Data-enhanced revealing of trends in Geoscience

Purpose:This article presents an in-depth analysis of global research trends in Geosciences from 2014 to 2023.By integrating bibliometric analysis with expert insights from the Deep-time Digital Earth(DDE)initiative,this article identifies key emerging themes shaping the landscape of Earth Sciences①.Design/methodology/approach:The identification process involved a meticulous analysis of over 400,000 papers from 466 Geosciences journals and approximately 5,800 papers from 93 interdisciplinary journals sourced from the Web of Science and Dimensions database.To map relationships between articles,citation networks were constructed,and spectral clustering algorithms were then employed to identify groups of related research,resulting in 407 clusters.Relevant research terms were extracted using the Log-Likelihood Ratio(LLR)algorithm,followed by statistical analyses on the volume of papers,average publication year,and average citation count within each cluster.Additionally,expert knowledge from DDE Scientific Committee was utilized to select top 30 trends based on their representation,relevance,and impact within Geosciences,and finalize naming of these top trends with consideration of the content and implications of the associated research.This comprehensive approach in systematically delineating and characterizing the trends in a way which is understandable to geoscientists.Findings:Thirty significant trends were identified in the field of Geosciences,spanning five domains:deep space,deep time,deep Earth,habitable Earth,and big data.These topics reflect the latest trends and advancements in Geosciences and have the potential to address real-world problems that are closely related to society,science,and technology.Research limitations:The analyzed data of this study only contain those were included in the Web of Science.Practical implications:This study will strongly support the organizations and individual scientists to understand the modern frontier of earth science,especially on solid earth.The organizations such as the surveys or natural science fund could map out areas for future exploration and analyze the hot topics reference to this study.Originality/value:This paper integrates bibliometric analysis with expert insights to highlight the most significant trends on earth science and reach the individual scientist and public by global voting.

Preliminary Study on Direct Assimilation of Cloud-affected Satellite Microwave Brightness Temperatures

Direct assimilation of cloud-affected microwave brightness temperatures from AMSU-A into the GSI three-dimensional variational （3D-Var） assimilation system is preliminarily studied in this paper. A combination of cloud microphysics param- eters retrieved by the 1D-Var algorithm （including vertical profiles of cloud liquid water content, ice water content, and rain water content） and atmospheric state parameters from objective analysis fields of an NWP model are used as background fields. Three cloud microphysics parameters （cloud liquid water content, ice water content, and rain water content） are ap- plied to the control variable. Typhoon Halong （2014） is selected as an example. The results show that direct assimilation of cloud-affected AMSU-A observations can effectively adjust the structure of large-scale temperature, humidity and wind anal- ysis fields due to the assimilation of more AMSU-A observations in typhoon cloudy areas, especially typhoon spiral cloud belts. These adjustments, with temperatures increasing and humidities decreasing in the movement direction of the typhoon, bring the forecasted typhoon moving direction closer to its real path. The assimilation of cloud-affected satellite microwave brightness temperatures can provide better analysis fields that are more similar to the actual situation. Furthermore, typhoon prediction accuracy is improved using these assimilation analysis fields as the initial forecast fields in NWP models.

A Multivariable Approach for Estimating Soil Moisture from Microwave Radiation Imager(MWRI)

Accurate measurements of soil moisture are beneficial to our understanding of hydrological processes in the earth system. A multivariable approach using the random forest(RF) machine learning technique is proposed to estimate the soil moisture from Microwave Radiation Imager(MWRI) onboard Fengyun-3 C satellite. In this study, Soil Moisture Operational Products System(SMOPS) products disseminated from NOAA are used as a truth to train the algorithm with the input of MWRI brightness temperatures(TBs) at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz, TB polarization ratios(PRs) at 10.65, 18.7, and 23.8 GHz, height in digital elevation model(DEM), and soil porosity. The retrieved soil moisture is also validated against the independent SMOPS data, and the correlation coefficient is about0.8 and mean bias is 0.002 m^3 m^-3 over the period from 1 August 2017 to 31 May 2019. Our retrieval of soil moisture also has a higher correlation with ECMWF ERA5 soil moisture data than the MWRI operational products. In the western part of China, the spatial distribution of MWRI soil moisture is much improved, compared to the MWRI operational products.

Source analysis of spaceborne microwave radiometer interference over land

Satellite microwave thermal emissions mixed with signals from active sensors are referred to as radio- frequency interference （RFI）. Based on Advanced Micro- wave Scanning Radiometer-Earth Observing System （AMSR-E） observations from June 1 to 16, 2011, RFI over Europe was identified and analyzed using the modified principal component analysis algorithm in this paper. The X band AMSR-E measurements in England and Italy are mostly affected by the stable, persistent, active microwave transmitters on the surface, while the RFI source of other European countries is the interference of the reflected geostationary TV satellite downlink signals to the measurements of spacebome microwave radiometers. The locations and intensities of the RFI induced by the geostationary TV and communication satellites changed with time within the observed period. The observations of spacebome microwave radiometers in ascending portions of orbits are usually interfered with over European land, while no RFI was detected in descending passes. The RFI locations and intensities from the reflection of downlink radiation are highly dependent upon the relative geometry between the geostationary satellite and the measuring passive sensor. Only these fields of view of a spaceborne instrument whose scan azimuths are close to the azimuth relative to the geostationary satellite are likely to be affected by RFI.

Truncated glycoprotein E of varicella-zoster virus is an ideal immunogen for Escherichia coli-based vaccine design

Varicella-zoster virus(VZV)is a highly infectious agent responsible for both varicella and herpes zoster disease.Despite high efficacy,there remain safety and accessibility concerns with the licensed vaccines.Here,we sought to produce a VZV g E immunogen using an E.coli expression system.We found that the soluble expression and yield of g E protein could be enhanced via C-terminal truncations to the protein,thereby facilitating a robust and scalable purification process for the purpose of vaccine manufacturing.The lead truncated g E(aa 31–358),hereafter referred to as tg E,was a homogenous monomer in solution and showed excellent antigenicity.Finally,we assessed and compared the immunogenicity of tg E with commercial v Oka LAV and Shingrix vaccine.We found that aluminum-adjuvanted tg E was immunogenic as compared with v Oka LAV.When adjuvanted with AS01B,a two-dose immunization of tg E showed comparable or better potency in antibody responses and cell-mediated immunity with those of the Shingrix vaccine at the same dosage,especially in terms of the proportion of IFN-γ-expressing CD4^(+)T cells.In conclusion,this method of E.coli-mediate tg E expression offers a cost-effective and scalable strategy to generate an ideal VZV g E immunogen for the development of both varicella and zoster vaccines.