Remote Sensing of CO2: Geostatistical Tools for Assessing Spatial Variability, Quantifying Representation Errors, and Gap-Filling.
Abstract: Currently, approximately half of the anthropogenic emissions of CO2 are absorbed by oceans and the terrestrial biosphere, thus greatly reducing the rate of atmospheric CO2 increase and related climate change. The current understanding of the global carbon cycle, and of the sustainability of natural carbon sinks, is limited, however. To enhance this knowledge, scientists use process-based biospheric models and atmospheric transport models, together with the limited global ground-based CO2 measurement network to infer global CO2 fluxes. Current estimates of carbon budgets at regional to continental scales vary significantly, however, in large part due to limited atmospheric observations of CO2. Satellite-based observations provide the possibility of global coverage of column-averaged CO2 (XCO2), which could improve the precision of estimated CO2 fluxes. XCO2 observations will have large data gaps, however, which will limit the use of XCO2 observations for evaluating CO2 flux estimates. In addition, remote sensing soundings will often be representative of fine scales relative to the resolution of typical atmospheric transport models, causing representation errors that should be quantified for accurate CO2 flux estimation. In this dissertation, the spatial variability of the XCO2 signal is quantified using geostatistical analysis. Geostatistical methods that depend on the knowledge of this spatial variability are then presented for evaluating representation errors. Unlike previous estimates of representation errors, the proposed method accounts for the regionally-variable XCO2 spatial variability, and the spatial distribution of retrievals. Further, a spatial mixed-effects statistical model that best represents the quantified XCO2 variability is presented for gap-filling XCO2 retrievals. The presented geostatistical gap-filling method, which is based on a multi-resolution model of the spatial trend and variability of XCO2, is tested using eight realistic scenarios of expected spatial distributions of XCO2 retrievals. The method yields XCO2 estimates over regions with data gaps, together with an estimate of the associated gap-filling uncertainties. The presented methods provide flexible tools that can be applied to estimate representation errors and gap-fill XCO2 or other remotely sensed data. As such, they provide the potential for improving and evaluating estimated CO2 fluxes, process-based models, and atmospheric transport models.
Subject(s): Quantification of the spatial variability of regional atmospheric carbon dioxide concentrations, Representation error evaluation of remote sensing observations, Using spatial mixed-effect statistical models to gap-fill remote sensing measurements
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.
http://bigapplewindowcleaning.com/ professional window cleaning.
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