A statistical model for the prediction of wind-speed probabilities in the atmospheric surface layer

Wind fields in the atmospheric surface layer (ASL) are highly three-dimensional and characterized by strong spatial and temporal variability. For various applications such as wind comfort assessments and structural design, an understanding of potentially hazardous wind extremes is important. Statistical models are designed to facilitate conclusions about the occurrence probability of wind speeds based on the knowledge of low-order flow statistics. Being particularly interested in the upper tail regions we show that the statistical behavior of near-surface wind speeds is adequately represented by the Beta distribution. By using the properties of the Beta probability density function in combination with a model for estimating extreme values based on readily available turbulence statistics, it is demonstrated that this novel modelling approach reliably predicts the upper margins of encountered wind speeds. The model’s basic parameter is derived from three substantially different calibrating datasets of flow in the ASL originating from boundary-layer wind-tunnel measurements and direct numerical simulation. Evaluating the model based on independent field observations of near-surface wind speeds showed a high level of agreement between the statistically modelled horizontal wind speeds and measurements. The results show that, based on the knowledge of only a few simple flow statistics (mean wind speed, wind speed fluctuations and integral time scales), the occurrence probability of velocity magnitudes at arbitrary flow locations in the ASL can be estimated with a high degree of confidence

Estimation of metabolite networks with regard to a specific covariable: applications to plant and human data

In systems biology, where a main goal is acquiring knowledge of biological systems, one of the challenges is inferring biochemical interactions from different molecular entities such as metabolites. In this area, the metabolome possesses a unique place for reflecting “true exposure” by being sensitive to variation coming from genetics, time, and environmental stimuli. While influenced by many different reactions, often the research interest needs to be focused on variation coming from a certain source, i.e. a certain covariable Xm . Objective Here, we use network analysis methods to recover a set of metabolite relationships, by finding metabolites sharing a similar relation to Xm . Metabolite values are based on information coming from individuals’ Xm status which might interact with other covariables. Methods Alternative to using the original metabolite values, the total information is decomposed by utilizing a linear regression model and the part relevant to Xm is further used. For two datasets, two different network estimation methods are considered. The first is weighted gene co-expression network analysis based on correlation coefficients. The second method is graphical LASSO based on partial correlations. Results We observed that when using the parts related to the specific covariable of interest, resulting estimated networks display higher interconnectedness. Additionally, several groups of biologically associated metabolites (very large density lipoproteins, lipoproteins, etc.) were identified in the human data example. Conclusions This work demonstrates how information on the study design can be incorporated to estimate metabolite networks. As a result, sets of interconnected metabolites can be clustered together with respect to their relation to a covariable of interest

THE MUST MODEL EVALUATION EXERCISE: STATISTICAL ANALYSIS OF MODELLING RESULTS

The first validation exercise of the COST action 732 lead to a substantial number of simulation results for comparison with the MUST wind tunnel experiments. Validation metrics for selected simulation results of the flow field and the concentrations are presented and compared to the state of the art. In addition mean metrics and corresponding scatter limits are computed from the individual results

THE MUST MODEL EVALUATION EXERCISE: PATTERNS IN MODEL PERFORMANCE

As part of the COST 732 action more than a dozen different research groups have modelled the MUST experiment, as simulated in a wind tunnel. The model evaluation guidance developed within COST 732 recommends \u27exploratory data analysis\u27 as one of the elements in model validation. Experience has shown that such exploratory analysis is crucial to reveal shortcomings of models that might otherwise pass unnoticed. Conditions are best for detecting common patterns and anomalies if you have a situation where several models are put into a common framework – like the case at hand. The available material provides a unique opportunity to identify and explore patterns within model performance

Forecasting air quality in the Greater Athens area for the year 2004: An intercomparison of the results of four different dispersion models

Forecasts for the NOx concentration levels in the Greater Athens area in 2004 are compared to the corresponding figures for 1990. Simulations are performed for two meteorological cases using four different dispersion models. Two different emission inventories are employed in the simulations. The first is based on the conditions for the year 1990, while the second is the reference scenario for the year 2004, taking into account all major public works under construction. In order to ensure the validity of the individual models, simulation results are compared with available measurements for the year 1990. All models show an overprediction of the maximum NOx concentrations, but in general the simulation results show satisfactory agreement with the observations. Excellent agreement is found between the results of all models with regard to the distribution of the 50 maximum hourly NOx concentrations. Reductions of the peak NOx levels of the order of 35% are forecast by all models between 1990 and the reference scenario for 2004

Sub-micron atmospheric aerosols in the surroundings of Marseille and Athens: physical characterization and new particle formation

International audienceThe properties of atmospheric aerosol particles in Marseille and Athens were investigated. The studies were performed in Marseille, France during July 2002 and in Athens Greece during June 2003. The aerosol size distribution and the formation and growth rates of newly formed particles were characterized using Differential Mobility Particle Sizers. Hygroscopic properties were observed using a Hygroscopic Tandem Differential Mobility Analyzer setup. During both campaigns, the observations were performed at suburban, almost rural sites, and the sites can be considered to show general regional background behavior depending on the wind direction. At both sites there were clear pattern for both aerosol number concentration and hygroscopic properties. Nucleation mode number concentration increased during the morning hours indicating new particle formation, which was observed during more than 30% of the days. The observed formation rate was typically more than 1 cm-3 s-1, and the growth rate was between 1.2–9.9 nm h-1. Based on hygroscopicity measurements in Athens, the nucleation mode size increase was due to condensation of both water insoluble and water soluble material. However, during a period of less anthropogenic influence, the growth was to a larger extent due to water insoluble components. When urban pollution was more pronounced, growth due to condensation of water soluble material dominated