Search Results (40)

The timing accuracy of eLoran systems is susceptible to meteorological fluctuations, with medium-to-long-range propagation delay variations reaching hundreds of nanoseconds to microseconds. While conventional models have been widely adopted for short-range delay prediction, they fail to accurately characterize the coupled effects of multiple factors in long-range scenarios. This study theoretically examines the influence mechanisms of temperature, humidity, and atmospheric pressure on signal propagation delays, proposing a hybrid prediction model integrating meteorological data with a back-propagation neural network (BPNN) through path-weighted Pearson correlation coefficient analysis. Long-term observational data from multiple differential reference stations and meteorological stations reveal that short-term delay fluctuations strongly correlate with localized instantaneous humidity variations, whereas long-term trends are governed by cumulative temperature–humidity effects in regional environments. A multi-tier neural network architecture was developed, incorporating spatial analysis of propagation distance impacts on model accuracy. Experimental results demonstrate enhanced prediction stability in long-range scenarios. The proposed model provides an innovative tool for eLoran system delay correction, while establishing an interdisciplinary framework that bridges meteorological parameters with signal propagation characteristics. This methodology offers new perspectives for reliable timing solutions in global navigation satellite system (GNSS)-denied environments and advances our understanding of meteorological–electromagnetic wave interactions. Full article

Earth’s energy imbalance (EEI) is a major indicator of climate change. Its metrics are top of the atmosphere radiation imbalance (EEI TOA) and net internal heat uptake. Both EEI and temperature are expected to respond gradually to forcing on annual timescales. This expectation was tested by analyzing regime changes in the inputs to EEI TOA along with increasing ocean heat content (OHC). Outward longwave radiation (OLR) displayed rapid shifts in three observational and two reanalysis records. The reanalysis records also contained shifts in surface fluxes and temperature. OLR, outward shortwave radiation (OSR) and TOA net radiation (Net) from the CERES Energy Balanced and Filled Ed-4.2.1 (2001–2023) record and from 27 CMIP5 historical and RCP4.5 forced simulations 1861–2100, were also analyzed. All variables from CERES contained shifts but the record was too short to confirm regime changes. Contributions of OLR and OSR to net showed high complementarity over space and time. EEI TOA was −0.47 ± 0.11 W m⁻² in 2001–2011 and −1.09 ± 0.11 W m⁻² in 2012–2023. Reduced OSR due to cloud feedback was a major contributor, coinciding with rapid increases in sea surface temperatures in 2014. Despite widely varying OLR and OSR, 26/27 climate models produced stable regimes for net radiation. EEI TOA was neutral from 1861, shifting downward in the 26 reliable records between 1963 and 1995, with 25 records showing it stabilizing by 2039. To investigate heat uptake, temperature and OHC 1955/57–2023 was analyzed for regime change in the 100 m, 700 m and 2000 m layers. The 100 m layer, about one third of total heat content, was dominated by regimes. Increases became more gradual with depth. Annual changes between the 700 m layer and 1300 m beneath were negatively correlated (−0.67), with delayed oscillations during lag years 2–9. Heat uptake at depth is dynamic. These changes reveal a complex thermodynamic response to gradual forcing. We outline a complex arrangement of naturally evolved heat engines, dominated by a dissipative heat engine nested within a radiative engine. EEI is a property of the dissipative heat engine. This far-from-equilibrium natural engine has evolved to take the path of least resistance while being constrained by its maximum power limit (~2 W m⁻²). It is open to the radiative engine, receiving solar radiation and emitting scattered shortwave and longwave radiation. Steady states maximize entropy within the dissipative engine by regulating spatial patterns in surface variables that influence outgoing OLR and OSR. Regime shifts to warmer climates balance the cost of greater irreversibility with increased energy rate density. The result is the regulation of EEI TOA through a form of thermodynamic metabolism. Full article

The rapid expansion of BeiDou satellite navigation applications has led to a growing demand for real-time high-precision positioning services. Currently, high-precision positioning services face challenges such as a long initialization time and heavy reliance on reference station networks, thereby failing to fulfill the requirements for real-time, wide-area, and centimeter-level positioning. In this study, we consider the multi-frequency signals that are broadcast by a satellite to share a common reference clock and possess identical RF channels and propagation paths with strict temporal, spectral, and spatial coupling between signal components, resulting in strongly coherent propagation delays. Firstly, we accurately establish a multi-frequency signal model that fully exploits those coherent characteristics among the multi-frequency BDS signals. Subsequently, we propose a rapid high-precision ranging technique using the code and carrier phases of multi-frequency signals. The proposed method unitizes multi-frequency signals via a coherent joint processing unit consisting of a joint tracking state estimator and a coherent signal generator. The joint tracking state estimator simultaneously estimates the biased pseudorange and its change rate, ionospheric delay and its change rate, and ambiguities. The coherent signal generator updates the numerically controlled oscillator (NCO) to adjust the local reference signal’s code and carrier replicas of different frequencies, changing them according to the state estimated by the joint tracking state estimator. Finally, the simulation results indicate that the proposed method efficiently diminishes the estimated biased pseudorange and ionospheric delay errors to below 0.1 m. Furthermore, this method reduces the carrier phase errors by more than 60% compared with conventional single-frequency-independent tracking methods. Consequently, the proposed method can achieve rapid centimeter-level results ranging for up to 1 min without using precise atmosphere corrections and provide enhanced tracking sensitivity and robustness. Full article

A ground-based microwave radiometer (MWR) provides continuous atmospheric profiles under various weather conditions. The change in total precipitable water vapor (PWV) and liquid water path (LWP) before rainfall events is particularly important for the improvement in the rainfall forecast. However, the analysis of the PWV and LWP before rainfall event on the plateau is especially worth exploring. In this study, the MWR installed at Xining, a city located over the northeastern Tibetan Plateau, was employed during September 2021 to August 2022. The results reveal that the MWR-retrieved temperature and vapor density demonstrate reliable accuracy, when compared with radiosonde observations; PWV and LWP values during the summer account for over 70% of the annual totals in the Xining area; both PWV and LWP at the initiating time of rainfall events are higher during summer, especially after sunset (during 20-00 local solar time); and notably, PWV and LWP anomalies are enhanced abruptly 8 and 28 min prior to the initiating time, respectively. Furthermore, the mean of LWP anomaly rises after the turning time (the moment rises abruptly) to the initiating time; as the intensity of rainfall events increases, the occurrence of the turning time is delayed, especially for PWV anomalies; while the occurrence of the turning time is similar for both convective cloud and stratiform cloud rainfall events, the PWV and LWP anomalies jump more the initiating time; as the intensity of rainfall events increases, the occurrence of the turning time is delayed, especially for PWV anomalies; while the occurrence of the turning time is similar for both convective cloud and stratiform cloud rainfall events, the PWV and LWP anomalies jump more dramatically after the turning time in convective cloud events. This study aims are to analyze the temporal characteristics of PWV and LWP, and assess their potential in predicting rainfall event. Full article

In many applications, such as atmospheric observation or disaster monitoring, cooperative control of a fleet of UAVs is crucial because it is effective in repeated tasks. In this work, we provide a workable and useful cooperative guiding algorithm for several fixed-wing UAVs to construct a path-following formation with communication delays. The two primary components of our concept are path-following (lateral guidance) and path formation (longitudinal guidance). The former is in charge of ensuring that, in the presence of wind disturbance, the lateral distance between the UAV and its targeted path converges using a well-known vector field technique. In the event of a communication delay, the latter ensures that several fixed-wing UAVs will create a predetermined formation shape. Furthermore, we provide a maximum delay bound that is dependent on the topology and a controller’s gain. Lastly, in order to confirm the viability and advantages of our suggested approach, we construct an effective platform for a hardware-in-the-loop (HIL) test. Full article

This work presents the design and implementation of an operational infrastructure for the monitoring of atmospheric parameters at sea through GNSS meteorology sensors installed on liners operating in the north-west Mediterranean Sea. A measurement system, capable of operationally and continuously providing the values of surface parameters, is implemented together with software procedures based on a float-PPP approach for estimating zenith path delay (ZPD) values. The values continuously registered over a three year period (2020–2022) from this infrastructure are compared with the data from a numerical meteorological reanalysis model (MERRA-2). The results clearly prove the ability of the system to estimate the ZPD from ship-based GNSS-meteo equipment, with the accuracy evaluated in terms of correlation and root mean square error reaching values between 0.94 and 0.65 and between 18.4 and 42.9 mm, these extreme values being from the best and worst performing installations, respectively. This offers a new perspective on the operational exploitation of GNSS signals over sea areas in climate and operational meteorological applications. Full article

The HY-2D satellite was successfully launched in 2022, which marks the first phase of the HY-2 satellite constellation. In order to reduce the deviation of wet path delay (WPD) between different satellites in the HY-2 satellite constellation and increase precision in the correction microwave radiometer (CMR) products, on-orbit calibration must be performed to the brightness temperature (BT) of the CMR in this constellation. This study describes the principle and process of on-orbit calibration for CMR in detail. For the three satellites of the HY-2 satellite constellation, after cross-matching with each other within a limited spatio-temporal range, the HY-2B satellite with sounding on the global ocean is selected to the calibration source, calibrating BT from the CMR of the HY-2C and HY-2D satellites to the BT dimension of the HY-2B satellite CMR. To check on-orbit calibration, a retrieval algorithm is built using atmospheric profile data from ECMWF and BT data, obtained from the CMR of the HY-2B satellite; this is used to calculate the atmospheric water vapor (AWV) and WPD from the HY-2 satellite constellation. After on-orbit calibration to the CMRs of the HY-2 satellite constellation, the deviation between the CMR products of different satellites is significantly reduced by over 20%, and the RMS of WPD for the same type of products from the Jason-3 satellite is less than 1 cm. It may be concluded that on-orbit calibration improves the accuracy of AWV and WPD by normalizing the BT dimension for CMRs of the HY-2 satellite constellation, so this calibration method is effective and credible for enhancing the quality of altimeter products in the HY-2 satellite constellation. Full article

Observations of precipitable water vapor (PWV) in the atmosphere play a crucial role in weather forecasting and global climate change research. Spaceborne Interferometric Synthetic Aperture Radar (InSAR), as a widely used modern geodetic technique, offers several advantages to the mapping of PWV, including all-weather capability, high accuracy, high resolution, and spatial continuity. In the process of PWV retrieval by using InSAR, accurately extracting the tropospheric wet delay phase and obtaining a high-precision tropospheric water vapor conversion factor are critical steps. Furthermore, the observations of InSAR are spatio-temporal differential results and the conversion of differential PWV (InSAR ΔPWV) into non-difference PWV (InSAR PWV) is a difficulty. In this study, the city of Jinan, Shandong Province, China is selected as the experimental area, and Sentinel-1A data in 2020 is used for mapping InSAR ΔPWV. The method of small baseline subset of interferometry (SBAS) is adopted in the data processing for improving the coherence of the interferograms. We extract the atmosphere phase delay from the interferograms by using SRTM-DEM and POD data. In order to evaluate the calculation of hydrostatic delay by using the ERA5 data, we compared it with the hydrostatic delay calculated by the Saastamoinen model. To obtain a more accurate water vapor conversion factor, the value of the weighted average temperature T_m was calculated by the path integral of the ERA5. In addition, GNSS PWV is used to calibrate InSAR PWV. This study demonstrates a robust consistency between InSAR PWV and GNSS PWV, with a correlation coefficient of 0.96 and a root-mean-square error (RMSE) of 1.62 mm. In conclusion, our method ensures the reliability of mapping PWV by using Sentinel-1A interferograms and GNSS observations. Full article

Coherent observations in GNSS reflectometry are prominent in regions with smooth reflecting surfaces and at grazing elevation angles. However, within these lower elevation ranges, GNSS signals traverse a more extensive atmospheric path, and increased ionospheric effects (e.g., delay biases) are expected. These biases can be mitigated by employing dual-frequency receivers or models tailored for single-frequency receivers. In preparation for the single-frequency GNSS-R ESA “PRETTY” mission, this study aims to characterize ionospheric effects under variable parameter conditions: elevation angles in the grazing range (5° to 30°), latitude-dependent regions (north, tropic, south) and diurnal changes (day and nighttime). The investigation employs simulations using orbit data from Spire Global Inc.’s Lemur-2 CubeSat constellation at the solar minimum (F10.7 index at 75) on March, 2021. Changes towards higher solar activity are accounted for with an additional scenario (F10.7 index at 180) on March, 2023. The electron density associated with each reflection event is determined using the Neustrelitz Electron Density Model (NEDM2020) and the NeQuick 2 model. The results from periods of low solar activity reveal fluctuations of up to approximately 300 TECUs in slant total electron content, 19 m in relative ionospheric delay for the GPS L1 frequency, 2 Hz in Doppler shifts, and variations in the peak electron density height ranging from 215 to 330 km. Sea surface height uncertainty associated with ionospheric model-based corrections in group delay altimetric inversion can reach a standard deviation at the meter level. Full article

During the catastrophic M7.8 earthquake in Turkey on 6 February 2023, anomalous effects were revealed in the ionosphere associated with various propagation mechanisms of seismogenic disturbance from the lithosphere up to the height of the ionosphere. Seventeen minutes after the main shock, a co-seismic disturbance was detected by a Doppler ionosonde on an inclined, 3010 km long, two-hop radio path “Kuwait—Institute of Ionosphere (Almaty)”. An appearance of acoustic waves at the height of 232 km in the ionosphere was fixed 568 s after arrival of the surface Rayleigh wave to the sub-ionospheric point, and such a delay agrees with the calculated propagation time of a vertically moving acoustic wave. The disturbance lasted 160 s, and its double amplitude was above 2 Hz, which noticeably exceeds the background fluctuation of Doppler frequency. The best coincidence between the waveforms of the Doppler signal and of the surface seismic wave was observed over the duration of the two leading periods, with correlation coefficients of 0.86 and 0.79, correspondingly. Pre-seismic effects in the ionosphere were revealed 8 days before the main shock both in the variations of the Doppler frequency and of the critical frequency $f0F2$. The probable origination mechanism of the pre-seismic ionospheric disturbances above the region of the earthquake preparation determined by the Dobrovolsky radius may be considered in accordance with the concept of lithospheric–atmospheric–ionospheric coupling. Full article

During the growth of biomass, there are two carbon storage paths for plant-derived fibers. One path is to assimilate carbon dioxide (CO₂) from the atmosphere through photosynthesis and temporarily store it in textile plants. Besides, the carbon can be captured and stored in soil. The carbon storage capacity of textile products made from plant-derived fibers such as cotton, flax, hemp, kenaf and bamboo fiber, etc., is a non-negligible part of greenhouse gas (GHG) accounting and reporting. However, there is a lack of systematic methods to evaluate carbon storage and the delayed emission effect of plant-derived fibers. In this study, the carbon storage and emission times of 100% hemp T-shirt, 100% hemp slipcover, and 100% hemp fiber handicraft were evaluated by using the soil organic carbon method, dry weight biomass method, and modeling method. The results revealed that the CO₂ storage of 1 kg hemp fiber is 1.833 kg. Meanwhile, the delayed emission effects of carbon temporarily stored in the 3 kinds of hemp fiber products are 3.83%, 19.68%, and 41.12% at different lifespans (i.e., 5, 25, or 50 years), in which case the landfill option for hemp fiber products may be preferable from carbon storage effect perspective. The results suggest that plant-derived fibers have a positive impact on climate change due to CO₂ storage, and that the carbon storage effect improves with the continued lifespan of the product. By quantifying carbon storage and the delayed emission effect of plant-derived fibers, it is beneficial to understand the potential for reducing carbon emissions, which in turn helps to promote and develop more environmentally friendly and low-carbon production processes and products. Full article

Troposcatter propagation delay is one of the most significant sources of errors in troposcatter time comparison. The existing methods of calculating troposcatter propagation delay face problems in terms of effectively reflecting the influence of the meteorological environment on troposcatter propagation delay, due to the insufficient spatial and temporal resolution of the meteorological data. This article proposes a high-precision calculation method for troposcatter propagation delay based on atmospheric reanalysis data. The troposcatter propagation path and the refractive index along the troposcatter data were obtained by combining 3D ray tracing with the European Centre for Medium-Range Weather Forecasts’ Reanalysis 5 (ERA5), with high spatial and temporal resolution. This found the hour-level time delay in troposcatter. The geometric delay, path delay, and total propagation delay of the troposcatter were calculated and analyzed via 12 scattering links in 6 typical geographical regions. It was found that the path delay was the main cause of the propagation delay of troposcatter, and that the proportion of geometric delay in the total propagation delay increased along with an increase in link distance. The propagation delay changed noticeably in different seasons. The path delay was higher in summer and lower in winter, the geometric delay was lower in summer and higher in winter, and the total propagation delay was mainly high in summer and low in winter. There were differences in delay date, seasonal fluctuation amplitude, and in hourly fluctuation standard deviation among different geographic areas. Full article

Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 10⁹ m³ from July 2002 to December 2022, with a growth rate of 5.3766 × 10⁸ m³/a. The growth rate from January 2005 to January 2015 is 4.4850 × 10⁸ m³/a, and from January 2015 to December 2022, the growth rate is 8.9206 × 10⁸ m³/a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years. Full article

Air relative humidity (RH) is an important control parameter in many industrial processes. The acoustic method is a novel technique to measure air humidity non-intrusively. Relevant research is limited. Existing methods use ultrasonic waves as a sound source and air humidity is measured by measuring the sound attenuation. In this paper, a novel air humidity measurement system using low-frequency sound waves as a sound source and two acoustic sensors is proposed. Air humidity is acquired by measuring sound speed in the air. Sound speed mainly depends on air temperature, humidity, atmospheric pressure, and air composition. The influence of air temperature, atmospheric pressure, and air constituent concentrations on the RH measurement is analyzed theoretically. A 0.1 s linear chirp signal in the frequency range of 200–500 Hz is selected as the sound source. Sound travel time is calculated by cross-correlating the sound signals received by the two acoustic sensors. To improve the accuracy of the sound speed measurement, sound speed under different RH points is obtained through reference RH experiments and substituted into the calibration equation. Then, equivalent sound path length and systematic delay are estimated using the least squares method. After obtaining these two parameter values, the sound speed measured by the system is closer to the theoretical value at the same RH point. In validation experiments using RH measured by a thermo-hygrometer as a comparison, the relative errors of the acoustically measured RH are within 9.9% in the RH range of 40.7–87.1%, and the standard deviation is within 4.8%. Full article

Simply speaking, automatic driving requires the calculation of a large amount of traffic data and, finally, the obtainment of the optimal driving route and speed. However, the key technical difficulty is the obtainment of data; thus, radar has become an indispensable hardware for automatic driving. Compared to the optical and infrared radar, millimeter-wave radar is not affected by the shape and color of the target, and it is not affected by the atmospheric turbulence, compared to ultrasonic, and so it has a stable detection performance and good environmental adaptability. It is little affected by changes in the weather, and the external environment, rain, snow, dust, and sunshine have no interference in it. The Doppler frequency shift is large, and the accuracy of the relative velocity measurement is improved. However, one challenge for vehicles in fast environments is millimeter-wave-based communication. Because of the short wavelength of the millimeter wave and the high path and penetration losses, the beamforming technology of a large-scale antenna array plays a key role in the construction and maintenance of millimeter-wave communication links. Millimeter waves have wide channel bandwidths, unique channel characteristics, and hardware limitations, and so there are many challenges in the direct use of beamforming technology in millimeter-wave communication. Traditional beam training cannot meet the requirements of low overhead and low delay. This paper, in order to obtain beam information, introduces the context-awareness module to the deep-learning net, which is derived from past observation data. This paper establishes a model that contains the receiver and the surrounding vehicles to perceive the environment. Then, a long short-term memory (LSTM) neural network is used to foresee the acquired power, which is quantized by several beam powers. According to the conclusion, the prediction accuracy is greatly increased, and the model could yield throughput with almost zero overhead and little performance loss. Full article