Intercomparison Exercise Research Articles

An international intercomparison exercise on passive samplers (DGT) for monitoring metals in marine waters under a regulatory context

In order to move forward in the acceptance of a novel contaminant monitoring technique (Diffusive Gradients in Thin-films: DGT) for assessment of marine water bodies, sensu the WFD, an Inter-Laboratories Comparison (ILC) exercise (nine Europeans laboratories) was organized in the framework of the Interreg Atlantic Area MONITOOL project, which focused on the use of the DGT technique for the measurement of WFD priority metals (Cd, Ni and Pb).Reproducible results were obtained for each metal by several laboratories, supporting the assertion that DGT analysis can be performed satisfactorily by laboratories experienced in measuring metals at trace levels in marine environments, even if they have limited practice in DGT analysis.According to the Z-score analysis, among the 9 participating laboratories, 3 had 100 % of satisfactory results for Cd, Ni, and Pb, 3 had >80 % satisfactory results and 2 had about 60 % satisfactory results.This work highlights the need to clearly describe the DGT method in order to control sources of contamination during analytical steps, in particular the resin gel retrieval and the elution steps.Such international intercomparison exercise is an important step to develop the laboratory network involved in DGT analysis and contributes to the improvement of data quality.

Benchmarking the vertically integrated ice-sheet model IMAU-ICE (version 2.0)

Abstract. Ice-dynamical processes constitute a large uncertainty in future projections of sea-level rise caused by anthropogenic climate change. Improving our understanding of these processes requires ice-sheet models that perform well at simulating both past and future ice-sheet evolution. Here, we present version 2.0 of the ice-sheet model IMAU-ICE, which uses the depth-integrated viscosity approximation (DIVA) to solve the stress balance. We evaluate its performance in a range of benchmark experiments, including simple analytical solutions and both schematic and realistic model intercomparison exercises. IMAU-ICE has adopted recent developments in the numerical treatment of englacial stress and sub-shelf melt near the grounding line, which result in good performance in experiments concerning grounding-line migration (MISMIP, MISMIP+) and buttressing (ABUMIP). This makes it a model that is robust, versatile, and user-friendly, which will provide a firm basis for (palaeo-)glaciological research in the coming years.

Interlaboratory comparison exercise using OSL personal dosimeters between the LMRI-DEN/UFPE, Brazil, and LCD/LAF-RAM, Nicaragua.

The aim of this paper is to present the results obtained in the intercomparison exercise organized by the Ionizing Radiation Metrology Laboratory at the Federal University of Pernambuco (LMRI-DEN/UFPE) and the Radiation Physics and Metrology Laboratory (LAF-RAM) of the National Autonomous University of Nicaragua, Managua (UNAN-Managua). The comparison aimed to assess the technical capabilities of the Dosimetry Calibration Laboratory (LCD) belonging to LAF-RAM for the irradiation of personal dosimeters in terms of Hp(d) magnitudes with a 137Cs radiation beam. The BeO OSL dosimeters were shipped by LMRI-DEN/UFPE to LAF-RAM for irradiation in March 2020. The evaluation of the Z-Score value obtained in the intercomparison was satisfactory for all irradiations of the dosimeters in the Hp (d) magnitude.

Lessons learnt from the recent EURADOS intercomparisons in computational dosimetry

Organized by Working Group 6 “Computational Dosimetry” of the European Radiation Dosimetry Group (EURADOS), a group of intercomparison exercises was conducted in which participants were asked to solve predefined problems in computational dosimetry. The results of these comparisons were published in a series of articles in this virtual special issue of Radiation Measurements. This paper reviews the experience gained from the various exercises and highlights the resulting conclusions for future exercises, as well as regarding the state of the art and the need for development in terms of quality assurance for computational dosimetry techniques.

Optimization of the direct LSC method for determination of biogenic component in liquids by applying 14C

Determination of fraction of biogenic component in liquid fuels by a direct radiocarbon measurement in liquid scintillation counter (direct-LSC method) has been validated by participation in the international intercomparison exercise. All the results for samples with the standard quench parameter SQP(E) value above ≈ 650 were accepted. Highly quenched sample of used edible oil was diluted with the 14C-free petroleum ether to optimize the region of applicability. It was established that quantitative results were obtained for SQP(E) values above 700, while in the SQP(E) region between 700 and 600 only the qualitative results of fbio can be taken.

Ensuring robust radiological risk assessment for wildlife: insights from the International Atomic Energy Agency EMRAS and MODARIA programmes

In response to changing international recommendations and national requirements, a number of assessment approaches, and associated tools and models, have been developed over the last circa 20 years to assess radiological risk to wildlife. In this paper, we summarise international intercomparison exercises and scenario applications of available radiological assessment models for wildlife to aid future model users and those such as regulators who interpret assessments. Through our studies, we have assessed the fitness for purpose of various models and tools, identified the major sources of uncertainty and made recommendations on how the models and tools can best be applied to suit the purposes of an assessment. We conclude that the commonly used tiered or graded assessment tools are generally fit for purpose for conducting screening-level assessments of radiological impacts to wildlife. Radiological protection of the environment (or wildlife) is still a relatively new development within the overall system of radiation protection and environmental assessment approaches are continuing to develop. Given that some new/developing approaches differ considerably from the more established models/tools and there is an increasing international interest in developing approaches that support the effective regulation of multiple stressors (including radiation), we recommend the continuation of coordinated international programmes for model development, intercomparison and scenario testing.

Intermodel comparison for the radiological assessment of the Zapadnoe and Tessenderlo case studies with implications for selection of remediation strategy

Risk assessment provides a key input for determining the need for and extent of remedial actions necessary for sites contaminated with naturally occurring radioactive material or nuclear legacy sites. The choice of a modelling approach for risk assessment, and the corresponding toolsets should fit the assessment context, taking account of the complexity, and be clearly related to the questions to be addressed in the decision-making process. One of the objectives of Working Group 1 of IAEA Modelling and Data for Radiological Impact Assessments II (MODARIA II) Programme is to perform intermodel comparisons for case studies of selected sites, in particular, to help illustrate the applicability of different models and approaches as inputs to decision-making processes. This intercomparison exercise, which included the analysis of potential consequences on the management strategy for contaminated sites, has been performed for two sites: The former uranium mill tailings facility at Zapadnoe, Ukraine, and the phosphate processing facility at Tessenderlo, Belgium. Several models and computer codes have been used for one or both of these cases: AMBER, GoldSim, NORM And LegacY Site Assessment, Preliminary Remediation Goals (PRG)-dose compliance concentration calculator, and RESRAD-OFFSITE. The assessments explore the implications of using differing assessment frameworks and assumptions, as well as alternative modelling tools, on model outputs and as input for corresponding decisions on remediation strategy. This paper reviews both similarities and differences in the results of assessments performed using these different models. It discusses how different approaches can complement one another to help build confidence in the evidence base underpinning decisions. It also discusses the appropriateness of the different modelling approaches in a given assessment context. In one of the case studies in particular (Tessenderlo case study), the remediation strategy is essentially driven by the contamination of the site with heavy metals, such as cadmium. This has significant consequences on the choice of the most adequate approaches and scenarios for assessing the radiological risk and balancing their relative importance with other impacts. The development of a holistic approach to risk assessment is, therefore, highlighted.

PERFORMANCE OF BARC NEUTRON PERSONAL DOSEMETER IN EURADOS INTERCOMPARISON EXERCISE IC2017N - A LESSON LEARNT.

Bhabha Atomic Research Centre (BARC) conducts fast neutron personnel monitoring service to radiation workers involved in reactors, accelerators, spent fuel processing plants, oil-well industries, etc., using CR-39 detector based dosemeter. In this study, performance of the BARC fast neutron personnel dosemeter has been checked through EURADOS intercomparison exercise for simulated workplace neutron fields. The overall performance of the dosemeter in the lower dose equivalent (≤ 5mSv) was found to be acceptable as per ISO-14146. The performance (ratio of estimated to reference dose equivalent) of the dosemeter in the higher dose equivalent (12mSv) was found to be in the range of 0.48-0.44 and not satisfactory as per the ISO-14146 criteria for any dosimetry service. Based on this performance, a new imaging system was developed and performance of the dosemeters were improved and found to be acceptable (within ±20%) as per ISO.

Intercomparison of model predictions of 14C concentrations in agricultural plants following acute exposures to airborne 14C

Carbon-14 (14C) is one of the main radionuclides released during normal operation by nuclear power plants, nuclear defense facilities and nuclear fuel reprocessing plants. It is mainly released in the form of carbon dioxide gas denoted 14CO2, which has the specificity of being incorporated into food webs via photosynthesis by primary producing organisms. In order to better assess the environmental and human impacts of 14CO2 under normal operating conditions - or after potential accidental releases - from nuclear facilities, it is necessary to improve our understanding and our predictions of the behaviour of this radionuclide along the human food chain. To achieve this goal, the International Atomic Energy Agency (IAEA) Environmental Modelling for Radiation Safety (EMRAS) model evaluation programme included the Tritium and 14C Working Group (TCWG) which dealt with the intercomparison exercises between several models of environmental transfer in the case of routine and accidental releases of these radionuclides into the environment, and their performance testing. The TOCATTA-χ model developed at IRSN is a dynamic compartment model with high temporal resolution, which simulates the transfer of 14C (and tritium) in grassland ecosystems exposed to gaseous 14CO2 (and HTO) from nuclear facilities under normal or accidental operating conditions. Following this work, IRSN proposed a related project to extend the application of the TOCATTA-χ model to 14C estimates in leafy vegetables, fruits and roots. This article deals with the application of the TOCATTA-χ model to a specific real-case scenario identified within the framework of the TCWG. The scenario provides experimental data and predicted results from models developed at the international level. Model-model and model-data intercomparison exercises were thus carried out to validate the evaluations of the TOCATTA-χ model. In addition, this paper discusses the parameterization of the TOCATTA-χ model for this scenario and the development of modules for 14C concentrations in potato tubers, based on the assumption that photosynthetic transfer occurs directly from leaves to tubers and depends mainly on the growth stage of the tubers. It is observed that the predictions of the TOCATTA-χ model for the concentrations of 14C in leaves and tubers are slightly better than the other models due to the modelling approaches adopted by TOCATTA-χ for the calculation of key ecophysiological processes that govern plant functioning. Overall, the TOCATTA-χ model reduces the Root Mean Square Error (RMSE) by a factor of less than 8 compared to other models. In addition, most of the predicted results of the TOCATTA-χ model better match the measurements and are within the measurement uncertainty limit, while a few are overestimated. This could be due to the high uncertainty associated with the experimentally measured 14C activities, which reflects the field variability in plant growth rate.

Antarctic-Scale Ice Flow Lines Map Generation and Basin Delineation

Ice flow lines are the most dominant feature of ice sheet surfaces. Accurate delineation of basins is a prerequisite for mass balance. We therefore propose a new weight balance-based approach to extract the present most comprehensive ice flow lines and then re-delineate the Antarctic basin on this basis. In our approach, we define three impact factors to represent directional accuracy, smoothness, and physical character. Following this, a weight balance-based method is proposed, in which those factors are integrated via weights, to determine the pointing pixels (points or subpixels of the ice flow line). After that, the ice flow lines are continuously tracked. Finally, Antarctic basins were re-delineated based on it. The results indicate that the derived ice flow lines exhibit a rational ice flow pattern against the DEM and the proposed method remarkably improves the performance of results. Furthermore, compared with the Antarctic IMBIE (Ice sheet Mass Balance Inter-Comparison Exercise) basins, the redivided basins exhibit slight diversity, with differences in area and length of less than 11% and a directional accuracy of less than 1° (covering more than 90% of area). As a result, the redivided basins are beneficial to the investigation of the in-depth mechanism of ice shelf calving and mass balance in the Antarctic.

THE IAEA FORENSICS PROGRAM: RESULTS OF THE AMS 14C INTERCOMPARISON EXERCISE ON CONTEMPORARY WINES AND COFFEES

ABSTRACTIn the frame of the IAEA-CRP (Coordinated Research Projects): Enhancing Nuclear Analytical Techniques to Meet the Needs of Forensic Sciences, an intercomparison exercise was organized between three AMS laboratories. Aim of the program is to promote the use of nuclear and accelerator-based techniques in routine forensics practice. In this view, one of the key points is the assessment of the precision and accuracy levels achievable on material of forensic interest. We review the general structure and status of the project, with emphasis on results obtained in the analysis of wines of different grape varieties and grounded coffee beans from different locations such as Brazil, Spain, and Italy. The three laboratories processed the samples according to different chemical protocols and performed the 14C measurements using different systems: MICADAS in Zurich and Debrecen and a HVEE 4130HC 3 MV Tandetron in Lecce. Within the quoted uncertainty, the results showed good reproducibility, indicating that uncertainty level of the order of 0.3% are achieved by AMS on a single sample while multiple sample analyses results in precision down to 0.1–0.2%. The measured 14C concentrations on coffee and wine samples resulted to be consistent with atmospheric 14C levels in the growing years.

Evaluation of statistical downscaling methods for climate change projections over Spain: Present conditions with imperfect predictors (global climate model experiment)

AbstractStatistical downscaling of climate projections is an active field of research and numerous intercomparison exercises of different techniques exist. Most evaluation studies make use of perfect predictors from a reanalysis, but neglect the analysis of how imperfect predictors from global climate models (GCMs) affect statistical downscaling. In this paper we evaluate and intercompare five statistical downscaling methods: (a) Analog, (b) Regression, (c) Artificial Neural Networks, (d) Support Vector Machines and (e) Kernel Ridge Regression, over a large ensemble of 18 GCMs participants in the CMIP5 project. These methods have been used to downscale maximum/minimum daily temperatures and daily accumulated precipitation on a high‐resolution observational grid (0.05°) over mainland Spain and the Balearic Islands. Our analysis focuses on the marginal aspects (mean values and tails) of the distributions by season. Results when driven by reanalysis and by GCMs have been compared in order to analyse the sensitivity of statistical downscaling methods to the use of imperfect predictors. Both for temperature and for precipitation all methods achieve, as expected, better results when applied over reanalysis, although no method displays a clearly higher sensitivity to the use of imperfect predictors from GCMs. We have found the existence of outlier GCMs, showing significantly higher errors than the rest of the ensemble. And we have quantified errors over GCMs, so this experiment provides a more precise idea of the reliability and accuracy of the overall performance (i.e., simulations by GCMs plus statistical downscaling) than the perfect predictor experiment and reveals an important source of uncertainty coming from GCMs even in a historical period.

A critical view on the suitability of machine learning techniques to downscale climate change projections: Illustration for temperature with a toy experiment

AbstractMachine learning is a growing field of research with many applications. It provides a series of techniques able to solve complex nonlinear problems, and that has promoted their application for statistical downscaling. Intercomparison exercises with other classical methods have so far shown promising results. Nevertheless, many evaluation studies of statistical downscaling methods neglect the analysis of their extrapolation capability. In this study, we aim to make a wakeup call to the community about the potential risks of using machine learning for statistical downscaling of climate change projections. We present a set of three toy experiments, applying three commonly used machine learning algorithms, two different implementations of artificial neural networks and a support vector machine, to downscale daily maximum temperature, and comparing them with the classical multiple linear regression. We have tested the four methods in and out of their calibration range, and have found how the three machine learning techniques can perform poorly under extrapolation. Additionally, we have analysed the impact of this extrapolation issue depending on the degree of overlapping between the training and testing datasets, and we have found very different sensitivities for each method and specific implementation.

Intercomparison of Radon Flux Monitors at Low and at High Radium Content Areas under Field Conditions.

Interlaboratory exercises are a good tool to compare the response of different systems to the same quantity and to identify possible inconsistencies between them. One of the main goals of the EMPIR 19ENV01 traceRadon project is to harmonize radon flux measurements based on different systems and methodologies. In the framework of the traceRadon Project, two radon flux intercomparison campaigns were carried out in October 2021 at high and at low radon source areas. Four institutions participated in the field intercomparison exercises with their own systems. Every system was based on a specific radon monitor (diffusion or pump mode) and an accumulation chamber (with manual or automatic opening). Radon fluxes were calculated by each participant using both exponential and linear fittings of the radon activity concentration measured over time within the accumulation chambers. The results of this study show mainly: (i) the exponential approach is not advisable due to the variability of the radon flux and the leakage of the systems during long-time measurements; (ii) the linear approach should be applied to minimize the measurement period in agreement with the time response and sensitivity of the monitors; (iii) radon flux measured at high radon source areas (radium content of about 800 Bq kg−1) risks being underestimated because of the influence of advective effects; (iv) radon flux measured at low radon source areas (radium content of about 30 Bq kg−1) may present large uncertainties if sensitive radon monitors with pump mode are not used.

The FastEddy® Resident‐GPU Accelerated Large‐Eddy Simulation Framework: Moist Dynamics Extension, Validation and Sensitivities of Modeling Non‐Precipitating Shallow Cumulus Clouds

AbstractHerein we describe the moist dynamics formulation implemented within the graphics processing unit‐resident large‐eddy simulation FastEddy® model, which includes a simple saturation adjustment scheme for condensation and evaporation processes. Two LES model intercomparison exercises for non‐precipitating shallow cumulus clouds are simulated in order to validate this model extension, including a static forcing and a time‐dependent forcing case. Overall, we find our dynamical, thermodynamical and microphysical quantities, along with turbulence variability and fluxes, to be commensurate with the corresponding model intercomparison results. In addition, sensitivities to specific model settings are investigated. Among these settings, it is shown that boundary layer and cloud layer structure and characteristics are sensitive to use of higher‐order advection schemes impacting the vertical distribution of cloud content and associated turbulence statistics. Increasing the timescale of the saturation scheme leads to enhanced liquid water presence and decreases vertical velocity variance within the cloud deck. In some cases, these sensitivities agree with the model‐to‐model variability reported in the intercomparison exercises, highlighting the important role of specific model implementation choices in the context of shallow cumulus convection simulations. These analyses and findings also provide the basis for future extensions and applications of FastEddy® for modeling moist convection and precipitation scenarios.

Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masking algorithms have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. This paper summarizes results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10–30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algorithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated against existing reference cloud mask datasets. Those datasets vary in sampling methods, geographical distribution, sample unit (points, polygons, full image labels), and generation approaches (experts, machine learning, sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in how the reference datasets were produced. The algorithms were in good agreement for thick cloud detection, which were opaque and had lower uncertainties in their identification, in contrast to thin/semi-transparent clouds detection. Not only did CMIX allow identification of strengths and weaknesses of existing algorithms and potential areas of improvements, but also the problems associated with the existing reference datasets. The paper concludes with recommendations on generating new reference datasets, metrics, and an analysis framework to be further exploited and additional input datasets to be considered by future CMIX activities.

Thoron Interference on Performance of Continuous Radon Monitors: An Experimental Study on Four Devices and a Proposal of an Indirect Method to Estimate Thoron Concentration.

The performance of continuous radon monitors (CRMs) is usually evaluated under controlled conditions in a radon chamber during calibrations or intercomparison exercises. The impact of thoron on CRMs response is rarely evaluated; in case the evaluation is performed, it is carried out in a controlled atmosphere with relatively constant, homogeneous, and generally high thoron concentrations and very low radon levels. In a real indoor environment, both radon and thoron concentrations are extremely variable, so the thoron interference evaluations reported in the literature are generally not applicable to CRMs used to measure radon concentration indoors. For this reason, an experimental study was carried out with four different CRMs in an indoor environment (an office room) where medium-to-high concentrations of both radon and thoron were expected. Thoron concentration has been separately evaluated throughout two different active monitors. Three CRMs resulted in overestimations of radon concentration by about 10% due to thoron interference, whereas such interference results were negligible for the fourth CRM. However, the thoron interference can also be used to assess thoron concentration by using CRM not specifically designed to do so. Based on the results of this study, an indirect method to assess thoron concentration is indeed proposed, relying on the combination of two identical monitors (one placed right close to the wall and the other one far enough from there).

Micro-Nano Plastic in the Aquatic Environment: Methodological Problems and Challenges.

Simple SummaryThe topic of plastic wastes, microplastic (MP) and nanoplastic (NP) particles in the aquatic environment has been the focus of much scientific effort over the last decade and has gained immense public attention through the media. While numerous scientific reports underscore the ubiquitous presence of MPs and NPs in aquatic environments, particularly the oceans, there are many unresolved issues involved in their sampling, identification and characterization. This paper addresses some of the main problems and suggests what needs to be undertaken to overcome these issues. An overriding problem is the lack of harmonization of the protocols used for sampling MPs at sea and identifying them in the laboratory. There are technological challenges in polymeric characterization of NPs in environmental samples. Researchers use a wide variety of net types and net mesh sizes to capture and separate MP floating in the sea which makes comparing MP concentrations from different teams and areas extremely difficult and calls for establishing inter-comparison exercises among the various research teams. Furthermore, the issue of whether chemicals in MPs and NPs leach following ingestion by biota or whether they transport and release contaminants adsorbed on MP/NP surfaces is still unresolved. In essence, these and other issues have to be addressed and resolved before society has an accurate picture of their importance as an aquatic pollutant. There are no datasets on the environmental concentration of NPs, hence their effect on biota is solely relied on laboratory experiments using extremely high concentrations. The legitimacy of these effects and interactions in the biotic system is something that warrants discussion.Microplastic research has become a buzz word. It is seen as one of the most pressing issues of Anthropocene contamination. There is certainly no doubt about the ubiquitous presence of microplastic (MP) in almost all environmental matrices. However, the validity of considering them as a vector for contaminants needs some reconsideration, there are other more potent pathways. Their effect on marine biota also calls for some realistic experiments with environmental concentrations of MP and nanoplastic (NP). It has been observed that in most published literature, polymer characterization is performed. Is it necessary to do, or will merely finding and confirming the particle as plastic suffice for environmental research? Harmonization of protocols is necessary, and there is likely a need for some inter-laboratory comparison exercises in order to produce comparable data and reliable assessments across regions. Samples collected from the same area using different techniques show an order of magnitude difference in MP concentration. The issue of nanoplastic is more contentious; are we technologically ready to identify NP in environmental samples?

Towards the Use of Dgt Technique in a Regulatory Context for Monitoring Metals in Marine Water Bodies: Results of an International Intercomparison Exercise

Towards the Use of Dgt Technique in a Regulatory Context for Monitoring Metals in Marine Water Bodies: Results of an International Intercomparison Exercise

High-Resolution Snow Depth on Arctic Sea Ice From Low-Altitude Airborne Microwave Radar Data

We present new high-resolution snow depth data on Arctic sea ice derived from airborne microwave radar measurements from the IceBird campaigns of the Alfred Wegener Institute (AWI) together with a new retrieval method using signal peakiness based on an intercomparison exercise of colocated data at different altitudes. We aim to demonstrate the capabilities and potential improvements of radar data, which were acquired at a lower altitude (200 ft) and slower speed (110 kn) and had a smaller radar footprint size (2-m diameter) than previous airborne snow radar data. So far, AWI Snow Radar data have been derived using a 2–18-GHz ultrawideband frequency-modulated continuous-wave (FMCW) radar in 2017–2019. Our results show that our method in combination with thorough calibration through coherent noise removal and system response deconvolution significantly improves the quality of the radar-derived snow depth data. The validation against a 2-D grid of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> snow depth measurements on level landfast first-year ice indicates a mean bias of only 0.86 cm between radar and ground truth. Comparison between the radar-derived snow depth estimates from different altitudes shows good consistency. We conclude that the AWI Snow Radar aboard the IceBird campaigns is able to measure the snow depth on Arctic sea ice accurately at higher spatial resolution than but consistent with the existing airborne snow radar data of NASA Operation IceBridge. Together with the simultaneous measurements of the total ice thickness and surface freeboard, the IceBird campaign data will be able to describe the whole sea-ice column on regional scales.