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Tian Y.,National Oceanic and Atmospheric Administration | Romanov P.,University of Maryland University College | Yu Y.,National Oceanic and Atmospheric Administration | Xu H.,National Oceanic and Atmospheric Administration | Tarpley D.,Short and Associates
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2010

Green Vegetation Fraction (GVF) is the fraction of area within the instrument footprint occupied by green vegetation. Information on GVF is needed to estimate the surface energy balance in numerical weather prediction (NWP) and climate models. For the Geostationary Operational Environmental Satellite-R Series (GOES-R) Advanced Baseline Imager (ABI) algorithm development, a normalized difference vegetation index (NDVI) based linear mixture algorithm has been chosen to convert NDVI into GVF. The GVF algorithm has been developed and tested using a proxy dataset from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the European Meteosat Second Generation (MSG) geostationary satellite. Studies of SEVIRI data have shown that NDVI strongly depends upon the viewing and illumination geometry of observations, especially over dense vegetation. If not corrected, this angular anisotropy of NDVI causes substantial spurious diurnal variations in the derived GVF. An empirical kernel-driven model to correct NDVI for angular anisotropy has been developed and implemented in the GVF algorithm. Its kernel weights for the GVF algorithm were also determined empirically from the SEVIRI clear-sky data. The preliminary validation estimates show that the model's performance is good. © 2010 IEEE. Source


Liu Y.,The Interdisciplinary Center | Yu Y.,National Oceanic and Atmospheric Administration | Sun D.,George Mason University | Tarpley D.,Short and Associates | Fang L.,George Mason University
IEEE Geoscience and Remote Sensing Letters | Year: 2013

The National Oceanic and Atmospheric Administration's National Environmental Satellite, Data, and Information Service is developing an operational land-surface temperature (LST) product from the U.S. Geostationary Operational Environmental Satellite (GOES) series 13, 14, and 15, which makes use of the Moderate Resolution Imaging Spectroradiometer (MODIS) monthly emissivity. However, there is a latency problem since the MODIS monthly emissivity data are available at least a month late. In this study, we investigated using alternative emissivity data sets, including the ten-year monthly average emissivity, the last month emissivity, and the same month emissivity in the last year. We also tested current monthly emissivity and current weekly emissivity for comparison and evaluation. The study area is in the continental United States (25°N-50°N and 125°W-65°W), and the temporal frame is April, July, October, and December, which represents the four seasons in a year. Based on the modified dual-window algorithm, LST is derived and validated against the SURFace RADiation (SURFRAD) budget network ground observations. The results show that the ten-year monthly average emissivity performs best by retrieving stable and accurate LST. © 2004-2012 IEEE. Source


Gao Y.,Chongqing University | Yao R.,Chongqing University | Yao R.,University of Reading | Li B.,Chongqing University | And 4 more authors.
Renewable Energy | Year: 2012

Airflow through urban environments is one of the most important factors affecting human health, outdoor and indoor thermal comfort, air quality and the energy performance of buildings. This paper presents a study on the effects of wind induced airflows through urban built form using statistical analysis. The data employed in the analysis are from the year-long simultaneous field measurements conducted at the University of Reading campus in the United Kingdom. In this study, the association between typical architectural forms and the wind environment are investigated; such forms include: a street canyon, a semi-closure, a courtyard form and a relatively open space in a low-rise building complex. Measured data captures wind speed and wind direction at six representative locations and statistical analysis identifies key factors describing the effects of built form on the resulting airflows. Factor analysis of the measured data identified meteorological and architectural layout factors as key factors. The derivation of these factors and their variation with the studied built forms are presented in detail. © 2012 Elsevier Ltd. Source


Short C.A.,Short and Associates | Short C.A.,University of Cambridge | Cook M.,Loughborough University | Cropper P.C.,De Montfort University | Al-Maiyah S.,Short and Associates
Journal of Building Performance Simulation | Year: 2010

Public health buildings contribute significantly to UK carbon emissions. New build initiatives have received more attention than the considerable opportunities to reduce carbon emissions within the retained health estate. The research reported here has considered the environmental performance of a typical medium rise, medium depth, concrete-framed, late 1960s acute hospital following low energy environmental design interventions. The interventions are made to optimize daylighting and natural ventilation/cooling whilst reducing overheating caused by summer time solar gains. Three options are investigated: advanced natural ventilation using plena and exhaust stacks; fan-assisted natural ventilation in which fans are used in the exhaust stacks; and mechanical ventilation/cooling with heat recovery. Computer simulations have been carried out to predict the influence on thermal performance (overheating risk) and energy consumption of each of these options on the original design. For each case, current weather data, and future weather data for the years 2020, 2050 and 2080, have been used. © 2010 International Building Performance Simulation Association (IBPSA). Source


Heidinger A.K.,National Oceanic and Atmospheric Administration | Laszlo I.,College Park | Molling C.C.,University of Wisconsin - Madison | Tarpley D.,Short and Associates
Journal of Atmospheric and Oceanic Technology | Year: 2013

Because of spectral shifts from instrument to instrument in the operational NOAA satellite imager longwave infrared channels, the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) has developed a single-channel land surface temperature (LST) algorithm based on the observed 11-mm radiances, numerical weather prediction data, and radiative transfer modeling that allows for consistent results from the Geostationary Operational Environmental Satellite-I/L (GOES-I/L), GOES-M-P, and Advanced Very High Resolution Radiometer (AVHRR)/1 through 3 sensor versions. This approach is implemented in the real-time NESDIS processing systems [GOES Surface and Insolation Products (GSIP) and Clouds fromAVHRR Extended (CLAVR-x)], and in the Pathfinder Atmospheres-Extended (PATMOS-x) climate dataset.An analysis of the PATMOS-x LST against that derived fromthe upwelling broadband longwave flux at each Surface RadiationNetwork (SURFRAD) site showed that biases in PATMOS-x were approximately 1Kor less. The standard deviations of the PATMOS-xminusSURFRADLST biases are generally 2.5Kor less at all sites for all sensors. Using the PATMOS-x minus SURFRAD LST distributions to validate the PATMOS-x cloud detection, the PATMOS-x cloud probability of correct detection values were shown to meet the GOES-R specifications for all sites. © 2013 American Meteorological Society. Source

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