The growth of populations and economy in Northern Cyprus has led to continuing utilization of fossil fuels as the primary source of electricity, which…The growth of populations and economy in Northern Cyprus has led to continuing utilization of fossil fuels as the primary source of electricity, which will raise environmental pollution. Thus, utilizing renewable energy, particularly solar energy, might be a solution to minimize this issue. This paper presents the potential of grid-connected solar PV power generation at Near East University Hospital (NEU Hospital), one of the largest and leading medical facilities in Northern Cyprus, to meet the energy demand during the daytime to reduce energy bills. For this purpose, the first objective of the study is to evaluate the solar energy potential as a power source for the NEU Hospital based on four datasets (actual measurement, Satellite Application Facility on Climate Monitoring (CMSAF), Surface Radiation Data Set-Heliosat (SARAH), and ERA-5, produced by the European Centre for Medium-range Weather Forecast). The results showed that the solar resource of the selected location is categorized as excellent (class 5), that is, the global solar radiation is within the range of 1843.8-2035.9 kWH/m(2). The second objective is to investigate the impact of orientation angles on PV output, capacity factor, economic feasibility indicators, and CO2 emissions by using different PV modules. The results are compared with optimum orientation angles found by Photovoltaic Geographical Information System (PVGIS) simulation software. This objective was achieved by using RETScreen Expert software. The results demonstrated that the highest performance of the proposed system was achieved for orientation angles of 180 & DEG; (azimuth angle) and -35 & DEG; (tilt angle). Consequently, it is recommended that orientation angles, PV modules, and market prices are considered to maximize energy production and reduce electricity production costs.more
Satellite sounder infrared radiances are among the most important contributions to the global observing system and have been assimilated into global n…Satellite sounder infrared radiances are among the most important contributions to the global observing system and have been assimilated into global numerical weather prediction (NWP) analyses for many years. They are also used as fundamental climate data records for climate monitoring. Prior to assimilation or producing climate records, the radiances should have all residual instrument biases removed. One way of estimating the mean biases is to continuously monitor the measured radiances against the NWP model equivalent radiances. This article is an extension of one published in 2012 which documented these biases for three years but now the time span of the monitoring has extended to beyond ten years, allowing the long-term stability of the instruments to be assessed. Data from high-resolution infrared sounder (HIRS), Advanced Along Track Scanning Radiometer (AATSR), and Spinning Enhanced Visible and Infrared Imager (SEVIRI), radiometers; atmospheric infrared sounder (AIRS), a spectrometer; and infrared atmospheric sounding interferometer (IASI), an interferometer, were included. Changes in mean biases and standard deviations were used to investigate the temporal stability of the bias and radiometric noise of the instruments over ten years. A double difference technique was employed to remove the effect of changes or deficiencies in the NWP system and radiative transfer (RT) model, which can contribute to the biases. The IASI and AIRS radiances were stable but with a different bias between the two instruments due to different versions of the RT model used. The SEVIRI radiometers were stable in most channels with the exception of the 13.4 mu m channel. The HIRS instruments were subject to sudden changes in bias and increases in standard deviation compared with NWP simulations during the past decade.more
The rising trend in fossil fuel prices and the depletion of natural resource reserves in the future force the authority of any country to find a more …The rising trend in fossil fuel prices and the depletion of natural resource reserves in the future force the authority of any country to find a more sustainable option for energy sources, so that future energy demand can be ensured for sustainable development. Assessing the trend and availability of sunshine duration (SD) at a spatiotemporal scale and the effect of different metrological parameters on the SD change is crucial to ensure the efficient utilization of solar energy, support the growth of renewable energy systems, and contribute to a sustainable future. In Saudi Arabia, The average monthly SD is 283 ± 18 hm<sup>-1</sup>, and there was a rising trend of SD that increased at a rate of 1.48 hy<sup>-1</sup> with a 95% confidence level. Most of the regions experienced an annual mean of SD between 3375 and 3754 hy<sup>-1</sup>, except for the southwest and the middle-eastern part where SD was between 3072 and 3375 hours in a year.&nbsp; The highest mean monthly SD was 318 ± 39 hm-1 during the summer season, but the trend of SD changes over the years was downward ( -0.21 hy<sup>-1</sup>). The mean monthly SD was lowest (244 ± 38 hm<sup>-1</sup>) in the winter season, and the changing pattern of SD was on the rise at a rate of 0.26 hy<sup>-1</sup> with a 95% confidence level. There was a decline in SD across the country between 1983 and 1998, whereas from 2000 onward the country experienced an upward trend in SD. Relative humidity (R = -0.53, p &lt; 0.01) and cloud cover (R = -0.42, p &lt; 0.05) as potential factors have a strong negative correlation with SD, whereas wind speed (R = 0.06, p &gt; 0.1) and temperature (R = 0.12, p &gt; 0.1) have a positive correlation with SD in the region.more
The Infrared Atmospheric Sounding Interferometers (IASIs) are three instruments flying on board the Metop satellites, launched in 2006 (IASI-A), 2012 …The Infrared Atmospheric Sounding Interferometers (IASIs) are three instruments flying on board the Metop satellites, launched in 2006 (IASI-A), 2012 (IASI-B), and 2018 (IASI-C). They measure infrared radiance from the Earth and atmosphere system, from which the atmospheric composition and temperature can be retrieved using dedicated algorithms, forming the Level 2 (L2) product. The operational near real-time processing of IASI data is conducted by the EUropean organisation for the exploitation of METeorological SATellites (EUMETSAT). It has improved over time, but due to IASI’s large data flow, the whole dataset has not yet been reprocessed backwards. A necessary step that must be completed before initiating this reprocessing is to uniformize the IASI radiance record (Level 1C), which has also changed with time due to various instrumental and software modifications. In 2019, EUMETSAT released a reprocessed IASI-A 2007–2017 radiance dataset that is consistent with both the L1C product generated after 2017 and with IASI-B. First, this study aimed to assess the changes in radiance associated with this update by comparing the operational and reprocessed datasets. The differences in the brightness temperature ranged from 0.02 K at 700 cm−1 to 0.1 K at 2200 cm−1. Additionally, two major updates in 2010 and 2013 were seen to have the largest impact. Then, we investigated the effects on the retrieved temperatures due to successive upgrades to the Level 2 processing chain. We compared IASI L2 with ERA5 reanalysis temperatures. We found differences of ~5–10 K at the surface and between 1 and 5 K in the atmosphere. These differences decreased abruptly after the release of the IASI L2 processor version 6 in 2014. These results suggest that it is not recommended to use the IASI inhomogeneous temperature products for trend analysis, both for temperature and trace gas trends.more
Arctic climate change has already resulted in amplified and accelerated regional warming, or the Arctic amplification. Satellite observations have cap…Arctic climate change has already resulted in amplified and accelerated regional warming, or the Arctic amplification. Satellite observations have captured this climate phenomenon in its development and in sufficient spatial details. As such, these observations have been—and still are—indispensable for monitoring of the amplification in this remote and inhospitable region, which is sparsely covered with ground observations. This study synthesizes the key contributions of satellite observations into an understanding and characterization of the amplification. The study reveals that the satellites were able to capture a number of important environmental transitions in the region that both precede and follow the emergence of the apparent amplification. Among those transitions, we find a rapid decline in the multiyear sea ice and subsequent changes in the surface radiation balance. Satellites have witnessed the impact of the amplification on phytoplankton and vegetation productivity as well as on human activity and infrastructure. Satellite missions of the European Space Agency (ESA) are increasingly contributing to amplification monitoring and assessment. The ESA Climate Change Initiative has become an essential provider of long-term climatic-quality remote-sensing data products for essential climate variables. Still, such synthesis has found that additional efforts are needed to improve cross-sensor calibrations and retrieval algorithms and to reduce uncertainties. As the amplification is set to continue into the 21st century, a new generation of satellite instruments with improved revisiting time and spectral and spatial resolutions are in high demand in both research and stakeholders’ communities.more
The work performed in this study evaluated the application of generalized pretrained object detection models for the identification and classification…The work performed in this study evaluated the application of generalized pretrained object detection models for the identification and classification of tropical storm (TS) systems through transfer learning. While the majority of literature focuses on developing bespoke models for this application, these typically require significantly more training data, compute resources, and time to train the models due to the large number of parameters the model has to tune to achieve similar results. These models also required additional preprocessing steps, such as extracting the storm from the image, and used a limited number of classes to describe the intensity of the storms. The approach presented here used considerably less data than the majority of other work (2–10x fewer input images) and a larger number of classes. The accuracies of the produced models trained on four different experimental datasets (varying the amount of data and number of classes) through this approach were 75%, 82%, 69%, and 89%. Overall, the models produced promising results, performing approximately equal to the bespoke models with scope to improve the performance of the model.more