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Thermophysical properties of Almahata Sitta meteorites (asteroid 2008 TC3) for high‐fidelity entry modeling 
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下载免费PDF全文 Stefan Loehle Peter Jenniskens Hannah Böhrk Thomas Bauer Henning Elsäβer Derek W. Sears Michael E. Zolensky Muawia H. Shaddad 《Meteoritics & planetary science》2017,52(2):197-205
Asteroid 2008 TC3 was characterized in a unique manner prior to impacting Earth's atmosphere, making its October 7, 2008, impact a suitable field test for or validating the application of high‐fidelity re‐entry modeling to asteroid entry. The accurate modeling of the behavior of 2008 TC3 during its entry in Earth's atmosphere requires detailed information about the thermophysical properties of the asteroid's meteoritic materials at temperatures ranging from room temperature up to the point of ablation (T ~ 1400 K). Here, we present measurements of the thermophysical properties up to these temperatures (in a 1 atm. pressure of argon) for two samples of the Almahata Sitta meteorites from asteroid 2008 TC3: a thick flat‐faced ureilite suitably shaped for emissivity measurements and a thin flat‐faced EL6 enstatite chondrite suitable for diffusivity measurements. Heat capacity was determined from the elemental composition and density from a 3‐D laser scan of the sample. We find that the thermal conductivity of the enstatite chondrite material decreases more gradually as a function of temperature than expected, while the emissivity of the ureilitic material decreases at a rate of 9.5 × 10?5 K?1 above 770 K. The entry scenario is the result of the actual flight path being the boundary to the load the meteorite will be affected with when entering. An accurate heat load prediction depends on the thermophysical properties. Finally, based on these data, the breakup can be calculated accurately leading to a risk assessment for ground damage. 相似文献
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Craig Loehle 《Climatic change》2009,94(3-4):233-245
Tree rings provide a primary data source for reconstructing past climates, particularly over the past 1,000 years. However, divergence has been observed in twentieth century reconstructions. Divergence occurs when trees show a positive response to warming in the calibration period but a lesser or even negative response in recent decades. The mathematical implications of divergence for reconstructing climate are explored in this study. Divergence results either because of some unique environmental factor in recent decades, because trees reach an asymptotic maximum growth rate at some temperature, or because higher temperatures reduce tree growth. If trees show a nonlinear growth response, the result is to potentially truncate any historical temperatures higher than those in the calibration period, as well as to reduce the mean and range of reconstructed values compared to actual. This produces the divergence effect. This creates a cold bias in the reconstructed record and makes it impossible to make any statements about how warm recent decades are compared to historical periods. Some suggestions are made to overcome these problems. 相似文献
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Natural Hazards - Because a change in the frequency (number/year) of hurricanes could be a result of climate change, we analyzed the historical record of Atlantic basin and US landfalling... 相似文献
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Craig Loehle 《Mathematical Geology》2005,37(2):127-140
Timeseries of estimated temperature have been combined to create global or hemispheric climate series over periods exceeding 1000 yr. The data used in these studies, however, may be subject to dating errors. It is shown that when timeseries with dating error are combined, the noise in the data smoothes periodic signals but leaves linear trends intact. This means that the effect of dating error of sample data in a timeseries reconstruction is to smooth out any signals (waves, cycles) that may be present. The purpose of this study was to develop signal extraction methods that will work for this type of historical data. The method used was nonlinear estimation of sample series where dating error has been added by Monte Carlo sampling. Several algorithms were tested for handling the dating error problem. Results were that using nonlinear model fitting, the periods of signals can be identified even from the averaged data. In a second stage of the estimation procedure, the cycle magnitudes can be estimated. Very good fits were achieved for two example cases. Temperature estimation error (white noise due to the use of proxies) was also considered and the method was extended to cover this case with quite good results. Using the new estimation methods, the information inherent in multiple series can be used to overcome the problem of dating error. 相似文献
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