Spatially consistent postprocessing of precipitation over complex topography

<p>Over the last decade statistical postprocessing has become a standard tool to reduce biases and dispersion errors of probabilistic numerical weather prediction (NWP) ensemble forecasts. Most established postprocessing approaches train a statistical model using raw ensemble statistics on a typically small set of stations.  While raw ensemble statistics are available from high resolution NWP grid data, observations are missing at most grid points. Hence, the generation of spatial fields of forecast scenarios requires both some kind of interpolation and reshuffling of forecast quantiles based on a dependence template. The most widely used reshuffling approach, ensemble copula coupling (ECC), applies a reordering based on the raw ensemble rank order structure. ECC relies on the assumption that the spatial dependence structure of the raw ensemble is spatially consistent with the observed fields. This assumption may not always hold for hourly precipitation in particular over complex topography, since even high resolution models do not achieve a perfect representation of the real topography.</p><p>In this study, hourly CombiPrecip fields, which are a blend of precipitation observations from station and radar data, at a spatial resolution of 1 km over Switzerland serve as observations. Hourly precipitation raw ensemble forecast fields covering lead times up to 120 hours with a spatial resolution of 2 km are provided by COSMO-E. This enables us to postprocess hourly  COSMO-E ensemble precipitation forecasts over Switzerland at different spatial scales, from a single global ensemble model output statistics type model, over regional quantile regression  models up to grid point-wise local analog models. The mismatch in spatial resolution between COSMO-E and CombiPrecip as well as  the general issue of non-representative model topography over Switzerland’s complex topography may affect the spatial consistency of the (postprocessed) forecast fields. Starting with an analysis of systematic errors and spatial consistency of COSMO-E precipitation forecasts , we assess the potential for spatially multivariate postprocessing approaches, which are able to incorporate the spatial information from CombiPrecip and are yet simple and computationally efficient. To this end, we analyse the effects of using standard and new postprocessing model designs that vary in the (analog-based) selection of training data, spatial aggregation, postprocessing model parametrizations, and methods to obtain physically realistic forecast scenarios in space. </p>

Grasping of Real-World Objects Is Not Biased by Ensemble Perception

The visual system is known to extract summary representations of visually similar objects which bias the perception of individual objects toward the ensemble average. Although vision plays a large role in guiding action, less is known about whether ensemble representation is informative for action. Motor behavior is tuned to the veridical dimensions of objects and generally considered resistant to perceptual biases. However, when the relevant grasp dimension is not available or is unconstrained, ensemble perception may be informative to behavior by providing gist information about surrounding objects. In the present study, we examined if summary representations of a surrounding ensemble display influenced grip aperture and orientation when participants reached-to-grasp a central circular target which had an explicit size but importantly no explicit orientation that the visuomotor system could selectively attend to. Maximum grip aperture and grip orientation were not biased by ensemble statistics during grasping, although participants were able to perceive and provide manual estimations of the average size and orientation of the ensemble display. Support vector machine classification of ensemble statistics achieved above-chance classification accuracy when trained on kinematic and electromyography data of the perceptual but not grasping conditions, supporting our univariate findings. These results suggest that even along unconstrained grasping dimensions, visually-guided behaviors toward real-world objects are not biased by ensemble processing.

Perceiving ensemble statistics of novel image sets

Perception, representation, and memory of ensemble statistics has attracted growing interest. Studies found that, at different abstraction levels, the brain represents similar items as unified percepts. We found that global ensemble perception is automatic and unconscious, affecting later perceptual judgments regarding individual member items. Implicit effects of set mean and range for low-level feature ensembles (size, orientation, brightness) were replicated for high-level category objects. This similarity suggests that analogous mechanisms underlie these extreme levels of abstraction. Here, we bridge the span between visual features and semantic object categories using the identical implicit perception experimental paradigm for intermediate novel visual-shape categories, constructing ensemble exemplars by introducing systematic variations of a central category base or ancestor. In five experiments, with different item variability, we test automatic representation of ensemble category characteristics and its effect on a subsequent memory task. Results show that observer representation of ensembles includes the group’s central shape, category ancestor (progenitor), or group mean. Observers also easily reject memory of shapes belonging to different categories, i.e. originating from different ancestors. We conclude that complex categories, like simple visual form ensembles, are represented in terms of statistics including a central object, as well as category boundaries. We refer to the model proposed by Benna and Fusi (bioRxiv 624239, 2019) that memory representation is compressed when related elements are represented by identifying their ancestor and each one’s difference from it. We suggest that ensemble mean perception, like category prototype extraction, might reflect employment at different representation levels of an essential, general representation mechanism.

Characteristics of Toxic Gas Leakages with Change in Duration

One of the emergencies rescue crews have to face is toxic gas leakages. The characteristics of the gas leakages differ with regard to their leakage duration. Long-term releases have plume-like behaviors that can be described by utilizing mean concentrations at individual exposed locations. In contrast, ensemble statistics of individual cloud characteristics are needed for short-term releases with puff-like behaviors to ensure fully aware risk assessment. The reason is that the time evolution of the concentration of short-term gas releases can differ wildly under the same mean ambient and leakage conditions. The duration from which the release can be classified as plume-like can be found only by studying the releases of different durations, which is the main aim of this paper. To investigate gas releases of different durations, wind tunnel experiments of gas releases in an idealized urban area were conducted. The results present a new method by which concentration signals of releases can be divided into three cloud phases: the arrival, the central and the departure cloud phase. The characteristics (e.g., lengths, mean concentrations) of the individual cloud phases are explored. The results indicate that the finite-duration releases for which the central cloud phase exists have the plume-like behavior for this cloud part.

Particle image velocimetry measurement of turbulent wake past circular cylinder at Reynolds numbers from 2.5·103 to 1.5·104

A canonical case of air flow past a circular cylinder is studied by using Particle Image Velocimetry technique. This contribution focus to the ensemble statistics (first and second moment) of the stream-wise and transverse velocity component as well as to the in-plane vorticity component. Although the range of explored Reynolds numbers is narrow, we observe a significant shortening of recirculation bubble within this range.

An Efficient Bi-Gaussian Ensemble Kalman Filter for Satellite Infrared Radiance Data Assimilation

The introduction of infrared water vapor channel radiance ensemble data assimilation (DA) has improved numerical weather forecasting at operational centers. Further improvements might be possible through extending ensemble data assimilation methods to better assimilate infrared satellite radiances. Here, we will illustrate that ensemble statistics under clear-sky conditions are different from cloudy conditions. This difference suggests that extending the ensemble Kalman filter (EnKF) to handle bi-Gaussian prior distributions may yield better results than the standard EnKF. In this study, we propose a computationally efficient bi-Gaussian ensemble Kalman filter (BGEnKF) to handle bi-Gaussian prior distributions. As a proof-of-concept, we used the 40-variable Lorenz 1996 model as a proxy to examine the impacts of assimilating infrared radiances with the BGEnKF and EnKF. A nonlinear observation operator that constructs radiance-like bimodal ensemble statistics was used to generate and assimilate pseudoradiances. Inflation was required for both methods to effectively assimilate pseudoradiances. In both 800- and 20-member experiments, the BGEnKF generally outperformed the EnKF. The relative performance of the BGEnKF with respect to the EnKF improved when the observation spacing and time between DA cycles (cycling interval) are increased from small values. The relative performance then degraded when observation spacing and cycling interval become sufficiently large. The BGEnKF generated less noise than the EnKF, suggesting that the BGEnKF produces more balanced analysis states than the EnKF. This proof-of-concept study motivates future investigation into using the BGEnKF to assimilate infrared observations into high-order numerical weather models.

Temporal bisection is influenced by ensemble statistics of the stimulus set

Although humans are well capable of precise time measurement, their duration judgments are nevertheless susceptible to temporal context. Previous research on temporal bisection has shown that duration comparisons are influenced by both stimulus spacing and ensemble statistics. However, theories proposed to account for bisection performance lack a plausible justification of how the effects of stimulus spacing and ensemble statistics are actually combined in temporal judgments. To explain the various contextual effects in temporal bisection, we develop a unified ensemble-distribution account (EDA), which assumes that the mean and variance of the duration set serve as a reference, rather than the short and long standards, in duration comparison. To validate this account, we conducted three experiments that varied the stimulus spacing (Experiment 1), the frequency of the probed durations (Experiment 2), and the variability of the probed durations (Experiment 3). The results revealed significant shifts of the bisection point in Experiments 1 and 2, and a change of the sensitivity of temporal judgments in Experiment 3—which were all well predicted by EDA. In fact, comparison of EDA to the extant prior accounts showed that using ensemble statistics can parsimoniously explain various stimulus set-related factors (e.g., spacing, frequency, variance) that influence temporal judgments.