Assimilation of 3D radar reflectivities with an ensemble Kalman filter on the convective scale

An ensemble data assimilation system for 3D radar reflectivity data is introduced for the convection-permitting numerical weather prediction model of the COnsortium for Small-scale MOdelling (COSMO) based on the Kilometre-scale ENsemble Data Assimilation system (KENDA), developed by Deutscher Wetterdienst and its partners. KENDA provides a state-of-the-art ensemble data assimilation system on the convective scale for operational data assimilation and forecasting based on the Local Ensemble Transform Kalman Filter (LETKF). In this study, the Efficient Modular VOlume RADar Operator is applied for the assimilation of radar reflectivity data to improve short-term predictions of precipitation. Both deterministic and ensemble forecasts have been carried out. A case-study shows that the assimilation of 3D radar reflectivity data clearly improves precipitation location in the analysis and significantly improves forecasts for lead times up to 4 h, as quantified by the Brier Score and the Continuous Ranked Probability Score. The influence of different update rates on the noise in terms of surface pressure tendencies and on the forecast quality in general is investigated. The results suggest that, while high update rates produce better analyses, forecasts with lead times of above 1 h benefit from less frequent updates. For a period of seven consecutive days, assimilation of radar reflectivity based on the LETKF is compared to that of DWD's current operational radar assimilation scheme based on latent heat nudging (LHN). It is found that the LETKF competes with LHN, although it is still in an experimental phase

Nonlinear bias correction for satellite data assimilation using Taylor series polynomials

Output from a high-resolution ensemble data assimilation system is used to assess the ability of an innovative nonlinear bias correction (BC) method that uses a Taylor series polynomial expansion of the observation-minus background departures to remove linear and nonlinear conditional biases from all-sky satellite infrared brightness temperatures. Univariate and multivariate experiments were performed in which the satellite zenith angle and variables sensitive to clouds and water vapor were used as the BC predictors. The results showed that even though the bias of the entire observation departure distribution is equal to zero regardless of the order of the Taylor series expansion, there are often large conditional biases that vary as a nonlinear function of the BC predictor. The linear 1st order term had the largest impact on the entire distribution as measured by reductions in variance; however, large conditional biases often remained in the distribution when plotted as a function of the predictor. These conditional biases were typically reduced to near zero when the nonlinear 2nd and 3rd order terms were used. The univariate results showed that variables sensitive to the cloud top height are effective BC predictors especially when higher order Taylor series terms are used. Comparison of the statistics for clear-sky and cloudy-sky observations revealed that nonlinear departures are more important for cloudy-sky observations as signified by the much larger impact of the 2nd and 3rd order terms on the conditional biases. Together, these results indicate that the nonlinear BC method is able to effectively remove the bias from all-sky infrared observation departures

Age of Child, More than HPV Type, Is Associated with Clinical Course in Recurrent Respiratory Papillomatosis

Background: RRP is a devastating disease in which papillomas in the airway cause hoarseness and breathing difficulty. The disease is caused by human papillomavirus (HPV), 6 or 11 and is very variable. Patients undergo multiple surgeries to maintain a patent airway and in order to communicate vocally. Several small studies have been published in which most have noted that HPV 11 is associated with a more aggressive course. Methodology/Principal Findings: Papilloma biopsies were taken from patients undergoing surgical treatment of RRP and were subjected to HPV typing. 118 patients with juvenile-onset RRP with a least 1 year of clinical data and infected with a single HPV type were analyzed. HPV 11 was encountered in 40% of the patients. By our definition, most of the patients in the sample (81%) had run an aggressive course. The odds of a patient with HPV 11 running an aggressive course were 3.9 times higher that that of patients with HPV 6 (Fisher's exact p=0.017). However, clinical course was more closely associated with age of the patient (at diagnosis and at the time of the current surgery) than with HPV type. Patients with HPV 11 were diagnosed at a younger age (2.4y) than were those with HPV 6 (3.4y) (p=0.014). Both by multiple linear regression and by multiple logistics regression HPV type was only weakly associated with metrics of disease course when simultaneously accounting for age. Conclusions/Significance Abstract: The course of RRP is variable and a quarter of the variability can be accounted for by the age of the patient. HPV 11 is more closely associated with a younger age at diagnosis than it is associated with an aggressive clinical course. These data suggest that there are factors other than HPV type and age of the patient that determine disease course. © 2008 Buchinsky et al

An FDA bioinformatics tool for microbial genomics research on molecular characterization of bacterial foodborne pathogens using microarrays

<p>Abstract</p> <p>Background</p> <p>Advances in microbial genomics and bioinformatics are offering greater insights into the emergence and spread of foodborne pathogens in outbreak scenarios. The Food and Drug Administration (FDA) has developed a genomics tool, ArrayTrack^TM, which provides extensive functionalities to manage, analyze, and interpret genomic data for mammalian species. ArrayTrack^TM has been widely adopted by the research community and used for pharmacogenomics data review in the FDA’s Voluntary Genomics Data Submission program. </p> <p>Results</p> <p>ArrayTrack^TM has been extended to manage and analyze genomics data from bacterial pathogens of human, animal, and food origin. It was populated with bioinformatics data from public databases such as NCBI, Swiss-Prot, KEGG Pathway, and Gene Ontology to facilitate pathogen detection and characterization. ArrayTrack^TM’s data processing and visualization tools were enhanced with analysis capabilities designed specifically for microbial genomics including flag-based hierarchical clustering analysis (HCA), flag concordance heat maps, and mixed scatter plots. These specific functionalities were evaluated on data generated from a custom Affymetrix array (FDA-ECSG) previously developed within the FDA. The FDA-ECSG array represents 32 complete genomes of <it>Escherichia coli</it> and<it> Shigella.</it> The new functions were also used to analyze microarray data focusing on antimicrobial resistance genes from <it>Salmonella</it> isolates in a poultry production environment using a universal antimicrobial resistance microarray developed by the United States Department of Agriculture (USDA).</p> <p>Conclusion</p> <p>The application of ArrayTrack^TM to different microarray platforms demonstrates its utility in microbial genomics research, and thus will improve the capabilities of the FDA to rapidly identify foodborne bacteria and their genetic traits (e.g., antimicrobial resistance, virulence, etc.) during outbreak investigations. ArrayTrack^TM is free to use and available to public, private, and academic researchers at <url>http://www.fda.gov/ArrayTrack</url>. </p

Smoke with Fire: Financial Crises and the Demand for Parliamentary Oversight in the European Union.

The handling of the 2008 financial crisis has reinforced the conviction that the European Union (EU) is undemocratic and that member states are forced to delegate overwhelming power to a supranational technocracy. However, European countries have engaged with this alleged power drift differently, with only a few member states demanding more parliamentary scrutiny of EU institutions. This article develops a political economy explanation for why only some states have enforced mechanisms to monitor the EU more closely. Our theory focuses on the role of the crisis and the impact of fiscal autonomy in countries outside and inside currency arrangements such as the European Economic and Monetary Union (EMU). We argue that, in the aftermath of a severe economic shock, member states outside the EMU possess more monetary and fiscal resources to handle the crisis. These would then demand oversight of EU decision-making if their fiscal sustainability depends on the Union. By contrast, Eurozone states that need policy changes cannot address the crisis independently or initiate reforms to scrutinize the EU. Hence, we argue that during the heated moments of severe economic downturns, parliaments in Eurozone countries discuss supranational supervision rarely. As these legislatures have nevertheless to give in to the popular demand for EU control, they express support for more EU supervision in the infrequent times of debate. We provide evidence for our theory with a cross-national analysis of EU oversight institutions, and a new original dataset of parliamentary debates during the Eurozone crisis. Our findings highlight the political consequences that financial nosedives have across the diverse membership of a supranational organization

Summary and Recommendations from Working Group 1: model uncertainty representations in convection-permitting / shorter lead-time / limited-area ensembles

WG3 discussed both the pros and cons of existing schemes as Working group 1 considered the treatment of model uncertainty (MU) in high-resolution ensembles, at grid spacings of order 1-5 km. These systems are often run for regional weather forecasting, perhaps over a single country, and for lead times of up to 5 days. Looking ahead, ECMWF’s strategy seeks to deliver global medium-range ensemble forecasts with 3-4 km grid spacings by 2030. It is questionable for what grid spacing we should dispense with a deep convection parameterization, but it will be either switched off or damped in these systems, such that deep convection can be assumed to be dominated by explicit motions. One of the problems with limited-area ensemble systems at this scale is that spread depends not only on the modelling system itself but also on the variability inherited from the large-scale boundary conditions. There is often thought to be a lack of spread in our high-resolution EPS (ensemble prediction systems), but this could reflect a lack of diversity on larger scales. The relative importance of lateral-boundary diversity and the model uncertainty mechanisms is regime dependent. The lateral boundaries will generally be more important in midlatitude winter but less so for summertime convection in relatively weak synoptic flow