COST 733 - WG4: Applications of weather type classification

The main objective of the COST Action 733 is to achieve a general numerical method for assessing, comparing and classifying typical weather situations in the European regions. To accomplish this goal, different workgroups are established, each with their specific aims: WG1: Existing methods and applications (finished); WG2: Implementation and development of weather types classification methods; WG3: Comparison of selected weather types classifications; WG4: Testing methods for various applications. The main task of Workgroup 4 (WG4) in COST 733 implies the testing of the selected weather type methods for various classifications. In more detail, WG4 focuses on the following topics:• Selection of dedicated applications (using results from WG1), • Performance of the selected applications using available weather types provided by WG2, • Intercomparison of the application results as a results of different methods • Final assessment of the results and uncertainties, • Presentation and release of results to the other WGs and external interested • Recommend specifications for a new (common) method WG2 Introduction In order to address these specific aims, various applications are selected and WG4 is divided in subgroups accordingly: 1.Air quality 2. Hydrology (& Climatological mapping) 3. Forest fires 4. Climate change and variability 5. Risks and hazards Simultaneously, the special attention is paid to the several wide topics concerning some other COST Actions such as: phenology (COST725), biometeorology (COST730), agriculture (COST 734) and mesoscale modelling and air pollution (COST728). Sub-groups are established to find advantages and disadvantages of different classification methods for different applications. Focus is given to data requirements, spatial and temporal scale, domain area, specifi

COST 733 – WG4: Applications of weather type classifications

Presentación realizada para: European Geosciences Union General Assembly celebrado del 19-24 de abril de 2009 en Viena

Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method

Spatial and temporal soil moisture dynamics are critically needed to improve the parameterization for hydrological and meteorological modeling processes. This study evaluates the statistical spatial structure of large-scale observed and simulated estimates of soil moisture under pre- and post-precipitation event conditions. This large scale variability is a crucial in calibration and validation of large-scale satellite based data assimilation systems. Spatial analysis using geostatistical approaches was used to validate modeled soil moisture by the Agriculture Meteorological (AGRMET) model using in situ measurements of soil moisture from a state-wide environmental monitoring network (Oklahoma Mesonet). The results show that AGRMET data produces larger spatial decorrelation compared to in situ based soil moisture data. The precipitation storms drive the soil moisture spatial structures at large scale, found smaller decorrelation length after precipitation. This study also evaluates the geostatistical approach for mitigation for quality control issues within in situ soil moisture network to estimates at soil moisture at unsampled stations

PROPHETIC: Prospective Identification of Pneumonia in Hospitalized Patients in the Intensive Care Unit

BACKGROUND: Pneumonia is the leading infection-related cause of death. Using simple clinical criteria and contemporary epidemiology to identify patients at high risk of nosocomial pneumonia should enhance prevention efforts and facilitate development of new treatments in clinical trials. RESEARCH QUESTION: What are the clinical criteria and contemporary epidemiology trends helpful in identifying patients at high risk of nosocomial pneumonia? STUDY DESIGN AND METHODS: Within the intensive care units of 28 United States hospitals, we conducted a prospective cohort study among adults hospitalized more than 48 hours and considered high risk for pneumonia (defined as treatment with invasive or noninvasive ventilatory support or high levels of supplemental oxygen). We estimated the proportion of high-risk patients developing nosocomial pneumonia. Using multivariable logistic regression, we identified patient characteristics and treatment exposures associated with increased risk of pneumonia development during the intensive care unit admission. RESULTS: Between February 6, 2016 and October 7, 2016, 4613 high-risk patients were enrolled. Among 1464/4613 (32%) high-risk patients treated for possible nosocomial pneumonia, 537/1464 (37%) met the study pneumonia definition. Among high-risk patients, a multivariable logistic model was developed to identify key patient characteristics and treatment exposures associated with increased risk of nosocomial pneumonia development (c-statistic 0.709, 95% confidence interval 0.686 to 0.731). Key factors associated with increased odds of nosocomial pneumonia included an admission diagnosis of trauma or cerebrovascular accident, receipt of enteral nutrition, documented aspiration risk, and receipt of systemic antibacterials within the preceding 90 days. INTERPRETATION: Treatment for nosocomial pneumonia is common among intensive care unit patients receiving high levels of respiratory support, yet more than half of patients treated do not fulfill standard diagnostic criteria for pneumonia. Application of simple clinical criteria may improve the feasibility of clinical trials of pneumonia prevention and treatment by facilitating prospective identification of patients at highest risk

Observation network design for environmental variables using a copula based approach

A. Bárdossy, J. Li and L. Guenn

A Copula based observation network design approach

In this paper, a method for environmental observation network design using the framework of spatial modeling with copulas is proposed. The methodology is developed to enlarge or redesign an existing monitoring network by taking the configuration which would increase the expected gain defined in a utility function. The utility function takes the estimation uncertainty, critical threshold value and gain-loss of a certain decision into account. In this approach, the studied spatial variable is considered as a random field in where variations in time is neglected and the variable of interest is static in nature. The uniqueness of this approach lies in the fact that the uncertainty estimation at the unsampled location is based on the full conditional distribution calculated as conditional copula in this study. Unlike the traditional Kriging variance which is a function of mere measurements density and spatial configuration of data points, the conditional copula account for the influence from data values. This is important specially if we are interested in purpose oriented network design (pond) as for example the detection of noncompliance with water quality standards, the detection of higher quantiles in the marginal probability distributions at ungauged locations, the presence or absence of a geophysical variable as soil contaminants, hydrocarbons, golds and so on. An application of the methodology to the groundwater quality parameters in the South-West region of Germany shows its potential. © 2011 Elsevier Ltd.Jing Li, András Bárdossy, Lelys Guenni, Min Li