlinear acoustic propagation of the SES sound pulses produces a comparably small foot print which results in high spatial resolution (decimeter in the xyz directions) and hence allows a more precise demarcation of hard substrate areas. Data for this study were recorded in the "Sylt Outer Reef" (German Bight, North Sea) in May 2013 and March 2015. The investigated area is characterized by heterogeneously distributed moraine deposits and rippled coarse sediments partly draped with Holocene fine sands. The relict sediments and the rippled coarse sediments indicate both high backscatter intensities but can be distinguished by means of the hyperbola locations. The northeast of the study area is dominated by rippled coarse sediments (without hyperbolas) and the southwestern part by relict sediments with a high amount of stones represented by hyperbolas which is also proven by extensive ground-truthing (grab sampling and high quality underwater videos). An automated procedure to identify and export the hyperbola positions makes the demarcation of hard substrate grounds (here: relict sediments) reproducible, faster and less complex in comparison to the visual-manual identification on the basis of sidescan sonar data.

10 Yr Spatial and Temporal Trends of PM2.5 Concentrations in the Southeastern US Estimated Using High-resolution Satellite Data

NASA Technical Reports Server (NTRS)

Hu, X.; Waller, L. A.; Lyapustin, A.; Wang, Y.; Liu, Y.

2013-01-01

Long-term PM2.5 exposure has been reported to be associated with various adverse health outcomes. However, most ground monitors are located in urban areas, leading to a potentially biased representation of the true regional PM2.5 levels. To facilitate epidemiological studies, accurate estimates of spatiotemporally continuous distribution of PM2.5 concentrations are essential. Satellite-retrieved aerosol optical depth (AOD) has been widely used for PM2.5 concentration estimation due to its comprehensive spatial coverage. Nevertheless, an inherent disadvantage of current AOD products is their coarse spatial resolutions. For instance, the spatial resolutions of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) are 10 km and 17.6 km, respectively. In this paper, a new AOD product with 1 km spatial resolution retrieved by the multi-angle implementation of atmospheric correction (MAIAC) algorithm was used. A two-stage model was developed to account for both spatial and temporal variability in the PM2.5-AOD relationship by incorporating the MAIAC AOD, meteorological fields, and land use variables as predictors. Our study area is in the southeastern US, centered at the Atlanta Metro area, and data from 2001 to 2010 were collected from various sources. The model was fitted for each year individually, and we obtained model fitting R2 ranging from 0.71 to 0.85, MPE from 1.73 to 2.50 g m3, and RMSPE from 2.75 to 4.10 g m3. In addition, we found cross validation R2 ranging from 0.62 to 0.78, MPE from 2.00 to 3.01 g m3, and RMSPE from 3.12 to 5.00 g m3, indicating a good agreement between the estimated and observed values. Spatial trends show that high PM2.5 levels occurred in urban areas and along major highways, while low concentrations appeared in rural or mountainous areas. A time series analysis was conducted to examine temporal trends of PM2.5 concentrations in the study area from 2001 to 2010. The results showed that the PM2.5 levels in the study area followed a generally declining trend from 2001 to 2010 and decreased about 20 during the period. However, there was an exception of an increase in year 2005, which is attributed to elevated sulfate concentrations in the study area in warm months of 2005. An investigation of the impact of wild and prescribed fires on PM2.5 levels in 2007 suggests a positive relationship between them.

Hierarchical coarse-graining model for photosystem II including electron and excitation-energy transfer processes.

PubMed

Matsuoka, Takeshi; Tanaka, Shigenori; Ebina, Kuniyoshi

2014-03-01

We propose a hierarchical reduction scheme to cope with coupled rate equations that describe the dynamics of multi-time-scale photosynthetic reactions. To numerically solve nonlinear dynamical equations containing a wide temporal range of rate constants, we first study a prototypical three-variable model. Using a separation of the time scale of rate constants combined with identified slow variables as (quasi-)conserved quantities in the fast process, we achieve a coarse-graining of the dynamical equations reduced to those at a slower time scale. By iteratively employing this reduction method, the coarse-graining of broadly multi-scale dynamical equations can be performed in a hierarchical manner. We then apply this scheme to the reaction dynamics analysis of a simplified model for an illuminated photosystem II, which involves many processes of electron and excitation-energy transfers with a wide range of rate constants. We thus confirm a good agreement between the coarse-grained and fully (finely) integrated results for the population dynamics. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Cryogenic scanning tunneling microscope with a magnetic coarse approach

NASA Astrophysics Data System (ADS)

Davydov, D. N.; Deltour, R.; Horii, N.; Timofeev, V. A.; Grokholski, A. S.

1993-11-01

A compact, rigid, and reliable cryogenic scanning tunneling microscope (CSTM) with a vertical electromagnetic coarse approach system was developed. This device can be used for topographic and local tunneling spectroscopy studies at liquid nitrogen and helium temperatures. Minimal step sizes of 28 nm for the electromagnetic translation device were achieved. The additional possibility of a coarse approach operation in the inertial slip-stick mode, without electromagnets, was successfully tested, making this STM compatible with external magnetic fields. A simple technique for characterizing the STM rigidity has been developed. Preliminary data, taken with this instrument are presented, demonstrating the achievement, at liquid helium temperature, of atomic resolution for topographic studies, and also the possibility of measuring simultaneously superconducting energy gap spectra.

Visual temporal processing in dyslexia and the magnocellular deficit theory: the need for speed?

PubMed

McLean, Gregor M T; Stuart, Geoffrey W; Coltheart, Veronika; Castles, Anne

2011-12-01

A controversial question in reading research is whether dyslexia is associated with impairments in the magnocellular system and, if so, how these low-level visual impairments might affect reading acquisition. This study used a novel chromatic flicker perception task to specifically explore temporal aspects of magnocellular functioning in 40 children with dyslexia and 42 age-matched controls (aged 7-11). The relationship between magnocellular temporal resolution and higher-level aspects of visual temporal processing including inspection time, single and dual-target (attentional blink) RSVP performance, go/no-go reaction time, and rapid naming was also assessed. The Dyslexia group exhibited significant deficits in magnocellular temporal resolution compared with controls, but the two groups did not differ in parvocellular temporal resolution. Despite the significant group differences, associations between magnocellular temporal resolution and reading ability were relatively weak, and links between low-level temporal resolution and reading ability did not appear specific to the magnocellular system. Factor analyses revealed that a collective Perceptual Speed factor, involving both low-level and higher-level visual temporal processing measures, accounted for unique variance in reading ability independently of phonological processing, rapid naming, and general ability.

Impact of Sub-grid Soil Textural Properties on Simulations of Hydrological Fluxes at the Continental Scale Mississippi River Basin

NASA Astrophysics Data System (ADS)

Kumar, R.; Samaniego, L. E.; Livneh, B.

2013-12-01

Knowledge of soil hydraulic properties such as porosity and saturated hydraulic conductivity is required to accurately model the dynamics of near-surface hydrological processes (e.g. evapotranspiration and root-zone soil moisture dynamics) and provide reliable estimates of regional water and energy budgets. Soil hydraulic properties are commonly derived from pedo-transfer functions using soil textural information recorded during surveys, such as the fractions of sand and clay, bulk density, and organic matter content. Typically large scale land-surface models are parameterized using a relatively coarse soil map with little or no information on parametric sub-grid variability. In this study we analyze the impact of sub-grid soil variability on simulated hydrological fluxes over the Mississippi River Basin (≈3,240,000 km2) at multiple spatio-temporal resolutions. A set of numerical experiments were conducted with the distributed mesoscale hydrologic model (mHM) using two soil datasets: (a) the Digital General Soil Map of the United States or STATSGO2 (1:250 000) and (b) the recently collated Harmonized World Soil Database based on the FAO-UNESCO Soil Map of the World (1:5 000 000). mHM was parameterized with the multi-scale regionalization technique that derives distributed soil hydraulic properties via pedo-transfer functions and regional coefficients. Within the experimental framework, the 3-hourly model simulations were conducted at four spatial resolutions ranging from 0.125° to 1°, using meteorological datasets from the NLDAS-2 project for the time period 1980-2012. Preliminary results indicate that the model was able to capture observed streamflow behavior reasonably well with both soil datasets, in the major sub-basins (i.e. the Missouri, the Upper Mississippi, the Ohio, the Red, and the Arkansas). However, the spatio-temporal patterns of simulated water fluxes and states (e.g. soil moisture, evapotranspiration) from both simulations, showed marked differences; particularly at a shorter time scale (hours to days) in regions with coarse texture sandy soils. Furthermore, the partitioning of total runoff into near-surface interflows and baseflow components was also significantly different between the two simulations. Simulations with the coarser soil map produced comparatively higher baseflows. At longer time scales (months to seasons) where climatic factors plays a major role, the integrated fluxes and states from both sets of model simulations match fairly closely, despite the apparent discrepancy in the partitioning of total runoff.

Assessing Temporal Stability for Coarse Scale Satellite Moisture Validation in the Maqu Area, Tibet

PubMed Central

Bhatti, Haris Akram; Rientjes, Tom; Verhoef, Wouter; Yaseen, Muhammad

2013-01-01

This study evaluates if the temporal stability concept is applicable to a time series of satellite soil moisture images so to extend the common procedure of satellite image validation. The area of study is the Maqu area, which is located in the northeastern part of the Tibetan plateau. The network serves validation purposes of coarse scale (25–50 km) satellite soil moisture products and comprises 20 stations with probes installed at depths of 5, 10, 20, 40, 80 cm. The study period is 2009. The temporal stability concept is applied to all five depths of the soil moisture measuring network and to a time series of satellite-based moisture products from the Advance Microwave Scanning Radiometer (AMSR-E). The in-situ network is also assessed by Pearsons's correlation analysis. Assessments by the temporal stability concept proved to be useful and results suggest that probe measurements at 10 cm depth best match to the satellite observations. The Mean Relative Difference plot for satellite pixels shows that a RMSM pixel can be identified but in our case this pixel does not overlay any in-situ station. Also, the RMSM pixel does not overlay any of the Representative Mean Soil Moisture (RMSM) stations of the five probe depths. Pearson's correlation analysis on in-situ measurements suggests that moisture patterns over time are more persistent than over space. Since this study presents first results on the application of the temporal stability concept to a series of satellite images, we recommend further tests to become more conclusive on effectiveness to broaden the procedure of satellite validation. PMID:23959237

Coarse-grained forms for equations describing the microscopic motion of particles in a fluid.

PubMed

Das, Shankar P; Yoshimori, Akira

2013-10-01

Exact equations of motion for the microscopically defined collective density ρ(x,t) and the momentum density ĝ(x,t) of a fluid have been obtained in the past starting from the corresponding Langevin equations representing the dynamics of the fluid particles. In the present work we average these exact equations of microscopic dynamics over the local equilibrium distribution to obtain stochastic partial differential equations for the coarse-grained densities with smooth spatial and temporal dependence. In particular, we consider Dean's exact balance equation for the microscopic density of a system of interacting Brownian particles to obtain the basic equation of the dynamic density functional theory with noise. Our analysis demonstrates that on thermal averaging the dependence of the exact equations on the bare interaction potential is converted to dependence on the corresponding thermodynamic direct correlation functions in the coarse-grained equations.

NASA Downscaling Project: Final Report

NASA Technical Reports Server (NTRS)

Ferraro, Robert; Waliser, Duane; Peters-Lidard, Christa

2017-01-01

A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether downscaling coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for downscaling is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA - 2 reanalyses were used to drive the NU - WRF regional climate model and a GEOS - 5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000 - 2010. The results of these experiments were compared to observational datasets to evaluate the output.

NASA Downscaling Project

NASA Technical Reports Server (NTRS)

Ferraro, Robert; Waliser, Duane; Peters-Lidard, Christa

2017-01-01

A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether downscaling coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for downscaling is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA-2 reanalyses were used to drive the NU-WRF regional climate model and a GEOS-5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000-2010. The results of these experiments were compared to observational datasets to evaluate the output.

Assessment of the effects of horizontal grid resolution on long ...

EPA Pesticide Factsheets

The objective of this study is to determine the adequacy of using a relatively coarse horizontal resolution (i.e. 36 km) to simulate long-term trends of pollutant concentrations and radiation variables with the coupled WRF-CMAQ model. WRF-CMAQ simulations over the continental United State are performed over the 2001 to 2010 time period at two different horizontal resolutions of 12 and 36 km. Both simulations used the same emission inventory and model configurations. Model results are compared both in space and time to assess the potential weaknesses and strengths of using coarse resolution in long-term air quality applications. The results show that the 36 km and 12 km simulations are comparable in terms of trends analysis for both pollutant concentrations and radiation variables. The advantage of using the coarser 36 km resolution is a significant reduction of computational cost, time and storage requirement which are key considerations when performing multiple years of simulations for trend analysis. However, if such simulations are to be used for local air quality analysis, finer horizontal resolution may be beneficial since it can provide information on local gradients. In particular, divergences between the two simulations are noticeable in urban, complex terrain and coastal regions. The National Exposure Research Laboratory’s Atmospheric Modeling Division (AMAD) conducts research in support of EPA’s mission to protect human health and the environment.

Mapping pre-European settlement vegetation at fine resolutions using a hierarchical Bayesian model and GIS

Treesearch

Hong S. He; Daniel C. Dey; Xiuli Fan; Mevin B. Hooten; John M. Kabrick; Christopher K. Wikle; Zhaofei. Fan

2007-01-01

In the Midwestern United States, the GeneralLandOffice (GLO) survey records provide the only reasonably accurate data source of forest composition and tree species distribution at the time of pre-European settlement (circa late 1800 to early 1850). However, GLO data have two fundamental limitations: coarse spatial resolutions (the square mile section and half mile...

A review of spatial downscaling of satellite remotely sensed soil moisture

NASA Astrophysics Data System (ADS)

Peng, Jian; Loew, Alexander; Merlin, Olivier; Verhoest, Niko E. C.

2017-06-01

Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed.

A method for generating high resolution satellite image time series

NASA Astrophysics Data System (ADS)

Guo, Tao

2014-10-01

There is an increasing demand for satellite remote sensing data with both high spatial and temporal resolution in many applications. But it still is a challenge to simultaneously improve spatial resolution and temporal frequency due to the technical limits of current satellite observation systems. To this end, much R&D efforts have been ongoing for years and lead to some successes roughly in two aspects, one includes super resolution, pan-sharpen etc. methods which can effectively enhance the spatial resolution and generate good visual effects, but hardly preserve spectral signatures and result in inadequate analytical value, on the other hand, time interpolation is a straight forward method to increase temporal frequency, however it increase little informative contents in fact. In this paper we presented a novel method to simulate high resolution time series data by combing low resolution time series data and a very small number of high resolution data only. Our method starts with a pair of high and low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and then projected onto the high resolution data plane and assigned to each high resolution pixel according to the predefined temporal change patterns of each type of ground objects. Finally the simulated high resolution data is generated. A preliminary experiment shows that our method can simulate a high resolution data with a reasonable accuracy. The contribution of our method is to enable timely monitoring of temporal changes through analysis of time sequence of low resolution images only, and usage of costly high resolution data can be reduces as much as possible, and it presents a highly effective way to build up an economically operational monitoring solution for agriculture, forest, land use investigation, environment and etc. applications.

Optical techniques: using coarse and detailed scans for the preventive acquisition of fingerprints with chromatic white-light sensors

NASA Astrophysics Data System (ADS)

Hildebrandt, Mario; Dittmann, Jana; Vielhauer, Claus; Leich, Marcus

2011-11-01

The preventive application of automated latent fingerprint acquisition devices can enhance the Homeland Defence, e.g. by improving the border security. Here, contact-less optical acquisition techniques for the capture of traces are subject to research; chromatic white light sensors allow for multi-mode operation using coarse or detailed scans. The presence of potential fingerprints could be detected using fast coarse scans. Those Regions-of- Interest can be acquired afterwards with high-resolution detailed scans to allow for a verification or identification of individuals. An acquisition and analysis of fingerprint traces on different objects that are imported or pass borders might be a great enhancement for security. Additionally, if suspicious objects require a further investigation, an initial securing of potential fingerprints could be very useful. In this paper we show current research results for the coarse detection of fingerprints to prepare the detailed acquisition from various surface materials that are relevant for preventive applications.

Stepping inside the niche: microclimate data are critical for accurate assessment of species' vulnerability to climate change

PubMed Central

Storlie, Collin; Merino-Viteri, Andres; Phillips, Ben; VanDerWal, Jeremy; Welbergen, Justin; Williams, Stephen

2014-01-01

To assess a species' vulnerability to climate change, we commonly use mapped environmental data that are coarsely resolved in time and space. Coarsely resolved temperature data are typically inaccurate at predicting temperatures in microhabitats used by an organism and may also exhibit spatial bias in topographically complex areas. One consequence of these inaccuracies is that coarsely resolved layers may predict thermal regimes at a site that exceed species' known thermal limits. In this study, we use statistical downscaling to account for environmental factors and develop high-resolution estimates of daily maximum temperatures for a 36 000 km2 study area over a 38-year period. We then demonstrate that this statistical downscaling provides temperature estimates that consistently place focal species within their fundamental thermal niche, whereas coarsely resolved layers do not. Our results highlight the need for incorporation of fine-scale weather data into species' vulnerability analyses and demonstrate that a statistical downscaling approach can yield biologically relevant estimates of thermal regimes. PMID:25252835

A new technique for online measurement of total and water-soluble copper (Cu) in coarse particulate matter (PM).

PubMed

Wang, Dongbin; Shafer, Martin M; Schauer, James J; Sioutas, Constantinos

2015-04-01

This study presents a novel system for online, field measurement of copper (Cu) in ambient coarse (2.5-10 μm) particulate matter (PM). This new system utilizes two virtual impactors combined with a modified liquid impinger (BioSampler) to collect coarse PM directly as concentrated slurry samples. The total and water-soluble Cu concentrations are subsequently measured by a copper Ion Selective Electrode (ISE). Laboratory evaluation results indicated excellent collection efficiency (over 85%) for particles in the coarse PM size ranges. In the field evaluations, very good agreements for both total and water-soluble Cu concentrations were obtained between online ISE-based monitor measurements and those analyzed by means of inductively coupled plasma mass spectrometry (ICP-MS). Moreover, the field tests indicated that the Cu monitor could achieve near-continuous operation for at least 6 consecutive days (a time resolution of 2-4 h) without obvious shortcomings. Copyright © 2015 Elsevier Ltd. All rights reserved.

Kilometric Scale Modeling of the North West European Shelf Seas: Exploring the Spatial and Temporal Variability of Internal Tides

NASA Astrophysics Data System (ADS)

Guihou, K.; Polton, J.; Harle, J.; Wakelin, S.; O'Dea, E.; Holt, J.

2018-01-01

The North West European Shelf break acts as a barrier to the transport and exchange between the open ocean and the shelf seas. The strong spatial variability of these exchange processes is hard to fully explore using observations, and simulations generally are too coarse to simulate the fine-scale processes over the whole region. In this context, under the FASTNEt program, a new NEMO configuration of the North West European Shelf and Atlantic Margin at 1/60° (˜1.8 km) has been developed, with the objective to better understand and quantify the seasonal and interannual variability of shelf break processes. The capability of this configuration to reproduce the seasonal cycle in SST, the barotropic tide, and fine-resolution temperature profiles is assessed against a basin-scale (1/12°, ˜9 km) configuration and a standard regional configuration (7 km resolution). The seasonal cycle is well reproduced in all configurations though the fine-resolution allows the simulation of smaller scale processes. Time series of temperature at various locations on the shelf show the presence of internal waves with a strong spatiotemporal variability. Spectral analysis of the internal waves reveals peaks at the diurnal, semidiurnal, inertial, and quarter-diurnal bands, which are only realistically reproduced in the new configuration. Tidally induced pycnocline variability is diagnosed in the model and shown to vary with the spring neap cycle with mean displacement amplitudes in excess of 2 m for 30% of the stratified domain. With sufficiently fine resolution, internal tides are shown to be generated at numerous bathymetric features resulting in a complex pycnocline displacement superposition pattern.

Partial Least Square Discriminant Analysis Based on Normalized Two-Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets.

PubMed

Shi, Yue; Huang, Wenjiang; Ye, Huichun; Ruan, Chao; Xing, Naichen; Geng, Yun; Dong, Yingying; Peng, Dailiang

2018-06-11

In recent decades, rice disease co-epidemics have caused tremendous damage to crop production in both China and Southeast Asia. A variety of remote sensing based approaches have been developed and applied to map diseases distribution using coarse- to moderate-resolution imagery. However, the detection and discrimination of various disease species infecting rice were seldom assessed using high spatial resolution data. The aims of this study were (1) to develop a set of normalized two-stage vegetation indices (VIs) for characterizing the progressive development of different diseases with rice; (2) to explore the performance of combined normalized two-stage VIs in partial least square discriminant analysis (PLS-DA); and (3) to map and evaluate the damage caused by rice diseases at fine spatial scales, for the first time using bi-temporal, high spatial resolution imagery from PlanetScope datasets at a 3 m spatial resolution. Our findings suggest that the primary biophysical parameters caused by different disease (e.g., changes in leaf area, pigment contents, or canopy morphology) can be captured using combined normalized two-stage VIs. PLS-DA was able to classify rice diseases at a sub-field scale, with an overall accuracy of 75.62% and a Kappa value of 0.47. The approach was successfully applied during a typical co-epidemic outbreak of rice dwarf (Rice dwarf virus, RDV), rice blast ( Magnaporthe oryzae ), and glume blight ( Phyllosticta glumarum ) in Guangxi Province, China. Furthermore, our approach highlighted the feasibility of the method in capturing heterogeneous disease patterns at fine spatial scales over the large spatial extents.

NASA Earth Exchange (NEX) Supporting Analyses for National Climate Assessments

NASA Astrophysics Data System (ADS)

Nemani, R. R.; Thrasher, B. L.; Wang, W.; Lee, T. J.; Melton, F. S.; Dungan, J. L.; Michaelis, A.

2015-12-01

The NASA Earth Exchange (NEX) is a collaborative computing platform that has been developed with the objective of bringing scientists together with the software tools, massive global datasets, and supercomputing resources necessary to accelerate research in Earth systems science and global change. NEX supports several research projects that are closely related with the National Climate Assessment including the generation of high-resolution climate projections, identification of trends and extremes in climate variables and the evaluation of their impacts on regional carbon/water cycles and biodiversity, the development of land-use management and adaptation strategies for climate-change scenarios, and even the exploration of climate mitigation through geo-engineering. Scientists also use the large collection of satellite data on NEX to conduct research on quantifying spatial and temporal changes in land surface processes in response to climate and land-cover-land-use changes. Researchers, leveraging NEX's massive compute/storage resources, have used statistical techniques to downscale the coarse-resolution CMIP5 projections to fulfill the demands of the community for a wide range of climate change impact analyses. The DCP-30 (Downscaled Climate Projections at 30 arcsecond) for the conterminous US at monthly, ~1km resolution and the GDDP (Global Daily Downscaled Projections) for the entire world at daily, 25km resolution are now widely used in climate research and applications, as well as for communicating climate change. In order to serve a broader community, the NEX team in collaboration with Amazon, Inc, created the OpenNEX platform. OpenNEX provides ready access to NEX data holdings, including the NEX-DCP30 and GDDP datasets along with a number of pertinent analysis tools and workflows on the AWS infrastructure in the form of publicly available, self contained, fully functional Amazon Machine Images (AMI's) for anyone interested in global climate change.

Estimating Evaporative Fraction From Readily Obtainable Variables in Mangrove Forests of the Everglades, U.S.A.

NASA Technical Reports Server (NTRS)

Yagci, Ali Levent; Santanello, Joseph A.; Jones, John; Barr, Jordan

2017-01-01

A remote-sensing-based model to estimate evaporative fraction (EF) the ratio of latent heat (LE; energy equivalent of evapotranspiration -ET-) to total available energy from easily obtainable remotely-sensed and meteorological parameters is presented. This research specifically addresses the shortcomings of existing ET retrieval methods such as calibration requirements of extensive accurate in situ micro-meteorological and flux tower observations, or of a large set of coarse-resolution or model-derived input datasets. The trapezoid model is capable of generating spatially varying EF maps from standard products such as land surface temperature [T(sub s)] normalized difference vegetation index (NDVI)and daily maximum air temperature [T(sub a)]. The 2009 model results were validated at an eddy-covariance tower (Fluxnet ID: US-Skr) in the Everglades using T(sub s) and NDVI products from Landsat as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results indicate that the model accuracy is within the range of instrument uncertainty, and is dependent on the spatial resolution and selection of end-members (i.e. wet/dry edge). The most accurate results were achieved with the T(sub s) from Landsat relative to the T(sub s) from the MODIS flown on the Terra and Aqua platforms due to the fine spatial resolution of Landsat (30 m). The bias, mean absolute percentage error and root mean square percentage error were as low as 2.9% (3.0%), 9.8% (13.3%), and 12.1% (16.1%) for Landsat-based (MODIS-based) EF estimates, respectively. Overall, this methodology shows promise for bridging the gap between temporally limited ET estimates at Landsat scales and more complex and difficult to constrain global ET remote-sensing models.

Estimating evaporative fraction from readily obtainable variables in mangrove forests of the Everglades, U.S.A.

USGS Publications Warehouse

Yagci, Ali Levent; Santanello, Joseph A.; Jones, John W.; Barr, Jordan G.

2017-01-01

A remote-sensing-based model to estimate evaporative fraction (EF) – the ratio of latent heat (LE; energy equivalent of evapotranspiration –ET–) to total available energy – from easily obtainable remotely-sensed and meteorological parameters is presented. This research specifically addresses the shortcomings of existing ET retrieval methods such as calibration requirements of extensive accurate in situ micrometeorological and flux tower observations or of a large set of coarse-resolution or model-derived input datasets. The trapezoid model is capable of generating spatially varying EF maps from standard products such as land surface temperature (Ts) normalized difference vegetation index (NDVI) and daily maximum air temperature (Ta). The 2009 model results were validated at an eddy-covariance tower (Fluxnet ID: US-Skr) in the Everglades using Ts and NDVI products from Landsat as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results indicate that the model accuracy is within the range of instrument uncertainty, and is dependent on the spatial resolution and selection of end-members (i.e. wet/dry edge). The most accurate results were achieved with the Ts from Landsat relative to the Ts from the MODIS flown on the Terra and Aqua platforms due to the fine spatial resolution of Landsat (30 m). The bias, mean absolute percentage error and root mean square percentage error were as low as 2.9% (3.0%), 9.8% (13.3%), and 12.1% (16.1%) for Landsat-based (MODIS-based) EF estimates, respectively. Overall, this methodology shows promise for bridging the gap between temporally limited ET estimates at Landsat scales and more complex and difficult to constrain global ET remote-sensing models.

Global tropospheric ozone modeling: Quantifying errors due to grid resolution

NASA Astrophysics Data System (ADS)

Wild, Oliver; Prather, Michael J.

2006-06-01

Ozone production in global chemical models is dependent on model resolution because ozone chemistry is inherently nonlinear, the timescales for chemical production are short, and precursors are artificially distributed over the spatial scale of the model grid. In this study we examine the sensitivity of ozone, its precursors, and its production to resolution by running a global chemical transport model at four different resolutions between T21 (5.6° × 5.6°) and T106 (1.1° × 1.1°) and by quantifying the errors in regional and global budgets. The sensitivity to vertical mixing through the parameterization of boundary layer turbulence is also examined. We find less ozone production in the boundary layer at higher resolution, consistent with slower chemical production in polluted emission regions and greater export of precursors. Agreement with ozonesonde and aircraft measurements made during the NASA TRACE-P campaign over the western Pacific in spring 2001 is consistently better at higher resolution. We demonstrate that the numerical errors in transport processes on a given resolution converge geometrically for a tracer at successively higher resolutions. The convergence in ozone production on progressing from T21 to T42, T63, and T106 resolution is likewise monotonic but indicates that there are still large errors at 120 km scales, suggesting that T106 resolution is too coarse to resolve regional ozone production. Diagnosing the ozone production and precursor transport that follow a short pulse of emissions over east Asia in springtime allows us to quantify the impacts of resolution on both regional and global ozone. Production close to continental emission regions is overestimated by 27% at T21 resolution, by 13% at T42 resolution, and by 5% at T106 resolution. However, subsequent ozone production in the free troposphere is not greatly affected. We find that the export of short-lived precursors such as NOx by convection is overestimated at coarse resolution.

Assessment of Near-Source Air Pollution at a Fine Spatial ...

EPA Pesticide Factsheets

Mobile monitoring is an emerging strategy to characterize spatially and temporally variable air pollution in areas near sources. EPA’s Geospatial Monitoring of Air Pollution (GMAP) vehicle – an all-electric vehicle measuring real-time concentrations of particulate and gaseous pollutants – was used to map air pollution levels near the Port of Charleston in South Carolina. High-resolution monitoring was performed along driving routes near several port terminals and rail yard facilities, recording geospatial coordinates and concentrations of pollutants including black carbon, size-resolved particle count ranging from ultrafine to coarse (6 nm to 20 um), carbon monoxide, carbon dioxide, and nitrogen dioxide. Additionally, a portable meteorological station was used to characterize local conditions. The primary objective of this work is to characterize the impact of port facilities on local scale air quality. It is found that elevated concentration measurements of Black Carbon and PM correlate to periods of increased port activity and a significant elevation in concentration is observed downwind of ports. However, limitations in study design prevent a more complete analysis of the port effect. As such, we discuss the ways in which this study is limited and how future work could be improved. Mobile monitoring is an emerging strategy to characterize spatially and temporally variable air pollution in areas near sources. EPA’s Geospatial Monitoring of Air Pollut

An expanded role for river networks

Treesearch

Jonathan P. Benstead; David S. Leigh

2012-01-01

Estimates of stream and river area have relied on observations at coarse resolution. Consideration of the smallest and most dynamic streams could reveal a greater role for river networks in global biogeochemical cycling than previously thought.

Employing temporal self-similarity across the entire time domain in computed tomography reconstruction

PubMed Central

Kazantsev, D.; Van Eyndhoven, G.; Lionheart, W. R. B.; Withers, P. J.; Dobson, K. J.; McDonald, S. A.; Atwood, R.; Lee, P. D.

2015-01-01

There are many cases where one needs to limit the X-ray dose, or the number of projections, or both, for high frame rate (fast) imaging. Normally, it improves temporal resolution but reduces the spatial resolution of the reconstructed data. Fortunately, the redundancy of information in the temporal domain can be employed to improve spatial resolution. In this paper, we propose a novel regularizer for iterative reconstruction of time-lapse computed tomography. The non-local penalty term is driven by the available prior information and employs all available temporal data to improve the spatial resolution of each individual time frame. A high-resolution prior image from the same or a different imaging modality is used to enhance edges which remain stationary throughout the acquisition time while dynamic features tend to be regularized spatially. Effective computational performance together with robust improvement in spatial and temporal resolution makes the proposed method a competitive tool to state-of-the-art techniques. PMID:25939621

Satellite image time series simulation for environmental monitoring

NASA Astrophysics Data System (ADS)

Guo, Tao

2014-11-01

The performance of environmental monitoring heavily depends on the availability of consecutive observation data and it turns out an increasing demand in remote sensing community for satellite image data in the sufficient resolution with respect to both spatial and temporal requirements, which appear to be conflictive and hard to tune tradeoffs. Multiple constellations could be a solution if without concerning cost, and thus it is so far interesting but very challenging to develop a method which can simultaneously improve both spatial and temporal details. There are some research efforts to deal with the problem from various aspects, a type of approaches is to enhance the spatial resolution using techniques of super resolution, pan-sharpen etc. which can produce good visual effects, but mostly cannot preserve spectral signatures and result in losing analytical value. Another type is to fill temporal frequency gaps by adopting time interpolation, which actually doesn't increase informative context at all. In this paper we presented a novel method to generate satellite images in higher spatial and temporal details, which further enables satellite image time series simulation. Our method starts with a pair of high-low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and the temporal change is then projected onto high resolution data plane and assigned to each high resolution pixel referring the predefined temporal change patterns of each type of ground objects to generate a simulated high resolution data. A preliminary experiment shows that our method can simulate a high resolution data with a good accuracy. We consider the contribution of our method is to enable timely monitoring of temporal changes through analysis of low resolution images time series only, and usage of costly high resolution data can be reduced as much as possible, and it presents an efficient solution with great cost performance to build up an economically operational monitoring service for environment, agriculture, forest, land use investigation, and other applications.

Definition of SMOS Level 3 Land Products for the Villafranca del Castillo Data Processing Centre (CP34)

NASA Astrophysics Data System (ADS)

Lopez-Baeza, E.; Monsoriu Torres, A.; Font, J.; Alonso, O.

2009-04-01

The ESA SMOS (Soil Moisture and Ocean Salinity) Mission is planned to be launched in July 2009. The satellite will measure soil moisture over the continents and surface salinity of the oceans at resolutions that are sufficient for climatological-type studies. This paper describes the procedure to be used at the Spanish SMOS Level 3 and 4 Data Processing Centre (CP34) to generate Soil Moisture and other Land Surface Product maps from SMOS Level 2 data. This procedure can be used to map Soil Moisture, Vegetation Water Content and Soil Dielectric Constant data into different pre-defined spatial grids with fixed temporal frequency. The L3 standard Land Surface Products to be generated at CP34 are: Soil Moisture products: maximum spatial resolution with no spatial averaging, temporal averaging of 3 days, daily generation maximum spatial resolution with no spatial averaging, temporal averaging of 10 days, generation frequency of once every 10 days. b': maximum spatial resolution with no spatial averaging, temporal averaging of monthly decades (1st to 10th of the month, 11th to 20th of the month, 21st to last day of the month), generation frequency of once every decade monthly average, temporal averaging from L3 decade averages, monthly generation Seasonal average, temporal averaging from L3 monthly averages, seasonally generation yearly average, temporal averaging from L3 monthly averages, yearly generation Vegetation Water Content products: maximum spatial resolution with no spatial averaging, temporal averaging of 10 days, generation frequency of once every 10 days. a': maximum spatial resolution with no spatial averaging, temporal averaging of monthly decades (1st to 10th of the month, 11th to 20th of the month, 21st to last day of the month) using simple averaging method over the L2 products in ISEA grid, generation frequency of once every decade monthly average, temporal averaging from L3 decade averages, monthly generation seasonal average, temporal averaging from L3 monthly averages, seasonally generation yearly average, temporal averaging from L3 monthly averages, yearly generation Dielectric Constant products: (the dielectric constant products are delivered together with soil moisture products, with the same averaging periods and generation frequency): maximum spatial resolution with no spatial averaging, temporal averaging of 3 days, daily generation maximum spatial resolution with no spatial averaging, temporal averaging of 10 days, generation frequency of once every 10 days. b': maximum spatial resolution with no spatial averaging, temporal averaging of monthly decades (1st to 10th of the month, 11th to 20th of the month, 21st to last day of the month), generation frequency of once every decade monthly average, temporal averaging from L3 decade averages, monthly generation seasonal average, temporal averaging from L3 monthly averages, seasonally generation yearly average, temporal averaging from L3 monthly averages, yearly generation.

Reaction-diffusion basis of retroviral infectivity

NASA Astrophysics Data System (ADS)

Sadiq, S. Kashif

2016-11-01

Retrovirus particle (virion) infectivity requires diffusion and clustering of multiple transmembrane envelope proteins (Env3) on the virion exterior, yet is triggered by protease-dependent degradation of a partially occluding, membrane-bound Gag polyprotein lattice on the virion interior. The physical mechanism underlying such coupling is unclear and only indirectly accessible via experiment. Modelling stands to provide insight but the required spatio-temporal range far exceeds current accessibility by all-atom or even coarse-grained molecular dynamics simulations. Nor do such approaches account for chemical reactions, while conversely, reaction kinetics approaches handle neither diffusion nor clustering. Here, a recently developed multiscale approach is considered that applies an ultra-coarse-graining scheme to treat entire proteins at near-single particle resolution, but which also couples chemical reactions with diffusion and interactions. A model is developed of Env3 molecules embedded in a truncated Gag lattice composed of membrane-bound matrix proteins linked to capsid subunits, with freely diffusing protease molecules. Simulations suggest that in the presence of Gag but in the absence of lateral lattice-forming interactions, Env3 diffuses comparably to Gag-absent Env3. Initial immobility of Env3 is conferred through lateral caging by matrix trimers vertically coupled to the underlying hexameric capsid layer. Gag cleavage by protease vertically decouples the matrix and capsid layers, induces both matrix and Env3 diffusion, and permits Env3 clustering. Spreading across the entire membrane surface reduces crowding, in turn, enhancing the effect and promoting infectivity. This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'.

Impact wave deposits provide new constraints on the location of the K/T boundary impact

NASA Technical Reports Server (NTRS)

Hildebrand, A. R.; Boynton, W. V.

1988-01-01

All available evidence is consistent with an impact into oceanic crust terminating the Cretaceous Period. Although much of this evidence is incompatible with an endogenic origin, some investigators still feel that a volcanic origin is possible for the Cretaceous/Tertiary (K/T) boundary clay layers. The commonly cited evidence for a large impact stems from delicate clay layers and their components and the impact site has not yet been found. Impact sites have been suggested all over the globe. The impact is felt to have occurred near North America by: the occurrence of a 2 cm thick ejecta layer only at North American locales, the global variation of shocked quartz grain sizes peaking in North America, the global variation of spinel compositions with most refractory compositions occurring in samples from the Pacific region and possibly uniquely severe plant extinctions in the North American region. The K/T boundary interval was investigated as preserved on the banks of the Brazos River, Texas. The K/T fireball and ejecta layers with associated geochemical anomalies were found interbedded with this sequence which apparently allows a temporal resolution 4 orders of magnitude greater than typical K/T boundary sections. A literature search reveals that such coarse deposits are widely preserved at the K/T boundary. Impact wave deposits have not been found elsewhere on the globe, suggesting the impact occurred between North and South America. The coarse deposits preserved in Deep Sea Drilling Project (DSDP) holes 151-3 suggest the impact occurred nearby. Subsequent tectonism has complicated the picture.

Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS

Treesearch

A. M. S. Smith; N. A. Drake; M. J. Wooster; A. T. Hudak; Z. A. Holden; C. J. Gibbons

2007-01-01

Accurate production of regional burned area maps are necessary to reduce uncertainty in emission estimates from African savannah fires. Numerous methods have been developed that map burned and unburned surfaces. These methods are typically applied to coarse spatial resolution (1 km) data to produce regional estimates of the area burned, while higher spatial resolution...

Spectral and temporal resolutions of information-bearing acoustic changes for understanding vocoded sentencesa)

PubMed Central

Stilp, Christian E.; Goupell, Matthew J.

2015-01-01

Short-time spectral changes in the speech signal are important for understanding noise-vocoded sentences. These information-bearing acoustic changes, measured using cochlea-scaled entropy in cochlear implant simulations [CSECI; Stilp et al. (2013). J. Acoust. Soc. Am. 133(2), EL136–EL141; Stilp (2014). J. Acoust. Soc. Am. 135(3), 1518–1529], may offer better understanding of speech perception by cochlear implant (CI) users. However, perceptual importance of CSECI for normal-hearing listeners was tested at only one spectral resolution and one temporal resolution, limiting generalizability of results to CI users. Here, experiments investigated the importance of these informational changes for understanding noise-vocoded sentences at different spectral resolutions (4–24 spectral channels; Experiment 1), temporal resolutions (4–64 Hz cutoff for low-pass filters that extracted amplitude envelopes; Experiment 2), or when both parameters varied (6–12 channels, 8–32 Hz; Experiment 3). Sentence intelligibility was reduced more by replacing high-CSECI intervals with noise than replacing low-CSECI intervals, but only when sentences had sufficient spectral and/or temporal resolution. High-CSECI intervals were more important for speech understanding as spectral resolution worsened and temporal resolution improved. Trade-offs between CSECI and intermediate spectral and temporal resolutions were minimal. These results suggest that signal processing strategies that emphasize information-bearing acoustic changes in speech may improve speech perception for CI users. PMID:25698018

Area-to-point regression kriging for pan-sharpening

NASA Astrophysics Data System (ADS)

Wang, Qunming; Shi, Wenzhong; Atkinson, Peter M.

2016-04-01

Pan-sharpening is a technique to combine the fine spatial resolution panchromatic (PAN) band with the coarse spatial resolution multispectral bands of the same satellite to create a fine spatial resolution multispectral image. In this paper, area-to-point regression kriging (ATPRK) is proposed for pan-sharpening. ATPRK considers the PAN band as the covariate. Moreover, ATPRK is extended with a local approach, called adaptive ATPRK (AATPRK), which fits a regression model using a local, non-stationary scheme such that the regression coefficients change across the image. The two geostatistical approaches, ATPRK and AATPRK, were compared to the 13 state-of-the-art pan-sharpening approaches summarized in Vivone et al. (2015) in experiments on three separate datasets. ATPRK and AATPRK produced more accurate pan-sharpened images than the 13 benchmark algorithms in all three experiments. Unlike the benchmark algorithms, the two geostatistical solutions precisely preserved the spectral properties of the original coarse data. Furthermore, ATPRK can be enhanced by a local scheme in AATRPK, in cases where the residuals from a global regression model are such that their spatial character varies locally.

Linking models and data on vegetation structure

NASA Astrophysics Data System (ADS)

Hurtt, G. C.; Fisk, J.; Thomas, R. Q.; Dubayah, R.; Moorcroft, P. R.; Shugart, H. H.

2010-06-01

For more than a century, scientists have recognized the importance of vegetation structure in understanding forest dynamics. Now future satellite missions such as Deformation, Ecosystem Structure, and Dynamics of Ice (DESDynI) hold the potential to provide unprecedented global data on vegetation structure needed to reduce uncertainties in terrestrial carbon dynamics. Here, we briefly review the uses of data on vegetation structure in ecosystem models, develop and analyze theoretical models to quantify model-data requirements, and describe recent progress using a mechanistic modeling approach utilizing a formal scaling method and data on vegetation structure to improve model predictions. Generally, both limited sampling and coarse resolution averaging lead to model initialization error, which in turn is propagated in subsequent model prediction uncertainty and error. In cases with representative sampling, sufficient resolution, and linear dynamics, errors in initialization tend to compensate at larger spatial scales. However, with inadequate sampling, overly coarse resolution data or models, and nonlinear dynamics, errors in initialization lead to prediction error. A robust model-data framework will require both models and data on vegetation structure sufficient to resolve important environmental gradients and tree-level heterogeneity in forest structure globally.

Towards a Near Real-Time Satellite-Based Flux Monitoring System for the MENA Region

NASA Astrophysics Data System (ADS)

Ershadi, A.; Houborg, R.; McCabe, M. F.; Anderson, M. C.; Hain, C.

2013-12-01

Satellite remote sensing has the potential to offer spatially and temporally distributed information on land surface characteristics, which may be used as inputs and constraints for estimating land surface fluxes of carbon, water and energy. Enhanced satellite-based monitoring systems for aiding local water resource assessments and agricultural management activities are particularly needed for the Middle East and North Africa (MENA) region. The MENA region is an area characterized by limited fresh water resources, an often inefficient use of these, and relatively poor in-situ monitoring as a result of sparse meteorological observations. To address these issues, an integrated modeling approach for near real-time monitoring of land surface states and fluxes at fine spatio-temporal scales over the MENA region is presented. This approach is based on synergistic application of multiple sensors and wavebands in the visible to shortwave infrared and thermal infrared (TIR) domain. The multi-scale flux mapping and monitoring system uses the Atmosphere-Land Exchange Inverse (ALEXI) model and associated flux disaggregation scheme (DisALEXI), and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) in conjunction with model reanalysis data and multi-sensor remotely sensed data from polar orbiting (e.g. Landsat and MODerate resolution Imaging Spectroradiometer (MODIS)) and geostationary (MSG; Meteosat Second Generation) satellite platforms to facilitate time-continuous (i.e. daily) estimates of field-scale water, energy and carbon fluxes. Within this modeling system, TIR satellite data provide information about the sub-surface moisture status and plant stress, obviating the need for precipitation input and a detailed soil surface characterization (i.e. for prognostic modeling of soil transport processes). The STARFM fusion methodology blends aspects of high frequency (spatially coarse) and spatially fine resolution sensors and is applied directly to flux output fields to facilitate daily mapping of fluxes at sub-field scales. A complete processing infrastructure to automatically ingest and pre-process all required input data and to execute the integrated modeling system for near real-time agricultural monitoring purposes over targeted MENA sites is being developed, and initial results from this concerted effort will be discussed.

Error Estimation in an Optimal Interpolation Scheme for High Spatial and Temporal Resolution SST Analyses

NASA Technical Reports Server (NTRS)

Rigney, Matt; Jedlovec, Gary; LaFontaine, Frank; Shafer, Jaclyn

2010-01-01

Heat and moisture exchange between ocean surface and atmosphere plays an integral role in short-term, regional NWP. Current SST products lack both spatial and temporal resolution to accurately capture small-scale features that affect heat and moisture flux. NASA satellite is used to produce high spatial and temporal resolution SST analysis using an OI technique.

Impaired temporal, not just spatial, resolution in amblyopia.

PubMed

Spang, Karoline; Fahle, Manfred

2009-11-01

In amblyopia, neuronal deficits deteriorate spatial vision including visual acuity, possibly because of a lack of use-dependent fine-tuning of afferents to the visual cortex during infancy; but temporal processing may deteriorate as well. Temporal, rather than spatial, resolution was investigated in patients with amblyopia by means of a task based on time-defined figure-ground segregation. Patients had to indicate the quadrant of the visual field where a purely time-defined square appeared. The results showed a clear decrease in temporal resolution of patients' amblyopic eyes compared with the dominant eyes in this task. The extent of this decrease in figure-ground segregation based on time of motion onset only loosely correlated with the decrease in spatial resolution and spanned a smaller range than did the spatial loss. Control experiments with artificially induced blur in normal observers confirmed that the decrease in temporal resolution was not simply due to the acuity loss. Amblyopia not only decreases spatial resolution, but also temporal factors such as time-based figure-ground segregation, even at high stimulus contrasts. This finding suggests that the realm of neuronal processes that may be disturbed in amblyopia is larger than originally thought.

Forest Classification Accuracy as Influenced by Multispectral Scanner Spatial Resolution. [Sam Houston National Forest, Texas

NASA Technical Reports Server (NTRS)

Nalepka, R. F. (Principal Investigator); Sadowski, F. E.; Sarno, J. E.

1976-01-01

The author has identified the following significant results. A supervised classification within two separate ground areas of the Sam Houston National Forest was carried out for two sq meters spatial resolution MSS data. Data were progressively coarsened to simulate five additional cases of spatial resolution ranging up to 64 sq meters. Similar processing and analysis of all spatial resolutions enabled evaluations of the effect of spatial resolution on classification accuracy for various levels of detail and the effects on area proportion estimation for very general forest features. For very coarse resolutions, a subset of spectral channels which simulated the proposed thematic mapper channels was used to study classification accuracy.

Flexible retrospective selection of temporal resolution in real-time speech MRI using a golden-ratio spiral view order.

PubMed

Kim, Yoon-Chul; Narayanan, Shrikanth S; Nayak, Krishna S

2011-05-01

In speech production research using real-time magnetic resonance imaging (MRI), the analysis of articulatory dynamics is performed retrospectively. A flexible selection of temporal resolution is highly desirable because of natural variations in speech rate and variations in the speed of different articulators. The purpose of the study is to demonstrate a first application of golden-ratio spiral temporal view order to real-time speech MRI and investigate its performance by comparison with conventional bit-reversed temporal view order. Golden-ratio view order proved to be more effective at capturing the dynamics of rapid tongue tip motion. A method for automated blockwise selection of temporal resolution is presented that enables the synthesis of a single video from multiple temporal resolution videos and potentially facilitates subsequent vocal tract shape analysis. Copyright © 2010 Wiley-Liss, Inc.

Weighted least squares phase unwrapping based on the wavelet transform

NASA Astrophysics Data System (ADS)

Chen, Jiafeng; Chen, Haiqin; Yang, Zhengang; Ren, Haixia

2007-01-01

The weighted least squares phase unwrapping algorithm is a robust and accurate method to solve phase unwrapping problem. This method usually leads to a large sparse linear equation system. Gauss-Seidel relaxation iterative method is usually used to solve this large linear equation. However, this method is not practical due to its extremely slow convergence. The multigrid method is an efficient algorithm to improve convergence rate. However, this method needs an additional weight restriction operator which is very complicated. For this reason, the multiresolution analysis method based on the wavelet transform is proposed. By applying the wavelet transform, the original system is decomposed into its coarse and fine resolution levels and an equivalent equation system with better convergence condition can be obtained. Fast convergence in separate coarse resolution levels speeds up the overall system convergence rate. The simulated experiment shows that the proposed method converges faster and provides better result than the multigrid method.

LSAH: a fast and efficient local surface feature for point cloud registration

NASA Astrophysics Data System (ADS)

Lu, Rongrong; Zhu, Feng; Wu, Qingxiao; Kong, Yanzi

2018-04-01

Point cloud registration is a fundamental task in high level three dimensional applications. Noise, uneven point density and varying point cloud resolutions are the three main challenges for point cloud registration. In this paper, we design a robust and compact local surface descriptor called Local Surface Angles Histogram (LSAH) and propose an effectively coarse to fine algorithm for point cloud registration. The LSAH descriptor is formed by concatenating five normalized sub-histograms into one histogram. The five sub-histograms are created by accumulating a different type of angle from a local surface patch respectively. The experimental results show that our LSAH is more robust to uneven point density and point cloud resolutions than four state-of-the-art local descriptors in terms of feature matching. Moreover, we tested our LSAH based coarse to fine algorithm for point cloud registration. The experimental results demonstrate that our algorithm is robust and efficient as well.

Proximity correction of high-dosed frame with PROXECCO

NASA Astrophysics Data System (ADS)

Eisenmann, Hans; Waas, Thomas; Hartmann, Hans

1994-05-01

Usefulness of electron beam lithography is strongly related to the efficiency and quality of methods used for proximity correction. This paper addresses the above issue by proposing an extension to the new proximity correction program PROXECCO. The combination of a framing step with PROXECCO produces a pattern with a very high edge accuracy and still allows usage of the fast correction procedure. Making a frame with a higher dose imitates a fine resolution correction where the coarse part is disregarded. So after handling the high resolution effect by means of framing, an additional coarse correction is still needed. Higher doses have a higher contribution to the proximity effect. This additional proximity effect is taken into account with the help of the multi-dose input of PROXECCO. The dose of the frame is variable, depending on the deposited energy coming from backscattering of the proximity. Simulation proves the very high edge accuracy of the applied method.

Accurate reconstruction of 3D cardiac geometry from coarsely-sliced MRI.

PubMed

Ringenberg, Jordan; Deo, Makarand; Devabhaktuni, Vijay; Berenfeld, Omer; Snyder, Brett; Boyers, Pamela; Gold, Jeffrey

2014-02-01

We present a comprehensive validation analysis to assess the geometric impact of using coarsely-sliced short-axis images to reconstruct patient-specific cardiac geometry. The methods utilize high-resolution diffusion tensor MRI (DTMRI) datasets as reference geometries from which synthesized coarsely-sliced datasets simulating in vivo MRI were produced. 3D models are reconstructed from the coarse data using variational implicit surfaces through a commonly used modeling tool, CardioViz3D. The resulting geometries were then compared to the reference DTMRI models from which they were derived to analyze how well the synthesized geometries approximate the reference anatomy. Averaged over seven hearts, 95% spatial overlap, less than 3% volume variability, and normal-to-surface distance of 0.32 mm was observed between the synthesized myocardial geometries reconstructed from 8 mm sliced images and the reference data. The results provide strong supportive evidence to validate the hypothesis that coarsely-sliced MRI may be used to accurately reconstruct geometric ventricular models. Furthermore, the use of DTMRI for validation of in vivo MRI presents a novel benchmark procedure for studies which aim to substantiate their modeling and simulation methods using coarsely-sliced cardiac data. In addition, the paper outlines a suggested original procedure for deriving image-based ventricular models using the CardioViz3D software. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Monitoring the lake area changes of the Qinghai-Tibet Plateau using coarse-resolution time series remote sensing data

NASA Astrophysics Data System (ADS)

Ma, M.

2015-12-01

The Qinghai-Tibet Plateau (QTP) is the world's highest and largest plateau and is occasionally referred to as "the roof of the world". As the important "water tower", there are 1,091 lakes of more than 1.0 km2 in the QTP areas, which account for 49.4% of the total area of lakes in China. Some studies focus on the lake area changes of the QTP areas, which mainly use the middle-resolution remote sensing data (e.g. Landsat TM). In this study, the coarse-resolution time series remote sensing data, MODIS data at a spatial resolution of 250m, was used to monitor the lake area changes of the QTP areas during the last 15 years. The dataset is the MOD13Q1 and the Normal Difference Vegetation Index (NDVI) is used to identify the lake area when the NDVI is less than 0. The results show the obvious inner-annual changes of most of the lakes. Therefore the annually average and maximum lake areas are calculated based on the time series remote data, which can better quantify the change characteristics than the single scene of image data from the middle-resolution data. The results indicate that there are big spatial variances of the lake area changes in the QTB. The natural driving factors are analyzed for revealing the causes of changes.

Graphics Processing Unit (GPU) Acceleration of the Goddard Earth Observing System Atmospheric Model

NASA Technical Reports Server (NTRS)

Putnam, Williama

2011-01-01

The Goddard Earth Observing System 5 (GEOS-5) is the atmospheric model used by the Global Modeling and Assimilation Office (GMAO) for a variety of applications, from long-term climate prediction at relatively coarse resolution, to data assimilation and numerical weather prediction, to very high-resolution cloud-resolving simulations. GEOS-5 is being ported to a graphics processing unit (GPU) cluster at the NASA Center for Climate Simulation (NCCS). By utilizing GPU co-processor technology, we expect to increase the throughput of GEOS-5 by at least an order of magnitude, and accelerate the process of scientific exploration across all scales of global modeling, including: The large-scale, high-end application of non-hydrostatic, global, cloud-resolving modeling at 10- to I-kilometer (km) global resolutions Intermediate-resolution seasonal climate and weather prediction at 50- to 25-km on small clusters of GPUs Long-range, coarse-resolution climate modeling, enabled on a small box of GPUs for the individual researcher After being ported to the GPU cluster, the primary physics components and the dynamical core of GEOS-5 have demonstrated a potential speedup of 15-40 times over conventional processor cores. Performance improvements of this magnitude reduce the required scalability of 1-km, global, cloud-resolving models from an unfathomable 6 million cores to an attainable 200,000 GPU-enabled cores.

Transpacific Transport of Dust to North American High-Elevation Sites: Integrated Dataset and Model Outputs

NASA Astrophysics Data System (ADS)

Kassianov, E.; Pekour, M. S.; Flynn, C. J.; Berg, L. K.; Beranek, J.; Zelenyuk, A.; Zhao, C.; Leung, L. R.; Ma, P. L.; Riihimaki, L.; Fast, J. D.; Barnard, J.; Hallar, G. G.; McCubbin, I.; Eloranta, E. W.; McComiskey, A. C.; Rasch, P. J.

2017-12-01

Understanding the effects of dust on the regional and global climate requires detailed information on particle size distributions and their changes with distance from the source. Awareness is now growing about the tendency of the dust coarse mode with moderate ( 3.5 µm) volume median diameter (VMD) to be rather insensitive to complex removal processes associated with long-range transport of dust from the main sources. Our study, with a focus on the transpacific transport of dust, demonstrates that the impact of coarse mode aerosol (VMD 3µm) is well defined at the high-elevation mountain-top Storm Peak Laboratory (SPL, about 3.2 km MSL) and nearby Atmospheric Radiation Measurement (ARM) Climate Research Facility Mobile Facility (AMF) during March 2011. Significant amounts of coarse mode aerosol are also found at the nearest Aerosol Robotic Network (AERONET) site. Outputs from the high-resolution Weather Research and Forecasting (WRF) Model coupled with chemistry (WRF-Chem) show that the major dust event is likely associated with transpacific transport of Asian and African plumes. Satellite data, including the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging SpectroRadiometer (MISR) aerosol optical depth (AOD) and plume height from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar data provide the observational support of the WRF-Chem simulations. Our study complements previous findings by indicating that the quasi-static nature of the coarse mode appears to be a reasonable approximation for Asian and African dust despite expected frequent orographic precipitation over mountainous regions in the western United States.

Temporal resolution in individuals with neurological disorders

PubMed Central

Rabelo, Camila Maia; Weihing, Jeffrey A; Schochat, Eliane

2015-01-01

OBJECTIVE: Temporal processing refers to the ability of the central auditory nervous system to encode and detect subtle changes in acoustic signals. This study aims to investigate the temporal resolution ability of individuals with mesial temporal sclerosis and to determine the sensitivity and specificity of the gaps-in-noise test in identifying this type of lesion. METHOD: This prospective study investigated differences in temporal resolution between 30 individuals with normal hearing and without neurological lesions (G1) and 16 individuals with both normal hearing and mesial temporal sclerosis (G2). Test performances were compared, and the sensitivity and specificity were calculated. RESULTS: There was no difference in gap detection thresholds between the two groups, although G1 revealed better average thresholds than G2 did. The sensitivity and specificity of the gaps-in-noise test for neurological lesions were 68% and 98%, respectively. CONCLUSIONS: Temporal resolution ability is compromised in individuals with neurological lesions caused by mesial temporal sclerosis. The gaps-in-noise test was shown to be a sensitive and specific measure of central auditory dysfunction in these patients. PMID:26375561

An investigation into the effects of temporal resolution on hepatic dynamic contrast-enhanced MRI in volunteers and in patients with hepatocellular carcinoma

NASA Astrophysics Data System (ADS)

Gill, Andrew B.; Black, Richard T.; Bowden, David J.; Priest, Andrew N.; Graves, Martin J.; Lomas, David J.

2014-06-01

This study investigated the effect of temporal resolution on the dual-input pharmacokinetic (PK) modelling of dynamic contrast-enhanced MRI (DCE-MRI) data from normal volunteer livers and from patients with hepatocellular carcinoma. Eleven volunteers and five patients were examined at 3 T. Two sections, one optimized for the vascular input functions (VIF) and one for the tissue, were imaged within a single heart-beat (HB) using a saturation-recovery fast gradient echo sequence. The data was analysed using a dual-input single-compartment PK model. The VIFs and/or uptake curves were then temporally sub-sampled (at interval ▵t = [2-20] s) before being subject to the same PK analysis. Statistical comparisons of tumour and normal tissue PK parameter values using a 5% significance level gave rise to the same study results when temporally sub-sampling the VIFs to HB < ▵t <4 s. However, sub-sampling to ▵t > 4 s did adversely affect the statistical comparisons. Temporal sub-sampling of just the liver/tumour tissue uptake curves at ▵t ≤ 20 s, whilst using high temporal resolution VIFs, did not substantially affect PK parameter statistical comparisons. In conclusion, there is no practical advantage to be gained from acquiring very high temporal resolution hepatic DCE-MRI data. Instead the high temporal resolution could be usefully traded for increased spatial resolution or SNR.

Quantum theory of multiscale coarse-graining.

PubMed

Han, Yining; Jin, Jaehyeok; Wagner, Jacob W; Voth, Gregory A

2018-03-14

Coarse-grained (CG) models serve as a powerful tool to simulate molecular systems at much longer temporal and spatial scales. Previously, CG models and methods have been built upon classical statistical mechanics. The present paper develops a theory and numerical methodology for coarse-graining in quantum statistical mechanics, by generalizing the multiscale coarse-graining (MS-CG) method to quantum Boltzmann statistics. A rigorous derivation of the sufficient thermodynamic consistency condition is first presented via imaginary time Feynman path integrals. It identifies the optimal choice of CG action functional and effective quantum CG (qCG) force field to generate a quantum MS-CG (qMS-CG) description of the equilibrium system that is consistent with the quantum fine-grained model projected onto the CG variables. A variational principle then provides a class of algorithms for optimally approximating the qMS-CG force fields. Specifically, a variational method based on force matching, which was also adopted in the classical MS-CG theory, is generalized to quantum Boltzmann statistics. The qMS-CG numerical algorithms and practical issues in implementing this variational minimization procedure are also discussed. Then, two numerical examples are presented to demonstrate the method. Finally, as an alternative strategy, a quasi-classical approximation for the thermal density matrix expressed in the CG variables is derived. This approach provides an interesting physical picture for coarse-graining in quantum Boltzmann statistical mechanics in which the consistency with the quantum particle delocalization is obviously manifest, and it opens up an avenue for using path integral centroid-based effective classical force fields in a coarse-graining methodology.

Implicit adaptive mesh refinement for 2D reduced resistive magnetohydrodynamics

NASA Astrophysics Data System (ADS)

Philip, Bobby; Chacón, Luis; Pernice, Michael

2008-10-01

An implicit structured adaptive mesh refinement (SAMR) solver for 2D reduced magnetohydrodynamics (MHD) is described. The time-implicit discretization is able to step over fast normal modes, while the spatial adaptivity resolves thin, dynamically evolving features. A Jacobian-free Newton-Krylov method is used for the nonlinear solver engine. For preconditioning, we have extended the optimal "physics-based" approach developed in [L. Chacón, D.A. Knoll, J.M. Finn, An implicit, nonlinear reduced resistive MHD solver, J. Comput. Phys. 178 (2002) 15-36] (which employed multigrid solver technology in the preconditioner for scalability) to SAMR grids using the well-known Fast Adaptive Composite grid (FAC) method [S. McCormick, Multilevel Adaptive Methods for Partial Differential Equations, SIAM, Philadelphia, PA, 1989]. A grid convergence study demonstrates that the solver performance is independent of the number of grid levels and only depends on the finest resolution considered, and that it scales well with grid refinement. The study of error generation and propagation in our SAMR implementation demonstrates that high-order (cubic) interpolation during regridding, combined with a robustly damping second-order temporal scheme such as BDF2, is required to minimize impact of grid errors at coarse-fine interfaces on the overall error of the computation for this MHD application. We also demonstrate that our implementation features the desired property that the overall numerical error is dependent only on the finest resolution level considered, and not on the base-grid resolution or on the number of refinement levels present during the simulation. We demonstrate the effectiveness of the tool on several challenging problems.

Dynamics of person-to-person interactions from distributed RFID sensor networks.

PubMed

Cattuto, Ciro; Van den Broeck, Wouter; Barrat, Alain; Colizza, Vittoria; Pinton, Jean-François; Vespignani, Alessandro

2010-07-15

Digital networks, mobile devices, and the possibility of mining the ever-increasing amount of digital traces that we leave behind in our daily activities are changing the way we can approach the study of human and social interactions. Large-scale datasets, however, are mostly available for collective and statistical behaviors, at coarse granularities, while high-resolution data on person-to-person interactions are generally limited to relatively small groups of individuals. Here we present a scalable experimental framework for gathering real-time data resolving face-to-face social interactions with tunable spatial and temporal granularities. We use active Radio Frequency Identification (RFID) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We analyze the dynamics of person-to-person interaction networks obtained in three high-resolution experiments carried out at different orders of magnitude in community size. The data sets exhibit common statistical properties and lack of a characteristic time scale from 20 seconds to several hours. The association between the number of connections and their duration shows an interesting super-linear behavior, which indicates the possibility of defining super-connectors both in the number and intensity of connections. Taking advantage of scalability and resolution, this experimental framework allows the monitoring of social interactions, uncovering similarities in the way individuals interact in different contexts, and identifying patterns of super-connector behavior in the community. These results could impact our understanding of all phenomena driven by face-to-face interactions, such as the spreading of transmissible infectious diseases and information.

Modelled air pollution levels versus EC air quality legislation - results from high resolution simulation.

PubMed

Chervenkov, Hristo

2013-12-01

An appropriate method for evaluating the air quality of a certain area is to contrast the actual air pollution levels to the critical ones, prescribed in the legislative standards. The application of numerical simulation models for assessing the real air quality status is allowed by the legislation of the European Community (EC). This approach is preferable, especially when the area of interest is relatively big and/or the network of measurement stations is sparse, and the available observational data are scarce, respectively. Such method is very efficient for similar assessment studies due to continuous spatio-temporal coverage of the obtained results. In the study the values of the concentration of the harmful substances sulphur dioxide, (SO2), nitrogen dioxide (NO2), particulate matter - coarse (PM10) and fine (PM2.5) fraction, ozone (O3), carbon monoxide (CO) and ammonia (NH3) in the surface layer obtained from modelling simulations with resolution 10 km on hourly bases are taken to calculate the necessary statistical quantities which are used for comparison with the corresponding critical levels, prescribed in the EC directives. For part of them (PM2.5, CO and NH3) this is done for first time with such resolution. The computational grid covers Bulgaria entirely and some surrounding territories and the calculations are made for every year in the period 1991-2000. The averaged over the whole time slice results can be treated as representative for the air quality situation of the last decade of the former century.

Spatial Frequency Priming of Scene Perception in Adolescents with and without ASD

ERIC Educational Resources Information Center

Vanmarcke, Steven; Noens, Ilse; Steyaert, Jean; Wagemans, Johan

2017-01-01

While most typically developing (TD) participants have a coarse-to-fine processing style, people with autism spectrum disorder (ASD) seem to be less globally and more locally biased when processing visual information. The stimulus-specific spatial frequency content might be directly relevant to determine this temporal hierarchy of visual…

MISR Level 2 TOA/Cloud Classifier parameters (MIL2TCCL_V2)

NASA Technical Reports Server (NTRS)

Diner, David J. (Principal Investigator)

The TOA/Cloud Classifiers contain the Angular Signature Cloud Mask (ASCM), a scene classifier calculated using support vector machine technology (SVM) both of which are on a 1.1 km grid, and cloud fractions at 17.6 km resolution that are available in different height bins (low, middle, high) and are also calculated on an angle-by-angle basis. [Location=GLOBAL] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=17.6 km; Longitude_Resolution=17.6 km; Horizontal_Resolution_Range=10 km - < 50 km or approximately .09 degree - < .5 degree; Temporal_Resolution=about 15 orbits/day; Temporal_Resolution_Range=Daily - < Weekly, Daily - < Weekly].

High density event-related potential data acquisition in cognitive neuroscience.

PubMed

Slotnick, Scott D

2010-04-16

Functional magnetic resonance imaging (fMRI) is currently the standard method of evaluating brain function in the field of Cognitive Neuroscience, in part because fMRI data acquisition and analysis techniques are readily available. Because fMRI has excellent spatial resolution but poor temporal resolution, this method can only be used to identify the spatial location of brain activity associated with a given cognitive process (and reveals virtually nothing about the time course of brain activity). By contrast, event-related potential (ERP) recording, a method that is used much less frequently than fMRI, has excellent temporal resolution and thus can track rapid temporal modulations in neural activity. Unfortunately, ERPs are under utilized in Cognitive Neuroscience because data acquisition techniques are not readily available and low density ERP recording has poor spatial resolution. In an effort to foster the increased use of ERPs in Cognitive Neuroscience, the present article details key techniques involved in high density ERP data acquisition. Critically, high density ERPs offer the promise of excellent temporal resolution and good spatial resolution (or excellent spatial resolution if coupled with fMRI), which is necessary to capture the spatial-temporal dynamics of human brain function.

In vivo quantification of plant starch reserves at micrometer resolution using X-ray microCT imaging and machine learning.

PubMed

Earles, J Mason; Knipfer, Thorsten; Tixier, Aude; Orozco, Jessica; Reyes, Clarissa; Zwieniecki, Maciej A; Brodersen, Craig R; McElrone, Andrew J

2018-03-08

Starch is the primary energy storage molecule used by most terrestrial plants to fuel respiration and growth during periods of limited to no photosynthesis, and its depletion can drive plant mortality. Destructive techniques at coarse spatial scales exist to quantify starch, but these techniques face methodological challenges that can lead to uncertainty about the lability of tissue-specific starch pools and their role in plant survival. Here, we demonstrate how X-ray microcomputed tomography (microCT) and a machine learning algorithm can be coupled to quantify plant starch content in vivo, repeatedly and nondestructively over time in grapevine stems (Vitis spp.). Starch content estimated for xylem axial and ray parenchyma cells from microCT images was correlated strongly with enzymatically measured bulk-tissue starch concentration on the same stems. After validating our machine learning algorithm, we then characterized the spatial distribution of starch concentration in living stems at micrometer resolution, and identified starch depletion in live plants under experimental conditions designed to halt photosynthesis and starch production, initiating the drawdown of stored starch pools. Using X-ray microCT technology for in vivo starch monitoring should enable novel research directed at resolving the spatial and temporal patterns of starch accumulation and depletion in woody plant species. No claim to original US Government works New Phytologist © 2018 New Phytologist Trust.

Geospatial relationships of air pollution and acute asthma events across the Detroit-Windsor international border: study design and preliminary results.

PubMed

Lemke, Lawrence D; Lamerato, Lois E; Xu, Xiaohong; Booza, Jason C; Reiners, John J; Raymond Iii, Delbert M; Villeneuve, Paul J; Lavigne, Eric; Larkin, Dana; Krouse, Helene J

2014-07-01

The Geospatial Determinants of Health Outcomes Consortium (GeoDHOC) study investigated ambient air quality across the international border between Detroit, Michigan, USA and Windsor, Ontario, Canada and its association with acute asthma events in 5- to 89-year-old residents of these cities. NO2, SO2, and volatile organic compounds (VOCs) were measured at 100 sites, and particulate matter (PM) and polycyclic aromatic hydrocarbons (PAHs) at 50 sites during two 2-week sampling periods in 2008 and 2009. Acute asthma event rates across neighborhoods in each city were calculated using emergency room visits and hospitalizations and standardized to the overall age and gender distribution of the population in the two cities combined. Results demonstrate that intra-urban air quality variations are related to adverse respiratory events in both cities. Annual 2008 asthma rates exhibited statistically significant positive correlations with total VOCs and total benzene, toluene, ethylbenzene and xylene (BTEX) at 5-digit zip code scale spatial resolution in Detroit. In Windsor, NO2, VOCs, and PM10 concentrations correlated positively with 2008 asthma rates at a similar 3-digit postal forward sortation area scale. The study is limited by its coarse temporal resolution (comparing relatively short term air quality measurements to annual asthma health data) and interpretation of findings is complicated by contrasts in population demographics and health-care delivery systems in Detroit and Windsor.

Optical magnetic detection of single-neuron action potentials using quantum defects in diamond.

PubMed

Barry, John F; Turner, Matthew J; Schloss, Jennifer M; Glenn, David R; Song, Yuyu; Lukin, Mikhail D; Park, Hongkun; Walsworth, Ronald L

2016-12-06

Magnetic fields from neuronal action potentials (APs) pass largely unperturbed through biological tissue, allowing magnetic measurements of AP dynamics to be performed extracellularly or even outside intact organisms. To date, however, magnetic techniques for sensing neuronal activity have either operated at the macroscale with coarse spatial and/or temporal resolution-e.g., magnetic resonance imaging methods and magnetoencephalography-or been restricted to biophysics studies of excised neurons probed with cryogenic or bulky detectors that do not provide single-neuron spatial resolution and are not scalable to functional networks or intact organisms. Here, we show that AP magnetic sensing can be realized with both single-neuron sensitivity and intact organism applicability using optically probed nitrogen-vacancy (NV) quantum defects in diamond, operated under ambient conditions and with the NV diamond sensor in close proximity (∼10 µm) to the biological sample. We demonstrate this method for excised single neurons from marine worm and squid, and then exterior to intact, optically opaque marine worms for extended periods and with no observed adverse effect on the animal. NV diamond magnetometry is noninvasive and label-free and does not cause photodamage. The method provides precise measurement of AP waveforms from individual neurons, as well as magnetic field correlates of the AP conduction velocity, and directly determines the AP propagation direction through the inherent sensitivity of NVs to the associated AP magnetic field vector.

Consistent integration of experimental and ab initio data into molecular and coarse-grained models

NASA Astrophysics Data System (ADS)

Vlcek, Lukas

As computer simulations are increasingly used to complement or replace experiments, highly accurate descriptions of physical systems at different time and length scales are required to achieve realistic predictions. The questions of how to objectively measure model quality in relation to reference experimental or ab initio data, and how to transition seamlessly between different levels of resolution are therefore of prime interest. To address these issues, we use the concept of statistical distance to define a measure of similarity between statistical mechanical systems, i.e., a model and its target, and show that its minimization leads to general convergence of the systems' measurable properties. Through systematic coarse-graining, we arrive at appropriate expressions for optimization loss functions consistently incorporating microscopic ab initio data as well as macroscopic experimental data. The design of coarse-grained and multiscale models is then based on factoring the model system partition function into terms describing the system at different resolution levels. The optimization algorithm takes advantage of thermodynamic perturbation expressions for fast exploration of the model parameter space, enabling us to scan millions of parameter combinations per hour on a single CPU. The robustness and generality of the new model optimization framework and its efficient implementation are illustrated on selected examples including aqueous solutions, magnetic systems, and metal alloys.

Resilience in Source to Sink Systems: A Millennial Record of Watershed Responses to Disturbance in Loon Lake, Umpqua River Basin, Oregon

NASA Astrophysics Data System (ADS)

Guerrero, F. J.; Richardson, K.; Hatten, J. A.

2017-12-01

Small mountainous watersheds are disproportionate sources of particulate organic matter (POM) to long-term sinks like lake bottoms and the ocean. Thus, alterations in sediment routing resulting from disturbances (e.g. earthquakes, fires, and timber harvesting) have profound consequences on watershed's (biogeochemical) resilience. The assessment of these biogeochemical impacts is complicated by the episodic signal propagation along these source-to-sink systems and therefore is seldom attempted. We report on a 1500-year record of historical changes in Loon Lake, a local sedimentary sink (1.2 km2) for a 230 km2 watershed in the Oregon Coast Range. Particle size distributions and POM elemental composition (C, N) were sampled at high temporal resolution ( 3 years). Stable isotopic composition and lignin biomarkers were sampled with varying temporal resolution depending on the period analyzed: 1939-2013 (3-year resolution); 515-1939 (15-year resolution). Disturbance history in Loon Lake catchment is recorded as a sequence of event beds deposited in sharp contrast within a matrix of background sedimentation. At least 8 out of 23 event beds were associated with >8.2 magnitude earthquakes (including the 9.0 megathrust earthquake in 1700). Forest fires in 1770 and 1890 were also recorded as event beds. After 1939, event beds record the impacts of landscape destabilization due to the interaction between intense storms and timber harvesting. At the onset of each event, %C, %N, and C:N ratios increased reflecting the input of coarse POM from surficial soil horizons. Top layers bracketing event beds are rich in clays and have low %C, suggesting a deep-soil sediment source. Isotopic signatures (i.e. δ13C, δ15N) confirm the allochthony of sediment inputs during events and lignin biomarkers suggest a replacement of riparian inputs by a strong gymnosperm signal, particularly after 1945. Thus, event beds record changes in the relative importance of different sediment sources within the catchment as they connect with their sink on the lake bottom. In contrast with continuous records of ecosystem changes from small watersheds, discontinuous records suggest the need for resilience assessments that go beyond the reconstruction of recovery paths to consider source to sink connectivity in small mountainous watersheds.

Daily monitoring of 30 m crop condition over complex agricultural landscapes

NASA Astrophysics Data System (ADS)

Sun, L.; Gao, F.; Xie, D.; Anderson, M. C.; Yang, Y.

2017-12-01

Crop progress provides information necessary for efficient irrigation, scheduling fertilization and harvesting operations at optimal times for achieving higher yields. In the United States, crop progress reports are released online weekly by US Department of Agriculture (USDA) - National Agricultural Statistics Service (NASS). However, the ground data collection is time consuming and subjective, and these reports are provided at either district (multiple counties) or state level. Remote sensing technologies have been widely used to map crop conditions, to extract crop phenology, and to predict crop yield. However, for current satellite-based sensors, it is difficult to acquire both high spatial resolution and frequent coverage. For example, Landsat satellites are capable to capture 30 m resolution images, while the long revisit cycles, cloud contamination further limited their use in detecting rapid surface changes. On the other hand, MODIS can provide daily observations, but with coarse spatial resolutions range from 250 to 1000 m. In recent years, multi-satellite data fusion technology such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) has been used to combine the spatial resolution of Landsat with the temporal frequency of MODIS. It has been found that this synthetic dataset could provide more valuable information compared to the images acquired from only one single sensor. However, accuracy of STARFM depends on heterogeneity of landscape and available clear image pairs of MODIS and Landsat. In this study, a new fusion method was developed using the crop vegetation index (VI) timeseries extracted from "pure" MODIS pixels and Landsat overpass images to generate daily 30 m VI for crops. The fusion accuracy was validated by comparing to the original Landsat images. Results show that the relative error in non-rapid growing period is around 3-5% and in rapid growing period is around 6-8% . The accuracy is much better than that of STARFM which is 4-9% in non-rapid growing period and 10-16% in rapid growing period based on 13 image pairs. The predicted VI from this approach looks consistent and smooth in the SLC-off gap stripes of Landsat 7 ETM+ image. The new fusion results will be used to map crop phenology and to predict crop yield at field scale in the complex agricultural landscapes.

A matter of scale: apparent niche differentiation of diploid and tetraploid plants may depend on extent and grain of analysis.

PubMed

Kirchheimer, Bernhard; Schinkel, Christoph C F; Dellinger, Agnes S; Klatt, Simone; Moser, Dietmar; Winkler, Manuela; Lenoir, Jonathan; Caccianiga, Marco; Guisan, Antoine; Nieto-Lugilde, Diego; Svenning, Jens-Christian; Thuiller, Wilfried; Vittoz, Pascal; Willner, Wolfgang; Zimmermann, Niklaus E; Hörandl, Elvira; Dullinger, Stefan

2016-03-22

Emerging polyploids may depend on environmental niche shifts for successful establishment. Using the alpine plant Ranunculus kuepferi as a model system, we explore the niche shift hypothesis at different spatial resolutions and in contrasting parts of the species range. European Alps. We sampled 12 individuals from each of 102 populations of R. kuepferi across the Alps, determined their ploidy levels, derived coarse-grain (100 × 100 m) environmental descriptors for all sampling sites by downscaling WorldClim maps, and calculated fine-scale environmental descriptors (2 × 2 m) from indicator values of the vegetation accompanying the sampled individuals. Both coarse and fine-scale variables were further computed for 8239 vegetation plots from across the Alps. Subsequently, we compared niche optima and breadths of diploid and tetraploid cytotypes by combining principal components analysis and kernel smoothing procedures. Comparisons were done separately for coarse and fine-grain data sets and for sympatric, allopatric and the total set of populations. All comparisons indicate that the niches of the two cytotypes differ in optima and/or breadths, but results vary in important details. The whole-range analysis suggests differentiation along the temperature gradient to be most important. However, sympatric comparisons indicate that this climatic shift was not a direct response to competition with diploid ancestors. Moreover, fine-grained analyses demonstrate niche contraction of tetraploids, especially in the sympatric range, that goes undetected with coarse-grained data. Although the niche optima of the two cytotypes differ, separation along ecological gradients was probably less decisive for polyploid establishment than a shift towards facultative apomixis, a particularly effective strategy to avoid minority cytotype exclusion. In addition, our results suggest that coarse-grained analyses overestimate niche breadths of widely distributed taxa. Niche comparison analyses should hence be conducted at environmental data resolutions appropriate for the organism and question under study.

Observations of Sand Dune Migration on the Colorado River in Grand Canyon using High-Resolution Multibeam Bathymetry

NASA Astrophysics Data System (ADS)

Kaplinski, M. A.; Buscmobe, D.; Ashley, T.; Tusso, R.; Grams, P. E.; McElroy, B. J.; Mueller, E. R.; Hamill, D.

2015-12-01

Repeat, high-resolution multibeam bathymetric surveys were conducted in March and July 2015 along a reach of the Colorado River in Grand Canyon near the Diamond Creek gage (362 km downstream of Lees Ferry, AZ) to characterize the migration of sand dunes. The surveys were collected as part of a study designed to quantify the relative importance of bedload and suspended sediment transport and develop a predictive relationship for bedload transport. Concurrent measurements of suspended-sediment concentrations, bed-sediment grain size, and water velocity were also collected. The study site is approximately 350 m long and 50 m wide; water depths are 7 to 10 m during normal flows; and a field of sand dunes form along its entire length with negligible coarse material at the bed surface. Full swath coverage of the site required about 6 to 10 minutes to complete with two passes of the survey vessel. Mapping occurred continuously during several survey periods. For each survey period, time-series of bathymetric maps were constructed from each pair of survey lines. In March, surveys were collected over durations of 2, 3, 9, and 11 hours, at discharges of 339 to 382 m3/s. In July, surveys were collected over durations of 4, 4, and 13 hours, at discharges ranging from 481 to 595 ft3/s. These surveys capture the migration of sand dunes over a wide range of discharge with an unprecedented temporal resolution. The dunes in March were between 30 and 50 cm in height, 5 m in length, and migrating downstream at about 1 m per hour. In July, dunes were between 75 and 130 cm in height and 10-15 m in length, and were migrating downstream at rates of 5 to 2 m per hour. The surveys also reveal that the dune migration is spatially and temporally variable, with fast-migrating small dunes variably superimposed on slower-moving larger dunes. The dunes also refract around shoreline talus piles and other flow constrictions collectively causing a large degree of dune deformation as they migrate.

Kawase & McDermott revisited with a proper ocean model.

NASA Astrophysics Data System (ADS)

Jochum, Markus; Poulsen, Mads; Nuterman, Roman

2017-04-01

A suite of experiments with global ocean models is used to test the hypothesis that Southern Ocean (SO) winds can modify the strength of the Atlantic Meridional Overturning Circulation (AMOC). It is found that for 3 and 1 degree resolution models the results are consistent with Toggweiler & Samuels (1995): stronger SO winds lead to a slight increase of the AMOC. In the simulations with 1/10 degree resolution, however, stronger SO winds weaken the AMOC. We show that these different outcomes are determined by the models' representation of topographic Rossby and Kelvin waves. Consistent with previous literature based on theory and idealized models, first baroclinic waves are slower in the coarse resolution models, but still manage to establish a pattern of global response that is similar to the one in the eddy-permitting model. Because of its different stratification, however, the Atlantic signal is transmitted by higher baroclinic modes. In the coarse resolution model these higher modes are dissipated before they reach 30N, whereas in the eddy-permitting model they reach the subpolar gyre undiminished. This inability of non-eddy-permitting ocean models to represent planetary waves with higher baroclinic modes casts doubt on the ability of climate models to represent non-local effects of climate change. Ideas on how to overcome these difficulties will be discussed.

Towards improved parameterization of a macroscale hydrologic model in a discontinuous permafrost boreal forest ecosystem

DOE PAGES

Endalamaw, Abraham; Bolton, W. Robert; Young-Robertson, Jessica M.; ...

2017-09-14

Modeling hydrological processes in the Alaskan sub-arctic is challenging because of the extreme spatial heterogeneity in soil properties and vegetation communities. Nevertheless, modeling and predicting hydrological processes is critical in this region due to its vulnerability to the effects of climate change. Coarse-spatial-resolution datasets used in land surface modeling pose a new challenge in simulating the spatially distributed and basin-integrated processes since these datasets do not adequately represent the small-scale hydrological, thermal, and ecological heterogeneity. The goal of this study is to improve the prediction capacity of mesoscale to large-scale hydrological models by introducing a small-scale parameterization scheme, which bettermore » represents the spatial heterogeneity of soil properties and vegetation cover in the Alaskan sub-arctic. The small-scale parameterization schemes are derived from observations and a sub-grid parameterization method in the two contrasting sub-basins of the Caribou Poker Creek Research Watershed (CPCRW) in Interior Alaska: one nearly permafrost-free (LowP) sub-basin and one permafrost-dominated (HighP) sub-basin. The sub-grid parameterization method used in the small-scale parameterization scheme is derived from the watershed topography. We found that observed soil thermal and hydraulic properties – including the distribution of permafrost and vegetation cover heterogeneity – are better represented in the sub-grid parameterization method than the coarse-resolution datasets. Parameters derived from the coarse-resolution datasets and from the sub-grid parameterization method are implemented into the variable infiltration capacity (VIC) mesoscale hydrological model to simulate runoff, evapotranspiration (ET), and soil moisture in the two sub-basins of the CPCRW. Simulated hydrographs based on the small-scale parameterization capture most of the peak and low flows, with similar accuracy in both sub-basins, compared to simulated hydrographs based on the coarse-resolution datasets. On average, the small-scale parameterization scheme improves the total runoff simulation by up to 50 % in the LowP sub-basin and by up to 10 % in the HighP sub-basin from the large-scale parameterization. This study shows that the proposed sub-grid parameterization method can be used to improve the performance of mesoscale hydrological models in the Alaskan sub-arctic watersheds.« less

Towards improved parameterization of a macroscale hydrologic model in a discontinuous permafrost boreal forest ecosystem

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

Endalamaw, Abraham; Bolton, W. Robert; Young-Robertson, Jessica M.

Modeling hydrological processes in the Alaskan sub-arctic is challenging because of the extreme spatial heterogeneity in soil properties and vegetation communities. Nevertheless, modeling and predicting hydrological processes is critical in this region due to its vulnerability to the effects of climate change. Coarse-spatial-resolution datasets used in land surface modeling pose a new challenge in simulating the spatially distributed and basin-integrated processes since these datasets do not adequately represent the small-scale hydrological, thermal, and ecological heterogeneity. The goal of this study is to improve the prediction capacity of mesoscale to large-scale hydrological models by introducing a small-scale parameterization scheme, which bettermore » represents the spatial heterogeneity of soil properties and vegetation cover in the Alaskan sub-arctic. The small-scale parameterization schemes are derived from observations and a sub-grid parameterization method in the two contrasting sub-basins of the Caribou Poker Creek Research Watershed (CPCRW) in Interior Alaska: one nearly permafrost-free (LowP) sub-basin and one permafrost-dominated (HighP) sub-basin. The sub-grid parameterization method used in the small-scale parameterization scheme is derived from the watershed topography. We found that observed soil thermal and hydraulic properties – including the distribution of permafrost and vegetation cover heterogeneity – are better represented in the sub-grid parameterization method than the coarse-resolution datasets. Parameters derived from the coarse-resolution datasets and from the sub-grid parameterization method are implemented into the variable infiltration capacity (VIC) mesoscale hydrological model to simulate runoff, evapotranspiration (ET), and soil moisture in the two sub-basins of the CPCRW. Simulated hydrographs based on the small-scale parameterization capture most of the peak and low flows, with similar accuracy in both sub-basins, compared to simulated hydrographs based on the coarse-resolution datasets. On average, the small-scale parameterization scheme improves the total runoff simulation by up to 50 % in the LowP sub-basin and by up to 10 % in the HighP sub-basin from the large-scale parameterization. This study shows that the proposed sub-grid parameterization method can be used to improve the performance of mesoscale hydrological models in the Alaskan sub-arctic watersheds.« less

Emotional cues enhance the attentional effects on spatial and temporal resolution.

PubMed

Bocanegra, Bruno R; Zeelenberg, René

2011-12-01

In the present study, we demonstrated that the emotional significance of a spatial cue enhances the effect of covert attention on spatial and temporal resolution (i.e., our ability to discriminate small spatial details and fast temporal flicker). Our results indicated that fearful face cues, as compared with neutral face cues, enhanced the attentional benefits in spatial resolution but also enhanced the attentional deficits in temporal resolution. Furthermore, we observed that the overall magnitudes of individuals' attentional effects correlated strongly with the magnitude of the emotion × attention interaction effect. Combined, these findings provide strong support for the idea that emotion enhances the strength of a cue's attentional response.

Assessing the Resolution Adaptability of the Zhang-McFarlane Cumulus Parameterization With Spatial and Temporal Averaging: RESOLUTION ADAPTABILITY OF ZM SCHEME

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

Yun, Yuxing; Fan, Jiwen; Xiao, Heng

Realistic modeling of cumulus convection at fine model resolutions (a few to a few tens of km) is problematic since it requires the cumulus scheme to adapt to higher resolution than they were originally designed for (~100 km). To solve this problem, we implement the spatial averaging method proposed in Xiao et al. (2015) and also propose a temporal averaging method for the large-scale convective available potential energy (CAPE) tendency in the Zhang-McFarlane (ZM) cumulus parameterization. The resolution adaptability of the original ZM scheme, the scheme with spatial averaging, and the scheme with both spatial and temporal averaging at 4-32more » km resolution is assessed using the Weather Research and Forecasting (WRF) model, by comparing with Cloud Resolving Model (CRM) results. We find that the original ZM scheme has very poor resolution adaptability, with sub-grid convective transport and precipitation increasing significantly as the resolution increases. The spatial averaging method improves the resolution adaptability of the ZM scheme and better conserves the total transport of moist static energy and total precipitation. With the temporal averaging method, the resolution adaptability of the scheme is further improved, with sub-grid convective precipitation becoming smaller than resolved precipitation for resolution higher than 8 km, which is consistent with the results from the CRM simulation. Both the spatial distribution and time series of precipitation are improved with the spatial and temporal averaging methods. The results may be helpful for developing resolution adaptability for other cumulus parameterizations that are based on quasi-equilibrium assumption.« less

Multi-resolution statistical image reconstruction for mitigation of truncation effects: application to cone-beam CT of the head

NASA Astrophysics Data System (ADS)

Dang, Hao; Webster Stayman, J.; Sisniega, Alejandro; Zbijewski, Wojciech; Xu, Jennifer; Wang, Xiaohui; Foos, David H.; Aygun, Nafi; Koliatsos, Vassilis E.; Siewerdsen, Jeffrey H.

2017-01-01

A prototype cone-beam CT (CBCT) head scanner featuring model-based iterative reconstruction (MBIR) has been recently developed and demonstrated the potential for reliable detection of acute intracranial hemorrhage (ICH), which is vital to diagnosis of traumatic brain injury and hemorrhagic stroke. However, data truncation (e.g. due to the head holder) can result in artifacts that reduce image uniformity and challenge ICH detection. We propose a multi-resolution MBIR method with an extended reconstruction field of view (RFOV) to mitigate truncation effects in CBCT of the head. The image volume includes a fine voxel size in the (inner) nontruncated region and a coarse voxel size in the (outer) truncated region. This multi-resolution scheme allows extension of the RFOV to mitigate truncation effects while introducing minimal increase in computational complexity. The multi-resolution method was incorporated in a penalized weighted least-squares (PWLS) reconstruction framework previously developed for CBCT of the head. Experiments involving an anthropomorphic head phantom with truncation due to a carbon-fiber holder were shown to result in severe artifacts in conventional single-resolution PWLS, whereas extending the RFOV within the multi-resolution framework strongly reduced truncation artifacts. For the same extended RFOV, the multi-resolution approach reduced computation time compared to the single-resolution approach (viz. time reduced by 40.7%, 83.0%, and over 95% for an image volume of 6003, 8003, 10003 voxels). Algorithm parameters (e.g. regularization strength, the ratio of the fine and coarse voxel size, and RFOV size) were investigated to guide reliable parameter selection. The findings provide a promising method for truncation artifact reduction in CBCT and may be useful for other MBIR methods and applications for which truncation is a challenge.

CAMELOT: A machine learning approach for coarse-grained simulations of aggregation of block-copolymeric protein sequences

PubMed Central

Ruff, Kiersten M.; Harmon, Tyler S.; Pappu, Rohit V.

2015-01-01

We report the development and deployment of a coarse-graining method that is well suited for computer simulations of aggregation and phase separation of protein sequences with block-copolymeric architectures. Our algorithm, named CAMELOT for Coarse-grained simulations Aided by MachinE Learning Optimization and Training, leverages information from converged all atom simulations that is used to determine a suitable resolution and parameterize the coarse-grained model. To parameterize a system-specific coarse-grained model, we use a combination of Boltzmann inversion, non-linear regression, and a Gaussian process Bayesian optimization approach. The accuracy of the coarse-grained model is demonstrated through direct comparisons to results from all atom simulations. We demonstrate the utility of our coarse-graining approach using the block-copolymeric sequence from the exon 1 encoded sequence of the huntingtin protein. This sequence comprises of 17 residues from the N-terminal end of huntingtin (N17) followed by a polyglutamine (polyQ) tract. Simulations based on the CAMELOT approach are used to show that the adsorption and unfolding of the wild type N17 and its sequence variants on the surface of polyQ tracts engender a patchy colloid like architecture that promotes the formation of linear aggregates. These results provide a plausible explanation for experimental observations, which show that N17 accelerates the formation of linear aggregates in block-copolymeric N17-polyQ sequences. The CAMELOT approach is versatile and is generalizable for simulating the aggregation and phase behavior of a range of block-copolymeric protein sequences. PMID:26723608

A highly accurate symmetric optical flow based high-dimensional nonlinear spatial normalization of brain images.

PubMed

Wen, Ying; Hou, Lili; He, Lianghua; Peterson, Bradley S; Xu, Dongrong

2015-05-01

Spatial normalization plays a key role in voxel-based analyses of brain images. We propose a highly accurate algorithm for high-dimensional spatial normalization of brain images based on the technique of symmetric optical flow. We first construct a three dimension optical model with the consistency assumption of intensity and consistency of the gradient of intensity under a constraint of discontinuity-preserving spatio-temporal smoothness. Then, an efficient inverse consistency optical flow is proposed with aims of higher registration accuracy, where the flow is naturally symmetric. By employing a hierarchical strategy ranging from coarse to fine scales of resolution and a method of Euler-Lagrange numerical analysis, our algorithm is capable of registering brain images data. Experiments using both simulated and real datasets demonstrated that the accuracy of our algorithm is not only better than that of those traditional optical flow algorithms, but also comparable to other registration methods used extensively in the medical imaging community. Moreover, our registration algorithm is fully automated, requiring a very limited number of parameters and no manual intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

Coarse-Grained Theory of Biological Charge Transfer with Spatially and Temporally Correlated Noise.

PubMed

Liu, Chaoren; Beratan, David N; Zhang, Peng

2016-04-21

System-environment interactions are essential in determining charge-transfer (CT) rates and mechanisms. We developed a computationally accessible method, suitable to simulate CT in flexible molecules (i.e., DNA) with hundreds of sites, where the system-environment interactions are explicitly treated with numerical noise modeling of time-dependent site energies and couplings. The properties of the noise are tunable, providing us a flexible tool to investigate the detailed effects of correlated thermal fluctuations on CT mechanisms. The noise is parametrizable by molecular simulation and quantum calculation results of specific molecular systems, giving us better molecular resolution in simulating the system-environment interactions than sampling fluctuations from generic spectral density functions. The spatially correlated thermal fluctuations among different sites are naturally built-in in our method but are not readily incorporated using approximate spectral densities. Our method has quantitative accuracy in systems with small redox potential differences (

Linear optical pulse compression based on temporal zone plates.

PubMed

Li, Bo; Li, Ming; Lou, Shuqin; Azaña, José

2013-07-15

We propose and demonstrate time-domain equivalents of spatial zone plates, namely temporal zone plates, as alternatives to conventional time lenses. Both temporal intensity zone plates, based on intensity-only temporal modulation, and temporal phase zone plates, based on phase-only temporal modulation, are introduced and studied. Temporal zone plates do not exhibit the limiting tradeoff between temporal aperture and frequency bandwidth (temporal resolution) of conventional linear time lenses. As a result, these zone plates can be ideally designed to offer a time-bandwidth product (TBP) as large as desired, practically limited by the achievable temporal modulation bandwidth (limiting the temporal resolution) and the amount of dispersion needed in the target processing systems (limiting the temporal aperture). We numerically and experimentally demonstrate linear optical pulse compression by using temporal zone plates based on linear electro-optic temporal modulation followed by fiber-optics dispersion. In the pulse-compression experiment based on temporal phase zone plates, we achieve a resolution of ~25.5 ps over a temporal aperture of ~5.77 ns, representing an experimental TBP larger than 226 using a phase-modulation amplitude of only ~0.8π rad. We also numerically study the potential of these devices to achieve temporal imaging of optical waveforms and present a comparative analysis on the performance of different temporal intensity and phase zone plates.

Testing of the new tuner design for the CEBAF 12 GeV upgrade SRF cavities

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

Edward Daly; G. Davis; William Hicks

2005-05-01

The new tuner design for the 12 GeV Upgrade SRF cavities consists of a coarse mechanical tuner and a fine piezoelectric tuner. The mechanism provides a 30:1 mechanical advantage, is pre-loaded at room temperature and tunes the cavities in tension only. All of the components are located in the insulating vacuum space and attached to the helium vessel, including the motor, harmonic drive and piezoelectric actuators. The requirements and detailed design are presented. Measurements of range and resolution of the coarse tuner are presented and discussed.

Temporal resolution required for accurate evaluation of the interplay effect in spot scanning proton therapy

NASA Astrophysics Data System (ADS)

Seo, Jeongmin; Han, Min Cheol; Yeom, Yeon Soo; Lee, Hyun Su; Kim, Chan Hyeong; Jeong, Jong Hwi; Kim, SeongHoon

2017-04-01

In proton therapy, the spot scanning method is known to suffer from the interplay effect induced from the independent movements of the proton beam and the organs in the patient during the treatment. To study the interplay effect, several investigators have performed four-dimensional (4D) dose calculations with some limited temporal resolutions (4 or 10 phases per respiratory cycle) by using the 4D computed tomography (CT) images of the patient; however, the validity of the limited temporal resolutions has not been confirmed. The aim of the present study is to determine whether the previous temporal resolutions (4 or 10 phases per respiratory cycle) are really high enough for adequate study of the interplay effect in spot scanning proton therapy. For this study, a series of 4D dose calculations were performed with a virtual water phantom moving in the vertical direction during dose delivery. The dose distributions were calculated for different temporal resolutions (4, 10, 25, 50, and 100 phases per respiratory cycle), and the calculated dose distributions were compared with the reference dose distribution, which was calculated using an almost continuously-moving water phantom ( i.e., 1000 phases per respiratory cycle). The results of the present study show that the temporal resolutions of 4 and 10 phases per respiratory cycle are not high enough for an accurate evaluation of the interplay effect for spot scanning proton therapy. The temporal resolution should be at least 14 and 17 phases per respiratory cycle for 10-mm and 20-mm movement amplitudes, respectively, even for rigid movement ( i.e., without deformation) of the homogeneous water phantom considered in the present study. We believe that even higher temporal resolutions are needed for an accurate evaluation of the interplay effect in the human body, in which the organs are inhomogeneous and deform during movement.

Stepping inside the niche: microclimate data are critical for accurate assessment of species' vulnerability to climate change.

PubMed

Storlie, Collin; Merino-Viteri, Andres; Phillips, Ben; VanDerWal, Jeremy; Welbergen, Justin; Williams, Stephen

2014-09-01

To assess a species' vulnerability to climate change, we commonly use mapped environmental data that are coarsely resolved in time and space. Coarsely resolved temperature data are typically inaccurate at predicting temperatures in microhabitats used by an organism and may also exhibit spatial bias in topographically complex areas. One consequence of these inaccuracies is that coarsely resolved layers may predict thermal regimes at a site that exceed species' known thermal limits. In this study, we use statistical downscaling to account for environmental factors and develop high-resolution estimates of daily maximum temperatures for a 36 000 km(2) study area over a 38-year period. We then demonstrate that this statistical downscaling provides temperature estimates that consistently place focal species within their fundamental thermal niche, whereas coarsely resolved layers do not. Our results highlight the need for incorporation of fine-scale weather data into species' vulnerability analyses and demonstrate that a statistical downscaling approach can yield biologically relevant estimates of thermal regimes. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Snow cover dynamics in the Catalan Pyrenees range using remote sensing data from 2002 to 2008 period

NASA Astrophysics Data System (ADS)

Cea, C.; Cristóbal, J.; Pons, X.

2009-04-01

Snow cover dynamics in the Catalan Pyrenees range using remote sensing data from 2002 to 2008 period. C. Cea (1), J. Cristóbal (1), X. Pons (1, 2) (1) Department of Geography. Autonomous University of Barcelona. Cerdanyola del Vallès, 08193. Cristina.Cea@uab.cat, (2) Center for Ecological Research and Forestry Applications (CREAF) Cerdanyola del Vallès, 08193. Water resources and its management are essential in many alpine mountainous areas. Snow cover monitoring in the Mediterranean zone requires obtaining accurate snow cartography to estimate the volume of water derived from snow melting and species distribution modelling. Snow data is usually obtained by field campaigns, but to obtain a spatial and temporal cover of enough detail and quality it is necessary collect an important number of data. However, when a continuous surface is needed, Remote Sensing could provide better snow cover estimation due to its spatial and temporal resolution. The aim of this study is to map snow cover and analyse its spatial and temporal dynamics using medium and coarse remote sensing data at a regional scale over an heterogeneous area, the Catalan Pyrenees (NE of the Iberian Peninsula). The seasonal snow cover period is from October to June. In this period, regular snowfalls usually take place from December to April, although during the rest of the period, punctual but important episodes of snowfalls are frequent. To perform this analysis, a set of 96 Landsat images (36 Landsat-5 TM and 60 Landsat-7 ETM+) of path 197 and 198 and rows 31 and 32 from January 2002 to April 2007, and 90 Terra-MODIS images from October 2007 to July 2008, with a different percentage of cloudiness, have been chosen. The computation of the Landsat-5 TM and Landsat-7 ETM+ data used in snow cover mapping has been carried out by means of the following methodologies. Images have been geometrically corrected by means of techniques based on first order polynomials taking into account the effect of the relief of the land surface using a Digital Elevation Model. Radiometric correction (non-thermal bands) has been done following the methodology which allows us to reduce the number of undesired artefacts that are due to the effects of the atmosphere or to the differential illumination. Finally, cloud removal has been carried out by means of a semi-automatic methodology. In the case of MODIS images, these have been imported by reading all the necessary metadata to document and interpret them. These products have already been radiometrically and geometrically corrected by USGS in a sinusoidal projection. Snow cover mapping has been carried out by means of the Normalized Snow Cover Index (NDSI), one of the most widely methodologies used. This methodology proposes a normalized index using green band 2 and Medium Infrared band because of snow reflectance is higher in the visible bands than in the medium infrared bands. NDSI pixels greater than 0.4 are select as snow cover. Although this threshold also selects water bodies, we have obtained optimal results using a mask of water bodies and generating a pre-boundary snow mask around the snow cover. In shadow cast areas, we have used a hybrid classification to obtain the snow cover. Preliminary results show the snow surface evolution of the Pyrenean basis during the hydrological period, as well as the differences between the different years of study. The obtained data shows how the basins with Mediterranean influence, placed in the east, receive less nival contribution than those with Atlantic influence, situated in the west. In addition, this data allow establishing the beginning and the ending of the snowfall cycle. Eastern basins usually have a shorter period than west basins, where the snow line is lower. The snow cover area average of the studied period is about 1200 km2 and stands out that for the particular period 2006-2007, this area was only about 55% of the average, due to the lack of snow precipitation. It affected the winter sports, an important sector in this area, the vegetation and the extensive livestock, just as the volume of water that flows towards river basins and reservoir when snow melts. Technical characteristics are different between both sensors. On the one hand, Landsat spatial resolution is better to obtain accuracy cartography, however it is not suitable to determine the frequency of snow falls. On the one other hand, MODIS with its daily temporal resolution allows better temporal monitoring, but with coarse spatial resolution.

Downscaling soil moisture over East Asia through multi-sensor data fusion and optimization of regression trees

NASA Astrophysics Data System (ADS)

Park, Seonyoung; Im, Jungho; Park, Sumin; Rhee, Jinyoung

2017-04-01

Soil moisture is one of the most important keys for understanding regional and global climate systems. Soil moisture is directly related to agricultural processes as well as hydrological processes because soil moisture highly influences vegetation growth and determines water supply in the agroecosystem. Accurate monitoring of the spatiotemporal pattern of soil moisture is important. Soil moisture has been generally provided through in situ measurements at stations. Although field survey from in situ measurements provides accurate soil moisture with high temporal resolution, it requires high cost and does not provide the spatial distribution of soil moisture over large areas. Microwave satellite (e.g., advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR2), the Advanced Scatterometer (ASCAT), and Soil Moisture Active Passive (SMAP)) -based approaches and numerical models such as Global Land Data Assimilation System (GLDAS) and Modern- Era Retrospective Analysis for Research and Applications (MERRA) provide spatial-temporalspatiotemporally continuous soil moisture products at global scale. However, since those global soil moisture products have coarse spatial resolution ( 25-40 km), their applications for agriculture and water resources at local and regional scales are very limited. Thus, soil moisture downscaling is needed to overcome the limitation of the spatial resolution of soil moisture products. In this study, GLDAS soil moisture data were downscaled up to 1 km spatial resolution through the integration of AMSR2 and ASCAT soil moisture data, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and Moderate Resolution Imaging Spectroradiometer (MODIS) data—Land Surface Temperature, Normalized Difference Vegetation Index, and Land cover—using modified regression trees over East Asia from 2013 to 2015. Modified regression trees were implemented using Cubist, a commercial software tool based on machine learning. An optimization based on pruning of rules derived from the modified regression trees was conducted. Root Mean Square Error (RMSE) and Correlation coefficients (r) were used to optimize the rules, and finally 59 rules from modified regression trees were produced. The results show high validation r (0.79) and low validation RMSE (0.0556m3/m3). The 1 km downscaled soil moisture was evaluated using ground soil moisture data at 14 stations, and both soil moisture data showed similar temporal patterns (average r=0.51 and average RMSE=0.041). The spatial distribution of the 1 km downscaled soil moisture well corresponded with GLDAS soil moisture that caught both extremely dry and wet regions. Correlation between GLDAS and the 1 km downscaled soil moisture during growing season was positive (mean r=0.35) in most regions.

10-year spatial and temporal trends of PM2.5 concentrations in the southeastern US estimated using high-resolution satellite data

PubMed Central

Hu, X.; Waller, L. A.; Lyapustin, A.; Wang, Y.; Liu, Y.

2017-01-01

Long-term PM2.5 exposure has been associated with various adverse health outcomes. However, most ground monitors are located in urban areas, leading to a potentially biased representation of true regional PM2.5 levels. To facilitate epidemiological studies, accurate estimates of the spatiotemporally continuous distribution of PM2.5 concentrations are important. Satellite-retrieved aerosol optical depth (AOD) has been increasingly used for PM2.5 concentration estimation due to its comprehensive spatial coverage. Nevertheless, previous studies indicated that an inherent disadvantage of many AOD products is their coarse spatial resolution. For instance, the available spatial resolutions of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) AOD products are 10 and 17.6 km, respectively. In this paper, a new AOD product with 1 km spatial resolution retrieved by the multi-angle implementation of atmospheric correction (MAIAC) algorithm based on MODIS measurements was used. A two-stage model was developed to account for both spatial and temporal variability in the PM2.5–AOD relationship by incorporating the MAIAC AOD, meteorological fields, and land use variables as predictors. Our study area is in the southeastern US centered at the Atlanta metro area, and data from 2001 to 2010 were collected from various sources. The model was fitted annually, and we obtained model fitting R2 ranging from 0.71 to 0.85, mean prediction error (MPE) from 1.73 to 2.50 μg m−3, and root mean squared prediction error (RMSPE) from 2.75 to 4.10 μg m−3. In addition, we found cross-validation R2 ranging from 0.62 to 0.78, MPE from 2.00 to 3.01 μgm−3, and RMSPE from 3.12 to 5.00 μgm−3, indicating a good agreement between the estimated and observed values. Spatial trends showed that high PM2.5 levels occurred in urban areas and along major highways, while low concentrations appeared in rural or mountainous areas. Our time-series analysis showed that, for the 10-year study period, the PM2.5 levels in the southeastern US have decreased by ∼20 %. The annual decrease has been relatively steady from 2001 to 2007 and from 2008 to 2010 while a significant drop occurred between 2007 and 2008. An observed increase in PM2.5 levels in year 2005 is attributed to elevated sulfate concentrations in the study area in warm months of 2005. PMID:28966656

Quantification of errors induced by temporal resolution on Lagrangian particles in an eddy-resolving model

NASA Astrophysics Data System (ADS)

Qin, Xuerong; van Sebille, Erik; Sen Gupta, Alexander

2014-04-01

Lagrangian particle tracking within ocean models is an important tool for the examination of ocean circulation, ventilation timescales and connectivity and is increasingly being used to understand ocean biogeochemistry. Lagrangian trajectories are obtained by advecting particles within velocity fields derived from hydrodynamic ocean models. For studies of ocean flows on scales ranging from mesoscale up to basin scales, the temporal resolution of the velocity fields should ideally not be more than a few days to capture the high frequency variability that is inherent in mesoscale features. However, in reality, the model output is often archived at much lower temporal resolutions. Here, we quantify the differences in the Lagrangian particle trajectories embedded in velocity fields of varying temporal resolution. Particles are advected from 3-day to 30-day averaged fields in a high-resolution global ocean circulation model. We also investigate whether adding lateral diffusion to the particle movement can compensate for the reduced temporal resolution. Trajectory errors reveal the expected degradation of accuracy in the trajectory positions when decreasing the temporal resolution of the velocity field. Divergence timescales associated with averaging velocity fields up to 30 days are faster than the intrinsic dispersion of the velocity fields but slower than the dispersion caused by the interannual variability of the velocity fields. In experiments focusing on the connectivity along major currents, including western boundary currents, the volume transport carried between two strategically placed sections tends to increase with increased temporal averaging. Simultaneously, the average travel times tend to decrease. Based on these two bulk measured diagnostics, Lagrangian experiments that use temporal averaging of up to nine days show no significant degradation in the flow characteristics for a set of six currents investigated in more detail. The addition of random-walk-style diffusion does not mitigate the errors introduced by temporal averaging for large-scale open ocean Lagrangian simulations.

Retrieving Aerosol in a Cloudy Environment: Aerosol Availability as a Function of Spatial and Temporal Resolution

NASA Technical Reports Server (NTRS)

Remer, Lorraine A.; Mattoo, Shana; Levy, Robert C.; Heidinger, Andrew; Pierce, R. Bradley; Chin, Mian

2011-01-01

The challenge of using satellite observations to retrieve aerosol properties in a cloudy environment is to prevent contamination of the aerosol signal from clouds, while maintaining sufficient aerosol product yield to satisfy specific applications. We investigate aerosol retrieval availability at different instrument pixel resolutions, using the standard MODIS aerosol cloud mask applied to MODIS data and a new GOES-R cloud mask applied to GOES data for a domain covering North America and surrounding oceans. Aerosol availability is not the same as the cloud free fraction and takes into account the technqiues used in the MODIS algorithm to avoid clouds, reduce noise and maintain sufficient numbers of aerosol retrievals. The inherent spatial resolution of each instrument, 0.5x0.5 km for MODIS and 1x1 km for GOES, is systematically degraded to 1x1 km, 2x2 km, 4x4 km and 8x8 km resolutions and then analyzed as to how that degradation would affect the availability of an aerosol retrieval, assuming an aerosol product resolution at 8x8 km. The results show that as pixel size increases, availability decreases until at 8x8 km 70% to 85% of the retrievals available at 0.5 km have been lost. The diurnal pattern of aerosol retrieval availability examined for one day in the summer suggests that coarse resolution sensors (i.e., 4x4 km or 8x8 km) may be able to retrieve aerosol early in the morning that would otherwise be missed at the time of current polar orbiting satellites, but not the diurnal aerosol properties due to cloud cover developed during the day. In contrast finer resolution sensors (i.e., 1x1 km or 2x2 km) have much better opportunity to retrieve aerosols in the partly cloudy scenes and better chance of returning the diurnal aerosol properties. Large differences in the results of the two cloud masks designed for MODIS aerosol and GOES cloud products strongly reinforce that cloud masks must be developed with specific purposes in mind and that a generic cloud mask applied to an independent aerosol retrieval will likely fail.

The Errors Sources Affect to the Results of One-Way Nested Ocean Regional Circulation Model

NASA Astrophysics Data System (ADS)

Pham, S. V.

2016-02-01

Pham-Van Sy1, Jin Hwan Hwang2 and Hyeyun Ku3 Dept. of Civil & Environmental Engineering, Seoul National University, KoreaEmail: 1phamsymt@gmail.com (Corresponding author) Email: 2jinhwang@snu.ac.krEmail: 3hyeyun.ku@gmail.comAbstractThe Oceanic Regional Circulation Model (ORCM) is an essential tool in resolving highly a regional scale through downscaling dynamically the results from the roughly revolved global model. However, when dynamic downscaling from a coarse resolution of the global model or observations to the small scale, errors are generated due to the different sizes of resolution and lateral updating frequency. This research evaluated the effect of four main sources on the results of the ocean regional circulation model (ORCMs) during downscaling and nesting the output data from the ocean global circulation model (OGCMs). Representative four error sources should be the way of the LBC formulation, the spatial resolution difference between driving and driven data, the frequency for up-dating LBCs and domain size. Errors which are contributed from each error source to the results of the ORCMs are investigated separately by applying the Big-Brother Experiment (BBE). Within resolution of 3km grid point of the ORCMs imposing in the BBE framework, it clearly exposes that the simulation results of the ORCMs significantly depend on the domain size and specially the spatial and temporal resolution of lateral boundary conditions (LBCs). The ratio resolution of spatial resolution between driving data and driven model could be up to 3, the updating frequency of the LBCs can be up to every 6 hours per day. The optimal domain size of the ORCMs could be smaller than the OGCMs' domain size around 2 to 10 times. Key words: ORCMs, error source, lateral boundary conditions, domain size Acknowledgement: This research was supported by grants from the Korean Ministry of Oceans and Fisheries entitled as "Developing total management system for the Keum river estuary and coast" and "Development of Technology for CO2 Marine Geological Storage". We also thank to the administrative supports of the Integrated Research Institute of Construction and Environmental Engineering of the Seoul National University.

On the effects of scale for ecosystem services mapping

USGS Publications Warehouse

Grêt-Regamey, Adrienne; Weibel, Bettina; Bagstad, Kenneth J.; Ferrari, Marika; Geneletti, Davide; Klug, Hermann; Schirpke, Uta; Tappeiner, Ulrike

2014-01-01

Ecosystems provide life-sustaining services upon which human civilization depends, but their degradation largely continues unabated. Spatially explicit information on ecosystem services (ES) provision is required to better guide decision making, particularly for mountain systems, which are characterized by vertical gradients and isolation with high topographic complexity, making them particularly sensitive to global change. But while spatially explicit ES quantification and valuation allows the identification of areas of abundant or limited supply of and demand for ES, the accuracy and usefulness of the information varies considerably depending on the scale and methods used. Using four case studies from mountainous regions in Europe and the U.S., we quantify information gains and losses when mapping five ES - carbon sequestration, flood regulation, agricultural production, timber harvest, and scenic beauty - at coarse and fine resolution (250 m vs. 25 m in Europe and 300 m vs. 30 m in the U.S.). We analyze the effects of scale on ES estimates and their spatial pattern and show how these effects are related to different ES, terrain structure and model properties. ES estimates differ substantially between the fine and coarse resolution analyses in all case studies and across all services. This scale effect is not equally strong for all ES. We show that spatially explicit information about non-clustered, isolated ES tends to be lost at coarse resolution and against expectation, mainly in less rugged terrain, which calls for finer resolution assessments in such contexts. The effect of terrain ruggedness is also related to model properties such as dependency on land use-land cover data. We close with recommendations for mapping ES to make the resulting maps more comparable, and suggest a four-step approach to address the issue of scale when mapping ES that can deliver information to support ES-based decision making with greater accuracy and reliability.

On the Effects of Scale for Ecosystem Services Mapping

PubMed Central

Grêt-Regamey, Adrienne; Weibel, Bettina; Bagstad, Kenneth J.; Ferrari, Marika; Geneletti, Davide; Klug, Hermann; Schirpke, Uta; Tappeiner, Ulrike

2014-01-01

Ecosystems provide life-sustaining services upon which human civilization depends, but their degradation largely continues unabated. Spatially explicit information on ecosystem services (ES) provision is required to better guide decision making, particularly for mountain systems, which are characterized by vertical gradients and isolation with high topographic complexity, making them particularly sensitive to global change. But while spatially explicit ES quantification and valuation allows the identification of areas of abundant or limited supply of and demand for ES, the accuracy and usefulness of the information varies considerably depending on the scale and methods used. Using four case studies from mountainous regions in Europe and the U.S., we quantify information gains and losses when mapping five ES - carbon sequestration, flood regulation, agricultural production, timber harvest, and scenic beauty - at coarse and fine resolution (250 m vs. 25 m in Europe and 300 m vs. 30 m in the U.S.). We analyze the effects of scale on ES estimates and their spatial pattern and show how these effects are related to different ES, terrain structure and model properties. ES estimates differ substantially between the fine and coarse resolution analyses in all case studies and across all services. This scale effect is not equally strong for all ES. We show that spatially explicit information about non-clustered, isolated ES tends to be lost at coarse resolution and against expectation, mainly in less rugged terrain, which calls for finer resolution assessments in such contexts. The effect of terrain ruggedness is also related to model properties such as dependency on land use-land cover data. We close with recommendations for mapping ES to make the resulting maps more comparable, and suggest a four-step approach to address the issue of scale when mapping ES that can deliver information to support ES-based decision making with greater accuracy and reliability. PMID:25549256

Simulations of hydrologic response in the Apalachicola-Chattahoochee-Flint River Basin, Southeastern United States

USGS Publications Warehouse

LaFontaine, Jacob H.; Jones, L. Elliott; Painter, Jaime A.

2017-12-29

A suite of hydrologic models has been developed for the Apalachicola-Chattahoochee-Flint River Basin (ACFB) as part of the National Water Census, a U.S. Geological Survey research program that focuses on developing new water accounting tools and assessing water availability and use at the regional and national scales. Seven hydrologic models were developed using the Precipitation-Runoff Modeling System (PRMS), a deterministic, distributed-parameter, process-based system that simulates the effects of precipitation, temperature, land cover, and water use on basin hydrology. A coarse-resolution PRMS model was developed for the entire ACFB, and six fine-resolution PRMS models were developed for six subbasins of the ACFB. The coarse-resolution model was loosely coupled with a groundwater model to better assess the effects of water use on streamflow in the lower ACFB, a complex geologic setting with karst features. The PRMS coarse-resolution model was used to provide inputs of recharge to the groundwater model, which in turn provide simulations of groundwater flow that were aggregated with PRMS-based simulations of surface runoff and shallow-subsurface flow. Simulations without the effects of water use were developed for each model for at least the calendar years 1982–2012 with longer periods for the Potato Creek subbasin (1942–2012) and the Spring Creek subbasin (1952–2012). Water-use-affected flows were simulated for 2008–12. Water budget simulations showed heterogeneous distributions of precipitation, actual evapotranspiration, recharge, runoff, and storage change across the ACFB. Streamflow volume differences between no-water-use and water-use simulations were largest along the main stem of the Apalachicola and Chattahoochee River Basins, with streamflow percentage differences largest in the upper Chattahoochee and Flint River Basins and Spring Creek in the lower Flint River Basin. Water-use information at a shorter time step and a fully coupled simulation in the lower ACFB may further improve water availability estimates and hydrologic simulations in the basin.

On the effects of scale for ecosystem services mapping.

PubMed

Grêt-Regamey, Adrienne; Weibel, Bettina; Bagstad, Kenneth J; Ferrari, Marika; Geneletti, Davide; Klug, Hermann; Schirpke, Uta; Tappeiner, Ulrike

2014-01-01

Ecosystems provide life-sustaining services upon which human civilization depends, but their degradation largely continues unabated. Spatially explicit information on ecosystem services (ES) provision is required to better guide decision making, particularly for mountain systems, which are characterized by vertical gradients and isolation with high topographic complexity, making them particularly sensitive to global change. But while spatially explicit ES quantification and valuation allows the identification of areas of abundant or limited supply of and demand for ES, the accuracy and usefulness of the information varies considerably depending on the scale and methods used. Using four case studies from mountainous regions in Europe and the U.S., we quantify information gains and losses when mapping five ES - carbon sequestration, flood regulation, agricultural production, timber harvest, and scenic beauty - at coarse and fine resolution (250 m vs. 25 m in Europe and 300 m vs. 30 m in the U.S.). We analyze the effects of scale on ES estimates and their spatial pattern and show how these effects are related to different ES, terrain structure and model properties. ES estimates differ substantially between the fine and coarse resolution analyses in all case studies and across all services. This scale effect is not equally strong for all ES. We show that spatially explicit information about non-clustered, isolated ES tends to be lost at coarse resolution and against expectation, mainly in less rugged terrain, which calls for finer resolution assessments in such contexts. The effect of terrain ruggedness is also related to model properties such as dependency on land use-land cover data. We close with recommendations for mapping ES to make the resulting maps more comparable, and suggest a four-step approach to address the issue of scale when mapping ES that can deliver information to support ES-based decision making with greater accuracy and reliability.

Deriving Continuous Fields of Tree Cover at 1-m over the Continental United States From the National Agriculture Imagery Program (NAIP) Imagery to Reduce Uncertainties in Forest Carbon Stock Estimation

NASA Astrophysics Data System (ADS)

Ganguly, S.; Basu, S.; Mukhopadhyay, S.; Michaelis, A.; Milesi, C.; Votava, P.; Nemani, R. R.

2013-12-01

An unresolved issue with coarse-to-medium resolution satellite-based forest carbon mapping over regional to continental scales is the high level of uncertainty in above ground biomass (AGB) estimates caused by the absence of forest cover information at a high enough spatial resolution (current spatial resolution is limited to 30-m). To put confidence in existing satellite-derived AGB density estimates, it is imperative to create continuous fields of tree cover at a sufficiently high resolution (e.g. 1-m) such that large uncertainties in forested area are reduced. The proposed work will provide means to reduce uncertainty in present satellite-derived AGB maps and Forest Inventory and Analysis (FIA) based regional estimates. Our primary objective will be to create Very High Resolution (VHR) estimates of tree cover at a spatial resolution of 1-m for the Continental United States using all available National Agriculture Imaging Program (NAIP) color-infrared imagery from 2010 till 2012. We will leverage the existing capabilities of the NASA Earth Exchange (NEX) high performance computing and storage facilities. The proposed 1-m tree cover map can be further aggregated to provide percent tree cover at any medium-to-coarse resolution spatial grid, which will aid in reducing uncertainties in AGB density estimation at the respective grid and overcome current limitations imposed by medium-to-coarse resolution land cover maps. We have implemented a scalable and computationally-efficient parallelized framework for tree-cover delineation - the core components of the algorithm [that] include a feature extraction process, a Statistical Region Merging image segmentation algorithm and a classification algorithm based on Deep Belief Network and a Feedforward Backpropagation Neural Network algorithm. An initial pilot exercise has been performed over the state of California (~11,000 scenes) to create a wall-to-wall 1-m tree cover map and the classification accuracy has been assessed. Results show an improvement in accuracy of tree-cover delineation as compared to existing forest cover maps from NLCD, especially over fragmented, heterogeneous and urban landscapes. Estimates of VHR tree cover will complement and enhance the accuracy of present remote-sensing based AGB modeling approaches and forest inventory based estimates at both national and local scales. A requisite step will be to characterize the inherent uncertainties in tree cover estimates and propagate them to estimate AGB.

Relative resolution: A hybrid formalism for fluid mixtures.

PubMed

Chaimovich, Aviel; Peter, Christine; Kremer, Kurt

2015-12-28

We show here that molecular resolution is inherently hybrid in terms of relative separation. While nearest neighbors are characterized by a fine-grained (geometrically detailed) model, other neighbors are characterized by a coarse-grained (isotropically simplified) model. We notably present an analytical expression for relating the two models via energy conservation. This hybrid framework is correspondingly capable of retrieving the structural and thermal behavior of various multi-component and multi-phase fluids across state space.

Relative resolution: A hybrid formalism for fluid mixtures

NASA Astrophysics Data System (ADS)

Chaimovich, Aviel; Peter, Christine; Kremer, Kurt

2015-12-01

We show here that molecular resolution is inherently hybrid in terms of relative separation. While nearest neighbors are characterized by a fine-grained (geometrically detailed) model, other neighbors are characterized by a coarse-grained (isotropically simplified) model. We notably present an analytical expression for relating the two models via energy conservation. This hybrid framework is correspondingly capable of retrieving the structural and thermal behavior of various multi-component and multi-phase fluids across state space.

Temporal and spatial resolution required for imaging myocardial function

NASA Astrophysics Data System (ADS)

Eusemann, Christian D.; Robb, Richard A.

2004-05-01

4-D functional analysis of myocardial mechanics is an area of significant interest and research in cardiology and vascular/interventional radiology. Current multidimensional analysis is limited by insufficient temporal resolution of x-ray and magnetic resonance based techniques, but recent improvements in system design holds hope for faster and higher resolution scans to improve images of moving structures allowing more accurate functional studies, such as in the heart. This paper provides a basis for the requisite temporal and spatial resolution for useful imaging during individual segments of the cardiac cycle. Multiple sample rates during systole and diastole are compared to determine an adequate sample frequency to reduce regional myocardial tracking errors. Concurrently, out-of-plane resolution has to be sufficiently high to minimize partial volume effect. Temporal resolution and out-of-plane spatial resolution are related factors that must be considered together. The data used for this study is a DSR dynamic volume image dataset with high temporal and spatial resolution using implanted fiducial markers to track myocardial motion. The results of this study suggest a reduced exposure and scan time for x-ray and magnetic resonance imaging methods, since a lower sample rate during systole is sufficient, whereas the period of rapid filling during diastole requires higher sampling. This could potentially reduce the cost of these procedures and allow higher patient throughput.

Temporal resolution for the perception of features and conjunctions.

PubMed

Bodelón, Clara; Fallah, Mazyar; Reynolds, John H

2007-01-24

The visual system decomposes stimuli into their constituent features, represented by neurons with different feature selectivities. How the signals carried by these feature-selective neurons are integrated into coherent object representations is unknown. To constrain the set of possible integrative mechanisms, we quantified the temporal resolution of perception for color, orientation, and conjunctions of these two features. We find that temporal resolution is measurably higher for each feature than for their conjunction, indicating that time is required to integrate features into a perceptual whole. This finding places temporal limits on the mechanisms that could mediate this form of perceptual integration.

An assessment of SEVIRI imagery at different temporal resolutions and the effect on accurate dust emission mapping

NASA Astrophysics Data System (ADS)

Hennen, Mark; White, Kevin; Shahgedanova, Maria

2017-04-01

This paper compares Dust RGB products derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) data at 15 minute, 30 minute and hourly temporal resolutions. From January 2006 to December 2006, observations of dust emission point sources were observed at each temporal resolution across the entire Middle East region (38.50N; 30.00E - 10.00N; 65.50E). Previous work has demonstrated that 15-minute resolution SEVIRI data can be used to map dust sources across the Sahara by observing dust storms back through sequential images to the point of first emission (Schepanski et al., 2007; 2009; 2012). These observations have improved upon lower resolution maps, based on daily retrievals of aerosol optical depth (AOD), whose maxima can be biased by prevalent transport routes, not necessarily coinciding with sources of emissions. Based on the thermal contrast of atmospheric dust to the surface, brightness temperature differences (BTD's) in the thermal infrared (TIR) wavelengths (8.7, 10.8 and 12.0 µm) highlight dust in the scene irrespective of solar illumination, giving both increased accuracy of dust source areas and a greater understanding of diurnal emission behaviour. However, the highest temporal resolution available (15-minute repeat capture) produces 96 images per day, resulting in significantly higher data storage demands than 30 minute or hourly data. To aid future research planning, this paper investigates what effect lowering the temporal resolution has on the number and spatial distribution of the observed dust sources. The results show a reduction in number of dust emission events observed with each step decrease in temporal resolution, reducing by 17% for 30-minute resolution and 50% for hourly. These differences change seasonally, with the highest reduction observed in summer (34% and 64% reduction respectively). Each resolution shows a similar spatial distribution, with the biggest difference seen near the coastlines, where near-shore convective cloud patterns obscure atmospheric dust soon after emission, restricting the opportunity to be observed at hourly resolution.

Design of 4D x-ray tomography experiments for reconstruction using regularized iterative algorithms

NASA Astrophysics Data System (ADS)

Mohan, K. Aditya

2017-10-01

4D X-ray computed tomography (4D-XCT) is widely used to perform non-destructive characterization of time varying physical processes in various materials. The conventional approach to improving temporal resolution in 4D-XCT involves the development of expensive and complex instrumentation that acquire data faster with reduced noise. It is customary to acquire data with many tomographic views at a high signal to noise ratio. Instead, temporal resolution can be improved using regularized iterative algorithms that are less sensitive to noise and limited views. These algorithms benefit from optimization of other parameters such as the view sampling strategy while improving temporal resolution by reducing the total number of views or the detector exposure time. This paper presents the design principles of 4D-XCT experiments when using regularized iterative algorithms derived using the framework of model-based reconstruction. A strategy for performing 4D-XCT experiments is presented that allows for improving the temporal resolution by progressively reducing the number of views or the detector exposure time. Theoretical analysis of the effect of the data acquisition parameters on the detector signal to noise ratio, spatial reconstruction resolution, and temporal reconstruction resolution is also presented in this paper.

Data for Figures and Tables in Journal Article Assessment of the Effects of Horizontal Grid Resolution on Long-Term Air Quality Trends using Coupled WRF-CMAQ Simulations, doi:10.1016/j.atmosenv.2016.02.036

EPA Pesticide Factsheets

The dataset represents the data depicted in the Figures and Tables of a Journal Manuscript with the following abstract: The objective of this study is to determine the adequacy of using a relatively coarse horizontal resolution (i.e. 36 km) to simulate long-term trends of pollutant concentrations and radiation variables with the coupled WRF-CMAQ model. WRF-CMAQ simulations over the continental United State are performed over the 2001 to 2010 time period at two different horizontal resolutions of 12 and 36 km. Both simulations used the same emission inventory and model configurations. Model results are compared both in space and time to assess the potential weaknesses and strengths of using coarse resolution in long-term air quality applications. The results show that the 36 km and 12 km simulations are comparable in terms of trends analysis for both pollutant concentrations and radiation variables. The advantage of using the coarser 36 km resolution is a significant reduction of computational cost, time and storage requirement which are key considerations when performing multiple years of simulations for trend analysis. However, if such simulations are to be used for local air quality analysis, finer horizontal resolution may be beneficial since it can provide information on local gradients. In particular, divergences between the two simulations are noticeable in urban, complex terrain and coastal regions.This dataset is associated with the following publication

Parameterized and resolved Southern Ocean eddy compensation

NASA Astrophysics Data System (ADS)

Poulsen, Mads B.; Jochum, Markus; Nuterman, Roman

2018-04-01

The ability to parameterize Southern Ocean eddy effects in a forced coarse resolution ocean general circulation model is assessed. The transient model response to a suite of different Southern Ocean wind stress forcing perturbations is presented and compared to identical experiments performed with the same model in 0.1° eddy-resolving resolution. With forcing of present-day wind stress magnitude and a thickness diffusivity formulated in terms of the local stratification, it is shown that the Southern Ocean residual meridional overturning circulation in the two models is different in structure and magnitude. It is found that the difference in the upper overturning cell is primarily explained by an overly strong subsurface flow in the parameterized eddy-induced circulation while the difference in the lower cell is mainly ascribed to the mean-flow overturning. With a zonally constant decrease of the zonal wind stress by 50% we show that the absolute decrease in the overturning circulation is insensitive to model resolution, and that the meridional isopycnal slope is relaxed in both models. The agreement between the models is not reproduced by a 50% wind stress increase, where the high resolution overturning decreases by 20%, but increases by 100% in the coarse resolution model. It is demonstrated that this difference is explained by changes in surface buoyancy forcing due to a reduced Antarctic sea ice cover, which strongly modulate the overturning response and ocean stratification. We conclude that the parameterized eddies are able to mimic the transient response to altered wind stress in the high resolution model, but partly misrepresent the unperturbed Southern Ocean meridional overturning circulation and associated heat transports.

Image resolution enhancement via image restoration using neural network

NASA Astrophysics Data System (ADS)

Zhang, Shuangteng; Lu, Yihong

2011-04-01

Image super-resolution aims to obtain a high-quality image at a resolution that is higher than that of the original coarse one. This paper presents a new neural network-based method for image super-resolution. In this technique, the super-resolution is considered as an inverse problem. An observation model that closely follows the physical image acquisition process is established to solve the problem. Based on this model, a cost function is created and minimized by a Hopfield neural network to produce high-resolution images from the corresponding low-resolution ones. Not like some other single frame super-resolution techniques, this technique takes into consideration point spread function blurring as well as additive noise and therefore generates high-resolution images with more preserved or restored image details. Experimental results demonstrate that the high-resolution images obtained by this technique have a very high quality in terms of PSNR and visually look more pleasant.

Auditory Processing Efficiency and Temporal Resolution in Children and Adults.

ERIC Educational Resources Information Center

Hill, Penelope R.; Hartley, Douglas E.H.; Glasberg, Brian R.; Moore, Brian C.J.; Moore, David R.

2004-01-01

Children have higher auditory backward masking (BM) thresholds than adults. One explanation for this is poor temporal resolution, resulting in difficulty separating brief or rapidly presented sounds. This implies that the auditory temporal window is broader in children than in adults. Alternatively, elevated BM thresholds in children may indicate…

Spatial Downscaling of TRMM Precipitation using MODIS product in the Korean Peninsula

NASA Astrophysics Data System (ADS)

Cho, H.; Choi, M.

2013-12-01

Precipitation is a major driving force in the water cycle. But, it is difficult to provide spatially distributed precipitation data from isolated individual in situ. The Tropical Rainfall Monitoring Mission (TRMM) satellite can provide precipitation data with relatively coarse spatial resolution (0.25° scale) at daily basis. In order to overcome the coarse spatial resolution of TRMM precipitation products, we conducted a downscaling technique using a scaling parameter from the Moderate Resolution Imaging Spectroradiometers (MODIS) sensor. In this study, statistical relations between precipitation estimates derived from the TRMM satellite and the normalized difference vegetation index (NDVI) which is obtained from the MODIS sensor in TERRA satellite are found for different spatial scales on the Korean peninsula in northeast Asia. We obtain the downscaled precipitation mapping by regression equation between yearly TRMM precipitations values and annual average NDVI aggregating 1km to 25 degree. The downscaled precipitation is validated using time series of the ground measurements precipitation dataset provided by Korea Meteorological Organization (KMO) from 2002 to 2005. To improve the spatial downscaling of precipitation, we will conduct a study about correlation between precipitation and land surface temperature, perceptible water and other hydrological parameters.

Downscaling Coarse Scale Microwave Soil Moisture Product using Machine Learning

NASA Astrophysics Data System (ADS)

Abbaszadeh, P.; Moradkhani, H.; Yan, H.

2016-12-01

Soil moisture (SM) is a key variable in partitioning and examining the global water-energy cycle, agricultural planning, and water resource management. It is also strongly coupled with climate change, playing an important role in weather forecasting and drought monitoring and prediction, flood modeling and irrigation management. Although satellite retrievals can provide an unprecedented information of soil moisture at a global-scale, the products might be inadequate for basin scale study or regional assessment. To improve the spatial resolution of SM, this work presents a novel approach based on Machine Learning (ML) technique that allows for downscaling of the satellite soil moisture to fine resolution. For this purpose, the SMAP L-band radiometer SM products were used and conditioned on the Variable Infiltration Capacity (VIC) model prediction to describe the relationship between the coarse and fine scale soil moisture data. The proposed downscaling approach was applied to a western US basin and the products were compared against the available SM data from in-situ gauge stations. The obtained results indicated a great potential of the machine learning technique to derive the fine resolution soil moisture information that is currently used for land data assimilation applications.

Mode Tracker for Mode-Hop-Free Operation of a Laser

NASA Technical Reports Server (NTRS)

Wysocki, Gerard; Tittel, Frank K.; Curl, Robert F.

2010-01-01

A mode-tracking system that includes a mode-controlling subsystem has been incorporated into an external-cavity (EC) quantum cascade laser that operates in a mid-infrared wavelength range. The mode-tracking system makes it possible to perform mode-hop-free wavelength scans, as needed for high-resolution spectroscopy and detection of trace gases. The laser includes a gain chip, a beam-collimating lens, and a diffraction grating. The grating is mounted on a platform, the position of which can be varied to effect independent control of the EC length and the grating angle. The position actuators include a piezoelectric stage for translation control and a motorized stage for coarse rotation control equipped with a piezoelectric actuator for fine rotation control. Together, these actuators enable control of the EC length over a range of about 90 m with a resolution of 0.9 nm, and control of the grating angle over a coarse-tuning range of +/-6.3deg and a fine-tuning range of +/-520 microrad with a resolution of 10 nrad. A mirror mounted on the platform with the grating assures always the same direction of the output laser beam.

Assumption-versus data-based approaches to summarizing species' ranges.

PubMed

Peterson, A Townsend; Navarro-Sigüenza, Adolfo G; Gordillo, Alejandro

2018-06-01

For conservation decision making, species' geographic distributions are mapped using various approaches. Some such efforts have downscaled versions of coarse-resolution extent-of-occurrence maps to fine resolutions for conservation planning. We examined the quality of the extent-of-occurrence maps as range summaries and the utility of refining those maps into fine-resolution distributional hypotheses. Extent-of-occurrence maps tend to be overly simple, omit many known and well-documented populations, and likely frequently include many areas not holding populations. Refinement steps involve typological assumptions about habitat preferences and elevational ranges of species, which can introduce substantial error in estimates of species' true areas of distribution. However, no model-evaluation steps are taken to assess the predictive ability of these models, so model inaccuracies are not noticed. Whereas range summaries derived by these methods may be useful in coarse-grained, global-extent studies, their continued use in on-the-ground conservation applications at fine spatial resolutions is not advisable in light of reliance on assumptions, lack of real spatial resolution, and lack of testing. In contrast, data-driven techniques that integrate primary data on biodiversity occurrence with remotely sensed data that summarize environmental dimensions (i.e., ecological niche modeling or species distribution modeling) offer data-driven solutions based on a minimum of assumptions that can be evaluated and validated quantitatively to offer a well-founded, widely accepted method for summarizing species' distributional patterns for conservation applications. © 2016 Society for Conservation Biology.

An approach for retrieval of atmospheric trace gases CO II, CH 4 and CO from the future Canadian micro earth observation satellite (MEOS)

NASA Astrophysics Data System (ADS)

Trishchenko, Alexander P.; Khlopenkov, Konstantin V.; Wang, Shusen; Luo, Yi; Kruzelecky, Roman V.; Jamroz, Wes; Kroupnik, Guennadi

2007-10-01

Among all trace gases, the carbon dioxide and methane provide the largest contribution to the climate radiative forcing and together with carbon monoxide also to the global atmospheric carbon budget. New Micro Earth Observation Satellite (MEOS) mission is proposed to obtain information about these gases along with some other mission's objectives related to studying cloud and aerosol interactions. The miniature suit of instruments is proposed to make measurements with reduced spectral resolution (1.2nm) over wide NIR range 0.9μm to 2.45μm and with high spectral resolution (0.03nm) for three selected regions: oxygen A-band, 1.5μm-1.7μm band and 2.2μm-2.4μm band. It is also planned to supplement the spectrometer measurements with high spatial resolution imager for detailed characterization of cloud and surface albedo distribution within spectrometer field of view. The approaches for cloud/clear-sky identification and column retrievals of above trace gases are based on differential absorption technique and employ the combination of coarse and high-resolution spectral data. The combination of high and coarse resolution spectral data is beneficial for better characterization of surface spectral albedo and aerosol effects. An additional capability for retrieval of the vertical distribution amounts is obtained from the combination of nadir and limb measurements. Oxygen A-band path length will be used for normalization of trace gas retrievals.

Experiments with the Mesoscale Atmospheric Simulation System (MASS) using the synthetic relative humidity

NASA Technical Reports Server (NTRS)

Chang, Chia-Bo

1994-01-01

This study is intended to examine the impact of the synthetic relative humidity on the model simulation of mesoscale convective storm environment. The synthetic relative humidity is derived from the National Weather Services surface observations, and non-conventional sources including aircraft, radar, and satellite observations. The latter sources provide the mesoscale data of very high spatial and temporal resolution. The synthetic humidity data is used to complement the National Weather Services rawinsonde observations. It is believed that a realistic representation of initial moisture field in a mesoscale model is critical for the model simulation of thunderstorm development, and the formation of non-convective clouds as well as their effects on the surface energy budget. The impact will be investigated based on a real-data case study using the mesoscale atmospheric simulation system developed by Mesoscale Environmental Simulations Operations, Inc. The mesoscale atmospheric simulation system consists of objective analysis and initialization codes, and the coarse-mesh and fine-mesh dynamic prediction models. Both models are a three dimensional, primitive equation model containing the essential moist physics for simulating and forecasting mesoscale convective processes in the atmosphere. The modeling system is currently implemented at the Applied Meteorology Unit, Kennedy Space Center. Two procedures involving the synthetic relative humidity to define the model initial moisture fields are considered. It is proposed to perform several short-range (approximately 6 hours) comparative coarse-mesh simulation experiments with and without the synthetic data. They are aimed at revealing the model sensitivities should allow us both to refine the specification of the observational requirements, and to develop more accurate and efficient objective analysis schemes. The goal is to advance the MASS (Mesoscal Atmospheric Simulation System) modeling expertise so that the model output can provide reliable guidance for thunderstorm forecasting.

Integration of High-resolution Data for Temporal Bone Surgical Simulations

PubMed Central

Wiet, Gregory J.; Stredney, Don; Powell, Kimerly; Hittle, Brad; Kerwin, Thomas

2016-01-01

Purpose To report on the state of the art in obtaining high-resolution 3D data of the microanatomy of the temporal bone and to process that data for integration into a surgical simulator. Specifically, we report on our experience in this area and discuss the issues involved to further the field. Data Sources Current temporal bone image acquisition and image processing established in the literature as well as in house methodological development. Review Methods We reviewed the current English literature for the techniques used in computer-based temporal bone simulation systems to obtain and process anatomical data for use within the simulation. Search terms included “temporal bone simulation, surgical simulation, temporal bone.” Articles were chosen and reviewed that directly addressed data acquisition and processing/segmentation and enhancement with emphasis given to computer based systems. We present the results from this review in relationship to our approach. Conclusions High-resolution CT imaging (≤100μm voxel resolution), along with unique image processing and rendering algorithms, and structure specific enhancement are needed for high-level training and assessment using temporal bone surgical simulators. Higher resolution clinical scanning and automated processes that run in efficient time frames are needed before these systems can routinely support pre-surgical planning. Additionally, protocols such as that provided in this manuscript need to be disseminated to increase the number and variety of virtual temporal bones available for training and performance assessment. PMID:26762105

Sensitivity to grid resolution in the ability of a chemical transport model to simulate observed oxidant chemistry under high-isoprene conditions

NASA Astrophysics Data System (ADS)

Yu, Karen; Jacob, Daniel J.; Fisher, Jenny A.; Kim, Patrick S.; Marais, Eloise A.; Miller, Christopher C.; Travis, Katherine R.; Zhu, Lei; Yantosca, Robert M.; Sulprizio, Melissa P.; Cohen, Ron C.; Dibb, Jack E.; Fried, Alan; Mikoviny, Tomas; Ryerson, Thomas B.; Wennberg, Paul O.; Wisthaler, Armin

2016-04-01

Formation of ozone and organic aerosol in continental atmospheres depends on whether isoprene emitted by vegetation is oxidized by the high-NOx pathway (where peroxy radicals react with NO) or by low-NOx pathways (where peroxy radicals react by alternate channels, mostly with HO2). We used mixed layer observations from the SEAC4RS aircraft campaign over the Southeast US to test the ability of the GEOS-Chem chemical transport model at different grid resolutions (0.25° × 0.3125°, 2° × 2.5°, 4° × 5°) to simulate this chemistry under high-isoprene, variable-NOx conditions. Observations of isoprene and NOx over the Southeast US show a negative correlation, reflecting the spatial segregation of emissions; this negative correlation is captured in the model at 0.25° × 0.3125° resolution but not at coarser resolutions. As a result, less isoprene oxidation takes place by the high-NOx pathway in the model at 0.25° × 0.3125° resolution (54 %) than at coarser resolution (59 %). The cumulative probability distribution functions (CDFs) of NOx, isoprene, and ozone concentrations show little difference across model resolutions and good agreement with observations, while formaldehyde is overestimated at coarse resolution because excessive isoprene oxidation takes place by the high-NOx pathway with high formaldehyde yield. The good agreement of simulated and observed concentration variances implies that smaller-scale non-linearities (urban and power plant plumes) are not important on the regional scale. Correlations of simulated vs. observed concentrations do not improve with grid resolution because finer modes of variability are intrinsically more difficult to capture. Higher model resolution leads to decreased conversion of NOx to organic nitrates and increased conversion to nitric acid, with total reactive nitrogen oxides (NOy) changing little across model resolutions. Model concentrations in the lower free troposphere are also insensitive to grid resolution. The overall low sensitivity of modeled concentrations to grid resolution implies that coarse resolution is adequate when modeling continental boundary layer chemistry for global applications.

Precise Aperture-Dependent Motion Compensation with Frequency Domain Fast Back-Projection Algorithm.

PubMed

Zhang, Man; Wang, Guanyong; Zhang, Lei

2017-10-26

Precise azimuth-variant motion compensation (MOCO) is an essential and difficult task for high-resolution synthetic aperture radar (SAR) imagery. In conventional post-filtering approaches, residual azimuth-variant motion errors are generally compensated through a set of spatial post-filters, where the coarse-focused image is segmented into overlapped blocks concerning the azimuth-dependent residual errors. However, image domain post-filtering approaches, such as precise topography- and aperture-dependent motion compensation algorithm (PTA), have difficulty of robustness in declining, when strong motion errors are involved in the coarse-focused image. In this case, in order to capture the complete motion blurring function within each image block, both the block size and the overlapped part need necessary extension leading to degeneration of efficiency and robustness inevitably. Herein, a frequency domain fast back-projection algorithm (FDFBPA) is introduced to deal with strong azimuth-variant motion errors. FDFBPA disposes of the azimuth-variant motion errors based on a precise azimuth spectrum expression in the azimuth wavenumber domain. First, a wavenumber domain sub-aperture processing strategy is introduced to accelerate computation. After that, the azimuth wavenumber spectrum is partitioned into a set of wavenumber blocks, and each block is formed into a sub-aperture coarse resolution image via the back-projection integral. Then, the sub-aperture images are straightforwardly fused together in azimuth wavenumber domain to obtain a full resolution image. Moreover, chirp-Z transform (CZT) is also introduced to implement the sub-aperture back-projection integral, increasing the efficiency of the algorithm. By disusing the image domain post-filtering strategy, robustness of the proposed algorithm is improved. Both simulation and real-measured data experiments demonstrate the effectiveness and superiority of the proposal.

Max CAPR: high-resolution 3D contrast-enhanced MR angiography with acquisition times under 5 seconds.

PubMed

Haider, Clifton R; Borisch, Eric A; Glockner, James F; Mostardi, Petrice M; Rossman, Phillip J; Young, Phillip M; Riederer, Stephen J

2010-10-01

High temporal and spatial resolution is desired in imaging of vascular abnormalities having short arterial-to-venous transit times. Methods that exploit temporal correlation to reduce the observed frame time demonstrate temporal blurring, obfuscating bolus dynamics. Previously, a Cartesian acquisition with projection reconstruction-like (CAPR) sampling method has been demonstrated for three-dimensional contrast-enhanced angiographic imaging of the lower legs using two-dimensional sensitivity-encoding acceleration and partial Fourier acceleration, providing 1mm isotropic resolution of the calves, with 4.9-sec frame time and 17.6-sec temporal footprint. In this work, the CAPR acquisition is further undersampled to provide a net acceleration approaching 40 by eliminating all view sharing. The tradeoff of frame time and temporal footprint in view sharing is presented and characterized in phantom experiments. It is shown that the resultant 4.9-sec acquisition time, three-dimensional images sets have sufficient spatial and temporal resolution to clearly portray arterial and venous phases of contrast passage. It is further hypothesized that these short temporal footprint sequences provide diagnostic quality images. This is tested and shown in a series of nine contrast-enhanced MR angiography patient studies performed with the new method.

The Dynamic General Vegetation Model MC1 over the United States and Canada at a 5-arcminute resolution: model inputs and outputs

Treesearch

Ray Drapek; John B. Kim; Ronald P. Neilson

2015-01-01

Land managers need to include climate change in their decisionmaking, but the climate models that project future climates operate at spatial scales that are too coarse to be of direct use. To create a dataset more useful to managers, soil and historical climate were assembled for the United States and Canada at a 5-arcminute grid resolution. Nine CMIP3 future climate...

Enhanced Axial Resolution of Wide-Field Two-Photon Excitation Microscopy by Line Scanning Using a Digital Micromirror Device.

PubMed

Park, Jong Kang; Rowlands, Christopher J; So, Peter T C

2017-01-01

Temporal focusing multiphoton microscopy is a technique for performing highly parallelized multiphoton microscopy while still maintaining depth discrimination. While the conventional wide-field configuration for temporal focusing suffers from sub-optimal axial resolution, line scanning temporal focusing, implemented here using a digital micromirror device (DMD), can provide substantial improvement. The DMD-based line scanning temporal focusing technique dynamically trades off the degree of parallelization, and hence imaging speed, for axial resolution, allowing performance parameters to be adapted to the experimental requirements. We demonstrate this new instrument in calibration specimens and in biological specimens, including a mouse kidney slice.

Enhanced Axial Resolution of Wide-Field Two-Photon Excitation Microscopy by Line Scanning Using a Digital Micromirror Device

PubMed Central

Park, Jong Kang; Rowlands, Christopher J.; So, Peter T. C.

2017-01-01

Temporal focusing multiphoton microscopy is a technique for performing highly parallelized multiphoton microscopy while still maintaining depth discrimination. While the conventional wide-field configuration for temporal focusing suffers from sub-optimal axial resolution, line scanning temporal focusing, implemented here using a digital micromirror device (DMD), can provide substantial improvement. The DMD-based line scanning temporal focusing technique dynamically trades off the degree of parallelization, and hence imaging speed, for axial resolution, allowing performance parameters to be adapted to the experimental requirements. We demonstrate this new instrument in calibration specimens and in biological specimens, including a mouse kidney slice. PMID:29387484

Climate model biases in jet streams, blocking and storm tracks resulting from missing orographic drag

NASA Astrophysics Data System (ADS)

Pithan, Felix; Shepherd, Theodore G.; Zappa, Giuseppe; Sandu, Irina

2016-07-01

State-of-the art climate models generally struggle to represent important features of the large-scale circulation. Common model deficiencies include an equatorward bias in the location of the midlatitude westerlies and an overly zonal orientation of the North Atlantic storm track. Orography is known to strongly affect the atmospheric circulation and is notoriously difficult to represent in coarse-resolution climate models. Yet how the representation of orography affects circulation biases in current climate models is not understood. Here we show that the effects of switching off the parameterization of drag from low-level orographic blocking in one climate model resemble the biases of the Coupled Model Intercomparison Project Phase 5 ensemble: An overly zonal wintertime North Atlantic storm track and less European blocking events, and an equatorward shift in the Southern Hemispheric jet and increase in the Southern Annular Mode time scale. This suggests that typical circulation biases in coarse-resolution climate models may be alleviated by improved parameterizations of low-level drag.

Whole-animal imaging with high spatio-temporal resolution

NASA Astrophysics Data System (ADS)

Chhetri, Raghav; Amat, Fernando; Wan, Yinan; Höckendorf, Burkhard; Lemon, William C.; Keller, Philipp J.

2016-03-01

We developed isotropic multiview (IsoView) light-sheet microscopy in order to image fast cellular dynamics, such as cell movements in an entire developing embryo or neuronal activity throughput an entire brain or nervous system, with high resolution in all dimensions, high imaging speeds, good physical coverage and low photo-damage. To achieve high temporal resolution and high spatial resolution at the same time, IsoView microscopy rapidly images large specimens via simultaneous light-sheet illumination and fluorescence detection along four orthogonal directions. In a post-processing step, these four views are then combined by means of high-throughput multiview deconvolution to yield images with a system resolution of ≤ 450 nm in all three dimensions. Using IsoView microscopy, we performed whole-animal functional imaging of Drosophila embryos and larvae at a spatial resolution of 1.1-2.5 μm and at a temporal resolution of 2 Hz for up to 9 hours. We also performed whole-brain functional imaging in larval zebrafish and multicolor imaging of fast cellular dynamics across entire, gastrulating Drosophila embryos with isotropic, sub-cellular resolution. Compared with conventional (spatially anisotropic) light-sheet microscopy, IsoView microscopy improves spatial resolution at least sevenfold and decreases resolution anisotropy at least threefold. Compared with existing high-resolution light-sheet techniques, such as lattice lightsheet microscopy or diSPIM, IsoView microscopy effectively doubles the penetration depth and provides subsecond temporal resolution for specimens 400-fold larger than could previously be imaged.

Variability of 4D flow parameters when subjected to changes in MRI acquisition parameters using a realistic thoracic aortic phantom.

PubMed

Montalba, Cristian; Urbina, Jesus; Sotelo, Julio; Andia, Marcelo E; Tejos, Cristian; Irarrazaval, Pablo; Hurtado, Daniel E; Valverde, Israel; Uribe, Sergio

2018-04-01

To assess the variability of peak flow, mean velocity, stroke volume, and wall shear stress measurements derived from 3D cine phase contrast (4D flow) sequences under different conditions of spatial and temporal resolutions. We performed controlled experiments using a thoracic aortic phantom. The phantom was connected to a pulsatile flow pump, which simulated nine physiological conditions. For each condition, 4D flow data were acquired with different spatial and temporal resolutions. The 2D cine phase contrast and 4D flow data with the highest available spatio-temporal resolution were considered as a reference for comparison purposes. When comparing 4D flow acquisitions (spatial and temporal resolution of 2.0 × 2.0 × 2.0 mm 3 and 40 ms, respectively) with 2D phase-contrast flow acquisitions, the underestimation of peak flow, mean velocity, and stroke volume were 10.5, 10 and 5%, respectively. However, the calculated wall shear stress showed an underestimation larger than 70% for the former acquisition, with respect to 4D flow, with spatial and temporal resolution of 1.0 × 1.0 × 1.0 mm 3 and 20 ms, respectively. Peak flow, mean velocity, and stroke volume from 4D flow data are more sensitive to changes of temporal than spatial resolution, as opposed to wall shear stress, which is more sensitive to changes in spatial resolution. Magn Reson Med 79:1882-1892, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Site Survey of the Martha's Vineyard Coastal Observatory: Backscatter, Grain Size and Temporal Evolution of Rippled Scour Depressions

NASA Astrophysics Data System (ADS)

Goff, J. A.; Mayer, L.; Schwab, B.; Traykovski, P.; Wilkins, R.; Jenkins, C.; Kraft, B.; Evans, R.; Buynevich, I.

2002-12-01

The Office of Naval Research's Mine Burial Prediction program has chosen the Martha's Vineyard Coastal Observatory (MVCO) as a natural laboratory for experimental observations of object burial by nearshore processes (e.g., bedform migration, scour). In support of this program, the MVCO has been subject to an intensive site survey program, involving, since early 2001: (1) three swath backscatter and/or bathymetry surveys; (2) three high resolution seismic surveys; (3) ultra-high resolution sector-scanning sonar on pole mounts; (4) in situ geotechnical (velocity and resistivity) measurements, (5) grab sampling, and (6) vibracoring. These efforts are concentrated in water depths between ~8 and 18 m, centered on the site of the MVCO permanent node at ~12 m water depth Rippled scour depressions (RSDs) are pervasive within the MVCO. RSDs are ~shore-perpendicular bands of coarse sands separated by overlying fine sands. The term itself implies that the coarse sands are heavily rippled (~0.5-1 m wavelength, ~0.1 m amplitude) and slightly depressed relative to the fine sands which, in the MVCO, are generally just a few 10's of cm thick. The RSDs are clearly evident on sidescan data as bands of high backscatter. For the most part, grain size measurements confirm a strong positive correspondence between mean grain size and backscatter intensity. However, a critical exception is seen in deeper water where, well within the area of fine sands, backscatter increases noticeably as mean grain size decreases from ~150μ to ~130μ. Topographic expression related to the RSDs is confined primarily to evident scour depressions at the edges. The RSDs are highly asymmetric: backscatter is higher, the coarse/fine transition is more sharply defined, and the scour depression is deeper on one side than the other. This pattern changes within the survey: the higher backscatter edge is always to the west in the western part of the survey, and vice versa to the east. The strike of the RSDs also changes, from being slightly east of north in the western part of the survey to slightly west of north to the east. The MVCO site survey work establishes a baseline set of observations against which physical changes in the seafloor with time can be measured. Early evidence of significant change has been provided by comparison of the first two sidescan surveys, which indicates a shift in the RSD boundaries by as much as 50 m between February and September of 2001. Continued seafloor evolution is evidenced by the August 2002 grab sampling and sector scanning sonar. This dynamic setting will continue to be monitored by additional swath mapping and sampling in conjunction with the planned winter 2003/2004 mine burial experiment.

A proxy for high-resolution regional reanalysis for the Southeast United States: assessment of precipitation variability in dynamically downscaled reanalyses

USGS Publications Warehouse

Stefanova, Lydia; Misra, Vasubandhu; Chan, Steven; Griffin, Melissa; O'Brien, James J.; Smith, Thomas J.

2012-01-01

We present an analysis of the seasonal, subseasonal, and diurnal variability of rainfall from COAPS Land- Atmosphere Regional Reanalysis for the Southeast at 10-km resolution (CLARReS10). Most of our assessment focuses on the representation of summertime subseasonal and diurnal variability.Summer precipitation in the Southeast United States is a particularly challenging modeling problem because of the variety of regional-scale phenomena, such as sea breeze, thunderstorms and squall lines, which are not adequately resolved in coarse atmospheric reanalyses but contribute significantly to the hydrological budget over the region. We find that the dynamically downscaled reanalyses are in good agreement with station and gridded observations in terms of both the relative seasonal distribution and the diurnal structure of precipitation, although total precipitation amounts tend to be systematically overestimated. The diurnal cycle of summer precipitation in the downscaled reanalyses is in very good agreement with station observations and a clear improvement both over their "parent" reanalyses and over newer-generation reanalyses. The seasonal cycle of precipitation is particularly well simulated in the Florida; this we attribute to the ability of the regional model to provide a more accurate representation of the spatial and temporal structure of finer-scale phenomena such as fronts and sea breezes. Over the northern portion of the domain summer precipitation in the downscaled reanalyses remains, as in the "parent" reanalyses, overestimated. Given the degree of success that dynamical downscaling of reanalyses demonstrates in the simulation of the characteristics of regional precipitation, its favorable comparison to conventional newer-generation reanalyses and its cost-effectiveness, we conclude that for the Southeast United states such downscaling is a viable proxy for high-resolution conventional reanalysis.

Trend analysis of GIMMS and MODIS NDVI time series for establishing a land degradation neutrality national baseline

NASA Astrophysics Data System (ADS)

Gichenje, Helene; Godinho, Sergio

2017-04-01

Land degradation is a key global environment and development problem that is recognized as a priority by the international development community. The Sustainable Development Goals (SDGs) were adopted by the global community in 2015, and include a goal related to land degradation and the accompanying target to achieve a land degradation-neutral (LDN) world by 2030. The LDN concept encompasses two joint actions of reducing the rate of degradation and increasing the rate of restoration. Using Kenya as the study area, this study aims to develop and test a spatially explicit methodology for assessing and monitoring the operationalization of a land degradation neutrality scheme at the national level. Time series analysis is applied to Normalized Difference Vegetation Index (NDVI) satellite data records, based on the hypothesis that the resulting NDVI residual trend would enable successful detection of changes in vegetation photosynthetic capacity and thus serve as a proxy for land degradation and regeneration processes. Two NDVI data sets are used to identify the spatial and temporal distribution of degraded and regenerated areas: the long term coarse resolution (8km, 1982-2015) third generation Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data record; and the shorter-term finer resolution (250m, 2001-2015) Moderate Resolution Imaging Spectroradiometer (MODIS) derived NDVI data record. Climate data (rainfall, temperature and soil moisture) are used to separate areas of human-induced vegetation productivity decline from those driven by climate dynamics. Further, weekly vegetation health (VH) indexes (4km, 1982-2015) developed by National Oceanic and Atmospheric Administration (NOAA), are assessed as indicators for early detection and monitoring of land degradation by estimating vegetation stress (moisture, thermal and combined conditions).

The Synergistic Use of NASA's A-Train Observations to Characterize the Planetary Boundary Layer and Enable Improved Understanding and Prediction of Land-Atmosphere Interactions

NASA Astrophysics Data System (ADS)

Zavodsky, B.; Santanello, J. A.; Friedl, M. A.; Susskind, J.; Palm, S. P.

2010-12-01

The planetary boundary layer (PBL) serves as a short-term memory of land-atmosphere (L-A) interactions through the diurnal integration of surface fluxes and subsequent evolution of PBL fluxes and states. Recent advances in satellite remote sensing offer the ability to monitor PBL and land surface properties at increasingly high spatial and temporal resolutions and, consequently, have the potential to provide valuable information on the terrestrial energy and water cycle across a range of scales. In this study, we evaluate the retrieval of PBL structure and temperature and moisture properties from measurements made by NASA's Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), Moderate Resolution Imaging Spectroradiometer (MODIS) , and Atmospheric Infrared Sounder (AIRS) instruments aboard the 'A-Train' constellation. The global coverage of these sensors greatly improves upon the coarse network of synoptic radiosonde and intermittent satellite and ground remote sensing currently available, and combining the high vertical and spectral resolution of these sensors allows for PBL retrievals to be evaluated in the context of their relationship with the land surface. Results include an evaluation of CALIPSO, MODIS, and AIRS temperature and humidity retrievals using radiosonde data, focusing on how well PBL properties (e.g. PBL height, temperature, humidity, and stability) can be discerned from each sensor under a range of conditions. Overall, this research is timely in assessing the potential for merging complimentary information from independent sensors, and provides a unique opportunity to evaluate and apply NASA data to answer fundamental questions regarding observation, understanding, and prediction of L-A interactions and coupling.

Fine Particulate Matter Predictions Using High Resolution Aerosol Optical Depth (AOD) Retrievals

NASA Technical Reports Server (NTRS)

Chudnovsky, Alexandra A.; Koutrakis, Petros; Kloog, Itai; Melly, Steven; Nordio, Francesco; Lyapustin, Alexei; Wang, Jujie; Schwartz, Joel

2014-01-01

To date, spatial-temporal patterns of particulate matter (PM) within urban areas have primarily been examined using models. On the other hand, satellites extend spatial coverage but their spatial resolution is too coarse. In order to address this issue, here we report on spatial variability in PM levels derived from high 1 km resolution AOD product of Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm developed for MODIS satellite. We apply day-specific calibrations of AOD data to predict PM(sub 2.5) concentrations within the New England area of the United States. To improve the accuracy of our model, land use and meteorological variables were incorporated. We used inverse probability weighting (IPW) to account for nonrandom missingness of AOD and nested regions within days to capture spatial variation. With this approach we can control for the inherent day-to-day variability in the AOD-PM(sub 2.5) relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance among others. Out-of-sample "ten-fold" cross-validation was used to quantify the accuracy of model predictions. Our results show that the model-predicted PM(sub 2.5) mass concentrations are highly correlated with the actual observations, with out-of- sample R(sub 2) of 0.89. Furthermore, our study shows that the model captures the pollution levels along highways and many urban locations thereby extending our ability to investigate the spatial patterns of urban air quality, such as examining exposures in areas with high traffic. Our results also show high accuracy within the cities of Boston and New Haven thereby indicating that MAIAC data can be used to examine intra-urban exposure contrasts in PM(sub 2.5) levels.

Development of Microbial Fuel Cell Prototypes for Examination of the Temporal and Spatial Response of Anodic Bacterial Communities in Marine Sediments

DTIC Science & Technology

2012-01-01

clumps. Approximately 3 grams of chitin (from crab shells, practical grade, coarse flakes, Sigma Aldrich, Product #C9213, CAS#1398-61-4), were then...Fig. 4A. post stirring to introduce new TOC; B. post microwaving to demonstrate power provided by microbes, C. upon addition of chitin to sand

Development of Microbial Fuel Cell Prototypes for Examination of the Temporal and Spatial Response of Anodic Bacterial Communities in Marine Sediments

DTIC Science & Technology

2014-01-01

clumps. Approximately 3 grams of chitin (from crab shells, practical grade, coarse flakes, Sigma Aldrich, Product #C9213, CAS#1398-61-4), were then...Fig. 4A. post stirring to introduce new TOC; B. post microwaving to demonstrate power provided by microbes, C. upon addition of chitin to sand

Temporal carbon dynamics of forests in Washington, US: implications for ecological theory and carbon management

Treesearch

Crystal L. Raymond; Donald McKenzie

2014-01-01

We quantified carbon (C) dynamics of forests in Washington, US using theoretical models of C dynamics as a function of forest age. We fit empirical models to chronosequences of forest inventory data at two scales: a coarse-scale ecosystem classification (ecosections) and forest types (potential vegetation) within ecosections. We hypothesized that analysis at the finer...

Regional distribution and dynamics of coarse woody debris in Midwestern old-growth forests

Treesearch

Martin A. Spetich; Stephen R. Shifley; George R. Parker

1999-01-01

Old-growth forests have been noted for containing significant quantities of deadwood. However, there has been no coordinated effort to quantify the deadwood component of old-growth remnants across large regions of temperate deciduous forest. We present results of a regional inventory that quantifies and examines regional and temporal trends for deadwood in upland old-...

Predicting longleaf pine coarse root decomposition in the southeastern US

Treesearch

Peter H. Anderson; Kurt H. Johnsen; John R. Butnor; Carlos A. Gonzalez-Benecke; Lisa J. Samuelson

2018-01-01

Storage of belowground carbon (C) is an important component of total forest C. However, belowground C changes temporally due to forest growth and tree mortality (natural and via harvesting) and these fluctuations are critical for modeling C in forests under varying management regimes. To date, little progress has been made in quantifying the rate of decay of southern...

High Resolution Freeze and Thaw States Detection Using Combination of Sentinel 1A SAR and Passive Microwave Measurements

NASA Astrophysics Data System (ADS)

Azarderakhsh, M.; McDonald, K. C.; Norouzi, H.; Rebolledo, M. A.; Prakash, S.

2017-12-01

The freeze and thaw (FT) cycles in high-latitude regions have great impact on many biogeochemical transitions, hydrology and ecosystem especially in wetland areas. Passive and active microwave remote sensing data from satellite observations have been deployed in the past to define the status of the surface in terms of freeze and thaw. While many progresses have been made in this field, the limitations attached to such observations have hindered our ability to fully predict the change of surface state in the scale that is appropriate for the aforementioned applications. The transition between freeze and thaw states may occur frequently (even within a day) especially during shifts from cold to warm seasons and vice versa. Passive microwave sensors have different acquisition times, and data fusion of these sensors may provide a complete diurnal variation estimate of FT states. However, the coarse spatial resolution of these measurements may undermine their applicability. However, active microwave backscatter measurements from sensors such as Sentinel 1A and the Advanced Land Observing Satellite Phased Array L-Band SAR (ALOS PALSAR) can deliver high resolution information about wetlands and FT status. In this project, Synthetic Aperture Radar (SAR) c-band backscatter data from Sentinel 1 from April 2014 to June 2017 are deployed to detect high resolution freeze/thaw states and wetland areas. The contrasts between frozen and thawed seasons are used to define FT states after performing required radiometric corrections and calibrations. A method based on phase changes in polarized images is developed for different land cover types to maximize the accuracy of the detections. The aggregated (up-scaled) estimates from active measurements are compared to passive microwave-based FT product. The results of this method reveal that the estimates are relatively in good agreement with SNOw TELemetry (SNOTEL) ground measurements. Finally, a downscaling method is tried to link passive emissivity-based FT product to high resolution active FT estimates to increase the temporal frequency of the high-resolution Sentinel data. The results of this study contribute to better understanding sources of positive carbon and methane (CH4) feedback to the atmosphere.

Large Contribution of Coarse Mode to Aerosol Microphysical and Optical Properties: Evidence from Ground-Based Observations of a Transpacific Dust Outbreak at a High-Elevation North American Site

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

Kassianov, E.; Pekour, M.; Flynn, C.

Our work is motivated by previous studies of the long-range trans-Atlantic transport of Saharan dust and the observed quasi-static nature of coarse mode aerosol with a volume median diameter (VMD) of approximately 3.5 µm. We examine coarse mode contributions from the trans-Pacific transport of Asian dust to North American aerosol microphysical and optical properties using a dataset collected at the high-elevation, mountain-top Storm Peak Laboratory (SPL, 3.22 km above sea level [ASL]) and the nearby Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF, 2.76 km ASL). Data collected during the SPL Cloud Property Validation Experiment (STORMVEX, March 2011) are complemented bymore » quasi-global high-resolution model simulations coupled with aerosol chemistry. We identify dust event associated mostly with Asian plume (about 70% of dust mass) where the coarse mode with moderate (~4 µm) VMD is distinct and contributes substantially to aerosol microphysical (up to 70% for total volume) and optical (up to 45% for total scattering and aerosol optical depth) properties. Our results, when compared with previous Saharan dust studies, suggest a fairly invariant behavior of coarse mode dust aerosols. If confirmed in additional studies, this invariant behavior may simplify considerably model parameterizations for complex and size-dependent processes associated with dust transport and removal.« less

Simulations of the temporal and spatial resolution for a compact time-resolved electron diffractometer

NASA Astrophysics Data System (ADS)

Robinson, Matthew S.; Lane, Paul D.; Wann, Derek A.

2016-02-01

A novel compact electron gun for use in time-resolved gas electron diffraction experiments has recently been designed and commissioned. In this paper we present and discuss the extensive simulations that were performed to underpin the design in terms of the spatial and temporal qualities of the pulsed electron beam created by the ionisation of a gold photocathode using a femtosecond laser. The response of the electron pulses to a solenoid lens used to focus the electron beam has also been studied. The simulated results show that focussing the electron beam affects the overall spatial and temporal resolution of the experiment in a variety of ways, and that factors that improve the resolution of one parameter can often have a negative effect on the other. A balance must, therefore, be achieved between spatial and temporal resolution. The optimal experimental time resolution for the apparatus is predicted to be 416 fs for studies of gas-phase species, while the predicted spatial resolution of better than 2 nm-1 compares well with traditional time-averaged electron diffraction set-ups.

Effects of vegetation heterogeneity and surface topography on spatial scaling of net primary productivity

NASA Astrophysics Data System (ADS)

Chen, J. M.; Chen, X.; Ju, W.

2013-03-01

Due to the heterogeneous nature of the land surface, spatial scaling is an inevitable issue in the development of land models coupled with low-resolution Earth system models (ESMs) for predicting land-atmosphere interactions and carbon-climate feedbacks. In this study, a simple spatial scaling algorithm is developed to correct errors in net primary productivity (NPP) estimates made at a coarse spatial resolution based on sub-pixel information of vegetation heterogeneity and surface topography. An eco-hydrological model BEPS-TerrainLab, which considers both vegetation and topographical effects on the vertical and lateral water flows and the carbon cycle, is used to simulate NPP at 30 m and 1 km resolutions for a 5700 km2 watershed with an elevation range from 518 m to 3767 m in the Qinling Mountain, Shaanxi Province, China. Assuming that the NPP simulated at 30 m resolution represents the reality and that at 1 km resolution is subject to errors due to sub-pixel heterogeneity, a spatial scaling index (SSI) is developed to correct the coarse resolution NPP values pixel by pixel. The agreement between the NPP values at these two resolutions is improved considerably from R2 = 0.782 to R2 = 0.884 after the correction. The mean bias error (MBE) in NPP modeled at the 1 km resolution is reduced from 14.8 g C m-2 yr-1 to 4.8 g C m-2 yr-1 in comparison with NPP modeled at 30 m resolution, where the mean NPP is 668 g C m-2 yr-1. The range of spatial variations of NPP at 30 m resolution is larger than that at 1 km resolution. Land cover fraction is the most important vegetation factor to be considered in NPP spatial scaling, and slope is the most important topographical factor for NPP spatial scaling especially in mountainous areas, because of its influence on the lateral water redistribution, affecting water table, soil moisture and plant growth. Other factors including leaf area index (LAI), elevation and aspect have small and additive effects on improving the spatial scaling between these two resolutions.

Effects of vegetation heterogeneity and surface topography on spatial scaling of net primary productivity

NASA Astrophysics Data System (ADS)

Chen, J. M.; Chen, X.; Ju, W.

2013-07-01

Due to the heterogeneous nature of the land surface, spatial scaling is an inevitable issue in the development of land models coupled with low-resolution Earth system models (ESMs) for predicting land-atmosphere interactions and carbon-climate feedbacks. In this study, a simple spatial scaling algorithm is developed to correct errors in net primary productivity (NPP) estimates made at a coarse spatial resolution based on sub-pixel information of vegetation heterogeneity and surface topography. An eco-hydrological model BEPS-TerrainLab, which considers both vegetation and topographical effects on the vertical and lateral water flows and the carbon cycle, is used to simulate NPP at 30 m and 1 km resolutions for a 5700 km2 watershed with an elevation range from 518 m to 3767 m in the Qinling Mountain, Shanxi Province, China. Assuming that the NPP simulated at 30 m resolution represents the reality and that at 1 km resolution is subject to errors due to sub-pixel heterogeneity, a spatial scaling index (SSI) is developed to correct the coarse resolution NPP values pixel by pixel. The agreement between the NPP values at these two resolutions is improved considerably from R2 = 0.782 to R2 = 0.884 after the correction. The mean bias error (MBE) in NPP modelled at the 1 km resolution is reduced from 14.8 g C m-2 yr-1 to 4.8 g C m-2 yr-1 in comparison with NPP modelled at 30 m resolution, where the mean NPP is 668 g C m-2 yr-1. The range of spatial variations of NPP at 30 m resolution is larger than that at 1 km resolution. Land cover fraction is the most important vegetation factor to be considered in NPP spatial scaling, and slope is the most important topographical factor for NPP spatial scaling especially in mountainous areas, because of its influence on the lateral water redistribution, affecting water table, soil moisture and plant growth. Other factors including leaf area index (LAI) and elevation have small and additive effects on improving the spatial scaling between these two resolutions.

Spatio-temporal Evolution of Velocity Structure, Concentration and Grain-size Stratification within Experimental Particulate Gravity Flows: Potential Input Parameters for Numerical Models

NASA Astrophysics Data System (ADS)

McCaffrey, W.; Choux, C.; Baas, J.; Haughton, P.

2001-12-01

Little is known about the combined spatio-temporal evolution of velocity structure, concentration and grain size stratification within particulate gravity currents. Yet these data are of primary importance for numerical model validation, prior to application to natural flows, such as pyroclastic density currents and turbidity currents. A comprehensive study was carried out on a series of experimental particulate gravity flows of 5% by volume initial concentration. The sediment analogue was polydisperse silica flour (mean grain size ~8 microns). A uniform 30 liter suspension was prepared in an overhead reservoir, then allowed to drain (in about one minute) into an flume 10 m long and 0.3 m wide, water-filled to a depth of 0.3 m. Each flow was siphoned continuously for 52 s at 5 different heights (spaced evenly from 0.6 to 4.6 cm) with samples collected at a frequency of 0.25Hz, generating 325 samples for grain-size and concentration analysis. Simultaneously, six 4-MHz UDVP (Ultrasonic Doppler Velocity Profiling) probes recorded the horizontal component of flow velocity. All but the highest probe were positioned at the same height as the siphons. The sampling location was shifted 1.32m down-current for each of five nominally identical flows, yielding sample locations at 1.32, 2.64, 3.96, 5.28 and 6.60m from the inlet point. These data can be combined to give both the temporal and spatial evolution of a single idealised flow. The concentration data can be used to defined the structure of the flow. The flow first propagated as a jet, then became stratified. The length of the head increased with increasing distance from the reservoir (although the head propagation velocity was uniform). The maximum concentration was located at the base of the flow towards the rear of the head. Grain-size analysis showed that the head was enriched in coarse particles even at the most distal sampling location. Distinct flow stratification developed at a distance between 1.3 m and 2.6 m from the reservoir. In the body of the current, the suspended sediment was normally graded, whereas the tail exhibited inverse grading. This inverse grading may be linked to coarse particles in the head being swept upwards and backwards, then falling back into the body of the current. Alternatively, body turbulence may inhibit the settling of coarse particles. Turbulence may also explain the presence of coarse particles in the flow's head, with turbulence intensity apparently correlated with the flow competence.

Integrating real-time and manual monitored data to predict hillslope soil moisture dynamics with high spatio-temporal resolution using linear and non-linear models

USDA-ARS?s Scientific Manuscript database

Spatio-temporal variability of soil moisture (') is a challenge that remains to be better understood. A trade-off exists between spatial coverage and temporal resolution when using the manual and real-time ' monitoring methods. This restricted the comprehensive and intensive examination of ' dynamic...

Sources and Loading of Nitrogen to U.S. Estuaries

EPA Science Inventory

Previous assessments of land-based nitrogen loading and sources to U.S. estuaries have been limited to estimates for larger systems with watersheds at the scale of 8-digit HUCs and larger, in part due to the coarse resolution of available data, including estuarine watershed bound...

TOWARDS AN IMPROVED UNDERSTANDING OF SIMULATED AND OBSERVED CHANGES IN EXTREME PRECIPITATION

EPA Science Inventory

The evaluation of climate model precipitation is expected to reveal biases in simulated mean and extreme precipitation which may be a result of coarse model resolution or inefficiencies in the precipitation generating mechanisms in models. The analysis of future extreme precip...

Coarsening of three-dimensional structured and unstructured grids for subsurface flow

NASA Astrophysics Data System (ADS)

Aarnes, Jørg Espen; Hauge, Vera Louise; Efendiev, Yalchin

2007-11-01

We present a generic, semi-automated algorithm for generating non-uniform coarse grids for modeling subsurface flow. The method is applicable to arbitrary grids and does not impose smoothness constraints on the coarse grid. One therefore avoids conventional smoothing procedures that are commonly used to ensure that the grids obtained with standard coarsening procedures are not too rough. The coarsening algorithm is very simple and essentially involves only two parameters that specify the level of coarsening. Consequently the algorithm allows the user to specify the simulation grid dynamically to fit available computer resources, and, e.g., use the original geomodel as input for flow simulations. This is of great importance since coarse grid-generation is normally the most time-consuming part of an upscaling phase, and therefore the main obstacle that has prevented simulation workflows with user-defined resolution. We apply the coarsening algorithm to a series of two-phase flow problems on both structured (Cartesian) and unstructured grids. The numerical results demonstrate that one consistently obtains significantly more accurate results using the proposed non-uniform coarsening strategy than with corresponding uniform coarse grids with roughly the same number of cells.

Per-Pixel Coded Exposure for High-Speed and High-Resolution Imaging Using a Digital Micromirror Device Camera

PubMed Central

Feng, Wei; Zhang, Fumin; Qu, Xinghua; Zheng, Shiwei

2016-01-01

High-speed photography is an important tool for studying rapid physical phenomena. However, low-frame-rate CCD (charge coupled device) or CMOS (complementary metal oxide semiconductor) camera cannot effectively capture the rapid phenomena with high-speed and high-resolution. In this paper, we incorporate the hardware restrictions of existing image sensors, design the sampling functions, and implement a hardware prototype with a digital micromirror device (DMD) camera in which spatial and temporal information can be flexibly modulated. Combined with the optical model of DMD camera, we theoretically analyze the per-pixel coded exposure and propose a three-element median quicksort method to increase the temporal resolution of the imaging system. Theoretically, this approach can rapidly increase the temporal resolution several, or even hundreds, of times without increasing bandwidth requirements of the camera. We demonstrate the effectiveness of our method via extensive examples and achieve 100 fps (frames per second) gain in temporal resolution by using a 25 fps camera. PMID:26959023

Per-Pixel Coded Exposure for High-Speed and High-Resolution Imaging Using a Digital Micromirror Device Camera.

PubMed

Feng, Wei; Zhang, Fumin; Qu, Xinghua; Zheng, Shiwei

2016-03-04

High-speed photography is an important tool for studying rapid physical phenomena. However, low-frame-rate CCD (charge coupled device) or CMOS (complementary metal oxide semiconductor) camera cannot effectively capture the rapid phenomena with high-speed and high-resolution. In this paper, we incorporate the hardware restrictions of existing image sensors, design the sampling functions, and implement a hardware prototype with a digital micromirror device (DMD) camera in which spatial and temporal information can be flexibly modulated. Combined with the optical model of DMD camera, we theoretically analyze the per-pixel coded exposure and propose a three-element median quicksort method to increase the temporal resolution of the imaging system. Theoretically, this approach can rapidly increase the temporal resolution several, or even hundreds, of times without increasing bandwidth requirements of the camera. We demonstrate the effectiveness of our method via extensive examples and achieve 100 fps (frames per second) gain in temporal resolution by using a 25 fps camera.

Biomechanics meets the ecological niche: the importance of temporal data resolution.

PubMed

Kearney, Michael R; Matzelle, Allison; Helmuth, Brian

2012-03-15

The emerging field of mechanistic niche modelling aims to link the functional traits of organisms to their environments to predict survival, reproduction, distribution and abundance. This approach has great potential to increase our understanding of the impacts of environmental change on individuals, populations and communities by providing functional connections between physiological and ecological response to increasingly available spatial environmental data. By their nature, such mechanistic models are more data intensive in comparison with the more widely applied correlative approaches but can potentially provide more spatially and temporally explicit predictions, which are often needed by decision makers. A poorly explored issue in this context is the appropriate level of temporal resolution of input data required for these models, and specifically the error in predictions that can be incurred through the use of temporally averaged data. Here, we review how biomechanical principles from heat-transfer and metabolic theory are currently being used as foundations for mechanistic niche models and consider the consequences of different temporal resolutions of environmental data for modelling the niche of a behaviourally thermoregulating terrestrial lizard. We show that fine-scale temporal resolution (daily) data can be crucial for unbiased inference of climatic impacts on survival, growth and reproduction. This is especially so for species with little capacity for behavioural buffering, because of behavioural or habitat constraints, and for detecting temporal trends. However, coarser-resolution data (long-term monthly averages) can be appropriate for mechanistic studies of climatic constraints on distribution and abundance limits in thermoregulating species at broad spatial scales.

ENSO in a warming world: interannual climate variability in the early Miocene Southern Hemisphere

NASA Astrophysics Data System (ADS)

Fox, Bethany; Wilson, Gary; Lee, Daphne

2016-04-01

The El Niño - Southern Oscillation (ENSO) is the dominant source of interannual variability in the modern-day climate system. ENSO is a quasi-periodic cycle with a recurrence interval of 2-8 years. A major question in modern climatology is how ENSO will respond to increased climatic warmth. ENSO-like (2-8 year) cycles have been detected in many palaeoclimate records for the Holocene. However, the temporal resolution of pre-Quaternary palaeoclimate archives is generally too coarse to investigate ENSO-scale variability. We present a 100-kyr record of ENSO-like variability during the second half of the Oligocene/Miocene Mi-1 event, a period of increasing global temperatures and Antarctic deglaciation (~23.032-2.93 Ma). This record is drawn from an annually laminated lacustrine diatomite from southern New Zealand, a region strongly affected by ENSO in the present day. The diatomite consists of seasonal alternations of light (diatom bloom) and dark (low diatom productivity) layers. Each light-dark couplet represents one year's sedimentation. Light-dark couplet thickness is characterised by ENSO-scale variability. We use high-resolution (sub-annual) measurements of colour spectra to detect couplet thickness variability. Wavelet analysis indicates that absolute values are modulated by orbital cycles. However, when orbital effects are taken into account, ENSO-like variability occurs throughout the entire depositional period, with no clear increase or reduction in relation to Antarctic deglaciation and increasing global warmth.

Anisotropic Mesoscale Eddy Transport in Ocean General Circulation Models

NASA Astrophysics Data System (ADS)

Reckinger, S. J.; Fox-Kemper, B.; Bachman, S.; Bryan, F.; Dennis, J.; Danabasoglu, G.

2014-12-01

Modern climate models are limited to coarse-resolution representations of large-scale ocean circulation that rely on parameterizations for mesoscale eddies. The effects of eddies are typically introduced by relating subgrid eddy fluxes to the resolved gradients of buoyancy or other tracers, where the proportionality is, in general, governed by an eddy transport tensor. The symmetric part of the tensor, which represents the diffusive effects of mesoscale eddies, is universally treated isotropically in general circulation models. Thus, only a single parameter, namely the eddy diffusivity, is used at each spatial and temporal location to impart the influence of mesoscale eddies on the resolved flow. However, the diffusive processes that the parameterization approximates, such as shear dispersion, potential vorticity barriers, oceanic turbulence, and instabilities, typically have strongly anisotropic characteristics. Generalizing the eddy diffusivity tensor for anisotropy extends the number of parameters to three: a major diffusivity, a minor diffusivity, and the principal axis of alignment. The Community Earth System Model (CESM) with the anisotropic eddy parameterization is used to test various choices for the newly introduced parameters, which are motivated by observations and the eddy transport tensor diagnosed from high resolution simulations. Simply setting the ratio of major to minor diffusivities to a value of five globally, while aligning the major axis along the flow direction, improves biogeochemical tracer ventilation and reduces global temperature and salinity biases. These effects can be improved even further by parameterizing the anisotropic transport mechanisms in the ocean.

LES on unstructured deforming meshes: Towards reciprocating IC engines

NASA Technical Reports Server (NTRS)

Haworth, D. C.; Jansen, K.

1996-01-01

A variable explicit/implicit characteristics-based advection scheme that is second-order accurate in space and time has been developed recently for unstructured deforming meshes (O'Rourke & Sahota 1996a). To explore the suitability of this methodology for Large-Eddy Simulation (LES), three subgrid-scale turbulence models have been implemented in the CHAD CFD code (O'Rourke & Sahota 1996b): a constant-coefficient Smagorinsky model, a dynamic Smagorinsky model for flows having one or more directions of statistical homogeneity, and a Lagrangian dynamic Smagorinsky model for flows having no spatial or temporal homogeneity (Meneveau et al. 1996). Computations have been made for three canonical flows, progressing towards the intended application of in-cylinder flow in a reciprocating engine. Grid sizes were selected to be comparable to the coarsest meshes used in earlier spectral LES studies. Quantitative results are reported for decaying homogeneous isotropic turbulence, and for a planar channel flow. Computations are compared to experimental measurements, to Direct-Numerical Simulation (DNS) data, and to Rapid-Distortion Theory (RDT) where appropriate. Generally satisfactory evolution of first and second moments is found on these coarse meshes; deviations are attributed to insufficient mesh resolution. Issues include mesh resolution and computational requirements for a specified level of accuracy, analytic characterization of the filtering implied by the numerical method, wall treatment, and inflow boundary conditions. To resolve these issues, finer-mesh simulations and computations of a simplified axisymmetric reciprocating piston-cylinder assembly are in progress.

On the stability of projection methods for the incompressible Navier-Stokes equations based on high-order discontinuous Galerkin discretizations

NASA Astrophysics Data System (ADS)

Fehn, Niklas; Wall, Wolfgang A.; Kronbichler, Martin

2017-12-01

The present paper deals with the numerical solution of the incompressible Navier-Stokes equations using high-order discontinuous Galerkin (DG) methods for discretization in space. For DG methods applied to the dual splitting projection method, instabilities have recently been reported that occur for small time step sizes. Since the critical time step size depends on the viscosity and the spatial resolution, these instabilities limit the robustness of the Navier-Stokes solver in case of complex engineering applications characterized by coarse spatial resolutions and small viscosities. By means of numerical investigation we give evidence that these instabilities are related to the discontinuous Galerkin formulation of the velocity divergence term and the pressure gradient term that couple velocity and pressure. Integration by parts of these terms with a suitable definition of boundary conditions is required in order to obtain a stable and robust method. Since the intermediate velocity field does not fulfill the boundary conditions prescribed for the velocity, a consistent boundary condition is derived from the convective step of the dual splitting scheme to ensure high-order accuracy with respect to the temporal discretization. This new formulation is stable in the limit of small time steps for both equal-order and mixed-order polynomial approximations. Although the dual splitting scheme itself includes inf-sup stabilizing contributions, we demonstrate that spurious pressure oscillations appear for equal-order polynomials and small time steps highlighting the necessity to consider inf-sup stability explicitly.

The Quasi-biennial Oscillation and Annual Variations in Tropical Ozone from SHADOZ and HALOE

NASA Technical Reports Server (NTRS)

Witte, J. C.; Schoeberl, M. R.; Douglass, A. R.; Thompson, A. M.

2008-01-01

We examine the tropical ozone mixing ratio perturbation fields generated from a monthly ozone climatology using 1998 to 2006 ozonesonde data from the Southern Hemisphere Additional Ozonesondes (SHADOZ) network and the 13-year satellite record from 1993 to 2005 obtained from the Halogen Occultation Experiment (HALOE). The long time series and high vertical resolution of the ozone and temperature profiles from the SHADOZ sondes coupled with good tropical coverage north and south of the equator gives a detailed picture of the ozone structure in the lowermost stratosphere down through the tropopause where the picture obtained from HALOE measurements is blurred by coarse vertical resolution. Ozone perturbations respond to annual variations in the Brewer-Dobson Circulation (BDC) in the region just above the cold-point tropopause to around 20 km. Annual cycles in ozone and temperature are well correlated. Above 20 km, ozone and temperature perturbations are dominated by the Quasi-biennial Oscillation (QBO). Both satellite and sonde records show good agreement between positive and negative ozone mixing ratio anomalies and alternating QBO westerly and easterly wind shears from the Singapore rawinsondes with a mean periodicity of 26 months for SHADOZ and 25 months for HALOE. There is a temporal offset of one to three months with the QBO wind shear ahead of the ozone anomaly field. The meridional length scales for the annual cycle and the QBO, obtained using the temperature anomalies and wind shears in the thermal wind equation, compare well with theoretical calculations.

Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems

PubMed Central

Selmants, Paul C.; Moreno, Alvaro; Running, Steve W.; Giardina, Christian P.

2017-01-01

Gross primary production (GPP) is the Earth’s largest carbon flux into the terrestrial biosphere and plays a critical role in regulating atmospheric chemistry and global climate. The Moderate Resolution Imaging Spectrometer (MODIS)-MOD17 data product is a widely used remote sensing-based model that provides global estimates of spatiotemporal trends in GPP. When the MOD17 algorithm is applied to regional scale heterogeneous landscapes, input data from coarse resolution land cover and climate products may increase uncertainty in GPP estimates, especially in high productivity tropical ecosystems. We examined the influence of using locally specific land cover and high-resolution local climate input data on MOD17 estimates of GPP for the State of Hawaii, a heterogeneous and discontinuous tropical landscape. Replacing the global land cover data input product (MOD12Q1) with Hawaii-specific land cover data reduced statewide GPP estimates by ~8%, primarily because the Hawaii-specific land cover map had less vegetated land area compared to the global land cover product. Replacing coarse resolution GMAO climate data with Hawaii-specific high-resolution climate data also reduced statewide GPP estimates by ~8% because of the higher spatial variability of photosynthetically active radiation (PAR) in the Hawaii-specific climate data. The combined use of both Hawaii-specific land cover and high-resolution Hawaii climate data inputs reduced statewide GPP by ~16%, suggesting equal and independent influence on MOD17 GPP estimates. Our sensitivity analyses within a heterogeneous tropical landscape suggest that refined global land cover and climate data sets may contribute to an enhanced MOD17 product at a variety of spatial scales. PMID:28886187

Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems

USGS Publications Warehouse

Kimball, Heather L.; Selmants, Paul; Moreno, Alvaro; Running Steve W,; Giardina, Christian P.

2017-01-01

Gross primary production (GPP) is the Earth’s largest carbon flux into the terrestrial biosphere and plays a critical role in regulating atmospheric chemistry and global climate. The Moderate Resolution Imaging Spectrometer (MODIS)-MOD17 data product is a widely used remote sensing-based model that provides global estimates of spatiotemporal trends in GPP. When the MOD17 algorithm is applied to regional scale heterogeneous landscapes, input data from coarse resolution land cover and climate products may increase uncertainty in GPP estimates, especially in high productivity tropical ecosystems. We examined the influence of using locally specific land cover and high-resolution local climate input data on MOD17 estimates of GPP for the State of Hawaii, a heterogeneous and discontinuous tropical landscape. Replacing the global land cover data input product (MOD12Q1) with Hawaii-specific land cover data reduced statewide GPP estimates by ~8%, primarily because the Hawaii-specific land cover map had less vegetated land area compared to the global land cover product. Replacing coarse resolution GMAO climate data with Hawaii-specific high-resolution climate data also reduced statewide GPP estimates by ~8% because of the higher spatial variability of photosynthetically active radiation (PAR) in the Hawaii-specific climate data. The combined use of both Hawaii-specific land cover and high-resolution Hawaii climate data inputs reduced statewide GPP by ~16%, suggesting equal and independent influence on MOD17 GPP estimates. Our sensitivity analyses within a heterogeneous tropical landscape suggest that refined global land cover and climate data sets may contribute to an enhanced MOD17 product at a variety of spatial scales.

Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems.

PubMed

Kimball, Heather L; Selmants, Paul C; Moreno, Alvaro; Running, Steve W; Giardina, Christian P

2017-01-01

Gross primary production (GPP) is the Earth's largest carbon flux into the terrestrial biosphere and plays a critical role in regulating atmospheric chemistry and global climate. The Moderate Resolution Imaging Spectrometer (MODIS)-MOD17 data product is a widely used remote sensing-based model that provides global estimates of spatiotemporal trends in GPP. When the MOD17 algorithm is applied to regional scale heterogeneous landscapes, input data from coarse resolution land cover and climate products may increase uncertainty in GPP estimates, especially in high productivity tropical ecosystems. We examined the influence of using locally specific land cover and high-resolution local climate input data on MOD17 estimates of GPP for the State of Hawaii, a heterogeneous and discontinuous tropical landscape. Replacing the global land cover data input product (MOD12Q1) with Hawaii-specific land cover data reduced statewide GPP estimates by ~8%, primarily because the Hawaii-specific land cover map had less vegetated land area compared to the global land cover product. Replacing coarse resolution GMAO climate data with Hawaii-specific high-resolution climate data also reduced statewide GPP estimates by ~8% because of the higher spatial variability of photosynthetically active radiation (PAR) in the Hawaii-specific climate data. The combined use of both Hawaii-specific land cover and high-resolution Hawaii climate data inputs reduced statewide GPP by ~16%, suggesting equal and independent influence on MOD17 GPP estimates. Our sensitivity analyses within a heterogeneous tropical landscape suggest that refined global land cover and climate data sets may contribute to an enhanced MOD17 product at a variety of spatial scales.

Coherent diffractive imaging of time-evolving samples with improved temporal resolution

DOE PAGES

Ulvestad, A.; Tripathi, A.; Hruszkewycz, S. O.; ...

2016-05-19

Bragg coherent x-ray diffractive imaging is a powerful technique for investigating dynamic nanoscale processes in nanoparticles immersed in reactive, realistic environments. Its temporal resolution is limited, however, by the oversampling requirements of three-dimensional phase retrieval. Here, we show that incorporating the entire measurement time series, which is typically a continuous physical process, into phase retrieval allows the oversampling requirement at each time step to be reduced, leading to a subsequent improvement in the temporal resolution by a factor of 2-20 times. The increased time resolution will allow imaging of faster dynamics and of radiation-dose-sensitive samples. Furthermore, this approach, which wemore » call "chrono CDI," may find use in improving the time resolution in other imaging techniques.« less

Feasibility of high temporal resolution breast DCE-MRI using compressed sensing theory.

PubMed

Wang, Haoyu; Miao, Yanwei; Zhou, Kun; Yu, Yanming; Bao, Shanglian; He, Qiang; Dai, Yongming; Xuan, Stephanie Y; Tarabishy, Bisher; Ye, Yongquan; Hu, Jiani

2010-09-01

To investigate the feasibility of high temporal resolution breast DCE-MRI using compressed sensing theory. Two experiments were designed to investigate the feasibility of using reference image based compressed sensing (RICS) technique in DCE-MRI of the breast. The first experiment examined the capability of RICS to faithfully reconstruct uptake curves using undersampled data sets extracted from fully sampled clinical breast DCE-MRI data. An average approach and an approach using motion estimation and motion compensation (ME/MC) were implemented to obtain reference images and to evaluate their efficacy in reducing motion related effects. The second experiment, an in vitro phantom study, tested the feasibility of RICS for improving temporal resolution without degrading the spatial resolution. For the uptake-curve reconstruction experiment, there was a high correlation between uptake curves reconstructed from fully sampled data by Fourier transform and from undersampled data by RICS, indicating high similarity between them. The mean Pearson correlation coefficients for RICS with the ME/MC approach and RICS with the average approach were 0.977 +/- 0.023 and 0.953 +/- 0.031, respectively. The comparisons of final reconstruction results between RICS with the average approach and RICS with the ME/MC approach suggested that the latter was superior to the former in reducing motion related effects. For the in vitro experiment, compared to the fully sampled method, RICS improved the temporal resolution by an acceleration factor of 10 without degrading the spatial resolution. The preliminary study demonstrates the feasibility of RICS for faithfully reconstructing uptake curves and improving temporal resolution of breast DCE-MRI without degrading the spatial resolution.

Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations

USGS Publications Warehouse

Cook, B.D.; Bolstad, P.V.; Naesset, E.; Anderson, R. Scott; Garrigues, S.; Morisette, J.T.; Nickeson, J.; Davis, K.J.

2009-01-01

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30??m to 1??km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600??ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400??m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine-resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire landscape. Failure to account for wetlands had little impact on landscape-scale estimates, because vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Monitoring Water Resources in Pastoral Areas of East Africa Using Satellite Data and Hydrologic Modeling

NASA Astrophysics Data System (ADS)

Alemu, H.; Senay, G. B.; Velpuri, N.; Asante, K. O.

2008-12-01

The nomadic pastoral communities in East Africa heavily depend on small water bodies and artificial lakes for domestic and livestock uses. The shortage of water in the region has made these water resources of great importance to them and sometimes even the reason for conflicts amongst rival communities in the region. Satellite-based data has significantly transformed the way we track and estimate hydrological processes such as precipitation and evapotranspiration. This approach has been particularly useful in remote places where conventional station-based weather networks are scarce. Tropical Rainfall Measuring Mission (TRMM) satellite data were extracted for the study region. National Oceanic and Atmospheric Administration's (NOAA) Global Data Assimilation System (GDAS) data were used to extract the climatic parameters needed to calculate reference evapotranspiration. The elevation data needed to delineate the watersheds were extracted from the Shuttle Radar Topography Mission (SRTM) with spatial resolution of 90m. The waterholes (most of which have average surface area less than a hectare) were identified using Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) images with a spatial resolution of 15 m. As part of National Aeronautics and Space Administration's (NASA) funded enhancement to a livestock early warning decision support system, a simple hydrologic water balance model was developed to estimate daily waterhole depth variations. The model was run for over 10 years from 1998 till 2008 for 10 representative waterholes in the region. Although there were no independent datasets to validate the results, the temporal patterns captured both the seasonal and inter-annual variations, depicting known drought and flood years. Future research includes the installation of staff-gauges for model calibration and validation. The simple modeling approach demonstrated the effectiveness of integrating dynamic coarse resolution datasets such as TRMM with high resolution static datasets such as ASTER and SRTM DEM (Digital Elevation Model) to monitor water resources for drought early warning applications.

Using LIDAR and Quickbird Data to Model Plant Production and Quantify Uncertainties Associated with Wetland Detection and Land Cover Generalizations

NASA Technical Reports Server (NTRS)

Cook, Bruce D.; Bolstad, Paul V.; Naesset, Erik; Anderson, Ryan S.; Garrigues, Sebastian; Morisette, Jeffrey T.; Nickeson, Jaime; Davis, Kenneth J.

2009-01-01

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the MOderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

High-Resolution Subtropical Summer Precipitation Derived from Dynamical Downscaling of the NCEP-DOE Reanalysis: How Much Small-Scale Information Is Added by a Regional Model?

NASA Technical Reports Server (NTRS)

Lim, Young-Kwon; Stefanova, Lydia B.; Chan, Steven C.; Schubert, Siegfried D.; OBrien, James J.

2010-01-01

This study assesses the regional-scale summer precipitation produced by the dynamical downscaling of analyzed large-scale fields. The main goal of this study is to investigate how much the regional model adds smaller scale precipitation information that the large-scale fields do not resolve. The modeling region for this study covers the southeastern United States (Florida, Georgia, Alabama, South Carolina, and North Carolina) where the summer climate is subtropical in nature, with a heavy influence of regional-scale convection. The coarse resolution (2.5deg latitude/longitude) large-scale atmospheric variables from the National Center for Environmental Prediction (NCEP)/DOE reanalysis (R2) are downscaled using the NCEP Environmental Climate Prediction Center regional spectral model (RSM) to produce precipitation at 20 km resolution for 16 summer seasons (19902005). The RSM produces realistic details in the regional summer precipitation at 20 km resolution. Compared to R2, the RSM-produced monthly precipitation shows better agreement with observations. There is a reduced wet bias and a more realistic spatial pattern of the precipitation climatology compared with the interpolated R2 values. The root mean square errors of the monthly R2 precipitation are reduced over 93 (1,697) of all the grid points in the five states (1,821). The temporal correlation also improves over 92 (1,675) of all grid points such that the domain-averaged correlation increases from 0.38 (R2) to 0.55 (RSM). The RSM accurately reproduces the first two observed eigenmodes, compared with the R2 product for which the second mode is not properly reproduced. The spatial patterns for wet versus dry summer years are also successfully simulated in RSM. For shorter time scales, the RSM resolves heavy rainfall events and their frequency better than R2. Correlation and categorical classification (above/near/below average) for the monthly frequency of heavy precipitation days is also significantly improved by the RSM.

Integrating Eddy Covariance, Penman-Monteith and METRIC based Evapotranspiration estimates to generate high resolution space-time ET over the Brazos River Basin

NASA Astrophysics Data System (ADS)

Mbabazi, D.; Mohanty, B.; Gaur, N.

2017-12-01

Evapotranspiration (ET) is an important component of the water and energy balance and accounts for 60 -70% of precipitation losses. However, accurate estimates of ET are difficult to quantify at varying spatial and temporal scales. Eddy covariance methods estimate ET at high temporal resolutions but without capturing the spatial variation in ET within its footprint. On the other hand, remote sensing methods using Landsat imagery provide ET with high spatial resolution but low temporal resolution (16 days). In this study, we used both eddy covariance and remote sensing methods to generate high space-time resolution ET. Daily, monthly and seasonal ET estimates were obtained using the eddy covariance (EC) method, Penman-Monteith (PM) and Mapping Evapotranspiration with Internalized Calibration (METRIC) models to determine cotton and native prairie ET dynamics in the Brazos river basin characterized by varying hydro-climatic and geological gradients. Daily estimates of spatially distributed ET (30 m resolution) were generated using spatial autocorrelation and temporal interpolations between the EC flux variable footprints and METRIC ET for the 2016 and 2017 growing seasons. A comparison of the 2016 and 2017 preliminary daily ET estimates showed similar ET dynamics/trends among the EC, PM and METRIC methods, and 5-20% differences in seasonal ET estimates. This study will improve the spatial estimates of EC ET and temporal resolution of satellite derived ET thus providing better ET data for water use management.

Accuracy Sampling Design Bias on Coarse Spatial Resolution Land Cover Data in the Great Lakes Region (United States and Canada)

EPA Science Inventory

A number of articles have investigated the impact of sampling design on remotely sensed landcover accuracy estimates. Gong and Howarth (1990) found significant differences for Kappa accuracy values when comparing purepixel sampling, stratified random sampling, and stratified sys...

Passive microwave soil moisture downscaling using vegetation index and skin surface temperature

USDA-ARS?s Scientific Manuscript database

Soil moisture satellite estimates are available from a variety of passive microwave satellite sensors, but their spatial resolution is frequently too coarse for use by land managers and other decision makers. In this paper, a soil moisture downscaling algorithm based on a regression relationship bet...

: “Developing Regional Modeling Techniques Applicable for Simulating Future Climate Conditions in the Carolinas”

EPA Science Inventory

Global climate models (GCMs) are currently used to obtain information about future changes in the large-scale climate. However, such simulations are typically done at coarse spatial resolutions, with model grid boxes on the order of 100 km on a horizontal side. Therefore, techniq...

Active–passive soil moisture retrievals during the SMAP validation experiment 2012

USDA-ARS?s Scientific Manuscript database

The goal of this study is to assess the performance of the active–passive algorithm for the NASA Soil Moisture Active Passive mission (SMAP) using airborne and ground observations from a field campaign. The SMAP active–passive algorithm disaggregates the coarse-resolution radiometer brightness tempe...

Structural alignment sensor. [laser applications and interferometry

NASA Technical Reports Server (NTRS)

Davis, L.; Buholz, N. E.; Gillard, C. W.; Huang, C. C.; Wells, W. M., III

1978-01-01

Comparative Michelson interferometers are discussed as well as the operating range potential of a structural alignment sensor (SAS) which requires only one laser mode. Schematics are presented for the distance measurement logic, the basic SAS system, the SAS optical layout, the coarse measurement signal processor, and the measured range resolution.

Analysis of North Atlantic Tropical Cyclone Intensify Change Using Data Mining

ERIC Educational Resources Information Center

Tang, Jiang

2010-01-01

Tropical cyclones (TC), especially when their intensity reaches hurricane scale, can become a costly natural hazard. Accurate prediction of tropical cyclone intensity is very difficult because of inadequate observations on TC structures, poor understanding of physical processes, coarse model resolution and inaccurate initial conditions, etc. This…

Object Manifold Alignment for Multi-Temporal High Resolution Remote Sensing Images Classification

NASA Astrophysics Data System (ADS)

Gao, G.; Zhang, M.; Gu, Y.

2017-05-01

Multi-temporal remote sensing images classification is very useful for monitoring the land cover changes. Traditional approaches in this field mainly face to limited labelled samples and spectral drift of image information. With spatial resolution improvement, "pepper and salt" appears and classification results will be effected when the pixelwise classification algorithms are applied to high-resolution satellite images, in which the spatial relationship among the pixels is ignored. For classifying the multi-temporal high resolution images with limited labelled samples, spectral drift and "pepper and salt" problem, an object-based manifold alignment method is proposed. Firstly, multi-temporal multispectral images are cut to superpixels by simple linear iterative clustering (SLIC) respectively. Secondly, some features obtained from superpixels are formed as vector. Thirdly, a majority voting manifold alignment method aiming at solving high resolution problem is proposed and mapping the vector data to alignment space. At last, all the data in the alignment space are classified by using KNN method. Multi-temporal images from different areas or the same area are both considered in this paper. In the experiments, 2 groups of multi-temporal HR images collected by China GF1 and GF2 satellites are used for performance evaluation. Experimental results indicate that the proposed method not only has significantly outperforms than traditional domain adaptation methods in classification accuracy, but also effectively overcome the problem of "pepper and salt".

Central tendency effects in time interval reproduction in autism

PubMed Central

Karaminis, Themelis; Cicchini, Guido Marco; Neil, Louise; Cappagli, Giulia; Aagten-Murphy, David; Burr, David; Pellicano, Elizabeth

2016-01-01

Central tendency, the tendency of judgements of quantities (lengths, durations etc.) to gravitate towards their mean, is one of the most robust perceptual effects. A Bayesian account has recently suggested that central tendency reflects the integration of noisy sensory estimates with prior knowledge representations of a mean stimulus, serving to improve performance. The process is flexible, so prior knowledge is weighted more heavily when sensory estimates are imprecise, requiring more integration to reduce noise. In this study we measure central tendency in autism to evaluate a recent theoretical hypothesis suggesting that autistic perception relies less on prior knowledge representations than typical perception. If true, autistic children should show reduced central tendency than theoretically predicted from their temporal resolution. We tested autistic and age- and ability-matched typical children in two child-friendly tasks: (1) a time interval reproduction task, measuring central tendency in the temporal domain; and (2) a time discrimination task, assessing temporal resolution. Central tendency reduced with age in typical development, while temporal resolution improved. Autistic children performed far worse in temporal discrimination than the matched controls. Computational simulations suggested that central tendency was much less in autistic children than predicted by theoretical modelling, given their poor temporal resolution. PMID:27349722

The influence of multispectral scanner spatial resolution on forest feature classification

NASA Technical Reports Server (NTRS)

Sadowski, F. G.; Malila, W. A.; Sarno, J. E.; Nalepka, R. F.

1977-01-01

Inappropriate spatial resolution and corresponding data processing techniques may be major causes for non-optimal forest classification results frequently achieved from multispectral scanner (MSS) data. Procedures and results of empirical investigations are studied to determine the influence of MSS spatial resolution on the classification of forest features into levels of detail or hierarchies of information that might be appropriate for nationwide forest surveys and detailed in-place inventories. Two somewhat different, but related studies are presented. The first consisted of establishing classification accuracies for several hierarchies of features as spatial resolution was progressively coarsened from (2 meters) squared to (64 meters) squared. The second investigated the capabilities for specialized processing techniques to improve upon the results of conventional processing procedures for both coarse and fine resolution data.

A 2.5-million-year perspective on coarse-filter strategies for conserving nature's stage.

PubMed

Gill, Jacquelyn L; Blois, Jessica L; Benito, Blas; Dobrowski, Solomon; Hunter, Malcolm L; McGuire, Jenny L

2015-06-01

Climate change will require novel conservation strategies. One such tactic is a coarse-filter approach that focuses on conserving nature's stage (CNS) rather than the actors (individual species). However, there is a temporal mismatch between the long-term goals of conservation and the short-term nature of most ecological studies, which leaves many assumptions untested. Paleoecology provides a valuable perspective on coarse-filter strategies by marshaling the natural experiments of the past to contextualize extinction risk due to the emerging impacts of climate change and anthropogenic threats. We reviewed examples from the paleoecological record that highlight the strengths, opportunities, and caveats of a CNS approach. We focused on the near-time geological past of the Quaternary, during which species were subjected to widespread changes in climate and concomitant changes in the physical environment in general. Species experienced a range of individualistic responses to these changes, including community turnover and novel associations, extinction and speciation, range shifts, changes in local richness and evenness, and both equilibrium and disequilibrium responses. Due to the dynamic nature of species responses to Quaternary climate change, a coarse-filter strategy may be appropriate for many taxa because it can accommodate dynamic processes. However, conservationists should also consider that the persistence of landforms varies across space and time, which could have potential long-term consequences for geodiversity and thus biodiversity. © 2015 Society for Conservation Biology.

Neural Tuning to Low-Level Features of Speech throughout the Perisylvian Cortex.

PubMed

Berezutskaya, Julia; Freudenburg, Zachary V; Güçlü, Umut; van Gerven, Marcel A J; Ramsey, Nick F

2017-08-16

Despite a large body of research, we continue to lack a detailed account of how auditory processing of continuous speech unfolds in the human brain. Previous research showed the propagation of low-level acoustic features of speech from posterior superior temporal gyrus toward anterior superior temporal gyrus in the human brain (Hullett et al., 2016). In this study, we investigate what happens to these neural representations past the superior temporal gyrus and how they engage higher-level language processing areas such as inferior frontal gyrus. We used low-level sound features to model neural responses to speech outside of the primary auditory cortex. Two complementary imaging techniques were used with human participants (both males and females): electrocorticography (ECoG) and fMRI. Both imaging techniques showed tuning of the perisylvian cortex to low-level speech features. With ECoG, we found evidence of propagation of the temporal features of speech sounds along the ventral pathway of language processing in the brain toward inferior frontal gyrus. Increasingly coarse temporal features of speech spreading from posterior superior temporal cortex toward inferior frontal gyrus were associated with linguistic features such as voice onset time, duration of the formant transitions, and phoneme, syllable, and word boundaries. The present findings provide the groundwork for a comprehensive bottom-up account of speech comprehension in the human brain. SIGNIFICANCE STATEMENT We know that, during natural speech comprehension, a broad network of perisylvian cortical regions is involved in sound and language processing. Here, we investigated the tuning to low-level sound features within these regions using neural responses to a short feature film. We also looked at whether the tuning organization along these brain regions showed any parallel to the hierarchy of language structures in continuous speech. Our results show that low-level speech features propagate throughout the perisylvian cortex and potentially contribute to the emergence of "coarse" speech representations in inferior frontal gyrus typically associated with high-level language processing. These findings add to the previous work on auditory processing and underline a distinctive role of inferior frontal gyrus in natural speech comprehension. Copyright © 2017 the authors 0270-6474/17/377906-15$15.00/0.

Extraction of temporal information in functional MRI

NASA Astrophysics Data System (ADS)

Singh, M.; Sungkarat, W.; Jeong, Jeong-Won; Zhou, Yongxia

2002-10-01

The temporal resolution of functional MRI (fMRI) is limited by the shape of the haemodynamic response function (hrf) and the vascular architecture underlying the activated regions. Typically, the temporal resolution of fMRI is on the order of 1 s. We have developed a new data processing approach to extract temporal information on a pixel-by-pixel basis at the level of 100 ms from fMRI data. Instead of correlating or fitting the time-course of each pixel to a single reference function, which is the common practice in fMRI, we correlate each pixel's time-course to a series of reference functions that are shifted with respect to each other by 100 ms. The reference function yielding the highest correlation coefficient for a pixel is then used as a time marker for that pixel. A Monte Carlo simulation and experimental study of this approach were performed to estimate the temporal resolution as a function of signal-to-noise ratio (SNR) in the time-course of a pixel. Assuming a known and stationary hrf, the simulation and experimental studies suggest a lower limit in the temporal resolution of approximately 100 ms at an SNR of 3. The multireference function approach was also applied to extract timing information from an event-related motor movement study where the subjects flexed a finger on cue. The event was repeated 19 times with the event's presentation staggered to yield an approximately 100-ms temporal sampling of the haemodynamic response over the entire presentation cycle. The timing differences among different regions of the brain activated by the motor task were clearly visualized and quantified by this method. The results suggest that it is possible to achieve a temporal resolution of /spl sim/200 ms in practice with this approach.

Mapping wildfire burn severity in the Arctic Tundra from downsampled MODIS data

USGS Publications Warehouse

Kolden, Crystal A.; Rogan, John

2013-01-01

Wildfires are historically infrequent in the arctic tundra, but are projected to increase with climate warming. Fire effects on tundra ecosystems are poorly understood and difficult to quantify in a remote region where a short growing season severely limits ground data collection. Remote sensing has been widely utilized to characterize wildfire regimes, but primarily from the Landsat sensor, which has limited data acquisition in the Arctic. Here, coarse-resolution remotely sensed data are assessed as a means to quantify wildfire burn severity of the 2007 Anaktuvuk River Fire in Alaska, the largest tundra wildfire ever recorded on Alaska's North Slope. Data from Landsat Thematic Mapper (TM) and downsampled Moderate-resolution Imaging Spectroradiometer (MODIS) were processed to spectral indices and correlated to observed metrics of surface, subsurface, and comprehensive burn severity. Spectral indices were strongly correlated to surface severity (maximum R2 = 0.88) and slightly less strongly correlated to substrate severity. Downsampled MODIS data showed a decrease in severity one year post-fire, corroborating rapid vegetation regeneration observed on the burned site. These results indicate that widely-used spectral indices and downsampled coarse-resolution data provide a reasonable supplement to often-limited ground data collection for analysis and long-term monitoring of wildfire effects in arctic ecosystems.

Acoustic estimates of zooplankton and micronekton biomass in cyclones and anticyclones of the northeastern Gulf of Mexico

NASA Astrophysics Data System (ADS)

Ressler, Patrick Henry

2001-12-01

In the Gulf of Mexico (GOM), coarse to mesoscale eddies can enhance the supply of limiting nutrients into the euphotic zone, elevating primary production. This leads to 'oases' of enriched standing stocks of zooplankton and micronekton in otherwise oligotrophic deepwater (>200 m bottom depth). A combination of acoustic volume backscattering (Sv) measurements with an acoustic Doppler current profiler (ADCP) and concurrent net sampling of zooplankton and micronekton biomass in GOM eddy fields between October 1996 and November 1998 confirmed that cyclones and flow confluences were areas of locally enhanced Sv and standing stock biomass. Net samples were used both to 'sea-truth' the acoustic measurements and to assess the influence of taxonomic composition on measured Sv. During October 1996 and August 1997, a mesoscale (200--300 km diameter) cyclone-anticyclone pair in the northeastern GOM was surveyed as part of a cetacean (whale and dolphin) and seabird habitat, study. Acoustic estimates of biomass in the upper 10--50 m of the water column showed that the cyclone and flow confluence were enriched relative to anticyclonic Loop Current Eddies during both years. Cetacean and seabird survey results reported by other project researchers imply that these eddies provide preferential habitat because they foster locally higher concentrations of higher-trophic-level prey. Sv measurements in November 1997 and 1998 showed that coarse scale eddies (30--150 km diameter) probably enhanced nutrients and S, in the deepwater GOM within 100 km of the Mississippi delta, an area suspected to be important habitat for cetaceans and seabirds. Finally, Sv, data collected during November-December 1997 and October-December 1998 from a mooring at the head of DeSoto Canyon in the northeastern GOM revealed temporal variability at a single location: characteristic temporal decorrelation scales were 1 day (diel vertical migration of zooplankton and micronekton) and 5 days (advective processes). A combination of acoustic and net sampling is a useful way to survey temporal and spatial patterns in zooplankton and micronekton biomass in coarse to mesoscale eddies. Further research should employ such a combination of methods to investigate plankton patterns in eddies and their implications for cetacean and seabird habitat.

Complementarity of PALM and SOFI for super-resolution live-cell imaging of focal adhesions

PubMed Central

Deschout, Hendrik; Lukes, Tomas; Sharipov, Azat; Szlag, Daniel; Feletti, Lely; Vandenberg, Wim; Dedecker, Peter; Hofkens, Johan; Leutenegger, Marcel; Lasser, Theo; Radenovic, Aleksandra

2016-01-01

Live-cell imaging of focal adhesions requires a sufficiently high temporal resolution, which remains a challenge for super-resolution microscopy. Here we address this important issue by combining photoactivated localization microscopy (PALM) with super-resolution optical fluctuation imaging (SOFI). Using simulations and fixed-cell focal adhesion images, we investigate the complementarity between PALM and SOFI in terms of spatial and temporal resolution. This PALM-SOFI framework is used to image focal adhesions in living cells, while obtaining a temporal resolution below 10 s. We visualize the dynamics of focal adhesions, and reveal local mean velocities around 190 nm min−1. The complementarity of PALM and SOFI is assessed in detail with a methodology that integrates a resolution and signal-to-noise metric. This PALM and SOFI concept provides an enlarged quantitative imaging framework, allowing unprecedented functional exploration of focal adhesions through the estimation of molecular parameters such as fluorophore densities and photoactivation or photoswitching kinetics. PMID:27991512

Complementarity of PALM and SOFI for super-resolution live-cell imaging of focal adhesions

NASA Astrophysics Data System (ADS)

Deschout, Hendrik; Lukes, Tomas; Sharipov, Azat; Szlag, Daniel; Feletti, Lely; Vandenberg, Wim; Dedecker, Peter; Hofkens, Johan; Leutenegger, Marcel; Lasser, Theo; Radenovic, Aleksandra

2016-12-01

Live-cell imaging of focal adhesions requires a sufficiently high temporal resolution, which remains a challenge for super-resolution microscopy. Here we address this important issue by combining photoactivated localization microscopy (PALM) with super-resolution optical fluctuation imaging (SOFI). Using simulations and fixed-cell focal adhesion images, we investigate the complementarity between PALM and SOFI in terms of spatial and temporal resolution. This PALM-SOFI framework is used to image focal adhesions in living cells, while obtaining a temporal resolution below 10 s. We visualize the dynamics of focal adhesions, and reveal local mean velocities around 190 nm min-1. The complementarity of PALM and SOFI is assessed in detail with a methodology that integrates a resolution and signal-to-noise metric. This PALM and SOFI concept provides an enlarged quantitative imaging framework, allowing unprecedented functional exploration of focal adhesions through the estimation of molecular parameters such as fluorophore densities and photoactivation or photoswitching kinetics.

Complementarity of PALM and SOFI for super-resolution live-cell imaging of focal adhesions.

PubMed

Deschout, Hendrik; Lukes, Tomas; Sharipov, Azat; Szlag, Daniel; Feletti, Lely; Vandenberg, Wim; Dedecker, Peter; Hofkens, Johan; Leutenegger, Marcel; Lasser, Theo; Radenovic, Aleksandra

2016-12-19

Live-cell imaging of focal adhesions requires a sufficiently high temporal resolution, which remains a challenge for super-resolution microscopy. Here we address this important issue by combining photoactivated localization microscopy (PALM) with super-resolution optical fluctuation imaging (SOFI). Using simulations and fixed-cell focal adhesion images, we investigate the complementarity between PALM and SOFI in terms of spatial and temporal resolution. This PALM-SOFI framework is used to image focal adhesions in living cells, while obtaining a temporal resolution below 10 s. We visualize the dynamics of focal adhesions, and reveal local mean velocities around 190 nm min -1 . The complementarity of PALM and SOFI is assessed in detail with a methodology that integrates a resolution and signal-to-noise metric. This PALM and SOFI concept provides an enlarged quantitative imaging framework, allowing unprecedented functional exploration of focal adhesions through the estimation of molecular parameters such as fluorophore densities and photoactivation or photoswitching kinetics.

Monitoring Ground Deformation at the Aquistore CO2 Storage Site in SE Saskatchewan, Canada

NASA Astrophysics Data System (ADS)

Samsonov, S. V.; White, D.; Craymer, M. R.; Murnaghan, K.; Chalaturnyk, R. J.

2012-12-01

The scientific objectives of the Aquistore CO2 storage project is to design, adapt, and test non-seismic monitoring methods that have not been systematically utilized to date for monitoring CO2 storage, and to integrate the data from these various monitoring tools to obtain quantitative estimates of the change in subsurface fluid distributions, pressure changes and associated surface deformation. For this an array of monitoring methodologies will be tested, including satellite-, surface- and wellbore-based monitoring systems. Interferometric Synthetic Aperture Radar (InSAR), GPS and tiltmeter monitoring will be used for measuring any ground deformation caused by CO2 injection and the associated subsurface pressure perturbation. In the spring-summer of 2012 we started collecting C-band SAR data from the Canadian Radarsat-2 satellite to provide baseline data over the study site. The Radarsat-2 data is acquired about every six days on average in five different geometries in order to achieve nearly uninterrupted coverage. We acquire ascending and descending spotlight data with sub-meter resolution (1.6x0.8 m), ascending and descending wide ultra fine data with moderate resolution (1.6x2.8 m) and descending fine quad-pol data with coarse resolution (5.2x7.6 m). Over the project life, this SAR coverage will be supplemented by X-band TerraSAR-X data, C-band Sentinel, and L-band ALOS-2 data. Availability of SAR data from all three wave-band sensors should allow us to measure ground deformation with a precision of a few mm/year. For mitigating temporal de-correlation and for improving precision during the winter when there will be snow cover, we will install 13 paired corner reflectors suitable for ascending and descending imaging. Multidimensional time series of ground deformation will be produced using MSBAS techniques (Samsonov and d'Oreye, 2012). PolInSAR methodology will be tested on fine quad-pol data. To obtain higher precision spatial and higher resolution temporal ground motion measurements we will install 13 continuous Global Positioning Systems (cGPS), and 5-6 tiltmeters in the fall of 2012. Various geodetic data will be integrated using the methodology of Samsonov et al., 2007 and resultant ground deformation maps will be used for validation of the geomechanical modelling. Here we will present maps of the injection site showing the locations and installation design of various geodetic sensors and provide initial results of InSAR measurements.

Surface albedo in relation to disturbance and early stand dynamics in the boreal forest: Implications for climate models

NASA Astrophysics Data System (ADS)

Halim, M. A.; Thomas, S. C.

2017-12-01

Surface albedo is the most important biophysical radiative forcing in the boreal forest. General Circulation Model studies have suggested that harvesting of boreal forest has a net cooling effect, in contrast to other terrestrial biomes, by increasing surface albedo. However, albedo estimation in these models has been achieved by simplifying processes governing albedo at a coarse scale (both spatial and temporal). Biophysical processes that determine albedo likely operate on small spatial and temporal scales, requiring more direct estimates of effects of landcover change on net radiation. We established a chronosequence study in post-fire and post-clearcut sites (2013, 2006, 1998), logging data from July 2013 to July 2017 in boreal forest sites in northwestern Ontario, Canada. Each age-class X disturbance had 3 three replicates, matched to 18 permanent circular plots (10-m radius) each with an instrumented tower measuring surface albedo, air and soil temperature, and soil moisture. We also measured leaf area index, species composition and soil organic matter content at each site. BRDF-corrected surface albedo was calculated from daily 30m x 30m reflectance data fused from the MODIS MOD09GA product and Landsat 7 reflectance data. Calculated albedo was verified using ground-based measurements. Results show that fire sites generally had lower (15-25%) albedo than clearcut sites in all seasons. Because of rapid forest regrowth, large perturbations of clearcut harvests on forest albedo started to fade out within a year. Albedo differences between fire and clearcut sites also declined sharply with stand age. Younger stands generally had higher albedo than older stands mainly due to the presence of broadleaf species (for example, Populus tremuloides). In spring, snow melted 10-12 days earlier in recent (2013) clearcut sites compared to closed-canopy sites, causing a sharp reduction in surface albedo in comparison to old clearcut/fire sites (2006 and 1998). Snow melted faster in post-fire sites than in clearcut sites, with concomitant effects on albedo associated with snow. Findings of this study strongly suggest that harvests in boreal forest do not have as strong a radiative cooling effect as previously inferred from GCM experiments based on coarse-resolution data or "biome substitution" approaches.

Temporal resolution and motion artifacts in single-source and dual-source cardiac CT.

PubMed

Schöndube, Harald; Allmendinger, Thomas; Stierstorfer, Karl; Bruder, Herbert; Flohr, Thomas

2013-03-01

The temporal resolution of a given image in cardiac computed tomography (CT) has so far mostly been determined from the amount of CT data employed for the reconstruction of that image. The purpose of this paper is to examine the applicability of such measures to the newly introduced modality of dual-source CT as well as to methods aiming to provide improved temporal resolution by means of an advanced image reconstruction algorithm. To provide a solid base for the examinations described in this paper, an extensive review of temporal resolution in conventional single-source CT is given first. Two different measures for assessing temporal resolution with respect to the amount of data involved are introduced, namely, either taking the full width at half maximum of the respective data weighting function (FWHM-TR) or the total width of the weighting function (total TR) as a base of the assessment. Image reconstruction using both a direct fan-beam filtered backprojection with Parker weighting as well as using a parallel-beam rebinning step are considered. The theory of assessing temporal resolution by means of the data involved is then extended to dual-source CT. Finally, three different advanced iterative reconstruction methods that all use the same input data are compared with respect to the resulting motion artifact level. For brevity and simplicity, the examinations are limited to two-dimensional data acquisition and reconstruction. However, all results and conclusions presented in this paper are also directly applicable to both circular and helical cone-beam CT. While the concept of total TR can directly be applied to dual-source CT, the definition of the FWHM of a weighting function needs to be slightly extended to be applicable to this modality. The three different advanced iterative reconstruction methods examined in this paper result in significantly different images with respect to their motion artifact level, despite exactly the same amount of data being used in the reconstruction process. The concept of assessing temporal resolution by means of the data employed for reconstruction can nicely be extended from single-source to dual-source CT. However, for advanced (possibly nonlinear iterative) reconstruction algorithms the examined approach fails to deliver accurate results. New methods and measures to assess the temporal resolution of CT images need to be developed to be able to accurately compare the performance of such algorithms.

Palliation of clinical signs in 48 dogs with nasal carcinomas treated with coarse-fraction radiation therapy.

PubMed

Gieger, Tracy; Rassnick, Kenneth; Siegel, Sheri; Proulx, David; Bergman, Philip; Anderson, Christine; LaDue, Tracy; Smith, Annette; Northrup, Nicole; Roberts, Royce

2008-01-01

Data from 48 dogs with nasal carcinomas treated with palliative radiation therapy (PRT) were retrospectively reviewed. Factors potentially influencing resolution of clinical signs and survival after PRT were evaluated. Clinical signs completely resolved in 66% of dogs for a median of 120 days. The overall median survival time was 146 days. Duration of response to PRT was shorter in dogs that had clinical signs for <90 days before PRT. Survival times were shorter in dogs that had partial or no resolution of clinical signs after PRT than in dogs that had complete resolution of clinical signs.

Prospects for Electron Imaging with Ultrafast Time Resolution

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

Armstrong, M R; Reed, B W; Torralva, B R

2007-01-26

Many pivotal aspects of material science, biomechanics, and chemistry would benefit from nanometer imaging with ultrafast time resolution. Here we demonstrate the feasibility of short-pulse electron imaging with t10 nanometer/10 picosecond spatio-temporal resolution, sufficient to characterize phenomena that propagate at the speed of sound in materials (1-10 kilometer/second) without smearing. We outline resolution-degrading effects that occur at high current density followed by strategies to mitigate these effects. Finally, we present a model electron imaging system that achieves 10 nanometer/10 picosecond spatio-temporal resolution.

Temporal and spectral manipulations of correlated photons using a time lens

NASA Astrophysics Data System (ADS)

Mittal, Sunil; Orre, Venkata Vikram; Restelli, Alessandro; Salem, Reza; Goldschmidt, Elizabeth A.; Hafezi, Mohammad

2017-10-01

A common challenge in quantum information processing with photons is the limited ability to manipulate and measure correlated states. An example is the inability to measure picosecond-scale temporal correlations of a multiphoton state, given state-of-the-art detectors have a temporal resolution of about 100 ps. Here, we demonstrate temporal magnification of time-bin-entangled two-photon states using a time lens and measure their temporal correlation function, which is otherwise not accessible because of the limited temporal resolution of single-photon detectors. Furthermore, we show that the time lens maps temporal correlations of photons to frequency correlations and could be used to manipulate frequency-bin-entangled photons. This demonstration opens a new avenue to manipulate and analyze spectral and temporal wave functions of many-photon states.

On the relation between SMMR 37-GHz polarization difference and the rainfall over Africa and Australia

NASA Technical Reports Server (NTRS)

Choudhury, Bhaskar J.; Digirolamo, Nicolo E.

1994-01-01

A major difficulty in interpreting coarse resolution satellite data in terms of land surface characteristics is unavailability of spatially and temporally representative ground observations. Under certain conditions rainfall has been found to provide a proxy measure for surface characteristics, and thus a relation between satellite observations and rainfall might provide an indirect approach for relating satellite data to these characteristics. Relationship between rainfall over Africa and Australia and 7-year average (1979-1985) polarization difference (PD) at 37 GHz from scanning multichannel microwave radiometer (SMMR) on board the Nimbus-7 satellite is studied in this paper. Quantitative methods have been used to screen (accept or reject) PD data considering antenna pattern, geolocation uncertainty, water contamination, surface roughness, and adverse effect of drought on the relation between rainfall and surface characteristics. The rainfall data used in the present analysis are climatologic averages and also 1979-1985 averages, and no screening has been applied to this data. The PD data has been screened considering only the location of rainfall stations, without any regard to rainfall amounts. The present analysis confirms a non-linear relation between rainfall and PD published previously.

Detecting switching and intermittent causalities in time series

NASA Astrophysics Data System (ADS)

Zanin, Massimiliano; Papo, David

2017-04-01

During the last decade, complex network representations have emerged as a powerful instrument for describing the cross-talk between different brain regions both at rest and as subjects are carrying out cognitive tasks, in healthy brains and neurological pathologies. The transient nature of such cross-talk has nevertheless by and large been neglected, mainly due to the inherent limitations of some metrics, e.g., causality ones, which require a long time series in order to yield statistically significant results. Here, we present a methodology to account for intermittent causal coupling in neural activity, based on the identification of non-overlapping windows within the original time series in which the causality is strongest. The result is a less coarse-grained assessment of the time-varying properties of brain interactions, which can be used to create a high temporal resolution time-varying network. We apply the proposed methodology to the analysis of the brain activity of control subjects and alcoholic patients performing an image recognition task. Our results show that short-lived, intermittent, local-scale causality is better at discriminating both groups than global network metrics. These results highlight the importance of the transient nature of brain activity, at least under some pathological conditions.

Rockfall hazard analysis using LiDAR and spatial modeling

NASA Astrophysics Data System (ADS)

Lan, Hengxing; Martin, C. Derek; Zhou, Chenghu; Lim, Chang Ho

2010-05-01

Rockfalls have been significant geohazards along the Canadian Class 1 Railways (CN Rail and CP Rail) since their construction in the late 1800s. These rockfalls cause damage to infrastructure, interruption of business, and environmental impacts, and their occurrence varies both spatially and temporally. The proactive management of these rockfall hazards requires enabling technologies. This paper discusses a hazard assessment strategy for rockfalls along a section of a Canadian railway using LiDAR and spatial modeling. LiDAR provides accurate topographical information of the source area of rockfalls and along their paths. Spatial modeling was conducted using Rockfall Analyst, a three dimensional extension to GIS, to determine the characteristics of the rockfalls in terms of travel distance, velocity and energy. Historical rockfall records were used to calibrate the physical characteristics of the rockfall processes. The results based on a high-resolution digital elevation model from a LiDAR dataset were compared with those based on a coarse digital elevation model. A comprehensive methodology for rockfall hazard assessment is proposed which takes into account the characteristics of source areas, the physical processes of rockfalls and the spatial attribution of their frequency and energy.

Modeling responses of large-river fish populations to global climate change through downscaling and incorporation of predictive uncertainty

USGS Publications Warehouse

Wildhaber, Mark L.; Wikle, Christopher K.; Anderson, Christopher J.; Franz, Kristie J.; Moran, Edward H.; Dey, Rima; Mader, Helmut; Kraml, Julia

2012-01-01

Climate change operates over a broad range of spatial and temporal scales. Understanding its effects on ecosystems requires multi-scale models. For understanding effects on fish populations of riverine ecosystems, climate predicted by coarse-resolution Global Climate Models must be downscaled to Regional Climate Models to watersheds to river hydrology to population response. An additional challenge is quantifying sources of uncertainty given the highly nonlinear nature of interactions between climate variables and community level processes. We present a modeling approach for understanding and accomodating uncertainty by applying multi-scale climate models and a hierarchical Bayesian modeling framework to Midwest fish population dynamics and by linking models for system components together by formal rules of probability. The proposed hierarchical modeling approach will account for sources of uncertainty in forecasts of community or population response. The goal is to evaluate the potential distributional changes in an ecological system, given distributional changes implied by a series of linked climate and system models under various emissions/use scenarios. This understanding will aid evaluation of management options for coping with global climate change. In our initial analyses, we found that predicted pallid sturgeon population responses were dependent on the climate scenario considered.

Spatial probabilistic pulsatility model for enhancing photoplethysmographic imaging systems

NASA Astrophysics Data System (ADS)

Amelard, Robert; Clausi, David A.; Wong, Alexander

2016-11-01

Photoplethysmographic imaging (PPGI) is a widefield noncontact biophotonic technology able to remotely monitor cardiovascular function over anatomical areas. Although spatial context can provide insight into physiologically relevant sampling locations, existing PPGI systems rely on coarse spatial averaging with no anatomical priors for assessing arterial pulsatility. Here, we developed a continuous probabilistic pulsatility model for importance-weighted blood pulse waveform extraction. Using a data-driven approach, the model was constructed using a 23 participant sample with a large demographic variability (11/12 female/male, age 11 to 60 years, BMI 16.4 to 35.1 kg·m-2). Using time-synchronized ground-truth blood pulse waveforms, spatial correlation priors were computed and projected into a coaligned importance-weighted Cartesian space. A modified Parzen-Rosenblatt kernel density estimation method was used to compute the continuous resolution-agnostic probabilistic pulsatility model. The model identified locations that consistently exhibited pulsatility across the sample. Blood pulse waveform signals extracted with the model exhibited significantly stronger temporal correlation (W=35,p<0.01) and spectral SNR (W=31,p<0.01) compared to uniform spatial averaging. Heart rate estimation was in strong agreement with true heart rate [r2=0.9619, error (μ,σ)=(0.52,1.69) bpm].

A Computational Approach for Modeling Neutron Scattering Data from Lipid Bilayers

DOE PAGES

Carrillo, Jan-Michael Y.; Katsaras, John; Sumpter, Bobby G.; ...

2017-01-12

Biological cell membranes are responsible for a range of structural and dynamical phenomena crucial to a cell's well-being and its associated functions. Due to the complexity of cell membranes, lipid bilayer systems are often used as biomimetic models. These systems have led to signficant insights into vital membrane phenomena such as domain formation, passive permeation and protein insertion. Experimental observations of membrane structure and dynamics are, however, limited in resolution, both spatially and temporally. Importantly, computer simulations are starting to play a more prominent role in interpreting experimental results, enabling a molecular under- standing of lipid membranes. Particularly, the synergymore » between scattering experiments and simulations offers opportunities for new discoveries in membrane physics, as the length and time scales probed by molecular dynamics (MD) simulations parallel those of experiments. We also describe a coarse-grained MD simulation approach that mimics neutron scattering data from large unilamellar lipid vesicles over a range of bilayer rigidity. Specfically, we simulate vesicle form factors and membrane thickness fluctuations determined from small angle neutron scattering (SANS) and neutron spin echo (NSE) experiments, respectively. Our simulations accurately reproduce trends from experiments and lay the groundwork for investigations of more complex membrane systems.« less

Impact of Antarctic Polar Front Variability on Southern Ocean Biogeochemistry

NASA Astrophysics Data System (ADS)

Freeman, N. M.; Lovenduski, N. S.; Gent, P. R.

2016-12-01

The Antarctic Polar Front (PF) is an important biogeochemical divide in the Southern Ocean, often coinciding with sharp gradients in silicate and nitrate concentration at the surface. Variability in the PF has the potential to influence Southern Ocean biogeochemistry and biological productivity both locally and at the basin scale. Characterizing PF variability is important for contextualizing recent biogeochemical observations from ORCAS, SOCCOM, and the Drake Passage time-series, as well as for understanding how anthropogenic change is influencing Southern Ocean biogeochemistry. Here, we employ a suite of remote sensing observations and output from the Community Earth System Model (CESM) to better understand the relationship between the PF and local biogeochemistry in the Southern Ocean. Using microwave SST measurements spanning 2002-2014 that avoid cloud contamination, we show that the PF has shifted northward (southward) in the Pacific (Indian) sector and intensified at nearly all longitudes along its circumpolar path. We identify the PF in CESM at both coarse (1°x1°) and fine (0.1°x0.1°) horizontal resolutions using temperature and silicate gradient maxima, and quantify its spatial and temporal variability. We further investigate co-variance between the position and intensity of the PF and local phytoplankton community structure.

A hidden Markov model for decoding and the analysis of replay in spike trains.

PubMed

Box, Marc; Jones, Matt W; Whiteley, Nick

2016-12-01

We present a hidden Markov model that describes variation in an animal's position associated with varying levels of activity in action potential spike trains of individual place cell neurons. The model incorporates a coarse-graining of position, which we find to be a more parsimonious description of the system than other models. We use a sequential Monte Carlo algorithm for Bayesian inference of model parameters, including the state space dimension, and we explain how to estimate position from spike train observations (decoding). We obtain greater accuracy over other methods in the conditions of high temporal resolution and small neuronal sample size. We also present a novel, model-based approach to the study of replay: the expression of spike train activity related to behaviour during times of motionlessness or sleep, thought to be integral to the consolidation of long-term memories. We demonstrate how we can detect the time, information content and compression rate of replay events in simulated and real hippocampal data recorded from rats in two different environments, and verify the correlation between the times of detected replay events and of sharp wave/ripples in the local field potential.

A Molecular Dynamics Investigation of the Physical-Chemical Properties of Calicivirus Capsid Protein Adsorption to Fomites

NASA Astrophysics Data System (ADS)

Peeler, David; Matysiak, Silvina

2013-03-01

Any inanimate object with an exposed surface bears the possibility of hosting a virus and may therefore be labeled a fomite. This research hopes to distinguish which chemical-physical differences in fomite surface and virus capsid protein characteristics cause variations in virus adsorption through an alignment of in silico molecular dynamics simulations with in vitro measurements. The impact of surface chemistry on the adsorption of the human norovirus (HNV)-surrogate calicivirus capsid protein 2MS2 has been simulated for monomer and trimer structures and is reported in terms of protein-self assembled monolayer (SAM) binding free energy. The coarse-grained MARTINI forcefield was used to maximize spatial and temporal resolution while minimizing computational load. Future work will investigate the FCVF5 and SMSVS4 calicivirus trimers and will extend beyond hydrophobic and hydrophilic SAM surface chemistry to charged SAM surfaces in varying ionic concentrations. These results will be confirmed by quartz crystal microbalance experiments conducted by Dr. Wigginton at the University of Michigan. This should provide a novel method for predicting the transferability of viruses that cannot be studied in vitro such as dangerous foodborne and nosocomially-acquired viruses like HNV.

Microwave brightness temperature and thermal inertia - towards synergistic method of high-resolution soil moisture retrieval

NASA Astrophysics Data System (ADS)

Lukowski, Mateusz; Usowicz, Boguslaw; Sagan, Joanna; Szlazak, Radoslaw; Gluba, Lukasz; Rojek, Edyta

2017-04-01

Soil moisture is an important parameter in many environmental studies, as it influences the exchange of water and energy at the interface between the land surface and the atmosphere. Accurate assessment of the soil moisture spatial and temporal variations is crucial for numerous studies; starting from a small scale of single field, then catchment, mesoscale basin, ocean conglomeration, finally ending at the global water cycle. Despite numerous advantages, such as fine accuracy (undisturbed by clouds or daytime conditions) and good temporal resolution, passive microwave remote sensing of soil moisture, e.g. SMOS and SMAP, are not applicable to a small scale - simply because of too coarse spatial resolution. On the contrary, thermal infrared-based methods of soil moisture retrieval have a good spatial resolution, but are often disturbed by clouds and vegetation interferences or night effects. The methods that base on point measurements, collected in situ by monitoring stations or during field campaigns, are sometimes called "ground truth" and may serve as a reference for remote sensing, of course after some up-scaling and approximation procedures that are, unfortunately, potential source of error. Presented research concern attempt to synergistic approach that join two remote sensing methods: passive microwave and thermal infrared, supported by in situ measurements. Microwave brightness temperature of soil was measured by ELBARA, the radiometer at 1.4 GHz frequency, installed at 6 meters high tower at Bubnow test site in Poland. Thermal inertia around the tower was modelled using the statistical-physical model whose inputs were: soil physical properties, its water content, albedo and surface temperatures measured by an infrared pyrometer, directed at the same footprint as ELBARA. The results coming from this method were compared to in situ data obtained during several field campaigns and by the stationary agrometeorological stations. The approach seems to be reasonable, as both variables, brightness temperature and thermal inertia, strongly depend on soil moisture. Despite the fact that the presented research focused on modelling in the small size, 4 ha test site, the method is promising for larger scales as well, due to similarities between ELBARA and SMOS and between pyrometer and satellite imaging spectrometers (Landsat, Sentinel etc.). The approach will merge advantages: high accuracy of passive microwave sensing with a good spatial resolution of thermal infrared methods. The work was partially funded under two ESA projects: 1) "ELBARA_PD (Penetration Depth)" No. 4000107897/13/NL/KML, funded by the Government of Poland through an ESA-PECS contract (Plan for European Cooperating States). 2) "Technical Support for the fabrication and deployment of the radiometer ELBARA-III in Bubnow, Poland" No. 4000113360/15/NL/FF/gp.

A stochastical event-based continuous time step rainfall generator based on Poisson rectangular pulse and microcanonical random cascade models

NASA Astrophysics Data System (ADS)

Pohle, Ina; Niebisch, Michael; Zha, Tingting; Schümberg, Sabine; Müller, Hannes; Maurer, Thomas; Hinz, Christoph

2017-04-01

Rainfall variability within a storm is of major importance for fast hydrological processes, e.g. surface runoff, erosion and solute dissipation from surface soils. To investigate and simulate the impacts of within-storm variabilities on these processes, long time series of rainfall with high resolution are required. Yet, observed precipitation records of hourly or higher resolution are in most cases available only for a small number of stations and only for a few years. To obtain long time series of alternating rainfall events and interstorm periods while conserving the statistics of observed rainfall events, the Poisson model can be used. Multiplicative microcanonical random cascades have been widely applied to disaggregate rainfall time series from coarse to fine temporal resolution. We present a new coupling approach of the Poisson rectangular pulse model and the multiplicative microcanonical random cascade model that preserves the characteristics of rainfall events as well as inter-storm periods. In the first step, a Poisson rectangular pulse model is applied to generate discrete rainfall events (duration and mean intensity) and inter-storm periods (duration). The rainfall events are subsequently disaggregated to high-resolution time series (user-specified, e.g. 10 min resolution) by a multiplicative microcanonical random cascade model. One of the challenges of coupling these models is to parameterize the cascade model for the event durations generated by the Poisson model. In fact, the cascade model is best suited to downscale rainfall data with constant time step such as daily precipitation data. Without starting from a fixed time step duration (e.g. daily), the disaggregation of events requires some modifications of the multiplicative microcanonical random cascade model proposed by Olsson (1998): Firstly, the parameterization of the cascade model for events of different durations requires continuous functions for the probabilities of the multiplicative weights, which we implemented through sigmoid functions. Secondly, the branching of the first and last box is constrained to preserve the rainfall event durations generated by the Poisson rectangular pulse model. The event-based continuous time step rainfall generator has been developed and tested using 10 min and hourly rainfall data of four stations in North-Eastern Germany. The model performs well in comparison to observed rainfall in terms of event durations and mean event intensities as well as wet spell and dry spell durations. It is currently being tested using data from other stations across Germany and in different climate zones. Furthermore, the rainfall event generator is being applied in modelling approaches aimed at understanding the impact of rainfall variability on hydrological processes. Reference Olsson, J.: Evaluation of a scaling cascade model for temporal rainfall disaggregation, Hydrology and Earth System Sciences, 2, 19.30

Intercomparison of Downscaling Methods on Hydrological Impact for Earth System Model of NE United States

NASA Astrophysics Data System (ADS)

Yang, P.; Fekete, B. M.; Rosenzweig, B.; Lengyel, F.; Vorosmarty, C. J.

2012-12-01

Atmospheric dynamics are essential inputs to Regional-scale Earth System Models (RESMs). Variables including surface air temperature, total precipitation, solar radiation, wind speed and humidity must be downscaled from coarse-resolution, global General Circulation Models (GCMs) to the high temporal and spatial resolution required for regional modeling. However, this downscaling procedure can be challenging due to the need to correct for bias from the GCM and to capture the spatiotemporal heterogeneity of the regional dynamics. In this study, the results obtained using several downscaling techniques and observational datasets were compared for a RESM of the Northeast Corridor of the United States. Previous efforts have enhanced GCM model outputs through bias correction using novel techniques. For example, the Climate Impact Research at Potsdam Institute developed a series of bias-corrected GCMs towards the next generation climate change scenarios (Schiermeier, 2012; Moss et al., 2010). Techniques to better represent the heterogeneity of climate variables have also been improved using statistical approaches (Maurer, 2008; Abatzoglou, 2011). For this study, four downscaling approaches to transform bias-corrected HADGEM2-ES Model output (daily at .5 x .5 degree) to the 3'*3'(longitude*latitude) daily and monthly resolution required for the Northeast RESM were compared: 1) Bilinear Interpolation, 2) Daily bias-corrected spatial downscaling (D-BCSD) with Gridded Meteorological Datasets (developed by Abazoglou 2011), 3) Monthly bias-corrected spatial disaggregation (M-BCSD) with CRU(Climate Research Unit) and 4) Dynamic Downscaling based on Weather Research and Forecast (WRF) model. Spatio-temporal analysis of the variability in precipitation was conducted over the study domain. Validation of the variables of different downscaling methods against observational datasets was carried out for assessment of the downscaled climate model outputs. The effects of using the different approaches to downscale atmospheric variables (specifically air temperature and precipitation) for use as inputs to the Water Balance Model (WBMPlus, Vorosmarty et al., 1998;Wisser et al., 2008) for simulation of daily discharge and monthly stream flow in the Northeast US for a 100-year period in the 21st century were also assessed. Statistical techniques especially monthly bias-corrected spatial disaggregation (M-BCSD) showed potential advantage among other methods for the daily discharge and monthly stream flow simulation. However, Dynamic Downscaling will provide important complements to the statistical approaches tested.

Tropospheric Emissions: Monitoring of Pollution (TEMPO)

NASA Astrophysics Data System (ADS)

Chance, Kelly; Liu, Xiong; Suleiman, Raid M.; Flittner, David E.; Al-Saadi, Jassim; Janz, Scott J.

2014-06-01

TEMPO, selected by NASA as the first Earth Venture Instrument, will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO measures from Mexico City to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution. TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry. Measurements are from geostationary (GEO) orbit, to capture the inherent high variability in the diurnal cycle of emissions and chemistry. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a GEO host spacecraft to provide a modest-cost mission that measures the spectra required to retrieve O3, NO2, SO2, H2CO, C2H2O2, H2O, aerosols, cloud parameters, and UVB radiation. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, reducing uncertainty in air quality predictions by 50 %. TEMPO quantifies and tracks the evolution of aerosol loading. It provides near-real-time air quality products that will be made widely, publicly available. TEMPO makes the first tropospheric trace gas measurements from GEO, by building on the heritage of five spectrometers flown in low-earth-orbit (LEO). These LEO instruments measure the needed spectra, although at coarse spatial and temporal resolutions, to the precisions required for TEMPO and use retrieval algorithms developed for them by TEMPO Science Team members and currently running in operational environments. This makes TEMPO an innovative use of a well-proven technique, able to produce a revolutionary data set. TEMPO provides much of the atmospheric measurement capability recommended for GEO-CAPE in the 2007 National Research Council Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. GEO-CAPE is not planned for implementation this decade. However, instruments from Europe (Sentinel 4) and Asia (GEMS) will form parts of a global GEO constellation for pollution monitoring later this decade, with a major focus on intercontinental pollution transport. TEMPO will launch at a prime time to be a component of this constellation.

Tropospheric Emissions: Monitoring of Pollution (TEMPO)

NASA Astrophysics Data System (ADS)

Chance, K.; Liu, X.; Suleiman, R. M.; Flittner, D. E.; Janz, S. J.

2012-12-01

TEMPO is a proposed concept to measure pollution for greater North America using ultraviolet/visible spectroscopy. TEMPO measures from Mexico City to the Canadian tar/oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (9 km2). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry. Measurements are from geostationary (GEO) orbit, to capture the inherent high variability in the diurnal cycle of emissions and chemistry. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve O3, NO2, SO2, H2CO, C2H2O2, H2O, aerosols, cloud parameters, and UVB radiation. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, reducing uncertainty in air quality predictions by 50%. TEMPO quantifies and tracks the evolution of aerosol loading. It provides near-real-time air quality products that will be made widely, publicly available. TEMPO makes the first tropospheric trace gas measurements from GEO, by building on the heritage of five spectrometers flown in low-earth-orbit (LEO). These LEO instruments measure the needed spectra, although at coarse spatial and temporal resolutions, to the precisions required for TEMPO and use retrieval algorithms developed for them by TEMPO Science Team members and currently running in operational environments. This makes TEMPO an innovative use of a well proven technique, able to produce a revolutionary data set. TEMPO provides much of the atmospheric measurement capability recommended for GEO-CAPE in the 2007 National Research Council Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. GEO-CAPE is not planned for implementation this decade. However, instruments from Europe (Sentinel 4) and Asia (GEMS) will form parts of a global GEO constellation for pollution monitoring later this decade, with a major focus on intercontinental pollution transport. TEMPO will launch at a prime time to be a component of this constellation.

Estimating PM2.5 speciation concentrations using prototype 4.4 km-resolution MISR aerosol properties over Southern California

NASA Astrophysics Data System (ADS)

Meng, Xia; Garay, Michael J.; Diner, David J.; Kalashnikova, Olga V.; Xu, Jin; Liu, Yang

2018-05-01

Research efforts to better characterize the differential toxicity of PM2.5 (particles with aerodynamic diameters less than or equal to 2.5 μm) speciation are often hindered by the sparse or non-existent coverage of ground monitors. The Multi-angle Imaging SpectroRadiometer (MISR) aboard NASA's Terra satellite is one of few satellite aerosol sensors providing information of aerosol shape, size and extinction globally for a long and continuous period that can be used to estimate PM2.5 speciation concentrations since year 2000. Currently, MISR only provides a 17.6 km product for its entire mission with global coverage every 9 days, a bit too coarse for air pollution health effects research and to capture local spatial variability of PM2.5 speciation. In this study, generalized additive models (GAMs) were developed using MISR prototype 4.4 km-resolution aerosol data with meteorological variables and geographical indicators, to predict ground-level concentrations of PM2.5 sulfate, nitrate, organic carbon (OC) and elemental carbon (EC) in Southern California between 2001 and 2015 at the daily level. The GAMs are able to explain 66%, 62%, 55% and 58% of the daily variability in PM2.5 sulfate, nitrate, OC and EC concentrations during the whole study period, respectively. Predicted concentrations capture large regional patterns as well as fine gradients of the four PM2.5 species in urban areas of Los Angeles and other counties, as well as in the Central Valley. This study is the first attempt to use MISR prototype 4.4 km-resolution AOD (aerosol optical depth) components data to predict PM2.5 sulfate, nitrate, OC and EC concentrations at the sub-regional scale. In spite of its low temporal sampling frequency, our analysis suggests that the MISR 4.4 km fractional AODs provide a promising way to capture the spatial hotspots and long-term temporal trends of PM2.5 speciation, understand the effectiveness of air quality controls, and allow our estimated PM2.5 speciation data to be linked with common spatial units such as census tract or zip code in epidemiological studies. This modeling strategy needs to be validated in other regions when more MISR 4.4 km data becoming available in the future.

Hi-C First Results

NASA Technical Reports Server (NTRS)

Cirtain, Jonathan

2013-01-01

Hi-C obtained the highest spatial and temporal resolution observatoins ever taken in the solar corona. Hi-C reveals dynamics and structure at the limit of its temporal and spatial resolution. Hi-C observed ubiquitous fine-scale flows consistent with the local sound speed.

Peripheral resolution and contrast sensitivity: Effects of stimulus drift.

PubMed

Venkataraman, Abinaya Priya; Lewis, Peter; Unsbo, Peter; Lundström, Linda

2017-04-01

Optimal temporal modulation of the stimulus can improve foveal contrast sensitivity. This study evaluates the characteristics of the peripheral spatiotemporal contrast sensitivity function in normal-sighted subjects. The purpose is to identify a temporal modulation that can potentially improve the remaining peripheral visual function in subjects with central visual field loss. High contrast resolution cut-off for grating stimuli with four temporal frequencies (0, 5, 10 and 15Hz drift) was first evaluated in the 10° nasal visual field. Resolution contrast sensitivity for all temporal frequencies was then measured at four spatial frequencies between 0.5 cycles per degree (cpd) and the measured stationary cut-off. All measurements were performed with eccentric optical correction. Similar to foveal vision, peripheral contrast sensitivity is highest for a combination of low spatial frequency and 5-10Hz drift. At higher spatial frequencies, there was a decrease in contrast sensitivity with 15Hz drift. Despite this decrease, the resolution cut-off did not vary largely between the different temporal frequencies tested. Additional measurements of contrast sensitivity at 0.5 cpd and resolution cut-off for stationary (0Hz) and 7.5Hz stimuli performed at 10, 15, 20 and 25° in the nasal visual field also showed the same characteristics across eccentricities. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Large Area Field of View for Fast Temporal Resolution Astronomy

NASA Astrophysics Data System (ADS)

Covarrubias, Ricardo A.

2018-01-01

Scientific CMOS (sCMOS) technology is especially relevant for high temporal resolution astronomy combining high resolution, large field of view with very fast frame rates, without sacrificing ultra-low noise performance. Solar Astronomy, Near Earth Object detections, Space Debris Tracking, Transient Observations or Wavefront Sensing are among the many applications this technology can be utilized. Andor Technology is currently developing the next-generation, very large area sCMOS camera with an extremely low noise, rapid frame rates, high resolution and wide dynamic range.

Integrating Enhanced Grace Terrestrial Water Storage Data Into the U.S. and North American Drought Monitors

NASA Technical Reports Server (NTRS)

Housborg, Rasmus; Rodell, Matthew

2010-01-01

NASA's Gravity Recovery and Climate Experiment (GRACE) satellites measure time variations nf the Earth's gravity field enabling reliable detection of spatio-temporal variations in total terrestrial water storage (TWS), including ground water. The U.S. and North American Drought Monitors are two of the premier drought monitoring products available to decision-makers for assessing and minimizing drought impacts, but they rely heavily on precipitation indices and do not currently incorporate systematic observations of deep soil moisture and groundwater storage conditions. Thus GRACE has great potential to improve the Drought Monitors hy filling this observational gap. Horizontal, vertical and temporal disaggregation of the coarse-resolution GRACE TWS data has been accomplished by assimilating GRACE TWS anomalies into the Catchment Land Surface Model using ensemble Kalman smoother. The Drought Monitors combine several short-term and long-term drought indices and indicators expressed in percentiles as a reference to their historical frequency of occurrence for the location and time of year in question. To be consistent, we are in the process of generating a climatology of estimated soil moisture and ground water based on m 60-year Catchment model simulation which will subsequently be used to convert seven years of GRACE assimilated fields into soil moisture and groundwater percentiles. for systematic incorporation into the objective blends that constitute Drought Monitor baselines. At this stage we provide a preliminary evaluation of GRACE assimilated Catchment model output against independent datasets including soil moisture observations from Aqua AMSR-E and groundwater level observations from the U.S. Geological Survey's Groundwater Climate Response Network.

Utilizing GNSS Reflectometry to Assess Surface Inundation Dynamics in Tropical Wetlands

NASA Astrophysics Data System (ADS)

Jensen, K.; McDonald, K. C.; Podest, E.; Chew, C. C.

2017-12-01

Tropical wetlands play a significant role in global atmospheric methane and terrestrial water storage. Despite the growing number of remote sensing products from satellite sensors, both spatial distribution and temporal variability of wetlands remain highly uncertain. An emerging innovative approach to mapping wetlands is offered by GNSS reflectometry (GNSS-R), a bistatic radar concept that takes advantage of GNSS transmitting satellites to yield observations with global coverage and rapid revisit time. This technology offers the potential to capture dynamic inundation changes in wetlands at higher temporal fidelity and sensitivity under the canopy than presently possible. We present an integrative analysis of radiometric modeling, ground measurements, and several microwave remote sensing datasets traditionally used for wetland observations. From a theoretical standpoint, GNSS-R sensitivities for vegetation and wetlands are investigated with a bistatic radar model in order to understand the interactions of the signal with various land surface components. GNSS reflections from the TechDemoSat-1 (TDS-1), Soil Moisture Active Passive (SMAP), and Cyclone GNSS (CYGNSS) missions are tested experimentally with contemporaneous (1) field measurements collected from the Pacaya Samiria National Reserve in the Peruvian Amazon, (2) imaging radar from Sentinel-1 and PALSAR-2 observed over a variety of tropical wetland systems, and (3) pan-tropical coarse-resolution (25km) microwave datasets (Surface Water Microwave Product Series). We find that GNSS-R data provide the potential to extend capabilities of current remote sensing techniques to characterize surface inundation extent, and we explore how to maximize synergism between different satellite sensors to produce an enhanced wetland monitoring product.

Basic quantitative assessment of visual performance in patients with very low vision.

PubMed

Bach, Michael; Wilke, Michaela; Wilhelm, Barbara; Zrenner, Eberhart; Wilke, Robert

2010-02-01

A variety of approaches to developing visual prostheses are being pursued: subretinal, epiretinal, via the optic nerve, or via the visual cortex. This report presents a method of comparing their efficacy at genuinely improving visual function, starting at no light perception (NLP). A test battery (a computer program, Basic Assessment of Light and Motion [BaLM]) was developed in four basic visual dimensions: (1) light perception (light/no light), with an unstructured large-field stimulus; (2) temporal resolution, with single versus double flash discrimination; (3) localization of light, where a wedge extends from the center into four possible directions; and (4) motion, with a coarse pattern moving in one of four directions. Two- or four-alternative, forced-choice paradigms were used. The participants' responses were self-paced and delivered with a keypad. The feasibility of the BaLM was tested in 73 eyes of 51 patients with low vision. The light and time test modules discriminated between NLP and light perception (LP). The localization and motion modules showed no significant response for NLP but discriminated between LP and hand movement (HM). All four modules reached their ceilings in the acuity categories higher than HM. BaLM results systematically differed between the very-low-acuity categories NLP, LP, and HM. Light and time yielded similar results, as did localization and motion; still, for assessing the visual prostheses with differing temporal characteristics, they are not redundant. The results suggest that this simple test battery provides a quantitative assessment of visual function in the very-low-vision range from NLP to HM.

Testing Snow Melt Algorithms in High Relief Topography Using Calibrated Enhanced-Resolution Brightness Temperatures, Hunza River Basin, Pakistan

NASA Astrophysics Data System (ADS)

Ramage, J. M.; Brodzik, M. J.; Hardman, M.; Troy, T. J.

2017-12-01

Snow is a vital part of the terrestrial hydrological cycle, a crucial resource for people and ecosystems. In mountainous regions snow is extensive, variable, and challenging to document. Snow melt timing and duration are important factors affecting the transfer of snow mass to soil moisture and runoff. Passive microwave brightness temperature (Tb) changes at 36 and 18 GHz are a sensitive way to detect snow melt onset due to their sensitivity to the abrupt change in emissivity. They are widely used on large icefields and high latitude watersheds. The coarse resolution ( 25 km) of historically available data has precluded effective use in high relief, heterogeneous regions, and gaps between swaths also create temporal data gaps at lower latitudes. New enhanced resolution data products generated from a scatterometer image reconstruction for radiometer (rSIR) technique are available at the original frequencies. We use these Calibrated Enhanced-resolution Brightness (CETB) Temperatures Earth System Data Records (ESDR) to evaluate existing snow melt detection algorithms that have been used in other environments, including the cross polarized gradient ratio (XPGR) and the diurnal amplitude variations (DAV) approaches. We use the 36/37 GHz (3.125 km resolution) and 18/19 GHz (6.25 km resolution) vertically and horizontally polarized datasets from the Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Radiometer for EOS (AMSR-E) and evaluate them for use in this high relief environment. The new data are used to assess glacier and snow melt records in the Hunza River Basin [area 13,000 sq. km, located at 36N, 74E], a tributary to the Upper Indus Basin, Pakistan. We compare the melt timing results visually and quantitatively to the corresponding EASE-Grid 2.0 25-km dataset, SRTM topography, and surface temperatures from station and reanalysis data. The new dataset is coarser than the topography, but is able to differentiate signals of melt/refreeze timing for different altitudes and land cover in this remote area with significant hazards from snow melt and glacier discharge. The improved spatial resolution, enhanced to 3-6 km, and retaining twice daily observations is a key improvement to fully analyze snowpack melt characteristics in remote mountainous regions.

Speech Perception in Tones and Noise via Cochlear Implants Reveals Influence of Spectral Resolution on Temporal Processing

PubMed Central

Kreft, Heather A.

2014-01-01

Under normal conditions, human speech is remarkably robust to degradation by noise and other distortions. However, people with hearing loss, including those with cochlear implants, often experience great difficulty in understanding speech in noisy environments. Recent work with normal-hearing listeners has shown that the amplitude fluctuations inherent in noise contribute strongly to the masking of speech. In contrast, this study shows that speech perception via a cochlear implant is unaffected by the inherent temporal fluctuations of noise. This qualitative difference between acoustic and electric auditory perception does not seem to be due to differences in underlying temporal acuity but can instead be explained by the poorer spectral resolution of cochlear implants, relative to the normally functioning ear, which leads to an effective smoothing of the inherent temporal-envelope fluctuations of noise. The outcome suggests an unexpected trade-off between the detrimental effects of poorer spectral resolution and the beneficial effects of a smoother noise temporal envelope. This trade-off provides an explanation for the long-standing puzzle of why strong correlations between speech understanding and spectral resolution have remained elusive. The results also provide a potential explanation for why cochlear-implant users and hearing-impaired listeners exhibit reduced or absent masking release when large and relatively slow temporal fluctuations are introduced in noise maskers. The multitone maskers used here may provide an effective new diagnostic tool for assessing functional hearing loss and reduced spectral resolution. PMID:25315376

Strategies for Large Scale Implementation of a Multiscale, Multiprocess Integrated Hydrologic Model

NASA Astrophysics Data System (ADS)

Kumar, M.; Duffy, C.

2006-05-01

Distributed models simulate hydrologic state variables in space and time while taking into account the heterogeneities in terrain, surface, subsurface properties and meteorological forcings. Computational cost and complexity associated with these model increases with its tendency to accurately simulate the large number of interacting physical processes at fine spatio-temporal resolution in a large basin. A hydrologic model run on a coarse spatial discretization of the watershed with limited number of physical processes needs lesser computational load. But this negatively affects the accuracy of model results and restricts physical realization of the problem. So it is imperative to have an integrated modeling strategy (a) which can be universally applied at various scales in order to study the tradeoffs between computational complexity (determined by spatio- temporal resolution), accuracy and predictive uncertainty in relation to various approximations of physical processes (b) which can be applied at adaptively different spatial scales in the same domain by taking into account the local heterogeneity of topography and hydrogeologic variables c) which is flexible enough to incorporate different number and approximation of process equations depending on model purpose and computational constraint. An efficient implementation of this strategy becomes all the more important for Great Salt Lake river basin which is relatively large (~89000 sq. km) and complex in terms of hydrologic and geomorphic conditions. Also the types and the time scales of hydrologic processes which are dominant in different parts of basin are different. Part of snow melt runoff generated in the Uinta Mountains infiltrates and contributes as base flow to the Great Salt Lake over a time scale of decades to centuries. The adaptive strategy helps capture the steep topographic and climatic gradient along the Wasatch front. Here we present the aforesaid modeling strategy along with an associated hydrologic modeling framework which facilitates a seamless, computationally efficient and accurate integration of the process model with the data model. The flexibility of this framework leads to implementation of multiscale, multiresolution, adaptive refinement/de-refinement and nested modeling simulations with least computational burden. However, performing these simulations and related calibration of these models over a large basin at higher spatio- temporal resolutions is computationally intensive and requires use of increasing computing power. With the advent of parallel processing architectures, high computing performance can be achieved by parallelization of existing serial integrated-hydrologic-model code. This translates to running the same model simulation on a network of large number of processors thereby reducing the time needed to obtain solution. The paper also discusses the implementation of the integrated model on parallel processors. Also will be discussed the mapping of the problem on multi-processor environment, method to incorporate coupling between hydrologic processes using interprocessor communication models, model data structure and parallel numerical algorithms to obtain high performance.

Initial stage corrosion of nanocrystalline copper particles and thin films

NASA Astrophysics Data System (ADS)

Tao, Weimin

1997-12-01

Corrosion behavior is an important issue in nanocrystalline materials research and development. A very fine grain size is expected to have significant effects on the corrosion resistance of these novel materials. However, both the macroscopic corrosion properties and the corresponding structure evolution during corrosion have not been fully studied. Under such circumstances, conducting fundamental research in this area is important and necessary. In this study, high purity nanocrystalline and coarse-grained copper were selected as our sample material, sodium nitrite aqueous solution at room temperature and air at a high temperature were employed as corrosive environments. The weight loss testing and electrochemical methods were used to obtain the macroscopic corrosion properties, whereas the high resolution transmission electron microscope was employed for the structure analysis. The weight loss tests indicate that the corrosion rate of nanocrystalline copper is about 5 times higher than that of coarse-grained copper at the initial stage of corrosion. The electrochemical measurements show that the corrosion potential of the nanocrystalline copper has a 230 mV negative shift in comparison with that of the coarse-grained copper. The nanocrystalline copper also exhibits a significantly higher exchange current density than the coarse-grained copper. High resolution TEM revealed that the surface structure changes at the initial stage of corrosion. It was found that the first copper oxide layer formed on the surface of nanocrystalline copper thin film contains a large density of high angle grain boundaries, whereas that formed on the surface of coarse-grained copper shows highly oriented oxide nuclei and appears to show a strong tendency for forming low angle grain boundaries. A correlation between the macroscopic corrosion properties and the structure characteristics is proposed for the nanocrystalline copper based on the concept of the "apparent" exchange current density associated with mass transport of ions in the oxide layer. A hypothesis is developed that the high corrosion rate of the nanocrystalline copper is closely associated with the structure of the copper oxide layer. Therefore, a high "apparent" exchange current density for the nanocrystalline copper is associated with the high angle grain boundary structure in the initial oxide layer. Additional structure analysis was also carried out: (a) High resolution TEM imaging has provided a cross sectional view of the epitaxial interface between nanocrystalline copper and copper (I) oxide and explicitly discloses the presence of interface defects such as misfit dislocations. Based on this observation, a mechanism was proposed to explain the Cu/Cusb2O interface misfit accommodation. This appears to be the first time this interface has been directly examined. (b) A nanocrystalline analogue to a cross-section of Gwathmey's copper single crystal sphere was revealed by high resolution TEM imaging. A partially oxidized nanocrystalline copper particle is used to examine the variation of the Cu/Cusb2O orientation relationship with respect to changes in surface orientation. A new orientation relationship, Cu (011) //Cusb2O (11), ˜ Cu(011)//Cusb2O(111), was found for the oxidation of nanocrystalline copper.

Tunable Coarse Graining for Monte Carlo Simulations of Proteins via Smoothed Energy Tables: Direct and Exchange Simulations

PubMed Central

2015-01-01

Many commonly used coarse-grained models for proteins are based on simplified interaction sites and consequently may suffer from significant limitations, such as the inability to properly model protein secondary structure without the addition of restraints. Recent work on a benzene fluid (LettieriS.; ZuckermanD. M.J. Comput. Chem.2012, 33, 268−27522120971) suggested an alternative strategy of tabulating and smoothing fully atomistic orientation-dependent interactions among rigid molecules or fragments. Here we report our initial efforts to apply this approach to the polar and covalent interactions intrinsic to polypeptides. We divide proteins into nearly rigid fragments, construct distance and orientation-dependent tables of the atomistic interaction energies between those fragments, and apply potential energy smoothing techniques to those tables. The amount of smoothing can be adjusted to give coarse-grained models that range from the underlying atomistic force field all the way to a bead-like coarse-grained model. For a moderate amount of smoothing, the method is able to preserve about 70–90% of the α-helical structure while providing a factor of 3–10 improvement in sampling per unit computation time (depending on how sampling is measured). For a greater amount of smoothing, multiple folding–unfolding transitions of the peptide were observed, along with a factor of 10–100 improvement in sampling per unit computation time, although the time spent in the unfolded state was increased compared with less smoothed simulations. For a β hairpin, secondary structure is also preserved, albeit for a narrower range of the smoothing parameter and, consequently, for a more modest improvement in sampling. We have also applied the new method in a “resolution exchange” setting, in which each replica runs a Monte Carlo simulation with a different degree of smoothing. We obtain exchange rates that compare favorably to our previous efforts at resolution exchange (LymanE.; ZuckermanD. M.J. Chem. Theory Comput.2006, 2, 656−666). PMID:25400525

Spatial models reveal the microclimatic buffering capacity of old-growth forests

Treesearch

Sarah J. K. Frey; Adam S. Hadley; Sherri L. Johnson; Mark Schulze; Julia A. Jones; Matthew. G. Betts

2016-01-01

Climate change is predicted to cause widespread declines in biodiversity, but these predictions are derived from coarse-resolution climate models applied at global scales. Such models lack the capacity to incorporate microclimate variability, which is critical to biodiversity microrefugia. In forested montane regions, microclimate is thought to be influenced by...

Comparing large-scale hydrological model predictions with observed streamflow in the Pacific Northwest: effects of climate and groundwater

Treesearch

Mohammad Safeeq; Guillaume S. Mauger; Gordon E. Grant; Ivan Arismendi; Alan F. Hamlet; Se-Yeun Lee

2014-01-01

Assessing uncertainties in hydrologic models can improve accuracy in predicting future streamflow. Here, simulated streamflows using the Variable Infiltration Capacity (VIC) model at coarse (1/16°) and fine (1/120°) spatial resolutions were evaluated against observed streamflows from 217 watersheds. In...

Validation of non-stationary precipitation series for site-specific impact assessment: Comparison of two statistical downscaling techniques

USDA-ARS?s Scientific Manuscript database

The generation of realistic future precipitation scenarios is crucial for assessing their impacts on a range of environmental and socio-economic impact sectors. A scale mismatch exists, however, between the coarse spatial resolution at which global climate models (GCMs) output future climate scenari...

GPCC - A weather generator-based statistical downscaling tool for site-specific assessment of climate change impacts

USDA-ARS?s Scientific Manuscript database

Resolution of climate model outputs are too coarse to be used as direct inputs to impact models for assessing climate change impacts on agricultural production, water resources, and eco-system services at local or site-specific scales. Statistical downscaling approaches are usually used to bridge th...

Musical structure analysis using similarity matrix and dynamic programming

NASA Astrophysics Data System (ADS)

Shiu, Yu; Jeong, Hong; Kuo, C.-C. Jay

2005-10-01

Automatic music segmentation and structure analysis from audio waveforms based on a three-level hierarchy is examined in this research, where the three-level hierarchy includes notes, measures and parts. The pitch class profile (PCP) feature is first extracted at the note level. Then, a similarity matrix is constructed at the measure level, where a dynamic time warping (DTW) technique is used to enhance the similarity computation by taking the temporal distortion of similar audio segments into account. By processing the similarity matrix, we can obtain a coarse-grain music segmentation result. Finally, dynamic programming is applied to the coarse-grain segments so that a song can be decomposed into several major parts such as intro, verse, chorus, bridge and outro. The performance of the proposed music structure analysis system is demonstrated for pop and rock music.

Hybrid femtosecond/picosecond rotational coherent anti-Stokes Raman scattering temperature and concentration measurements using two different picosecond-duration probes.

PubMed

Kearney, Sean P; Scoglietti, Daniel J; Kliewer, Christopher J

2013-05-20

A hybrid fs/ps pure-rotational CARS scheme is characterized in furnace-heated air at temperatures from 290 to 800 K. Impulsive femtosecond excitation is used to prepare a rotational Raman coherence that is probed with a ps-duration beam generated from an initially broadband fs pulse that is bandwidth limited using air-spaced Fabry-Perot etalons. CARS spectra are generated using 1.5- and 7.0-ps duration probe beams with corresponding coarse and narrow spectral widths. The spectra are fitted using a simple phenomenological model for both shot-averaged and single-shot measurements of temperature and oxygen mole fraction. Our single-shot temperature measurements exhibit high levels of precision and accuracy when the spectrally coarse 1.5-ps probe beam is used, demonstrating that high spectral resolution is not required for thermometry. An initial assessment of concentration measurements in air is also provided, with best results obtained using the higher resolution 7.0-ps probe. This systematic assessment of the hybrid CARS technique demonstrates its utility for practical application in low-temperature gas-phase systems.

Folding 19 proteins to their native state and stability of large proteins from a coarse-grained model.

PubMed

Kapoor, Abhijeet; Travesset, Alex

2014-03-01

We develop an intermediate resolution model, where the backbone is modeled with atomic resolution but the side chain with a single bead, by extending our previous model (Proteins (2013) DOI: 10.1002/prot.24269) to properly include proline, preproline residues and backbone rigidity. Starting from random configurations, the model properly folds 19 proteins (including a mutant 2A3D sequence) into native states containing β sheet, α helix, and mixed α/β. As a further test, the stability of H-RAS (a 169 residue protein, critical in many signaling pathways) is investigated: The protein is stable, with excellent agreement with experimental B-factors. Despite that proteins containing only α helices fold to their native state at lower backbone rigidity, and other limitations, which we discuss thoroughly, the model provides a reliable description of the dynamics as compared with all atom simulations, but does not constrain secondary structures as it is typically the case in more coarse-grained models. Further implications are described. Copyright © 2013 Wiley Periodicals, Inc.

Ballistic Imaging and Scattering Measurements for Diesel Spray Combustion: Optical Development and Phenomenological Studies

DTIC Science & Technology

2016-04-01

polystyrene spheres in a water suspension. The impact of spatial filtering , temporal filtering , and scattering path length on image resolution are...The impact of spatial filtering , temporal filtering , and scattering path length on image resolution are reported. The technique is demonstrated...cell filled with polystyrene spheres in a water suspension. The impact of spatial filtering , temporal filtering , and scattering path length on image

The Effect of Spatial and Temporal Resolution of Cine Phase Contrast MRI on Wall Shear Stress and Oscillatory Shear Index Assessment

PubMed Central

Gijsen, Frank J.; Marquering, Henk; van Ooij, Pim; vanBavel, Ed; Wentzel, Jolanda J.; Nederveen, Aart J.

2016-01-01

Introduction Wall shear stress (WSS) and oscillatory shear index (OSI) are associated with atherosclerotic disease. Both parameters are derived from blood velocities, which can be measured with phase-contrast MRI (PC-MRI). Limitations in spatiotemporal resolution of PC-MRI are known to affect these measurements. Our aim was to investigate the effect of spatiotemporal resolution using a carotid artery phantom. Methods A carotid artery phantom was connected to a flow set-up supplying pulsatile flow. MRI measurement planes were placed at the common carotid artery (CCA) and internal carotid artery (ICA). Two-dimensional PC-MRI measurements were performed with thirty different spatiotemporal resolution settings. The MRI flow measurement was validated with ultrasound probe measurements. Mean flow, peak flow, flow waveform, WSS and OSI were compared for these spatiotemporal resolutions using regression analysis. The slopes of the regression lines were reported in %/mm and %/100ms. The distribution of low and high WSS and OSI was compared between different spatiotemporal resolutions. Results The mean PC-MRI CCA flow (2.5±0.2mL/s) agreed with the ultrasound probe measurements (2.7±0.02mL/s). Mean flow (mL/s) depended only on spatial resolution (CCA:-13%/mm, ICA:-49%/mm). Peak flow (mL/s) depended on both spatial (CCA:-13%/mm, ICA:-17%/mm) and temporal resolution (CCA:-19%/100ms, ICA:-24%/100ms). Mean WSS (Pa) was in inverse relationship only with spatial resolution (CCA:-19%/mm, ICA:-33%/mm). OSI was dependent on spatial resolution for CCA (-26%/mm) and temporal resolution for ICA (-16%/100ms). The regions of low and high WSS and OSI matched for most of the spatiotemporal resolutions (CCA:30/30, ICA:28/30 cases for WSS; CCA:23/30, ICA:29/30 cases for OSI). Conclusion We show that both mean flow and mean WSS are independent of temporal resolution. Peak flow and OSI are dependent on both spatial and temporal resolution. However, the magnitude of mean and peak flow, WSS and OSI, and the spatial distribution of OSI and WSS did not exhibit a strong dependency on spatiotemporal resolution. PMID:27669568

Effects of temporal averaging on short-term irradiance variability under mixed sky conditions

NASA Astrophysics Data System (ADS)

Lohmann, Gerald M.; Monahan, Adam H.

2018-05-01

Characterizations of short-term variability in solar radiation are required to successfully integrate large numbers of photovoltaic power systems into the electrical grid. Previous studies have used ground-based irradiance observations with a range of different temporal resolutions and a systematic analysis of the effects of temporal averaging on the representation of variability is lacking. Using high-resolution surface irradiance data with original temporal resolutions between 0.01 and 1 s from six different locations in the Northern Hemisphere, we characterize the changes in representation of temporal variability resulting from time averaging. In this analysis, we condition all data to states of mixed skies, which are the most potentially problematic in terms of local PV power volatility. Statistics of clear-sky index k* and its increments Δk*τ (i.e., normalized surface irradiance and changes therein over specified intervals of time) are considered separately. Our results indicate that a temporal averaging time scale of around 1 s marks a transition in representing single-point irradiance variability, such that longer averages result in substantial underestimates of variability. Higher-resolution data increase the complexity of data management and quality control without appreciably improving the representation of variability. The results do not show any substantial discrepancies between locations or seasons.

Coarse-Grain Molecular Dynamics Simulations To Investigate the Bulk Viscosity and Critical Micelle Concentration of the Ionic Surfactant Sodium Dodecyl Sulfate (SDS) in Aqueous Solution.

PubMed

Ruiz-Morales, Yosadara; Romero-Martínez, Ascención

2018-04-12

The first critical micelle concentration (CMC) of the ionic surfactant sodium dodecyl sulfate (SDS) in diluted aqueous solution has been determined at room temperature from the investigation of the bulk viscosity, at several concentrations of SDS, by means of coarse-grain molecular dynamics simulations. The coarse-grained model molecules at the mesoscale level are adopted. The bulk viscosity of SDS was calculated at several millimolar concentrations of SDS in water using the MARTINI force field by means of NVT shear Mesocite molecular dynamics. The definition of each bead in the MARTINI force field is established, as well as their radius, volume, and mass. The effect of the size of the simulation box on the obtained CMC has been investigated, as well as the effect of the number of SDS molecules, in the simulations, on the formation of aggregates. The CMC, which was obtained from a graph of the calculated viscosities versus concentration, is in good agreement with the reported experimental data and does not depend on the size of the box used in the simulation. The formation of a spherical micelle-like aggregate is observed, where the dodecyl sulfate tails point inward and the heads point outward the aggregation micelle, in accordance with experimental observations. The advantage of using coarse-grain molecular dynamics is the possibility of treating explicitly charged beads, applying a shear flow for viscosity calculation, and processing much larger spatial and temporal scales than atomistic molecular dynamics can. Furthermore, the CMC of SDS obtained with the coarse-grained model is in much better agreement with the experimental value than the value obtained with atomistic simulations.

Global Surface Net-Radiation at 5 km from MODIS Terra

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

Verma, Manish; Fisher, Joshua; Mallick, Kaniska

Reliable and fine resolution estimates of surface net-radiation are required for estimating latent and sensible heat fluxes between the land surface and the atmosphere. However, currently, fine resolution estimates of net-radiation are not available and consequently it is challenging to develop multi-year estimates of evapotranspiration at scales that can capture land surface heterogeneity and are relevant for policy and decision-making. We developed and evaluated a global net-radiation product at 5 km and 8-day resolution by combining mutually consistent atmosphere and land data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra. Comparison with net-radiation measurements from 154 globally distributedmore » sites (414 site-years) from the FLUXNET and Surface Radiation budget network (SURFRAD) showed that the net-radiation product agreed well with measurements across seasons and climate types in the extratropics (Wilmott's index ranged from 0.74 for boreal to 0.63 for Mediterranean sites). Mean absolute deviation between the MODIS and measured net-radiation ranged from 38.0 ± 1.8 W.m -2 in boreal to 72.0 ± 4.1 W.m -2 in the tropical climates. The mean bias was small and constituted only 11%, 0.7%, 8.4%, 4.2%, 13.3%, and 5.4% of the mean absolute error in daytime net-radiation in boreal, Mediterranean, temperate-continental, temperate, semi-arid, and tropical climate, respectively. To assess the accuracy of the broader spatiotemporal patterns, we upscaled error-quantified MODIS net-radiation and compared it with the net-radiation estimates from the coarse spatial (1° x 1°) but high temporal resolution gridded net-radiation product from the Clouds and Earth's Radiant Energy System (CERES). Our estimates agreed closely with the net-radiation estimates from the CERES. Difference between the two was less than 10W.m -2 in 94% of the total land area. MODIS net-radiation product will be a valuable resource for the science community studying turbulent fluxes and energy budget at the Earth's surface.« less

Global Surface Net-Radiation at 5 km from MODIS Terra

DOE PAGES

Verma, Manish; Fisher, Joshua; Mallick, Kaniska; ...

2016-09-06

Reliable and fine resolution estimates of surface net-radiation are required for estimating latent and sensible heat fluxes between the land surface and the atmosphere. However, currently, fine resolution estimates of net-radiation are not available and consequently it is challenging to develop multi-year estimates of evapotranspiration at scales that can capture land surface heterogeneity and are relevant for policy and decision-making. We developed and evaluated a global net-radiation product at 5 km and 8-day resolution by combining mutually consistent atmosphere and land data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra. Comparison with net-radiation measurements from 154 globally distributedmore » sites (414 site-years) from the FLUXNET and Surface Radiation budget network (SURFRAD) showed that the net-radiation product agreed well with measurements across seasons and climate types in the extratropics (Wilmott's index ranged from 0.74 for boreal to 0.63 for Mediterranean sites). Mean absolute deviation between the MODIS and measured net-radiation ranged from 38.0 ± 1.8 W.m -2 in boreal to 72.0 ± 4.1 W.m -2 in the tropical climates. The mean bias was small and constituted only 11%, 0.7%, 8.4%, 4.2%, 13.3%, and 5.4% of the mean absolute error in daytime net-radiation in boreal, Mediterranean, temperate-continental, temperate, semi-arid, and tropical climate, respectively. To assess the accuracy of the broader spatiotemporal patterns, we upscaled error-quantified MODIS net-radiation and compared it with the net-radiation estimates from the coarse spatial (1° x 1°) but high temporal resolution gridded net-radiation product from the Clouds and Earth's Radiant Energy System (CERES). Our estimates agreed closely with the net-radiation estimates from the CERES. Difference between the two was less than 10W.m -2 in 94% of the total land area. MODIS net-radiation product will be a valuable resource for the science community studying turbulent fluxes and energy budget at the Earth's surface.« less

High Resolution Modeling in Mountainous Terrain for Water Resource Management: AN Extreme Precipitation Event Case Study

NASA Astrophysics Data System (ADS)

Masarik, M. T.; Watson, K. A.; Flores, A. N.; Anderson, K.; Tangen, S.

2016-12-01

The water resources infrastructure of the Western US is designed to deliver reliable water supply to users and provide recreational opportunities for the public, as well as afford flood control for communities by buffering variability in precipitation and snow storage. Thus water resource management is a balancing act of meeting multiple objectives while trying to anticipate and mitigate natural variability of water supply. Currently, the forecast guidance available to personnel managing resources in mountainous terrain is lacking in two ways: the spatial resolution is too coarse, and there is a gap in the intermediate time range (10-30 days). To address this need we examine the effectiveness of using the Weather Research and Forecasting (WRF) model, a state of the art, regional, numerical weather prediction model, as a means to generate high-resolution weather guidance in the intermediate time range. This presentation will focus on a reanalysis and hindcasting case study of the extreme precipitation and flooding event in the Payette River Basin of Idaho during the period of June 2nd-4th, 2010. For the reanalysis exercise we use NCEP's Climate Forecast System Reanalysis (CFSR) and the North American Regional Reanalysis (NARR) data sets as input boundary conditions to WRF. The model configuration includes a horizontal spatial resolution of 3km in the outer nest, and 1 km in the inner nest, with output temporal resolution of 3 hrs and 1 hr, respectively. The hindcast simulations, which are currently underway, will make use of the NCEP Climate Forecast System Reforecast (CFSRR) data. The current state of these runs will be discussed. Preparations for the second of two components in this project, weekly WRF forecasts during the intense portion of the water year, will be briefly described. These forecasts will use the NCEP Climate Forecast System version 2 (CFSv2) operational forecast data as boundary conditions to provide forecast guidance geared towards water resource managers out to a lead time of 30 days. We are particularly interested in the degree to which there is forecast skill in basinwide precipitation occurrence, departure from climatology, timing, and amount in the intermediate time range.

Fusion of multi-source remote sensing data for agriculture monitoring tasks

NASA Astrophysics Data System (ADS)

Skakun, S.; Franch, B.; Vermote, E.; Roger, J. C.; Becker Reshef, I.; Justice, C. O.; Masek, J. G.; Murphy, E.

2016-12-01

Remote sensing data is essential source of information for enabling monitoring and quantification of crop state at global and regional scales. Crop mapping, state assessment, area estimation and yield forecasting are the main tasks that are being addressed within GEO-GLAM. Efficiency of agriculture monitoring can be improved when heterogeneous multi-source remote sensing datasets are integrated. Here, we present several case studies of utilizing MODIS, Landsat-8 and Sentinel-2 data along with meteorological data (growing degree days - GDD) for winter wheat yield forecasting, mapping and area estimation. Archived coarse spatial resolution data, such as MODIS, VIIRS and AVHRR, can provide daily global observations that coupled with statistical data on crop yield can enable the development of empirical models for timely yield forecasting at national level. With the availability of high-temporal and high spatial resolution Landsat-8 and Sentinel-2A imagery, course resolution empirical yield models can be downscaled to provide yield estimates at regional and field scale. In particular, we present the case study of downscaling the MODIS CMG based generalized winter wheat yield forecasting model to high spatial resolution data sets, namely harmonized Landsat-8 - Sentinel-2A surface reflectance product (HLS). Since the yield model requires corresponding in season crop masks, we propose an automatic approach to extract winter crop maps from MODIS NDVI and MERRA2 derived GDD using Gaussian mixture model (GMM). Validation for the state of Kansas (US) and Ukraine showed that the approach can yield accuracies > 90% without using reference (ground truth) data sets. Another application of yearly derived winter crop maps is their use for stratification purposes within area frame sampling for crop area estimation. In particular, one can simulate the dependence of error (coefficient of variation) on the number of samples and strata size. This approach was used for estimating the area of winter crops in Ukraine for 2013-2016. The GMM-GDD approach is further extended for HLS data to provide automatic winter crop mapping at 30 m resolution for crop yield model and area estimation. In case of persistent cloudiness, addition of Sentinel-1A synthetic aperture radar (SAR) images is explored for automatic winter crop mapping.

The 'fine structure' of nutrient dynamics in rivers: ten years of study using high-frequency monitoring

NASA Astrophysics Data System (ADS)

Jordan, Phil; Melland, Alice; Shore, Mairead; Mellander, Per-Erik; Shortle, Ger; Ryan, David; Crockford, Lucy; Macintosh, Katrina; Campbell, Julie; Arnscheidt, Joerg; Cassidy, Rachel

2014-05-01

A complete appraisal of material fluxes in flowing waters is really only possibly with high time resolution data synchronous with measurements of discharge. Defined by Kirchner et al. (2004; Hydrological Processes, 18/7) as the high-frequency wave of the future and with regard to disentangling signal noise from process pattern, this challenge has been met in terms of nutrient flux monitoring by automated bankside analysis. In Ireland over a ten-year period, time-series nutrient data collected on a sub-hourly basis in rivers have been used to distinguish fluxes from different catchment sources and pathways and to provide more certain temporal pictures of flux for the comparative definition of catchment nutrient dynamics. In catchments where nutrient fluxes are particularly high and exhibit a mix of extreme diffuse and point source influences, high time resolution data analysis indicates that there are no satisfactory statistical proxies for seasonal or annual flux predictions that use coarse datasets. Or at least exposes the limits of statistical approaches to catchment scale and hydrological response. This has profound implications for catchment monitoring programmes that rely on modelled relationships. However, using high resolution monitoring for long term assessments of catchment mitigation measures comes with further challenges. Sustaining continuous wet chemistry analysis at river stations is resource intensive in terms of capital, maintenance and quality assurance. Furthermore, big data capture requires investment in data management systems and analysis. These two institutional challenges are magnified when considering the extended time period required to identify the influences of land-based nutrient control measures on water based systems. Separating the 'climate signal' from the 'source signal' in river nutrient flux data is a major analysis challenge; more so when tackled with anything but higher resolution data. Nevertheless, there is scope to lower costs in bankside analysis through technology development, and the scientific advantages of these data are clear and exciting. When integrating its use with policy appraisal, it must be made clear that the advances in river process understanding from high resolution monitoring data capture come as a package with the ability to make more informed decisions through an investment in better information.

Unified treatment and measurement of the spectral resolution and temporal effects in frequency-resolved sum-frequency generation vibrational spectroscopy (SFG-VS).

PubMed

Velarde, Luis; Wang, Hong-Fei

2013-12-14

The lack of understanding of the temporal effects and the restricted ability to control experimental conditions in order to obtain intrinsic spectral lineshapes in surface sum-frequency generation vibrational spectroscopy (SFG-VS) have limited its applications in surface and interfacial studies. The emergence of high-resolution broadband sum-frequency generation vibrational spectroscopy (HR-BB-SFG-VS) with sub-wavenumber resolution [Velarde et al., J. Chem. Phys., 2011, 135, 241102] offers new opportunities for obtaining and understanding the spectral lineshapes and temporal effects in SFG-VS. Particularly, the high accuracy of the HR-BB-SFG-VS experimental lineshape provides detailed information on the complex coherent vibrational dynamics through direct spectral measurements. Here we present a unified formalism for the theoretical and experimental routes for obtaining an accurate lineshape of the SFG response. Then, we present a detailed analysis of a cholesterol monolayer at the air/water interface with higher and lower resolution SFG spectra along with their temporal response. With higher spectral resolution and accurate vibrational spectral lineshapes, it is shown that the parameters of the experimental SFG spectra can be used both to understand and to quantitatively reproduce the temporal effects in lower resolution SFG measurements. This perspective provides not only a unified picture but also a novel experimental approach to measuring and understanding the frequency-domain and time-domain SFG response of a complex molecular interface.

Solvation free energies and partition coefficients with the coarse-grained and hybrid all-atom/coarse-grained MARTINI models.

PubMed

Genheden, Samuel

2017-10-01

We present the estimation of solvation free energies of small solutes in water, n-octanol and hexane using molecular dynamics simulations with two MARTINI models at different resolutions, viz. the coarse-grained (CG) and the hybrid all-atom/coarse-grained (AA/CG) models. From these estimates, we also calculate the water/hexane and water/octanol partition coefficients. More than 150 small, organic molecules were selected from the Minnesota solvation database and parameterized in a semi-automatic fashion. Using either the CG or hybrid AA/CG models, we find considerable deviations between the estimated and experimental solvation free energies in all solvents with mean absolute deviations larger than 10 kJ/mol, although the correlation coefficient is between 0.55 and 0.75 and significant. There is also no difference between the results when using the non-polarizable and polarizable water model, although we identify some improvements when using the polarizable model with the AA/CG solutes. In contrast to the estimated solvation energies, the estimated partition coefficients are generally excellent with both the CG and hybrid AA/CG models, giving mean absolute deviations between 0.67 and 0.90 log units and correlation coefficients larger than 0.85. We analyze the error distribution further and suggest avenues for improvements.

Solvation free energies and partition coefficients with the coarse-grained and hybrid all-atom/coarse-grained MARTINI models

NASA Astrophysics Data System (ADS)

Genheden, Samuel

2017-10-01

We present the estimation of solvation free energies of small solutes in water, n-octanol and hexane using molecular dynamics simulations with two MARTINI models at different resolutions, viz. the coarse-grained (CG) and the hybrid all-atom/coarse-grained (AA/CG) models. From these estimates, we also calculate the water/hexane and water/octanol partition coefficients. More than 150 small, organic molecules were selected from the Minnesota solvation database and parameterized in a semi-automatic fashion. Using either the CG or hybrid AA/CG models, we find considerable deviations between the estimated and experimental solvation free energies in all solvents with mean absolute deviations larger than 10 kJ/mol, although the correlation coefficient is between 0.55 and 0.75 and significant. There is also no difference between the results when using the non-polarizable and polarizable water model, although we identify some improvements when using the polarizable model with the AA/CG solutes. In contrast to the estimated solvation energies, the estimated partition coefficients are generally excellent with both the CG and hybrid AA/CG models, giving mean absolute deviations between 0.67 and 0.90 log units and correlation coefficients larger than 0.85. We analyze the error distribution further and suggest avenues for improvements.

Radial Bias Is Not Necessary For Orientation Decoding

PubMed Central

Pratte, Michael S.; Sy, Jocelyn L.; Swisher, Jascha D.; Tong, Frank

2015-01-01

Multivariate pattern analysis can be used to decode the orientation of a viewed grating from fMRI signals in early visual areas. Although some studies have reported identifying multiple sources of the orientation information that make decoding possible, a recent study argued that orientation decoding is only possible because of a single source: a coarse-scale retinotopically organized preference for radial orientations. Here we aim to resolve these discrepant findings. We show that there were subtle, but critical, experimental design choices that led to the erroneous conclusion that a radial bias is the only source of orientation information in fMRI signals. In particular, we show that the reliance on a fast temporal-encoding paradigm for spatial mapping can be problematic, as effects of space and time become conflated and lead to distorted estimates of a voxel’s orientation or retinotopic preference. When we implement minor changes to the temporal paradigm or to the visual stimulus itself, by slowing the periodic rotation of the stimulus or by smoothing its contrast-energy profile, we find significant evidence of orientation information that does not originate from radial bias. In an additional block-paradigm experiment where space and time were not conflated, we apply a formal model comparison approach and find that many voxels exhibit more complex tuning properties than predicted by radial bias alone or in combination with other known coarse-scale biases. Our findings support the conclusion that radial bias is not necessary for orientation decoding. In addition, our study highlights potential limitations of using temporal phase-encoded fMRI designs for characterizing voxel tuning properties. PMID:26666900