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We have used spherical harmonic representations of the Venus topography and geopotential, obtained from Magellan data, to evaluate isostatic support in several areas within the Ishtar Terra highlands including the Lakshmi plateau, its surrounding mountain belts namely Akna and Freyja, and Maxwell Montes, and the Fortuna Tessera province.
We have analyzed GRACE Level 1B data to resolve time-variable gravity using a local mascon approach. The spherical harmonic solutions released to date resolve the signal from surface hydrology over land areas at spatial scales of 750 to 1000 km over one month intervals [Wahr et al., 2004; Tapley et al., 2004]. In our local approach, we solve explicitly for the mass of water in surface blocks using only the KBRR data collected as GRACE overflies the region of interest. The local representation of gravity minimizes leakage of errors from other areas due to aliasing or mismodelling. In this paper, we report on the analysis of GRACE data from January 2003 through August 2004 over three regions: the Amazon, the Indian subcontinent, and the continental United States. We solve for mass change at 10-day intervals using 4 deg x 4 deg blocks. We give an overview of our latest results, and we present the results of error analyses, and comparisons to both hydrology models and in-situ data.
Models of the main geomagnetic field are generally represented by a scalar potential gamma expanded in a finite number of spherical harmonics. Very accurate observations of F were used, but indications exist that the accuracy of models derived from them is considerably lower. One problem is that F does not always characterize gamma uniquely. It is not clear whether such ambiguity can be encountered in deriving gamma from F in geomagnetic surveys, but there exists a connection, due to the fact that the counterexamples of Backus are related to the dipole field, while the geomagnetic field is dominated by its dipole component. If the models are recovered with a finite error (i.e. they cannot completely fit the data and consequently have a small spurious component), this connection allows the error in certain sequences of harmonic terms in gamma to be enhanced without unduly large effects on the fit of F to the model.
A refinement has been obtained in the earth's gravitational field by using satellite and surface data. In addition to a more complete treatment of data previously employed on 27 satellites, the new satellite solution Gem 7 (Goddard Earth Model 7) includes 64,000 laser measurements taken on seven satellites. Gem 7, containing 400 harmonic terms, is complete through degree and order 16. The companion solution Gem 8 combines the same satellite data as Gem 7 with surface gravimetry over 39% of the earth. Gem 8 is complete to degree and order 25. Extensive tests on data independent of the solution show that the undulations of the geoidal surface computed by Gem 7 have an accuracy of about 2.5 m (rms). The overall accuracy of the geoid calculated by Gem 8 is estimated to be about 4 m (rms). The new combination solution is the first to show signs of 'convection rolls' in the upper mantle below the Pacific Ocean.
Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.
A bump-on-tail unstable reduced velocity distribution, constructed from data obtained at the upstream boundary of the electron foreshock by the GSFC electron spectrometer experiment on the ISEE-1 satellite, is used as the initial plasma state for a numerical integration of the 1D-Vlasov-Maxwell system of equations. The integration is carried through the growth of the instability, beyond its saturation, and well into the stabilized plasma regime. A power spectrum computed for the electric field of the stabilized plasma is dominated by a narrow peak at the Bohm-Gross frequency of the unstable field mode but also contains significant power at the harmonics of the Bohm-Gross frequency. The harmonic power is in sharp peaks which are split into closely spaced doublets. The fundamental peak at the Bohm-Gross frequency is split into a closely spaced triplet. The mechanism for excitation of the second harmonic is shown to be second order wave-wave coupling.
We measure the impact of source galaxy clustering on higher order summary statistics of weak gravitational lensing data. By comparing simulated data with galaxies that either trace or do not trace the underlying density field, we show that this effect can exceed measurement uncertainties for common higher order statistics for certain analysis choices. We evaluate the impact on different weak lensing observables, finding that third moments and wavelet phase harmonics are more affected than peak count statistics. Using Dark Energy Survey (DES) Year 3 (Y3) data, we construct null tests for the source-clustering-free case, finding a p-value of p = 4 × 10 −3 (2.6σ) using third-order map moments and p = 3 × 10 −11 (6.5σ) using wavelet phase harmonics. The impact of source clustering on cosmological inference can be either included in the model or minimized through ad hoc procedures (e.g. scale cuts). We verify that the procedures adopted in existing DES Y3 cosmological analyses were sufficient to render this effect negligible. Failing to account for source clustering can significantly impact cosmological inference from higher order gravitational lensing statistics, e.g. higher order N-point functions, wavelet-moment observables, and deep learning or field-level summary statistics of weak lensing maps.
The gcamfaostat R package is designed for the preparation, processing, and synthesis of the Food and Agriculture Organization (FAO) Statistics (FAOSTAT) agroeconomic data. The primary purpose is to facilitate FAOSTAT data use in global economic and multisector dynamic models while ensuring transparency, traceability, and reproducibility. Here, we provide an overview of the development of gcamfaostat (v1.0) and demonstrate its capabilities in generating and maintaining agroeconomic data required for the Global Change Analysis Model (GCAM). Our initiative seeks to enhance the quality and accessibility of data for the global agroeconomic modeling community, with the aim of fostering more robust and harmonized outcomes in a collaborative, efficient, and open-source framework. One of the important features of the package is the possibility to construct the FAO Food Balance Sheets at the disaggregated commodity level (with over 500 commodities), which provides a comprehensive and detailed data input for a variety of analytical and modeling applications. The processed data and visualizations offered by gcamfaostat can also be valuable to a broader audience interested in gaining insights into the intricacies of global agriculture.
The Pannonian Basin is a deep intra-continental basin that formed as part of the Alpine orogeny. It is some 600 by 500 km in area and centered on Hungary. This area was chosen since it has one of the thinnest continental crusts in Europe and is the region of complex tectonic structures. In order to study the nature of the crustal basement we used the long-wavelength magnetic anomalies acquired by the CHAMP satellite. The SWARM constellation, scheduled to be launched next year, will have two lower altitude satellites flying abreast, with a separation of between ca. 150 to 200 km. to record the horizontal magnetic gradient. Since the CHAMP satellite has been in orbit for eight years and has obtained an extensive range of data, both vertically and horizontally there is a large enough data base to compute the horizontal magnetic gradients over the Pannonian Basin region using these many CHAMP orbits. We recomputed a satellite magnetic anomaly map, using the spherical-cap method of Haines (1985), the technique of Alsdorf et al. (1994) and from spherical harmonic coefficients of MF6 (Maus et aI., 2008) employing the latest and lowest altitude CHAMP data. We then computed the horizontal magnetic anomaly gradients (Kis and Puszta, 2006) in order to determine how these component data will improve our interpretation and to preview what the SW ARM mission will reveal with reference to the horizontal gradient anomalies. The gradient amplitude of an 1000 km northeast-southwest profile through our horizontal component anomaly map varied from 0 to 0.025 nT/km with twin positive anomalies (0.025 and 0.023 nT/km) separated by a sharp anomaly negative at o nT/km. Horizontal gradient indicate major magnetization boundaries in the crust (Dole and Jordan, 1978 and Cordell and Grauch, 1985). Our gradient anomaly was modeled with a twodimensional body and the anomaly, of some 200 km, correlates with a 200 km area of crustal thinning in the southwestern Pannonian Basin.
On June 3-4, 2025, we evaluated three types of electric-field (E-field) sensors: capacitive electrodes, stainless-steel stakes, and porous pots - at two electromagnetic (EM) stations (PWBVI and PWAOF) on Aqueduct Mesa at the Nevada National Security Site. We compared calibrated time series and derived metrics including power spectral density, root-mean-square (RMS) amplitude, signal-to-noise ratio (SNR), and detectability of power line harmonics and Schumann resonance. The objective was to identify an alternative E field sensor with higher SNR than the existing capacitive electrodes. Our data demonstrates that stainless-steel electrodes perform best, with the highest SNR and the most consistent detection of power-line harmonics and Schumann resonance among the three sensor types; they may be used as a supplement to, or replacement for, the existing capacitive electrodes.
Evidence is presented that radiated power line harmonics leak into high-altitude regions of the magnetosphere with sufficient intensity to control the starting frequencies of chorus emissions. OGO-3 data from three passes show that the starting frequencies of all measurable chorus emissions were within a few hertz of power line harmonics. It is also found that emissions detected over Western Europe were controlled by harmonics of 50 Hz; over the eastern United States and Canada by 60 Hz; and along the Alaska-New Zealand meridian by harmonics of both 50 and 60 Hz. These results indicate that man-made VLF noise plays an important role in the generation of chorus, one of the commonly observed forms of wave activity in the outer magnetosphere.
We present a spherical harmonic solution of the static gravity field of Mars to degree and order 120, GMM-3, that has been calculated using the Deep Space Network tracking data of the NASA Mars missions, Mars Global Surveyor (MGS), Mars Odyssey (ODY), and the Mars Reconnaissance Orbiter (MRO). We have also jointly determined spherical harmonic solutions for the static and time-variable gravity field of Mars, and the Mars k(sub 2) Love numbers, exclusive of the gravity contribution of the atmosphere. Consequently, the retrieved time-varying gravity coefficients and the Love number k(sub 2) solely yield seasonal variations in the mass of the polar caps and the solid tides of Mars, respectively. We obtain a Mars Love number k(sub 2) of 0.1697 +/- 0.0027 (3- sigma). The inclusion of MRO tracking data results in improved seasonal gravity field coefficients C(sub 30) and, for the first time, C 50. Refinements of the atmospheric model in our orbit determination program have allowed us to monitor the odd zonal harmonic C(sub 30) for approximately 1.5 solar cycles (16 years). This gravity model shows improved correlations with MOLA topography up to 15% larger at higher harmonics ( l = 60-80) than previous solutions.
We present an improved 360 degree and order spherical harmonic solution for Venus' topography. The new model uses the most recent set of Venus altimetry data with spacecraft positions derived from a recent high resolution gravity model.
The production of the MQXFA low-beta quadrupole magnets for the High Luminosity LHC (HL-LHC) is quite advanced with about 20 magnets cold tested in vertical cryostat. The MQXFA magnets are fabricated by the US HL-LHC Accelerator Upgrade Project (AUP) and are being used in the Q1 and Q3 Inner Triplet (IT) elements of the HL-LHC, whereas CERN is fabricating similar magnets for Q2a and Q2b IT elements. These large-aperture, high-gradient quadrupole magnets are first-of-the-kind accelerator magnets introducing Nb_3Sn conductor to High Energy Physics colliders. At this point of MQXFA production there is sufficient data to look at warm to cold correlations for the HL-LHC and for future use of Nb_3Sn magnets in particle accelerators. Examples of warm-cold correlations discussed in this paper are: field quality (including the use of magnetic shims for low-order harmonics correction), magnetic length, magnetic axis and magnetic field angle. A summary of main test data will also be presented and discussed.
A radio source in the outer heliosphere has been detected by the plasma wave receivers on Voyagers 1 and 2. The radio emission is observed in the frequency range 2-3 kHz, and is above the local solar wind electron plasma frequency whenever supporting plasma density data are available. The maximum spectral density of the emission recorded is about 10 to the -14th V-squared/m-squared/Hz. The bandwidth of the radio noise is about 1 kHz. Possible sources include continuum radiation from Jupiter's distant magnetotail and radiation at the second harmonic of the plasma frequency at the heliopause. If the latter interpretation is correct, these data represent the first remote observations of the heliopause.
Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.
The computation is described of a geopotential model to deg 360, a sea surface topography model to deg 10/15, and adjusted Geosat orbits for the first year of the exact repeat mission (ERM). This study started from the GEM-T2 potential coefficient model and it's error covariance matrix and Geosat orbits (for 22 ERMs) computed by Haines et al. using the GEM-T2 model. The first step followed the general procedures which use a radial orbit error theory originally developed by English. The Geosat data was processed to find corrections to the a priori geopotential model, corrections to a radial orbit error model for 76 Geosat arcs, and coefficients of a harmonic representation of the sea surface topography. The second stage of the analysis took place by doing a combination of the GEM-T2 coefficients with 30 deg gravity data derived from surface gravity data and anomalies obtained from altimeter data. The analysis has shown how a high degree spherical harmonic model can be determined combining the best aspects of two different analysis techniques. The error analysis was described that has led to the accuracy estimates for all the coefficients to deg 360. Significant work is needed to improve the modeling effort.
The paper presents data on the accumulated V-band orbital phase curves of polarization in Cygnus X-1 which disagree with the analysis of Simmons, Aspin, and Brown (1980). No curves which conform to the canonical model were observed, and a noncanonical structure is indicated by F tests on third, fourth, and fifth harmonics, which have a probability of 0.85 as a group. The fifth harmonic has a probability of 0.99; in contrast, the fundamental harmonic has a probability of only 0.6, which is below the detection threshold. Therefore, major conclusions of Simmons et al based on a nonsymmetrical canonical model assuming that fundamental harmonics are present, but harmonics with n greater than 2 are absent, are unacceptable. Analysis of the author's data by Simmons et al is criticized on the grounds that he did not take into account the cumulative aspects of the data and did not recognize the need to average out the random noise generated by Cygnus X-1 over many orbital cycles.