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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Particulate backscattering in the global ocean: A comparison of independent assessements

How well do we know the particulate backscattering coefficient (bbp) in the global ocean? Satellite lidar bbp has never been validated globally and few studies have compared lidar bbp to bbp derived from reflectances (via ocean color) or in-situ observations. Here we validate lidar bbp with autonomous biogeochemical Argo floats using a decorrelation analysis to identify relevant spatiotemporal matchup scales inspired by geographical variability in the Rossby radius of deformation. We compare lidar, float, and ocean color bbp at the same locations and times to assess performance. Lidar bbp outperforms ocean color, with a median percent error of 18% compared to 24% in the best case and a relative bias of -11% compared to -21%, respectively. Phytoplankton carbon calculated from ocean color and lidar exhibits basin-scale differences that can reach ± 50%.

ocean color↗
NASA NTRS2014

Application of Spectral Analysis Techniques in the Intercomparison of Aerosol Data: Part III. Using Combined PCA to Compare Spatiotemporal Variability of MODIS, MISR and OMI Aerosol Optical Depth

Satellite measurements of global aerosol properties are very useful in constraining aerosol parameterization in climate models. The reliability of different data sets in representing global and regional aerosol variability becomes an essential question. In this study, we present the results of a comparison using combined principal component analysis (CPCA), applied to monthly mean, mapped (Level 3) aerosol optical depth (AOD) product from Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Ozone Monitoring Instrument (OMI). This technique effectively finds the common space-time variability in the multiple data sets by decomposing the combined AOD field. The results suggest that all of the sensors capture the globally important aerosol regimes, including dust, biomass burning, pollution, and mixed aerosol types. Nonetheless, differences are also noted. Specifically, compared with MISR and OMI, MODIS variability is significantly higher over South America, India, and the Sahel. MODIS deep blue AOD has a lower seasonal variability in North Africa, accompanied by a decreasing trend that is not found in either MISR or OMI AOD data. The narrow swath of MISR results in an underestimation of dust variability over the Taklamakan Desert. The MISR AOD data also exhibit overall lower variability in South America and the Sahel. OMI does not capture the Russian wild fire in 2010 nor the phase shift in biomass burning over East South America compared to Central South America, likely due to cloud contamination and the OMI row anomaly. OMI also indicates a much stronger (boreal) winter peak in South Africa compared with MODIS and MISR.

satellite observation↗
NASA NTRS2017

Scale Size-Dependent Characteristics of the Nightside Aurora

We have determined the spatiotemporal characteristics of the magnetosphere-ionosphere (M-I) coupling using auroral imaging. Observations at fixed positions for an extended period of time are provided by a ground-based all-sky imager measuring the 557.7 nanometer auroral emissions. We report on a single event of nightside aurora (at approximately 22 magnetic local time) preceding a substorm onset. To determine the spatiotemporal characteristics, we perform an innovative analysis of an all-sky imager movie (19 minutes duration, images at 3.31 hertz) that combines a two-dimensional spatial fast Fourier transform with a temporal correlation. We find a scale size-dependent variability where the largest scale sizes are stable on timescales of minutes while the small scale sizes are more variable. When comparing two smaller time intervals of different types of auroral displays, we find a variation in their characteristics. The characteristics averaged over the event are in remarkable agreement with the spatiotemporal characteristics of the nightside field-aligned currents during moderately disturbed times. Thus, two different electrodynamical parameters of the M-I coupling show similar behavior. This gives independent support to the claim of a system behavior that uses repeatable solutions to transfer energy and momentum from the magnetosphere to the ionosphere.

electrodynamical↗
NASA NTRS2019

In Situ Observation of Growth Dynamics in DECLIC Directional Solidification Insert Onboard ISS: DSI-R Flight Campaign

The study of solidification microstructure formation is of utmost importance for materials design and processing, as solid-liquid interface patterns largely govern mechanical and physical properties. Pattern selection occurs under dynamic conditions of growth in which the initial morphological instability evolves nonlinearly and undergoes a reorganization process. The dynamic and nonlinear nature of this instability renders in situ observation of the interface an invaluable tool to gain knowledge on the time-evolution of the interface pattern. Transparent organic analogs, which solidify like metallic alloys, allow direct visualization of interface dynamics. Extensive ground-based studies of both metallic and organic bulk samples have established the presence of significant convection during solidification processes that alters the formation of interfacial microstructures. A reduced-gravity environment is therefore mandatory for fluid flow elimination in bulk samples. In the framework of the CNES project MISOL3D (MIcrostrutures de SOLidification 3D) and the NASA projects DSIP (Dynamical Selection of 3D Interface Patterns), SPADES (SPAtiotemporal Evolution of three-dimensional DEndritic array Structures) and CAMUS (ComputAtional Studies of MicrostrUcture Formation During Alloy Solidification in Microgravity), we participated in the development of the Directional Solidification Insert (DSI) of the DEvice for the study of Critical Liquids and Crystallization (DECLIC). The DECLIC-DSI is dedicated to in situ and real-time characterization of solid-liquid interface patterns during directional solidification of transparent alloys in diffusive transport regime. Between April 2010 and March 2011, the first ISS campaign (DSI) explored the entire range of microstructures resulting in unprecedented observations. A second campaign (DSI-R), performed between October 2017 and December 2018, in which the insert contained an alloy of higher solute concentration, allowed to complete the benchmark database. The increase of solute concentration resulted in well-developed dendritic patterns at lower velocities (lower interface curvature and larger tip radius). The microstructure resulting from dendritic growth is dominant in metallurgy so that it is fundamental to understand the mechanisms of its formation. The main aims of this experimental campaign are to understand: the mechanisms of the cell to dendrite transition, the fundamental mechanisms of sidebranching formation, the dependence of dendrite tip shapes on growth conditions, the interaction of primary array and secondary sidebranches, and the influence of subgrain boundaries on the spatiotemporal organization of the array structure. Preparation, analysis and interpretation of the experiments performed onboard ISS are considerably enhanced by experiments performed on ground using thin-samples (Pr. Trivedi’s group) and phase-field simulations of microstructure formation in a diffuse growth regime (Pr. Karma’s group). In this summary, we will present an initial assessment of the results obtained during the DSI-R campaign. Nomenclature Vp: pulling velocity G: thermal gradient L: solidified length rtip: tip curvature radius Acronyms/Abbreviations CADMOS: Centre d'Aide au Développement des activités en Micro-pesanteur et des Opérations Spatiales CNES: Centre National d’Études Spatiales DECLIC: Device for the study of Critical LIquids and Crystallization DSI: Directional Solidification Insert DSI-R: Directional Solidification Insert - Refurbish ISS: International Space Station NASA: National Aeronautics and Space Administration PF: phase-field SCN: Succinonitrile 3D: three-dimensional

Mota, F.L.↗
NASA NTRS2017

Limiting Data Friction by Reducing Data Download Using Spatiotemporally Aligned Data Organization Through STARE

Current data processing practice limits the volume and variety of relevant geoscience data that can practically be applied to important problems. File archives in centralized data centers are the principal means by which Earth Science data are accessed. This approach, however, requires laborious search, retrieval, and eventual customization/adaptation for the data to be used. Such fractionation makes it even more difficult to share outcomes, i.e. research artifacts and data products, hampering reusability and repeatability, since end users generally have their own research agenda and preferences as well as scarce resources. Thus, while finding and downloading data files from central data centers are already costly for end users working in their own field, using data products from other disciplines rapidly becomes prohibitive. This curtails scientific productivity, limits avenues of study, and endangers quality and reproducibility. The Spatio-Temporal Adaptive Resolution Encoding (STARE) is a unifying scheme that facilitates the indexing, access, and fusion of diverse Earth Science data. STARE implements an innovative encoding of geo-spatiotemporal information, originally developed for aligning datasets with diverse spatiotemporal characteristics in an array database. The spatial component of STARE recursively quadfurcates a root polyhedron, producing a hierarchical scheme for addressing geographic locations and regions. The temporal component of STARE uses conventional date-time units as an indexing hierarchy. The additional encoding of spatial and temporal resolution information in STARE enables comparisons and conditional selections across diverse datasets. Moreover, spatiotemporal set-operations, e.g. union and intersection, are mapped to efficient integer operations with STARE. Applied to existing data models (point, grid, spacecraft swath) and corresponding granules, STARE indexes provide a streamlined description usable as geo-spatiotemporal metadata. When coupled with large scale, distributed hardware and software, STARE-based data access reduces pre-analysis data preparation costs by offering a convenient means to align different datasets spatiotemporally without specialized effort in parallel computing or distributed data management.

Kuo, Kwo-Sen↗
NASA NTRS2022

Chile Wildfires: Utilizing NASA and NOAA Earth Observations to Determine Lightning-ignited Wildfire Risks in Central Chile

In recent years, Central Chile has experienced wildfires of increasing frequency and intensity which threaten natural resources and communities. The Corporación Nacional Forestal (CONAF) responds to wildfires caused by a variety of ignitions, including lightning, but it is difficult to determine the prevalence of lightning-ignited wildfires based solely on ground observations. In collaboration with CONAF and the Embassy of Chile, Agricultural Office, the team used Earth observations to map coincidence of lightning strikes and wildfire ignitions. The Active Fire Product of Suomi NPP Visible Infrared Imaging Radiometer Suite (VIIRS) identified wildfires as thermal anomalies, which the team compared to the lightning events detected by NOAA’s GOES-16 Geostationary Lightning Mapper (GLM). Next, the team mapped lightning strike frequency and lightning related wildfires across the study area. Finally, the team calculated and mapped a relative estimate of lightning-ignited wildfire vulnerability across the year, fire season (December – March), and off-season (April – November) by summing the following factors: lightning frequency, the Normalized Difference Moisture Index (NDMI) and land surface temperature (LST). These risks were then weighted by fuel availability. Preliminary analysis of the lightning fire relationship showed a spatiotemporal coincidence, primarily in the South-central region of study, near Temuco, and isolated areas on the Andean front. The team identified areas at risk of lightning-induced wildfires, predominantly in the northern third of the study area and along the Andean front. Adjusting the relative weight of risk factors and improving the lightning and fire coincidence map by clustering VIIRS thermal anomalies into fire events could reduce discrepancies and improve risk assessments for future work.

Christopher Matechik↗
NASA NTRS2024

A Feasibility Study of 4-D Tomography of Soft X-ray Magnetosheath Emissivities Using Multi-Spacecraft Measurements

Upcoming heliophysics missions utilize state-of-the-art wide field-of-view (FOV) imaging technology to measure and investigate the space plasma environment on a global scale. At Earth, remote sensing of soft X-ray emissions, which are generated via the charge exchange interaction between heavy solar wind ions and exospheric neutral atoms, is a promising means to investigate the global magnetosheath structure, its response to varying solar wind conditions, and the spatiotemporal properties of the dayside magnetic reconnection. Data analysis techniques such as optical tomography can provide additional structural and time-varying information from the observed target and thus enhance the mission’s scientific return. In this work, we simulate multiple and simultaneous observations of the dayside magnetosphere using soft X-ray imagers located at long-distance vantage points to reconstruct the time-dependent, three-dimensional (3-D) structure of the magnetosheath using a dynamic tomographic approach. The OpenGCCM MHD model is used to simulate the time-varying response of the magnetosheath to solar wind conditions and, subsequently, generate synthetic soft X-ray images from multiple spacecraft vantage points separated along a common orbit. A detailed analysis is then performed to identify the nominal set of spacecraft that produces the highest fidelity tomographic reconstruction of the magnetopause. This work aims to (i) demonstrate, for the first time, the use of dynamic tomography to retrieve the time-varying magnetosheath structure and (ii) identify a nominal mission design for multi-spacecraft configurations aiming for optical tomography.

3-D/4_d tomography↗
NASA NTRS1999

Nitrogen Species in the Post-Pinatubo Stratosphere: Model Analysis Utilizing UARS Measurements

We present an analysis of the impact of heterogeneous chemistry on the partitioning of nitrogen species measured by the Upper Atmosphere Research Satellite (UARS) instruments. The UARS measurements utilized include N2O, HNO3, and ClONO2 from the cryogenic limb array etalon spectrometer (CLAES), version 7 (v.7), and temperature, methane, ozone, H2O, HCl, NO and NO2 from the halogen occultation experiment (HALOE), version 18. The analysis is carried out for the UARS data obtained between January 1992 and September 1994 in the 100-to 1-mbar (approx. 17-47 km) altitude range and over 10 degrees latitude bins from 70 S to 70 N. The spatiotemporal evolution of aerosol surface area density (SAD) is adopted from analysis of the Stratospheric Aerosol and Gas Experiment (SAGE) II data. A diurnal steady state photochemical box model, constrained by the temperature, ozone, H2O, CH4, aerosol SAD, and columns of O2 and O3 above the point of interest, has been used as the main tool to analyze these data. Total inorganic nitrogen (NOy) is obtained by three different methods: (1) as a sum of the UARS-measured NO, NO2, HNO3, and ClONO2; (2) from the N2O-NOy correlation, and (3) from the CH4-NOy correlation. To validate our current understanding of stratospheric heterogeneous chemistry for post-Pinatubo conditions, the model-calculated monthly averaged NOx/NOy ratios and the NO, NO2, and HNO3 profiles are compared with the UARS-derived data. In general, the UARS-constrained box model captures the main features of nitrogen species partitioning in the post-Pinatubo years, such as recovery of NOx after the eruption, their seasonal variability and vertical profiles. However, the model underestimates the NO2 content, particularly in the 30- to 7-mbar (approx.23-32 km) range. Comparisons of the calculated temporal behavior of the partial columns of NO2 and HNO3 and ground-based measurements at 45 S and 45 N are also presented. Our analysis indicates that ground-based and HALOE v.18 measurements of the NO2 vertical columns are consistent within the range of their uncertainties and are systematically higher (up to 50%) than the model results at midlatitudes in both hemispheres. Reasonable agreement is obtained for HNO3 columns at 45 S, suggesting some problems with nitrogen species partitioning in the model. Outstanding uncertainties are discussed.

Danilin, Michael Y.↗
NASA NTRS1999

Nitrogen Species in the Post-Pinatubo Stratosphere: Model Analysis Utilizing UARS Measurements

We present an analysis of the impact of heterogeneous chemistry on the partitioning of nitrogen species measured by the Upper Atmosphere Research Satellite (UARS) instruments. The UARS measurements utilized include N2O, HNO3, and ClONO2 from the cryogenic limb array etalon spectrometer (CLAES), version 7 (v.7), and temperature, methane, ozone, H2O, HCl, NO and NO2 from the halogen occultation experiment (HALOE), version 18. The analysis is carried out for the UARS data obtained between January 1992 and September 1994 in the 100- to 1-mbar (approx. 17-47 km) altitude range and over 10 deg latitude bins from 70 deg S to 70 deg N. The spatiotemporal evolution of aerosol surface area density (SAD) is adopted from analysis of the Stratospheric Aerosol and Gas Experiment (SAGE) II data. A diurnal steady state photochemical box model, constrained by the temperature, ozone, H2O, CH4, aerosol SAD, and columns of O2 and O3 above the point of interest, has been used as the main tool to analyze these data. Total inorganic nitrogen (NOY) is obtained by three different methods: (1) as a sum of the UARS-measured NO, NO2, HNO3, and ClONO2; (2) from the N2O-NOY correlation; and (3) from the CH4-NOY correlation. To validate our current understanding of stratospheric heterogeneous chemistry for post-Pinatubo conditions, the model-calculated monthly averaged NO(x)/NO(y) ratios and the NO, NO2, and HNO3 profiles are compared with the UARS-derived data. In general, the UARS-constrained box model captures the main features of nitrogen species partitioning in the post-Pinatubo years, such as recovery of NO(x) after the eruption, their seasonal variability and vertical profiles. However, the model underestimates the NO2 content, particularly in the 30- to 7-mbar (approx. 23-32 km) range. Comparisons of the calculated temporal behavior of the partial columns of NO2 and HNO3 and ground-based measurements at 45 deg S and 45 deg N are also presented. Our analysis indicates that ground-based and HALOE v.18 measurements of the NO2 vertical columns are consistent within the range of their uncertainties and are systematically higher (up to 50%) than the model results at midlatitudes in both hemispheres. Reasonable agreement is obtained for HNO3 columns at 45 deg S, suggesting some problems with nitrogen species partitioning in the model. Outstanding uncertainties are discussed.

Danilin, Michael Y.↗
NASA NTRS2012

Multiscale Dynamics of Solar Magnetic Structures

Multiscale topological complexity of the solar magnetic field is among the primary factors controlling energy release in the corona, including associated processes in the photospheric and chromospheric boundaries.We present a new approach for analyzing multiscale behavior of the photospheric magnetic flux underlying these dynamics as depicted by a sequence of high-resolution solar magnetograms. The approach involves two basic processing steps: (1) identification of timing and location of magnetic flux origin and demise events (as defined by DeForest et al.) by tracking spatiotemporal evolution of unipolar and bipolar photospheric regions, and (2) analysis of collective behavior of the detected magnetic events using a generalized version of the Grassberger-Procaccia correlation integral algorithm. The scale-free nature of the developed algorithms makes it possible to characterize the dynamics of the photospheric network across a wide range of distances and relaxation times. Three types of photospheric conditions are considered to test the method: a quiet photosphere, a solar active region (NOAA 10365) in a quiescent non-flaring state, and the same active region during a period of M-class flares. The results obtained show (1) the presence of a topologically complex asymmetrically fragmented magnetic network in the quiet photosphere driven by meso- and supergranulation, (2) the formation of non-potential magnetic structures with complex polarity separation lines inside the active region, and (3) statistical signatures of canceling bipolar magnetic structures coinciding with flaring activity in the active region. Each of these effects can represent an unstable magnetic configuration acting as an energy source for coronal dissipation and heating.

Magnetic fields↗
NASA NTRSDate not supplied

Evaluation of Planetary Boundary Layer Structure from NASA Global Modeling and Assimilation Office’s Next Retrospective Analysis Product MERRA-21C

The Planetary Boundary Layer (PBL) is a complex interface between Earth’s surface and the atmosphere with high spatiotemporal variability in its characteristics, and accurate simulation and observation of the PBL has proven to be a challenge. In this study, we evaluate PBL structures from NASA Global Modeling and Assimilation Office (GMAO)’s next retrospective analysis product: the Goddard Earth Observing System Retrospective Analysis of the early 21st Century (GEOS-R21C), with 25-km horizontal resolution. The GEOS-R21C incorporates a wide range of observing systems and various improvements over previous GMAO reanalysis. We compare the PBL thermodynamic structure as well as PBL Height (PBLH) from GEOS-R21C with various PBL observations. For PBLH, definitions are different depending on each observing system. PBLHs derived from radiosonde observations and Global Navigation Satellite System Radio Occultation (GNSS RO) are based on bulk Richardson number and refractivity gradient, respectively. The results of the evaluation of PBL structure from GMAO’s next retrospective analysis product will be presented. In addition, comparison between near-real-time (NRT) commercial and NASA commercial GNSS RO PBLH will also be discussed.

Eun-Gyeong Yang↗
NASA NTRSDate not supplied

Monte Carlo Simulations for Evaluating the Accuracy of GLM Detection Efficiency and False Alarm Rate Retrievals

The performance of the Geostationary Lightning Mapper has been evaluated through comparison with other satellite lightning sensors and ground-based networks. However, because the performance of these reference sensors is both imperfect and imperfectly known, the true performance of GLM can only be estimated through such a process. In particular, GLM performance metrics such as detection efficiency (DE) and false alarm rate (FAR) retrieved through comparison with reference networks are affected by those networks’ own DE, FAR, and spatiotemporal accuracy, as well as the selected flash matching criteria. This study presents follow-on results from an ongoing analysis that uses a Monte Carlo simulation-based inversion technique to quantify the effect on retrieved GLM performance of a reasonable range of reference network DE, FAR, spatiotemporal accuracy, and the associated geographic patterns of each. These results help quantify and bound the actual performance of GLM and the attendant uncertainties when comparing GLM to imperfect reference networks.

Katrina Virts↗
NASA NTRS2023

Optimal Estimation Inversion of Ionospheric Electron Density from GNSS-POD Limb Measurements: Part I-Algorithm and Morphology

GNSS-LEO radio links from Precise Orbital Determination (POD) and Radio Occultation (RO) antennas have been used increasingly in characterizing the global 3D distribution and variability of ionospheric electron density (N e ). In this study, we developed an optimal estimation (OE) method to retrieve N e profiles from the slant total electron content (hTEC) measurements acquired by the GNSS-POD links at negative elevation angles (ε < 0°). Although both OE and onion-peeling (OP) methods use the Abel weighting function in the N e inversion, they are significantly different in terms of performance in the lower ionosphere. The new OE results can overcome the large N e oscillations, sometimes negative values, seen in the OP retrievals in the E-region ionosphere. In the companion paper in this Special Issue, the HmF2 and NmF2 from the OE retrieval are validated against ground-based ionosondes and radar observations, showing generally good agreements in NmF2 from all sites. Nighttime hmF2 measurements tend to agree better than the daytime when the ionosonde heights tend to be slightly lower. The OE algorithm has been applied to all GNSS-POD data acquired from the COSMIC-1 (2006–2019), COSMIC-2 (2019–present), and Spire (2019–present) constellations, showing a consistent ionospheric N e morphology. The unprecedented spatiotemporal sampling of the ionosphere from these constellations now allows a detailed analysis of the frequency–wavenumber spectra for the N e variability at different heights. In the lower ionosphere (~150 km), we found significant spectral power in DE1, DW6, DW4, SW5, and SE4 wave components, in addition to well-known DW1, SW2, and DE3 waves. In the upper ionosphere (~450 km), additional wave components are still present, including DE4, DW4, DW6, SE4, and SW4. The co-existence of eastward- and westward-propagating wave4 components implies the presence of a stationary wave4 (SPW4), as suggested by other earlier studies. Further improvements to the OE method are proposed, including a tomographic inversion technique that leverages the asymmetric sampling about the tangent point associated with GNSS-LEO links.

GNSS constellation↗
NASA NTRS1988

Capacity for patterns and sequences in Kanerva's SDM as compared to other associative memory models

The information capacity of Kanerva's Sparse Distributed Memory (SDM) and Hopfield-type neural networks is investigated. Under the approximations used here, it is shown that the total information stored in these systems is proportional to the number connections in the network. The proportionality constant is the same for the SDM and Hopfield-type models independent of the particular model, or the order of the model. The approximations are checked numerically. This same analysis can be used to show that the SDM can store sequences of spatiotemporal patterns, and the addition of time-delayed connections allows the retrieval of context dependent temporal patterns. A minor modification of the SDM can be used to store correlated patterns.

Keeler, James D.↗
NASA NTRS1987

Capacity for patterns and sequences in Kanerva's SDM as compared to other associative memory models

The information capacity of Kanerva's Sparse Distributed Memory (SDM) and Hopfield-type neural networks is investigated. Under the approximations used, it is shown that the total information stored in these systems is proportional to the number connections in the network. The proportionality constant is the same for the SDM and Hopfield-type models independent of the particular model, or the order of the model. The approximations are checked numerically. This same analysis can be used to show that the SDM can store sequences of spatiotemporal patterns, and the addition of time-delayed connections allows the retrieval of context dependent temporal patterns. A minor modification of the SDM can be used to store correlated patterns.

Keeler, James D.↗
NASA NTRS2021

The Scientific Legacy of NASA’s Operation IceBridge

The National Aeronautics and Space Administration (NASA)’s Operation IceBridge (OIB) was a 13-year (2009–2021) airborne mission to survey land and sea ice across the Arctic, Antarctic, and Alaska. Here, we review OIB’s goals, instruments, campaigns, key scientific results, and implications for future investigations of the cryosphere. OIB’s primary goal was to use airborne laser altimetry to bridge the gap in fine-resolution elevation measurements of ice from space between the conclusion of NASA’s Ice, Cloud, and land Elevation Satellite (ICESat; 2003–2009) and its follow-on, ICESat-2 (launched 2018). Additional scientific requirements were intended to contextualize observed elevation changes using a multisensor suite of radar sounders, gravimeters, magnetometers, and cameras. Using 15 different aircraft, OIB conducted 968 science flights, of which 42% were repeat surveys of land ice, 42% were surveys of previously unmapped terrain across the Greenland and Antarctic ice sheets, Arctic ice caps, and Alaskan glaciers, and 16% were surveys of sea ice. The combination of an expansive instrument suite and breadth of surveys enabled numerous fundamental advances in our understanding of the Earth’s cryosphere. For land ice, OIB dramatically improved knowledge of interannual outlet-glacier variability, ice-sheet, and outlet-glacier thicknesses, snowfall rates on ice sheets, fjord and sub-ice-shelf bathymetry, and ice-sheet hydrology. Unanticipated discoveries included a reliable method for constraining the thickness within difficult-to-sound incised troughs beneath ice sheets, the extent of the firn aquifer within the Greenland Ice Sheet, the vulnerability of many Greenland and Antarctic outlet glaciers to ocean-driven melting at their grounding zones, and the dominance of surface-melt-driven mass loss of Alaskan glaciers. For sea ice, OIB significantly advanced our understanding of spatiotemporal variability in sea ice freeboard and its snow cover, especially through combined analysis of fine-resolution altimetry, visible imagery, and snow radar measurements of the overlying snow thickness. Such analyses led to the unanticipated discovery of an interdecadal decrease in snow thickness on Arctic sea ice and numerous opportunities to validate sea ice freeboards from satellite radar altimetry. While many of its data sets have yet to be fully explored, OIB’s scientific legacy has already demonstrated the value of sustained investment in reliable airborne platforms, airborne instrument development, interagency and international collaboration, and open and rapid data access to advance our understanding of Earth’s remote polar regions and their role in the Earth system.

Joseph A. MacGregor↗