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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Integrating biological knowledge for mechanistic inference in the host-associated microbiome

Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Causal relationships of vegetation productivity with root zone water availability and atmospheric dryness at the catchment scale

Abstract. This study explores the causal relationships between catchment water availability, vapor pressure deficit, and gross primary productivity (GPP) across 341 catchments in the contiguous US. Seasonal climatic, hydrological, and vegetation characteristics were represented using the Horton index, ecological aridity index, evaporative fraction index, and carbon uptake efficiency. Statistical methods, including circularity statistics, correlation analysis, and causality tests, were employed to determine the complex interactions between catchment wetness, atmospheric dryness, and vegetation carbon uptake. The results revealed a maximum lag of 2 months in the intra-annual variability of catchment water supply–productivity and atmospheric water demand–productivity relationships, with hysteresis patterns varying with the catchment's hydrological characteristics. In catchments not permanently under water-limited or energy-limited conditions, vegetation experiences hydrological stress during the peak growing period, coinciding with the highest gross primary productivity and carbon uptake efficiency being out of phase with the Horton index and in phase with the evaporative fraction index. Causality analysis highlights strong temporal continuity in GPP seasonal characteristics, with a cause–effect relationship between catchment water supply, atmospheric demand, and vegetation productivity spanning a maximum of 2 months. These findings underscore the need for a comprehensive functional framework that integrates catchment water supply, atmospheric demand, and vegetation productivity to enhance our understanding and predictive capabilities with regard to ecosystem responses to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Spatial mapping of plant N-glycosylation cellular heterogeneity inside soybean root nodules provided insights into legume-rhizobia symbiosis

Although ubiquitously present, information on the function of complex N-glycan posttranslational modification in plants is very limited and it is often neglected. In this work, we adopted an enzyme-assisted matrix-assisted laser desorption/ionization mass spectrometry imaging strategy to visualize the distribution and identity of N-glycans in soybean root nodules at a cellular resolution. We additionally performed proteomics analysis to probe the potential correlation to proteome changes during symbiotic rhizobia-legume interactions. Our ion images reveal that intense N-glycosylation occurs in the sclerenchyma layer, and inside the infected cells within the infection zone, while morphological structures such as the cortex, uninfected cells, and cells that form the attachment with the root are less N-glycosylated. Notably, we observed different N-glycan profiles between soybean root nodules infected with wild type rhizobia and those infected with mutant rhizobia incapable of efficiently fixing atmospheric nitrogen. The majority of complex N-glycan structures, particularly those with characteristic Lewis-a epitopes, are overexpressed in the mutant nodules. Our proteomic results revealed that these glycans likely originated from proteins that maintain the redox balance crucial for proper nitrogen fixation, but also from enzymes involved in N-glycan and phenylpropanoid biosynthesis. These findings indicate possible involvement of Lewis-a glycans in these critical pathways during legume-rhizobia symbiosis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning

Arsenic, a potent carcinogen and neurotoxin, affects over 200 million people globally. Current detection methods are laborious, expensive, and unscalable, being difficult to implement in developing regions and during crises such as COVID-19. This study attempts to determine if a relationship exists between soil’s hyperspectral data and arsenic concentration using NASA’s Hyperion satellite. It is the first arsenic study to use satellite-based hyperspectral data and apply a classification approach. Four regression machine learning models are tested to determine this correlation in soil with bare land cover. Raw data are converted to reflectance, problematic atmospheric influences are removed, characteristic wavelengths are selected, and four noise reduction algorithms are tested. The combination of data augmentation, Genetic Algorithm, Second Derivative Transformation, and Random Forest regression (R 2 =0.840 and normalized root mean squared error (re-scaled to [0,1]) = 0.122) shows strong correlation, performing better than past models despite using noisier satellite data (versus lab-processed samples). Three binary classification machine learning models are then applied to identify high-risk shrub-covered regions in ten U.S. states, achieving strong accuracy (=0.693) and F1-score (=0.728). Overall, these results suggest that such a methodology is practical and can provide a sustainable alternative to arsenic contamination detection.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2020

An acidic polysaccharide (AGC3) isolated from North American ginseng ( Panax quinquefolius ) suspension culture as a potential immunomodulatory nutraceutical

Polysaccharides isolated from Panax quinquefolius roots are widely used as nutraceuticals due to their immunomodulatory properties. Despite their popularity, several challenges exist in isolating ginseng root polysaccharides such as batch-to-batch structural inconsistencies and bacterial endotoxin contamination. A plant tissue culture-based platform offers a potential solution to isolate natural polysaccharide fractions with consistent chemical characteristics and reduced endotoxin content. In this study, an acidic polysaccharide fraction (AGC3) with immunomodulatory properties was isolated from Panax quinquefolius suspension cultures. The heterogeneous fraction (molecular weight: 4.81 and 32.14 kDa), purified by anion exchange chromatography, was predominantly composed of galactose (>60%) along with the presence of rhamnose, arabinose, glucose, glucuronic acid and galacturonic acid. The major glycosidic linkages were found to be t-Galp (47.7%), 4-Galp (15.6%), 2,4-Rhap (8.1%), 6-Galp (8.1%) and 4-GalAp (6.8%). Structural analyses indicated the presence of a pectic rhamnogalacturonan I polysaccharide in AGC3. AGC3 significantly (p < 0.05) stimulated RAW 264.7 murine macrophage cells and primary murine splenocytes by enhancing the production of several immunomodulatory mediators such as IL-6, TNF-α, GM-CSF and MCP-1. The results also indicated the putative roles of NF-κB (p65/RelA) and MAPK (p38) signaling pathways in the immunostimulatory response. Additionally, AGC3 induced murine splenocyte proliferation, another major indicator of immunostimulation. Overall, AGC3 has the potential to be used as an immunostimulatory nutraceutical.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Organic carbon feedbacks to soil structure in Profundihumic and Haplic Ferralsols in Campinas, Sao Paulo, Brazil

Increasing soil organic matter content coincides with increasing soil porosity and decreasing bulk density, but the mechanisms that lead to changes in soil structure are unclear. We quantified and compared soil characteristics across two contrasting soils (heavily weathered ferralsols in Brazil) to clarify the effects of organic matter content on soil porosity. We found that high organic matter content fosters higher biologic activity, including root growth and animal burrowing, which in turn increase soil porosity and decreases bulk density.This dataset includes soil physical, morophological, and chemical variables from 6 soil profiles were sampled in Campinas, Sa ̃o Paulo state, southeastern Brazil to 1 m depth. Sites are regenerated mixed savanna and rainforest in the protected area of Viracopos Airport in Campinas, lightly grazed by cattle.This dataset includes soil color, bulk density, texture (percent sand, silt, clay, and coarse fragments as well as class), soil pH, cation exchange capacity, C stock, C concentration, N stock, N concentration, delta 13C, Delta 14C, and soil porosity in Excel and csv format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Impact of a track trigger at a multi-TeV muon collider

In the panorama of future particle accelerators, the Muon Collider seems to be the best choice for several reasons that will be explored in section 1.2. The main challenge is represented by the instability of the muons. At the desired instantaneous luminosities, an intense flow of secondary and tertiary particles from the interactions of the beam decay products with the machine elements reaches the detector, compromising its performance. Several studies are currently underway to mitigate the background and to have an efficient trigger. My work represents a first step towards understanding the basic characteristics of a trigger selection, which will subsequently guide the design of the trigger system. With this aim I produced and analyzed Higgs and di-Higgs signals with MadGraph and CERN-ROOT. In first section there is an introduction to Muon Collider, a brief history, a discussion of its benefits, a description of the detector and of the effects of the Beam Induced Background. Several operating center-of-mass energies are possible for this machine, in this report a center-of-mass of 3 TeV is assumed. Then I describe the event generation process. Finally I describe the entire analysis, in particular I analyzed the efficiency and the number of events for each process.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

NGEE Arctic Plant Traits: Fine Roots, Kougarok Road Mile Marker 64, Seward Peninsula, Alaska, 2016

Soil cores were collected from twelve vegetation biomass plots at the Kougarok hillslope in late July of 2016. The sampled plots were located across six ecotypes present at this site (n=2 replicates per ecotype). Soil cores were separated into depth intervals in the field and frozen for transport to Oak Ridge National Laboratory. Data package contains one *.csv and one *.pdf.Laboratory sample processing of these soils generated depth-specific data on soil properties as well as fine root biomass, length, %C, %N, delta13C, and delta15N. Only live fine roots were analyzed. Fine roots were not separated by species or plant functional type.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A daily, 250 m and real-time gross primary productivity product (2000–present) covering the contiguous United States

Abstract. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time and observation-based GPP products. To address this critical gap, here we leverage a state-of-the-art vegetation index, near-infrared reflectance of vegetation (NIRV), along with accurate photosynthetically active radiation (PAR), to produce a SatelLite Only Photosynthesis Estimation (SLOPE) GPP product for the contiguous United States (CONUS). Compared to existing GPP products, the proposed SLOPE product is advanced in its spatial resolution (250 m versus >500 m), temporal resolution (daily versus 8 d), instantaneity (latency of 1 d versus >2 weeks) and quantitative uncertainty (on a per-pixel and daily basis versus no uncertainty information available). These characteristics are achieved because of several technical innovations employed in this study: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRV (SANIRV) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Cropland Data Layer (CDL) product to predict dynamic C4 crop fraction. Through developing a parsimonious model with only two slope parameters, the proposed SLOPE product explains 85 % of the spatial and temporal variations in GPP acquired from 49 AmeriFlux eddy-covariance sites (324 site years), with a root-mean-square error (RMSE) of 1.63 gC m−2 d−1. The median R2 over C3 and C4 crop sites reaches 0.87 and 0.94, respectively, indicating great potentials for monitoring crops, in particular bioenergy crops, at the field level. With such a satisfactory performance and its distinct characteristics in spatiotemporal resolution and instantaneity, the proposed SLOPE GPP product is promising for biological and environmental research, carbon cycle research, and a broad range of real-time applications at the regional scale. The archived dataset is available at https://doi.org/10.3334/ORNLDAAC/1786 (download page: https://daac.ornl.gov/daacdata/cms/SLOPE_GPP_CONUS/data/, last access: 20 January 2021) (Jiang and Guan, 2020), and the real-time dataset is available upon request.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Tropical forests and global change: biogeochemical responses and opportunities for cross-site comparisons, an organized INSPIRE session at the 108th Annual Meeting, Ecological Society of America, Portland, Oregon, USA, August 2023

In this study, tropical forests play a critical role in the global carbon (C) cycle. These ecosystems maintain the highest rates of net primary production (NPP) on Earth, contain c. 30% of terrestrial C stocks, and have some of the largest stores of fine-root biomass globally, as well as higher fine-root production and turnover rates compared with other biomes. Tropical forest responses to projected warming, altered rainfall regimes, and elevated CO 2 concentrations are likely to be different from other ecosystems because of their unique characteristics (Box 1), making targeted research and model development important for understanding tropical forest–climate feedbacks. There is now a critical mass of long-term global change field experiments and modeling efforts in tropical forests, yet thus far there has been little synthesis, cross-site comparison, or multi-site standardized experimentation among tropical forests to help us understand how these biomes are changing. An organized INSPIRE session at the 108th Annual Meeting of the Ecological Society of America set out to tackle just this. Speakers covered large-scale tropical forest field experiments and modeling efforts, with an emphasis on changes in ecosystem biogeochemistry under warming, drying, elevated atmospheric CO 2 , and changing nutrient status. In this Meeting report, we provide an overview of the large-scale global change experiments presented and highlight the main objectives and opportunities for tropical forest research that emerged, including cross-site comparisons and integration with ecosystem-scale models (Fig. 1).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Validation of density pump-out by pedestal-foot magnetic island formation prior to ELM suppression in KSTAR and DIII-D tokamaks

Abstract TM1 nonlinear two-fluid simulations reveal key characteristics of density pump-out caused by pedestal-foot island formation when applying resonant magnetic perturbation (RMP). These characteristics include a bifurcation in pump-out with a low RMP coil current threshold, a sensitivity of pump-out magnitude to q 95 , and the magnitude of pump-out scaling as the square root of the RMP coil current. Dedicated experiments are carried out in the KSTAR and DIII-D tokamaks to validate these features and show that: (1) a staircase bifurcation in density pump-out is observed when slowly ramping up the n = 1 RMP current, and the density fluctuations are found to be slightly decreased from the pedestal-foot to the pedestal-top; (2) the magnitude of density pump-out becomes weaker when decreasing q 95 from 5.5 to 4.9, and a partial recovery of density pump-out is observed when q 95 is ramped down to lower than 4.9; and (3) analysis of a DIII-D database of n = 3 RMP edge-localized mode control experiments finds that the magnitude of density pump-out is proportional to the square root of RMP coil current Δ n e / n e ∝ I RMP 0.5 . These experimental observations are consistent with TM1 simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network

Abstract Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein–protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision–recall curve of 0.63 and a Cohen’s Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network’s information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.

Wang, Xiaowen↗
DOE OSTI2023

Knowledge acquisition and innovation quality: The moderating role of geographical characteristics of technology

Acquiring and absorbing knowledge at different geographic levels is an important step in the technological learning and innovation processes for firms. Geographically bounded knowledge searching and spillover have been shown in various network formats, industries, geographic scopes, and country contexts. This study finds evidence of localized learning in citation networks in the solar photovoltaic balance-of-systems (PV BOS) innovations, and that the levels of localized learning vary based on the degree to which technologies are relevant to local contexts. Thus, this study proposes that the value of local and non-local knowledge on patent quality is contingent on technological characteristics. Further, leveraging the different degrees of local dependence on four types of technologies in the PV BOS industry, we find that the impact of local knowledge on innovation quality is higher for technologies that are more rooted in local contexts and that the impact of non-local knowledge is higher for technologies that are less relevant to local contexts.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Belowground allocation and dynamics of recently fixed plant carbon in a California annual grassland

Plant roots and the organisms that surround them are a primary source for stabilized soil organic carbon (SOC). While grassland soils have a large capacity to store organic carbon (C), few field-based studies have quantified the amount of plant-fixed C that moves into soil and persists belowground over multiple years. Yet this characteristic of the soil C cycle is critical to C storage, soil water holding capacity, nutrient provisions, and the management of soil health. Here, we tracked the fate of plant-fixed C following a five-day 13 CO 2 labeling of a Northern California annual grassland, measuring C pools starting at the end of the labeling period, at three days, four weeks, six months, one year, and two years. Soil organic carbon was fractionated using a density-based approach to separate the free-light fraction (FLF), occluded-light fraction (OLF), and heavy fraction (HF). Using isotope ratio mass spectrometry, we measured 13 C enrichment and total C content for plant shoots, roots, soil, soil dissolved organic carbon (DOC), and the FLF, OLF, and HF. The chemical nature of C in the HF was further analyzed by solid state 13 C nuclear magnetic resonance (NMR) spectroscopy.

13C-NMR↗
DOE OSTI2022

ACORN Review: NPK fertilizer use in loblolly pine plantations: Who are we really feeding?

Optimizing loblolly pine (Pinus taeda L.) productivity using fertilizers and various site management practices has been a goal of foresters for decades. Nitrogen (N), phosphorus (P), and potassium (K) are the three most operationally applied fertilizers to loblolly pine silviculture and are of primary importance to their total productivity. Fertilizer recommendations for N, P, and K in loblolly pine are primarily made on abiotic factors such as site and soil characteristics, while the biological factors controlling nutrient uptake are typically overlooked in the production and optimization of these stands. Arguably the most important of these biological factors are the diverse ectomycorrhizal fungal (ECM) communities that colonize the fine roots of almost all loblolly pine trees. The mantle formed by ECM fungi on short-root tips presents a barrier for direct apoplastic uptake of N, P, and K from soil solution by pine roots. In well-colonized roots, the tree is dependent on symplastic fungal transport of N, P, and K foraged from the soil by the extraradical hyphal network. This raises the question: Who are we really feeding if the ECM fungi are the ones assimilating most of the tree's total nutritional requirements? Considering multiple species of ECM fungi can inhabit a single root system, many questions remain regarding the drivers of colonization, why some species are more efficient at taking up and exchanging nutrients with their hosts than others, and why certain fertilizers directly affect the morphology of ECM growth. The purposes of this review here are (1) to explore how the most commonly commercially applied macronutrients, N, P, and K, affect the relationship between loblolly pine and ECM communities, and (2) to propose future directions to investigate, preserve, and manipulate these interactions in pine plantations to optimize productivity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Development of a Unified Taxonomy for HVAC System Faults

Detecting and diagnosing HVAC faults is critical for maintaining building operation performance, reducing energy waste, and ensuring indoor comfort. An increasing deployment of commercial fault detection and diagnostics (FDD) software tools in commercial buildings in the past decade has significantly increased buildings’ operational reliability and reduced energy consumption. A massive amount of data has been generated by the FDD software tools. However, efficiently utilizing FDD data for ‘big data’ analytics, algorithm improvement, and other data-driven applications is challenging because the format and naming conventions of those data are very customized, unstructured, and hard to interpret. This paper presents the development of a unified taxonomy for HVAC faults. A taxonomy is an orderly classification of HVAC faults according to their characteristics and causal relations. The taxonomy includes fault categorization, physical hierarchy, fault library, relation model, and naming/tagging scheme. The taxonomy employs both a physical hierarchy of HVAC equipment and a cause-effect relationship model to reveal the root causes of faults in HVAC systems. A structured and standardized vocabulary library is developed to increase data representability and interpretability. The developed fault taxonomy can be used for HVAC system ‘big data’ analytics such as HVAC system fault prevalence analysis or the development of an HVAC FDD software standard. A common type of HVAC equipment-packaged rooftop unit (RTU) is used as an example to demonstrate the application of the developed fault taxonomy. Two RTU FDD software tools are used to show that after mapping FDD data according to the taxonomy, the meta-analysis of the multiple FDD reports is possible and efficient.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Discerning the thermodynamic feasibility of the spontaneous coexistence of multiple functional vegetation groups

Can the Second Law of Thermodynamics explain why ecosystems naturally organize into a complex structure composed of multiple vegetation species and functional groups? Ecosystem structure, which refers to the number and type of plant functional groups, is the result of self-organization, or the spontaneous emergence of order from random fluctuations. By considering ecosystems as open thermodynamic systems, we model and study these fluctuations of throughput signatures on short timescales to determine the drivers and characteristics of ecosystem structure. This diagnostic approach allows us to use fluxes of energy and entropy to calculate an ecosystem’s estimated work and understand the thermodynamic behavior of the system. We use a multi-layer canopy-root-soil model to calculate the energy and entropy fluxes of different scenarios for field sites across various climates. At each site, scenarios comprised of native individual plant functional groups and a coexisting multi-group composition scenario including all functional groups observed at the site are compared. Ecosystem-scale calculations demonstrate that entropy fluxes and work efficiency—the work performed for the amount of radiation entering the ecosystem—are greatest in the multi-group scenario when its leaf area is significantly larger than each of its individual functional groups. Thus, we conclude that ecosystems self-organize towards the vegetation structure with the greatest outgoing entropy flux and work efficiency, resulting in the coexistence of multiple functional groups and performing the maximum amount of work within the constraints of locally available energy, water, and nutrients.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Uncertainty quantification and optimization of precipitating hydrometeor parameters for winter precipitation in a cloud microphysics scheme

The precipitating hydrometeor parameters used in cloud microphysics schemes carry inherent uncertainties. The quantification of these uncertainties, together with parameter optimization, can significantly improve precipitation forecasts. This study investigates the effects of 13 parameters in the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme, which define the hydrometeor characteristics such as fall velocity–diameter and mass–diameter relationships, as well as the shape parameter of the drop size distribution for precipitating particles such as rain, snow, and graupel on simulated winter precipitation. A comparison between the model's pre-defined parameters and observations from the International Collaborative Experiments for the PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018) field campaign reveals that the fall velocity–diameter relationship for rain, the mass–diameter relationships for snow and graupel, and the shape parameters for all precipitating particles in the WDM6 scheme deviate from the median values observed by the two-dimensional video disdrometer (2DVD). To quantify parameter sensitivities, a perturbed parameter ensemble (PPE) of 256 simulations was conducted within parameter ranges constrained by 2DVD observations for three winter precipitation cases. Bayesian optimization was then applied to identify parameter sets that minimized the root mean square error (RMSE) for each case, achieving reductions of up to 30.2 %. These results demonstrate that ensemble-based uncertainty quantification and parameter optimization can help identify key parameters and provide a pathway to improving precipitation simulation performance. In addition, measurement sites can be strategically selected based on regions that show high sensitivity to variations in hydrometeor characteristic parameters.

Bayesian optimization↗