“This course will introduce a suite of geostatistical methods for the spatial analysis of environmental data. Participants will learn how to apply geostatistics for the description of spatial patterns and identification of scales of variability, spatial interpolation and stochastic modeling of environmental attributes, creation of risk maps and their use in decision-making. Lectures will alternate with analysis of environmental data using the Stanford Geostatistical Modeling Software (S-GeMS) and the TerraSeer Space-time Information System (STIS).Test datasets will be prepared so that results of geostatistical prediction and impacts on decision-making can be compared and discussed during the course. Each participant will receive a set of lecture notes and have the opportunity to purchase a copy of Dr. Goovaerts’ textbook. A copy of the public domain S-GeMS software will be provided. Ample time will be allocated to discussion, and participants are invited to bring their own case studies to seek Dr. Goovaerts opinion. The course provides 30 hours of training and instruction, and a Certificate of Completion will be provided upon conclusion. Enrollment is limited, so register early to secure a seat in the course. Advance registration is required.
“A key feature of environmental information is that each observation relates to a particular location in space. Knowledge of an attribute value, say a pollutant concentration or a soil property, is of little interest unless the location of the measurement is known and accounted for in the analysis. Another feature is that the information available is usually sparse which, in combination with the imperfect knowledge of underlying processes, leads to a large uncertainty about the actual spatial distribution of values. Such an uncertainty needs to be quantified and accounted for in decision-making, hence probabilistic (statistical) tools are increasingly preferred to a deterministic approach where a single (error-free) representation is sought. Geostatistics provides a set of statistical tools for the analysis of data distributed in space and time. It allows the description of spatial patterns in the data, the incorporation of multiple sources of information in the mapping of environmental attributes, the modeling of the spatial uncertainty and its propagation through decision-making. Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences, to agriculture, soil science, environmental studies, and more recently exposure assessment and environmental epidemiology. The recognition of the importance of geostatistical analysis is illustrated by the inclusion of geostatistical functions in a growing number of products, including ArcGIS Geostatistical Analyst and TerraSeer Space-time Information System (STIS).”
Anything new about stirling engines in Cal?
Looking for an update. Everything I find from google news is almost a year old. This is from the stirling engine deal with Southern Edison to build a 500MW plant outside of LA.
What geographical areas are best suited for a solar dish farm?
The southwest region of the United States is ideally suited for this. In fact, a solar farm 100 miles by 100 miles could satisfy 100% of the Americaâs annual electrical needs. Solar technology primarily addresses the peak power demands facing utility companies in the Southwest U.S. and other solar-rich areas.
The cost of living and job markets are better than the national average, but the best job strategy is not to go for averages, but look at your specific skills and experiences, figure out which careers that relates to, and then go to that geographical area:
technology - Silicon Valley
finance - New York
There are other factors to consider. How important are mountains? the ocean? good weather? I have met many midwesterners in Acapulco during the winter, and none ever told me
"I got to get back to Omaha. I just miss those snow covered plains."
4,000 Year Old Greenlander
WASHINGTON (Reuters) â Scientists have sequenced the DNA from four frozen hairs of a Greenlander who died 4,000 years ago in a study they say takes genetic technology into several new realms.
Surprisingly, the long-dead man appears to have originated in Siberia and is unrelated to modern Greenlanders, Morten Rasmussen of the University of Copenhagen and colleagues found.
"This provides evidence for a migration from Siberia into the New World some 5,500 years ago, independent of that giving rise to the modern Native Americans and Inuit," the researchers wrote in Thursday's issue of the journal Nature