76 research outputs found
Identification of storm surge events over the German Bight from atmospheric reanalysis and climate model data
A new procedure for the identification of storm surge situations for the
German Bight is developed and applied to reanalysis and global climate model
data. This method is based on the empirical approach for estimating storm
surge heights using information about wind speed and wind direction. Here, we
hypothesize that storm surge events are caused by high wind speeds from north-
westerly direction in combination with a large-scale wind storm event
affecting the North Sea region. The method is calibrated for ERA-40 data,
using the data from the storm surge atlas for Cuxhaven. It is shown that using
information of both wind speed and direction as well as large-scale wind storm
events improves the identification of storm surge events. To estimate possible
future changes of potential storm surge events, we apply the new
identification approach to an ensemble of three transient climate change
simulations performed with the ECHAM5/MPIOM model under A1B greenhouse gas
scenario forcing. We find an increase in the total number of potential storm
surge events of about 12 % [(2001–2100)–(1901–2000)], mainly based on changes
of moderate events. Yearly numbers of storm surge relevant events show high
interannual and decadal variability and only one of three simulations shows a
statistical significant increase in the yearly number of potential storm surge
events between 1900 and 2100. However, no changes in the maximum intensity and
duration of all potential events is determined. Extreme value statistic
analysis confirms no frequency change of the most severe events
Optical Propagation and Communication
Contains an introduction and reports on four research project.Maryland Procurement Office Contract MDA 904-90-C-5070Charles S. Draper Laboratories Contract DL-H-441698National Institute of Standards and Technology Grant 60-NANBOD-1052U.S. Army Research Office Grant DAAL03-90-G-0128U.S. Navy - Office of Naval Research Grant N00014-89-J-1163U.S. Air Force - Office of Scientific Research Contract F49620-90-C-003
Recommended from our members
DNA methylation-based classification of central nervous system tumours.
Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology
DNA methylation-based classification of central nervous system tumours.
Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology
- …