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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Characterization of wind speed and directional shear at the AWAKEN field campaign site

The American wake experiment (AWAKEN) is taking place in northern Oklahoma, USA, close to the Atmospheric Radiation Measurement Southern Great Plains (ARM SGP) atmospheric observatory. The planning for the deployment of the instruments in this observational field campaign required an assessment of the wind characteristics of the site. This paper analyzes long-term data collected by instruments at the ARM SGP observatory to characterize the winds near the AWAKEN site. The analysis shows that this site experiences high wind shear and veer events with a large number of nocturnal low-level jets. A total of 7086 low-level jet wind profiles over 6 years are examined and found to be dominant from the south and southeast. Significant nocturnal wind veer is observed, which causes southerly wind near the surface to become westerly wind aloft. By identifying a strong relationship between atmospheric stability and wind shear, the wind shear at the site is predicted using the Monin–Obukhov similarity theory (MOST) and validated with the observational data collected by a scanning Doppler lidar. The results show that wind speed at a height of 91 m, a proxy hub height for wind turbines in this area, can be predicted from data collected at a height of 10 m with a bias of −0.35 and 0.65 m s−1 in unstable and stable atmospheric boundary layers, respectively. The bias of the predicted wind speed is mostly in the region of low wind speed, and wind speed above 5 m s−1 at a height of 91 m can be predicted with a bias of less than 0.2 m s−1, and the limitations of the MOST in predicting winds during the stably stratified boundary layer is well-observed.

17 WIND ENERGY↗
DOE OSTI2021

Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data

Abstract. Tikhonov regularization is a tool for reducing noise amplification during data inversion. This work introduces RegularizationTools.jl, a general-purpose software package for applying Tikhonov regularization to data. The package implements well-established numerical algorithms and is suitable for systems of up to ∼ 1000 equations. Included is an abstraction to systematically categorize specific inversion configurations and their associated hyperparameters. A generic interface translates arbitrary linear forward models defined by a computer function into the corresponding design matrix. This obviates the need to explicitly write out and discretize the Fredholm integral equation, thus facilitating fast prototyping of new regularization schemes associated with measurement techniques. Example applications include the inversion involving data from scanning mobility particle sizers (SMPSs) and humidified tandem differential mobility analyzers (HTDMAs). Inversion of SMPS size distributions reported in this work builds upon the freely available software DifferentialMobilityAnalyzers.jl. The speed of inversion is improved by a factor of ∼ 200, now requiring between 2 and 5 ms per SMPS scan when using 120 size bins. Previously reported occasional failure to converge to a valid solution is reduced by switching from the L-curve method to generalized cross-validation as the metric to search for the optimal regularization parameter. Higher-order inversions resulting in smooth, denoised reconstructions of size distributions are now included in DifferentialMobilityAnalyzers.jl. This work also demonstrates that an SMPS-style matrix-based inversion can be applied to find the growth factor frequency distribution from raw HTDMA data while also accounting for multiply charged particles. The outcome of the aerosol-related inversion methods is showcased by inverting multi-week SMPS and HTDMA datasets from ground-based observations, including SMPS data obtained at Bodega Marine Laboratory during the CalWater 2/ACAPEX campaign and co-located SMPS and HTDMA data collected at the US Department of Energy observatory located at the Southern Great Plains site in Oklahoma, USA. Results show that the proposed approaches are suitable for unsupervised, nonparametric inversion of large-scale datasets as well as inversion in real time during data acquisition on low-cost reduced-instruction-set architectures used in single-board computers. The included software implementation of Tikhonov regularization is freely available, general, and domain-independent and thus can be applied to many other inverse problems arising in atmospheric measurement techniques and beyond.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Use of Satellite, Surface Observations and Numerical Weather Prediction Model Data to Improve Cloud Base Height and Cloud Base Vertical Velocity Estimation

Cloud base height (CBH) and cloud base vertical velocity (CBVV) are important variables that impact the overall climate in a region as they influence the formulation, longevity, and evolution of clouds. Retrieval of both parameters have long used ground instrumentation (e.g., Doppler lidar (DL), ground base radar); however, retrieving CBH from satellites is particularly challenging given that space-based instruments only observe cloud tops. In this manuscript, CBH is retrieved using a multi-linear regression equation, while CBVV used a random forests model. Both retrievals combine satellite and numerical weather prediction data. The satellite data used are the Visible Infrared Imaging Radiometer Suite imagery, while measurements of CBH and CBVV include DL and radiosonde data at the Southern Great Plains (SGP) Atmospheric Radiation Measurement observatory. Data from 83 summer days (May-August) in 2018–2021 featuring cumulus clouds forced by solar heating were examined and used to train the models, with years 2022–2023 used for validation. Various spatial domains were defined with one large (2.4° longitude by 2.0° latitude) SGP domain being split into smaller sections (smallest being 0.99° and 0.61° longitude and latitude respectably). CBH and CBVV values obtained from the DL as compared to the models show root mean square errors between 150 and 200 m, with CBVV values between 0.45 and 1 ms -1 . Finally, it was found that the CBH formulation performs well over all domains, while the CBVV retrievals become less accurate due to more turbulence being introduced into the observations as the number of DL stations decreases in the smaller domains.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Climatological sensitivities of shallow-cumulus bulk entrainment in continental and oceanic locations

Cumulus entrainment is a complex process that has long challenged conceptual understanding and atmospheric prediction. To investigate this process observationally, two retrievals are used to generate multi-year climatologies of shallow-cumulus bulk entrainment ( ϵ ) at two Atmospheric Radiation Measurement cloud observatories, one in the US southern Great Plains (SGP) and the other in the Azores archipelago in the eastern North Atlantic (ENA). The statistical distributions of ϵ thus obtained, as well as certain environmental and cloud-related sensitivities of ϵ , are consistent with previous findings from large-eddy simulations. The retrieved ϵ robustly increases with cloudlayer relative humidity and decreases in wider clouds and cloud ensembles with larger cloud-base mass fluxes. While ϵ also correlates negatively with measures of cloud-layer vigor (e.g., maximum in-cloud vertical velocity and cloud depth), the extent to which these metrics actually regulate ϵ (or vice-versa) is unclear. Novel sensitivities of ϵ include a robust decrease of ϵ with increasing subcloud wind speed in oceanic flows, as well as a decrease of ϵ with increasing cloud-base mass flux in individual cumuli. A strong land–ocean contrast in ϵ is also found, with median values of 0.5-0.6 km -1 at the continental SGP site and and 1.0-1.1 km -1 at the oceanic ENA site. This trend is associated with drier and deeper cloud layers, along with larger cloud-base mass fluxes, at SGP, all of which favor reduced ϵ . The flow-dependence of retrieved ϵ implies that its various sensitivities should be accounted for in cumulus parameterization schemes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Source Attribution of Methane Enhancements at the Southern Great Plains Atmospheric Radiation Measurement Site Field Campaign Report

Tower data from 2017 to 2020 revealed that the U.S. Department of Energy’s Southern Great Plains (SGP) Atmospheric Radiation Measurements (ARM) observatory in Oklahoma exhibited significant CH 4 enhancements compared to other U.S. tower sites. On average, near-surface CH 4 at the ARM site was higher, had larger diurnal and seasonal variations, and had sharper near-surface increases at night compared to other tower sites in the U.S. A field campaign was conducted in June 2024 to identify and quantify the surrounding CH 4 emission sources by two researchers from the University of Oklahoma. During the campaign, a mobile measurement was conducted using a LI-COR 7810 tracer gas analyzer mounted on a vehicle during June 23-26, 2024 near the SGP site (within 7 km distance). We sampled CH 4 along predefined routes surrounding a suspected source, i.e., a large animal feeding operation (AFO) farm 5.8 km northwest of the SGP ARM site. Measurements were collected between 04:30 and 09:00 local time when the planetary boundary layer (PBL) was shallow, increasing sensitivity to surface emissions. Driving speed was kept below 5 mph to minimize pressure-related artifacts. CH 4 concentrations downwind of the AFO exceeded 6000 ppb on the early morning of June 26, corresponding to an emission rate of ~95 kg⋅hr -1 estimated using the mass balance method. Additional sources identified during the campaign include nearby open-range cattle and leaks from a nearby natural gas pipeline.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

ARM FY2024 Aerosol Operations Plan

The Atmospheric Radiation Measurement user facility (ARM) deploys a suite of aerosol and trace gas (further mentions of aerosols will assume inclusion of trace gases) instrumentation at each of ARM's observatories. ARM currently deploys five Aerosol Observing Systems, one each at: Southern Great Plains (SGP); Eastern North Atlantic (ENA); ARM Mobile Fabilities (AMFI 1, 2, and 3). Aerosol measurements at ARM’s North Slope of Alaska (NSA) observatory have historically been made by the National Oceanic and Atmospheric Administration (NOAA) and provided to ARM through a collaboration. As ARM’s support for aerosol instrumentation increases, it is important for ARM to communicate plans and priorities for aerosol measurements to the community to maximize the benefit and planning around scientific activities and to advance confidence in ARM’s aerosol measurements. ARM will develop a yearly aerosol operations plan for the upcoming fiscal year (October 1-September 30) starting with FY24. This plan will be open and available through the ARM aerosol instrument webpages and will include: Review of the previous activities since the last plan (in this case, 2018); Planned activities and their priorities for the upcoming FY; Calibration timing and efforts; Planned activities for data products and value-added products (VAP).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Solar fusion III: New data and theory for hydrogen-burning stars

In stars that lie on the main sequence in the Hertzsprung-Russell diagram, like our Sun, hydrogen is fused to helium in a number of nuclear reaction chains and series, such as the proton-proton chain and the carbon-nitrogen-oxygen cycles. Precisely determined thermonuclear rates of these reactions lie at the foundation of the standard solar model. This review, the third decadal evaluation of the nuclear physics of hydrogen-burning stars, is motivated by the great advances made in recent years by solar neutrino observatories, putting experimental knowledge of the proton-proton (𝑝⁢𝑝)-chain neutrino fluxes in the few-percent precision range. The basis of the review is a one-week community meeting held in July 2022 in Berkeley, California, and many subsequent digital meetings and exchanges. The relevant reactions of solar and stellar hydrogen burning are reviewed here from both theoretical and experimental perspectives. Recommendations for the state of the art of the astrophysical 𝑆 factor and its uncertainty are formulated for each of them. Furthermore, several other topics of paramount importance for the solar model are reviewed as well: recent and future neutrino experiments, electron screening, radiative opacities, and current and upcoming experimental facilities. In addition to reaction-specific recommendations, general recommendations are also formed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

AmeriFlux FLUXNET-1F US-xWD NEON Woodworth (WOOD)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xWD NEON Woodworth (WOOD). This is the FLUXNET version of the carbon flux data for the site US-xWD NEON Woodworth (WOOD) produced by applying the standard ONEFlux (1F) software. Site Description - The Northern Plains-Woodworth site (Woodworth), is a restored prairie in an undulating matrix of small lakes, ponds and ephemeral water bodies (re. prairie potholes). The site is administered by the Department of the Interior (DOI), National Fish and Wildlife, National Wildlife Refuge. The Northern Plains-Woodworth site is located at ~ 3 miles SE of the town of Woodworth, ND. The Northern Plains-Woodworth site is part of the Chase Lake National Wildlife Refuge and is at the center of one of the largest roadless blocks of native prairie left in the Upper Great Plains.

Network), NEON (National Ecological Observatory↗
DOE OSTI2025

Fixed-Site KAZR b1 Data Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) User Facility operates three fixed-site observatories: Eastern North Atlantic (ENA), North Slope of Alaska (NSA), and Southern Great Plains (SGP). Each fixed site has a wide variety of atmospheric instrumentation that has been collecting data for at least 10 years (NSA and SGP over 25 years). Each site is unique because it represents a different climate state. The environment at ENA is characterized by marine stratocumulus clouds and one of the key scientific areas of focus is the interaction of these clouds with aerosols. At NSA the focus is on arctic climate and cloud and radiative processes in a high-latitude environment. SGP represents a mid-latitude, mid-continent climate that experiences environmental cycles on diurnal and seasonal time scales. The ARM observatories are all meant to improve understanding of atmospheric processes that can then be better incorporated into weather and climate models. Another common thread between the fixed sites is the focus on cloud processes. Each site is equipped with at least one radar that provides continuous remote-sensing observations of clouds and precipitation. This report details the analysis of a1-level radar data at the fixed sites and the process for generating b1-level data. While b1-level data have been produced for ARM campaigns at the mobile facilities, this is the first analysis led by ARM radar mentors to correct data at the fixed sites. In particular, we focus on calibrations and corrections of the Ka-band ARM Zenith Radars (KAZRs) at ENA, NSA, and SGP. Ongoing and future work will involve corrections applied to other fixed-site radars.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Operational Uncrewed Aircraft Systems Development for Meteorology and Atmospheric Physics Field Campaign Report

The overarching goal of the project is to develop integrated small, unmanned aircraft systems (SUAS) capabilities for enhanced atmospheric physics measurements. This team included atmospheric scientists, meteorologists, engineers, computer scientists, geographers, and chemists necessary to evaluate the needs and develop the advanced sensing and imaging, robust autonomous navigation, enhanced data communication, and data management capabilities required to use SUAS in atmospheric physics. The flight campaigns at the Southern Great Plains (SGP) U.S. Department of Energy Atmospheric Radiation Measurement (ARM) observatory were an integrated evaluation of the systems in coordinated field tests. This enabled sensor testing, and validation of SUAS technology and integration of unmanned aircraft into the airspace. The set of experiments enabled evaluation and progression of remote UAS deployment operations with observing atmospheric sensors. Each flight test endeavor was notable for the significant lessons learned during every deployment event. Team size and roles, equipment use, aircraft instruments, meteorological instruments, and deployment logistics, were all evaluated and improved at every flight-testing day. The details of these are further discussed in the Results section.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Characterizing Subsiding Shells in Shallow Cumulus Using Doppler Lidar and Large‐Eddy Simulation

Abstract The existence of subsiding shells on the periphery of shallow cumulus clouds has major implications concerning the parameterization of shallow convection, with the mass exchange between the shell and cloudy air representing a significant deviation from the commonly used bulk‐plume parameterization. We examine the structure and frequency of subsiding shells in shallow cumulus convection using Doppler lidars at the Atmospheric Radiation Measurement Southern Great Plains facility in the central United States and at the Jülich ObservatorY for Cloud Evolution in western Germany. Doppler lidar indicates that the vertical subsiding shell extent is asymmetric, while shell width is typically ~100 m. Large‐eddy simulation can reasonably simulate the observed shell structure using a grid spacing of 10 m and suggests that much of the observed asymmetry is not a result of transient cloud evolution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

AmeriFlux FLUXNET-1F US-xNG NEON Northern Great Plains Research Laboratory (NOGP)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xNG NEON Northern Great Plains Research Laboratory (NOGP). This is the FLUXNET version of the carbon flux data for the site US-xNG NEON Northern Great Plains Research Laboratory (NOGP) produced by applying the standard ONEFlux (1F) software. Site Description - This site is located at the Northern Great Plains Research Laboratory

Network), NEON (National Ecological Observatory↗
DOE OSTI2023

LSE-163: Data Products Definition Document

This document describes the data products and processing services to be delivered by the NSF-DOE Vera C. Rubin Observatory whilst performing the Legacy Survey of Space and Time (LSST). LSST will deliver three levels of data products and services. Prompt data products are computed and released within 24 hours of observation, and include images, difference images, catalogs of sources and objects detected in difference images, and catalogs of Solar System objects. Their primary purpose is to enable rapid follow-up of time-domain events. Data Release data products are computed during annual processing campaigns, and include well-calibrated single-epoch images, deep coadds, and catalogs of objects, sources, and forced sources, enabling static sky and precision time-domain science. The Science Platform will allow for the creation of User Generated data products and will enable science cases that greatly benefit from co-location of user processing and/or data within the Rubin Observatory Data Access Center. LSST will also devote 10% of observing time to programs with special cadence. Their data products will be created using the same software and hardware as Prompt and Data Release products. All data products will be made available using user-friendly databases and web services.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

ARM data-oriented metrics and diagnostics package for climate model evaluation (ARM-DIAGS-V3) version 3

A Python-based metrics and diagnostics package is currently being developed by the ARM Infrastructure Team at Lawrence Livermore National Laboratory to facilitate the use of long-term high frequency measurements from the ARM program in evaluating the regional climate simulation of clouds, radiation, precipitation, and aerosols. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The CMIP model data sets are also included in the package to enable model inter-comparison as demonstrated in Zhang et al. (2018) and Zhang et al. (2020). The mean of the CMIP model can be served as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, aerosol optical depth, and radiative fluxes, with plan to extend to more fields, such as the evaluation of model simulated aerosol physicochemical properties and cloud microphysics properties. Process-oriented diagnostics focusing on aerosol, cloud, and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. In addition to the Southern Great Plains (SGP), North Slope of Alaska (NSA) and Tropical Western Pacific (TWP) atmospheric observatories in the ARMDIAGS version 2.0, the version 3.0 package have extended to the data collected at the ARM Eastern North Atlantic (ENA) atmospheric observatory and the Observation and Modeling of the Green Ocean Amazon (GOAMAZON) field campaign. The metrics and diagnostics package are currently built upon standard Python libraries and additional Python packages developed by DOE (CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first provide an overview of major metrics in section 2. The input data, which constitutes the core content of the metrics and diagnostics package, is summarized in section 3. A user's guide documenting the workflow/structure of the version 3.0 codes and including step-by-step instruction for running the package is described in section 4.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

AmeriFlux CA-TPA Ontario Turkey Point Observatory Agricultural Site

This is the AmeriFlux version of the carbon flux data for the site CA-TPA Ontario Turkey Point Observatory Agricultural Site. Site Description - This agricultural flux tower site is located about 15 km southwest of Simcoe in southern Ontario, Canada. It was planted with corn (Zea mays) in 2020 and 2021, sweet potato (Ipomoea batatas) in 2022 and tobacco (Nicotiana tabacum) in 2023. The site is part of Turkey Point Environmental Observatory (TPEO). The establishment of the agricultural site has allowed TPEO to become representative of the major biomes in the Great Lakes region, encompassing coniferous and deciduous forests, as well as agricultural crops. The soil at this agricultural site is well-drained fine sandy loam. The area has a humid continental climate and has one of the longest-growing seasons in Canada with at least 150–160 frost-free days in a year.

Arain, M. Altaf [McMaster University]↗
DOE OSTI2026

AmeriFlux FLUXNET-1F CA-TPA Ontario Turkey Point Observatory Agricultural Site

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-TPA Ontario Turkey Point Observatory Agricultural Site. This is the FLUXNET version of the carbon flux data for the site CA-TPA Ontario Turkey Point Observatory Agricultural Site produced by applying the standard ONEFlux (1F) software. Site Description - This agricultural flux tower site is located about 15 km southwest of Simcoe in southern Ontario, Canada. It was planted with corn (Zea mays) in 2020 and 2021, sweet potato (Ipomoea batatas) in 2022 and tobacco (Nicotiana tabacum) in 2023. The site is part of Turkey Point Environmental Observatory (TPEO). The establishment of the agricultural site has allowed TPEO to become representative of the major biomes in the Great Lakes region, encompassing coniferous and deciduous forests, as well as agricultural crops. The soil at this agricultural site is well-drained fine sandy loam. The area has a humid continental climate and has one of the longest-growing seasons in Canada with at least 150–160 frost-free days in a year.

Arain, M. Altaf [McMaster University]↗
DOE OSTI2020

The Stellar Velocity Distribution Function in the Milky Way Galaxy

The stellar velocity distribution function in the solar vicinity is reexamined using data from the Sloan Digital Sky Survey Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey’s DR16 and Gaia DR2. By exploiting APOGEE’s ability to chemically discriminate with great reliability the thin-disk, thick-disk, and (accreted) halo populations, we can, for the first time, derive the three-dimensional velocity distribution functions (DFs) for these chemically separated populations. We employ this smaller but more data-rich APOGEE+Gaia sample to build a data-driven model of the local stellar population velocity DFs and use these as basis vectors for assessing the relative density proportions of these populations over the 5 < R < 12 kpc and −1.5 < z < 2.5 kpc range as derived from the larger, more complete (i.e., all-sky, magnitude-limited) Gaia database. We find that 81.9% ± 3.1% of the objects in the selected Gaia data set are thin-disk stars, 16.6% ± 3.2% are thick-disk stars, and 1.5% ± 0.1% belong to the Milky Way stellar halo. We also find the local thick-to-thin-disk density normalization to be ρ {sub T}(R {sub ⊙})/ρ {sub t}(R {sub ⊙}) = 2.1% ± 0.2%, a result consistent with, but determined in a completely different way from, typical star-count/density analyses. Using the same methodology, the local halo-to-disk-density normalization is found to be ρ {sub H}(R {sub ⊙})/(ρ {sub T}(R {sub ⊙}) + ρ {sub t}(R {sub ⊙})) = 1.2% ± 0.6%, a value that may be inflated due to the chemical overlap of halo and metal-weak thick-disk stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Snowfall and snow accumulation during the MOSAiC winter and spring seasons

Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth and density retrievals from a SnowMicroPen and approximately weekly measured snow depths along fixed transect paths. We used the derived SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were also compared with ERA5 reanalysis snowfall rates for the drift track. We found an accumulated snow mass of 38 mm SWE between the end of October 2019 and end of April 2020. The initial SWE over first-year ice relative to second-year ice increased from 50 % to 90 % by end of the investigation period. Further, we found that the Vaisala Present Weather Detector 22, an optical precipitation sensor, and installed on a railing on the top deck of research vessel Polarstern, was least affected by blowing snow and showed good agreements with SWE retrievals along the transect. On the contrary, the OTT Pluvio 2 pluviometer and the OTT Parsivel 2 laser disdrometer were largely affected by wind and blowing snow, leading to too high measured precipitation rates. These are largely reduced when eliminating drifting snow periods in the comparison. ERA5 reveals good timing of the snowfall events and good agreement with ground measurements with an overestimation tendency. Retrieved snowfall from the ship-based K a -band ARM zenith radar shows good agreements with SWE of the snow cover and differences comparable to those of ERA5. Based on the results, we suggest the K a -band radar-derived snowfall as an upper limit and the present weather detector on RV Polarstern as a lower limit of a cumulative snowfall range. Based on these findings, we suggest a cumulative snowfall of 72 to 107 mm and a precipitation mass loss of the snow cover due to erosion and sublimation as between 47 % and 68 %, for the time period between 31 October 2019 and 26 April 2020. Extending this period beyond available snow cover measurements, we suggest a cumulative snowfall of 98–114 mm.

54 ENVIRONMENTAL SCIENCES↗