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DOE OSTI2026

KCG Baseline Air Pollution Station LIFSO2 v1 Data from December 2024 to March 2025

Sulfur Dioxide is a key precursor to the formation of new particles within the marine environment, yet commercial instrumentation lack sufficient precision or sensitivity to resolve the levels present in these environments. As such, the LIFSO2, a custom built fibre laser from the University of York, UK, was developed (based on the one developed by Rollins et al 2016). We are able to resolve down to the ppt level with this instrument. We present version 1 (v1) data for Dec 2024 to Mar 2025 for SO2 (1min time average) from the Cape-k precursors field campaign.

Sulfur dioxide (SO2) mixing ratio↗

DOE OSTI2021

Gridded daily weather data for North America with comprehensive uncertainty quantification

Abstract Access to daily high-resolution gridded surface weather data based on direct observations and over long time periods is essential for many studies and applications including vegetation, wildlife, soil health, hydrological modelling, and as driver data in Earth system models. We present Daymet V4, a 40-year daily meteorological dataset on a 1 km grid for North America, Hawaii, and Puerto Rico, providing temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset includes an objective quantification of uncertainty based on strict cross-validation analysis for temperature and precipitation results. The dataset represents several improvements from a previous version, and this data descriptor provides complete documentation for updated methods. Improvements include: reductions in the timing bias of input reporting weather station measurements; improvement to the three-dimensional regression model techniques in the core algorithm; and a novel approach to handling high elevation temperature measurement biases. We show cross-validation analyses with the underlying weather station data to demonstrate the technical validity of new dataset generation methods, and to quantify improved accuracy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

WHONDRS River Corridor Sediment and Water Geochemistry and In Situ Sensor Data from Machine-Learning-Informed Sites across the Contiguous United States (v6)

This dataset supports a broader study examining hyporheic zone respiration rates to improve predictive models at a contiguous United States (CONUS) scale. The CONUS-Scale Model-Sample Study (CM) was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the CONUS. New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Sampling began in April 2022 and ended in October 2023. In addition to the widely distributed CONUS sites, a more spatially focused sampling occurred in the Yakima River Basin, WA in summer 2022. Data from this more spatially intensive sampling occurred under the label “Second Spatial Study (SSS)” and were also included in the machine learning models. Other data types collected from SSS that were not part of CM were published in a separate data package (https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566). This data package was originally published in February 2023. It was updated in June 2023 (v2; new and modified files); December 2023 (v3; new and modified files); June 2024 (v4; new and modified files); April 2024 (v5; new and modified files); and September 2025 (v6; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of two folders of field photos and videos, one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) international generic sample number (IGSN) mapping file; (6) field protocols; (7) a subfolder with sample data; and (8) a subfolder with sensor data. The sample data subfolder contains (1) surface water and sediment dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) surface water and sediment total nitrogen data and averages; (3) surface water major cations and anions and averages; (4) sediment grain size data; (5) sediment iron (II) data and averages; (6) wet sediment mass, dry sediment mass, water mass, and wet sediment volume in incubation and sediment ICR vials; (7) sediment incubation respiration rate data and averages; (8) normalized respiration rate data and averages; (9) methods codes; (10) sediment specific surface area; (11) sediment percent carbon and nitrogen; (12) sediment gravimetric moisture and averages; (15) sediment X-ray diffraction (XRD) data; (16) sediment adenosine triphosphate (ATP) and averages; (17) a subfolder with sediment incubation respiration data, scripts, and plots; (18) surface water and sediment FTICR methods; and (19) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains five subfolders, one containing the sediment .xml data files, one containing the water .xml files, one containing the sediment CoreMS output files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS).The sensor data subfolder contains (1) a subfolder with miniDOT dissolved oxygen and temperature data and plots; (2) miniDOT dissolved oxygen and temperature summary data; and (3) miniDOT installation methods. All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4. CORRECTION: Carbon and nitrogen content are reported as percentages. The current column headers "01395_C_percent_per_mg" and "01397_N_percent_per_mg" are incorrect. These should read "01395_C_percent" and "01397_N_percent" and will be corrected in the next version of this data package. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Photometric cross-calibration of the SDSS Stripe 82 Standard Stars catalogue with Gaia EDR3, and comparison with Pan-STARRS1, DES, CFIS, and GALEX catalogues

ABSTRACT We extend the SDSS Stripe 82 Standard Stars catalogue with post-2007 SDSS imaging data. This improved version lists averaged SDSS ugriz photometry for nearly a million stars brighter than r ∼ 22 mag. With 2–3 times more measurements per star, random errors are 1.4–1.7 times smaller than in the original catalogue, and about three times smaller than for individual SDSS runs. Random errors in the new catalogue are $\lesssim$0.01 mag for stars brighter than 20.0, 21.0, 21.0, 20.5, and 19.0 mag in u, g, r, i, and z-bands, respectively. We achieve this error threshold by using the Gaia Early Data Release 3 (EDR3) Gmag photometry to derive grey photometric zeropoint corrections, as functions of RA and Declination, for the SDSS catalogue, and use the Gaia BP–RP colour to derive corrections in the ugiz bands, relative to the r-band. The quality of the recalibrated photometry, tested against Pan-STARRS1, DES, CFIS, and GALEX surveys, indicates spatial variations of photometric zeropoints ≦ 0.01 mag (rms), with typical values of 3–7 millimag in the RA, and 1–2 millimag in the Declination directions, except for $\lesssim$6 millimag scatter in the u-band. We also report a few minor photometric problems with other surveys considered here, including a magnitude-dependent ∼0.01 mag bias between 16 ≤ GGaia ≤ 20 in the Gaia EDR3. Our new, publicly available catalogue offers robust calibration of ugriz photometry below 1 per cent level, and will be helpful during the commissioning of the Vera C. Rubin Observatory Legacy Survey of Space and Time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Transforming Drainage Research Data (USDA-NIFA Award No. 2015-68007-23193)

This dataset contains research data compiled by the “Managing Water for Increased Resiliency of Drained Agricultural Landscapes” project a.k.a. Transforming Drainage. This project was funded from 2015-2021 by the United States Department of Agriculture, National Institute of Food and Agriculture (USDA-NIFA, Award No. 2015-68007-23193). Data are also available from a separate web-accessible application (drainagedata.org). At drainagedata.org, users can visualize the data with customized tools, query based on specific sites and measurements of interest, and access site photographs, maps, summaries, and publications. Additional data or edits made following the publication of this data here at USDA NAL Ag Data Commons will be posted under the Versions tab on drainagedata.org. These data began in 1996 and include plot- and field-level measurements for 39 experiments across the Midwest and North Carolina. Practices studied include controlled drainage, drainage water recycling, and saturated buffers. In total, 219 variables are reported and span 207 site-years for tile drainage, 154 for nitrate-N load, 181 for water quality, 92 for water table, and 201 for crop yield.

Modeling↗

DOE OSTI2022

HPXML Version Translator

HPXML is a consensus data transfer standard for residential buildings. Over several years, multiple versions of the standard have been released. This tool accepts an HPXML file in an older version and translates it to a newer version.

Merket, Noel↗

DOE OSTI2025

Monthly Quality-filtered Aggregation of NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 5

This dataset contains gridded monthly Leaf Area Index (LAI) derived from the daily NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 5. This data record spans from 1981 to 2018 using data from eight NOAA polar orbiting satellites: NOAA-7, -9, -11, -14, -16, -17, -18 and -19. The data are projected on a 0.05 degree x 0.05 degree global grid, as in the original CDR. The original CDR is one of the Land Surface CDR Version 5 products produced by the NASA Goddard Space Flight Center (GSFC) and the University of Maryland (UMD), which is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program. This dataset is in the netCDF-4 file format following ACDD and CF Conventions. This dataset has applied quality assurance information to only include "OK" data from the original CDR in the monthly aggregation.

Vermote, Eric [NASA Goddard Space Flight Center (G↗

DOE OSTI2023

Testing SOAR tools in use

Investigations within Security Operation Centers (SOCs) are tedious as they rely on manual efforts to query diverse data sources, overlay related logs, correlate the data into information, and then document results in a ticketing system. Security Orchestration, Automation, and Response (SOAR) tools are a relatively new technology that promise, with appropriate configuration, to collect, filter, and display needed diverse information; automate many of the common tasks that unnecessarily require SOC analysts’ time; facilitate SOC collaboration; and, in doing so, improve both efficiency and consistency of SOCs. There has been no prior research to test SOAR tools in practice; hence, understanding and evaluation of their effect is nascent and needed. Here, in this paper, we design and administer the first hands-on user study of SOAR tools, involving 24 participants and six commercial SOAR tools. Our contributions include the experimental design, itemizing six characteristics of SOAR tools, and a methodology for testing them. We describe configuration of a cyber range test environment, including network, user, and threat emulation; a full SOC tool suite; and creation of artifacts allowing multiple representative investigation scenarios to permit testing. We present the first research results on SOAR tools. Concisely, our findings are that: per-SOC SOAR configuration is extremely important; SOAR tools increase efficiency and reduce context switching, although with potentially decreased ticketing accuracy/completeness; user preference is slightly negatively correlated with their performance with the tool; internet dependence varies widely among SOAR tools; and balance of automation with assisting decision making is preferred by senior participants. We deliver a public user- and tool-anonymized and -obfuscated version of the data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

AmeriFlux FLUXNET-1F US-GLE GLEES

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-GLE GLEES. This is the FLUXNET version of the carbon flux data for the site US-GLE GLEES produced by applying the standard ONEFlux (1F) software. Site Description - The Glacier Lakes Ecosystem Experiments Site (GLEES) site is located on land owned by the U.S. government and managed by US Forest Service as part of the Medicine Bow National Forest. Many of the trees in the immediate vicinity of the site are older than 400 years, inter-dispersed among trees much younger in age. This widespread age distribution is most likely a derivation of one of two scenarios: 1) A widespread stand replacement about 400 years ago followed by a slow replacement; 2) Intermittent random disturbances over the past 400 years (Bradford et al. 2008). A decade long spruce beetle outbreak that peaked in 2008 resulted in the mortality of 85% of the forested basal area. There are a few private land holdings in the area, with scattered uncorked mining claims. Following the establishment of the National Forest, mining was banned and grazing was closed in the early 1990's in the upper portion of the GLEES site. Recreation in the winter, when snow can remain in patches into the summer months, snow mobiling and cross country skiing are popular. During the limited summer, hiking, camping and fishing are common activities. The site is accessible by vehicle only during the summer on Forest Road FDR 317, and in the winter, the tower is only reachable via snowmobile.

Frank, John↗

DOE OSTI2025

AmeriFlux FLUXNET-1F US-Sta Saratoga

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Sta Saratoga. This is the FLUXNET version of the carbon flux data for the site US-Sta Saratoga produced by applying the standard ONEFlux (1F) software. Site Description - Sagebrush steppe ecosystem

Ewers, Brent [University of Wyoming]↗

DOE OSTI2026

AmeriFlux FLUXNET-1F US-Cwt Coweeta

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Cwt Coweeta. This is the FLUXNET version of the carbon flux data for the site US-Cwt Coweeta produced by applying the standard ONEFlux (1F) software. Site Description - Mature, secondary, deciduous-dominated forest. Located in southern Appalachian mountains, cove location, near base of forested catchment. Proximity close to USFS Coweeta Hydrologic Laboratory offices. Forest harvesting until early 1930s, unmanaged since.

Oishi, A. Christopher [USDA Forest Service]↗

DOE OSTI2026

AmeriFlux FLUXNET-1F CA-LU2 Lutose

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-LU2 Lutose. This is the FLUXNET version of the carbon flux data for the site CA-LU2 Lutose produced by applying the standard ONEFlux (1F) software. Site Description - Lutose is a peat plateau that burned from a moderate forest fire in June 2007. Prior to the fire the site was likley an open canopy of stunted black spruce and a ground layer of Labrador tea shurbs and lichen or sphagnum. No black spruce survived the fire and lichens were still completely absent during this study. By 2019, most charred tree boles has fallen over and vegetation recovery was dominated by dense Labrador tea shrubs and sparse regenerating black spruce with around >150cm of peat.

Schulze, Christopher [University of Alberta]↗

DOE OSTI2026

AmeriFlux FLUXNET-1F BR-Xpw XomanoFlux

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site BR-Xpw XomanoFlux. This is the FLUXNET version of the carbon flux data for the site BR-Xpw XomanoFlux produced by applying the standard ONEFlux (1F) software. Site Description - The site is located in the northern Pantanal, in central South America. The site is a grazing area used for cattle ranching and is subject to seasonal flooding during rainy seasons (November to April). Dry seasons are typically May to October.

Dalmagro, Higo [Universidade de Cuiabá - UNIC]↗

DOE OSTI2020

Mapping the Concentration Distribution of Contaminant Plumes to the Computational Grid of the Plateau to River Model (Ver. 8.3)

This environmental calculation file (ECF) outlines the process conducted for defining the initial condition groundwater plumes for select contaminants of interest (COIs) to the Plateau River model version 8.3 (P2Rv8.3) domain (CP-57037, Model Package Report: Plateau to River Groundwater Model Version 8.3). Various data sources were combined into a single set of files defining plume conditions at time 1/1/2018. The resulting files are intended for use as inputs for transport modeling to support forecasts of the Central Plateau contaminant plume fate and transport.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Crosslink V.0.11.x User Manual

CrossLink is a novel two-dimensional and three-dimensional geometry and mesh generation software package developed by the Simulation Tools team at Los Alamos National Laboratory. This software represents the third generation of topology-based mesh generation technology developed by the Department of Defense and the Department of Energy with a special focus on complex multi-material hydrodynamic applications, mesh scalability, and high-order element mesh generation. The topology-based meshing approach offered by CrossLink enables users to quickly and easily mesh complex geometries in a repeatable and robust manner. CrossLink’s topology-based meshing approach is well-suited for parametric design studies, parametric design optimization, damage scenario assessment, and iterative design modification (i.e. feature addition and/or removal). CrossLink’s python API allows workflow scripting of the geometry creation and mesh generation process for traceability, repeatability, data provenance, and version control. CrossLink consists of three main components: a graphical user interface (GUI), a geometry creation and mesh generation engine, and a python API that provides a workflow scripting interface to the geometry and meshing functions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Initial development of viscoplastic constitutive model of Alloy 800H in support of the use of inelastic analysis methods for ASME Section III, Division 5, Class A applications

This report describes the development of a preliminary inelastic constitutive model for the thermomechanical behavior of Alloy 800H. The objective is to develop a model suitable for incorporating into Nonmandatory Appendix Z to Section III, Division 5 of the ASME Boiler & Pressure Vessel Code, which provides guidance and reference constitutive models for the ASME design by inelastic analysis rules for Class A components. The report describes the process of collecting experimental data, developing a mathematical form for the model, and training the model against the test data. The initial version of the model captures most of the relevant material deformation mechanisms, including dynamic strain aging effects. However, further development of the model form will be required to develop a final model suitable for ASME use.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Triton Initiative: FY22 Communications, Outreach, and Engagement End-of-Year Report

The Department of Energy (DOE) Water Power Technologies Office (WPTO) Triton Initiative supports the advancement of the marine energy (ME) industry through environmental monitoring research and technology development. This report presents the results and analysis of the Triton Initiative’s communications, outreach, and engagement (TCOE) efforts in fiscal year 2022. The primary TCOE goals were to: (1) educate and raise awareness of ME and the role of Triton's environmental monitoring research in supporting the industry; (2) build trust with audiences through transparent communications and outreach; and (3) evaluate and refine TCOE tactics based on feedback and metrics. To support these goals, the TCOE team used multiple platforms and approaches. Notable achievements include: (1) 12 newsletter issues sent to 185 subscribers with an average open rate of 56.4%. (2) 10 Triton Stories, resulting in 2,871 collective views contributing to 46% of all Triton website views. (3) 78 Triton-specific social media posts, which generated a total of 81,896 impressions, 1,037 post-clicks, and 7,814 video views. (4) 6,320 Triton website views with increased search engine optimization ranking in several categories. (5) 3 Triton researchers interviewed as guests on two different podcasts, Water Women and Big Deep: An Ocean Podcast. (6) A Triton special issue of the Journal of Marine Science and Engineering (JMSE) featuring 10 peer reviewed publications. All articles ranked in the top 25%, and two in the top 5%, for digital attention of all research outputs scored by Altmetric. (7) A seven-part webinar series called Triton Talks to share and discuss research and results from the research published in the JMSE special issue. Triton leveraged unique opportunities, particularly the JMSE special issue, to disseminate research results to end users, educate stakeholders, and gain valuable feedback. These concerted communication efforts increased exposure across platforms ultimately resulting in greater reach and access across audiences. Based on audience analyses of webinar attendees and newsletter subscribers, the TCOE team was able to successfully engage with general audiences, research partners, and ME stakeholders, including subject matter experts from government agencies, research organizations, and the regulatory community. The TCOE task established channels to gather input and create opportunities for two-way communication with people engaged with Triton’s outreach efforts. The feedback received will enable the TCOE team to support its goals for ongoing evaluation of outreach and engagement tools while continuing to build trust in the ME community and educate diverse audiences about the impactful research conducted by the Triton Initiative. This report is a Triton Initiative Fiscal Year 2022 Quarter 4 Milestone Deliverable due to the DOE WPTO Sponsor on 9/30/2022. An updated version to include data through 9/30/2022 will be delivered in October 2022.

16 TIDAL AND WAVE POWER↗

DOE OSTI2022

Comparison of scattering ratio profiles retrieved from ALADIN/Aeolus and CALIOP/CALIPSO observations and preliminary estimates of cloud fraction profiles

The space-borne active sounders have been contributing invaluable vertically resolved information of atmospheric optical properties since the launch of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) in 2006. To build long-term records from space-borne lidars useful for climate studies, one has to understand the differences between successive space lidars operating at different wavelengths, flying on different orbits, and using different viewing geometries, receiving paths, and detectors. In this article, we compare the results of Atmospheric Laser Doppler INstrument (ALADIN) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidars for the period from 28 June to 31 December 2019. First, we build a dataset of ALADIN–CALIOP collocated profiles (Δdist<1°; Δtime<6 h). Then we convert ALADIN's 355 nm particulate backscatter and extinction profiles into the scattering ratio vertical profiles SR(z) at 532 nm using molecular density profiles from Goddard Earth Observing System Data Assimilation System, version 5 (GEOS-5 DAS). And finally, we build the CALIOP and ALADIN globally gridded cloud fraction profiles CF(z) by applying the same cloud detection threshold to the SR(z) profiles of both lidars at the same spatial resolution. Before comparing the SR(z) and CF(z) profiles retrieved from the two analyzed lidar missions, we performed a numerical experiment to estimate the best achievable cloud detection agreement CDA norm (z) considering the differences between the instruments. We define CDA norm (z) in each latitude–altitude bin as the occurrence frequency of cloud layers detected by both lidars, divided by a cloud fraction value for the same latitude–altitude bin. We simulated the SR(z) and CF(z) profiles that would be observed by these two lidars if they were flying over the same atmosphere predicted by a global model. By analyzing these simulations, we show that the theoretical limit for CDA$_{norm}^{theor}$(z) for a combination of ALADIN and CALIOP instruments is equal to 0.81±0.07 at all altitudes. In other words, 19 % of the clouds cannot be detected simultaneously by two instruments due to said differences. The analyses of the actual observed CALIOP–ALADIN collocated dataset containing ~78 000 pairs of nighttime SR(z) profiles revealed the following points: (a) the values of SR(z) agree well up to ~3 km height. (b) The CF(z) profiles show agreement below ~3 km, where ~80 % of the clouds detected by CALIOP are detected by ALADIN as expected from the numerical experiment. (c) Above this height, the CDA$_{norm}^{obs}$(z) reduces to ~50 %. (d) On average, better sensitivity to lower clouds skews ALADIN's cloud peak height in pairs of ALADIN–CALIOP profiles by ~0.5±0.6 km downwards, but this effect does not alter the heights of polar stratospheric clouds and high tropical clouds thanks to their strong backscatter signals. (e) The temporal evolution of the observed CDA$_{norm}^{obs}$(z) does not reveal any statistically significant change during the considered period. This indicates that the instrument-related issues in ALADIN L0/L1 have been mitigated, at least down to the uncertainties of the following CDA$_{norm}^{obs}$(z) values: 68±12 %, 55±14 %, 34±14 %, 39±13 %, and 42±14 % estimated at 0.75, 2.25, 6.75, 8.75, and 10.25 km, respectively.

54 ENVIRONMENTAL SCIENCES↗