A comparison among four different retrieval methods for ice-cloud properties using data from CloudSat, CALIPSO, and MODIS

The A-Train constellation of satellites provides a new capability to measure vertical cloud profiles that leads to more detailed information on ice-cloud microphysical properties than has been possible up to now. A variational radar–lidar ice-cloud retrieval algorithm (VarCloud) takes advantage of the complementary nature of the CloudSat radar and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar to provide a seamless retrieval of ice water content, effective radius, and extinction coefficient from the thinnest cirrus (seen only by the lidar) to the thickest ice cloud (penetrated only by the radar). In this paper, several versions of the VarCloud retrieval are compared with the CloudSat standard ice-only retrieval of ice water content, two empirical formulas that derive ice water content from radar reflectivity and temperature, and retrievals of vertically integrated properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) radiometer. The retrieved variables typically agree to within a factor of 2, on average, and most of the differences can be explained by the different microphysical assumptions. For example, the ice water content comparison illustrates the sensitivity of the retrievals to assumed ice particle shape. If ice particles are modeled as oblate spheroids rather than spheres for radar scattering then the retrieved ice water content is reduced by on average 50% in clouds with a reflectivity factor larger than 0 dBZ. VarCloud retrieves optical depths that are on average a factor-of-2 lower than those from MODIS, which can be explained by the different assumptions on particle mass and area; if VarCloud mimics the MODIS assumptions then better agreement is found in effective radius and optical depth is overestimated. MODIS predicts the mean vertically integrated ice water content to be around a factor-of-3 lower than that from VarCloud for the same retrievals, however, because the MODIS algorithm assumes that its retrieved effective radius (which is mostly representative of cloud top) is constant throughout the depth of the cloud. These comparisons highlight the need to refine microphysical assumptions in all retrieval algorithms and also for future studies to compare not only the mean values but also the full probability density function

Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagrams

We consider the problem of structure learning for bow-free acyclic path diagrams (BAPs). BAPs can be viewed as a generalization of linear Gaussian DAG models that allow for certain hidden variables. We present a first method for this problem using a greedy score-based search algorithm. We also prove some necessary and some sufficient conditions for distributional equivalence of BAPs which are used in an algorithmic ap- proach to compute (nearly) equivalent model structures. This allows us to infer lower bounds of causal effects. We also present applications to real and simulated datasets using our publicly available R-package

Projection operator approach to spin diffusion in the anisotropic Heisenberg chain at high temperatures

We investigate spin transport in the anisotropic Heisenberg chain in the limit of high temperatures ({\beta} \to 0). We particularly focus on diffusion and the quantitative evaluation of diffusion constants from current autocorrelations as a function of the anisotropy parameter {\Delta} and the spin quantum number s. Our approach is essentially based on an application of the time-convolutionless (TCL) projection operator technique. Within this perturbative approach the projection onto the current yields the decay of autocorrelations to lowest order of {\Delta}. The resulting diffusion constants scale as 1/{\Delta}^2 in the Markovian regime {\Delta}<<1 (s=1/2) and as 1/{\Delta} in the highly non-Markovian regime above {\Delta} \sim 1 (arbitrary s). In the latter regime the dependence on s appears approximately as an overall scaling factor \sqrt{s(s+1)} only. These results are in remarkably good agreement with diffusion constants for {\Delta}>1 which are obtained directly from the exact diagonalization of autocorrelations or have been obtained from non-equilibrium bath scenarios.Comment: 4 pages, 3 figure

Quantum Darwinism in quantum Brownian motion: the vacuum as a witness

We study quantum Darwinism -- the redundant recording of information about a decohering system by its environment -- in zero-temperature quantum Brownian motion. An initially nonlocal quantum state leaves a record whose redundancy increases rapidly with its spatial extent. Significant delocalization (e.g., a Schroedinger's Cat state) causes high redundancy: many observers can measure the system's position without perturbing it. This explains the objective (i.e. classical) existence of einselected, decoherence-resistant pointer states of macroscopic objects.Comment: 5 page

Biases in Expansion Distances of Novae Arising from the Prolate Geometry of Nova Shells

(abridged) Expansion distances (or expansion parallaxes) for classical novae are based on comparing a measurement of the shell expansion velocity, multiplied by the time since outburst, with some measure of the angular size of the shell. We review and formalize this method in the case of prolate spheroidal shells. We present expressions for the maximum line-of-sight velocity from a complete, expanding shell and for its projected major and minor axes, in terms of the intrinsic axis ratio and the inclination of the polar axis to the line of sight. For six distinct definitions of ``angular size'', we tabulate the error in distance that is introduced under the assumption of spherical symmetry (i.e., without correcting for inclination and axis ratio). The errors can be significant and systematic, affecting studies of novae whether considered individually or statistically. Each of the six estimators overpredicts the distance when the polar axis is close to the line of sight, and most underpredict the distance when the polar axis is close to the plane of the sky. The straight mean of the projected semimajor and semiminor axes gives the least distance bias for an ensemble of randomly oriented prolate shells. The best individual expansion distances, however, result from a full spatio-kinematic modeling of the nova shell. We discuss several practical complications that affect expansion distance measurements of real nova shells. Nova shell expansion distances be based on velocity and angular size measurements made contemporaneously if possible, and the same ions and transitions should be used for the imaging and velocity measurements. We emphasize the need for complete and explicit reporting of measurement procedures and results, regardless of the specific method used.Comment: 21 pages, LaTeX, uses aasms4.sty, to be published in Publ. Astron. Soc. of the Pacific, May 200

Infrared and Raman Spectroscopy of the Dirhodium Tetraacetate Complexes Rh2(O2CCH3)4, Rh2(18O2CCH3)4, Rh2(O2CCD3)4 and Rh2(O2CCH3)4(H2O)2

The infrared (3500-50 cm-I), and Raman (3550-30 cm-I) spectra of the dirhodium tetraacetate species Rh2(02CCHs)4, Rh2(IB02CCHs)4, Rh2(02CCDS)4, and Rh2(02CCHs)4(H20)2 have been recorded and the key bands assigned, The oxygen-18 and deuteration studies, in particular, assisted with ma king the important band assignments for the anhydrous complex, for which 11 (RhRh) occurs at 355-351 cm-I, and 11 (RhO) at 389-319 cm-I (Raman) and 398-341 cm-I (infrared). The band attributed to 11 (RhRh) is typically intense, sharp, and relatively insensitive to either IBO_ or CDs-substitution. Thus 11 (RhRh) shifts only 4 cm-Ion either IBO or CDs substitution whereas 11 (RhO) shifts 4-6 cm-I in the Raman and 3-7 cm-I in the infrared spectra on IBO substitution but 12 cm-I in the Raman and 11-18 cm-I in the infrared spectra on CDs substitution. Some preliminary isotopic work for the complex Rh2(02CCHS)4(H20l2 is also presented