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NASA NTRS2016

Pluto and Charon Seen with the New Horizons Spacecraft

After nearly a decade en route, New Horizons flew through the Pluto system in July 2015. The encounter hemisphere of Pluto shows ongoing surface geological activity centered on a vast basin (Sputnik Planum [SP]*) containing a thick layer of volatile ices with a crater retention age no greater than approximately 10 Ma. Surrounding terrains show active glacial flow, apparent transport and rotation of large buoyant water-ice crustal blocks, and pitting, likely by sublimation erosion and/or collapse. Also seen are constructional mounds with central depressions, and ridges with complex bladed textures. Pluto has ancient cratered terrains up to approximately 4 Ga old that are fractured and mantled, and perhaps eroded by glacial processes. Charon does not appear to be currently active, but experienced major tectonism and resurfacing nearly 4 Ga ago. Imaging spectrometer observations of Pluto reveal the encounter hemisphere to be dominated by volatile ices of N2, CO, and CH4, along with non-volatile components that include H2O and tholins. The most volatile of Pluto's ices (N2 and CO) are especially prevalent in the western half of Tombaugh Regio (TR), and the strikingly flat Sputnik Planum basin, which lies a few km below surrounding elevations. The high mobility of N2 and CO ices enables SP's surface to refresh itself sufficiently rapidly that no impact craters are seen there. This likely occurs through a combination of solid state convective overturning and sublimation/ condensation that produces regular patterns of pits and ridges on scales of 102 to 103 m. In many areas, CH4 appears to favor topographically high regions. Its propensity to condense on ridges could play a role in forming the bladed terrain seen in Tartarus Dorsa. H2O can be discerned across much of Cthulhu Regio, and also in a few isolated spots. In many regions, H2O ice is associated with reddish tholin coloration. Pluto's atmosphere was probed with the radio science experiment (REX) and the Alice UV spectrometer, as well as imaging at high phase angles. The surface pressure, due mostly to N2, is approximately 11 microbars. Extensive multiple haze layers are seen in the images. Alice has detected hydrocarbons in addition to CH4 in the atmosphere. Since both are inert, H2O ice and tholin could have similar geological behaviors on Pluto, possibly including aeolian transport or mobilization by volatile ice glaciation. While Pluto's H2O ice is sculpted and at least partially veiled by more volatile ices, Charon's heavily cratered H2O ice is exposed globally. H2O ice spectral bands characteristic of crystalline ice are seen everywhere on the encounter hemisphere. Charon's north polar region is strikingly red, possibly the result of the unique thermal environment of Charon's poles, which become exceptionally cold during the long, dark winters. Extremely cold regions on Charon' could cold trap gases expanding outward from Pluto as ices, and thereby subject them to rapid radiolytic processing. Charon also exhibits a weak NH3 absorption band over most or all of its surface, with small local concentrations. Detailed results of the radio science, small satellite, particles and plasma, and atmosphere investigations are in press.

Pluto↗

NASA NTRS2017

Precision Projector Laboratory: Detector Characterization with an Astronomical Emulation Testbed

As astronomical observations benefit from improved sensitivity, the effectiveness of scientific programs is becoming limited by systematics that often originate in poorly understood image sensor behavior. Traditional, bottom-up detector characterization methods provide one way to model underlying detector physics, and generate ever more faithful numerical simulations, but this approach is vulnerable to preconceptions and over-simplification. The alternative top-down approach is laboratory emulation, which enables observation, calibration, and analysis scenarios to be tested without relying on a complete understanding of the underlying physics. This complements detector characterization and simulation efforts by testing their validity. We describe a laboratory facility and experimental testbed that supports the emulation of a wide range of mission concepts such as gravitational weak lensing measurements by WFIRST and high precision spectrophotometry of transiting exoplanets by JWST. An Offner relay projects readily customizable "scenes" (e.g. stars, galaxies, spectra) with very low optical aberration over the full area of a typical optical or near infrared image sensor. f/8 and slower focal ratios may be selected, spanning those of most proposed space missions and approximating the point spread function (PSF) size of seeing limited ground based surveys. Diffraction limited PSFs are projected over a wide field of view and wavelength range to deliver highly predictable image patterns down to sub-pixel scales with stable intensity and fine motion control. The testbed enables realistic validation of detector performance on science-like images, which aids mission design and survey strategy, as well as targeted investigations of various detector effects.

Seshadri, Suresh↗

NASA NTRS2021

Linear and Nonlinear Trend Analyzes in Global Satellite‐Based Precipitation, 1998–2017

Precipitation varies spatio‐temporally in amount, intensity, and frequency. Although, much research has been conducted on analyzing precipitation patterns and variation at the global scale, trend types have still not received much attention. This study developed a new polynomial‐based model for detecting nonlinear and linear trends in a satellite precipitation product (TRMM 3B43) for the 1998–2017 period at a near‐global scale. We used an automated trend classification method that detects significant trends and classifies them into linear and nonlinear (cubic, quadratic, and concealed) trend types in satellite‐based precipitation at near‐global, continental, and climate zone scales. We found that 12.3% of pixel‐based precipitation time series across the globe have significant trend at 0.05 significance level (50% positive and 50% negative trends). In all continents except Asia, decreasing trends were found to cover larger areas than corresponding increasing trends. Regarding climate zone and precipitation trend change, our results indicate that a linear trend is dominant in the warm temperate (77.7%) and equatorial climates (80.4%) while the least linear change was detected in the polar climate (68.9%). The combined results of continental and climate zone scales indicate significant increasing trends in Asia and arid climate over the last 20 yr. Furthermore, positive trends were found to be more significant at the continental scale, particularly, in Asia relative to the climate zone scale. Linear change in precipitation (80%) was the most dominant trend observed as opposed to nonlinear (quadratic [11%] and cubic [9%]) trend types at the global scale.

Majid Kazemzadeh↗

NASA NTRSDate not supplied

Monte Carlo Study of the Formation of Chromosome Aberrations by Direct Ion Traversal vs. Delta-Electrons

Space radiation constitutes a major risk for the safety of space travel. Galactic Cosmic Rays (GCRs) are made of high energy protons (87%), high energy helium ions (12%) and high-charge and energy (HZE) ions [1]. At a cellular scale, HZE ions display a characteristic pattern of energy deposition, known as the ion track, that correlates with the ion linear energy transfer (LET). These energy deposition events (ionizations/excitations) create DNA breaks that, if misrepaired, lead to the formation of chromosome aberrations (CAs). Compared to low-LET terrestrial radiations (X-rays and γ-rays), high LET ions are known to create complex, clustered DNA breaks that are particularly efficient at forming CAs. Indeed, ion tracks are usually described as a dense ionization core, where clustered breaks are formed, and a penumbra, made of low-LET δ-electrons that have sufficient energy to travel across many cells and induce breaks that are more homogeneously distributed. In this work, we investigated the formation of CAs, and separated them into two contributions (direct ion traversal of the nucleus vs.δ-electrons), to elucidate to which extent δ-electrons contribute to CA formation.

Floriane Poignant↗

DOE OSTI2025

Evaluating the Impact of Power Outages on Occupancy Patterns During the 2021 Texas Power Crisis

Large-scale power outages, such as those caused by extreme weather events, have a big impact on human behavior. A short power outage is merely a nuisance for most, and may not change people's locations. An outage that lasts for a few hours can result in spoiled food and medical supplies, and people will have to restock spoiled items. Long outages result in temperatures outside tolerable levels in homes, and may prompt people to acquire supplies, such as generators and gas, or change location. The long outages during Winter Storm Uri in Texas resulted in millions of dollars in property damage due to freezing pipes. This level of damage is expected to result in a sharp increase in supply runs and contractor activity. In this paper, we present a tool to explore differences in visiting patterns before, during, and after power outages. It allows to compare different points of interest like medical facilities, grocery stores, hardware stores, and other types of businesses.

big data↗

DOE OSTI2021

Nanoscale dynamics during self-organized ion beam patterning of Si. I. Ar + bombardment

X-ray photon correlation spectroscopy (XPCS) is used to investigate the fluctuation dynamics during self- organized nanopatterning of silicon by Ar + bombardment at 65° polar angle. Rich structure is observed in the development of the correlation dynamics as seen in the evolving correlation time τ(q || ) and fluctuation relaxation exponent n(q || ). Furthermore, on length scales of the ripple structure, local structure becomes ever more long lived as coarsening progresses. In addition, τ (q || ) develops a peak on length scales corresponding to the ripple wavelength. As patterning progresses, correlation times become asymmetric between the positive and negative directions, suggesting the possibility of different dynamics on the slopes facing toward and away from the ion beam. Relaxation exponents show evolution from linear dynamics at early times to compressed exponential relaxation at low wave numbers and stretched exponential relaxation at high wave numbers. Compressed exponential behavior is reminiscent of stress relaxation processes observed in glasses.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Optimizing DOE Opportunities to Research Land–Atmosphere Interactions in the U.S. Southeast (Workshop Report)

The southeastern United States (Southeast), with its complex and varied environments, is an area of tremendous economic, ecological, and societal importance to the country. The region is characterized by heterogeneous landscapes (i.e., geology and soil type) and a long history of human land use coupled with a warm temperature regime and high precipitation. As a result, soil erosion and deposition are pronounced, vegetation recovery is rapid, and human modification is extensive across the region. To better understand land–atmosphere interactions in this important and complex region, research communities supported by the U.S. Department of Energy’s Biological and Environmental Research (BER) program identified the Southeast as a priority region of interest. In fall 2024, the third Atmospheric Radiation Measurement Mobile Facility (AMF3), one of three mobile monitoring facilities designed to collect atmospheric and climate data from undersampled regions around the world, will begin operations in northwestern Alabama’s Bankhead National Forest (BNF). The AMF3-BNF 5-year deployment, from 2024–2029, will monitor the effects of feedbacks among aerosols, clouds, and precipitation on plant physiology and canopy-scale fluxes. It will also focus on scale aggregation to resolve the role of local forcing on larger-scale processes. To enable broader AMF3 involvement by the science community, the BER Environmental System Science (ESS) program organized the Southeast Land– Atmosphere Research Opportunities (SELARO) workshop in August 2023. The purpose was to identify gaps in scientific understanding of terrestrial processes in the Southeast (defined as states bounded by the Gulf of Mexico to the south, the Atlantic Ocean to east, the Mississippi River to the west, and extending through Tennessee and North Carolina to the north) and explore opportunities to use the AMF3-BNF deployment to coordinate and leverage research efforts across the region. Many parts of the Southeast have experienced repeated anthropogenic forcings. Farming, hunting, burning, and settlement of the region by Indigenous Peoples first shaped the distribution of plant communities, which in turn influenced European colonization patterns. Timber harvesting was common during the expansion of European settlements, and production forestry continues today. Agricultural production was extensive and then waned through the 20th century, creating a period of afforestation following agricultural abandonment. Today, many formerly agricultural landscapes are undergoing rapid urbanization and suburbanization. Overlying these patterns of anthropogenic land use are frequent disturbances from hurricanes, tornadoes, wildfires, drought, flooding, ice storms, and the occasional blizzard. An additional characteristic of the Southeast is its overall landscape complexity. Unlike the western United States, where broad expanses may share similar characteristics, Southeast topography, drainage patterns, vegetation, and development patterns vary widely across relatively small spatial scales (<1 km). This is due to the region’s underlying geology and soil development, species biodiversity patterns, and land ownership and use coupled with strong forces of erosion, weathering, and rapid plant growth in the warm, wet climate.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2019

Coupled Modes of North Atlantic Ocean Atmosphere Variability and the Onset of the Little Ice Age

Hydroclimate extremes in North America, Europe, and the Mediterranean are linked to ocean and atmospheric circulation anomalies in the Atlantic, but the limited length of the instrumental record prevents complete identification and characterization of these patterns of covariability especially at decadal to centennial timescales. Here we analyze the coupled patterns of drought variability on either side of the North Atlantic Ocean basin using independent climate field reconstructions spanning the last millennium in order to detect and attribute epochs of coherent basin-wide moisture anomalies to ocean and atmosphere processes. A leading mode of broad-scale moisture variability is characterized by distinct patterns of North Atlantic atmosphere circulation and sea surface temperatures. We infer a negative phase of the North Atlantic Oscillation and colder Atlantic sea surface temperatures in the middle of the 15th century, coincident with weaker solar irradiance and prior to strong volcanic forcing associated with the early Little Ice Age.

Kevin J Anchukaitis↗

DOE OSTI2020

Rendezvous algorithms for large-scale modeling and simulation

Rendezvous algorithms encode a communication pattern that is useful when processors sending data do not know who the receiving processors should be, or vice versa. The idea is to define an intermediate decomposition where datums from different sending processors can ”rendezvous” to perform a computation, in a manner that both the senders and eventual receivers of the results can identify the appropriate rendezvous processor. Though they were originally designed for interpolating between overlaid grids with independent parallel decompositions (Plimpton et al., 2004), we have recently found rendezvous algorithms useful for a variety of operations in particle- or grid-based simulation codes when running large problems on large numbers of processors. In particular, we show they can perform well when a load-balanced intermediate decomposition is randomized and not spatial, requiring all-to-all communication to move data between processors. In this case rendezvous algorithms leverage the large bisection communication bandwidths which parallel machines provide. We describe how rendezvous algorithms work in a scientific computing context and give specific examples for molecular dynamics and Direct Simulation Monte Carlo codes which result in dramatic performance improvements versus simpler algorithms which do not scale as well. We explain how a generic rendezvous algorithm can be implemented, and also point out similarities with the MapReduce paradigm popularized by Google and Hadoop.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1988

Magnetic fine structure of solar coronal loops

A numerical simulation of the effect of a random photospheric flow on the magnetic structure of a coronal loop is presented. An initially uniform field embedded in a perfectly conducting plasma is assumed, extending between two flat parallel plates representing the solar photosphere at both ends of the loop. The field is perturbed by a sequence of randomly phased, sinusoidal flow patterns applied at one of the boundary plates, and the corresponding sequence of force-free fields is determined. It is found that the electric currents generated by these flows develop a fine structure on a scale significantly smaller than the wavelength of the velocity patterns. This suggests that magnetic energy is transferred to smaller scale via a cascade process.

Van Ballegooijen, A. A.↗

NASA NTRS1980

Development of test methods for scale model simulation of aerial applications in the NASA Langley Vortex Research Facility

As part of basic research to improve aerial applications technology, methods were developed at the Langley Vortex Research Facility to simulate and measure deposition patterns of aerially-applied sprays and granular materials by means of tests with small-scale models of agricultural aircraft and dynamically-scaled test particles. Interactions between the aircraft wake and the dispersed particles are being studied with the objective of modifying wake characteristics and dispersal techniques to increase swath width, improve deposition pattern uniformity, and minimize drift. The particle scaling analysis, test methods for particle dispersal from the model aircraft, visualization of particle trajectories, and measurement and computer analysis of test deposition patterns are described. An experimental validation of the scaling analysis and test results that indicate improved control of chemical drift by use of winglets are presented to demonstrate test methods.

Jordan, F. L., Jr.↗

DOE OSTI2025

Data Readiness for Scientific AI at Scale

This paper examines how Data Readiness for AI (DRAI) principles apply to leadership-scale scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, bio/health, and materials—to identify common preprocessing patterns and domain-specific constraints. We introduce a two-dimensional readiness framework that combines canonical preprocessing patterns with a five-level operational readiness scale, both tailored to high-performance computing (HPC) environments. This framework helps outline key challenges in transforming large-scale scientific data into formats suitable for scalable AI training. Together, these dimensions form a conceptual maturity matrix that characterizes scientific data readiness and guides infrastructure development toward standardized, cross-domain support for scalable and reproducible AI for science.

Brewer, Wes [ORNL] (ORCID:0000000236393956)↗

DOE OSTI2024

Snow distribution patterns revisited: A physics-based and machine learning hybrid approach to snow distribution mapping in the sub-Arctic Supporting Data

Snow in the Arctic and sub-Arctic is highly variable at fine scales, with deep drifts and shallow scoured areas creating a complex pattern of snow on the landscape. This fine-scale variation in snow is driven primarily by landscape and vegetation properties. Some landscape features, such as river beds, will rapidly fill in with snow during the wintertime due to high winds, while shrubs will trap blowing snow, resulting in drifts. Meanwhile, snow will blow off of exposed areas, resulting in abnormally shallow snow. These complex interactions between wind, vegetation, and terrain are difficult to represent well with physically-based models, but machine learning techniques have shown promise in the past. Here, we propose a hybrid modeling approach, where we use machine learning derived snow pattern maps to inform SnowModel, a physically-based snow process model. We develop and test this technique at the Teller 27 Seward Peninsula NGEE-Arctic study site. This dataset includes 5 *.nc files of model inputs and outputs plus one user guide (*.pdf). We present the data we use to drive SnowModel (vegetation type, digital elevation model), the snow pattern maps used to inform SnowModel (Standardized Depth Values maps, Machine Learning Snow Distribution Pattern), and our machine learning, SnowModel, and Hybrid snow depth and snow water equivalent results. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1978

Status of aerial applications research in the Langley vortex research facility and the Langley full-scale wind tunnel

Small scale models of agricultural airplanes were tested and numerical methods were utilized to study interactions between the airplane wake and the dispersed spray and granular materials. Methods were developed to measure and predict dispersal transport and wake characteristics and dispersal techniques to obtain interactions more favorable to wide, uniform deposition patterns and reduced drift. In the full scale wind tunnel, full scale agricultural airplanes and dispersal systems for both liquid and solid applications were evaluated to improve aircraft aerodynamics and dispersal systems efficiency. The program status in these two facilities is reported with emphasis on wake interactions and dispersal systems research.

Jordan, F. L., Jr.↗

DOE OSTI2025

Amplified Mesoscale and Submesoscale Variability and Increased Concentration of Precipitation under Global Warming over Western North America

Abstract Cold-season precipitation statistics in simulations from the storm-resolving WRF Model at 6-km and 1-h resolution over western North America are analyzed. Pseudo–global warming future simulations for the 2041–80 period, constrained by GCMs under the RCP8.5 scenario, are compared to the 1981–2020 historical simulation. The analysis focuses on the dynamical properties of precipitation time series at subdaily scales and on the morphology of storms. The statistical distribution of precipitation intensities in each pixel of the simulation domain is characterized through nonparametric statistical indicators: frequency of wet hours, mean wet-hour precipitation intensity, and Gini coefficient as a measure of the temporal concentration of the precipitation volume. Additionally, the temporal and spatial Fourier power spectra of precipitation time series and precipitation fields are analyzed. The half-power period (HPP) and half-power wavelength (HPW) are defined as spectral measures of the characteristic scales of precipitation’s temporal and spatial patterns. The results show statistically significant increases in the mean wet-hour precipitation intensity and in the Gini coefficient in 99% of the pixels, indicating that the seasonal precipitation volume becomes more concentrated within a smaller number of hours with higher precipitation intensity. The statistics of change in the frequency of wet hours are more contrasted across the simulation domain. The changes are also reflected in the power spectra, which show the spatial and temporal variability increasing proportionally more with finer spatial and temporal scales and the HPW and HPP decreasing. These projected changes are expected to have consequences, not only in terms of hydrologic impacts but also in terms of the predictability of precipitation patterns. Significance Statement The precipitation characteristics of winter storms over the western United States and southwestern Canada are analyzed in future climate simulations for the 2041–80 period. As compared to present-day climate, the most intense parts of the storms are projected to produce a higher rainfall volume, with increased concentration over smaller areas and shorter time intervals. The propensity of rainfall intensity to vary rapidly over time will be enhanced in the future according to the simulations. These model predictions imply an increased risk of rapid flooding in small basins. They also suggest that predicting several hours ahead the time and location at which a storm will produce maximum rainfall may become more challenging in the future.

Climate change↗

DOE OSTI2025

Cyclic Background Noise Variations on Infrasound Microbarometers From Micrometeorology and Human Activity

Infrasound microbarometers deployed on the Earth's surface capture pressure fluctuations and acoustic signatures, revealing trends in surface wind speed and cycles in ambient sound. Previous studies investigated wind noise trends at quarter day resolution and urban acoustic background variations on hourly to weekly scales. Cyclic variations at sub-hourly resolution over local distances remain unaddressed. We show that topography-driven micrometeorology impacts diurnal background noise levels below 10 Hz. Anthropogenic noise occurs over daily and weekly cycles, with inputs from traffic, ventilation systems, and power lines. These noise patterns vary at stations spaced less than a kilometer apart. We observe these phenomena by using a circular spectrogram to visualize frequency trends over a periodic temporal scale. This study demonstrates that infrasound microbarometer deployments can highlight localized wind patterns and time scales of human activity. The results suggest that future microbarometer deployments may benefit from site noise surveys before selecting final sensor locations.

Malach, Amrit Kaur [Sandia National Lab. (SNL-CA),↗

DOE OSTI2021

Determining Spatial Scales of Soil Moisture—Cloud Coupling Pathways Using Semi-Idealized Simulations

The spatial scales of land-atmosphere interactions over heterogeneous, realistic soil moisture patterns are critical to improve our understanding of land-atmosphere interactions. However, these scales are poorly quantified. We use high-resolution numerical experiments to quantify the spatial scales for a selected day during the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) campaign in Oklahoma. A semi-idealized approach (removing background winds and surface topography while retaining the real-world data for other boundary conditions) enables examination of the local influence from the surface. Simulations that apply uniform surface, variable land cover with uniform soil moisture, and variable land cover with variable soil moisture are then compared. Land cover variations, especially water and urban surfaces, create two variance peaks in the surface sensible heat flux in the scales >10 km (meso-β) and <6 km (meso-γ). Both peaks are enhanced by the observation-based soil moisture distribution that has most of its variance in the meso-β scale. Atmospheric response in the meso-β scale is hydrostatic with weak vertical motion. The land cover variations induce secondary circulations in the scales of 2.5–6 km. The additional soil moisture variability intensifies extreme circulations over dry soil patches and sharp sensible heat flux gradients, slightly extending the characteristic scales to 2.5–9 km. While their areal coverage is small, soil moisture variations on such “hotspots” lead to substantially larger convective cloud structure and domain-mean rainfall, calling for a consideration of their non-linear effect in large-scale models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Improving Geolocation Accuracy of the Advanced Meteorological Imager on the GEO-KOMPSAT-2A

GeoNEX is a collaborative project led by scientists from NASA and many other international institutes to generate Earth monitoring products using data streams from the latest geostationary (GEO) sensors. Its consistent processing and common gridding systems can produce research-quality data products from GEO sensors and leverage GEO-GEO or GEO-LEO (low earth orbit) synergistic uses. Currently, GeoNEX has produced and disseminated L1G (geometrically corrected Level 1 products) from GOES 16/17 ABIs and Himawari-8 AHI, but a new Korean geostationary sensor (Advanced Meteorological Imager, AMI) onboard Geo-KOMPSAT-2A covering a large proportion of Asia and all of Oceania is in development. Our recent efforts on assessing geolocation accuracy in ABI and AHI suggest a nontrivial residual exists in both level 1B data with varying spatiotemporal patterns. The findings urge us to prioritize identifying and correcting geolocation residuals of AMI to generate accurate and consistent GeoNEX top-of-atmosphere (TOA) reflectance products and following processing chains. Here we implement a phase correlation correction approach to a visible band (0.64 μm, 500 m) using landmarks prepared from finer scale digital terrain models. We characterize spatiotemporal patterns (e.g., diurnal & daily) of geolocation residuals of AMI before and after correction. We then assess stability of datasets and quantify impact of unexpected geolocation errors on terrestrial monitoring. The geolocation corrected AMI data are further compared with GeoNEX AHI L1G products which are able to create unique stereo-type observations with AMI through leveraging the similarities of spectral bands and the sun-target-sensor geometry. Further, we discuss challenges in utilizing the GEO-GEO (e.g., AMI & AHI) satellite data for potential applications.

Geostationary Satellites↗