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Stochastic modelling of polyhedral gravity signal variations. Part II: Second-order derivatives of gravitational potential

Journal of Geodesy - Thu, 02/13/2025 - 00:00

Abstract

The stochastic representation of an uncertain shape model allows the dynamic evaluation of its induced gravity signal. This can be also applied for representing a time variable gravity field to model mass changes. The algorithm for estimating variations in gravitational potential is extended for the case of second-order derivatives. Two different harmonic synthesis formulas are used to derive the sought variations: one expressed in spherical coordinates using the traditional associated Legendre functions (ALF) and their derivatives up to the second order, while the other expressed in Cartesian coordinates by including the derived Legendre functions (DLF). The obtained variations are compared in terms of convergence with gravity signal differences referring to the specific shape changes using the line integral analytical approach for three asteroid shape models. Both approaches provide results that differ from the analytical method at a 1E−1 level, while the differences between them are at the 1E−15 level. The obtained results are highly influenced by the geometry of the examined shape model, with the ALF approach providing variations with closer agreement with the analytical method only for the almost spherical shape. Both harmonic synthesis expressions can be used to derive accurate results, as they differ at a very low level, and one can choose based on the convenience of their algorithmic characteristics.

Benefits of refined 10-day effective angular momentum forecasts for earth rotation parameter prediction

Journal of Geodesy - Thu, 02/13/2025 - 00:00

Abstract

Effective angular momentum (EAM) forecasts are widely used as an important input for predicting both polar motion and dUT1. So far, model predictions for atmosphere, ocean, and terrestrial hydrosphere utilized in Earth rotation research reach only 6-days into the future. GFZ’s oceanic and land-surface model forecasts are forced with operational 6-day high-resolution deterministic numerical weather predictions provided by the European Centre for Medium-range Weather Forecasts. Those atmospheric forecasts extend also further into the future with a reduced sampling rate of just 6 h but the prediction skill decreases rapidly after roughly one week. To decide about publishing 10-day instead of 6-day model-based EAM forecasts, we generated a test set of 454 individual 10-day forecasts and used it with GFZ’s EAM Predictor method to calculate Earth rotation predictions. Using 10-day instead of 6-day EAM forecasts leads to slight improvements in y-pole and dUT1 predictions for 10 to 30 days ahead. By introducing additional neural network models trained on the errors of the EAM forecasts when compared to their subsequently available analysis runs, Earth rotation prediction can be enhanced even further. A reduction of the mean absolute errors for polar motion and length-of-day prediction at a forecast horizon of 10 days of 26.8% in x-pole, 15.5% in y-pole, 27.6% in dUT1, and 47.1% in \(\Delta \) LOD is achieved. This test application successfully demonstrates the potential of the extended EAM forecasts for Earth rotation prediction although the success rate has to be further improved to arrive at robust routine predictions. GFZ publishes from October 2024 onwards raw uncorrected 10-day instead of 6-day EAM forecasts at www.gfz-potsdam.de/en/esmdata for the individual contributions of atmosphere, ocean, and terrestrial hydrosphere. Users interested in the summarized effect of all subsystems are advised to use the 90-day combined EAM forecast product that also makes use of the presented corrections to the EAM forecasts.

A strategy to determine GRACE-FO kinematic orbit during the activation of flex power

GPS Solutions - Thu, 02/13/2025 - 00:00

Abstract

GPS flex power can improve anti-jamming capability by enhancing the transmitting power of individual signals. However, during the active periods of GPS flex power in 2020, it was found that the accuracy of kinematic orbit for GRACE-FO satellites is decreased. In this paper, the impact of flex power on kinematic orbit determination of GRACE-FO is investigated. With the analysis of 30-day epoch-differenced geometry-free combinations of phase, i.e., \(\:\varDelta\:{{\Phi\:}}_{\text{G}\text{F}}\) and signal-to-noise ratio (SNR) for GRACE-FO satellites, a new strategy which considers the impact of flex power on the continuity of ambiguity is put forward to improve the kinematic orbit of GRACE-FO. After considering flex power, the 3D root-mean-square (RMS) of GRACE-C and GRACE-D are reduced to 4.10 and 4.42 cm, with improvements of 36% and 21%, respectively. The improvements of SLR validation are 34% and 14% for GRACE-C and GRACE-D. The above results prove the effectiveness of the proposed strategy.

Deep neural network based anti-jamming processing in the GNSS array receiver

GPS Solutions - Thu, 02/13/2025 - 00:00

Abstract

Signal anti-jamming has always been a difficult problem in GNSS (global navigation satellite system) signal processing. There are many GNSS anti-jamming techniques in the existing research, which can achieve good results if the interferences are sparsely distinguishable in some signal feature domains. Specifically, the single antenna based anti-jamming techniques cannot deal with wideband Gaussian noise interference because it is not sparse in time or frequency domain, while the only effective method currently is using multiple antennas to apply the space array processing (SAP) technique since the wideband Gaussian noise interference is sparse in the spatial domain. However, when the incoming directions of the different interferences are not less than that of antennas, the interferences are not sparse to the array anymore, and the SAP anti-jamming performance would decrease drastically. In this paper, a LSTM (long short-term memory) deep neural network (DNN) based algorithm is proposed to enhance the array anti-jamming performance in this situation. The proposed network estimates the interferences as an integrity by exploring the non-linear relationship of the array data received by antennas. Especially, a new loss function is designed exclusively for GNSS anti-jamming problem. The proposed DNN method is verified in the simulation that two wideband Gaussian interferences with JSR (jamming to signal ratio) 50 dB can be eliminated by using two antennas’ data, and the interference cancellation ratio improvement is about 24 dB compared to some other widely used classical SAP algorithms.

RamBO: Randomized blocky Occam, a practical algorithm for generating blocky models and associated uncertainties

Geophysical Journal International - Thu, 02/13/2025 - 00:00

SummaryWe present new numerical tools for geophysical inversion and uncertainty quantification (UQ), with an emphasis on blocky (piecewise-constant) layered models that can reproduce sharp contrasts in geophysical or geological properties. The new tools are inspired by an “old” and very successful inversion tool: regularized, nonlinear inversion. We combine Occam’s inversion with total variation (TV) regularization and a split Bregman method to obtain an inversion algorithm that we call blocky Occam, because it determines the blockiest model that fits the data adequately. To generate a UQ, we use a modified randomize-then-optimize approach (RTO) and call the resulting algorithm RamBO (randomized blocky Occam), because it essentially amounts to running blocky Occam in a randomized parallel for-loop. Blocky Occam and RamBO inherit computational advantages and stability from the combination of Occam’s inversion, split Bregman and RTO, and, therefore, can be expected to be robustly applicable across geophysics.

Wildfires intensifying more due to changes in vegetation and humidity than to lightning, supercomputer simulation finds

Phys.org: Earth science - Wed, 02/12/2025 - 19:58

Extreme fire seasons in recent years highlight the urgent need to better understand wildfires within the broader context of climate change. Under climate change, many drivers of wildfires are expected to change, such as the amount of carbon stored in vegetation, rainfall, and lightning strikes.