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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

A Global Assessment of Added Value in the SMAP Level-4 Soil Moisture Product Relative to Its Baseline Land Surface Model

The Soil Moisture Active Passive (SMAP) Level-4 product provides enhanced soil moisture estimates by assimilating SMAP brightness temperature observations into a land surface model. Here, an unbiased qualitative estimate of the relative skill of SMAP Level-4 and model-only surface soil moisture (versus true soil moisture) is derived using only one additional noisy (but independent) soil moisture product. The method is applied globally and verified using high-quality, ground-based measurements where available. Results demonstrate that assimilating SMAP brightness temperature has relatively little impact in data-rich areas like the United States and Europe. In contrast, much larger improvement is observed in data-sparse regions, including much of Africa and central Australia, where model-only simulations are disproportionately impacted by low-quality model forcing. Therefore, ground validation conducted in data-rich areas does not adequately sample the added value of SMAP data assimilation for data-sparse regions and substantially underestimates the added skill provided by the SMAP Level-4 system.

SMAP L4↗
NASA NTRS1993

A theoretical and numerical study of polarimetric scattering and emission from random rough surfaces with anisotropic directional spectrum

In this paper, theoretical and numerical results of the polarimetric scattering and emission from random rough surfaces with anisotropic directional spectrum are presented for the remote sensing of ocean and soil surfaces. The polarimetric scattered field for rough dielectric surfaces is derived to the second order by the small perturbation method (SPM). It is found that the second-order scattered field is coherent in nature, and its coefficients for different polarizations present the lowest-order corrections to the Fresnel reflection coefficients of the surfaces. In addition, the cross-polarized (HV and VH) components of the coherent fields are reciprocal and not zero for surfaces with anisotropic directional spectrum when the azimuth angle of the incident direction is not aligned with the symmetry directions of surfaces. In order to verify the energy conservation condition of the theoretical results, which is important if the theory is to be applied to the passive polarimetry of rough surfaces, a Monte Carlo simulation is performed to numerically calculate the polarimetric reflectivities of one-dimensional random rough surfaces which are generated with a prescribed power-law spectrum in the spectral domain and transformed to the spatial domain by the FFT. The surfaces simulated by this approach are periodic with the period corresponding to the low-wavenumber cutoff. To calculate the scattering from periodic dielectric surfaces, the authors present a new numerical technique which applies the Floquet theorem to reduce the problem to one period and does not require the evaluation of one-dimensional periodic Green's function used in the conventional method of moment formulation. Once the scattering coefficients are obtained, the polarimetric Stokes vectors for the emission from the random surfaces are then calculated according to the Kirchhoff's law and are illustrated as functions of relative azimuth observation and row directions. The second-order SPM is also reduced to this one-dimensional case, and theoretical results for all four Stokes parameters are in excellent agreement with the numerical results obtained from the Monte Carlo simulation. Moreover, the second order coherent fields are found to be necessary for the theoretical evaluation of the third and fourth Stokes parameters. Without that, the reflectivities of random rough surfaces are significantly over-estimated, and the signs of the third and the fourth Stokes parameters are found to be incorrect, if calculated by using the first-order incoherent fields alone.

Yueh, S. H.↗
NASA NTRS1984

Target detection using microwave irradiances from natural sources: A passive, local and global surveillance system

Detection of metal objects on or near the Earth's surface was investigated using existing, passive, microwave sensors operating from Earth orbit. The range equations are derived from basic microwave principles and theories and the expressions are given explicitly to estimate the signal to noise ratio for detecting metal targets operating as bistatic scatterers. Actual measurements are made on a range of metal objects observed from orbit using existing passive microwave receiving systems. The details of the measurements and the results are tabulated and discussed. The advantages of a passive microwave sensor as it is applied to surveillance of metal objects as viewed from aerial platforms or from orbit, are examined.

Stacey, J. M.↗
NASA NTRS1993

Passive microwave remote sensing of thin sea ice using principal component analysis

The possibility of developing a satellite-based sea-ice-concentration algorithm which solves for the presence of thinner ice is explored on the basis of time sequences of surface-based measurements of passive MW emission from growing saline ice reported by Wensnahan et al. (1993). It is shown that two classes of thinner ice can be distinguished from mixtures of open water, first-year ice, and multiyear ice. Newly formed ice appears to be optically thick at 37 and 90 GHz and has a relatively dry surface. The thin ice spectrum occurs when the ice is greater than 4 cm thick and appears to result from the accumulation of brine at the ice surface. Thin ice has a relatively stable spectrum characterized by high brightness temperatures, a near-zero spectral gradient at vertical polarization, and a large difference between vertical and horizontal polarizations. It is concluded that thin ice can be detected using satellite data.

Wensnahan, Mark↗
NASA NTRS1993

Property changes induced by the space environment in composite materials on LDEF: Solar array materials passive LDEF experiment A0171 (SAMPLE)

Surface modifications to composite materials induced by long term exposure in low earth orbit (LEO) were dominated by atomic oxygen erosion and micrometeoroid and space debris impacts. As expected, calculated erosion rates were peculiar to material type and within the predicted order of magnitude. Generally, about one ply of the carbon fiber composites was eroded during the 70 month LDEF experiment. Matrix erosion was greater than fiber erosion and was more evident for a polysulfone matrix than for epoxy matrices. Micrometeoroid and space debris impacts resulted in small (less than 1mm) craters and splattered contaminants on all samples. Surfaces became more diffuse and darker with small increases in emissivity and absorption. Tensile strength decreased roughly with thickness loss, and epoxy matrices apparently became slightly embrittled, probably as a result of continued curing under UV and/or electron bombardment. However, changes in the ultimate yield stress of the carbon reinforced epoxy composites correlate neither with weave direction nor fiber type. Unexpected developments were the discovery of new synergistic effects of the space environment in the interaction of atomic oxygen and copious amounts of contamination and in the induced luminescence of many materials.

Source record↗
NASA NTRS2012

Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors

Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. Precipitation impacts latent heating profiles locally while global circulation patterns distribute precipitation and energy from the equator to the poles. For the hydrological cycle, falling snow is a primary contributor in northern latitudes during the winter seasons. Falling snow is the source of snow pack accumulations that provide fresh water resources for many communities in the world. Furthermore, falling snow impacts society by causing transportation disruptions during severe snow events. In order to collect information on the complete global precipitation cycle, both liquid and frozen precipitation must be collected. The challenges of estimating falling snow from space still exist though progress is being made. These challenges include weak falling snow signatures with respect to background (surface, water vapor) signatures for passive sensors over land surfaces, unknowns about the spherical and non-spherical shapes of the snowflakes, their particle size distributions (PSDs) and how the assumptions about the unknowns impact observed brightness temperatures or radar reflectivities, differences in near surface snowfall and total column snow amounts, and limited ground truth to validate against. While these challenges remain, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Since falling snow from space is the next precipitation measurement challenge from space, information must be determined in order to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (approx 2.5 km) cloud tops having an ice water content (IWC) at the surface of 0.25 g / cubic m and dendrite snowflakes be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. Here, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The results rely on simulated Weather Research Forecasting (WRF) simulations of falling snow cases since simulations provide all the information to determine the measurements from space and the ground truth. Sensitivity analyses were performed to better ascertain the relationships between multifrequency microwave and millimeter-wave sensor observations and the falling snow/underlying field of view. In addition, thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types were studied. Results will be presented for active radar at Ku, Ka, and W-band and for passive radiometer channels from 10 to 183 GHz.

Gail Skofronick-Jackson↗
NASA NTRS2018

Physics-Based Retrieval of Surface Roughness Parameters for Bare Soils from Combined Active-Passive Microwave Signatures

In the past the effect of soil roughness was often considered secondary within the determination of soil moisture from remote sensing data. Several studies showed that accurate determination of soil roughness leads to an improved estimation of soil moisture. Two default parameters to describe the surface roughness are the standard deviation of the surface height variation 𝑠 and the surface correlation length 𝑙 with its corresponding autocorrelation function. Both parameters (𝑠,𝑙) affect the emissivity measured by radiometers as well as the backscattering observed by radars. In this study, we develop a physics-based approach to retrieve 𝑠 and 𝑙 by combining both microwave signals based on active-passive microwave covariation. To test the approach, containing a forward model and a retrieval algorithm, we used active/passive microwave data measured with the ComRAD truck-based SMAP simulator at L-band. Results and validations with corresponding field measurements on ground show that 𝑠 and 𝑙 can be estimated simultaneously when using this approach. The physics-based retrieval algorithm works robustly for two investigated test fields having an RMS-Error of 0.68 cm and 0.69 cm between the microwave-based and field-measured 𝑠-values, and of 3.13 cm and 3.04 cm for 𝑙-values. The first validation of the results reveals that the influence of the autocorrelation function, needed within the retrieval, is distinct.

correlation length↗
NASA NTRS1975

Passive microwave sensing of the earth

The natural thermal radio radiation emitted by the terrestrial atmosphere and surface can be measured by passive microwave sensors. The receivers and antennas for the required sensing systems are briefly considered along with questions regarding the geophysical information obtainable. Satellite, aircraft, and ground-based observations are discussed. Attention is given to the possibility to monitor continually the evolution of the ice caps in polar regions.

Staelin, D. H.↗
NASA NTRS1993

Polarimetric thermal emission from rough surfaces

Recent theoretical works have suggested the potential of passive polarimetry in the remote sensing of geophysical media. It was shown that the third Stokes parameter U of the thermal emission may become larger for azimuthally asymmetric fields of observation. In order to investigate the potential applicability of passive polarimetry to the remote sensing of ocean surface, measurements of the polarimetric thermal emission from a sinusoidal water surface and a numerical study of the polarimetric thermal emission from randomly rough ocean surfaces were performed. Measurements of sinusoidal water surface thermal emission were performed using a sinusoidal water surface which was created by placing a thin sheet of fiberglass with a sinusoidal profile in two dimensions extended infinitely in the third dimension onto a water surface. The theory of thermal emission from a 'two-layer' periodic surface is derived and the exact solution is performed using both the extended boundary condition method (EBC) and the method of moments (MOM). The theoretical predictions are found to be in good agreement with the experimental results once the effects of the radiometer antenna pattern are included and the contribution of background noise to the measurements is modeled. The experimental results show that the U parameter indicates the direction of periodicity of the water surface and can approach values of up to 30 K for the surface observed. Next, a numerical study of polarimetric thermal emission from randomly rough surfaces was performed. A Monte Carlo technique utilizing an exact method for calculating thermal emission was chosen for the study to avoid any of the limitations of the commonly used approximate methods in rough surface scattering. In this Monte Carlo technique, a set of finite rough surface profiles in two dimensions with desired statistics was generated and extended periodically. The polarimetric thermal emission from each surface of the set was then calculated using both the EBC and the MOM and the results were averaged. The surface statistics chosen were intended to model a wind perturbed ocean surface in the X to Ku band microwave region. The results indicate that the U parameter is sensitive to the azimuthal angle between the surface periodicity and the looking angle and to the rms height of the surface, and that the U parameter is fairly insensitive to variations in polar angle, permittivity, surface power law spectrum, and surface spectrum high frequency cutoff. These properties give further strength to the idea of using the U parameter to detect wind direction over the ocean.

Johnson, J. T.↗
NASA NTRSDate not supplied

Assessment of Retrieved GMI Emissivity Over Land, Snow and Sea Ice in the GEOS System

Measurements from microwave sounders and imagers provide a valuable source of information including atmospheric temperature and water vapor in Numerical Weather Prediction (NWP) systems that assimilate these observations directly over water surfaces (oceans and other large water bodies). In a recent decadal survey, targeted observables in the Planetary Boundary Layer (PBL) were cited as a key need for future observations (NASEM, 2018). Microwave observations which sense in the PBL are currently available, however, utilizing surface-sensitive microwave observations for atmospheric data assimilation remains a challenge over land, snow and sea ice. This is in part due to the inability of surface emissivity models used by NWP data assimilation systems to simulate observations with sufficient accuracy. The GEOS-ADAS (Todling and el Akkraoui, 2018) which utilizes the Community Radiative Transfer Model (CRTM) (Han, 2006; Chen 2009) is no exception. The ECMWF system has retrieved instantaneous surface emissivity from surface-sensitive channels for SSMI/S and MHS radiance observations, and apply these estimates to the closest channels higher in frequency (Baordo and Geer 2016) in the calculation of simulated radiances. This approach currently is also being tested in the GEOS-ADAS for AMSU-A and ATMS radiances (Zhu et al. 2021). No or minimal emissivity spectral variability has been assumed in the above-mentioned studies. Recently, work by Munchak et al., 2020 (hereby referred to as M2020) provided a new database for emissivity over land, snow and sea ice retrieved from the NASA Global Precipitation Mission (GPM). Compared with Tool to Estimate Land Surface Emissivities at Microwave (TELSEM2; Wang et al., 2017), M2020 provides emissivities for more frequencies(i.e., 10.7 GHz V/H). Moreover, this database is unique in that it utilizes both active and passive data to retrieve surface emissivity and normalized radar cross section. While the emissivity values may be useful for other sensors, they are most applicable to the GPM Microwave Imager (GMI). In this work the GEOS-ADAS is modified to utilize emissivity values from Munchak et al, 2020 in place of values used by CRTM. Presently, only GMI radiances over ocean are used in the operational GEOS-ADAS. This study will focus on the GMI radiances over land, snow, and ice, as a first attempt to evaluate GMI radiances over these non-water surface types. Two cases are then presented, one with one week of observation minus background departures using the modified GEOS-ADAS, and one utilizing the original GEOS-ADAS. It should be noted that the surface emissivity models in CRTM are not state of the art and are scheduled to be replaced by the Community Surface Emissivity Module (CSEM; Chen and Weng, 2016). Simulations using default CRTM emissivity values are used merely as reference comparing against M2020, and is not a thorough comparison against other more state of the art modules such as CSEM.

GMI↗
NASA NTRS2001

Advanced Aerodynamic Design of Passive Porosity Control Effectors

This paper describes aerodynamic design work aimed at developing a passive porosity control effector system for a generic tailless fighter aircraft. As part of this work, a computational design tool was developed and used to layout passive porosity effector systems for longitudinal and lateral-directional control at a low-speed, high angle of attack condition. Aerodynamic analysis was conducted using the NASA Langley computational fluid dynamics code USM3D, in conjunction with a newly formulated surface boundary condition for passive porosity. Results indicate that passive porosity effectors can provide maneuver control increments that equal and exceed those of conventional aerodynamic effectors for low-speed, high-alpha flight, with control levels that are a linear function of porous area. This work demonstrates the tremendous potential of passive porosity to yield simple control effector systems that have no external moving parts and will preserve an aircraft's fixed outer mold line.

Hunter, Craig A.↗
NASA NTRS1998

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model: Overview - Part 1

Knowledge of the amount of water in the soil is of great importance to many earth science disciplines. Soil moisture is a key variable in controlling the exchange of water and energy between the land surface and the atmosphere. Thus, soil moisture information is valuable in a wide range of applications including weather and climate, runoff potential and flood control, early warning of droughts, irrigation, crop yield forecasting, soil erosion, reservoir management, geotechnical engineering, and water quality. Despite the importance of soil moisture information, widespread and continuous measurements of soil moisture are not possible today. Although many earth surface conditions can be measured from satellites, we still cannot adequately measure soil moisture from space. Research in soil moisture remote sensing began in the mid 1970s shortly after the surge in satellite development. Recent advances in remote sensing have shown that soil moisture can be measured, at least qualitatively, by several methods. Quantitative measurements of moisture in the soil surface layer have been most successful using both passive and active microwave remote sensing, although complications arise from surface roughness and vegetation type and density. Early attempts to measure soil moisture from space-borne microwave instruments were hindered by what is now considered sub-optimal wavelengths (shorter than 5 cm) and the coarse spatial resolution of the measurements. L-band frequencies between 1 and 3 GHz (10-30 cm) have been deemed optimal for detection of soil moisture in the upper few centimeters of soil. The Electronically Steered Thinned Array Radiometer (ESTAR), an aircraft-based instrument operating a 1,4 GHz, has shown great promise for soil moisture determination. Initiatives are underway to develop a similar instrument for space. Existing space-borne synthetic aperture radars (SARS) operating at C- and L-band have also shown some potential to detect surface wetness. The advantage of radar is its much higher resolution than passive microwave systems, but it is currently hampered by surface roughness effects and the lack of a good algorithm based on a single frequency and single polarization. In addition, its repeat frequency is generally low (about 40 days). In the meantime, two new radiometers offer some hope for remote sensing of soil moisture from space. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in November 1997, possesses a 10.65 GHz channel and the Advanced Microwave Scanning Radiometer (AMSR) on both the ADEOS-11 and Earth Observing System AM-1 platforms to be launched in 1999 possesses a 6.9 GHz channel. Aside from issues about interference from vegetation, the coarse resolution of these data will provide considerable challenges pertaining to their application. The resolution of TMI is about 45 km and that of AMSR is about 70 km. These resolutions are grossly inconsistent with the scale of soil moisture processes and the spatial variability of factors that control soil moisture. Scale disparities such as these are forcing us to rethink how we assimilate data of various scales in hydrologic models. Of particular interest is how to assimilate soil moisture data by reconciling the scale disparity between what we can expect from present and future remote sensing measurements of soil moisture and modeling soil moisture processes. It is because of this disparity between the resolution of space-based sensors and the scale of data needed for capturing the spatial variability of soil moisture and related properties that remote sensing of soil moisture has not met with more widespread success. Within a single footprint of current sensors at the wavelengths optimal for this application, in most cases there is enormous heterogeneity in soil moisture created by differences in landcover, soils and topography, as well as variability in antecedent precipitation. It is difficult to interpret the meaning of 'mean' soil moisture under such conditions and even more difficult to apply such a value. Because of the non-linear relationships between near-surface soil moisture and other variables of interest, such as surface energy fluxes and runoff, mean soil moisture has little applicability at such large scales. It is for these reasons that the use of remote sensing in conjunction with a hydrologic model appears to be of benefit in capturing the complete spatial and temporal structure of soil moisture. This paper is Part I of a four-part series describing a method for intermittently assimilating remotely-sensed soil moisture information to improve performance of a distributed land surface hydrology model. The method, summarized in section II, involves the following components, each of which is detailed in the indicated section of the paper or subsequent papers in this series: Forward radiative transfer model methods (section II and Part IV); Use of a Kalman filter to assimilate remotely-sensed soil moisture estimates with the model profile (section II and Part IV); Application of a soil hydrology model to capture the continuous evolution of the soil moisture profile within and below the root zone (section III); Statistical aggregation techniques (section IV and Part II); Disaggregation techniques using a neural network approach (section IV and Part III); and Maximum likelihood and Bayesian algorithms for inversely solving for the soil moisture profile in the upper few cm (Part IV).

Crosson, William L.↗
NASA NTRS2012

Achievable Performance and Effective Interrogator Design for SAW RFID Sensor Tags

For many NASA missions, remote sensing is a critical application that supports activities such as environmental monitoring, planetary science, structural shape and health monitoring, non-destructive evaluation, etc. The utility of the remote sensing devices themselves is greatly increased if they are passive V that is, they do not require any on-board power supply such as batteries V and if they can be identified uniquely during the sensor interrogation process. Additional passive sensor characteristics that enable greater utilization in space applications are small size and weight, long read ranges with low interrogator power, ruggedness, and operability in extreme environments (vacuum, extreme high/low temperature, high radiation, etc.) In this paper, we consider one very promising passive sensor technology, called surface acoustic wave (SAW) radio-frequency identification (RFID), that satisfies all of these criteria. In general, RFID is a method of identifying items using radio waves to interrogate tags encoded with a unique identifier that are affixed to the items of interest. In the case of passive tags, only the interrogator, which transmits power to the tags in the form of radio-frequency electromagnetic radiation, requires access to a power supply. Passive RFID technologies are used today in many applications, including asset tracking and management, security and access control, and remote sensing. To date, most of the development and application in RFID technology has focused on either asset/inventory tracking and control or security and access control because these are the largest commercial application areas. Recently however, there has been growing interest in using passive RFID technology for remote sensing applications, and SAW devices are at the forefront of RFID sensing technology development. Although SAW RFID tags have great potential for use in numerous space-based remote sensing applications, the limited collision resolution capability of current generation tags limits the performance in a cluttered sensing environment. That is, as more SAW-based sensors are added to the environment, numerous tag responses are superimposed at the receiver and decoding all or even a subset of the telemetry becomes increasingly difficult. Background clutter generated by reflectors other than the sensors themselves is also a problem, as is multipath interference and signal distortion, but the limiting factor in many remote sensing applications can be expected to be tag mutual interference. This problem may be greatly mitigated by proper design of the SAW tag waveform, but that remains an open research problem, and in the meantime, several other related questions remain to be answered including: (1) What are the fundamental relationships between tag parameters such as bit-rate, time-bandwidth-product, SNR, and achievable collision resolution? (2) What are the differences in optimal or near-optimal interrogator designs between noise-limited environments and interference-limited environments? (3) What are the performance characteristics of different interrogator designs in term of parameters such as transmitter power level, range, and number of interfering tags? In this paper, we will present the results of a research effort aimed at providing at least partial answers to all of these questions.

Barton Richard J.↗
NASA NTRS2016

Evaluation of Observed and Modelled Aerosol Lifetimes Using Radioactive Tracers of Opportunity and an Ensemble of 19 Global Models

Aerosols have important impacts on air quality and climate, but the processes affecting their removal from the atmosphere are not fully understood and are poorly constrained by observations. This makes modelled aerosol lifetimes uncertain. In this study, we make use of an observational constraint on aerosol lifetimes provided by radionuclide measurements and investigate the causes of differences within a set of global models. During the Fukushima Dai-Ichi nuclear power plant accident of March 2011, the radioactive isotopes cesium-137 (Cs-137) and xenon-133 (Xe-133) were released in large quantities. Cesium attached to particles in the ambient air, approximately according to their available aerosol surface area. Cs-137 size distribution measurements taken close to the power plant suggested that accumulation mode (AM) sulfate aerosols were the main carriers of cesium. Hence, Cs-137 can be used as a proxy tracer for the AM sulfate aerosol's fate in the atmosphere. In contrast, the noble gas Xe-133 behaves almost like a passive transport tracer. Global surface measurements of the two radioactive isotopes taken over several months after the release allow the derivation of a lifetime of the carrier aerosol. We compare this to the lifetimes simulated by 19 different atmospheric transport models initialized with identical emissions of Cs-137that were assigned to an aerosol tracer with each model's default properties of AM sulfate, and Xe-133 emissions that were assigned to a passive tracer. We investigate to what extent the modelled sulfate tracer can reproduce the measurements, especially with respect to the observed loss of aerosol mass with time. Modelled Cs-137and Xe-133 concentrations sampled at the same location and times as station measurements allow a direct comparison between measured and modelled aerosol lifetime. The e-folding lifetime e, calculated from station measurement data taken between 2 and 9 weeks after the start of the emissions, is 14.3 days (95% confidence interval 13.1-15.7 days). The equivalent modelled e lifetimes have a large spread, varying between 4.8 and 26.7 days with a model median of 9.42.3 days, indicating too fast a removal in most models. Because sufficient measurement data were only available from about 2 weeks after the release, the estimated lifetimes apply to aerosols that have undergone long-range transport, i.e. not for freshly emitted aerosol. However, modelled instantaneous lifetimes show that the initial removal in the first 2 weeks was quicker (lifetimes between 1 and 5 days) due to the emissions occurring at low altitudes and co-location of the fresh plume with strong precipitation. Deviations between measured and modelled aerosol lifetimes are largest for the northernmost stations and at later time periods, suggesting that models do not transport enough of the aerosol towards the Arctic. The models underestimate passive tracer (Xe-133) concentrations in the Arctic as well but to a smaller extent than for the aerosol (Cs-137) tracer. This indicates that in addition to too fast an aerosol removal in the models, errors in simulated atmospheric transport towards the Arctic in most models also contribute to the underestimation of the Arctic aerosol concentrations.

Radioactive isotopes↗