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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Chlorophyll- a dynamics in the lower Amazon River: insights from in situ and hyperspectral remote sensing using OCI-PACE

Chlorophyll-a concentration (Chla) is a key indicator of phytoplankton biomass and aquatic trophic status. However, satellite-derived Chla in sediment-rich waters, such as those found in the Lower Amazon River, remains challenging. The present study characterizes in situ Chla levels and their relationships with geographic, physical, and biogeochemical parameters in the Lower Amazon. Data collected between 2014 and 2017 across four hydrological seasons included measurements of Chla, remote sensing reflectance, and water quality parameters such as total suspended sediment, conductivity, water surface temperature, dissolved oxygen, pH, dissolved organic carbon and coloured dissolved organic matter. An empirical model was developed to estimate Chla using simulated hyperspectral bands from NASA’s PACE mission, achieving high performance (R 2 = 0.76; RMSE = 0.11 μg·L −1 ). Red bands proved particularly effective for Chla retrieval, while the addition of ultraviolet bands further enhanced model accuracy. The application of the developed model to satellite imagery yielded results consistent with in situ observations for the same hydrologic season. Seasonal variation and geographic location were major factors influencing Chla dynamics. This study provides a novel contribution to Chla estimation in optically complex, highly turbid waters and highlights the potential of the PACE mission to enhance global aquatic ecosystem monitoring. In conclusion, by offering freely available hyperspectral data with high radiometric resolution, PACE represents a significant advancement in the realm of remote sensing of aquatic environments.

Amazon River↗
DOE OSTI2024

Importance of viewing angle: Hotspot effect improves the ability of satellites to track terrestrial photosynthesis

The product of near-infrared reflectance of vegetation and photosynthetic active radiation (NIRvP) is a new tool for monitoring gross primary productivity (GPP) dynamics in terrestrial ecosystems, due to the discovered linear correlation between NIRvP and GPP. While remote sensing-based NIRvP is considerably influenced by sensor geometry, such geometry impacts on the NIRvP-GPP relationship remain underexplored. In this study, we calculate NIRvP using observations from the Deep Space Climate Observatory (DSCOVR) that provide unique hotspot observation geometry in which the sensor viewing angle coincides with the sun direction. We evaluated the linear correlation between NIRvP and GPP in both the common nadir direction and the special hotspot direction. The results indicate that NIRvP in the hotspot direction significantly outperforms that in the nadir direction for tracking GPP variations across different ecosystems from diurnal to daily scales. This conclusion is further supported by data from the MODerate resolution Imaging Spectroradiometer (MODIS) and simulations using the Soil Canopy Observation Photosynthesis Energy (SCOPE) model. Finally, our research highlights the value of using the unconventional hotspot-based sun-tracking satellite observations for a more accurate characterization of GPP dynamics in terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Characterization of volcanic tuff pores pre- and post-underground nuclear detonation using ultra-small and small angle neutron scattering

The ability to accurately model the subsurface transport of radionuclides is fundamental to the remote detection and characterization of underground nuclear explosion (UNE) events. Developing more sophisticated transport models presents a significant opportunity to enhance monitoring capabilities, particularly in the reliable prediction of signature migration. Experimentally determined characterization of geologic materials associated with transport properties is the pertinent base information for such robust model development and calibration. Here, we report results from an unprecedented study demonstrating changes to the pore and fracture network structures in geological materials in response to UNEs over nanometer to micrometer length scales. Volcanic tuffs of five different lithological formations from pre- and post-UNE environments were collected from the Nevada National Security Site. Combined ultra-small and small-angle neutron scattering techniques were used to characterize the tuff pore structure. The results demonstrate measurable differences in the specific surface area and porosity of samples pre- and post-shot from texturally similar lithological formations, indicating that pore properties can serve as a direct physical signature of a UNE. The results also provide experimentally determined transport parameters in support of advanced model development through the integration of gas migration, hydrodynamic simulations, and geologic framework models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Enhanced Laser-Induced Graphene Microfluidic Integrated Sensors (LIGMIS) for On-Site Biomedical and Environmental Monitoring

The convergence of microfluidic and electrochemical biosensor technologies offers significant potential for rapid, in-field diagnostics in biomedical and environmental applications. Traditional systems face challenges in cost, scalability, and operational complexity, especially in remote settings. Addressing these issues, laser-induced graphene microfluidic integrated sensors (LIGMIS) are presented as an innovative platform that integrates microfluidics and electrochemical sensors both comprised of laser-induced graphene. This study advances the LIGMIS concept by resolving issues of uneven fluid transport, increased hydrophobicity during storage, and sensor biofunctionalization challenges. Key innovations include Y-shaped reservoirs for consistent fluid flow, hydrophilic polyethyleneimine coatings to maintain wettability, and separable microfluidic and electrochemical components enabling isolated electrode nanoparticle metallization and biofunctionalization. Multiplexed electrochemical detection of the neonicotinoid imidacloprid and nitrate ions in environmental water samples yields detection limits of 707 nm and 10 -5.4 m with wide sensing ranges of 5–100 µm and 10 -5 –10 -1 m, respectively. Similarly, uric acid and calcium ions are detected in saliva, demonstrating detection limits of 217 nm and 10 -5.3 m with sensing ranges of 10–50 µm, and 10 -5 –10 -2.5 m, respectively. Overall, this biosensing demonstrates the capability of the LIGMIS platform for multiplexed detection in biologically complex solutions, with applications in environmental water quality monitoring and oral cancer screening.

environmental monitoring↗
DOE OSTI2026

Sedimentation and Nonlinear Trapping in Texas Reservoirs Identified Using Remote Sensing and Bathymetric Survey Records

Decreasing reservoir storage capacity due to sedimentation poses great challenges to aging U.S. reservoirs, as it reduces the efficacy and reliability of their socio‐economic services. However, systematic assessments of reservoir sedimentation rates and associated issues remain limited because of sparse and infrequent bathymetry survey data. In this study, we use remote sensing‐driven estimates of sediment concentrations to identify regions experiencing rapid reservoir capacity loss, as observed in repeated bathymetry surveys. Our analysis focuses on Texas, where one of the most reliable state‐level reservoir capacity loss data sets is available through a unique long‐term monitoring program by the Texas Water Development Board. We find that reservoirs with large storage capacities and high sedimentation rate are concentrated in Northeast Texas. We also show that reduced forest, increased barren land, and erosive soil properties are co‐varying with high reservoir sedimentation rates. Temporal changes in the longitudinal gradient of the remotely sensed sediment flux highlight the nonlinear nature of sediment trapping processes, and can be used to estimate the reservoir storage capacity loss over time. In combination with standard bathymetry surveys, our approach shows potential for more cost‐effective and frequent assessment of reservoir storage loss.

Lee, Jiyong [ORNL] (ORCID:0000000198957406)↗
DOE OSTI2025

Basal Melting and Oceanic Observations Beneath Central Fimbulisen, East Antarctica

Abstract Basal melting of ice shelves is fundamental to Antarctic ice sheet mass loss, yet direct observations remain sparse. We present the first year‐round melt record (2017–2021) from a phase‐sensitive radar on Fimbulisen, one of the fastest flowing ice shelves in Dronning Maud Land, East Antarctica. The observed long‐term mean ablation rate at 350 m depth below the central ice shelf was 1.0 ± 0.5 m yr −1 , marked by substantial sub‐weekly variability ranging from 0.4 to 3.5 m yr −1 . 36‐h filtered basal melt rate fluctuations closely align with ocean velocity. On seasonal time scales, melt rates peak during austral spring to autumn (September–March), driven by both elevated ocean velocities and thermal driving near the base. The combined effect of thermal driving and current speed explains the majority of the melt rate variability ( r = 0.84), highlighting the dominant role of shear‐driven turbulence. This relationship enables parameterization of melt rates for the decade‐long ocean record (2010–2021), although deviations appear under low and high forcing conditions. Both observed and parameterized melt rates show similar yearly mean magnitudes compared to satellite‐derived melt rates but with a tenfold lower seasonal amplitude and a 3‐month delay in seasonality. These detailed concurrent ice–ocean observations provide essential validation data for remote sensing and numerical models that aim to quantify and project ice‐shelf response to a change in ocean forcing. In situ measurements and continued monitoring are crucial for accurately assessing and modeling future basal melt rates, and for understanding the complex dynamics driving ice‐shelf stability and sea‐level change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Underwater thermomagnetic generator for remote marine thermal energy harvesting and sensing

Thermomagnetic generators offer a promising approach for sustainable power generation in remote marine environments. Here, this study presents the design, thermal modeling, and experimental validation of a passively driven underwater thermomagnetic generator developed for powering ocean observation and monitoring sensors. The generator was evaluated under varying working fluids, thermal boundary conditions, and extended operation to assess real-world applicability. Two fluids, deionized water and silicone-based Thermal C5, were tested under simulated shallow- and deep-ocean conditions. Deionized water outperformed Thermal C5, especially in colder environments (~5°C), achieving a peak output of 2.7 mW due to larger temperature gradients and enhanced convective-evaporative heat transfer. Long-duration tests revealed a transient evaporation-condensation cycle that temporarily reduced rotor immersion and performance before stabilizing. The generator powered commercial marine sensors for over 6 h without external batteries, demonstrating the viability of compact, passively cooled thermomagnetic systems for autonomous, off-grid marine sensing.

13 HYDRO ENERGY↗
DOE OSTI2025

Leveraging transfer learning and leaf spectroscopy for leaf trait prediction with broad spatial, species, and temporal applicability

Accurate and reliable prediction of leaf traits is crucial for understanding plant adaptations to environmental variation, monitoring terrestrial ecosystems, and enhancing comprehension of functional diversity and ecosystem functioning. Currently, various approaches (e.g., statistical, physical models) have been developed to estimate leaf traits through hyperspectral remote sensing and leaf spectroscopy. However, the absence of high-performing, transferable, and stable models across various domains of space, plant functional types (PFTs) and seasons hinder our ability to quantify and comprehend spatiotemporal variations in leaf traits. This study proposes robust and highly transferable models for better predicting leaf traits with hyperspectral reflectance. Initially, three datasets were assembled, pairing common leaf traits — chlorophyll (Chla+b), carotenoids (Ccar), leaf mass per area (LAM), equivalent water thickness (EWT) — with leaf spectra measurements collected across diverse geographic locations in the U.S. and Europe, PFTs, and seasons. Measurements were acquired using spectroradiometers (e.g., ASD FieldSpec 3/4/Pro and SVC HR-1024i) with integrating spheres, leaf clips, and contact probes. Here, we then developed transfer learning-based hybrid models that incorporated the domain knowledge of radiative transfer models (RTMs) through pretraining processes and were well-constrained by fine-tuning with field measurements. Through comparison with other state-of-the-art statistical models, including partial-least squares regression (PLSR) and Gaussian Process Regression (GPR), as well as pure physical models, we found that the proposed transfer learning models achieved better predictive performance and higher transferability. Specifically, compared to other statistical models and pure RTMs, the transfer learning model exhibited higher coefficient of determination (R 2 ) values with range of 0.01 to 0.79, lower normalized root mean square error (NRMSE) with range of 0.06 % to 33.25 % in model performance. Additionally, the models exhibited improved transferability, with higher R 2 values range from 0.04 to 0.32, lower NRMSE range from 0.08 % to 30.81 %. The findings underscore that transfer learning models through integrating domain knowledge from RTMs and limited observations, can harness the advantages of both RTMs and statistical models and serve as a promising approach for effectively predicting leaf traits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Advanced Laboratory and Field Arrays (ALFA)/Lab Collaboration Project (LCP) for Marine Energy (Final Scientific/Technical Report)

The objective of the Advanced Laboratory and Field Arrays (ALFA) project was to reduce the Levelized Cost of Energy (LCOE) of Marine and Hydrokinetic (MHK) energy by leveraging research, development, and testing capabilities at Oregon State University, University of Washington, and the University of Alaska, Fairbanks. ALFA is a project within the Pacific Marine Energy Center (PMEC; formerly NNMREC), a multi-institution entity with a diverse funding base that focuses on research and development for marine renewables. The ALFA project aimed to accelerate the development of next-generation arrays of wave energy conversion (WEC) and tidal energy conversion (TEC) devices through a suite of field-focused R&D activities spanning a broad range of strategic opportunity areas identified in the Funding Opportunity Announcement: • Device and/or array operation and maintenance (O&M) logistics development; • High-fidelity resource characterization and/or modeling technique development and validation; • Array-specific component technology development (e.g. moorings and foundations, transmission, and other offshore grid components); • Array performance testing and evaluation; and • Novel cost-effective environmental monitoring techniques and instrumentation testing and evaluation. The objective of the Lab Collaboration Project (LCP) was to accelerate the development of next-generation marine energy conversion systems. The LCP aimed to achieve these project objectives in collaboration with the national laboratories by: • Developing concept generation and assessment tools; • Improving access to existing testing resources; • Validating collision risk models between fish and turbines; and • Advancing analysis and simulation capabilities for wave-WEC interactions and PTO analysis in nonlinear ocean waves. The ALFA portion of the project was comprised of six overarching technical tasks: • Task 1: Debris Modeling, Detection and Mitigation; • Task 2: Autonomous Monitoring & Intervention; • Task 3: Resource Characterization for Extreme Conditions; • Task 4: Robust Models for Design of Offshore Anchoring and Mooring Systems; • Task 5: Performance Enhancement for Marine Energy Converter (MEC) Arrays; and • Task 6: Evaluating Sampling Techniques for MHK Biological Monitoring. The LCP was divided into four overarching technical tasks: • Task 7: Project Management and Reporting • Task 8: Novel Design and Assessment Methodologies for Wave Energy Converter Design (Wave- SPARC) • Task 9: Testing Access for Commercial Marine Renewable Energy Technology Developers • Task 10: Quantifying Collision Risk for Fish and Turbines • Task 11: Nonlinear Ocean Waves and PTO Control Strategy Each ALFA/LCP task listed above functioned as a separate and discreet project. A final Technical Report was written for each individual task and these reports were uploaded to OSTI, after receiving DOE approval. The following document is a compilation of each of these final, approved reports arranged as individual chapters.

13 HYDRO ENERGY↗
DOE OSTI2025

Optimized V1G and V2G Electric Vehicle Fleet Management and Grid Transaction at Marine Corps Air Station Miramar in San Diego, CA

The overall technical goal of the project was to demonstrate an all-electric bi-directional non-tactical fleet at Marine Corps Air Station (MCAS) Miramar that was integrated and controlled with other distributed energy resources (DERs) (i.e., PV, stationary battery, and building loads) to provide resilience to critical electric loads in the event of grid outages, to minimize charging costs, and to provide economic energy resources to electricity markets. In this project, the specific, technical objectives were: 1. Demonstrate that bi-directional electric vehicles can provide critical complementary services to fixed storage batteries in microgrid applications while performing function as non-tactical vehicles. 2. Demonstrate participation of bi-directional (V2G) and unidirectional (V1G) PEVs for demand management and minimization of charging costs. 3. Demonstrate integration of multiple DERs for grid service participation. US Marine Corps Air Station (MCAS) Miramar in San Diego was the site of this electric vehicle-to-microgrid-utility grid test and demonstration project. Existing microgrid assets in this study included (1) a public works building; (2) a 30-kW rooftop photovoltaic (PV) system and (3) a separate 250 kW carport PV system. In this project, six bi-directional V2G vans were located at the MCAS Miramar’s showcase building-scale microgrid to develop and test technical capabilities that V2G can provide in microgrid applications (e.g., cost reduction and resiliency). These resources provided aggregated demand management and simulated participation in current retail DR programs. The vehicles used in this demonstration were selected because they provided functionality that MCAS Miramar needed, 15 passenger transport and facilities work cargo carrying capacity, and bi-directional charging capability that the research project required. All vehicles in this study were manufactured and distributed by VIA Motors, Inc. There were six vehicles total and each was VIA’s VTRUX eREV V2G model, a modified General Motors Chevrolet 2500 2WD van. Three of the vans were configured as passenger vans and the other three were configured as cargo vans. Each van had an on-board bi-direcrtional inverter/charger, Bel Power Solutions model 350INVCHGT150-120-240-8G nominally rated at +/-15 kW. The VIA van’s charging connector follows the J1772 charging protocol. The bi-directional EVSEs demonstrated in this study were manufactured by Coritech, Inc. Each VGI-80-AC charging station enabled enhanced V2G charging capability to a Clipper Creek CS-100 charging module. The enhanced capabilities included ethernet communication following the SEP2.0 protocol with a distributed energy resource function set and an operator screen displaying real-time SOC, voltage, and current. The VGI-80-AC charging stations are classified as level 2 with a maximum current output of 80 A or effectively 19 kW. The VIA van’s onboard charger limited the charging and discharging power to 15 kW in each direction. A control computer was installed in the EWOC and connected to an existing monitor. The V2G control communication network was a completely stand-alone closed system that did not have any connection to any other networks on the base. A cybersecure remote communication connection was created with a cellular modem, firewall hardware, and a virtual private network configuration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Daily Arctic Lightning Strokes

In recent decades, lightning activity at high latitudes has increased. Tall thunderstorm clouds affect radiation balance directly, as well as indirectly through lightning-initiated fires and the resulting smoke. One can remotely sense lightning strokes over the globe through their VLF radio emission, and, with multiple receivers, it is possible to precisely locate lightning strokes. This technique makes it possible to continuously monitor arctic lightning--a capability not possible by other means. In this research effort, a World-Wide Lightning Location Network station, consisting of a VLF receiver, signal processing hardware, and analysis software, were installed at the North Slope of Alaska (NSA) facility and planned to operate for several years. This far north location is expected to improve the network's high-latitude detection efficiency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Analyzing Risks of Virtual Private Network Connections

The use of Splunk for analyzing VPN logs is an effective approach for identifying vulnerabilities in network endpoints. Splunk, a powerful platform for searching, monitoring, and analyzing machine-generated data, enables organizations to aggregate VPN logs in real-time, providing insights into network activity, user behavior, and potential security risks. By indexing VPN traffic and authentication logs, security teams can track abnormal patterns such as multiple failed login attempts, unusual IP addresses, or unexpected changes in bandwidth usage, all of which could indicate potential vulnerabilities or breaches. With Splunk’s advanced search and reporting capabilities, users can create custom dashboards and alerts to detect suspicious activities. Automated searches can flag endpoints exhibiting unusual behavior, while correlation analysis can identify links between compromised devices and broader network vulnerabilities. In particular, Splunk's machine learning capabilities can be leveraged to predict and prevent threats by identifying trends that might otherwise be missed in traditional log analysis. This proactive approach to monitoring VPN logs allows for the early detection of security weaknesses, enabling rapid response and minimizing potential damage to network integrity. By enhancing endpoint visibility, Splunk plays a crucial role in securing remote connections and safeguarding sensitive information. Additionally, Splunk’s automation and alerting features allow teams to create custom workflows that notify them of vulnerable or misconfigured endpoints identified through Shodan. This synergy between Splunk’s log analysis and Shodan’s device intelligence enhances an organization’s ability to proactively identify and mitigate security risks, improving the overall resilience of their VPN infrastructure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Energy Efficiency Assistance for Galena Alaska

The City of Galena sits along the north bank of the Yukon River with no road access. Temperatures regularly reach negative 20°F in the winter and sunlight extends for up to 21 hours a day in the summer. Galena’s 400 residents, including members of the Koyukuk Athabascan culture, primarily use fuel oil and locally forested wood for heating and electricity generation. Because these resources are costly to obtain, local households have a high energy burden, spending 7% of their annual income on energy (averaging $4,000 per year). To reduce this burden, the City of Galena joined the U.S. Department of Energy’s (DOE) Remote Alaska Community Energy Efficiency (RACEE) Competition, where they received financial and technical assistance to implement energy savings solutions across their community, including building envelope improvements, solar panels, and LED lighting. Additionally, Galena installed new energy monitoring equipment to continue measuring the impacts of this work beyond the RACEE project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

PAVC: The foundation for a Pan-Arctic Vegetation Cover database

Field-measured Arctic vegetation cover data is essential for creating accurate, high-quality vegetation structure and composition maps. Extrapolating field data into high-resolution cover maps provides detailed, function-specific information for use in Earth System Models, vegetation classifications, and monitoring vegetation change over time and space. However, field campaigns that collect plant cover vary substantially in scope, method, and purpose, which makes them difficult to unify across data stores, and they are often not designed to meet remote sensing needs. In this work, we synthesized and harmonized field-based fractional cover data from various data stores to create a high-quality, consistent repository schema for remote sensing-based vegetation cover mapping applications. We developed a reproducible workflow for synthesizing visual estimate and point-intercept fractional cover data. The resultant Pan-Arctic Vegetation Cover (PAVC) database contains synthesized fractional cover at both the species and plant functional type levels. The latter includes absolute foliar cover for deciduous shrubs and trees, evergreen shrubs and trees, forbs, graminoids, lichen, bryophytes, and “other” vegetation, as well as absolute cover for litter and top cover for water and bare ground.

Steckler, Morgan R. [Oak Ridge National Laboratory↗
DOE OSTI2025

Remote Americium Detection Using an Optical Sensor: A D-Optimal Strategy for Efficient PLS-Based Modeling

A fiber-optic visible–near-infrared absorption spectroscopy system in a glove box was demonstrated for remote quantification of Am(III) (0–500 µM) and HNO 3 (0.1–9 M) using partial least squares regression (PLSR) models. The sensor platform, featuring a simple plug-and-play spectrophotometer, can enable noninvasive, real-time monitoring of actinide process solutions. To establish a flexible PLSR model calibration strategy, a D-optimal design developed using Nd(III) in previous studies was successfully extended to an actinide system with Am(III) to effectively minimize sample set size while maintaining robust prediction performance. The results suggest strong spectral similarities between Nd(III) and Am(III) and validate Nd(III) as an effective optical surrogate for trivalent actinide species. This work also supports the generalizability of a D-optimal training set selection approach for two-factor systems. The PLS1 models for Am(III) and HNO 3 outperformed a PLS2 model and maintained reasonable performance in the presence of interfering U(VI). The resulting sensor system and multivariate approach provides a flexible and scalable solution for process monitoring, control, and safety in diverse nuclear applications.

actinide↗
DOE OSTI2024

Beyond the Bridge: Contention-Based Covert and Side Channel Attacks on Multi-GPU Interconnect

Recently, high-speed interconnects like NVLink have become a standard integration in modern multi-GPU systems, serving as a crucial bridge between CPUs and GPUs. This study highlights the vulnerability of multi-GPU systems to covert and side-channel attacks arising from congestion on these interconnects. An adversary, operating without special permissions, can extract private information about a victim's activities by monitoring NVLink congestion. Specifically, we leverage this insight to develop a covert channel attack across two GPUs, achieving a respectable bandwidth of 33 Kbps. Additionally, we introduce a side-channel attack that allows attackers to fingerprint applications on a remote GPU by probing the shared NVLink interconnect. We underscore the urgent nature of the threats posed by these attacks and advocate for heightened awareness within both industry and academia to implement multi-GPU interconnects with enhanced security measures.

Zhang, Yicheng↗
DOE OSTI2025

MOSAiC studies of long-lasting mixed-phase cloud events and analysis of the liquid-phase properties of Arctic clouds

Vertically resolved observations of the temporal evolution of mixed-phase clouds (MPCs) were performed over the central Arctic during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition, which lasted from October 2019 to September 2020. The research icebreaker Polarstern , drifting with the pack ice for more than 7 months, mostly at latitudes > 85° N, served as a platform for state-of-the-art remote sensing of aerosols and clouds. The use of the recently introduced dual field-of-view (FOV) polarization lidar technique in combination with the well-established lidar-radar retrieval technique provided, for the first time, a robust instrumental basis to monitor the evolution of the liquid and the ice phase of MPCs and the interplay between the two phases. Two long-lasting Arctic MPC events observed close to the North Pole in mid-winter (December 2019) and late summer (September 2020) are discussed to provide new insight into Arctic MPC evolution processes. In the second part of the article, cloud statistics, covering all seasons of a year, are presented. The focus is on the optical and microphysical properties of the liquid phase. These results are solely derived from the dual-FOV lidar observations. The key findings of the study can be summarized as follows: persistent activation of aerosol particles to form water droplets is of great importance for the longevity of MPCs. The observations confirm that ice formation occurs predominantly via immersion freezing. The field studies suggest that the free tropospheric reservoirs of cloud condensation nuclei (CCN) and of ice-nucleating particles (INPs) were always well filled, i.e., the clouds did not exhaust their supply of activatable and activated particles. The observation of long-lasting MPC events, low ice production rates, and a sufficiently large INP reservoir leads to the recommendation to use a time-dependent immersion freezing parameterization in MPC modeling efforts.

Jimenez, Cristofer [Leibniz Inst. for Tropospheric↗