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Publication date: Available online 12 September 2024

Source: Advances in Space Research

Author(s): Yiling Ye, Lilong Liu, Fade Chen, Liangke Huang

Publication date: Available online 11 September 2024

Source: Advances in Space Research

Author(s): Sandeep Nagar, Ehsan Farahbakhsh, Joseph Awange, Rohitash Chandra

Publication date: Available online 11 September 2024

Source: Advances in Space Research

Author(s): Alexander Burton, Liam Robinson, Carolin Frueh

Publication date: Available online 7 September 2024

Source: Advances in Space Research

Author(s): N.Z. Mohd Afandi, R. Umar, N.H. Sabri, S. Safei, C. Monstein, C.C. Lau, S.N.A. Syed Zafar

Publication date: Available online 7 September 2024

Source: Advances in Space Research

Author(s): T.V. Sruthi, G. Manju, K.S. Vishnupriya

Stanford researchers have developed an artificial intelligence-based tool—dubbed SandAI—that can reveal the history of quartz sand grains going back hundreds of millions of years. With SandAI, researchers can tell with high accuracy if wind, rivers, waves, or glacial movements shaped and deposited motes of sand.

The atmospheric lidar ATLID, the last of four instruments on board the EarthCARE satellite launched in May, has now been successfully put into operation. The joint mission of the European Space Agency (ESA) and the Japanese Space Agency (JAXA) is designed to measure clouds, aerosols and radiation more accurately than ever before.

The core-mantle boundary (CMB) is a crucial interface within the Earth, marking the boundary between the outer core and the lower mantle. For the past two decades, seismological studies have identified anomalous low-velocity zones above the CMB, such as the large low-shear-velocity provinces (LLSVPs) beneath Africa and the Pacific. Smaller ultralow-velocity zones (ULVZs) have been detected in these regions, characterized by significantly lower seismic wave speeds and higher densities compared to the surrounding mantle.

A pioneering method for soil moisture retrieval using satellite navigation systems has been introduced, significantly boosting the accuracy and efficiency of global data collection. The research, published in the journal Satellite Navigation, tackles the challenges posed by geographical disparities in soil moisture assessment, providing a critical advancement for monitoring a key parameter in climate, agriculture, and environmental applications.

You're a hotshot working to contain a wildfire. The conflagration jumps the fire line, forcing your crew to flee using pre-determined escape routes. At the start of the day, the crew boss estimated how long it should take to get to the safety zone. With the flames at your back, you check your watch and hope they were right.

Products that we depend on and use every day arrive by way of massive container ships to ports around the world. But the maritime shipping industry is also responsible for polluting the air and oceans with sulfur dioxide, which can negatively affect human health, cause ocean acidification, and oxidize to form sulfate aerosols.

Dr. Souran Chatterjee, from the University of Plymouth, has made a major contribution to a second United Nations report exploring the best ways of harnessing climate change and Sustainable Development Goals (SDG) synergies.

A new study allays fears that rerouting flights to avoid forming climate-warming contrails could result in inadvertently making climate warming worse.

Author(s): G. J. Li and S. Davidovits

Ablator materials used for inertial confinement fusion, such as high-density carbon (HDC) and beryllium, have grain structure which may lead to small-scale density nonuniformity and the generation of perturbations when the materials are shocked and compressed. Here, we use a combination of a linear …

[Phys. Rev. E 110, 035206] Published Mon Sep 16, 2024

Abstract

Satellite remote sensing serves as a crucial means to acquire cloud physical parameters. However, existing official cloud products from the advanced geostationary radiation imager (AGRI) onboard the Fengyun-4A geostationary satellite lack spatiotemporal continuity and important micro-physical properties. In this study, an image-based transfer learning ResUnet (TL-ResUnet) model was applied to realize all-day and high-precision retrieval of cloud physical parameters from AGRI thermal infrared measurements. Combining the observation advantages of geostationary and polar-orbiting satellites, the TL-ResUnet model was pre-trained with official cloud products from advanced Himawari imager (AHI) and transfer-trained with official cloud products from moderate resolution imaging spectroradiometer (MODIS), respectively. For comparison, a pixel-based transfer learning random forest (TL-RF) model was trained using the equally distributed data sets. Taking MODIS official products as the benchmarks, the TL-ResUnet model achieved an overall accuracy of 79.82% for identifying cloud phase and root mean squared errors of 1.99 km, 7.11 μm, and 12.87 for estimating cloud top height, cloud effective radius, and cloud optical thickness, outperforming the precision of AGRI and AHI official products. Compared to the TL-RF model, the TL-ResUnet model utilized the spatial information of clouds to significantly improve the retrieval performance and achieve more than a 6-fold increase in speed for single full-disk retrieval. Moreover, AGRI TL-ResUnet products with spatiotemporal continuity and high precision were used to accurately describe the spatial distribution characteristics of cloud fractions and cloud properties over the Tibetan Plateau, and provide the diurnal variation of cloud cover and cloud properties across different seasons for the first time.

Abstract

Substorms are a rapid release of energy that is redistributed throughout the magnetosphere-ionosphere system, resulting in many observable signals, such as enhancements in the aurora, energetic particle injections, and ground magnetic field perturbations. Numerous substorm identification techniques and onset lists based on each of these signals have been provided in the literature, but often with no cross-calibration. Since the signals produced are not necessarily unique to substorms and may not be sufficiently similar to be identified for each and every substorm, individual event lists may miss or misidentify substorms, hindering our understanding and the development and validation of substorm models. To gauge the scale of this problem, we use metrics derived from contingency tables to quantify the association between lists of substorms derived from SuperMAG SML/SMU indices, midlatitude magnetometer data, particle injections, and auroral enhancements. Overall, although some degree of pairwise association is found between the lists, even lists generated by applying conceptually similar gradient-based identification to ground magnetometer data achieve an association with less than 50% event coincidence. We discuss possible explanations of the levels of association seen from our results, as well as their implications for substorm analyses.

Abstract

Accurate determination of the ionospheric parameters is one of the important objectives of the Ionospheric Connection Explorer (ICON) mission. Recent analyses of the current ICON Level 2.5 (L2.5) data product have shown that the ionospheric parameters (e.g., the peak electron density, \(n_{\mathrm{m}}F_{2}\) , and the peak height, \(h_{\mathrm{m}}F_{2}\) ) that are retrieved from the nighttime OI 135.6 nm emission observed by ICON’s Far Ultraviolet (FUV) imager exhibit a systematic bias when compared to external radio measurements. In this study, we demonstrate that the bias was introduced by Tikhonov regularization that was used for the FUV Level 1 data inversion to generate the L2.5 data product. To address the bias, we develop a Bayesian framework for accurate determination of the nighttime ionospheric parameters through the Maximum A Posteriori (MAP) estimation. We show through analysis of synthetic observations that the key to an accurate MAP estimation is to construct a series of prior distributions associated with different \(h_{\mathrm{m}}F_{2}\) using climatological empirical models. Implementation of the MAP estimation with this series of prior distributions to the ICON FUV observations and comparison of the ionospheric retrievals with external radio measurements verify that the Bayesian method can reduce the systematic bias to a negligible level of ∼1% in the retrieved \(n_{\mathrm{m}}F_{2}\) and ∼1 km in the retrieved \(h_{\mathrm{m}}F_{2}\) . Our study provides a novel method for FUV remote sensing data analysis and an improved data set for ionospheric research.

Abstract

With the rapid development of multi-GNSS and multi-frequency observations, the code observable‑specific bias (OSB) emerges due to its flexibility and convenience compared to the traditional differential code bias (DCB). To estimate the GNSS multi-frequency code OSBs, the geometry-free model is commonly used, where ionospheric error handling is a key issue. For ionospheric handling, previous studies mostly focus on the ionospheric-float approach based on ionospheric modeling and the ionospheric-fixed approach using an external empirical model. We proposed an improved estimation approach aided by geometry-free and ionospheric-free (GFIF) observations to estimate multi-frequency code OSBs. The proposed approach expands the DCB to multiple code bias (MCB) using the GFIF model, eliminating the first-order ionospheric error. Then, the satellite-plus-receiver MCB observations with high precision and reliability are extracted. Finally, a full-rank estimable functional model after the identification and elimination of rank deficiencies is proposed. The effectiveness of the proposed approach is verified, where the multi-frequency code OSBs of GPS, BDS-3, and Galileo are estimated. Taking the code OSB products provided by the Chinese Academy of Sciences (CAS) as the references, the estimated code OSBs exhibit good consistency, showing an average RMS of 0.115, 0.130, and 0.161 ns for GPS, BDS-3, and Galileo, respectively. The average STD of all code OSBs for GPS, BDS-3, and Galileo is 0.187, 0.296, and 0.331 ns, respectively, which is smaller than that of CAS products for GPS and Galileo. In conclusion, the proposed approach can estimate the reliable GNSS multi-frequency code OSBs with high consistency and stability.

Abstract

Several of the icy moons in the Jupiter and Saturn systems appear to possess internal liquid water oceans. Our knowledge of the Uranian moons is more limited but a future tour of the system has the potential to detect subsurface oceans. Planning for this requires an understanding of how the moons' internal structures—with and without oceans—relate to observable quantities. Here, we show that the amplitude of forced physical librations could be diagnostic of the presence or absence of subsurface oceans within the Uranian moons. In the presence of a decoupling global ocean, ice shell libration amplitudes at Miranda, Ariel, and Umbriel will exceed 100 m if the shells are <30 ${< } 30$ km $\mathrm{k}\mathrm{m}$ thick. The presence of oceans could also imply significant tidal heating within the last few hundred million years. Combining librations with the quadrupole gravity field could provide comprehensive constraints on the internal structures and histories of the Uranian moons.