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Nicosia, Cyprus

Papadavid G.,Cyprus University of Technology | Hadjimitsis D.,Agricultural Research Institute of Cyprus | Fedra K.,Environmental Software and Services GmbH | Michaelides S.,Meteorological Service
Advances in Geosciences | Year: 2011

This paper presents a research project which integrates technological tools for developing a complete system for monitoring and determining irrigation demand on a systematic basis in Cyprus. Such tools are multi-spectral remotely sensed data dynamic water budget simulation and optimization, crop evapotranspiration (ETc) models and micro-sensor technology. The main aim is to estimate ETc in Cyprus and, furthermore, to undertake the required measures for an effective irrigation water management in the future. Evapotranspiration is difficult to determine since it combines various meteorological and field parameters while in literature quite many different models for estimating ETc are put forward. The proposed wireless sensor network acts as a monitoring tool for providing measurements of the necessary parameters: meteorological, climatic data and other auxiliary parameters required by the irrigation model in order to determine the irrigation demand. Reflectance is determined directly from satellite images. Finally, using the WaterWare irrigation software, irrigation scheduling is planned for the area of interest in Paphos, Cyprus. This area is located at almost sea level and is characterized by mild micro-climate. The results of the paper refer to year 2009 and show the daily water requirements of the specific crop in study. © Author(s) 2011.

Themistocleous K.,Cyprus University of Technology | Hadjimitsis D.G.,Cyprus University of Technology | Retalis A.,Institute of Environmental Research and Sustainable Development | Chrysoulakis N.,Foundation for Research and Technology Hellas | Michaelides S.,Meteorological Service
Atmospheric Research | Year: 2013

One of the most well-established atmospheric correction methods of satellite imagery is the use of the empirical line method using non-variant targets. Non-variant targets serve as pseudo-invariant targets since their reflectance values are stable across time. A recent adaptation of the empirical line method incorporates the use of ground reflectance measurements of selected non-variant targets. Most of the users are not aware of the existing conditions of the pseudo-invariant targets; i.e., whether they are dry or wet. Any omission of such effects may cause erroneous results; therefore, remote sensing users must be aware of such effects. This study assessed the effects of precipitation on five types of commonly located surfaces, including asphalt, concrete and sand, intended as pseudo-invariant targets for atmospheric correction. Spectroradiometric measurements were taken in wet and dry conditions to obtain the spectral signatures of the targets, from January 2010 to May 2011 (46 campaigns). An atmospheric correction of eleven Landsat TM/ETM. +. satellite images using the empirical line method was conducted. To identify the effects of precipitation, a comparison was conducted of the atmospheric path radiance component for wet and dry conditions. It was found that precipitation conditions such as rainfall affected the reflectance values of the surfaces, especially sand. Therefore, precipitation conditions need to be considered when using non-variant targets in atmospheric correction methods. © 2012 Elsevier B.V.

Hadjimitsis D.G.,Cyprus University of Technology | Papadavid G.,Cyprus University of Technology | Papadavid G.,Agricultural Research Institute of Cyprus | Agapiou A.,Cyprus University of Technology | And 7 more authors.
Natural Hazards and Earth System Science | Year: 2010

Solar radiation reflected by the Earth's surface to satellite sensors is modified by its interaction with the atmosphere. The objective of applying an atmospheric correction is to determine true surface reflectance values and to retrieve physical parameters of the Earth's surface, including surface reflectance, by removing atmospheric effects from satellite images. Atmospheric correction is arguably the most important part of the pre-processing of satellite remotely sensed data. Such a correction is especially important in cases where multi-temporal images are to be compared and analyzed. For agricultural applications, in which several vegetation indices are applied for monitoring purposes, multi-temporal images are used. The integration of vegetation indices from remotely sensed images with other hydro-meteorological data is widely used for monitoring natural hazards such as droughts. Indeed, the most important task.

Gascon E.,University of Leon | Sanchez J.L.,University of Leon | Charalambous D.,Meteorological Service | Fernandez-Gonzalez S.,Complutense University of Madrid | And 3 more authors.
Atmospheric Research | Year: 2015

On 4 March 2011, an exceptionally heavy snowfall event affected the Madrid region on the central Iberian Peninsula. At altitudes of 1200 m, snowfall reached a record of 34. cm in 24. h and produced considerable damage and disruption to electricity distribution and transport systems. Maximum intensity precipitation was identified between 1600 and 1800 UTC. Associated precipitation was particularly intense in the Guadarrama Mountains (at the center of the Peninsula, near Madrid).Analysis of Meteosat Second Generation (MSG) satellite images revealed a dark area, generated by a stratospheric intrusion originating in the Atlantic and reaching the Iberian Peninsula. We studied synoptic conditions and mesoscale factors involved in the event, using the Weather Research and Forecasting (WRF) model. This permitted analysis of the evolution of the dry intrusion caused by a tropopause fold, its movement, and frontogenesis-related mechanisms during its crossing of the Guadarrama Mountains. The blocking of a wet warm mass at altitude owing to a descent of the tropopause but mainly at low levels because of orographic effects, helped concentrate moisture and generate potential instability (PI). This was subsequently released in deep convection, owing to the formation of frontogenesis. © 2014 Elsevier B.V..

News Article
Site: www.nature.com

Following a record winter in many ways, Arctic sea-ice cover seems poised to reach one of its smallest winter maxima ever. As of 28 February, ice covered 14.525 million square kilometres, or  938,000 square kilometres less than the 1981–2010 average. And researchers are using a new technique to capture crucial information about the thinning ice pack in near real time, to better forecast future changes. Short-term weather patterns and long-term climate trends have conspired to create an extraordinary couple of months, even by Arctic standards. “This winter will be the topic of research for many years to come,” says Jennifer Francis, a climate scientist at Rutgers University in New Brunswick, New Jersey. “There’s such an unusual cast of characters on the stage that have never played together before.” The characters include the El Niño weather pattern that is pumping heat and moisture across the globe, and the Arctic Oscillation, a large-scale climate pattern whose shifts in recent months have pushed warm air northward. Together, they are exacerbating the long-term decline of Arctic sea ice, which has shrunk by an average of 3% each February since satellite records began in 1979. A persistent ridge of high-pressure air perched off the US West Coast has steered weather systems around drought-stricken California, funnelling warmth northward. As a consequence, sea ice is particularly scarce this year in the Bering Sea. “The ice would normally be extensive and cold, but we have open water instead,” says Francis. A storm last December compounded the situation by pushing warm air — more than 20 °C above average — to the North Pole. In January, an Arctic Oscillation-driven warm spell heated the air above most of the Arctic Ocean. By February, ice had begun to circulate clockwise around the Arctic basin and out through the Fram Strait, says Julienne Stroeve, a researcher at the US National Snow and Ice Data Center (NSIDC) in Boulder, Colorado. Given the Arctic’s notoriously unpredictable weather, the low maximum doesn’t necessarily foretell record-low melting this summer, when sea ice will reach its annual minimum. (The biggest summer melt on record happened in 2012, a year without an El Niño.) But researchers have one new tool with which to track the changes as they happen this year — the first detailed, near-real-time estimates of ice thickness, from the European Space Agency’s CryoSat-2 satellite. Three research groups currently calculate Arctic ice thickness from satellite data, but with a lag time of at least a month. Faster estimates would allow shipping companies to better plot routes through the Arctic, and scientists to improve their longer-term forecasts of ice behaviour. “The quicker you have these estimates of sea-ice thickness, the quicker you can start assimilating them into models and make more timely predictions of what’s going to happen,” says Rachel Tilling, a sea-ice researcher at University College London. She and her colleagues have developed a faster way to get information on ice thickness from CryoSat-2 (see ‘Measuring stick’). The satellite measures thickness by comparing the time that it takes for radar signals to bounce off the ice, as opposed to open water. Normally, it takes several months for satellite operators to calculate Cryo-Sat-2’s precise orbit (and therefore the exact location of the ice and water that it flew over). But Tilling’s group instead runs a quick-and-dirty analysis of orbital data, then combines it with near-real-time information on ice concentration from the NSIDC and ice type from the Norwegian Meteorological Service (R. L. Tilling et al. Cryosphere Discuss. http://doi.org/bcw5; 2016). The result is ice-thickness measurements that are ready in just 3 days, and accurate to within 1.5% of those produced months later. The current winter cycle is the first complete season for the near-real-time data. (The measurements cannot be done in the summer, when melt ponds on the ice confuse the satellite.) Tilling has begun to speak to shipping companies, among others, that are interested in using the data as fast as they are produced. “It really is a new era for CryoSat-2,” she says. More-accurate ice-thickness data would improve climate models and give better forecasts for the possible impacts of thick or thin sea ice, says Nathan Kurtz, a cryosphere scientist at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. Kurtz helps to lead NASA’s IceBridge project, which will begin flying aeroplanes north of Greenland later this month to measure ice thickness using lasers and an infrared camera that can detect heat from the underlying water. Thickness measurements are more crucial than ever, given the changing Arctic, says David Barber, a sea-ice specialist at the University of Manitoba in Winnipeg, Canada. He and his colleagues reported last year that there is increased open water all around the edge of the Arctic ice pack every month of the year (D. G. Barber et al. Prog. Oceanogr. 139, 122–150; 2015). “We’re getting more open water in the winter than we were expecting,” Barber says. “These changes are happening very quickly, and I don’t think people are fully aware of how dramatic they are.”

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