Search Results (10)

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

This study addresses the issue of atmospheric delay correction for the rational polynomial coefficient (RPC) model associated with spaceborne synthetic aperture radar (SAR) imagery under conditions lacking ephemeris data, proposing a novel approach to enhance the geometric positioning accuracy of RPC models. A satellite position inversion method based on the vector-autonomous intersection technique was developed, incorporating ionospheric delay and neutral atmospheric delay models to derive atmospheric delay errors. Additionally, an RPC model reconstruction approach, which integrates atmospheric correction, is proposed. Validation experiments using GF-3 satellite imagery demonstrated that the atmospheric delay values obtained by this method differed by only 0.0001 m from those derived using the traditional ephemeris-based approach, a negligible difference. The method also exhibited high robustness in long-strip imagery. The reconstructed RPC parameters improved image-space accuracy by 18–44% and object-space accuracy by 19–32%. The results indicate that this approach can fully replace traditional ephemeris-based methods for atmospheric delay extraction under ephemeris-free conditions, significantly enhancing the geometric positioning accuracy of SAR imagery RPC models, with substantial application value and development potential. Full article

The delay of GNSS signals in the neutral atmosphere allow the determination of atmospheric water vapor. The conversion factor of the delay in the water vapor uses the water vapor-weighted mean temperature, T_m, which is a crucial parameter to improve the quality of conversion. This study analyzed two different types of models: linear models such as Bevis, Mendes and Ortiz de Galisteo, and empirical models such as GPT2w, GPT3 and GWMT_D. The performance of the models was analyzed using the models as the source of T_m to obtain the precipitable water vapor (PWV), which was compared to a reference set of PWV obtained from a matched radiosonde site. The results show a better performance of the linear models, with the Bevis model achieving the best performance. Full article

The launch of a series of Starlink internet satellites on 3 February 2022 (S-36), and 7 July 2022 (S-49), coincided with the development of two-phase geomagnetic storms. The first launch S-36 took place in the middle of the moderate two-phase space weather storm, which induced significant technological consequences. After liftoff on 3 February at 18:13 UT, all Starlink satellites reached an initial altitude of 350 km in perigee and had to reach an altitude of ~550 km after the maneuver. However, 38 of 49 launched spacecrafts did not reach the planned altitude, left orbit due to increased drag and reentered the atmosphere on 8 February. A geomagnetic storm on 3–4 February 2022 has increased the density of the neutral atmosphere up to 50%, increasing drag of the satellites and dooming most of them. The second launch of S-49 at 13:11 UT on 7 July 2022 was successful at the peak of the two-phase geomagnetic storm. The global ionospheric maps of the total electron content (GIM-TEC) have been used to produce the ionospheric weather GIM-W index maps and Global Electron Content (GEC). We observed a GEC increment from 10 to 24% for the storm peak after the Starlink launch at both storms, accompanying the neutral density increase identified earlier. GIM-TEC maps are available with a lag (delay) of 1–2 days (real-time GIMs have a lag less than 15 min), so the GIMs forecast is required by the time of the launch. Comparisons of different GIMs forecast techniques are provided including the Center for Orbit Determination in Europe (CODE), Beijing (BADG and CASG) and IZMIRAN (JPRG) 1- and 2-day forecasts, and the Universitat Politecnica de Catalunya (UPC-ionSAT) forecast for 6, 12, 18, 24 and 48 h in advance. We present the results of the analysis of evolution of the ionospheric parameters during both events. The poor correspondence between observed and predicted GIM-TEC and GEC confirms an urgent need for the industry–science awareness of now-casting/forecasting/accessibility of GIM-TECs during the space weather events. Full article

To reach sustainable aviation, one approach is to use electro-fuels (e-fuels) within the gas turbine engines. E-fuels are CO₂-neutral synthetic fuels which are produced employing electrical energy generated from renewable resources, where the carbon is taken out of the atmosphere or from biomass. Our approach is, to find e-fuels, which can be utilized in the lean premixed prevaporized (LPP) combustion, where most of the non-CO₂ emissions are prevented. One of the suitable e-fuel classes is alcohols with a low number of carbons. In this work, the autoignition properties of propanol isomers and butanol isomers as e-fuels were investigated in a high-pressure shock tube (HPST) at temperatures from 1200 to 1500 K, the pressure of 10 bar, and lean fuel-air conditions. Additional investigations on the low-temperature oxidation and flame speed of C3 and C4 alcohols from the literature were employed to develop a comprehensive mechanism for the prediction of ignition delay time (IDT) and laminar burning velocity (LBV) of the above-mentioned fuels. A numerical model based on newly developed chemical kinetics was applied to further study the IDT and LBV of fuels in comparison to the Jet-A surrogate at the engine-related conditions along with the emissions prediction of the model at lean fuel-air conditions. Full article

Currently, global warming has been a serious issue, which is closely related to anthropogenic emission of Greenhouse Gas (GHG) in the atmosphere, particularly Carbon Dioxide (CO₂). To help achieve carbon neutrality by decreasing CO₂ emissions, Oxy-Fuel Combustion (OFC) technology is becoming a hot topic in recent years. However, few findings have been reported about the implementation of OFC in dual-injection Spark Ignition (SI) engines. This work numerically explores the effects of Water Injection (WI) strategies on OFC characteristics in a practical dual-injection engine, including GDI (only using GDI), P₅₀-G₅₀ (50% PFI and 50% GDI) and PFI (only using PFI). The findings will help build a conceptual and theoretical foundation for the implementation of OFC technology in dual-injection SI engines, as well as exploring a solution to increase engine efficiency. The results show that compared to Conventional Air Combustion (CAC), there is a significant increase in BSFC under OFC. Ignition delay (${\theta }_F$) is significantly prolonged, and the spark timing is obviously advanced. Combustion duration (${\theta }_C$) of PFI is a bit shorter than that of GDI and P₅₀-G₅₀. There is a small benefit to BSFC under a low water-fuel mass ratio ($R_{wf}$). However, with the further increase of $R_{wf}$ from 0.2 to 0.9, there is an increment of 4.29%, 3.6% and 3.77% in BSFC for GDI, P₅₀-G₅₀ and PFI, respectively. As WI timing ($t_{WI}$) postpones to around −30 °CA under the conditions of $R_{wf}$ ≥ 0.8, BSFC has a sharp decrease of more than 6 g/kWh, and this decline is more evident under GDI injection strategy. The variation of maximum cylinder pressure ($P_{max}$) and combustion phasing is less affected by WI temperature ($T_{WI}$) compared to the effects of $R_{wf}$ or $t_{WI}$. BSFC just has a small decline with the increase of $T_{WI}$ from 298 K to 368 K regardless of the injection strategy. Consequently, appropriate WI strategies are beneficial to OFC combustion in a dual-injection SI engine, but the benefit in fuel economy is limited. Full article

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model. Full article

The delays of radio signals transmitted by global navigation satellite system (GNSS) satellites and induced by neutral atmosphere, which are usually represented by zenith tropospheric delay (ZTD), are required as critical information both for GNSS positioning and navigation and GNSS meteorology. Establishing a stable and reliable ZTD model is one of the interests in GNSS research. In this study, we proposed a regional ZTD model that makes full use of the ZTD calculated from regional GNSS data and the corresponding ZTD estimated by global pressure and temperature 3 (GPT3) model, adopting the artificial neutral network (ANN) to construct the correlation between ZTD derived from GPT3 and GNSS observations. The experiments in Hong Kong using Satellite Positioning Reference Station Network (SatRet) were conducted and three statistical values, i.e., bias, root mean square error (RMSE), and compound relative error (CRE) were adopted for our comparisons. Numerical results showed that the proposed model outperformed the parameter ZTD model (Saastamoinen model) and the empirical ZTD model (GPT3 model), with an approximately 56%/52% and 52%/37% RMSE improvement in the internal and external accuracy verification, respectively. Moreover, the proposed method effectively improved the systematic deviation of GPT3 model and achieved better ZTD estimation in both rainy and rainless conditions. Full article

Precise point positioning (PPP) is one of the well-known applications of Global Navigation Satellite System (GNSS) and provides precise positioning solutions using accurate satellite orbit and clock products. The tropospheric delay due to the neutral atmosphere for microwave signals is one of the main sources of measurement error in PPP. As one component of this delay, the hydrostatic delay is usually compensated by using an empirical correction model. However, how to eliminate the effects of the wet delay during a weather event is a challenge because current troposphere models are not capable of considering the complex atmosphere around the receiver during situations such as typhoons, storms, heavy rainfall, et cetera. Thus, how positioning results can be improved if the residual wet delays are taken into account needs to be investigated . In this contribution, a real-time procedure of recursive detection, identification and adaptation (DIA) is applied to detect the model errors which have the same effects on both phase and code observables; e.g., the model error caused by the tropospheric delay. Once the model errors are identified, additional parameters are added to the functional model to account for the measurement residuals. This approach is evaluated with Global Positioning System (GPS) data during two rainfall events in Darwin, Australia, proving the usefulness of compensated residual slant wet delay for positioning results. Comparisons with the standard approach show that the precision of the up component is improved significantly during the periods of the weather events; for the two case studies, $72.46\%$ and $64.41\%$ improvements of root mean squared error (RMS) resulted, and the precision of the horizontal component obtained by the proposed approach is also improved more than $30\%$ compared to the standard approach. The results also show that the identified model errors are concentrated at the beginning of both heavy rainfall processes when the front causes significant spatial and temporal gradients of the integrated water vapor above the receiver. Full article

The neutral atmospheric delay has a great impact on synthetic aperture radar (SAR) absolute ranging and on differential interferometry. In this paper, we demonstrate its effective mitigation by means of the direction integration method using two products from the European Centre for Medium-Range Weather Forecast: ERA-Interim and operational data. Firstly, we shortly review the modeling of the neutral atmospheric delay for the direct integration method, focusing on the different refractivity models and constant coefficients available. Secondly, a thorough validation of the method is performed using two approaches. In the first approach, numerical weather prediction (NWP) derived zenith path delay (ZPD) is validated against ZPD from permanent GNSS (global navigation satellite system) stations on a global scale, demonstrating a mean accuracy of 14.5 mm for ERA-Interim. Local analysis shows a 1 mm improvement using operational data. In the second approach, NWP derived slant path delay (SPD) is validated against SAR SPD measured on corner reflectors in more than 300 TerraSAR-X High Resolution SpotLight acquisitions, demonstrating an accuracy in the centimeter range for both ERA-Interim and operational data. Finally, the application of this accurate delay estimate for the mitigation of the impact of the neutral atmosphere on SAR absolute ranging and on differential interferometry, both for individual interferograms and multi-temporal processing, is demonstrated. Full article