The Collection Efficiency of Shielded and Unshielded Precipitation Gauges. Part II: Modeling Particle Trajectories

The use of windshields to reduce the impact of wind on snow measurements is common. This paper investigates the catching performance of shielded and unshielded gauges using numerical simulations. In Part II, the role of the windshield and gauge aerodynamics, as well as the varying flow field due to the turbulence generated by the shield–gauge configuration, in reducing the catch efficiency is investigated. This builds on the computational fluid dynamics results obtained in Part I, where the airflow patterns in the proximity of an unshielded and single Alter shielded Geonor T-200B gauge are obtained using both time-independent [Reynolds-averaged Navier–Stokes (RANS)] and time-dependent [large-eddy simulation (LES)] approaches. A Lagrangian trajectory model is used to track different types of snowflakes (wet and dry snow) and to assess the variation of the resulting gauge catching performance with the wind speed. The collection efficiency obtained with the LES approach is generally lower than the one obtained with the RANS approach. This is because of the impact of the LES-resolved turbulence above the gauge orifice rim. The comparison between the collection efficiency values obtained in case of shielded and unshielded gauge validates the choice of installing a single Alter shield in a windy environment. However, time-dependent simulations show that the propagating turbulent structures produced by the aerodynamic response of the upwind single Alter blades have an impact on the collection efficiency. Comparison with field observations provides the validation background for the model results

Impact of Wind Direction, Wind Speed, and Particle Characteristics on the Collection Efficiency of the Double Fence Intercomparison Reference

The accurate measurement of snowfall is important in various fields of study such as climate variability, transportation, and water resources. A major concern is that snowfall measurements are difficult and can result in significant errors. For example, collection efficiency of most gauge–shield configurations generally decreases with increasing wind speed. In addition, much scatter is observed for a given wind speed, which is thought to be caused by the type of snowflake. Furthermore, the collection efficiency depends strongly on the reference used to correct the data, which is often the Double Fence Intercomparison Reference (DFIR) recommended by the World Meteorological Organization. The goal of this study is to assess the impact of weather conditions on the collection efficiency of the DFIR. Note that the DFIR is defined as a manual gauge placed in a double fence. In this study, however, only the double fence is being investigated while still being called DFIR. To address this issue, a detailed analysis of the flow field in the vicinity of the DFIR is conducted using computational fluid dynamics. Particle trajectories are obtained to compute the collection efficiency associated with different precipitation types for varying wind speed. The results show that the precipitation reaching the center of the DFIR can exceed 100% of the actual precipitation, and it depends on the snowflake type, wind speed, and direction. Overall, this study contributes to a better understanding of the sources of uncertainty associated with the use of the DFIR as a reference gauge to measure snowfall

An Improved Trajectory Model to Evaluate the Collection Performance of Snow Gauges

Recent studies have used numerical models to estimate the collection efficiency\ud of solid precipitation gauges when exposed to the wind, in both\ud shielded and unshielded configurations. The models used computational fluid\ud dynamics (CFD) simulations of the airflow pattern generated by the aerodynamic\ud response to the gauge/shield geometry. These are used as initial conditions\ud to perform Lagrangian tracking of solid precipitation particles. Validation\ud of the results against field observations yielded similarities in the overall\ud behavior, but the model output only approximately reproduced the dependence\ud of the experimental collection efficiency on wind speed. This paper\ud presents an improved snowflake trajectory modeling scheme due to the inclusion\ud of a dynamically-determined drag coefficient. The drag coefficient\ud was estimated using the local Reynolds number as derived from CFD simulations\ud within a time-independent Reynolds Averaged Navier-Stokes (RANS)\ud approach. The proposed dynamic model greatly improves the consistency of\ud results with the field observations recently obtained at the Marshall, CO Winter\ud Precipitation Testbed

Dynamics of Cloud-Top Generating Cells in Winter Cyclones. Part III: Shear and Convective Organization

Cloud-top generating cells (GCs) are a common feature atop stratiform clouds within the comma head of winter cyclones. The dynamics of cloud-top GCs are investigated using very high-resolution idealized WRF Model simulations to examine the role of shear in modulating the structure and intensity of GCs. Simulations were run for the same combinations of radiative forcing and instability as in Part II of this series, but with six different shear profiles ranging from 0 to 10ms21 km21 within the layer encompassing the GCs. The primary role of shear was to modulate the organization of GCs, which organized as closed convective cells in simulations with radiative forcing and no shear. In simulations with shear and radiative forcing, GCs organized in linear streets parallel to the wind. No GCs developed in the initially stable simulations with no radiative forcing. In the initially unstable and neutral simulations with no radiative forcing or shear, GCs were exceptionally weak, with no clear organization. In moderate-shear (Du/Dz 5 2, 4ms21 km21) simulations with no radiative forcing, linear organization of the weak cells was apparent, but this organization was less coherent in simulations with high shear (Du/Dz 5 6, 8, 10ms21 km21). The intensity of the updrafts was primarily related to the mode of radiative forcing but was modulated by shear. The more intense GCs in nighttime simulations were either associated with no shear (closed convective cells) or strong shear (linear streets). Updrafts within GCs under conditions with radiative forcing were typically ;1–2 ms21 with maximum values , 4ms21

A Case Study of Processes Impacting Precipitation Phase and Intensity during the Vancouver 2010 Winter Olympics

Accurate forecasting of precipitation phase and intensity was critical information for many of the Olympic venue managers during the Vancouver 2010 Olympic and Paralympic Winter Games. Precipitation forecasting was complicated because of the complex terrain and warm coastal weather conditions in the Whistler area of British Columbia, Canada. The goal of this study is to analyze the processes impacting precipitation phase and intensity during a winter weather storm associated with rain and snow over complex terrain. The storm occurred during the second day of the Olympics when the downhill ski event was scheduled. At 0000 UTC 14 February, 2 h after the onset of precipitation, a rapid cooling was observed at the surface instrumentation sites. Precipitation was reported for 8 h, which coincided with the creation of a nearly 0°C isothermal layer, as well as a shift of the valley flow from up valley to down valley. Widespread snow was reported on Whistler Mountain with periods of rain at the mountain base despite the expectation derived from synoptic-scale models (15-km grid spacing) that the strong warm advection would maintain temperatures above freezing. Various model predictions are compared with observations, and the processes influencing the temperature, wind, and precipitation types are discussed. Overall, this case study provided a well-observed scenario of winter storms associated with rain and snow over complex terrain

Infinite factorization of multiple non-parametric views

Combined analysis of multiple data sources has increasing application interest, in particular for distinguishing shared and source-specific aspects. We extend this rationale of classical canonical correlation analysis into a flexible, generative and non-parametric clustering setting, by introducing a novel non-parametric hierarchical mixture model. The lower level of the model describes each source with a flexible non-parametric mixture, and the top level combines these to describe commonalities of the sources. The lower-level clusters arise from hierarchical Dirichlet Processes, inducing an infinite-dimensional contingency table between the views. The commonalities between the sources are modeled by an infinite block model of the contingency table, interpretable as non-negative factorization of infinite matrices, or as a prior for infinite contingency tables. With Gaussian mixture components plugged in for continuous measurements, the model is applied to two views of genes, mRNA expression and abundance of the produced proteins, to expose groups of genes that are co-regulated in either or both of the views. Cluster analysis of co-expression is a standard simple way of screening for co-regulation, and the two-view analysis extends the approach to distinguishing between pre- and post-translational regulation

High expression of miR-17-5p in tumor epithelium is a predictor for poor prognosis for prostate cancer patients

MicroRNAs (miRs) are small non-coding RNA molecules, which are involved in the development of various malignancies, including prostate cancer (PCa). miR-17-5p is considered the most prominent member of the miR-17-92 cluster, with an essential regulatory function of fundamental cellular processes. In many malignancies, up-regulation of miR-17-5p is associated with worse outcome. In PCa, miR-17-5p has been reported to increase cell proliferation and the risk of metastasis. In this study, prostatectomy specimens from 535 patients were collected. Tissue microarrays were constructed and in situ hybridization was performed, followed by scoring of miR-17-5p expression on different tumor compartments. High expression of miR-17-5p in tumor epithelium was associated with biochemical failure (BF, p p = 0.019). In multivariate analyses, high miR-17-5p expression in tumor epithelial cells was an independent negative prognostic factor for BF (HR 1.87, 95% CI 1.32–2.67, p < 0.001). In vitro analyses confirmed association between overexpression of miR-17-5p and proliferation, migration and invasion in prostate cancer cell lines (PC3 and DU145). In conclusion, our study suggests that a high cancer cell expression of miR-17-5p was an independent negative prognostic factor in PCa

A Bayesian Nonparametric Approach to Modeling Motion Patterns

The most difficult—and often most essential— aspect of many interception and tracking tasks is constructing motion models of the targets to be found. Experts can often provide only partial information, and fitting parameters for complex motion patterns can require large amounts of training data. Specifying how to parameterize complex motion patterns is in itself a difficult task. In contrast, nonparametric models are very flexible and generalize well with relatively little training data. We propose modeling target motion patterns as a mixture of Gaussian processes (GP) with a Dirichlet process (DP) prior over mixture weights. The GP provides a flexible representation for each individual motion pattern, while the DP assigns observed trajectories to particular motion patterns. Both automatically adjust the complexity of the motion model based on the available data. Our approach outperforms several parametric models on a helicopter-based car-tracking task on data collected from the greater Boston area