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Analyzing the spatial variation characteristics of grid TEC using long-term GIM data

Publication date: 1 August 2024

Source: Advances in Space Research, Volume 74, Issue 3

Author(s): Qisheng Wang, Jiaru Zhu

Forecasting ionospheric TEC using least squares support vector machine and moth-flame optimization methods in China

Publication date: 1 August 2024

Source: Advances in Space Research, Volume 74, Issue 3

Author(s): Jun Tang, Chang Liu, Cihang Fan

Memory sampled-data control design for attitude stabilization of uncertain spacecraft with randomly missing measurements

Publication date: 1 August 2024

Source: Advances in Space Research, Volume 74, Issue 3

Author(s): Janani Moorthy, Visakamoorthi Balasubramani, Muthukumar Palanisamy, Sung-ho Hur

Trajectory planning of a free-floating dual-arm space robot with minimal base disturbance in obstacle environments

Publication date: 1 August 2024

Source: Advances in Space Research, Volume 74, Issue 3

Author(s): Mengqing Hong, Lu Wang, Liaoxue Liu, Qun Wang, Yu Guo

D-region ionospheric disturbances due to the December 2019 solar eclipse observed using multi-station VLF radio network

Publication date: 1 August 2024

Source: Advances in Space Research, Volume 74, Issue 3

Author(s): Kheyali Barman, Bakul Das, Sujay Pal, Prabir Kumar Haldar, Subrata Kumar Midya, Sabyasachi Pal, Sushanta Kumar Mondal

Potential and performance for classifying Earth surface only with ICESat-2 altimetric data

Publication date: 1 August 2024

Source: Advances in Space Research, Volume 74, Issue 3

Author(s): Yuan Sun, Huan Xie, Chunhui Wang, Kuifeng Luan, Shijie Liu, Binbin Li, Qi Xu, Peiqi Huang, Changda Liu, Min Ji, Xiaohua Tong

Prediction of the amplitude of solar cycle 25 from the ratio of sunspot number to sunspot-group area, low latitude activity, and 130-year solar cycle

Publication date: 1 August 2024

Source: Advances in Space Research, Volume 74, Issue 3

Author(s): J. Javaraiah

Analysis of long-term changes in algal bloom pattern and their association with Ocean, atmosphere, and land-based processes across the northern Indian Ocean

Publication date: 1 August 2024

Source: Advances in Space Research, Volume 74, Issue 3

Author(s): Punya P., Rama Rao Nidamanuri

Modelling of spacecraft apparent brightness A study on OneWeb constellation satellites

Publication date: 1 August 2024

Source: Advances in Space Research, Volume 74, Issue 3

Author(s): Gerardo Littoriano, Camilla Colombo, Alessandro Nastasi, Carmelo Falco

Active fault-tolerant attitude control based on Q-learning for rigid spacecraft with actuator faults

Publication date: 1 August 2024

Source: Advances in Space Research, Volume 74, Issue 3

Author(s): Sajad Rafiee, Mohammadrasoul Kankashvar, Parisa Mohammadi, Hossein Bolandi

New routine NLTE15µmCool-E v1.0 for calculating the non-local thermodynamic equilibrium (non-LTE) CO2 15 µm cooling in general circulation models (GCMs) of Earth's atmosphere

Geoscientific Model Development - Thu, 07/11/2024 - 18:58
New routine NLTE15µmCool-E v1.0 for calculating the non-local thermodynamic equilibrium (non-LTE) CO2 15 µm cooling in general circulation models (GCMs) of Earth's atmosphere
Alexander Kutepov and Artem Feofilov
Geosci. Model Dev., 17, 5331–5347, https://doi.org/10.5194/gmd-17-5331-2024, 2024
Infrared CO2 cooling of the middle and upper atmosphere is increasing. We developed a new routine for very fast and accurate calculations of this cooling in general circulation models. The new algorithm accounts for non-local thermodynamic equilibrium and is about 1000 times faster than the standard matrix algorithms. It is based on advanced techniques for non-equilibrium emission calculations in stellar atmospheres, which so far have not been used in Earth’s and planetary atmospheres.

Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier

Geoscientific Model Development - Thu, 07/11/2024 - 18:58
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, 2024
Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.

tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena

Geoscientific Model Development - Thu, 07/11/2024 - 18:58
tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena
G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, 2024
Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.

Integrating monitoring data to analyze greenhouse gas emissions from reservoirs in the Yellow River Basin

Phys.org: Earth science - Thu, 07/11/2024 - 18:57
A study published in the journal Science China Earth Sciences integrates existing monitoring data to discuss the characteristics of greenhouse gas (GHG) emissions from reservoirs in the Yellow River Basin. While CO2 emission flux from reservoirs is lower than that from river channels, the emission fluxes of CH4 and N2O are 1.9 times and 10 times those from rivers, respectively, indicating that the emission of CH4 and N2O is significantly enhanced in reservoirs.

Scientists use cosmic rays to study twisters and other severe storms

Phys.org: Earth science - Thu, 07/11/2024 - 18:37
Cosmic rays could offer scientists another way to track and study violent tornadoes and other severe weather phenomena, a new study suggests.

A multi-instrument fuzzy logic boundary-layer-top detection algorithm

Atmos. Meas. techniques - Thu, 07/11/2024 - 18:16
A multi-instrument fuzzy logic boundary-layer-top detection algorithm
Elizabeth N. Smith and Jacob T. Carlin
Atmos. Meas. Tech., 17, 4087–4107, https://doi.org/10.5194/amt-17-4087-2024, 2024
Boundary-layer height observations remain sparse in time and space. In this study we create a new fuzzy logic method for synergistically combining boundary-layer height estimates from a suite of instruments. These estimates generally compare well to those from radiosondes; plus, the approach offers near-continuous estimates through the entire diurnal cycle. Suspected reasons for discrepancies are discussed. The code for the newly presented fuzzy logic method is provided for the community to use.

Tree ring records reveal influence of North Atlantic sea surface temperature fluctuations on climate

Phys.org: Earth science - Thu, 07/11/2024 - 18:14
With the intensification of global climate change, understanding historical climate patterns is crucial for predicting future trends in climate change.

A Global Marine Sediment Compilation and a Cerium Anomaly Perspective on Metasomatized Mantle Sources for REE‐Mineralized Carbonatites

JGR–Solid Earth - Thu, 07/11/2024 - 17:35
Abstract

Rare earth elements (REE) are vital for powerful permanent magnets used in electric motors and wind turbines. These elements are chiefly sourced from carbonatites and their weathering products. The economic attractiveness of carbonatites is explained by the 10,000-fold enrichment of REE in their mineralized portions relative to the average continental crust. Carbonatites form from mantle-derived melts, but the ultimate origin of their REE is not completely clear. One widely cited model invokes subduction of marine sediments which accumulate REE-rich material, priming the mantle to produce REE-rich carbonatite melts which subsequently form deposits in the upper crust. Here we examine a global marine sediment compilation, revealing a wide variety in REE abundances and patterns. We use the sensitive lambda method that separates REE pattern curvature from redox-related element anomalies to examine both marine sediments and presumably derived carbonatite rocks. We find that the most REE-rich marine sediments are characterized by strongly negative Ce anomalies, which if recycled via subduction, mineralized carbonatites are expected to inherit. In contrast, we find that mineralized carbonatite rocks do not contain Ce anomalies. This indicates that the REE from the most REE-rich marine sediments are not recycled into carbonatite deposits, and a different REE source is needed to explain carbonatite fertilities. We also find evidence that raises questions on whether any sediment-derived REE are present in carbonatite deposits to a significant amount. We suggest that a REE-rich source may not be required and REE enrichment occurs primarily during crustal magmatic differentiation.

Prediction of volume of shallow landslides due to rainfall using data-driven models

Natural Hazards and Earth System Sciences - Thu, 07/11/2024 - 17:29
Prediction of volume of shallow landslides due to rainfall using data-driven models
Jérémie Tuganishuri, Chan-Young Yune, Manik Das Adhikari, Seung Woo Lee, Gihong Kim, and Sang-Guk Yum
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-90,2024
Preprint under review for NHESS (discussion: open, 0 comments)
To reduce the consequences of landslides due to rainfall, such as of life and economic losses, and disruption of order of our daily living; this study describes the process of building a machine learning model which can help to estimate the volume of landslides material that can occur in a particular region taking into account of antecedent rainfall, soil characteristics, type of vegetation etc. The findings can be useful for land use, infrastructure design and rainfall disaster management.

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