Filippos Tymvios

The role of blocking in the summer 2014 collapse of Etesians over the eastern Mediterranean

We investigate the dynamical harbingers leading to the remarkable summer 2014 decline of the northerly flow (Etesians) over the eastern Mediterranean. From mid-July to mid-August four distinct episodes of unseasonal southerly flow were identified and associated with upper level troughs over central Europe and the Balkans. These features developed as repeated episodes of wave breaking, leading to blocking over Europe in July, and triggered equatorward streamers of high potential vorticity. During July a twofold increase in blocking occurrence against climatology was identified over parts of Europe and was part of a five-wave hemispheric pattern featuring abundant high-latitude blocking also over central Asia, the central Pacific, and western Atlantic. Overall, the frequent European blocking resulted in the southward displacement of the midlatitude storm track toward the Balkans and the relaxation of the traditional sharp east-west pressure gradient that triggered the collapse of Etesians. The bifurcation of the midlatitude jet caused by blocking led to the intensification of the westerly flow over the Mediterranean, accompanying the passing disturbances farther to the north, which combined with the weak Etesians resulting in a dramatic modification of the large-scale circulation over the Mediterranean Basin.

Artificial Neural Networks for the Diagnosis and Prediction of Desert Dust Transport Episodes

Artificial Neural Networks (ANN) are widely used as diagnostic and predictive tools in atmospheric sciences. This Chapter presents how such practical applications of ANN can be employed in the study of various aspects of a quite complex atmospheric phenomenon as the atmospheric pollution by particulate matter, due to dust transport episodes. It is also discussed how ANN can be utilized in assembling a useful predictive tool for such events. The diagnosis and prediction of dust episodes is very important for human welfare: indeed, some severe health issues are related to the presence of particulate matter in the atmosphere. Also, several human operations are affected by widespread dust presence: indeed, transportation and the increasing use of renewable energy systems utilizing solar radiation are profoundly affected.

Downscaling CHIRPS precipitation data: an artificial neural network modelling approach

ABSTRACT The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) is a high-resolution climatic database of precipitation embracing monthly precipitation climatology, quasi-global geostationary thermal infrared satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 product, atmospheric model rainfall fields from National Oceanic and Atmospheric Administration – Climate Forecast System (NOAA CFS), and precipitation observations from various sources. The key difference with all other existing precipitation databases is the high-resolution of the available data, since the inherent 0.05° resolution is a rather unique threshold. Monthly data for the period from January 1999 to December 2012 were processed in the present research. The main aim of this article is to propose a novel downscaling method in order to attain high resolution (1 km × 1 km) precipitation datasets, by correlating the CHIRPS dataset with altitude information and the normalized difference vegetation index from satellite images at 1 km × 1 km, utilizing artificial neural network models. The final result was validated with precipitation measurements from the rain gauge network of the Cyprus Department of Meteorology.

Monitoring and Forecasting Air Pollution Levels by Exploiting Satellite, Ground-Based, and Synoptic Data, Elaborated with Regression Models

This paper presents some of the results of a project that aimed at the design and implementation of a system for the spatial mapping and forecasting the temporal evolution of air pollution from dust transport from the Sahara Desert into the eastern Mediterranean and secondarily from anthropogenic sources, focusing over Cyprus. Monitoring air pollution (aerosols) in near real-time is accomplished by using spaceborne and in situ platforms. The results of the development of a system for forecasting pollution levels in terms of particulate matter concentrations are presented. The aim of the present study is to utilize the recorded PM10 (particulate matter with aerodynamic diameter less than 10 μm) ground measurements, Aerosol Optical Depth retrievals from satellite, and the prevailing synoptic conditions established by Artificial Neural Networks, in order to develop regression models that will be able to predict the spatial and temporal variability of PM10 in Cyprus. The core of the forecasting system comprises an appropriately designed neural classification system which clusters synoptic maps, Aerosol Optical Depth data from the Aqua satellite, and ground measurements of particulate matter. By exploiting the above resources, statistical models for forecasting pollution levels were developed.

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.

Satellite and Ground Measurements for Studying the Urban Heat Island Effect in Cyprus

An urban heat island (UHI) is a phenomenon whereby an urban area experiences elevated air temperatures due to anthropogenic modification of the environment and is usually more evident at night. During heat waves the local effect of an UHI is superimposed on the re‐ gional temperature field and as a result heat stress is enhanced. Both the intensity and the spatial structure of the observed thermal contrast of the UHI depend on various parameters, such as the structure of the urban tissue, the population density and its associated heat re‐ lease, the land use patterns, the vegetation cover, the surface topography and relief etc. In general terms, the UHI is becoming more intense as city sizes increase. Traditional measure‐ ments of the near-surface UHI are based on measurements of the air temperature using ur‐ ban and rural weather stations or air temperature transects. Thermal satellite sensors, which primarily measure the radiance at the top of the atmosphere in the thermal infrared, retrieve the so called land surface temperature (LST) which is the temperature measured at the Earth’s surface and is regarded as its skin temperature. Given that LST is different from the surface air temperature, a distinction is made in remote sensing studies between surface ur‐ ban heat island (SUHI) and atmospheric heat island (e.g., Nichol, 1996).

Study of Precipitation Extremes in Cyprus

The influence of climate change on the precipitation extremes is examined using results from theory, modeling and observations. Observations and simulations with climate models show that a warming climate typically results in an intensification of precipitation extremes. Towards this direction, an analysis of the daily precipitation database for the island of Cyprus is performed, for a period of 50 years. A number of climatic indices for precipitation are calculated using the dense network of rain gauges of the Cyprus Department of Meteorology. These parameters are calculated for the recent period spanning from 1981 to 2010. The results are compared with those of a previous period, namely the period 1961–1990, in order to investigate the changes regarding the occurrence of extreme rainfall events, along with the differences in the recording of periods with drought, showing a decrease in the number of rainy days along with the number of days with heavy rainfall.

Air Pollution from Space

Diofantos G. Hadjimitsis, Rodanthi-Elisavet Mamouri, Argyro Nisantzi, Natalia Kouremerti, Adrianos Retalis, Dimitris Paronis, Filippos Tymvios, Skevi Perdikou, Souzana Achilleos, Marios A. Hadjicharalambous, Spyros Athanasatos, Kyriacos Themistocleous, Christiana Papoutsa, Andri Christodoulou, Silas Michaelides, John S. Evans, Mohamed M. Abdel Kader, George Zittis, Marilia Panayiotou, Jos Lelieveld and Petros Koutrakis

Validation of the First Years of GPM Operation over Cyprus

Global Precipitation Measurement (GPM) high-resolution product is validated against rain gauges over the island of Cyprus for a three-year period, starting from April 2014. The precipitation estimates are available in both high temporal (half hourly) and spatial (10 km) resolution and combine data from all passive microwave instruments in the GPM constellation. The comparison performed is twofold: first the GPM data are compared with the precipitation measurements on a monthly basis and then the comparison focuses on extreme events, recorded throughout the first 3 years of GPM’s operation. The validation is based on ground data from a dense and reliable network of rain gauges, also available in high temporal (hourly) resolution. The first results show very good correlation regarding monthly values; however, the correspondence of GPM in extreme precipitation varies from “no correlation” to “high correlation”, depending on case. This study aims to verify the GPM rain estimates, since such a high-resolution dataset has numerous applications, including the assimilation in numerical weather prediction models and the study of flash floods with hydrological models.

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 5km×5km 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 1km×1km, by correlating the CHIRPS dataset with elevation information, the NDVI index (Normalized Difference Vegetation Index) from satellite images at 1km×1km 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.