Search Results (8)

We report ground-based measurements of the atmospheric electric field (Ez = −potential gradient (PG)) and current density (Jz) that were conducted at two locations in Israel. One is at the Emilio Segre cosmic ray station located on Mt. Hermon (34.45° N, 2020 m AMSL) in northern Israel near the Syrian-Lebanon border, and the other is at the Wise astronomical observatory in the Negev desert highland plateau of southern Israel (31.18° N, 870 m AMSL). We searched for possible effects of strong, short-term solar events on the potential gradient and the vertical current density, as disruptions to the global electric circuit are often observed following strong solar events. The first case study (St. Patrick’s Day, 17 March 2015) was classified as the strongest event of 2015. The second case study (8 September 2017) was categorized as the strongest event of 2017 and one of the twenty strongest events on record to date. The results show that the electrical parameters measured at ground level at both stations were not affected during the two massive proton events and the ensuing geomagnetic storms. The magnetospheric shielding in lower latitudes is strong enough to shield against the flux of energetic particles from solar events, obscuring any impact that may be noticeable above the local daily variations induced by local meteorological conditions (aerosol concentrations, clouds, high humidity, and wind speed), which were investigated as well. Full article

At present, Rn subsurface flow can be described only by diffusion and advection transportation models within porous media that currently exist. Even though the temperature is a strong driving force in climate and gas thermodynamics, the impact of the surface heating is missing from all gas flow models within geological porous media. In this work, it is shown that heating the ground surface by the sun, every day up to a maximum temperature at noon, creates a downward vertical temperature gradient related to the constant temperature in the upper shallow layer whose measured thickness is several meters. Undersurface, the Rn gas in the porous media is propelled in nonlinear dependency by the surface temperature gradient to flow downward, up to a measured depth of 100 m, revealing a daily periodicity with time delay depending on depth, similar to the diurnal cycle of the surface temperature. Moreover, regression analysis applied with the data implies a non-linear relationship between Rn and the temporal surface temperature. The relationship is non-linear and the best fit for it from a thermodynamic point of view is an exponential dependency. From now on, it will be possible according to the model to predict and extract, if required, by the time series of the surface-measured parameters (the ambient temperature and pressure), the semi-diurnal, diurnal, multiday, and seasonal Rn temporal variation at a shallow depth. Full article

Real−time single−frequency precise point positioning (PPP) is a promising low−cost technique for achieving high−precision navigation with sub−meter or centimeter−level accuracy. However, its effectiveness depends heavily on the availability and quality of the real−time ionospheric state estimations required for correcting the delay in global navigation satellite system (GNSS) signals. In this study, the dynamic mode decomposition (DMD) model is used with global ionospheric vertical total electron content (vTEC) RMS maps to construct 24 h global ionospheric vTEC RMS map forecasts. These forecasts are assimilated with C1P forecast products, and L1 single−frequency positioning solutions are compared with different ionospheric correction models. The study examines the impact of assimilating predicted RMS data and evaluates the presented approach’s practicality in utilizing the IGRG product. The results show that the IGSG RMS prediction−based model improves positioning accuracy up to five hours ahead and achieves comparable results to other models, making it a promising technique for obtaining high−precision navigation. Full article

Flash floods in the Eastern Mediterranean (EM) region are considered among the most destructive natural hazards, which pose a significant challenge to model due to their high complexity. Machine learning (ML) methods have made a significant contribution to the advancement of flash flood prediction systems by providing cost-effective solutions with improved performance, enabling the modeling of the complex mathematical expressions underlying physical processes of flash floods. Thus, the development of ML methods for flash flood prediction holds the potential to mitigate risks, inform policy recommendations, minimize loss of human life, and reduce property damage caused by flash floods. Here, we present a novel approach for improving flash flood predictions in the EM region using Support Vector Machines (SVMs) with a combination of precipitable water vapor (PWV) data, derived from ground-based global navigation satellite system (GNSS) receivers, along with surface pressure measurements, and nearby lightning occurrence data to predict flash floods in an arid region of the EM. The SVM model was trained on historical data from 2004 to 2019 and was used to forecast the likelihood of flash floods in the region. The study found that integrating nearby lightning data with the other variables significantly improved the accuracy of flash flood prediction compared to using only PWV and surface pressure measurements. The results of the SVM model were validated using observed flash flood events, and the model was found to have a high predictive accuracy with an area under the receiver operating characteristic curve of 0.93 for the test set. The study provides valuable insights into the potential of utilizing a combination of meteorological and lightning data for improving flash flood forecasting in the Eastern Mediterranean region. Full article

In this study, we assess the Dynamic Mode Decomposition (DMD) model applied with global ionospheric vertical Total Electron Content (vTEC) maps to construct 24-h global ionospheric vTEC map forecasts using the available International GNSS Service (IGS) 2-h cadence vTEC maps. In addition, we examine the impact of a EUV 121.6 nm time series data source with the DMD control (DMDc) framework, which shows an improvement in the vTEC Root Mean Square Error (RMSE) values compared with the IGS final solution vTEC maps. Both the DMD and DMDc predictions present close RMSE scores compared with the available CODE 1-day predicted ionospheric maps, both for quiet and disturbed solar activity. Finally, we evaluate the predicted global ionospheric vTEC maps with the East-North-Up (ENU) coordinate system errors metric, as an ionospheric correction source for L1 single-frequency GPS/GNSS Single Point Positioning (SPP) solutions. Based on these findings, we argue that the commonly adopted vTEC map comparison RMSE metric fails to correctly reflect an informative impact with L1 single-frequency positioning solutions using dual-frequency ionospheric corrections. Full article

There are significant controversies surrounding the detection of precursors that may precede earthquakes. Natural hazard signatures associated with strong earthquakes can appear in the lithosphere, troposphere, and ionosphere, where current remote sensing technologies have become valuable tools for detecting and measuring early warning signals of stress build-up deep in the Earth’s crust (presumably associated with earthquake events). Here, we propose implementing a machine learning support vector machine (SVM) technique, applied with GPS ionospheric total electron content (TEC) pre-processed time series estimations, to evaluate potential precursors caused by earthquakes and manifested as disturbances in the TEC data. After filtering and screening our data for solar or geomagnetic influences at different time scales, our results indicate that for large earthquakes (>Mw 6), true negative predictions can be achieved with 85.7% accuracy, and true positive predictions with an accuracy of 80%. We tested our method with different skill scores, such as accuracy (0.83), precision (0.85), recall (0.8), the Heidke skill score (0.66), and true skill statistics (0.66). Full article

Motivated by poor forecasting of a deadly convective event within the Levant, the factor separation technique was used to investigate the impact of non-local versus local moisture sources on simulated precipitation and lightning rates in central and southern Israel on 25 and 26 April 2018. Both days saw unusually heavy rains, and it was hypothesized that antecedent precipitation on 25 April contributed to the development of deadly flooding late morning on the 26th, as well as strong lightning and heavy rains later the same day. Antecedent precipitation led to an increase in the precipitable water content and an overall increase in instability as measured by the Convective Available Potential Energy (CAPE). The deadly flood occurred in the area of the Tzafit river gorge (hereafter, Tzafit river), about 25 km southeast of the city of Dimona, a semi-arid region in the northeastern Negev desert. The heavy rains and strong lightning occurred throughout the Levant with local peaks in the vicinity of Jerusalem. Factor separation conducted in model simulations showed that local ground moisture sources had a large impact on the CAPE and subsequent precipitation and lightning rates in the area of Jerusalem, while non-local moisture sources enabled weak convection to occur over broad areas, with particularly strong convection in the area of the Tzafit river. The coupled impact of both moisture sources also led to localized enhanced areas of convective activity. The results suggest that forecast models for the Levant should endeavor to incorporate an accurate depiction of soil moisture to predict convective rain, especially during the typically drier spring-time season. Full article

Improving the accuracy of numerical weather predictions remains a challenging task. The absence of sufficiently detailed temporal and spatial real-time in-situ measurements poses a critical gap regarding the proper representation of atmospheric moisture fields, such as water vapor distribution, which are highly imperative for improving weather predictions accuracy. The estimated amount of the total vertically integrated water vapor (IWV), which can be derived from the attenuation of global positioning systems (GPS) signals, can support various atmospheric models at global, regional, and local scales. Currently, several existing atmospheric numerical models can estimate the IWV amount. However, they do not provide accurate results compared with in-situ measurements such as radiosondes. Here, we present a new strategy for assimilating 2D IWV regional maps estimations, derived from combined GPS and METEOSAT satellite imagery data, to improve Weather Research and Forecast (WRF) model predictions accuracy in Israel and surrounding areas. As opposed to previous studies, which used point measurements of IWV in the assimilation procedure, in the current study, we assimilate quasi-continuous 2D GPS IWV maps, combined with METEOSAT-11 data. Using the suggested methodology, our results indicate an improvement of more than 30% in the root mean square error (RMSE) of WRF forecasts after assimilation relative standalone WRF, when both are compared to the radiosonde measured data near the Mediterranean coast. Moreover, significant improvements along the Jordan Rift Valley and Dead Sea Valley areas are obtained when compared to 2D IWV regional maps estimations. Improvements in these areas suggest the impact of the assimilated high resolution IWV maps, with initialization times which coincide with the Mediterranean Sea Breeze propagation from the coastline to highland stations, as the distance to the Mediterranean Sea shore, along with other features, dictates its arrival times. Full article