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

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

Regional Evaluation of ERA-40 Reanalysis Data with Marine Atmospheric Observations in the North Sea Area

An important task of the departmental research programme KLIWAS is the evaluation and assessment of climate model results by means of a comprehensive reference data set. For validation purposes, and to create a North Sea wide maritime atmospheric and oceanographic reference database, in-situ observations of the Centre for Global Marine Meteorological Observations (GZS) of the National Meteorological Service DWD have been compared to the ERA-40 reanalysis. ERA-40 is used as forcing for the hindcast runs of the ENSEMBLES regional climate models, which is used within the KLIWAS model chain. The GZS hosts a regularly updated, quality controlled, world-wide data bank of weather observations from the oceans. It includes data from all sorts of observation platforms as Voluntary Observing Ships (VOS), drifting and moored buoys, light vessels, and offshore platforms, either from real-time (RT) via the Global Telecommunication System (GTS) or from international exchange in delayed-mode (DM). In addition to the automated set of programs applied for high quality control, erroneous data are also manually corrected to a certain extent, if possible. To assure reliable statistics for the evaluation, the corrected observations are gridded to a resolution of 2.25 degree, so each grid box includes four ERA-40 reanalysis grid points. The temporal coverage of the grid boxes depends on shipping routes and the positions of automated systems. Observed air temperatures, covering a period of 40 years (1961?2000), show noticeable differences to the reanalysis data for all land influenced boxes, specifically in the winter months. The same differences can be found if ERA-40 data alone are compared between land- and sea facing boxes. They can not be found in GZS data. It can be assumed that the differences are not resulting from measurement errors or uncertain fraction variabilities, since they are small during the winter months. A comparison of the differences basing on the 1981?2000 period to those of the 1961?1980 period shows an increase. Further investigations reveal that differences are largest in the landward part of the grid boxes, whereas in the parts facing to the open sea, observations and ERA-40 are in fair agreement. This leads to the conclusion, that the resolution of the ERA-40 reanalysis is not sufficient for detailed analyses of air temperatures near the coasts. However, this problem does not occur for the sea level pressure. The described land influence does not interfere with parameters, that are more or less insensitive to the land-sea distribution