Old Fib

That\u27s a mighty fine lookin\u27 mare you\u27re drivin\u27 there, mister. You say she throwed a shoe about a mile from here? Well, I guess we can fix that soon as I run down some tools..

We Killed Some, We Loved Some

She certainly didn\u27t look like the German women whom we had read about before the war. She was tiny and slim, gray and wrinkled, about five feet two inches tall and weighed about a hundred pounds. Her sparkling eyes were sharp and expressive, and she was quick, scarcely stooped and very spry for a woman of such age. Her name was Betsy Holtzendoner. She was seventy-five years old, a widow and the mother of five children

Cost-efficient design and production of flexible and re-usable near real-time tactical human-machine interfaces

International audienceMaking complex systems accessible to human operators supposes to design HMIs that provide the operator with means to manage the complexity in an efficient manner. This is particularly true in the aeronautics domain for tactical HMIs where complexity is present in many dimensions. Current technical requirements, such as being able to display thousands of objects updated on the basis of time intervals inferior to half a second, coupled with economic requirements such as manning and cost reductions, make this issue even more crucial. We present our approach to the design and production of near real-time tactical HMIs, that enables us to devise HMIs that meet such requirements while being flexible enough to be re- used in a wide variety of contexts and produced at a reasonable cost

The utility of continuous atmospheric measurements for identifying biospheric CO 2 flux variability

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95026/1/jgrd16859.pd

Evaluation of simulated soil carbon dynamics in Arctic-Boreal ecosystems

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Huntzinger, D. N., Schaefer, K., Schwalm, C., Fisher, J. B., Hayes, D., Stofferahn, E., Carey, J., Michalak, A. M., Wei, Y., Jain, A. K., Kolus, H., Mao, J., Poulter, B., Shi, X., Tang, J., & Tian, H. Evaluation of simulated soil carbon dynamics in Arctic-Boreal ecosystems. Environmental Research Letters, 15(2), (2020): 025005, doi:10.1088/1748-9326/ab6784.Given the magnitude of soil carbon stocks in northern ecosystems, and the vulnerability of these stocks to climate warming, land surface models must accurately represent soil carbon dynamics in these regions. We evaluate soil carbon stocks and turnover rates, and the relationship between soil carbon loss with soil temperature and moisture, from an ensemble of eleven global land surface models. We focus on the region of NASA's Arctic-Boreal vulnerability experiment (ABoVE) in North America to inform data collection and model development efforts. Models exhibit an order of magnitude difference in estimates of current total soil carbon stocks, generally under- or overestimating the size of current soil carbon stocks by greater than 50 PgC. We find that a model's soil carbon stock at steady-state in 1901 is the prime driver of its soil carbon stock a hundred years later—overwhelming the effect of environmental forcing factors like climate. The greatest divergence between modeled and observed soil carbon stocks is in regions dominated by peat and permafrost soils, suggesting that models are failing to capture the frozen soil carbon dynamics of permafrost regions. Using a set of functional benchmarks to test the simulated relationship of soil respiration to both soil temperature and moisture, we find that although models capture the observed shape of the soil moisture response of respiration, almost half of the models examined show temperature sensitivities, or Q10 values, that are half of observed. Significantly, models that perform better against observational constraints of respiration or carbon stock size do not necessarily perform well in terms of their functional response to key climatic factors like changing temperature. This suggests that models may be arriving at the right result, but for the wrong reason. The results of this work can help to bridge the gap between data and models by both pointing to the need to constrain initial carbon pool sizes, as well as highlighting the importance of incorporating functional benchmarks into ongoing, mechanistic modeling activities such as those included in ABoVE.This work was supported by NASA'S Arctic Boreal Vulnerability Experiment (ABoVE; https://above.nasa.gov); NNN13D504T. Funding for the Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP; https://nacp.ornl.gov/MsTMIP.shtml) activity was provided through NASA ROSES Grant #NNX10AG01A. Data management support for preparing, documenting, and distributing model driver and output data was performed by the Modeling and Synthesis Thematic Data Center at Oak Ridge National Laboratory (MAST-DC; https://nacp.ornl.gov), with funding through NASA ROSES Grant #NNH10AN681. Finalized MsTMIP data products are archived at the ORNL DAAC (https://daac.ornl.gov). We also acknowledge the modeling groups that provided results to MsTMIP. The synthesis of site-level soil respiration, temperature, and moisture data reported in Carey et al 2016a, 2016b) was funded by the US Geological Survey (USGS) John Wesley Powell Center for Analysis and Synthesis Award G13AC00193. Additional support for that work was also provided by the USGS Land Carbon Program. JBF carried out the research at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. California Institute of Technology. Government sponsorship acknowledged

Sensitivity of inferred climate model skill to evaluation decisions: a case study using CMIP5 evapotranspiration

Confrontation of climate models with observationally-based reference datasets is widespread and integral to model development. These comparisons yield skill metrics quantifying the mismatch between simulated and reference values and also involve analyst choices, or meta-parameters, in structuring the analysis. Here, we systematically vary five such meta-parameters (reference dataset, spatial resolution, regridding approach, land mask, and time period) in evaluating evapotranspiration (ET) from eight CMIP5 models in a factorial design that yields 68 700 intercomparisons. The results show that while model–data comparisons can provide some feedback on overall model performance, model ranks are ambiguous and inferred model skill and rank are highly sensitive to the choice of meta-parameters for all models. This suggests that model skill and rank are best represented probabilistically rather than as scalar values. For this case study, the choice of reference dataset is found to have a dominant influence on inferred model skill, even larger than the choice of model itself. This is primarily due to large differences between reference datasets, indicating that further work in developing a community-accepted standard ET reference dataset is crucial in order to decrease ambiguity in model skill

On-line liquid-effluent monitoring of sewage at Lawrence Livermore National Laboratory

LLNL's sanitary sewer system is a possible route for the escape of toxic materials. Liquid effluents are released to Livermore's sanitary sewer system and the effluent is treated at the Livermore Water Reclamation Plant (LWRP). The plant is a secondary-treatment operation that returns most of the water to the San Francisco Bay via a transport pipeline. The remaining portion is used for irrigating vegetation along the roadways and a local golf course. An automatic on-line, sewage-effluent-monitoring system has been developed that diverts a representative fraction of the total waste stream leaving the site. This portion is monitored for pH, radiation, and heavy metals as it passes through a detection assembly. The assembly consists of an industrial pH probe, two NaI radiation detectors, and an x-ray fluorescence metal detector. A microprocessor collects, reduces, and analyzes the data to determine if the levels are acceptable by established environmental limits