Airborne lidar observations of Arctic polar stratospheric clouds

Polar stratospheric clouds (PSC's) have been detected repeatedly during Arctic and Antarctic winters since 1978/1979 by the SAM II (Stratospheric Aerosol Measurement II) instrument aboard the NIMBUS-7 satellite. PSC's are believed to form when supercooled sulfuric acid droplets freeze, and subsequently grow by deposition of ambient water vapor as the local stratospheric temperature falls below the frost point. In order to study the characteristics of PSC's at higher spatial and temporal resolution than that possible from the satellite observations, aircraft missions were conducted within the Arctic polar night vortex in Jan. 1984 and Jan. 1986 using the NASA Langley Research Center airborne dual polarization ruby lidar system. A synopsis of the 1984 and 1986 PSC observations is presented illustrating short range spatial changes in cloud structure, the variation of backscatter ratio with temperature, and the depolarization characterics of cloud layers. Implications are noted with regard to PSC particle characteristics and the physical process by which the clouds are thougth to form

Crotalus tortugensis

Number of Pages: 5Integrative BiologyGeological Science

Crotalus enyo

Number of Pages: 6Integrative BiologyGeological Science

Sauromalus hispidus

Number of Pages: 4Integrative BiologyGeological Science

Monte Carlo energy and variance minimization techniques for optimizing many-body wave functions

We investigate Monte Carlo energy and variance minimization techniques for optimizing many-body wave functions. Several variants of the basic techniques are studied, including limiting the variations in the weighting factors which arise in correlated sampling estimations of the energy and its variance. We investigate the numerical stability of the techniques and identify two reasons why variance minimization exhibits superior numerical stability to energy minimization. The characteristics of each method are studied using a non-interacting 64-electron model of crystalline silicon. While our main interest is in solid state systems, the issues investigated are relevant to Monte Carlo studies of atoms, molecules and solids. We identify a robust and efficient variance minimization scheme for optimizing wave functions for large systems.Comment: 14 pages, including 7 figures. To appear in Phys. Rev. B. For related publications see http://www.tcm.phy.cam.ac.uk/Publications/many_body.htm

Systematic reduction of sign errors in many-body calculations of atoms and molecules

The self-healing diffusion Monte Carlo algorithm (SHDMC) [Phys. Rev. B {\bf 79}, 195117 (2009), {\it ibid.} {\bf 80}, 125110 (2009)] is shown to be an accurate and robust method for calculating the ground state of atoms and molecules. By direct comparison with accurate configuration interaction results for the oxygen atom we show that SHDMC converges systematically towards the ground-state wave function. We present results for the challenging N$_2$ molecule, where the binding energies obtained via both energy minimization and SHDMC are near chemical accuracy (1 kcal/mol). Moreover, we demonstrate that SHDMC is robust enough to find the nodal surface for systems at least as large as C$_{20}$ starting from random coefficients. SHDMC is a linear-scaling method, in the degrees of freedom of the nodes, that systematically reduces the fermion sign problem.Comment: Final version accepted in Physical Review Letters. The review history (referees' comments and our replies) is included in the source