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NASA NTRSDate not supplied

Detecting Hail from Space: Algorithms, Climatologies, and Challenges Going Forward

In addition to the myriad threats that severe hailstorms pose to society, infrastructure and agriculture, severe hail is difficult to measure in situ, and surface-based hail reporting and detection methods are inconsistent and subject to geographical or societal biases. This motivates the use of spaceborne remote-sensing platforms to retrieve hail and construct climatologies in a globally uniform way. We have developed a hail detection algorithm that leverages the sensitivity of spaceborne passive-microwave radiometers to scattering by hail, particularly in the channels from 10 to 89 GHz. We use this retrieval to construct global climatologies of severe hail using several different spaceborne sensors: the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Global Precipitation Measurement Mission (GPM) Microwave Imager, Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors and are working to extend into the late 1980’s using the Special Sensor Microwave Imager/Sounder (SSMI/(S)) data. Using coincident Global Precipitation Measurement (GPM) Ku-band precipitation radar, we assessed this retrieval and several others in the literature for their effectiveness and regional variability. We developed a passive-microwave algorithm that corresponds tightly to radar reflectivity and gives the least appearance of regional biases compared to other passive-microwave approaches in the literature. Satellite platforms offer consistent observations, even in remote, data-sparse, and oceanic regions that ground-based networks exclude. There are, however, potential disconnects between the processes identified aloft by the satellite and the resultant weather at the ground, leading to uncertainties in the retrievals that may propagate into satellite-based climatologies, particularly in the Tropics, where there are abundant strong - but not necessarily hailing - storms that are strongly represented in the current satellite climatologies. We will discuss ongoing efforts to assess and mitigate the contributing factors to these uncertainties, chiefly among them the effects of non-uniform beam filling in the passive-microwave footprint, and the relationships between the size distributions of hailstones aloft and the dynamic processes and environments with which they interact throughout their trajectories.

Sarah D Bang↗

NASA NTRS1994

Multifractal characterizations of nonstationarity and intermittency in geophysical fields: Observed, retrieved, or simulated

Geophysical data rarely show any smoothness at any scale, and this often makes comparison with theoretical model output difficult. However, highly fluctuating signals and fractal structures are typical of open dissipative systems with nonlinear dynamics, the focus of most geophysical research. High levels of variability are excited over a large range of scales by the combined actions of external forcing and internal instability. At very small scales we expect geophysical fields to be smooth, but these are rarely resolved with available instrumentation or simulation tools; nondifferentiable and even discontinuous models are therefore in order. We need methods of statistically analyzing geophysical data, whether measured in situ, remotely sensed or even generated by a computer model, that are adapted to these characteristics. An important preliminary task is to define statistically stationary features in generally nonstationary signals. We first discuss a simple criterion for stationarity in finite data streams that exhibit power law energy spectra and then, guided by developments in turbulence studies, we advocate the use of two ways of analyzing the scale dependence of statistical information: singular measures and qth order structure functions. In nonstationary situations, the approach based on singular measures seeks power law behavior in integrals over all possible scales of a nonnegative stationary field derived from the data, leading to a characterization of the intermittency in this (gradient-related) field. In contrast, the approach based on structure functions uses the signal itself, seeking power laws for the statistical moments of absolute increments over arbitrarily large scales, leading to a characterization of the prevailing nonstationarity in both quantitative and qualitative terms. We explain graphically, step by step, both multifractal statistics which are largely complementary to each other. The geometrical manifestations of nonstationarity and intermittency, 'roughness' and 'sparseness', respectively, are illustrated and the associated analytical (differentiability and continuity) properties are discussed. As an example, the two techniques are applied to a series of recent measurements of liquid water distributions inside marine stratocumulus decks; these are found to be multifractal over scales ranging from approximately 60 m to approximately 60 km. Finally, we define the 'mean multifractal plane' and show it to be a simple yet comprehensive tool with many applications including data intercomparison, (dynamical or stochastic) model and retrieval validations.

Davis, Anthony↗

DOE OSTI2026

Automatic Calibration and Health Monitoring of Infrastructure Sensors

Smart transportation infrastructure relies on networks of heterogeneous sensors - cameras, radars, and lidars - continuously monitoring traffic conditions. However, executing the initial spatial calibration of multiple sensors and the subsequent health monitoring presents significant operational challenges. Environmental factors, mechanical vibrations, and gradual drift cause spatial misalignment, degrading fusion performance and tracking accuracy. Traditional calibration approaches require manual intervention with specialized targets or survey equipment, resulting in service interruptions and high maintenance costs. This work presents an automated framework for initial calibration and continuous health monitoring without human intervention or service disruption. Our approach addresses two critical problems: (1) detecting when sensors become miscalibrated during operation, and (2) automatically re-establishing spatial alignment using only operational traffic data. The health monitoring component analyzes measurement innovations - differences between sensor observations and predicted object states - to detect systematic biases indicative of calibration drift. By computing bias magnitude, directional consistency, and rejection rates, the system identifies miscalibrations as small as 0.5 meters. Unlike traditional methods requiring known calibration targets, our diagnostic operates continuously on live traffic observations, enabling early detection before fusion quality degrades. The automatic recalibration algorithm leverages overlapping sensor fields-of-view and temporal correlation of vehicle observations. Using graph-based optimization, the system automatically discovers which sensor pairs observe common regions, estimates pairwise spatial transformations using RANSAC-based robust estimation, and jointly optimizes all sensor poses through bundle adjustment. The framework handles practical deployment challenges, including different sensor sampling rates (1-10 Hz), varying installation positions, unknown orientations, and limited overlap regions (>10%). When approximate sensor positions are available from installation surveys (+/-1m accuracy), the algorithm additionally estimates sensor orientations, refining both position and rotation to sub-meter and sub-degree accuracy. We validate the framework on multi-hour traffic datasets from six heterogeneous sensors with sampling rates ranging from 1 Hz to 10 Hz. Results demonstrate successful calibration even with sparse overlap (<20%) and automatic detection of miscalibrations exceeding 0.8 meters. This work enables a "deploy-and-forget" sensor infrastructure that maintains calibration autonomously, reducing maintenance costs while improving tracking accuracy. The techniques generalize beyond transportation to any multi-sensor monitoring application requiring robust spatial alignment, including smart cities, industrial monitoring, and surveillance systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Large spatiotemporal variability in aerosol properties over central Argentina during the CACTI field campaign

Abstract. Few field campaigns with extensive aerosol measurements have been conducted over continental areas in the Southern Hemisphere. To address this data gap and better understand the interactions of convective clouds and the surrounding environment, extensive in situ and remote sensing measurements were collected during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign conducted between October 2018 and April 2019 over the Sierras de Córdoba range of central Argentina. This study describes measurements of aerosol number, size, composition, mixing state, and cloud condensation nuclei (CCN) collected on the ground and from a research aircraft during 7 weeks of the campaign. Large spatial and multiday variations in aerosol number, size, composition, and CCN were observed due to transport from upwind sources controlled by mesoscale to synoptic-scale meteorological conditions. Large vertical wind shears, back trajectories, single-particle measurements, and chemical transport model predictions indicate that different types of emissions and source regions, including biogenic emissions and biomass burning from the Amazon and anthropogenic emissions from Chile and eastern Argentina, contribute to aerosols observed during CACTI. Repeated aircraft measurements near the boundary layer top reveal strong spatial and temporal variations in CCN and demonstrate that understanding the complex co-variability of aerosol properties and clouds is critical to quantify the impact of aerosol–cloud interactions. In addition to quantifying aerosol properties in this data-sparse region, these measurements will be valuable to evaluate predictions over the midlatitudes of South America and improve parameterized aerosol processes in local, regional, and global models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Basal Melting and Oceanic Observations Beneath Central Fimbulisen, East Antarctica

Abstract Basal melting of ice shelves is fundamental to Antarctic ice sheet mass loss, yet direct observations remain sparse. We present the first year‐round melt record (2017–2021) from a phase‐sensitive radar on Fimbulisen, one of the fastest flowing ice shelves in Dronning Maud Land, East Antarctica. The observed long‐term mean ablation rate at 350 m depth below the central ice shelf was 1.0 ± 0.5 m yr −1 , marked by substantial sub‐weekly variability ranging from 0.4 to 3.5 m yr −1 . 36‐h filtered basal melt rate fluctuations closely align with ocean velocity. On seasonal time scales, melt rates peak during austral spring to autumn (September–March), driven by both elevated ocean velocities and thermal driving near the base. The combined effect of thermal driving and current speed explains the majority of the melt rate variability ( r = 0.84), highlighting the dominant role of shear‐driven turbulence. This relationship enables parameterization of melt rates for the decade‐long ocean record (2010–2021), although deviations appear under low and high forcing conditions. Both observed and parameterized melt rates show similar yearly mean magnitudes compared to satellite‐derived melt rates but with a tenfold lower seasonal amplitude and a 3‐month delay in seasonality. These detailed concurrent ice–ocean observations provide essential validation data for remote sensing and numerical models that aim to quantify and project ice‐shelf response to a change in ocean forcing. In situ measurements and continued monitoring are crucial for accurately assessing and modeling future basal melt rates, and for understanding the complex dynamics driving ice‐shelf stability and sea‐level change.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2022

The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model

This work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test such hypothesis across the Contiguous United States during April 2015 – December 2018. The performance of the proposed methodology is assessed for several modeled vegetation and water variables (evapotranspiration, net ecosystem exchange, and soil moisture) in terms of random errors and anomaly correlation coefficients against a set of independent validation datasets (i.e., Global Land Evaporation Amsterdam Model, FLUXCOM, and International Soil Moisture Network). Results show that the assimilation of leaf area index mostly improves the estimation of evapotranspiration and net ecosystem exchange, whereas the assimilation of surface soil moisture alone improves surface soil moisture content, especially in the western US, in terms of both root mean squared error and anomaly correlation coefficient. The joint assimilation of vegetation and soil moisture information combines the results of individual vegetation and soil moisture assimilations and reduces errors (and increases correlations with the reference datasets) in evapotranspiration, net ecosystem exchange, and surface soil moisture simulated by the land surface model. However, because soil moisture satellite observations only provide information on the water content in the top 5 cm of the soil column, the impact of the proposed data assimilation technique on root zone soil moisture is limited. This work moves one step forward in the direction of improving our estimation and understanding of land surface interactions using a multi-variate data assimilation approach, which can be particularly useful in regions of the world where ground observations are sparse or missing altogether.

Data assimilation↗

NASA NTRS2010

Visible Nulling Coronagraphy Testbed Development for Exoplanet Detection

Three of the recently completed NASA Astrophysics Strategic Mission Concept (ASMC) studies addressed the feasibility of using a Visible Nulling Coronagraph (VNC) as the prime instrument for exoplanet science. The VNC approach is one of the few approaches that works with filled, segmented and sparse or diluted aperture telescope systems and thus spans the space of potential ASMC exoplanet missions. NASA/Goddard Space Flight Center (GSFC) has a well-established effort to develop VNC technologies and has developed an incremental sequence of VNC testbeds to advance the this approach and the technologies associated with it. Herein we report on the continued development of the vacuum Visible Nulling Coronagraph testbed (VNT). The VNT is an ultra-stable vibration isolated testbed that operates under high bandwidth closed-loop control within a vacuum chamber. It will be used to achieve an incremental sequence of three visible light nulling milestones of sequentially higher contrasts of 10(exp 8) , 10(exp 9) and 10(exp 10) at an inner working angle of 2*lambda/D and ultimately culminate in spectrally broadband (>20%) high contrast imaging. Each of the milestones, one per year, is traceable to one or more of the ASMC studies. The VNT uses a modified Mach-Zehnder nulling interferometer, modified with a modified "W" configuration to accommodate a hex-packed MEMS based deformable mirror, a coherent fiber bundle and achromatic phase shifters. Discussed will be the optical configuration laboratory results, critical technologies and the null sensing and control approach.

Lyon, Richard G.↗

NASA NTRSDate not supplied

Spaceborne Passive-Microwave Hail Detection: Global Climatologies, Validation, and Challenges Going Forward

In addition to the myriad threats that severe hailstorms pose to society, infrastructure and agriculture, severe hail is difficult to measure in situ, and surface-based hail reporting and detection methods are inconsistent and vulnerable to nonmeteorological biases. This motivates the use of spaceborne remote-sensing platforms to retrieve hail and construct climatologies in a globally uniform way. We have developed a hail detection algorithm that leverages the sensitivity of spaceborne passive-microwave radiometers to scattering by hail, particularly in the channels from 10 to 89 GHz. We use this retrieval to construct global passive-microwave climatologies of severe hail using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Global Precipitation Measurement Mission (GPM) Microwave Imager, Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors and are working to extend into the pre-TRMM era to the Special Sensor Microwave Imager/Sounder (SSMI/(S)) data. Using coincident Global Precipitation Measurement (GPM) Ku-band precipitation radar, we assessed this retrieval and several others in the literature for their effectiveness and regional variability. We find that this retrieval, which leverages a signature in the Minimum 19-GHz polarization corrected temperature (PCT) combined with the 37-GHz PCT depression normalized by tropopause height constrains the radar reflectivity most tightly, and gives the least appearance of regional biases compared to other passive-microwave approaches in the literature. Satellite platforms offer consistent observations, even in remote, data-sparse, and oceanic regions that ground-based networks exclude. There are, however, potential disconnects between the processes identified aloft by the satellite and the resultant weather at the ground, leading to uncertainties in the retrievals that may propagate into satellite-based climatologies, particularly in the Tropics, where there are abundant strong - but not necessarily hailing - storms that are strongly represented in the current satellite climatologies. We will discuss ongoing efforts to assess and mitigate the contributing factors to these uncertainties, chiefly among them the effects of non-uniform beam filling in the passive-microwave footprint, and the relationships between the size distributions of hailstones aloft and the dynamic processes and environments with which they interact throughout their trajectories.

Sarah D. Bang↗

NASA NTRS2006

Improved Oxygen-Beam Texturing of Glucose-Monitoring Optics

An improved method has been devised for using directed, hyperthermal beams of oxygen atoms and ions to impart desired textures to the tips of polymethylmethacrylate [PMMA] optical fibers to be used in monitoring the glucose content of blood. The improved method incorporates, but goes beyond, the method described in Texturing Blood-Glucose- Monitoring Optics Using Oxygen Beams (LEW-17642-1), NASA Tech Briefs, Vol. 29, No. 4 (April 2005), page 11a. The basic principle of operation of such a glucose-monitoring sensor is as follows: The textured surface of the optical fiber is coated with chemicals that interact with glucose in such a manner as to change the reflectance of the surface. Light is sent down the optical fiber and is reflected from, the textured surface. The resulting change in reflectance of the light is measured as an indication of the concentration of glucose. The required texture on the ends of the optical fibers is a landscape of microscopic cones or pillars having high aspect ratios (microscopic structures being taller than they are wide). The average distance between hills must be no more than about 5 mso that blood cells (which are wider) cannot enter the valleys between the hills, where they would interfere with optical sensing of glucose in the blood plasma. On the other hand, the plasma is required to enter the valleys, and high aspect ratio structures are needed to maximize the surface area in contact with the plasma, thereby making it possible to obtain a given level of optical glucose-measurement sensitivity with a relatively small volume of blood. There is an additional requirement that the hills be wide enough that a sufficient amount of light can propagate into them and, after reflection, can propagate out of them. The method described in the cited prior article produces a texture comprising cones and pillars that conform to the average-distance and aspect-ratio requirements. However, a significant fraction of the cones and pillars are so narrow that not enough light can propagate along them. The improved method makes it possible to form wider cones and pillars while still satisfying the average-distance and aspect-ratio requirements. In the improved method, as in the previously reported method, multiple optical fibers are first bundled together for simultaneous texturing of their distal tips. However, prior to texturing by exposure to an oxygen beam, the tips are first coated by vapor deposition of a thin, sparse layer of aluminum: The exposure to the aluminum vapor must be short enough (typically of the order of seconds) so that the aluminum nucleates into islands separated by uncoated areas. The coated tips are textured by exposure to a directed beam of hyperthermal (kinetic energy >1 eV) oxygen atoms and/or ions in a vacuum chamber, as in the previously reported method. The aluminum islands partially shield the underlying PMMA from oxidation and erosion by the beam, so that the cones or pillars remaining after texturing are wider than they would otherwise be. To some extent, the dimensions of the hills and the distances between them can be tailored through choice of the thickness of the aluminum coat and/or the oxygen-beam fluence. The figure illustrates an example of texturing of the tip of a PMMA optical fiber without and with prior aluminum coating.

Banks, Bruce A.↗

NASA NTRS2022

The Earth's Exosphere and Its Response to Space Weather

Neutral-Plasma charge exchange is a fundamental physical process that occurs ubiquitously across the universe. In geospace, charge exchange occurs in the Earth’s topside ionosphere, polar wind, plasmasphere, inner magnetosphere, and magnetosheath. Past and current space missions have profiled plasma and electromagnetic characteristics in various parts of the Earth’s magnetospheric system. However, observations of the exosphere, i.e., neutrals above 500 km altitude, are still sparse and, in some regions, non-existent, which limits our understanding of the neutral contribution to the overall dynamics of the geospace environment. Cold exospheric neutrals (< 10 eV) play an important role in the Sun-Earth interaction. Variability of exospheric density provides key information of the Earth’s atmospheric loss under dynamic space environment conditions. Various neutral species and their density variations in the polar wind can alter ion outflow patterns, modifying global magnetospheric dynamics. Exospheric neutrals also provide an energy sink for the inner magnetosphere by creating Energetic Neutral Atoms (ENAs) through charge exchange with high-energy ring current ions, which subsequently leave our geospace system unimpeded by magnetic fields. Exospheric neutrals also provide a means to observe the global interaction of the solar wind – magnetosphere, through global imaging of the system via ENAs (e.g., the TWINS and IMAGE missions) and soft X-rays (e.g., the upcoming LEXI and SMILE missions), the byproducts of neutral-plasma charge exchange. In the coming decade, we advocate that our community needs to increase our exploration of the neutral populations in the outermost reaches of the Earth’s atmosphere. It is imperative that we improve both in-situ and remote-sensing technologies for measuring key neutral species in our exosphere. We also encourage dedicated exosphere missions and to stimulate model developments of our exosphere and its interaction with the co-located magnetospheric system and neighboring Ionosphere - Thermosphere - Mesosphere system.

Hyunju Connor↗

NASA NTRS2010

Analysis of Snow Bidirectional Reflectance from ARCTAS Spring-2008 Campaign

The spring 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) experiment was one of major intensive field campaigns of the International Polar Year aimed at detailed characterization of atmospheric physical and chemical processes in the Arctic region. A part of this campaign was a unique snow bidirectional reflectance experiment on the NASA P-3B aircraft conducted on 7 and 15 April by the Cloud Absorption Radiometer (CAR) jointly with airborne Ames Airborne Tracking Sunphotometer (AATS) and ground-based Aerosol Robotic Network (AERONET) sunphotometers. The CAR data were atmospherically corrected to derive snow bidirectional reflectance at high 1 degree angular resolution in view zenith and azimuthal angles along with surface albedo. The derived albedo was generally in good agreement with ground albedo measurements collected on 15 April. The CAR snow bidirectional reflectance factor (BRF) was used to study the accuracy of analytical Ross-Thick Li-Sparse (RTLS), Modified Rahman-Pinty-Verstraete (MRPV) and Asymptotic Analytical Radiative Transfer (AART) BRF models. Except for the glint region (azimuthal angles phi less than 40 degrees), the best fit MRPV and RTLS models fit snow BRF to within 0.05. The plane-parallel radiative transfer (PPRT) solution was also analyzed with the models of spheres, spheroids, randomly oriented fractal crystals, and with a synthetic phase function. The latter merged the model of spheroids for the forward scattering angles with the fractal model in the backscattering direction. The PPRT solution with synthetic phase function provided the best fit to measured BRF in the full range of angles. Regardless of the snow grain shape, the PPRT model significantly over-/underestimated snow BRF in the glint/backscattering regions, respectively, which agrees with other studies. To improve agreement with experiment, we introduced a model of macroscopic snow surface roughness by averaging the PPRT solution over the slope distribution function and by adding a simple model of shadows. With macroscopic roughness described by two parameters, the AART model achieved an accuracy of about plus or minus 0.05 with a possible bias of plus or minus 0.03 in the spectral range 0.4-2.2 micrometers. This high accuracy holds at view zenith angles below 55-60 degrees covering the practically important range for remote sensing applications, and includes both glint and backscattering directions.

Lyapustin, A.↗

NASA NTRSDate not supplied

Development of the GeoNEX Level 2G Products: Exploiting the Diurnal Variability of TOA Reflectance in Atmospheric Correction

This study develops a new atmospheric correction algorithm to generate the Level 2G products, in particular the gap-filled Surface Reflectance at 10-minute time steps, for the Geostationary-NASA Earth Exchange (GeoNEX) project. The algorithm is based on the MODIS MAIAC (Multi-Angle Implementation of Atmospheric Correction) framework but with significant modifications to exploit angular/temporal information from the diurnal variability of the GeoNEX L1G TOA (Top-of-Atmosphere) reflectance. The algorithm starts by evaluating the roughness/smoothness of the diurnal time series of the TOA reflectance. Because rapid changes in TOA reflectance are generally caused by passing clouds or shadows, rough segments of the time series are automatically filtered out while the smooth segments are further tested for brightness and temperature to identify clear-sky and snow-free observations. Next the algorithm runs the MAIAC RTM (Radiative Transfer Model) to retrieve the Ross-Thick-Li-Sparse (RTLS) BRDF model parameters and the daily-mean atmospheric optical depth (AOD) that allow the RTM to optimally simulate the observed diurnal variability of clear-sky TOA reflectance. Once the initial RTLS parameters are retrieved after the algorithm’s burn-in period, they are used as the prior information to predict the AOD level for the next days, while the subsequent clear-sky observations are used to make necessary adjustments to the RTLS parameters in an continuous fashion. This “prediction-analysis” cycle is then iterated to process the full time series of the L1G data, skipping only total-cloudy days or when surface snow is detected. We tested the algorithm over a list of selected AERONET sites. The retrieved results (the daily mean AOD and the RTLS parameters) reasonably agree with the ground-based measurements. Importantly, the results indicate that the diurnal cycles of surface reflectance are continuous functions of the illumination-view geometry. Thus we can use the retrieved RTLS model to accurately fill in data gaps on partial cloudy days. Also, our algorithm is totally independent from the traditional approaches based on the use of spectral band ratios between the shortwave infrared (e.g., 2.2µm) and the visible (e.g., 0.47µm and 0.64µm) bands. Our results thus demonstrate that the high-frequent diurnal geostationary observations contain unique information that helps us improve atmospheric correction of remote sensing data.

GeoNEX↗

NASA NTRS2014

Lightning Imaging Sensor (LIS) for the International Space Station (ISS): Mission Description and Science Goals

In recent years, NASA Marshall Space Flight Center, the University of Alabama in Huntsville, and their partners have developed and demonstrated space-based lightning observations as an effective remote sensing tool for Earth science research and applications. The Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) continues to provide global observations of total lightning after 17 years on-orbit. In April 2013, a space-qualified LIS built as the flight spare for TRMM, was selected for flight as a science mission on the International Space Station. The ISS LIS (or I-LIS as Hugh Christian prefers) will be flown as a hosted payload on the Department of Defense Space Test Program (STP) H5 mission, which has a January 2016 baseline launch date aboard a SpaceX launch vehicle for a 2-4 year or longer mission. The LIS measures the amount, rate, and radiant energy of global lightning. More specifically, it measures lightning during both day and night, with storm scale resolution, millisecond timing, and high, uniform detection efficiency, without any land-ocean bias. Lightning is a direct and most impressive response to intense atmospheric convection. It has been found that the characteristics of lightning that LIS measures can be quantitatively coupled to both thunderstorm and other geophysical processes. Therefore, the ISS LIS lightning observations will provide important gap-filling inputs to pressing Earth system science issues across a broad range of disciplines, including weather, climate, atmospheric chemistry, and lightning physics. A unique contribution from the ISS platform will be the availability of real-time lightning, especially valuable for operational applications over data sparse regions such as the oceans. The ISS platform will also uniquely enable LIS to provide simultaneous and complementary observations with other payloads such as the European Space Agency's Atmosphere-Space Interaction Monitor (ASIM) that will be exploring the connection between thunderstorms and lightning with terrestrial gamma-ray flashes (TGFs). Another important function of the ISS LIS will be to provide cross-sensor calibration/validation with a number of other payloads, including the TRMM LIS and the next generation geostationary lightning mappers (e.g., GOES-R Geostationary Lightning Mapper and Meteosat Third Generation Lightning Imager). This inter-calibration will improve the long term climate monitoring provided by all these systems. Finally, the ISS LIS will extend the time-series climate record of LIS lightning observations and expand the latitudinal coverage of LIS lightning to the climate significant upper middle-latitudes.

Blakeslee, R. J.↗

NASA NTRS2014

Lightning Imaging Sensor (LIS) for the International Space Station (ISS): Mission Description and Science Goals

Blakeslee, R. J.↗

NASA NTRS2007

NASA Tech Briefs, January 2007

Topics covered include: Flexible Skins Containing Integrated Sensors and Circuitry; Artificial Hair Cells for Sensing Flows; Video Guidance Sensor and Time-of-Flight Rangefinder; Optical Beam-Shear Sensors; Multiple-Agent Air/Ground Autonomous Exploration Systems; A 640 512-Pixel Portable Long-Wavelength Infrared Camera; An Array of Optical Receivers for Deep-Space Communications; Microstrip Antenna Arrays on Multilayer LCP Substrates; Applications for Subvocal Speech; Multiloop Rapid-Rise/Rapid Fall High-Voltage Power Supply; The PICWidget; Fusing Symbolic and Numerical Diagnostic Computations; Probabilistic Reasoning for Robustness in Automated Planning; Short-Term Forecasting of Radiation Belt and Ring Current; JMS Proxy and C/C++ Client SDK; XML Flight/Ground Data Dictionary Management; Cross-Compiler for Modeling Space-Flight Systems; Composite Elastic Skins for Shape-Changing Structures; Glass/Ceramic Composites for Sealing Solid Oxide Fuel Cells; Aligning Optical Fibers by Means of Actuated MEMS Wedges; Manufacturing Large Membrane Mirrors at Low Cost; Double-Vacuum-Bag Process for Making Resin- Matrix Composites; Surface Bacterial-Spore Assay Using Tb3+/DPA Luminescence; Simplified Microarray Technique for Identifying mRNA in Rare Samples; High-Resolution, Wide-Field-of-View Scanning Telescope; Multispectral Imager With Improved Filter Wheel and Optics; Integral Radiator and Storage Tank; Compensation for Phase Anisotropy of a Metal Reflector; Optical Characterization of Molecular Contaminant Films; Integrated Hardware and Software for No-Loss Computing; Decision-Tree Formulation With Order-1 Lateral Execution; GIS Methodology for Planning Planetary-Rover Operations; Optimal Calibration of the Spitzer Space Telescope; Automated Detection of Events of Scientific Interest; Representation-Independent Iteration of Sparse Data Arrays; Mission Operations of the Mars Exploration Rovers; and More About Software for No-Loss Computing.

Source record↗

NASA NTRS2015

Lightning Imaging Sensor (LIS) for the International Space Station (ISS): Mission Description and Science Goals

In recent years, the NASA Marshall Space Flight Center, the University of Alabama in Huntsville, and their partners have developed and demonstrated space-based lightning observations as an effective remote sensing tool for Earth science research and applications. The Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) continues to acquire global observations of total (i.e., intracloud and cloud-to-ground) lightning after 17 years on-orbit. However, TRMM is now low on fuel, so this mission will soon be completed. As a follow on to this mission, a space-qualified LIS built as the flight spare for TRMM has been selected for flight as a science mission on the International Space Station (ISS). The ISS LIS will be flown as a hosted payload on the Department of Defense Space Test Program (STP) H5 mission, which has a January 2016 baseline launch date aboard a SpaceX launch vehicle for a 2-4 year or longer mission. The LIS measures the amount, rate, and radiant energy of total lightning over the Earth. More specifically, it measures lightning during both day and night, with storm scale resolution (approx. 4 km), millisecond timing, and high, uniform detection efficiency, without any land-ocean bias. Lightning is a direct and most impressive response to intense atmospheric convection. It has been found that lightning measured by LIS can be quantitatively related to thunderstorm and other geophysical processes. Therefore, the ISS LIS lightning observations will continue to provide important gap-filling inputs to pressing Earth system science issues across a broad range of disciplines, including weather, climate, atmospheric chemistry, and lightning physics. A unique contribution from the ISS platform will be the availability of real-time lightning data, especially valuable for operational applications over data sparse regions such as the oceans. The ISS platform will also uniquely enable LIS to provide simultaneous and complementary observations with other ISS payloads such as the European Space Agency's Atmosphere-Space Interaction Monitor (ASIM) that will be exploring the connection between thunderstorms and lightning with terrestrial gamma-ray flashes (TGFs) and the Japan Aerospace Exploration Agency's Global LIghtning and Sprites MeasurementS (GLIMS) with its focus on global lightning and sprite connections. Another important function of the ISS LIS will be to provide cross-sensor calibration/validation with a number of other payloads, including the TRMM LIS and the next generation geostationary lightning mappers such as the GOES-R Geostationary Lightning Mapper (GLM) and Meteosat Third Generation Lightning Imager (MTG LI), as well as with ground-based lightning detection systems. These inter-calibrations will improve the long term climate monitoring record provided by all these systems. Finally, the ISS LIS will extend the time-series climate record of LIS lightning observations and expand the latitudinal coverage of LIS lightning to the climate significant upper middle-latitudes.

Blakeslee, R. J.↗

DOE OSTI2026

Integrating very-high-resolution imagery, Sentinel-2 time-series data, and machine learning to map shrub fractional abundance across arid and semi-arid ecosystems in China

Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, we generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km 2 each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R 2 = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R 2 = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 °C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.

Arid and semi-arid landscapes↗

NASA NTRS2018

Lightning Imaging Sensor on the International Space Station: Assessments and Results from First Year Operations

Over two decades, the NASA Marshall Space Flight Center, the University of Alabama in Huntsville, and their partners have demonstrated the effectiveness and value of space-based lightning observations as a remote sensing tool for Earth science research and applications, and, in the process, established a robust global lightning climatology. The Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) provided global observations of tropical lightning for an impressive 17 years before that mission came to a close in April 2015. Now a space-qualified LIS, built as the flight spare for TRMM, has been installed on the International Space Station (ISS) for a minimum two year mission following its SpaceX launch on February 19, 2017. The LIS, flown as a hosted payload on the Department of Defense Space Test Program-Houston 5 (STP-H5) mission, was delivered to the ISS in the Dragon trunk and robotically installed in an Earth-viewing position on the outside of the ISS. Following successful activation and checkout, LIS has continuously observed the amount, rate, and radiant energy lightning within its field-of-view as it orbits the Earth. Placing LIS on the Space Station provides a great opportunity to not only extend the 17-year TRMM LIS record of tropical lightning measurements but also to expand that coverage to higher latitudes missed by the previous mission. Furthermore, this mission continues the important science focus to better understand the processes which cause lightning, as well as the connections between lightning and subsequent severe weather events. This understanding is a key to improving weather predictions and saving lives and property here in the United States and around the world. The LIS measurements, along with observations from the new Geostationary Lightning Mapper (GLM) operating on NOAA's newest weather satellites, the Geosynchronous Operational Environmental Satellite-16/17 (GOES-16/17), are being used to cross-validate both systems. An especially unique contribution from the ISS platform is the production of real-time lightning data, especially valuable for operational forecasting and warning applications over data sparse regions such as the oceans. Finally, LIS provides simultaneous and complementary observations with other ISS payloads such as the European Space Agency's Atmosphere-Space Interaction Monitor (ASIM) that is exploring the connection between thunderstorms and lightning with terrestrial gamma-ray flashes (TGFs). Leveraging TRMM's well-established processing and data handling assures that LIS data can be quickly delivered to users.

space-based lightning↗