Validation of non‑stationary precipitation series for site‑specific impact assessment: comparison of two statistical downscaling techniques

Statistical downscaling (SD) methods have become a popular, low-cost and accessible means of bridging the gap between the coarse spatial resolution at which climate models output climate scenarios and the finer spatial scale at which impact modellers require these scenarios, with various different SD techniques used for a wide range of applications across the world. This paper compares the Generator for Point Climate Change (GPCC) model and the Statistical DownScaling Model (SDSM)—two contrasting SD methods—in terms of their ability to generate precipitation series under non-stationary conditions across ten contrasting global climates. The mean, maximum and a selection of distribution statistics as well as the cumulative frequencies of dry and wet spells for four different temporal resolutions were compared between the models and the observed series for a validation period. Results indicate that both methods can generate daily precipitation series that generally closely mirror observed series for a wide range of non-stationary climates. However, GPCC tends to overestimate higher precipitation amounts, whilst SDSM tends to underestimate these. This infers that GPCC is more likely to overestimate the effects of precipitation on a given impact sector, whilst SDSM is likely to underestimate the effects. GPCC performs better than SDSM in reproducing wet and dry day frequency, which is a key advantage for many impact sectors. Overall, the mixed performance of the two methods illustrates the importance of users performing a thorough validation in order to determine the influence of simulated precipitation on their chosen impact sector.

A new statistical framework for the quantification of covariate associations with species distributions

Identifying processes that shape species geographical ranges is a prerequisite for understanding environmental change. Currently, species distribution modelling methods do not offer credible statistical tests of the relative influence of climate factors and typically ignore other processes (e.g. biotic interactions and dispersal limitation). We use a hierarchical model fitted with Markov Chain Monte Carlo to combine ecologically plausible niche structures using regression splines to describe unimodal but potentially skewed response terms. We apply spatially explicit error terms that account for (and may help identify) missing variables. Using three example distributions of European bird species, we map model results to show sensitivity to change in each covariate. We show that the overall strength of climatic association differs between species and that each species has considerable spatial variation in both the strength of the climatic association and the sensitivity to climate change. Our methods are widely applicable to many species distribution modelling problems and enable accurate assessment of the statistical importance of biotic and abiotic influences on distributions.

The subjective numeracy scale as a tool for assessing statistical numeracy in older adult populations

BACKGROUND:
Statistical numeracy, necessary for making informed medical decisions, is reduced among older adults who make more decisions about their medical care and treatment than at any other stage of life. Objective numeracy scales are a source of anxiety among patients, heightened among older adults.
OBJECTIVE:
We investigate the subjective numeracy scale as an alternative tool for measuring statistical numeracy with older adult samples.
METHODS:
Numeracy was assessed using objective measures for 526 adults ranging in age from 18 to 93 years, and all participants provided subjective numeracy ratings.
RESULTS:
Subjective numeracy correlated highly with objective measurements among oldest adults (70+ years; r = 0.51, 95% CI 0.32, 0.66), and for younger age groups. Subjective numeracy explained 33.2% of age differences in objective numeracy.
CONCLUSION:
The subjective numeracy scale provides an effective tool for assessing statistical numeracy for broad age ranges and circumvents problems associated with objective numeracy measures.

Statistical Analysis of Small Ellerman Bomb Events

The properties of Ellerman bombs (EBs), small-scale brightenings in the Hα line wings, have proved difficult to establish because their size is close to the spatial resolution of even the most advanced telescopes. Here, we aim to infer the size and lifetime of EBs using high-resolution data of an emerging active region collected using the Interferometric BIdimensional Spectrometer (IBIS) and Rapid Oscillations of the Solar Atmosphere (ROSA) instruments as well as the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). We develop an algorithm to track EBs through their evolution, finding that EBs can often be much smaller (around 0.3″) and shorter-lived (less than one minute) than previous estimates. A correlation between G-band magnetic bright points and EBs is also found. Combining SDO/HMI and G-band data gives a good proxy of the polarity for the vertical magnetic field. It is found that EBs often occur both over regions of opposite polarity flux and strong unipolar fields, possibly hinting at magnetic reconnection as a driver of these events.The energetics of EB events is found to follow a power-law distribution in the range of a nanoflare (1022-25 ergs).

Statistical Eigenmode Transmission for the MU-MIMO Downlink in Rician Fading

In this paper, we study the achievable ergodic sum-rate of multiuser multiple-input multiple-output downlink systems in Rician fading channels. We first derive a lower bound on the average signal-to-leakage-and-noise ratio by using the Mullen’s inequality, and then use it to analyze the effect of channel mean information on the achievable ergodic sum-rate. A novel statistical-eigenmode space-division multiple-access (SESDMA) downlink transmission scheme is then proposed. For this scheme, we derive an exact analytical closed-form expression for the achievable ergodic rate and present tractable tight upper and lower bounds. Based on our analysis, we gain valuable insights into the system parameters, such as the number of transmit antennas, the signal-to-noise ratio (SNR) and Rician K-factor on the system sum-rate. Results show that the sum-rate converges to a saturation value in the high SNR regime and tends to a lower limit for the low Rician K-factor case. In addition, we compare the achievable ergodic sum-rate between SE-SDMA and zeroforcing beamforming with perfect channel state information at the base station. Our results reveal that the rate gap tends to zero in the high Rician K-factor regime. Finally, numerical results are presented to validate our analysis.

Statistical data correction for unreliable memories

In this paper, we introduce a statistical data-correction framework that aims at improving the DSP system performance in presence of unreliable memories. The proposed signal processing framework implements best-effort error mitigation for signals that are corrupted by defects in unreliable storage arrays using a statistical correction function extracted from the signal statistics, a data-corruption model, and an application-specific cost function. An application example to communication systems demonstrates the efficacy of the proposed approach.

On the statistical memory architecture exploration and optimization

The worsening of process variations and the consequent increased spreads in circuit performance and consumed power hinder the satisfaction of the targeted budgets and lead to yield loss. Corner based design and adoption of design guardbands might limit the yield loss. However, in many cases such methods may not be able to capture the real effects which might be way better than the predicted ones leading to increasingly pessimistic designs. The situation is even more severe in memories which consist of substantially different individual building blocks, further complicating the accurate analysis of the impact of variations at the architecture level leaving many potential issues uncovered and opportunities unexploited. In this paper, we develop a framework for capturing non-trivial statistical interactions among all the components of a memory/cache. The developed tool is able to find the optimum memory/cache configuration under various constraints allowing the designers to make the right choices early in the design cycle and consequently improve performance, energy, and especially yield. Our, results indicate that the consideration of the architectural interactions between the memory components allow to relax the pessimistic access times that are predicted by existing techniques.

A Statistical Characterization of Shadowed Fading in Indoor Off-Body Communications Channels At 5.8 GHz

This paper investigates the characteristics of the shadowed fading observed in off-body communications channels at 5.8 GHz using the κ-μ / gamma composite fading model. Realistic measurements have been conducted considering four individual scenarios namely line of sight (LOS) and non-LOS (NLOS) walking, rotation and random movements within an indoor laboratory environment. It is shown that the κ-μ / gamma composite fading model provides a better fit to the fading observed in off-body communications channels compared to the conventional Nakagami-m and Rician fading models.

Shadowed Fading in Indoor Off-Body Communications Channels: A Statistical Characterization using the κ – μ / Gamma Composite Fading Model

This paper investigates the characteristics of the shadowed fading observed in off-body communications channels at 5.8 GHz. This is realized with the aid of the $\kappa-\mu$ / gamma composite fading model which assumes that the transmitted signal undergoes $\kappa-\mu$ fading which is subject to \emph{multiplicative} shadowing. Based on this, the total power of the multipath components, including both the dominant and scattered components, is subject to non-negligible variations that follow the gamma distribution. For this model, we present an integral form of the probability density function (PDF) as well as important analytic expressions for the PDF, cumulative distribution function, moments and moment generating function. In the case of indoor off-body communications, the corresponding measurements were carried out in the context of four explicit individual scenarios namely: line of sight (LOS) and non-LOS (NLOS) walking, rotational and random movements. The measurements were repeated within three different indoor environments and considered three different hypothetical body worn node locations. With the aid of these results, the parameters for the $\kappa-\mu$ / gamma composite fading model were estimated and analyzed extensively. Interestingly, for the majority of the indoor environments and movement scenarios, the parameter estimates suggested that dominant signal components existed even when the direct signal path was obscured by the test subject's body. Additionally, it is shown that the $\kappa-\mu$ / gamma composite fading model provides an adequate fit to the fading effects involved in off-body communications channels. Using the Kullback-Leibler divergence, we have also compared our results with another recently proposed shadowed fading model, namely the $\kappa-\mu$ / lognormal LOS shadowed fading model. It was found that the $\kappa-\mu$ / gamma composite fading model provided a better fit for the majority of the scenarios considered in this study.