Biography: Chris Elvidge received a Ph.D. in Applied Earth Sciences from Stanford University in 1985. He was a National Research Council post-doctoral fellow at NASA’s Jet Propulsion Laboratory from 1985-1987 and was on the Biological Sciences faculty at the Desert Research Institute in Reno starting in 1988. In 1994 he moved to NOAA’s National Geophysical Data Center in Boulder, Colorado to lead an effort to construct global maps of fires and lights using the low-light imaging data from the U.S. Air Force Defense Meteorological Satellite program (DMSP) Operational Linescan System. He currently leads NGDC’s Earth Observation Group.

Recent Publications:

Elvidge, C.D., Erwin, E.H., Baugh, K.E., Tuttle, B.T., Howard, A.T., Pack, D.W., Milesi, C., 2007, Satellite data estimate worldwide flared gas volumes. Oil and Gas Journal, November 12, 2007, p. 50-58.
Elvidge, C.D., Cinzano, P., Pettit, D.R., Arvesen, J., Sutton, P., Small, C., Nemani, R., Longcore, T., Rich, C., Safran, J., Weeks, J., Ebener, S., 2007, The Nightsat mission concept, International Journal of Remote Sensing, Volume 28, Issue 12 January 2007 , pages 2645 - 2670. DOI: 10.1080/01431160600981525.
Elvidge, C.D., Tuttle, B.T., Sutton, P.C., Baugh, K.E., Howard, A.T., Milesi, C., Bhaduri, B., Nemani R., 2007, Global distribution and density of constructed impervious surfaces. Sensors, v. 7, p. 1962-1979.
Elvidge, C.D., Safran, J., Tuttle, B., Sutton, P., Cinzano, P., Pettit, D., Arvesen, J., Small, C., 2007, Potential for global mapping of development via a Nightsat mission. GeoJournal v. 69, 45-53.
Elvidge, C.D., Baugh, K.E., Tuttle, B.T., Howard, A.T., Pack, D.W., Milesi, C., Erwin, E.H., 2007, A Twelve Year Record of Natural Gas Flaring Derived From Satellite Data. Submitted to the International Journal of Energy Research.

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All societies whether developing or advanced are becoming increasingly sensitive to weather related environmental hazards. These range from the quiescent events such as fog and its effects on transportation systems, to the more obvious headline-grabbing storm impacts, such as severe winds, heavy snowfall, river flood, rain-induced land sliding, damaging waves, storm surge and coastal inundation. However the effects extend still further when the management of power systems is considered such as the active management of renewable resources (wind, water, solar / cloud) to the effect of weather related loads on electricity networks. Similarly, weather events are responsible for the delivery of all water resources, whether directly or indirectly.
Owing to the complexity of the systems being modelled and the use of very large near real-time data sets, High Performance Computing and high speed data networks lie at the heart of environmental forecasting. Advances in satellite, aircraft, and surface based observing systems and the need for increasingly accurate predictions will further increase the demand for HPC computational and data management resources.
Here we outline (and demonstrate) the key aspects of a real-time Environmental Forecasting system that is using a high resolution weather prediction model coupled to a physically formulated distributed hydrological model, a probabilistic landslide model, a third generation wave model and a finite element storm surge model to forecast weather, river flows, river catchment states, sea state, storm surge (including astronomical tide) and, in the future, the probability of landslides. This integrated forecasting system runs on NIWA’s Cray HPC system and ancillary services, and utilises extensive observations from both conventional and satellite based observing systems, as well as fixed high resolution geospatial data sets. Model interactions and dependences are managed automatically using an object oriented approach and metadata rich data streams. Forecasts are delivered through an n-tier platform that supports workflow based decision systems as well as presenting the information via a rich internet application built on WPF. The decision tool provides explicit access to forecast uncertainty information and to verifying real time observations.
Michael Uddstrom is NIWA’s Science Leader for Environmental Forecasting, and leads NIWA’s weather related hazards forecasting research effort. He has more than 30 years experience in the development of satellite algorithms for application across a wide range of science problems - from fisheries analysis to numerical weather prediction, and was responsible for the development of the science and technology proposals that resulted in the acquisition of New Zealand’s first capability-class supercomputer in 1999, a Cray T3E 1200. He has been a champion for the development of High Performance Computing in New Zealand, is NIWA’s “e-Science champion”, and a member of the KAREN Capability Build Fund review committee. Michael also leads NIWA’s EcoConnect initiative which has developed an integrated environmental forecasting and information system that is built on new advances in physical system simulation models, better use of real time observing systems, the power of High Performance Computing, and the use of high bandwidth networks to deliver forecasts to end users via web services and a rich internet application.

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Since mid-1990s, we have begun to build a national operative dynamic information serving systems on natural resources and environment across China. 520 scenes of remotely sensed images of Landsat Thematic Mapper (TM) were interpreted into land-use categories at a scale of 1:100,000 under overall digital software environment after being geo-referenced and ortho-rectified in the process. The vector map of land uses in China at the scale of 1:100,000 was recently converted into a 1-km raster database, using the algrithems of 1-km area percentage data model, that captures all of the high-resolution land-use information by calculating area percentage for each kind of land use category within every cells. Being designed as an operative dynamic information serving systems, monitoring the change in land-use/land-cover at national level was executed. Now we have updated the land use dataset into 2000 for the entire China and 2005 for some specific provinces of China. The land use database has supported greatly the national LUCC research program in China.

Dr Xiangzheng Deng is Associate Professor, Center for Chinese Agricultural Policy, Chinese Academy of Sciences; Visiting Scholar, Walter H. Shorenstein Asia-Pacific Research Center, Stanford University; and Director Associate, Global Land Project, Beijing Office. His current research interests include the impacts of climatic changes on agricultural bio-productivity, simulation and investigation of nature-human interactions, spatial econometrics and spatially explicit methodology and tools, and spatial-temporal patterns and determinants of land uses.

SCENZ-Grid is a REANNZ Capability Build Fund collaboration between Landcare Research and GNS Science. Niels will describe the technologies used in the first prototype SCENZ-Grid workflow. Lithology is the surface layer of the earth's crust between the bottom of the soil profile and the underlying geology. Lithology is particularly important for the behaviour of aquifers, but New Zealand doesn't have a lithology map. However both GNS and Landcare Research have data that can be used to infer lithology. This proof of concept workflow combines two mapping web-services and a look-up web-service to demonstrate the way that a number of distributed databases and web services could be orchestrated into a workflow to provide an inference about lithology.

Bio Niels is a Geospatial researcher and programmer at Landcare Research. He has a background in Physical Geography and worked as a technical consultant in the GIS industry. He is involved in the development of Landcare's geospatial databases and applications. Current research interests are Land Use classification and sustainable environments as well as the implementation of distributed databases and modelling environments.

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Peyman Zawar-Reza Towards a multi-scale multi-model ensemble system for research and education
The implementation of the BlueFern supercomputing system comprising of an IBM p575 and Blue Gene has lead to a rapid growth in research and educational capabilities in atmospheric sciences at the University of Canterbury. This presentation will cover advances made in the past six-months in regards to providing the research community and students in New Zealand a state-of-the-art computing resource.

Components of the multi-model ensemble prediction system
Three non-hydrostatic atmospheric models with weather prediction capabilities will be providing daily high-resolution forecasts for New Zealand and other regions upon demand. Plans are underway to bring online a real-time system for the Antarctic, focusing on the Peninsula and the Ross Ice Shelf.

Real-time forecasting system as an educational tool
Plans are underway to include easy to access real-time forecasts as an educational tool for such courses as GEOG310 (entitled ‘Weather Systems’ taught by Prof Andrew Sturman). Students will have access to daily high-resolution (3 km) forecasts for Canterbury Region of South Island and will compare – validate – modelled data with measurements.
Research Applications Due to the impressive computational power available with BlueFern, it is now possible to target research at a hierarchy of atmospheric spatial and temporal scales. Consequently, the number of graduate students that perform computationally based research has increased dramatically. The list of research topics will be discussed.

BioPeyman Zawar-Reza joined the Geography Department at the University of Canterbury in July 2004, after being a postdoctoral fellow for 4 years. He was subsequently appointed as the Director of Centre for Atmospheric Research. His research interests are mainly focused on numerical modelling of the atmosphere at meso-scales. Simulating the atmosphere has many applications for environmental studies including air pollution dispersion, estimating human exposure to air pollutants, regional climate analysis, wind farm prospecting, and short-term weather forecasting.

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