9,704 research outputs found
Multispectral scanner data applications evaluation. Volume 1: User applications study
A six-month systems study of earth resource surveys from satellites was conducted and is reported. SKYLAB S-192 multispectral scanner (MSS) data were used as a baseline to aid in evaluating the characteristics of future systems using satellite MSS sensors. The study took the viewpoint that overall system (sensor and processing) characteristics and parameter values should be determined largely by user requirements for automatic information extraction performance in quasi-operational earth resources surveys, the other major factor being hardware limitations imposed by state-of-the-art technology and cost. The objective was to use actual aircraft and spacecraft MSS data to outline parametrically the trade-offs between user performance requirements and hardware performance and limitations so as to allow subsequent evaluation of compromises which must be made in deciding what system(s) to build
FLIAT, an object-relational GIS tool for flood impact assessment in Flanders, Belgium
Floods can cause damage to transportation and energy infrastructure, disrupt the delivery of services, and take a toll on public health, sometimes even causing significant loss of life. Although scientists widely stress the compelling need for resilience against extreme events under a changing climate, tools for dealing with expected hazards lag behind. Not only does the socio-economic, ecologic and cultural impact of floods need to be considered, but the potential disruption of a society with regard to priority adaptation guidelines, measures, and policy recommendations need to be considered as well. The main downfall of current impact assessment tools is the raster approach that cannot effectively handle multiple metadata of vital infrastructures, crucial buildings, and vulnerable land use (among other challenges). We have developed a powerful cross-platform flood impact assessment tool (FLIAT) that uses a vector approach linked to a relational database using open source program languages, which can perform parallel computation. As a result, FLIAT can manage multiple detailed datasets, whereby there is no loss of geometrical information. This paper describes the development of FLIAT and the performance of this tool
Hydrological Alteration Index as an Indicator of the Calibration Complexity of Water Quantity and Quality Modeling in the Context of Global Change
Modeling is a useful way to understand human and climate change impacts on the water resources of agricultural watersheds. Calibration and validation methodologies are crucial in forecasting assessments. This study explores the best calibration methodology depending on the level of hydrological alteration due to human-derived stressors. The Soil and Water Assessment Tool (SWAT) model is used to evaluate hydrology in South-West Europe in a context of intensive agriculture and water scarcity. The Index of Hydrological Alteration (IHA) is calculated using discharge observation data. A comparison of two SWAT calibration methodologies are done; a conventional calibration (CC) based on recorded in-stream water quality and quantity and an additional calibration (AC) adding crop managements practices. Even if the water quality and quantity trends are similar between CC and AC, water balance, irrigation and crop yields are different. In the context of rainfall decrease, water yield decreases in both CC and AC, while crop productions present opposite trends (+33% in CC and -31% in AC). Hydrological performance between CC and AC is correlated to IHA: When the level of IHA is under 80%, AC methodology is necessary. The combination of both calibrations appears essential to better constrain the model and to forecast the impact of climate change or anthropogenic influences on water resources
Modelling District Heating in a Renewable Electricity System
With the decarbonisation of electricity generation, large scale heat pumps are becoming increasingly viable for district heating combined with thermal energy storage, district heating can provide flexibility to the electricity grid by decoupling demand from supply. This thesis examines how district heating with heat pumps and thermal energy storage can integrate with and provide a benefit to an electricity system with predominantly renewable generation. The scope of work comprises three interlinked models underpinned by the same set of meteorology data, fundamentally coupling supply and demand.
First, heat load data are surveyed, and an hourly demand profile is simulated. Disaggregation of district heating loads from the national demand is accomplished via segmentation of the building stock to model heat demand at high spatiotemporal resolution.
Second, a novel method of pricing hourly electricity in a zero carbon, capital-intensive renewable system with electricity storage is developed and applied to a dispatch simulation to generate hourly electricity prices.
Third, a dynamic model of district heating is constructed to simulate the meeting of heat loads with different design configurations using electricity as the energy source. Model predictive control is applied with varying forecast horizons so as to minimise the cost of electricity to meet the heat demand given a time series of hourly prices and consequently optimising the capacity of thermal energy storage. It was found that a thermal energy storage capacity equivalent to 1.3% of annual demand is sufficient to minimise operating costs.
Finally, the potential impact of district heating on balancing the electricity system is analysed and an equivalence between thermal and electric storage is examined. While this is highly dependent on annual conditions, it can be as much as 3.5 units of thermal storage for every unit of electrical grid storage on the system. This could potentially reduce the investment in grid storage by £36 billion, underlining the significant financial benefits of thermal storage to the whole system. The research highlights the important potential of district heating to the UK’s energy system strategy
Remote Sensing Monitoring of Land Surface Temperature (LST)
This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research
The Physics of Galaxy Cluster Outskirts
As the largest virialized structures in the universe, galaxy clusters
continue to grow and accrete matter from the cosmic web. Due to the low gas
density in the outskirts of clusters, measurements are very challenging,
requiring extremely sensitive telescopes across the entire electromagnetic
spectrum. Observations using X-rays, the Sunyaev-Zeldovich effect, and weak
lensing and galaxy distributions from the optical band, have over the last
decade helped to unravel this exciting new frontier of cluster astrophysics,
where the infall and virialization of matter takes place. Here, we review the
current state of the art in our observational and theoretical understanding of
cluster outskirts, and discuss future prospects for exploration using newly
planned and proposed observatories.Comment: 56 pages. Review paper. Published in Space Science Review
An evaluation of a severe smog episode in the Eastern U.S. using regional modeling and satellite measurements
An ensemble of regional chemical modeling (WRF/Chem with RADM2) simulations, satellite, ozonesonde, and surface observations during July 7-11, 2007 was used to examine the horizontal and vertical signature of one of the worst smog events in the eastern U.S. in the past decade. The general features of this event -- a broad area of high pressure, weak winds and heavy pollution, terminated by the passage of a cold front -- were well simulated by the model. Average 8-hr maximum O3 has a mean (±Σ) bias of 0.59 (±11.0) ppbv and a root mean square error of 11.0 ppbv. WRF/Chem performed the best on poor air quality days, simulating correctly the spatial pattern of surface O3. Yet the model underpredicted O3 maxima by 5-7 ppbv in the Northeast and overpredicted by 8-11 ppbv in the Southeast. High O3 biases in the Southeast are explained by overpredicted temperatures in the model (>1.5°C). Sensitivity simulations with 1) accelerated O3 dry deposition velocity and 2) suppressed multiphase nitric acid formation pushed the model closer to observations. Simulated O3 vertical profiles over Beltsville, MD showed good agreement with ozonesonde measurements, but the modeled boundary layer depth was overpredicted on July 9, contributing to the low bias over this region.
During this severe smog episode, space-borne TES detected high total tropospheric column ozone (TCO) over the Western Atlantic Ocean off the coast near North and South Carolina. The standard product (OMI/MLS) missed the magnitude of these local maxima, but the level-2 ozone profile (OMI) confirmed the TES observations. HYSPLIT back trajectories from these O3 maxima intersected regions of strong convection over the Southeast and Great Lakes regions. When lightning NO emissions were implemented in WRF/Chem, the high concentrations of NOx and O3 off the coast were well reproduced, showing that the exported O3 was produced by a combination of natural NO and pollutants lofted from the lower atmosphere. Lastly, WINTER MONEX O3 data from 1978 are presented for the first time here in discussion of open cell convection over Indonesia
Scaling Effect of Fused ASTER-MODIS Land Surface Temperature in an Urban Environment
There is limited research in land surface temperatures (LST) simulation using image fusion techniques, especially studies addressing the downscaling effect of LST image fusion. LST simulation and associated downscaling effect can potentially benefit the thermal studies requiring both high spatial and temporal resolutions. This study simulated LSTs based on observed Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LST imagery with Spatial and Temporal Adaptive Reflectance Fusion Model, and investigated the downscaling effect of LST image fusion at 15, 30, 60, 90, 120, 250, 500, and 1000 m spatial resolutions. The study area partially covered the City of Los Angeles, California, USA, and surrounding areas. The reference images (observed ASTER and MODIS LST imagery) were acquired on 04/03/2007 and 07/01/2007, with simulated LSTs produced for 4/28/2007. Three image resampling methods (Cubic Convolution, Bilinear Interpolation, and Nearest Neighbor) were used during the downscaling and upscaling processes, and the resulting LST simulations were compared. Results indicated that the observed ASTER LST and simulated ASTER LST images (date 04/28/2007, spatial resolution 90 m) had high agreement in terms of spatial variations and basic statistics based on a comparison between the observed and simulated ASTER LST maps. Urban developed lands possessed higher LSTs with lighter tones and mountainous areas showed dark tones with lower LSTs. The Cubic Convolution and Bilinear Interpolation resampling methods yielded better results over Nearest Neighbor resampling method across the scales from 15 to 1000 m. The simulated LSTs with image fusion can be used as valuable inputs in heat related studies that require frequent LST measurements with fine spatial resolutions, e.g., seasonal movements of urban heat islands, monthly energy budget assessment, and temperature-driven epidemiology. The observation of scale-independency of the proposed image fusion method can facilitate with image selections of LST studies at various locations
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