ALBERT

Description: The Neural Decision Tree (NDT) is a hybrid supervised machine-learning algorithm that combines the self-limiting property of a decision tree (Classification and Regression Tree [CART]) algorithm with the artificial neural network. We demonstrate the use of NDT for a regression problem of building a prediction model for the plasmasphere electron density with solar and geomagnetic measurements as inputs. Our work replicates the work by Zhelavskaya et al. reported in their 2017 article (I. S. Zhelavskaya et al., 2017, https://doi.org/10.1002/2017JA024406) to show that NDT makes available sophisticated network layout for building a predictive model, thus taking advantage of deep-learning potential of the neural network. We also demonstrate that with the ability to automatically select an appropriate network layout, as well as, effective initialization, the NDT algorithm allows research scientists in space weather to focus more of their attention on physically and statistically relevant aspects of using machine-learning techniques. In fact, our example highlights the fact that the basic assumptions of standard supervise machine-learning problems are often unsatisfied in real-world space weather applications. Greater attention to these fundamental issues may create significantly different solutions to space weather forecast problems.

Description: The Sun has an obvious quasi-11-year cycle and numerous short-term eruptive activities. From the aspect of energy, there are four processes of energy transmission in the effectuation chain of solar forcing to the climate system: the solar energy input to the atmosphere, the atmospheric absorption to the input energy, the transformation of the absorbed energy to dynamic and thermodynamic responses in the atmosphere, and the coupling among all the layers affected by solar forcings. However, the four processes have not been discussed in their entirety. In this present paper, studies in recent decade on how the solar radiation varies during the solar cycle as well as the eruptions, and correspondingly how the terrestrial atmosphere absorbs the input solar energy are reviewed.

Description: The GNSS monument and its nearby bedrock both exhibit thermal expansion effect due to changes of surface temperature. However, previous studies treat the monument and its nearby bedrock below the ground surface as a whole without considering their bedrock depth, type, and material inconsistencies. Moreover, the existing finite element method (FEM) that consider nonseasonal variations is not easy to extend to consider the bedrock information. To address these problems, we propose a refined full-spectrum half-space bedrock thermal expansion model that consider both nonseasonal variations and bedrock information. Results show that the bedrock thermal vertical displacements (BTVD) estimated by our method is about 0.2 mm larger than that from FEM. The amplitude of BTVD becomes larger with increasing bedrock depth, and the phase lead is also obvious. The correlation coefficient between GNSS height and the environmental loading displacement improved by 1.6% to 21.9% after deducting the BTVD estimated by our method compared with FEM. Through investigating 80 globally distributed IGS stations, we find that the annual amplitude increases by up to 0.5 mm and the annual phase lead by up to 10° after considering bedrock depths. Different bedrock types also have a significant impact on the annual amplitude and phase of vertical displacement, with the correlation coefficient increased by 1.4% to 30.4%. Our work confirms the advantage of more rigorous bedrock thermal expansion modeling to correct the nonlinear variations of global GNSS reference stations, which might provide a possible clue to establish the terrestrial reference frame with 1 mm level.

Description: Due to different reanalysis methods and input data, there exist big discrepancies between hydrological models from regions to regions. Therefore, accuracy assessment of different models is necessary before applications in specific areas. In this report, six hydrological models, including GLDAS, FLDAS, ERA5, MERRA-Land, NCEP and WGHM were evaluated and analyzed through inter-comparison between models and outer-comparison with Global Positioning System (GPS) station height time series, GRACE/GRACE-FO RL06 Mascon solutions, and Precipitation data from Global Precipitation Climatology Centre (CPCC) for a comprehensive assessment of model differences. We then try to combine the six models through variance component estimation (VCE), entropy weight method (EWF), coefficient of variation method (CVM) and other mathematical models, so as to improve the accuracy, integrity and applicability of hydrological models. Our results show that the root mean square (RMS) of global GPS height time series improves by up to 17% after correcting the hydrological effect obtained from combined models compared with that from individual models, while the correlation with rainfall also improves by 30% at most. Compared with TWSC derived from satellite gravity inversion, the correlation coefficient increases from 0.2 to 0.8 at the highest. Finally, the combined methods exhibit much higher signal-to-noise ratio (SNR) value than that of the pre-combined models, and the VCE combined model performs as the optimal hydrology model to correct the GPS height. Therefore, we conclude that the combined hydrological model could provide a better data source for monitoring hydrological changes and surface load deformation at a global scale.

Description: The Pengchiayu, located at the offshore island in north Taiwan, is a volcanic island with controversial eruptive ages ranging from 2.1 Ma to less than 0.29 Ma, dated by K-Ar method. Geomorphologically, a crater with diameter about 50 m wide and fresh reddish scoria in top crops out in northwest tip, and a depression, probably was a tuff cone, occurs in central part of the island. Geologically, well bedded base surges with thickness totally over 30 m thick and ranging from few to tens of centimeters expose in southeast and west island. The lava flows with Aa and pahoehoe structures cover on the top or outflow from a breach of cone to the south and the north. Meanwhile, glassy scoria shows very fresh characters and almost no weathering on surface. In addition, a whole-coring about 100 m deep has been drilled and showed the lithology being composed of the thick lavas with vesicles, reddish scoria, weathered soils, base-surges and lavas from bottom to top. This sequence infers that the eruptions occurred in subaerial condition firstly, then the hydrovolcanism followed to produce thick base surges in shallow marine, and finally erupted in subaerial again. Combining several lines of evidence from surface observations and drilled core records, the last activity of pengchiayu volcano was a surtsey-type eruption and occurred probably in latest glacial sea level rising, which is in the Holocene period.

Description: The incorporation of multi-frequency signals into global navigation satellite systems (GNSS) has presented new possibilities for precise positioning and rapid ambiguity resolution. Inter-frequency clock bias (IFCB) pertains to the time-varying biases among distinct frequencies within carrier phase observations in GNSS signals. The appropriate handling of IFCB is critical in enhancing the accuracy and convergence time of precise point positioning (PPP) solutions. The focus of this study is on the proper modeling of phase IFCB in multi-GNSS multi-frequency PPP. In this paper, the optimal IFCB power spectral density value of 0.6 m/sqrt(s) is first determined. To obtain the optimal stochastic model for IFCB, a thorough comparison and analysis of the product correction and parameter estimation methods is conducted. Additionally, experiments are conducted on the effect of IFCB modeling on the performance of undifferenced and uncombined PPP using data from 130 multi-GNSS experiment stations across the globe over a period of one week in January 2022. The study reveals that the optimal power spectral density for IFCB is within [60, 0.006] m/sqrt(s), modeling IFCB as a random walk is feasible, and the PPP is comparable for the three IFCB processing schemes of product correction, random walk, and white noise. Meanwhile, it is not reasonable to treat IFCB as a random constant or neglect it in the multi-GNSS multi-frequency PPP. In the absence of product correction or for users who require immediate and continuous positioning solutions, modeling IFCBs as random walks can lead to more reliable positioning results and improved convergence performance.

Description: Interactions between soil quality and climate change may influence the capacity of croplands to produce sufficient food. Here, we address this issue by using a new dataset of soil, climate and associated yield observations for 12,115 site-years representing 90% of total cereal production in China. Across crops and environmental conditions, we show that high-quality soils reduced the sensitivity of crop yield to climate variability leading to both higher mean crop yield (10.3 ± 6.7%) and higher yield stability (decreasing variability by 15.6 ± 14.4%). High-quality soils improve the outcome for yields under climate change by 1.7% (0.5–4.0%), compared to low-quality soils. Climate-driven yield change could result in reductions of national cereal production of 11.4 Mt annually under representative concentration pathway RCP 8.5 by 2080–2099. While this production reduction was exacerbated by 14% due to soil degradation, it can be reduced by 21% through soil improvement. This study emphasizes the vital role of soil quality in agriculture under climate change.