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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Assessment of the Contribution of the Atmosphere to Uncertainties in Normalized Water-Leaving Radiance: A Combined Modeling and Data Analysis Approach

Our research for the Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) program has been focused on modeling and simulation studies as well as the development of atmospheric correction algorithms. Based on our original proposal and discussions at the SIMBIOS team meetings and workshops, the objectives of our research can be summarized as follows: (1) Use our radiative transfer model for the coupled atmosphere-ocean system to simulate the radiation field at arbitrary levels and in any desired direction in the atmosphere and ocean so as to provide a firm connection between the signal received by the satellite sensor and by a sensor looking down into the water column just above the surface and just below it; (2) Use the simulations to quantify the influence of atmospheric aerosols on the water-leaving radiance, and to quantify the error in the water-leaving radiance as a function of uncertainties in the aerosol optical properties, mass loading and vertical extent; (3) Use the model simulations in conjunction with validation measurements taken by other SIMBIOS investigators (for satellite overpasses) to assess our understanding of the radiative transfer process in the coupled atmosphere-ocean column, and to examine the extent to which the model provides a realistic prediction of simultaneously measured in situ water-leaving radiance and the radiance received by the satellite sensor; (4) Modify and improve an existing atmospheric correction algorithm, based on the work above, as needed by constructing new look-up tables that include scattering by ocean particles; and (5) Carry out approaches to developing an atmospheric correction algorithm for ocean color imagery with strongly absorbing aerosols.

Stamnes, Knut↗
NASA NTRSDate not supplied

Assessment of Cloud Fraction Derived from a New Geostationary Satellite Cloud Retrieval Algorithm for CERES and Progress Towards Cross-platform Continuity

For over two decades The Clouds and the Earth’s Radiant Energy System (CERES) project has produced long-term records of top-of-atmosphere (TOA) and surface irradiances for detecting changes in the Earth’s radiation budget and advancing understanding of how clouds contribute to those changes. Accurate characterization of the spatial and temporal distributions of clouds are a critical component for producing CERES datasets. The CERES Cloud Working Group (CWG) derives cloud properties from both geostationary (GEO) and low-Earth orbit (LEO) satellite sensors in order to provide complete global coverage at hourly temporal resolution. However, the use of multiple sensors to provide this spatiotemporal coverage throughout a long-term record presents some challenges, because the various sensors generally have different spectral band characteristics (e.g., spectral band width and response) and spatial resolution. These differences can result in spatial artifacts at the coverage boundary between two sensors or temporal artifacts when one sensor replaces another in the record. For the upcoming Edition 5 release of CERES products, the CWG is developing cloud retrieval algorithms which utilize only spectral bands common to most modern passive satellite radiometers and account for differences in spectral width and response. The goal with this approach is to provide global cloud properties for CERES with greater cross-platform consistency than the previous Edition 4 products and thus mitigate artifacts which are evident at the interface of two sensors. This study focuses on retrieval of total cloud fraction from various GEO sensors, and we use Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations to assess the accuracy of total cloud fraction derived from the various sensors. The Edition 5 cloud mask algorithm depends on estimates of cloud-free TOA spectral radiances to differentiate clear and cloudy conditions, and CALIOP is used to assess the accuracy of those estimates when cloud-free conditions are indeed observed.

CALIOP↗
NASA NTRS2014

A Physically Based Algorithm for Non-Blackbody Correction of Cloud-Top Temperature and Application to Convection Study

Cloud-top temperature (CTT) is an important parameter for convective clouds and is usually different from the 11-micrometers brightness temperature due to non-blackbody effects. This paper presents an algorithm for estimating convective CTT by using simultaneous passive [Moderate Resolution Imaging Spectroradiometer (MODIS)] and active [CloudSat 1 Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)] measurements of clouds to correct for the non-blackbody effect. To do this, a weighting function of the MODIS 11-micrometers band is explicitly calculated by feeding cloud hydrometer profiles from CloudSat and CALIPSO retrievals and temperature and humidity profiles based on ECMWF analyses into a radiation transfer model.Among 16 837 tropical deep convective clouds observed by CloudSat in 2008, the averaged effective emission level (EEL) of the 11-mm channel is located at optical depth; approximately 0.72, with a standard deviation of 0.3. The distance between the EEL and cloud-top height determined by CloudSat is shown to be related to a parameter called cloud-top fuzziness (CTF), defined as the vertical separation between 230 and 10 dBZ of CloudSat radar reflectivity. On the basis of these findings a relationship is then developed between the CTF and the difference between MODIS 11-micrometers brightness temperature and physical CTT, the latter being the non-blackbody correction of CTT. Correction of the non-blackbody effect of CTT is applied to analyze convective cloud-top buoyancy. With this correction, about 70% of the convective cores observed by CloudSat in the height range of 6-10 km have positive buoyancy near cloud top, meaning clouds are still growing vertically, although their final fate cannot be determined by snapshot observations.

Physical Algorithm↗
NASA NTRS2005

Algorithm Science to Operations for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Visible/Infrared Imager/Radiometer Suite (VIIRS)

The VIIRS sensor provides measurements for 22 Environmental Data Records (EDRs) addressing the atmosphere, ocean surface temperature, ocean color, land parameters, aerosols, imaging for clouds and ice, and more. That is, the VIIRS collects visible and infrared radiometric data of the Earth's atmosphere, ocean, and land surfaces. Data types include atmospheric, clouds, Earth radiation budget, land/water and sea surface temperature, ocean color, and low light imagery. This wide scope of measurements calls for the preparation of a multiplicity of Algorithm Theoretical Basis Documents (ATBDs), and, additionally, for intermediate products such as cloud mask, et al. Furthermore, the VIIRS interacts with three or more other sensors. This paper addresses selected and crucial elements of the process being used to convert and test an immense volume of a maturing and changing science code to the initial operational source code in preparation for launch of NPP. The integrity of the original science code is maintained and enhanced via baseline comparisons when re-hosted, in addition to multiple planned code performance reviews.

NPOESS (NATIONAL POLAR-ORBITING OPERATIONAL ENVIRO↗
NASA NTRS1986

Atmospheric, climatic and environmental research

Research conducted during the past year in the climate and atmospheric modeling programs was focused on the development of appropriate atmospheric and upper ocean models, and preliminary applications of these models. Prinicpal models are a one-dimensional radiative-convection model, a three-dimensional global climate model, and an upper ocean model. Principal application is the study of the impact of CO2, aerosols and the solar constant on climate. Also the performance of the International Satellite Cloud Climatology Project cloud detection algorithm was evaluated, concentrating initially on its application to geosynchronous data, with an eventual switch of the developed methodologies to data from polar orbiting satellites. In the process, a number of improvements were made, in particular: an improved technique for tracking small scale day to day variability in clear sky continental temperatures; a number of techniques for the statistical assessment of cloud detection uncertainties due to cloud types which are spatially and temporally invariant; and a method used to detect those cloudy regions which have long term spatial and temporal stability.

Broecker, W. S.↗
DOE OSTI2025

NeuralMie (v1.0): an aerosol optics emulator

The direct interactions of atmospheric aerosols with radiation significantly impact the Earth's climate and weather and are important to represent accurately in simulations of the atmosphere. This work introduces two contributions to enable a more accurate representation of aerosol optics in atmosphere models: (1) NeuralMie, a neural network Mie scattering emulator that can directly compute the bulk optical properties of a diverse range of aerosol populations and is appropriate for use in atmosphere simulations where aerosol optical properties are parameterized, and (2) TAMie, a fast Python-based Mie scattering code based on the Toon and Ackerman (1981) Mie scattering algorithm that can represent both homogeneous and coated particles. TAMie achieves speed and accuracy comparable to established Fortran Mie codes and is used to produce training data for NeuralMie. NeuralMie is highly flexible and can be used for a wide range of particle types, wavelengths, and mixing assumptions. It can represent core-shell scattering and, by directly estimating bulk optical properties, is more efficient than existing Mie code and Mie code emulators while incurring negligible error compared to existing aerosol optics parameterization schemes (0.08 % mean absolute percentage error).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

MODIS Collection 6 MAIAC Algorithm

This paper describes the latest version of the algorithm MAIAC (Multi-Angle Implementation of Atmospheric Correction) used for processing the MODIS (Moderate-resolution Imaging Spectroradiometer) Collection6 data record. Since initial publication in 2011-2012, MAIAC has changed considerably to adapt to global processing and improve cloud/snow detection, aerosol retrievals and atmospheric correction of MODIS data. The main changes include (1) transition from a 25 to 1 km scale for retrieval of the spectral regression coefficient (SRC) which helped to remove occasional blockiness at 25 km scale in the aerosol optical depth (AOD) and in the surface reflectance, (2) continuous improvements of cloud detection, (3) introduction of smoke and dust tests to discriminate absorbing fine- and coarse mode aerosols, (4) adding over-water processing, (5) general optimization of the LUT (LookUp-Table)-based radiative transfer for the global processing, and others. MAIAC provides an interdisciplinary suite of atmospheric and land products, including cloud mask (CM), column water vapor (CWV), AOD at 0.47 and 0.55 m, aerosol type (background, smoke or dust) and fine-mode fraction over water; spectral bidirectional reflectance factors (BRF), parameters of Ross-thick Lisparse (RTLS) bidirectional reflectance distribution function (BRDF) model and instantaneous albedo. For snow-covered surfaces, we provide subpixel snow fraction and snow grain size. All products come in standard HDF4 (software library) format at 1 km resolution, except for BRF, which is also provided at 500 m resolution on a sinusoidal grid adopted by the MODIS Land team. All products are provided on per-observation basis in daily files except for the BRDF/Albedo product, which is reported every 8 days. Because MAIAC uses a time series approach, BRDF/Albedo is naturally gap-filled over land where missing values are filled-in with results from the previous retrieval. While the BRDF model is reported for MODIS Land bands 1-7 and ocean band 8, BRF is reported for both land and ocean bands 1-12. This paper focuses on MAIAC cloud detection, aerosol retrievals and atmospheric correction and describes MCD19 data products and quality assurance (QA) flags.

MAIAC Algorithm↗
NASA NTRS2023

Relationship Between the Sub-Micron Fraction (SMF) and Fine-Mode Fraction (FMF) in the Context of AERONET Retrievals

The sub-micron (SM) aerosol optical depth (AOD) is an optical separation based on the fraction of particles below a specified cutoff radius of the particle size distribution (PSD) at a given particle radius. It is fundamentally different from spectrally separated FM (fine-mode) AOD. We present a simple (AOD-normalized) SM fraction versus FM fraction (SMF vs. FMF) linear equation that explains the well-recognized empirical result of SMF generally being greater than the FMF. The AERONET inversion (AERinv) products (combined inputs of spectral AOD and sky radiance) and the spectral deconvolution algorithm (SDA) products (input of AOD spectra) enable, respectively, an empirical SMF vs. FMF comparison at similar (columnar) remote sensing scales across a variety of aerosol types. SMF (AERinv-derived) vs. FMF (SDA-derived) behavior is primarily dependent on the relative truncated portion (ε c ) of the coarse-mode (CM) AOD associated with the cutoff portion of the CM PSD and, to a second order, the cutoff FM PSD and FM AOD (ε f ). The SMF vs. FMF equation largely explains the SMF vs. FMF behavior of the AERinv vs. SDA products as a function of PSD cutoff radius (“inflection point”) across an ensemble of AERONET sites and aerosol types (urban-industrial, biomass burning, dust, maritime and a mixed class of Arctic aerosols). The overarching dynamic was that the linear SMF vs. FMF relation pivots clockwise about the approximate (SMF, FMF) singularity of (1, 1) in a “linearly inverse” fashion (slope and intercept of approximately 1−ε c and ε c ) with increasing cutoff radius. SMF vs. FMF slopes and intercepts derived from AERinv and SDA retrievals confirmed the general domination of ε c over ε f in controlling that dynamic. A more general conclusion is the apparent confirmation that the optical impact of truncating modal (whole) PSD features can be detected by an SMF vs. FMF analysis.

Norman T. O'Neill↗
NASA NTRS2000

A New Raman Water Vapor Lidar Calibration Technique and Measurements in the Vicinity of Hurricane Bonnie

The NAcA/Goddard Space Flight Center Scanning Raman Lidar has made measurements of water vapor and aerosols for almost ten years. Calibration of the water vapor data has typically been performed by comparison with another water vapor sensor such as radiosondes. We present a new method for water vapor calibration that only requires low clouds, and surface pressure and temperature measurements. A sensitivity study was performed and the cloud base algorithm agrees with the radiosonde calibration to within 10- 15%. Knowledge of the true atmospheric lapse rate is required to obtain more accurate cloud base temperatures. Analysis of water vapor and aerosol measurements made in the vicinity of Hurricane Bonnie are discussed.

Evans, Keith D.↗
NASA NTRS2000

A New Raman Water Vapor Lidar Calibration Technique and Measurements in the Vicinity of Hurricane Bonnie

The NASA/Goddard Space Flight Center Scanning Raman Lidar has made measurements of water vapor and aerosols for almost ten years. Calibration of the water vapor data has typically been performed by comparison with another water vapor sensor such as radiosondes. We present a new method for water vapor calibration that only requires low clouds, and surface pressure and temperature measurements. A sensitivity study was performed and the cloud base algorithm agrees with the radiosonde calibration to within 10-15%. Knowledge of the true atmospheric lapse rate is required to obtain more accurate cloud base temperatures. Analysis of water vapor and aerosol measurements made in the vicinity of Hurricane Bonnie are discussed.

Evans, Keith D.↗
NASA NTRS1999

Optical Properties of Aerosols from Long Term Ground-Based Aeronet Measurements

AERONET is an optical ground-based aerosol monitoring network and data archive supported by NASA's Earth Observing System and expanded by federation with many non-NASA institutions including AEROCAN (AERONET CANada) and PHOTON (PHOtometrie pour le Traiteinent Operatonnel de Normalisation Satellitaire). The network hardware consists of identical automatic sun-sky scanning spectral radiometers owned by national agencies and universities purchased for their own monitoring and research objectives. Data are transmitted hourly through the data collection system (DCS) on board the geostationary meteorological satellites GMS, GOES and METEOSAT and received in a common archive for daily processing utilizing a peer reviewed series of algorithms thus imposing a standardization and quality control of the product data base. Data from this collaboration provides globally distributed near real time observations of aerosol spectral optical depths, aerosol size distributions, and precipitable water in diverse aerosol regimes. Access to the AERONET data base has shifted from the interactive program 'demonstrat' (reserved for PI's) to the AERONET homepage allowing faster access and greater development for GIS object oriented retrievals and analysis with companion geocoded data sets from satellites, LIDAR and solar flux measurements for example. We feel that a significant yet under utilized component of the AERONET data base are inversion products made from hourly principal plane and almucanter measurements. The current inversions have been shown to retrieve aerosol volume size distributions. A significant enhancement to the inversion code has been developed and is presented in these proceedings.

Holben, B. N.↗
NASA NTRS2000

The Atmospheric Channels of GLAS: Near Real-Time Global Lidar Remote Sensing of Clouds and Aerosols from Space

The Geoscience Laser Altimeter System (GLAS) will be placed into orbit in 2001 aboard the Ice, Cloud and Land Elevation Satellite (ICESat). From its nearly polar orbit (94 degree inclination), GLAS will provide continuous global measurements of the vertical distribution of clouds and aerosols while simultaneously providing high accuracy topographic profiling of surface features. During the mission, which is slated to last 3 to 5 years, the data collected by GLAS will be in near-real time to produce level 1 and 2 data products at the NASA GLAS Science Computing Facility (SCF) at Goddard Space Flight Center in Greenbelt, Maryland. The atmospheric products include cloud and aerosol layer heights, planetary boundary layer depth, polar stratospheric clouds and thin cloud and aerosol optical depth. These products will be made available to the science community within days of their creation. The processing algorithms must be robust, adaptive, efficient, and clever enough to run autonomously for the widely varying atmospheric conditions that will be encountered. This paper presents an overview of the GLAS atmospheric data products and briefly discusses the design of the processing algorithms.

Palm, Stephen P.↗
NASA NTRS2001

A Nanometer Aerosol Size Analyzer (nASA) for Rapid Measurement of High-Concentration Size Distributions

We have developed a fast-response Nanometer Aerosol Size Analyzer (nASA) that is capable of scanning 30 size channels between 3 and 100 nm in a total time of 3 seconds. The analyzer includes a bipolar charger (P0210), an extended-length Nanometer Differential Mobility Analyzer (Nano-DMA), and an electrometer (TSI 3068). This combination of components provides particle size spectra at a scan rate of 0.1 second per channel free of uncertainties caused by response-time-induced smearing. The nASA thus offers a fast response for aerosol size distribution measurements in high-concentration conditions and also eliminates the need for applying a de-smearing algorithm to resulting data. In addition, because of its thermodynamically stable means of particle detection, the nASA is useful for applications requiring measurements over a broad range of sample pressures and temperatures. Indeed, experimental transfer functions determined for the extended-length Nano-DMA using the Tandem Differential Mobility Analyzer (TDMA) technique indicate the nASA provides good size resolution at pressures as low as 200 Torr. Also, as was demonstrated in tests to characterize the soot emissions from the J85-GE engine of a T38 aircraft, the broad dynamic concentration range of the nASA makes it particularly suitable for studies of combustion or particle formation processes. Further details of the nASA performance as well as results from calibrations, laboratory tests and field applications are presented.

Han, Hee-Siew↗
NASA NTRS2003

Remote Sensing of Liquid Water and Ice Cloud Optical Thickness and Effective Radius in the Arctic: Application of Airborne Multispectral MAS Data

A multispectral scanning spectrometer was used to obtain measurements of the reflection function and brightness temperature of clouds, sea ice, snow, and tundra surfaces at 50 discrete wavelengths between 0.47 and 14.0 microns. These observations were obtained from the NASA ER-2 aircraft as part of the FIRE Arctic Clouds Experiment, conducted over a 1600 x 500 km region of the north slope of Alaska and surrounding Beaufort and Chukchi Seas between 18 May and 6 June 1998. Multispectral images of the reflection function and brightness temperature in 11 distinct bands of the MODIS Airborne Simulator (MAS) were used to derive a confidence in clear sky (or alternatively the probability of cloud), shadow, and heavy aerosol over five different ecosystems. Based on the results of individual tests run as part of the cloud mask, an algorithm was developed to estimate the phase of the clouds (water, ice, or undetermined phase). Finally, the cloud optical thickness and effective radius were derived for both water and ice clouds that were detected during one flight line on 4 June. This analysis shows that the cloud mask developed for operational use on MODIS, and tested using MAS data in Alaska, is quite capable of distinguishing clouds from bright sea ice surfaces during daytime conditions in the high Arctic. Results of individual tests, however, make it difficult to distinguish ice clouds over snow and sea ice surfaces, so additional tests were added to enhance the confidence in the thermodynamic phase of clouds over the Beaufort Sea. The cloud optical thickness and effective radius retrievals used 3 distinct bands of the MAS, with the newly developed 1.62 and 2.13 micron bands being used quite successfully over snow and sea ice surfaces. These results are contrasted with a MODIS-based algorithm that relies on spectral reflectance at 0.87 and 2.13 micron.

King, Michael D.↗
NASA NTRS2008

Provenance in Data Interoperability for Multi-Sensor Intercomparison

As our inventory of Earth science data sets grows, the ability to compare, merge and fuse multiple datasets grows in importance. This requires a deeper data interoperability than we have now. Efforts such as Open Geospatial Consortium and OPeNDAP (Open-source Project for a Network Data Access Protocol) have broken down format barriers to interoperability; the next challenge is the semantic aspects of the data. Consider the issues when satellite data are merged, cross-calibrated, validated, inter-compared and fused. We must match up data sets that are related, yet different in significant ways: the phenomenon being measured, measurement technique, location in space-time or quality of the measurements. If subtle distinctions between similar measurements are not clear to the user, results can be meaningless or lead to an incorrect interpretation of the data. Most of these distinctions trace to how the data came to be: sensors, processing and quality assessment. For example, monthly averages of satellite-based aerosol measurements often show significant discrepancies, which might be due to differences in spatio- temporal aggregation, sampling issues, sensor biases, algorithm differences or calibration issues. Provenance information must be captured in a semantic framework that allows data inter-use tools to incorporate it and aid in the intervention of comparison or merged products. Semantic web technology allows us to encode our knowledge of measurement characteristics, phenomena measured, space-time representation, and data quality attributes in a well-structured, machine-readable ontology and rulesets. An analysis tool can use this knowledge to show users the provenance-related distrintions between two variables, advising on options for further data processing and analysis. An additional problem for workflows distributed across heterogeneous systems is retrieval and transport of provenance. Provenance may be either embedded within the data payload, or transmitted from server to client in an out-of-band mechanism. The out of band mechanism is more flexible in the richness of provenance information that can be accomodated, but it relies on a persistent framework and can be difficult for legacy clients to use. We are prototyping the embedded model, incorporating provenance within metadata objects in the data payload. Thus, it always remains with the data. The downside is a limit to the size of provenance metadata that we can include, an issue that will eventually need resolution to encompass the richness of provenance information required for daata intercomparison and merging.

Lynnes, Chris↗
NASA NTRS2014

On the Stratospheric Aerosol and Gas Experiment III on the International Space Station

The Stratospheric Aerosol and Gas Experiment III on International Space Station (SAGE3/ISS) is anticipated to be delivered to Cape Canaveral in the spring of 2015. This is the fourth generation, fifth instrument, of visible/near-IR solar occultation instruments operated by the National Aeronautics and Space Agency (NASA) to investigate the Earth's upper atmosphere. The instrument is a moderate resolution spectrometer covering wavelengths from 290 nm to 1550 nm. The nominal science products include vertical profiles of trace gases, such as ozone, nitrogen dioxide and water vapor, along with multi-wavelength aerosol extinction. The SAGE3/ISS validation program will be based upon internal consistency of the measurements, detailed analysis of the retrieval algorithm, and comparisons with independent correlative measurements. The Instrument Payload (IP), mission architecture, and major challenges are also discussed.

Hernandez, Gloria↗
NASA NTRS2014

High-Speed On-Board Data Processing for Science Instruments

A new development of on-board data processing platform has been in progress at NASA Langley Research Center since April, 2012, and the overall review of such work is presented in this paper. The project is called High-Speed On-Board Data Processing for Science Instruments (HOPS) and focuses on a high-speed scalable data processing platform for three particular National Research Council's Decadal Survey missions such as Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS), Aerosol-Cloud-Ecosystems (ACE), and Doppler Aerosol Wind Lidar (DAWN) 3-D Winds. HOPS utilizes advanced general purpose computing with Field Programmable Gate Array (FPGA) based algorithm implementation techniques. The significance of HOPS is to enable high speed on-board data processing for current and future science missions with its reconfigurable and scalable data processing platform. A single HOPS processing board is expected to provide approximately 66 times faster data processing speed for ASCENDS, more than 70% reduction in both power and weight, and about two orders of cost reduction compared to the state-of-the-art (SOA) on-board data processing system. Such benchmark predictions are based on the data when HOPS was originally proposed in August, 2011. The details of these improvement measures are also presented. The two facets of HOPS development are identifying the most computationally intensive algorithm segments of each mission and implementing them in a FPGA-based data processing board. A general introduction of such facets is also the purpose of this paper.

Beyon, Jeffrey Y.↗
NASA NTRS2019

Optimizing Umkehr Ozone Profile Retrievals

NOAA Dobson Umkehr ozone profile records have been collected since the 1970s. Umkehr ozone profiles are used to monitor stratospheric ozone recovery predicted to occur by the 2050s. Current operational Dobson Umkehr profile algorithms produce data that have uncertainty on the order of ~ 5 % in the stratosphere. However, when large volcanic eruptions inject aerosols into the stratosphere, the errors can be as large as 70 %. In order to evaluate Umkehr records for aerosol-related and instrumental artifacts, we compare observations with a Hindcast simulation of the NASA Merra-2 Global Modeling Initiative (GMI) Replay (M2GMI, Orbe et al, 2017; Wargan et al, 2018) and Chemistry Transport Model (GMI CTM, Strahan et al, 2013, Strahan et al, 2016). The biases found between the models and observations are summarized for each Dobson calibration and volcanic eruption period, thus providing a reference tool for homogenization of the Umkehr time series and removal of volcanic aerosol errors.

Petropavlovskikh, I.↗