A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons

In this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge-based, satellite-related, and reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high-latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation

Fast Predictive Image Registration

We present a method to predict image deformations based on patch-wise image appearance. Specifically, we design a patch-based deep encoder-decoder network which learns the pixel/voxel-wise mapping between image appearance and registration parameters. Our approach can predict general deformation parameterizations, however, we focus on the large deformation diffeomorphic metric mapping (LDDMM) registration model. By predicting the LDDMM momentum-parameterization we retain the desirable theoretical properties of LDDMM, while reducing computation time by orders of magnitude: combined with patch pruning, we achieve a 1500x/66x speed up compared to GPU-based optimization for 2D/3D image registration. Our approach has better prediction accuracy than predicting deformation or velocity fields and results in diffeomorphic transformations. Additionally, we create a Bayesian probabilistic version of our network, which allows evaluation of deformation field uncertainty through Monte Carlo sampling using dropout at test time. We show that deformation uncertainty highlights areas of ambiguous deformations. We test our method on the OASIS brain image dataset in 2D and 3D

Local stabilisation of polar order at charged antiphase boundaries in antiferroelectric (Bi_0.85Nd_0.15)(Ti_0.1Fe_0.9)O₃

Observation of an unusual, negatively-charged antiphase boundary in (Bi<sub>0.85</sub>Nd<sub>0.15</sub>)(Ti<sub>0.1</sub>Fe<sub>0.9</sub>)O<sub>3</sub> is reported. Aberration corrected scanning transmission electron microscopy is used to establish the full three dimensional structure of this boundary including O-ion positions to ~ ± 10 pm. The charged antiphase boundary stabilises tetragonally distorted regions with a strong polar ordering to either side of the boundary, with a characteristic length scale determined by the excess charge trapped at the boundary. Far away from the boundary the crystal relaxes into the well-known Nd-stabilised antiferroelectric phase

Some Low Dimensional Evidence for the Weak Gravity Conjecture

We discuss a few examples in 2+1 dimensions and 1+1 dimensions supporting a recent conjecture concerning the relation between the Planck scale and the coupling strength of a non-gravitional interaction, unlike those examples in 3+1 dimensions, we do not have to resort to exotic physics such as small black holes. However, the result concerning these low dimensional examples is a direct consequence of the 3+1 dimensional conjecture.Comment: 7 pages, harvma

Some estimates of Wang-Yau quasilocal energy

Given a spacelike 2-surface $\Sigma$ in a spacetime $N$ and a constant future timelike unit vector $T_0$ in $\R^{3,1}$, we derive upper and lower estimates of Wang-Yau quasilocal energy $E(\Sigma, X, T_0)$ for a given isometric embedding $X$ of $\Sigma$ into a flat 3-slice in $\R^{3,1}$. The quantity $E(\Sigma, X, T_0)$ itself depends on the choice of $X$, however the infimum of $E(\Sigma, X, T_0)$ over $T_0$ does not. In particular, when $\Sigma$ lies in a time symmetric 3-slice in $N$ and has nonnegative Brown-York quasilocal mass \mby(\Sigma), our estimates show that $\inf\limits_{T_0}E(\Sigma, X, T_0)$ equals \mby (\Sigma). We also study the spatial limit of $\inf\limits_{T_0}E(S_r,X_r,T_0)$, where $S_r$ is a large coordinate sphere in a fixed end of an asymptotically flat initial data set $(M, g, p)$ and $X_r$ is an isometric embeddings of $S_r$ into $\mathbb{R}^3 \subset \mathbb{R}^{3,1}$. We show that if $(M, g, p)$ has future timelike ADM energy-momentum, then $\lim\limits_{r\to\infty}\inf\limits_{T_0}E(S_r,X_r,T_0)$ equals the ADM mass of $(M, g, p)$.Comment: 17 page