Search Results (52)

Localized ionization enhancements (LIEs) in altitude range corresponding to the D-region ionosphere, disrupting Very-Low-Frequency (VLF) signal propagation. This case study focuses on Lightning-induced Electron Precipitation (LEP), analyzing amplitude and phase variations in VLF signals recorded in Belgrade, Serbia, from worldwide transmitters. Due to the localized, transient nature of Energetic Electron Precipitation (EEP) events and the path-dependence of VLF responses, research relies on event-specific case studies to model reflection height and sharpness via numerical simulations. Findings show LIEs are typically under 1000 × 500 km, with varying internal structure. Accumulated case studies and corresponding data across diverse conditions contribute to a broader understanding of ionospheric dynamics and space weather effects. These findings enhance regional modeling, support aerosol–electricity climate research, and underscore the value of VLF-based ionospheric monitoring and collaboration in Europe. Full article

Accurate prediction of Global Navigation Satellite System-derived precipitable water vapor (GNSS-PWV), which is a crucial indicator for climate change monitoring, holds significant scientific value for climate disaster prevention and mitigation. In the study of GNSS-PWV prediction, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm within a decomposition–integration framework effectively addresses the non-stationarity and complexity of PWV sequences, enhancing prediction accuracy. However, residual noise and pseudo-modes from decomposition can distort signals, reducing the predictor system’s reliability. Additionally, independent modeling of all decomposed components decreases computational efficiency. To address these challenges, this paper proposes a hybrid model combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), autoregressive integrated moving average (ARIMA), and long short-term memory (LSTM) networks. Enhanced by local mean optimization and adaptive noise regulation, the ICEEMDAN algorithm effectively suppresses pseudo-modes and minimizes residual noise, enabling its decomposed intrinsic mode functions (IMFs) to more accurately capture the multi-scale features of GNSS-PWV. Sample entropy (SE) is used to quantify the complexity of IMFs, and components with similar entropy values are reconstructed into the following three sub-sequences: high-frequency, low-frequency, and trend. This process significantly reduces modeling complexity and improves computational efficiency. We propose different modeling strategies tailored to the dynamics of various subsequences. For the nonlinear and non-stationary high-frequency components, the LSTM network is used to effectively capture their complex patterns. The LSTM’s gating mechanism and memory cell design proficiently address the long-term dependency issue. For the stationary and weakly nonlinear low-frequency and trend components, linear patterns are extracted using ARIMA. Differencing eliminates trends and moving average operations capture random fluctuations, effectively addressing periodicity and trends in the time series. Finally, the prediction results of the three components are linearly combined to obtain the final prediction value. To validate the model performance, experiments were conducted using measured GNSS-PWV data from several stations in Hong Kong. The results demonstrate that the proposed model reduces the root mean square error by 56.81%, 37.91%, and 13.58% at the 1 h scale compared to the LSTM, EMD-LSTM, and ICEEMDAN-SE-LSTM benchmark models, respectively. Furthermore, it exhibits strong robustness in cross-month forecasts (accounting for seasonal influences) and multi-step predictions over the 1–6 h period. By improving the accuracy and efficiency of PWV predictions, this model provides reliable technical support for the real-time monitoring and early warning of extreme weather events in Hong Kong while offering a universal methodological reference for multi-scale modeling of geophysical parameters. Full article

We present a two-dimensional reconstruction of blocking frequency indices in the Atlantic-European region spanning the last 400 years. Our approach is based on a simple field reconstruction scheme similar to the principal component regression method. The particularity of our reconstruction scheme is that we select the blocking predictors using observed and reconstructed surface temperature and precipitation gridded data based on the correlation stability criteria. This approach avoids the problem of non-stationarity between predictand and predictors that commonly affects the quality of climate field reconstructions. First, we reconstruct the blocking field back to 1891 using observed gridded surface temperature and precipitation data. Then, the reconstruction is extended back in time to 1602 using seasonal-resolution paleo-reanalysis temperature and precipitation fields. The reconstruction is validated against various observed blocking frequency fields and climate reconstruction indices. The methodology presented in this study offers an opportunity for extracting paleo-weather signals from seasonal-resolution gridded datasets, which enables an improved understanding of the forcing of low-frequency variability for atmospheric blockings and related extremes. Full article

This study investigates the impact of rain-induced attenuation on cross-polarization discrimination (XPD) in Earth–space satellite links operating at millimeter-wave frequencies in Pretoria, South Africa. The traditional method of computing XPD employs a constant annual mean rain height and annual mean co-polar attenuation (CPA) over a certain location. This research utilized seasonal rain height data obtained from a recent study and the latest ITU-R P.618-14 guidelines, to compute and analyze XPD variations across six selected frequencies (11.7 GHz to 35 GHz) for different percentages of time exceedance in Pretoria. The study reveals significant seasonal dependencies of rain heights, with XPD reaching its maximum during winter due to lower rain height, and lower rain-induced attenuation and its minimum during summer, characterized by intense convective rainfall and maximum rain height. For instance, the estimated XPD for a 35 GHz signal at 0.01% of the time in the summer, spring, winter, and autumn are 13, 14, 15, and 14 dB, respectively. This implies that radio signals suffer severe attenuation caused by low XPD in the summer. The relationship between CPA and XPD highlights the need for increased XPD margins at higher frequencies to mitigate signal degradation caused by rain depolarization. Practical recommendations include the adoption of adaptive modulation and coding schemes to maintain link reliability during adverse weather conditions, particularly in summer. This research highlights the significance of incorporating frequency-dependent parameters and rain height variability in XPD estimation to enhance the design of satellite communication systems, ensuring optimized performance and reliable operation in a tropical climate. Full article

High-frequency (HF) emissions, referred to as supraharmonics (SHs), are proliferating in low- and medium-voltage networks due to the increasing use of technologies that generate distortions in the 2 kHz to 150 kHz range. The propagation of SHs through the electrical grid causes interference with power supply components and end-user equipment. With the increasing frequency of these incidents, it is imperative to establish guidelines and regulations that facilitate diagnosis and limit the amount of emissions injected into the electrical grid. The proliferation of SH emissions from active power electronics devices is a significant concern, especially considering the growing importance of photovoltaic (PV) systems in the context of climate change. The aim of this paper is to address and analyze the emissions from different PV inverters present in an electrical network. Several scenarios were simulated to understanding and identifying possible correlations. This study examines real signals from PV systems, which exhibit narrowband, broadband and time-varying emissions. This paper concludes by emphasizing the need for specific regulations for this frequency range while also providing indications for future research. Full article

In addition to technical issues related to the instruments used, differences between soil moisture (SM) measured using ground-based methods and microwave remote sensing (RS) can be related to the main features of the study areas, which are intricately connected to hydraulic–hydrological conditions and soil properties. When long-term analysis is performed, these discrepancies are mitigated by the contribution of SM seasonality and are only evident when high-frequency variations (i.e., signal anomalies) are investigated. This study sought to examine the responsiveness of SM to seasonal variations in terrestrial ecoregions located in areas covered by the in situ Romanian Soil Moisture Network (RSMN). To achieve this aim, several remote sensing-derived retrievals were considered: (i) NASA’s Soil Moisture Active and Passive (SMAP) L4 V5 model assimilated product data; (ii) the European Space Agency’s Soil Moisture and Ocean Salinity INRA–CESBIO (SMOS-IC) V2.0 data; (iii) time-series data extracted from the H115 and H116 SM products, which are derived from the analysis of Advanced Scatterometer (ASCAT) data acquired via MetOp satellites; (iv) Copernicus Global Land Service SSM 1 km data; and (v) the “combined” European Space Agency’s Climate Change Initiative for Soil Moisture (ESA CCI SM) product v06.1. An initial assessment of the performance of these products was conducted by checking the anomaly of long-term fluctuations, quantified using the Absolute Variation of Local Change of Environment (ALICE) index, within a time frame spanning 2015 to 2020. These correlations were then compared with those based on raw data and anomalies computed using a moving window of 35 days. Prominent correlations were observed with the SMAP L4 dataset and across all ecoregions, and the Balkan mixed forests (646) exhibited strong concordance regardless of the satellite source (with a correlation coefficient R_ALICE > 0.5). In contrast, neither the Central European mixed forests (No. 654) nor the Pontic steppe (No. 735) were adequately characterized by any satellite dataset (R_ALICE < 0.5). Subsequently, the phenological seasonality and dynamic behavior of SM were computed to investigate the effects of the wetting and drying processes. Notably, the Central European mixed forests (654) underwent an extended dry phase (with an extremely low p-value of 2.20 × 10⁻¹⁶) during both the growth and dormancy phases. This finding explains why the RSMN showcases divergent behavior and underscores why no satellite dataset can effectively capture the complexities of the ecoregions covered by this in situ SM network. Full article

The accurate extraction of river water bodies is crucial for the utilization of water resources and understanding climate patterns. Compared with traditional methods of extracting rivers using remote sensing imagery, the launch of satellite-based photon-counting LiDAR (ICESat-2) provides a novel approach for river water body extraction. The use of ICESat-2 ATL03 photon data for inland river water body extraction is relatively underexplored and thus warrants investigation. To extract inland river water bodies accurately, this study proposes a method based on the spatial distribution of ATL03 photon data and the elevation variation characteristics of inland river water bodies. The proposed method first applies low-pass filtering to denoised photon data to mitigate the impact of high-frequency signals on data processing. Then, the elevation’s standard deviation of the low-pass-filtered data is calculated via a sliding window, and the photon data are classified on the basis of the standard deviation threshold obtained through Gaussian kernel density estimation. The results revealed that the average overall accuracy (OA) and Kappa coefficient (KC) for the extraction of inland river water bodies across the four study areas were 99.12% and 97.81%, respectively. Compared with the improved RANSAC algorithm and the combined RANSAC and DBSCAN algorithms, the average OA of the proposed method improved by 17.98% and 7.12%, respectively, and the average KC improved by 58.38% and 17.69%, respectively. This study provides a new method for extracting inland river water bodies. Full article

Major explosive volcanic eruptions impact the climate by altering the radiative balance of the atmosphere and through feedback mechanisms in the climate system. The extent of the impact depends on the magnitude (aerosol mass loading) and the number or frequency of such eruptions. Multiple Antarctica ice core records of past volcanic eruptions reveal that the number (5) of major eruptions (volcanic sulfate deposition flux greater than 10 kg km⁻²) was the highest in the 13th century over the last two millennia. Signals of four of the five eruptions are dated to the second half of the century, indicating consecutive major eruptions capable of causing sustained climate impact via known feedback processes. The fact that signals of four corresponding eruptions have been found in a Greenland ice core indicates that four of the five 13th century eruptions were probably by volcanoes in the low latitudes (between 20° N and 20° S) with substantial aerosol mass loading. These eruptions in the low latitudes likely exerted the strongest volcanic impact on climate in the last two millennia. Full article

Madden–Julian Oscillation (MJO) modulates the generation of typhoons (TYs) in the western North Pacific (WNP). Using IBTrACS v04 tropical cyclone best path data, ERA5 reanalysis data, and the MJO index from the Climate Prediction Center (CPC), this paper defines an index to describe the persistent anomalies of the MJO and to examine the statistical characteristics of TYs over 44 years (1978–2021), focusing on the analysis of major differences in environmental conditions after the removal of the ENSO signal over the WNP. The results indicate that the persistent anomalous state of the MJO influences the change in large-scale environmental factors, which, in turn, affects the generation of TYs, as follows: (1) For the I high-value years, the center of the MJO stagnates in the Indian Ocean–South China Sea (SCS), the monsoon trough retreats westward, the warm pool becomes warmer, and the Walker circulation is enhanced. There is stronger upper-level divergence and low-level convergence, larger low-level relative vorticity, higher mid-level relative humidity, and smaller vertical wind shear in the SCS and the seas near the Philippines. Consequently, these conditions foster a conducive environment for TY genesis in the SCS and the seas near the Philippines. (2) For the I low-value years, the center of the MJO stagnates in the WNP–North America region, the monsoon trough extends eastward, the warm pool becomes colder, and the Walker circulation is weakened. Consequently, these conditions are more likely to facilitate TY genesis in the central–eastern WNP. The results show that persistent anomalies in MJO active centers can effectively improve the predictive ability of TY frequency. Full article

Acceleration sensors are vital for assessing engineering structures by measuring properties like natural frequencies. In practice, engineering structures often have low natural frequencies and face harsh environmental conditions. Understanding sensor behavior on such structures is crucial for reliable measurements. The research focus is on understanding the behavior of acceleration sensors in harsh environmental conditions within the low-frequency acceleration range. The main question is how to distinguish sensor behavior from structural influences to minimize errors in assessing engineering structure conditions. To investigate this, the sensors are tested using a long-stroke calibration unit under varying temperature and humidity conditions. Additionally, a mini-monitoring system configured with four IEPE sensors is applied to a small-scale support structure within a climate chamber. For the evaluation, a signal-energy approach is employed to distinguish sensor behavior from structural behavior. The findings show that IEPE sensors display temperature-dependent nonlinear transmission behavior within the low-frequency acceleration range, with humidity having negligible impact. To ensure accurate engineering structure assessment, it is crucial to separate sensor behavior from structural influences using signal energy in the time domain. This study underscores the need to compensate for systematic effects, preventing the underestimation of vibration energy at low temperatures and overestimation at higher temperatures when using IEPE sensors for engineering structure monitoring. Full article

As a challenge in the construction of a “seamless forecast” system, improving the prediction skills of subseasonal forecasts is a key issue for meteorologists. In view of the evolution characteristics of numerical models and deep-learning models for subseasonal forecasts, as forecast times increase, the prediction skill for high-frequency components will decrease, as the lead time is already far beyond the predictability. Meanwhile, intraseasonal low-frequency components are essential to the change in general circulation on subseasonal timescales. In this paper, the Global Subseasonal Forecast Model (GSFM v1.0) first extracted the intraseasonal oscillation (ISO) components of atmospheric signals and used an improved deep-learning model (SE-ResNet) to train and predict the ISO components of geopotential height at 500 hPa (Z500) and temperature at 850 hPa (T850). The results show that the 10–30 day prediction performance of the SE-ResNet model is better than that of the model trained directly with original data. Compared with other models/methods, this model has a good ability to depict the subseasonal evolution of the ISO components of Z500 and T850. In particular, although the prediction results from the Climate Forecast System Version 2 have better performance through 10 days, the SE-ResNet model is substantially superior to CFSv2 through 10–30 days, especially in the middle and high latitudes. The SE-ResNet model also has a better effect in predicting planetary waves with wavenumbers of 3–8. Thus, the application of data-driven subseasonal forecasts of atmospheric ISO components may shed light on improving the skill of seasonal forecasts. Full article

The atmospheric total water vapor content (TWVC) affects climate change, weather patterns, and radio signal propagation. Recent techniques such as global navigation satellite systems (GNSS) are used to measure TWVC but with either compromised accuracy, temporal resolution, or spatial coverage. This study demonstrates the feasibility of predicting, mapping, and measuring TWVC using spread spectrum (SS) radio signals and software-defined radio (SDR) technology on low Earth-orbiting (LEO) satellites. An intersatellite link (ISL) communication network from a constellation of small satellites is proposed to achieve three-dimensional (3D) mapping of TWVC. However, the calculation of TWVC from satellites in LEO contains contribution from the ionospheric total electron content (TEC). The TWVC and TEC contribution are determined based on the signal propagation time delay and the satellites’ positions in orbit. Since TEC is frequency dependent unlike TWVC, frequency reconfiguration algorithms have been implemented to distinguish TWVC. The novel aspects of this research are the implementation of time stamps to deduce time delay, the unique derivation of TWVC from a constellation setup, the use of algorithms to remotely tune frequencies in real time, and ISL demonstration using SDRs. This mission could contribute to atmospheric science, and the measurements could be incorporated into the global atmospheric databases for climate and weather prediction models. Full article

Climate change can exacerbate the occurrence of extreme precipitation events, thereby affecting both the frequency and intensity of rainfall-induced landslides. It is important to study the threat of rainfall-induced landslides under future climate conditions for the formulation of disaster prevention and mitigation policies. Due to the complexity of the climate system, there is great uncertainty in the climate variables simulated by a global climate model (GCM), which will be further propagated in landslide prediction. In this study, we investigate the spatial and temporal trends of future landslide hazards in China under climate change, using data from a multi-model ensemble of GCMs based on two scenarios, RCP4.5 and RCP8.5. The uncertainty characteristics are then estimated based on signal-to-noise ratios (SNRs) and the ratio of agreement in sign (RAS). The results show that the uncertainty of landslide prediction is mainly dominated by the GCM ensemble and the RCP scenario settings. Spatially, the uncertainty of landslide prediction is high in the western areas of China and low in the eastern areas of China. Temporally, the uncertainty of landslide prediction is evolving, with characteristics of high uncertainty in the near future and characteristics of low uncertainty in the distant future. The annual average SNRs in the 21st century are 0.44 and 0.50 in RCP4.5 and RCP8.5, respectively, and the RAS of landslide prediction in Southeastern China is only 50–60%. This indicates that more than half of the patterns show trends that are opposite to those of the ensemble, suggesting that their landslide change trends are not universally recognized in the pattern ensemble. Considering the uncertainty of climate change in landslide prediction can enable studies to provide a more comprehensive picture of the possible range of future landslide changes, effectively improving the reliability of landslide hazard prediction and disaster prevention. Full article

Natural thermoluminescence (TL) from the core of MD81349 marine calcareous biological ooze samples in the Ninetyeast Ridge of the equatorial northeast Indian Ocean and from the core of IODP306-U1312B in the high latitudes of the North Atlantic Ocean was studied. The spurious TL intensity of 395 °C at its peak is dose independent when the heating rate is 6 °C/s in a nitrogen atmosphere. TL signals have exhibited a significant correlation with the marine isotope stages (MIS) in the two oceans since the mid-Pleistocene era. High TL intensity corresponds to a negative δ¹⁸O in the interglacial stages, and low TL intensity corresponds to a positive δ¹⁸O in the glacial stages. The TL of both cores from the two oceans reveal that global climate has experienced eight cold and warm cycles since the mid-Pleistocene era. In this study, a single-frequency spectrum analysis of the MD81349 and U1312B TL cores in the last 300 ka is performed. Near the equator of the northeast Indian Ocean, the short cycles of 38 ka and 5 ka are more significant, while the cycle of 8 ka is more significant in the North Atlantic Ocean. In addition, a correlation analysis shows that the TL has a significant positive correlation with the trace element ¹³⁵Ba and a significant negative correlation with ⁴⁷Ti. The impurity ions (e.g., Ba²⁺, Mn²⁺ and Ti²⁺) doped in carbonate act as activators and suppressants, respectively. A time series of the TL of the calcareous biological ooze tests provide an important record of climate change. The source of the TL signal is also discussed. Full article

El Niño is generally associated with negative rainfall anomalies (below-average rainfall) in northern Northeast Brazil (NNEB). In 2019, however, the opposite rainfall pattern was observed during an El Niño episode. Here, we explore the mechanisms that overwhelmed typical El Niño-related conditions and resulted in positive rainfall anomalies (above-average rainfall) in NNEB. We focus on the austral autumn when El Niño is most prone to rainfall anomalies in the region. The analysis of several datasets, including weather station data, satellite data, reanalysis data, and modelled data derived from a dry linear baroclinic model, allowed us to identify that the austral autumn 2019 above-average rainfall in NNEB was likely associated with four combined factors; these are (1) the weak intensity of the 2019 El Niño; (2) the negative phase of the Atlantic Meridional Mode; (3) local and remote diabatic heating anomalies, especially over the western South Pacific and tropical South Atlantic, which resulted in anticyclonic and cyclonic circulations in the upper and lower troposphere, respectively, over the tropical South Atlantic; and (4) sub-seasonal atmospheric convection anomalies over the western South Pacific, which reinforced the low-frequency convection signal over that region. This latter factor suggests the influence of the Madden–Julian Oscillation on rainfall in NNEB during the first ten days of March 2019. We discuss these mechanisms in detail and provide evidence that, even during an El Niño event, above-average rainfall in NNEB in the austral autumn may occur, and its modulation is not limited to the influence of a single climate phenomenon. Our results may assist in the planning of several crucial activities, such as water resources management and agriculture. Full article