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In this study, we investigate the representation of the global mean energy balance components in 10 atmospheric reanalyses, and compare their magnitudes with recent reference estimates as well as the ones simulated by the latest generation of climate models from the 6th phase of the coupled model intercomparison project (CMIP6). Despite the assimilation of comprehensive observational data in reanalyses, the spread amongst the magnitudes of their global energy balance components generally remains substantial, up to more than 20 Wm−2 in some quantities, and their consistency is typically not higher than amongst the much less observationally constrained CMIP6 models. Relative spreads are particularly large in the reanalysis global mean latent heat fluxes (exceeding 20%) and associated intensity of the global water cycle, as well as in the energy imbalances at the top-of-atmosphere and surface. A comparison of reanalysis runs in full assimilation mode with corresponding runs constrained only by sea surface temperatures reveals marginal differences in their global mean energy balance components. This indicates that discrepancies in the global energy balance components caused by the different model formulations amongst the reanalyses are hardly alleviated by the imposed observational constraints from the assimilation process. Similar to climate models, reanalyses overestimate the global mean surface downward shortwave radiation and underestimate the surface downward longwave radiation by 3–7 Wm−2. While reanalyses are of tremendous value as references for many atmospheric parameters, they currently may not be suited to serve as references for the magnitudes of the global mean energy balance components.

Earth’s energy imbalance (EEI) is a fundamental metric of global Earth system change, quantifying the cumulative impact of natural and anthropogenic radiative forcings and feedback. To date, the most precise measurements of EEI change are obtained through radiometric observations at the top of the atmosphere (TOA), while the quantification of EEI absolute magnitude is facilitated through heat inventory analysis, where ~ 90% of heat uptake manifests as an increase in ocean heat content (OHC). Various international groups provide OHC datasets derived from in situ and satellite observations, as well as from reanalyses ingesting many available observations. The WCRP formed the GEWEX-EEI Assessment Working Group to better understand discrepancies, uncertainties and reconcile current knowledge of EEI magnitude, variability and trends. Here, 21 OHC datasets and ocean heat uptake (OHU) rates are intercompared, providing OHU estimates ranging between 0.40 ± 0.12 and 0.96 ± 0.08 W m−2 (2005–2019), a spread that is slightly reduced when unequal ocean sampling is accounted for, and that is largely attributable to differing source data, mapping methods and quality control procedures. The rate of increase in OHU varies substantially between − 0.03 ± 0.13 (reanalysis product) and 1.1 ± 0.6 W m−2 dec−1 (satellite product). Products that either more regularly observe (satellites) or fill in situ data-sparse regions based on additional physical knowledge (some reanalysis and hybrid products) tend to track radiometric EEI variability better than purely in situ-based OHC products. This paper also examines zonal trends in TOA radiative fluxes and the impact of data gaps on trend estimates. The GEWEX-EEI community aims to refine their assessment studies, to forge a path toward best practices, e.g., in uncertainty quantification, and to formulate recommendations for future activities.

Carrier phase integer ambiguities must be determined for BDS-3/inertial navigation system (INS) tightly coupled (TC) integration to achieve centimetre-level positioning accuracy. However, cycle slip breaks the consistency of the integer ambiguities. Conventional multi-frequency cycle slip methods use the pseudorange; thus, requiring improvement when applied to kinematic situations. Furthermore, a concise and nonprior information-dependent model is crucial for real-time processing. In this study, an inertial-aided BDS-3 cycle slip detection and repair (I-CDR) method was developed. First, a BDS-3/INS TC model with I-CDR was created. The ionospheric delays were modelled as part of the TC states; therefore, they could be estimated and eliminated. Investigations were conducted on the effects of carrier phase noise, residual ionosphere delay, and INS-predicted position error on combined cycle slip detection (CCD) accuracy. The optimal CCDs under various frequency available configurations were determined. The effectiveness of I-CDR was demonstrated using land vehicle test data. The false alarm ratio was less than 1.0%, and the missed detection ratio was almost zero even in situations with challenging abundant 1-cycle slips in random epochs. Furthermore, the right determination ratio reached 100%. In addition, BDS-3 signal loss-recovery cases were simulated, and all cycle slips for all satellites could be repaired within 40s. I-CDR exhibits outstanding cycle slip detection and repair performance for dense 1-cycle slip and signal loss-recovery cases, demonstrating its suitability for BDS-3/INS TC integration.

This paper focuses on the retrieval of refractivity fields from GNSS measurements by means of least-squares collocation. Collocation adjustment estimates parameters that relate delays and refractivity without relying on a grid. It contains functional and stochastic models that define the characteristics of the retrieved refractivity fields. This work aims at emphasizing the capabilities and limitations of the collocation method in modeling refractivity and to present it as a valuable alternative to GNSS tomography. Initially, we analyze the stochastic models in collocation and compare the theoretical errors of collocation with those of tomography. We emphasize the low variability of collocation formal variances/covariances compared to tomography and its lower dependence on a-priori fields. Then, based on real and simulated data, we investigate the importance of station resolution and station heights for collocation. Increasing the network resolution, for example, from 10 to 2 km, results in improved a-posteriori statistics, including a 10% reduction in the error statistic for the retrieved refractivity up to 6 km. In addition, using additional stations at higher altitudes has an impact on the retrieved refractivity fields of about 1 ppm in terms of standard deviation up to 6 km, and a bias reduction of more than 3 ppm up to 3 km. Furthermore, we compare refractivity fields retrieved through tomography and collocation, where data of the COSMO weather model are utilized in a closed-loop validation mode to simulate tropospheric delays and validate the retrieved profiles. While tomography estimates are less biased, collocation captures relative changes in refractivity more effectively among the voxels within one height level. Finally, we apply tomography and collocation to test their capabilities to detect an approaching weather front. Both methods can sense the weather front, but their atmospheric structures appear more similar when the GNSS network has a well-distributed height coverage.

Deformation phenomena on Earth are inherently three dimensional. With SAR interferometry (InSAR), in many practical situations the maximum number of observations is two (ascending and descending), resulting in an infinite number of possible displacement estimates. Here we propose a practical solution to this underdeterminancy problem in the form of the strapdown approach. With the strapdown approach, it is possible to obtain “3D-global/2D-local” solutions, by using minimal and largely undisputed contextual information, on the expected driving mechanisms and/or spatial geometry. It is a generic method that defines a local reference system with transversal, longitudinal, and normal (TLN) axes, with displacement occurring in the transversal-normal plane only. Since the orientation of the local frame is based on the physics of the problem at hand, the strapdown approach gives physically more relevant estimates compared to conventional approaches. Moreover, using an a-priori uncertainty approximation on the orientation of the local frame it is possible to assess the precision of the final estimates. As a result, appropriate cartographic visualization using a vector map with confidence ellipses enables an improved interpretation of the results.

Accurate information on satellite antenna phase center offsets (PCOs) and phase variations (PVs) is indispensable for high-precision geodetic applications. In the absence of consistent pre-flight calibrations, satellite antenna PCOs and PVs of global navigation satellite systems are commonly estimated based on observations from a global network, constraining the scale to a given reference frame. As part of this estimation, flatness and zero-mean conditions need to be applied to unambiguously separate PCOs, PVs, and constant phase ambiguities. Within this study, we analytically investigate the impact of different boresight-angle-dependent weighting functions for PV minimization, and we compare antenna models generated with different observation-based weighting schemes with those based on uniform weighting. For the case of the GPS IIR/-M and III satellites, systematic differences of 10 mm in the PVs and 65 cm in the corresponding PCOs are identified. In addition, new antenna models for the different blocks of BeiDou-3 satellites in medium Earth orbit are derived using different processing schemes. As a drawback of traditional approaches estimating PCOs and PVs consecutively in distinct steps, it is shown that different, albeit self-consistent, PCO/PV pairs may result depending on whether PCOs or PVs are estimated first. This apparent discrepancy can be attributed to potentially inconsistent weighting functions in the individual processing steps. Use of a single-step process is therefore proposed, in which a dedicated constraint for PCO-PV separation is applied in the solution of the normal equations. Finally, the impact of neglecting phase patterns in precise point positioning applications is investigated. In addition to an overall increase of the position scatter, the occurrence of systematic height biases is illustrated. While observation-based weighting in the pattern estimation can help to avoid such biases, the possible benefit depends critically on the specific elevation-dependent weighting applied in the user’s positioning model. As such, the practical advantage of such antenna models would remain limited, and uniform weighting is recommended as a lean and transparent approach for the pattern estimation of satellite antenna models from observations.

Author(s): Raffaello Foldes, Silvio Sergio Cerri, Raffaele Marino, and Enrico Camporeale

The properties of energy transfer in the kinetic range of plasma turbulence have fundamental implications on the turbulent heating of space and astrophysical plasmas. It was suggested that magnetic reconnection may be responsible for driving the subion scale cascade, and that this process would be c…

[Phys. Rev. E 110, 055207] Published Tue Nov 26, 2024

An attractive feature of PPP-RTK is the possibility of reducing the amount of data that needs to be transferred to users. By leveraging the state-space Representation (SSR) of the corrections, the correction provider (i.e., a GNSS network) can consider distinct transfer rates for each of the individual corrections according to their temporal characteristics. Reducing the transfer rates comes at the cost of delivering time-delayed corrections, urging the user to time predict the corrections to bridge the gap between the corrections’ generation time and the positioning time. Consequently, the user Kalman filter needs to be equipped with a strategy to account for the errors caused by such predictions, minimizing the precision loss of the user parameter solutions. In this contribution, we apply a processing strategy for both the network and user filters to handle the latency of corrections. This enables the network to update corrections over longer time-intervals. To have the strategy applicable to regional networks, an ionosphere-weighted model is adopted for the corresponding observations, delivering minimum-variance spatially predicted ionospheric corrections to users. It is shown that certain components of the network filter’s dynamic model are duplicated and should be excluded from processing. To illustrate the performance of the strategy at work, three globally distributed regional networks are employed, and maximum correction latencies to meet different positioning criteria are evaluated. In terms of both the positioning precision and time-to-first-fix (TTFF), the strategy is numerically shown to outperform the user processing case in which the uncertainty of corrections is discarded.

Author(s): Gabe Schumm, Sibin Yang, and Anders W. Sandvik

Stochastic analytic continuation (SAC) of quantum Monte Carlo (QMC) imaginary-time correlation function data is a valuable tool in connecting many-body models to experimentally measurable dynamic response functions. Recent developments of the SAC method have allowed for spectral functions with sharp…

[Phys. Rev. E 110, 055307] Published Mon Nov 25, 2024

Alongside magnetic reconnection, turbulence is another fundamental nonlinear plasma phenomenon that plays a key role in energy transport and conversion in space and astrophysical plasmas. From a numerical, theoretical, and observational point of view there is a long history of exploring the interplay between these two phenomena in space plasma environments; however, recent high-resolution, multi-spacecraft observations have ushered in a new era of understanding this complex topic. The interplay between reconnection and turbulence is both complex and multifaceted, and can be viewed through a number of different interrelated lenses - including turbulence acting to generate current sheets that undergo magnetic reconnection (turbulence-driven reconnection), magnetic reconnection driving turbulent dynamics in an environment (reconnection-driven turbulence) or acting as an intermediate step in the excitation of turbulence, and the random diffusive/dispersive nature of the magnetic field lines embedded in turbulent fluctuations enabling so-called stochastic reconnection. In this paper, we review the current state of knowledge on these different facets of the interplay between turbulence and reconnection in the context of collisionless plasmas, such as those found in many near-Earth astrophysical environments, from a theoretical, numerical, and observational perspective. Particular focus is given to several key regions in Earth’s magnetosphere – namely, Earth’s magnetosheath, magnetotail, and Kelvin-Helmholtz vortices on the magnetopause flanks – where NASA’s Magnetospheric Multiscale mission has been providing new insights into the topic.

We propose a benchmark for comparing gap-filling techniques used on global time-variable gravity field time-series. The Gravity Recovery and Climate Experiment (GRACE) and the GRACE Follow-On missions provide products to study the Earth’s time-variable gravity field. However, the presence of missing months in the measurements poses challenges for understanding specific Earth processes through the gravity field. We reproduce, adapt, and compare satellite-monitoring and interpolation techniques for filling these missing months in GRACE and GRACE Follow-On products on a global scale. Satellite-monitoring techniques utilize solutions from Swarm and satellite laser ranging, while interpolation techniques rely on GRACE and/or Swarm solutions. We assess a wide range of interpolation techniques, including least-squares fitting, principal component analysis, singular spectrum analysis, multichannel singular spectrum analysis, auto-regressive models, and the incorporation of prior data in these techniques. To inter-compare these techniques, we employ a remove-and-restore approach, removing existing GRACE products and predicting missing months using interpolation techniques. We provide detailed comparisons of the techniques and discuss their strengths and limitations. The auto-regressive interpolation technique delivers the best score according to our evaluation metric. The interpolation based on a least-squares fitting of constant, trend, annual, and semi-annual cycles offers a simple and effective prediction with a good score. Through this assessment, we establish a starting benchmark for gap-filling techniques in Earth’s time-variable gravity field analysis.

Author(s): Nicholas Babusis, Adam Patel, Rokas Jutas, Zahra Manzoor, Mikhail N. Shneider, Audrius Pugzlys, Andrius Baltuska, and Alexey Shashurin

The authors propose a new microwave scattering probe technique that allows spatially resolved measurements of electron number density to be made in midinfrared filaments.

[Phys. Rev. E 110, 055206] Published Thu Nov 21, 2024

Inverting fault geometry and slip distribution simultaneously with geodetic observations based on Bayesian theory is becoming increasingly prevalent. A widely used approach, proposed by (Fukuda and Johnson, Geophys J Int 181:1441–1458, 2010) (F-J method), employs the least-squares method to solve the linear parameters of slip distribution after sampling the nonlinear parameters, including fault geometry, data weights and smoothing factor. Here, we present a modified version of the F-J method (MF-J method), which treats data weights and the smoothing factor as hyperparameters not directly linked to surface deformation. Additionally, we introduce the variance component estimation (VCE) method to resolve these hyperparameters. To validate the effectiveness of the MF-J method, we conducted inversion tests using both synthetic data and a real earthquake case. In our comparison of the MF-J and F-J methods using synthetic experiments, we found that the F-J method's inversion results for fault geometry were highly sensitive to the initial values and step sizes of hyperparameters, whereas the MF-J method exhibited greater robustness and stability. The MF-J method also exhibited a higher and more stable acceptance rate, enabling convergence to simulated values and ensuring greater accuracy of the parameter estimation. Furthermore, treating the fault length and width as unknown parameters and solving them simultaneously with other fault geometry parameters and hyperparameters using the MF-J method successfully resolved the issue of non-uniqueness in fault location solutions caused by the excessively large no-slip areas. In the 2017 Mw 7.3 Sarpol-e Zahab earthquake case study, the MF-J method produced a fault slip distribution with low uncertainty that accurately fit surface observation data, aligning with results from other research institutions. This validated the method's applicability and robustness in real-world scenarios. Additionally, we inferred that the second slip asperity was caused by early afterslip.

Seismic and electromagnetic (EM) imaging are essential tools for characterizing velocity and conductivity. However, the separate inversion of seismic and EM data is challenging due to the noisy measurements, inadequate data collection, and reliance on prior information, consequently resulting in uncertainty and ambiguity of the solutions. Moreover, the two methods are different in sensitivity and spatial resolution, making it difficult to discover consistencies in the inverted models. Joint inversion of seismic and EM data takes advantage of both methods and significantly improves the imaging capability of subsurface structures. In this paper, we review various coupling strategies for the joint inversion of seismic and EM data and highlight the application advances from 1-D to 3-D inversion. Specifically, we investigate the integration of machine learning techniques to tackle ill-posed inverse problems and showcase their effectiveness in coupling. Following this, we construct a deep-learning-based joint inversion workflow and provide a synthetic test to demonstrate its superiority by applying an attention mechanism, which enhances the model’s capability to focus on specific features within the data. This study proves the potential of integrating artificial intelligence into joint inversion and understanding the deep Earth interior by incorporating multiple geophysical data.

Author(s): Mikhail Gennadievich Belov and Vladislav Gennadievich Malyshkin

The problem of an optimal mapping between Hilbert spaces IN of |ψ〉 and OUT of |ϕ〉 based on a set of wavefunction measurements (within a phase) ψl→ϕl, l=1,⋯,M, is formulated as an optimization problem maximizing the total fidelity ∑l=1Mω(l)|〈ϕl|U|ψl〉|2 subject to probability preservation constraints …

[Phys. Rev. E 110, 055306] Published Tue Nov 19, 2024

NASA’s Europa Clipper mission is designed to provide a diversity of measurements to further our understanding of the potential habitability of this intriguing ocean world. The Europa mission’s Ultraviolet Spectrograph (Europa-UVS), built at the Southwest Research Institute (SwRI), is primarily a “plume finder” and tenuous atmosphere investigation. The science objectives of Europa-UVS are to: 1) Search for and characterize any current activity, notably plumes; and 2) Characterize the composition and sources of volatiles to identify the signatures of non-ice materials, including organic compounds, in the atmosphere and local space environment. Europa-UVS observes photons in the 55–206 nm wavelength range at moderate spectral and spatial resolution along a 7.5° slit composed of 7.3°×0.1° and 0.2°×0.2° contiguous sections. A variety of observational techniques including nadir pushbroom imaging, disk scans, stellar and solar occultations, Jupiter transit observations, and neutral cloud/plasma torus stares are employed to perform a comprehensive study of Europa’s atmosphere, plumes, surface, and local space environment. This paper describes the Europa-UVS investigation’s science plans, instrument details, concept of operations, and data formats in the context of the Europa Clipper mission’s primary habitability assessment goals.

Author(s): Brett D. Keenan

Collisionless shocks are ubiquitous in space and astrophysical plasmas, and they are essential dynamical features of these systems. Lacking Coulomb collisions, these shocks are mediated by the anomalous dissipation provided by nonlinear plasma instabilities. By numerically resolving the structure of…

[Phys. Rev. E 110, 055204] Published Mon Nov 18, 2024

Author(s): Pontus Svensson, Yusuf Aziz, Tobias Dornheim, Sam Azadi, Patrick Hollebon, Amy Skelt, Sam M. Vinko, and Gianluca Gregori

We present two methods for computing the dynamic structure factor for warm dense hydrogen without invoking either the Born-Oppenheimer approximation or the Chihara decomposition, by employing a wave-packet description that resolves the electron dynamics during ion evolution. First, a semiclassical m…

[Phys. Rev. E 110, 055205] Published Mon Nov 18, 2024

This review article provides a synthesis and perspective on how weather and climate extreme events can play a role in influencing tipping elements and triggering tipping points in the Earth System. An example of a potential critical global tipping point, induced by climate extremes in an increasingly warmer climate, is Amazon rainforest dieback that could be driven by regional increases in droughts and exacerbated by fires, in addition to deforestation. A tipping element associated with the boreal forest might also be vulnerable to heat, drought and fire. An oceanic example is the potential collapse of the Atlantic meridional overturning circulation due to extreme variability in freshwater inputs, while marine heatwaves and high acidity extremes can lead to coral reef collapse. Extreme heat events may furthermore play an important role in ice sheet, glacier and permafrost stability. Regional severe extreme events could also lead to tipping in ecosystems, as well as in human systems, in response to climate drivers. However, substantial scientific uncertainty remains on mechanistic links between extreme events and tipping points. Earth observations are of high relevance to evaluate and constrain those links between extreme events and tipping elements, by determining conditions leading to delayed recovery with a potential for tipping in the atmosphere, on land, in vegetation, and in the ocean. In the subsurface ocean, there is a lack of consistent, synoptic and high frequency observations of changes in both ocean physics and biogeochemistry. This review article shows the importance of considering the interface between extreme events and tipping points, two topics usually addressed in isolation, and the need for continued monitoring to observe early warning signs and to evaluate Earth system response to extreme events as well as improving model skill in simulating extremes, compound extremes and tipping elements.

The 3D ionosphere structure is of interest in many fields such as radio frequency communication and global navigation satellite system (GNSS) applications. However, the limited temporal and spatial coverage of measurements poses a challenge for 3D electron density modeling. To overcome this challenge, we explore the use of kriging interpolation technique. The kriging interpolation is performed to obtain 3D representation of the ionosphere over electron density measurements retrieved by GNSS radio-occultation (RO) data. RO measurements are first reduced to “shape function,” the ratio of electron density to vertical total electron content (VTEC), aiming to create a background model. Then, the empirical residual semivariogram is analyzed for variation characteristics of the shape functions under different solar geomagnetic conditions. Finally, 3D kriging is adopted for shape function interpolation. Compared to the modeling results without kriging, the maximum root mean square error (RMSE) reduction reaches \(3.4\times {10}^{-4}~\text {km}^{-1}\) , which amounts to \(3.4\times {10}^{11}~\text {el/m}^{3}\) of electron density when VTEC is assumed as 100 TECU. This improvement accounts for 17.8% of root mean square (RMS) of shape function.