We implement the first blind analysis of cluster abundance data to derive cosmological constraints from the abundance and weak lensing signal of redMaPPer clusters in the Sloan Digital Sky Survey (SDSS). We simultaneously fit for cosmological parameters and the richness-mass relation of the clusters. For a flat cold dark matter cosmological model with massive neutrinos, we find S 8 ≡ σ 8 ( m /0.3) 0.5 = 0.79 +0.05 −0.04 . This value is both consistent and competitive with that derived from cluster catalogues selected in different wavelengths. Our result is also consistent with the combined probes analyses by the Dark Energy Survey (DES), the Kilo-Degree Survey (KiDS), and with the cosmic microwave background (CMB) anisotropies as measured by Planck. We demonstrate that the cosmological posteriors are robust against variation of the richness-mass relation model and to systematics associated with the calibration of the selection function. In combination with baryon acoustic oscillation data and big bang nucleosynthesis data (Cooke et al.), we constrain the Hubble rate to be h = 0.66 ± 0.02, independent of the CMB. Future work aimed at improving our understanding of the scatter of the richness-mass relation has the potential to significantly improve the precision of our cosmological posteriors. The methods described in this work were developed for use

While current atmospheric general circulation models (GCMs) still treat the surface as a blackbody in their longwave radiation scheme, recent studies suggest the need for taking realistic surface spectral emissivity into account. There have been few measurements available for the surface emissivity in the far IR (<650 cm−1). Based on first-principle calculation, the authors compute the spectral emissivity over the entire longwave spectrum for a variety of surface types. MODIS-retrieved mid-IR surface emissivity at 0.05° × 0.05° spatial resolution is then regressed against the calculated spectral emissivity to determine the surface type for each grid. The derived spectral emissivity data are then spatially averaged onto 0.5° × 0.5° grids and spectrally integrated onto the bandwidths used by the RRTMG_LW—a longwave radiation scheme widely used in current climate and numerical weather models. The band-by-band surface emissivity dataset is then compared with retrieved surface spectral emissivities from Infrared Atmospheric Sounding Interferometer (IASI) measurements. The comparison shows favorable agreement between two datasets in all the bands covered by the IASI measurements. The authors further use the dataset in conjunction with ERA-Interim to evaluate its impact on the top-of-atmosphere radiation budget. Depending on the blackbody surface assumptions used in the original calculation, the globally averaged difference caused by the inclusion of realistic surface emissivity ranges from −1.2 to −1.5 W m−2 for clear-sky OLR and from −0.67 to −0.94 W m−2 for all-sky OLR. Moreover, the difference is not spatially uniform and has a distinct spatial pattern.

Widely-used 1-D/2-D speckle tracking techniques in elasticity imaging often experience significant speckle decorrelation in applications involving large elevational motion (i.e., out of plane motion). The problem is more pronounced for cardiac strain rate imaging (SRI) since it is very difficult to confine cardiac motion to a single image plane. Here, we present a 3-D correlation-based speckle tracking algorithm. Conceptually, 3-D speckle tracking is just an extension of 2-D phase-sensitive correlation-based speckle tracking. However, due to its high computational cost, optimization schemes, such as dynamic programming, decimation and two-path processing, are introduced to reduce the computational burden. To evaluate the proposed approach, a 3-D bar phantom under uniaxial compression was simulated for benchmark tests. A more sophisticated 3-D simulation of the left ventricle of the heart was also made to test the applicability of 3-D speckle tracking in cardiac SRI. Results from both simulations clearly demonstrated the feasibility of 3-D correlation-based speckle tracking. With the ability to follow 3-D speckle in 3-D space, 3-D speckle tracking outperforms lower-dimensional speckle tracking by minimizing decorrelation caused by pure elevational translation. In other words, 3-D tracking can push toward solely deformation-limited, decorrelation-optimized speckle tracking. Hardware implementation of the proposed 3-D speckle tracking algorithm using field programmable gate arrays (FPGA) is also discussed.

The open source ALPS (Algorithms and Libraries for Physics Simulations) project provides a collection of physics libraries and applications, with a focus on simulations of lattice models and strongly correlated systems. The libraries provide a convenient set of well-documented and reusable components for developing condensed matter physics simulation code, and the applications strive to make commonly used and proven computational algorithms available to a non-expert community. In this paper we present an updated and refactored version of the core ALPS libraries geared at the computational physics software development community, rewritten with focus on documentation, ease of installation, and software maintainability. PROGRAM SUMMARY

The authors present a new method to derive both the broadband and spectral longwave observation-based cloud radiative kernels (CRKs) using cloud radiative forcing (CRF) and cloud fraction (CF) for different cloud types using multisensor A-Train observations and MERRA data collocated on the pixel scale. Both observation-based CRKs and model-based CRKs derived from the Fu-Liou radiative transfer model are shown. Good agreement between observation-and model-derived CRKs is found for optically thick clouds. For optically thin clouds, the observation-based CRKs show a larger radiative sensitivity at TOA to cloud-cover change than model-derived CRKs. Four types of possible uncertainties in the observed CRKs are investigated: 1) uncertainties in Moderate Resolution Imaging Spectroradiometer cloud properties, 2) the contributions of clearsky changes to the CRF, 3) the assumptions regarding clear-sky thresholds in the observations, and 4) the assumption of a single-layer cloud. The observation-based CRKs show the TOA radiative sensitivity of cloud types to unit cloud fraction change as observed by the A-Train. Therefore, a combination of observation-based CRKs with cloud changes observed by these instruments over time will provide an estimate of the short-term cloud feedback by maintaining consistency between CRKs and cloud responses to climate variability.