Association of geopotential height patterns with heavy rainfall events in Cyprus

Dynamically induced rainfall is strongly connected with synoptic atmospheric circulation patterns at the upper levels. This study investigates the relationship between days of high precipitation volume events in the eastern Mediterranean and the associated geopotential height patterns at 500 hPa. To reduce the number of different patterns and to simplify the statistical processing, the input days were classified into clusters of synoptic cases having similar characteristics, by utilizing Kohonen Self Organizing Maps (SOM) architecture. Using this architecture, synoptic patterns were grouped into 9, 18, 27 and 36 clusters which were subsequently used in the analysis. The classification performance was tested by applying the method to extreme rainfall events in the eastern Mediterranean. The relationship of the synoptic upper air patterns (500 hPa height) and surface features (heavy rainfall events) was established, while the 36 member classification proved to be the most efficient

Investigation of trends in synoptic patterns over Europe with artificial neural networks

The present study is a comprehensive application of a methodology developed for the classification of synoptic situations using artificial neural networks. In this respect, the 500 hPa geopotential height patterns at 12:00 UTC (Universal Time Coordinated) determined from the reanalysis data (ERA-40 dataset) of the European Centre for Medium range Weather Forecasts (ECMWF) over Europe were used. The dataset covers a period of 45 years (1957–2002) and the neural network methodology applied is the SOM architecture (Self Organizing Maps). The classification of the synoptic scale systems was conducted by considering 9, 18, 27 and 36 synoptic patterns. The statistical analysis of the frequency distribution of the classification results for the 36 clusters over the entire 44-year period revealed significant tendencies in the frequency distribution of certain clusters, thus substantiating a possible climatic change. In the following, the database was split into two periods, the "reference" period that includes the first 30 years and the "test" period comprising the remaining 14 years

Tropospheric delay performance for GNSS integrated water vapor estimation by using GPT2w model, ECMWF's IFS operational model and in situ meteorological data

Tropospheric delay comprises one of the most important error sources in satellite navigation and is caused when radio signals broadcasted by GPS satellites propagate into the atmosphere. It is usually projected onto zenith direction by using mapping functions named as Zenith Tropospheric Delay (ZTD). ZTD is described as the sum of the Zenith Hydrostatic Delay (ZHD) and the Zenith Wet Delay (ZWD) and with the aid of surface pressure and temperature the integrated water vapor can be estimated. The main objective of this study is to evaluate the tropospheric delay performance for GNSS integrated water vapor estimation by using GPT2w model, ECMWF's IFS (ECMWF stands for the European Centre for Medium-Range Weather Forecasts) reanalysis model and ground meteorological data from two stations of the permanent network of Cyprus and Greece. The period from 27 May to 3 June 2018 is characterized by two different synoptic conditions: high pressure with fair weather in central Mediterranean (Greece), on the one hand, and high instability over the upper levels of the atmosphere that resulted in thunderstorms inland and mountainous areas during midday over the Eastern Mediterranean (Cyprus), on the other hand. In general, the results show that both the empirical blind model GPT2w and the ECMWF (IFS) operational model perform well in particular over Nicosia when used for the retrieval of Integrated Water Vapor (IWV) from GNSS measurements, although appreciable deviations were observed between ECMWF (IFS)-retrieved IWV and the one retrieved from GNSS observations by using meteorological measurements. A sharp increase of IWV prior to the abrupt rainfall events during noon on 30 and 31 May over Nicosia was also found.</p

Thrombosis Is Reduced by Inhibition of COX-1, but Unaffected by Inhibition of COX-2, in an Acute Model of Platelet Activation in the Mouse

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

An Integrated Microfluidic Device for Monitoring Changes in Nitric Oxide Production in Single T-Lymphocyte (Jurkat) Cells

A considerable amount of attention has been focused on the analysis of single cells in an effort to better understand cell heterogeneity in cancer and neurodegenerative diseases. Although microfluidic devices have several advantages for single cell analysis, few papers have actually demonstrated the ability of these devices to monitor chemical changes in perturbed biological systems. In this paper, a new microfluidic channel manifold is described that integrates cell transport, lysis, injection, electrophoretic separation, and fluorescence detection into a single device, making it possible to analyze individual cells at a rate of 10 cells/min in an automated fashion. The system was employed to measure nitric oxide (NO) production in single T-lymphocytes (Jurkat cells) using a fluorescent marker, 4-amino-5-methylamino-2',7'-difluorofluorescein diacetate (DAF-FM DA). The cells were also labeled with 6-carboxyfluorescein diacetate (6-CFDA) as an internal standard. The NO production by control cells was compared to that of cells stimulated using lipopolysaccharide (LPS), which is known to cause the expression of inducible nitric oxide synthase (iNOS) in immune-type cells. Statistical analysis of the resulting electropherograms from a population of cells indicated a twofold increase in NO production in the induced cells. These results compare nicely to a recently published bulk cell analysis of NO

Comparative Forecasts of a Local Area Model (WRF) in Summer for Cyprus

This paper presents a first attempt to increase the Cyprus Department of Meteorology operational model’s forecasting skill in Summer predictions. This is pursued with a comparative study of temperature and wind forecasts between the operational model, on the one hand, and other model variants, on the other hand, concerning the spatial resolution and other planetary boundary layer and radiation parameterizations

Intercomparison of boundary layer parameterizations for summer conditions in the eastern Mediterranean island of Cyprus using the WRF - ARW model

Ten planetary boundary-layer (PBL) parameterization schemes in the Weather Research and Forecasting (WRF) model have been evaluated with respect to temperature and wind forecasts for typical summer conditions in the eastern Mediterranean island of Cyprus. An ensemble of twenty-two simulations was performed, for a combination of the ten PBL and compatible surface layer parameterization schemes and each of the setups has been evaluated in view of forecasting skill and other statistical indices. Comparison of the model results with measurements from eight sites in Cyprus revealed significant differences in forecast skill, during both daytime (unstable conditions) and nighttime (stable conditions). The time series produced by the simulations were assessed against observations, in an effort to identify biases and skills in the forecasting ability of the models, employing commonly used statistical performance measures, such as the Mean Error, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. In summary, most simulations exhibit a negative bias for all stations concerning temperature and nearly all have positive biases for wind speed and direction. Statistical analysis of the results identified the best performing parameterization configurations for further investigation, to determine the most suitable setup for summertime forecasting in Cyprus, focusing on coastal stations, where land – sea breeze circulations govern the diurnal weather characteristics

Increasing spatial resolution of CHIRPS rainfall datasets for Cyprus with artificial neural networks

The use of high resolution rainfall datasets is an alternative way of studying climatological regions where conventional rain measurements are sparse or not available. Starting in 1981 to near-present, the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) dataset incorporates a 5kmx5km resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis, severe events and seasonal drought monitoring. The aim of this work is to further increase the resolution of the rainfall dataset for Cyprus to 1kmx1km, by correlating the CHIRPS dataset with elevation information, the NDVI index (Normalized Difference Vegetation Index) from satellite images at 1kmx1km and precipitation measurements from the official raingauge network of the Cyprus' Department of Meteorology, utilizing Artificial Neural Networks. The Artificial Neural Networks' architecture that was implemented is the Multi-Layer Perceptron (MLP) trained with the back propagation method, which is widely used in environmental studies. Seven different network architectures were tested, all with two hidden layers. The number of neurons ranged from 3 to10 in the first hidden layer and from 5 to 25 in the second hidden layer. The dataset was separated into a randomly selected training set, a validation set and a testing set; the latter is independently used for the final assessment of the models' performance. Using the Artificial Neural Network approach, a new map of the spatial analysis of rainfall is constructed which exhibits a considerable increase in its spatial resolution. A statistical assessment of the new spatial analysis was made using the rainfall ground measurements from the raingauge network. The assessment indicates that the methodology is promising for several applications

Natural Hazards and Earth System Sciences Outcomes of the 10th EGU Plinius Conference on Mediterranean

www.nat-hazards-earth-syst-sci.net/11/259/2011/ doi:10.5194/nhess-11-259-201

An Integrated Framework of Environmental Physics and Epidemiology: The Biometeorological Aspect of Thermal Environment and Health Project (BeAT Heat)

This article presents the Biometeorological Aspect of Thermal environment and Health (BeAT Heat) project, an integrated framework for the assessment of thermal perception that could assist in developing successful public health measures by local authorities and policymakers. BeAT Heat is conducted in Cyprus, a country of the eastern Mediterranean region. The study focuses on identifying models that best simulate thermal sensation, evaluating the spatial distribution of the state of the thermal environment and by means of this the thermal burden using satellite remote sensing methods, and determining the impact of thermal environment on the health of general and vulnerable populations. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG