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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Urban Land Surface Temperature Downscaling in Chicago: Addressing Ethnic Inequality and Gentrification

In this study, we developed a XGBoost-based algorithm to downscale 2 km-resolution land surface temperature (LST) data from the GOES satellite to a finer 70 m resolution, using ancillary variables including NDVI, NDBI, and DEM. This method demonstrated a superior performance over the conventional TsHARP technique, achieving a reduced RMSE of 1.90 °C, compared to 2.51 °C with TsHARP. Our approach utilizes the geostationary GOES satellite data alongside high-resolution ECOSTRESS data, enabling hourly LST downscaling to 70 m—a significant advancement over previous methodologies that typically measure LST only once daily. Applying these high-resolution LST data, we examined the hottest days in Chicago and their correlation with ethnic inequality. Our analysis indicated that Hispanic/Latino communities endure the highest LSTs, with a maximum LST that is 1.5 °C higher in blocks predominantly inhabited by Hispanic/Latino residents compared to those predominantly occupied by White residents. This study highlights the intersection of urban development, ethnic inequality, and environmental inequities, emphasizing the need for targeted urban planning to mitigate these disparities. The enhanced spatial and temporal resolution of our LST data provides deeper insights into diurnal temperature variations, crucial for understanding and addressing the urban heat distribution and its impact on vulnerable communities.

Lee, Jangho (ORCID:0000000289421092)↗
DOE OSTI2021

Land Surface Temperature from GOES-East and GOES-West

Land surface temperature (LST) is an important climate parameter that controls the surface energy budget. For climate applications, information is needed at the global scale with representation of the diurnal cycle. To achieve global coverage there is a need to merge about five independent geostationary (GEO) satellites that have different observing capabilities. An issue of practical importance is the merging of independent satellite observations in areas of overlap. An optimal approach in such areas could eliminate the need for redundant computations by differently viewing satellites. We use a previously developed approach to derive information on LST from GOES-East (GOES-E), modify it for application to GOES-West (GOES-W) and implement it simultaneously across areas of overlap at 5-km spatial resolution. We evaluate the GOES-based LST against in situ observations and an independent MODIS product for the period of 2004–09. The methodology proposed minimizes differences between satellites in areas of overlap. The mean and median values of the differences in monthly mean LST retrieved from GOES-E and GOES-W at 0600 UTC for July are 0.01 and 0.11 K, respectively. Similarly, at 1800 UTC the respective mean and median value of the differences were 0.15 and 1.33 K. These findings can provide guidelines for potential users to decide whether the reported accuracy based on one satellite alone, meets their needs in area of overlap. Since the 6 yr record of LST was produced at hourly time scale, the data are well suited to address scientific issues that require the representation of LST diurnal cycle or the diurnal temperature range (DTR).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multi-channel Imager Algorithm (MIA): A novel cloud-top phase classification algorithm

The current Geostationary Operational Environmental Satellites (GOES-16 and 17) cloud-top phase classification algorithm is based primarily on empirical thresholds at multiple wavelengths that have varying absorption capabilities for water and ice. The performance of current GOES-16 cloud-top phase product largely depends on the accuracy of the selection of reflectance ratios. Here this study aims at presenting a novel cloud-top phase classification algorithm (the Multi-channel Imager Algorithm, MIA) that provides a more judicious selection of relationships between channels using a supervised K-mean clustering method on multi-channel Red-Green-Blue images. The K-mean clustering method works analogously to how human eyes separate different colors in a microphysical color rendering set of satellite images, which differentiates water, ice and unclassified thin clouds. For water phase, cloud-top temperature information is used to further distinguish supercooled water. To evaluate the performance of the MIA, an extensive comparison with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer, and current GOES-16 cloud-top phase products is conducted, using CALIOP as the benchmark. Compared to the current GOES-16 cloud-top phase product, MIA demonstrates a substantial improvement in phase classification, where hit rate increases from 69% to 76% over the Continental United States and 58% to 66% over the full disk domain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Summertime Continental Shallow Cumulus Cloud Detection Using GOES-16 Satellite and Ground-Based Stereo Cameras at the DOE ARM Southern Great Plains Site

Summertime continental shallow cumulus clouds (ShCu) are detected using Geostationary Operational Environmental Satellite (GOES)-16 reflectance data, with cross-validation by observations from ground-based stereo cameras at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. A ShCu cloudy pixel is identified when the GOES reflectance exceeds the clear-sky surface reflectance by a reflectance detection threshold of ShCu, ΔR. We firstly construct diurnally varying clear-sky surface reflectance maps and then estimate the ΔR. A GOES simulator is designed, projecting the clouds reconstructed by stereo cameras towards the surface along the satellite’s slanted viewing direction. The dynamic ShCu detection threshold ΔR is determined by making the GOES cloud fraction (CF) equal to the CF from the GOES simulator. Although there are temporal variabilities in ΔR, cloud fractions and cloud size distributions can be well reproduced using a constant ΔR value of 0.045. The method presented in this study enables daytime ShCu detection, which is usually falsely reported as clear sky in the GOES-16 cloud mask data product. Using this method, a new ShCu dataset can be generated to bridge the observational gap in detecting ShCu, which may transition into deep precipitating clouds, and to facilitate further studies on ShCu development over heterogenous land surface.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Local limits of detection for anthropogenic aerosol-cloud interactions

Ship tracks are quasi-linear cloud patterns produced from the interaction of ship emissions with low boundary layer clouds. They are visible throughout the diurnal cycle in satellite images from space-borne assets like the Advanced Baseline Imagers (ABI) aboard the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellites (GOES-R). However, complex atmospheric dynamics often make it difficult to identify and characterize the formation and evolution of tracks. Ship tracks have the potential to increase a cloud's albedo and reduce the impact of global warming. Thus, it is important to study these patterns to better understand the complex atmospheric interactions between aerosols and clouds to improve our climate models, and examine the efficacy of climate interventions, such as marine cloud brightening. Over the course of this 3-year project, we have developed novel data-driven techniques that advance our ability to assess the effects of ship emissions on marine environments and the risks of future marine cloud brightening efforts. The three main innovative technical contributions we will document here are a method to track aerosol injections using optical flow, a stochastic simulation model for track formations and an automated detection algorithm for efficient identification of ship tracks in large datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Outer Radiation Belt Dynamics During the October 2012 Storm Revisited: Rapid Inward Radial Transport From a Dynamic Outer Boundary

Earth's outer radiation belt electron flux is highly variable and can be enhanced by over an order of magnitude over timescales less than one day, as observed during the October 2012 storm. Previous studies of this storm (e.g., Reeves et al., 2013, https://doi.org/10.1126/science.1237743) have invoked local acceleration to explain this. However, here, we argue that the observations can instead be explained by fast inward radial transport. One method often invoked to distinguish between these two acceleration processes is the existence of local peaks in electron phase space density (PSD) as a function of L* at fixed first, M, and second, K, adiabatic invariants. However, this method relies on the assumption that the evolution of the PSD as a function of L* occurs over timescales slower than the satellite orbital period. Here, high spatiotemporal resolution data from the Global Positioning System (GPS) spacecraft constellation is used to show that enhancements in the PSD occur during the October 2012 storm over short timescales not resolvable by the Van Allen Probes. In addition, Geostationary Operational Environmental Satellite spacecraft data also indicate that these enhancements are consistent with relativistic electron injections. A radial diffusion model is shown to reproduce the PSD dynamics observed by the Van Allen Probes, once rapid variations at the simulation outer boundary are included, consistent with GPS data. This verifies that apparently “locally growing” peaks in PSD along high apogee satellite orbits can be produced by fast inward radial transport without requiring the action of any local acceleration processes.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Thunderstorm Cloud-Type Classification from Space-Based Lightning Imagers

The organization and structure of thunderstorms determines the extent and severity of their hazards to the general public and their consequences for the Earth system. Distinguishing vigorous convective regions that produce heavy rain and hail from adjacent regions of stratiform clouds or overhanging anvil clouds that produce light to no rainfall is valuable in operations and physical research. Cloud-type algorithms that partition convection from stratiform regions have been developed for space-based radar, passive microwave, and now Geostationary Operational Environmental Satellites (GOES) Advanced Baseline Imager (ABI) multispectral products. However, there are limitations for each of these products including temporal availability, spatial coverage, and the degree to which they based on cloud microphysics. We report we have developed a cloud-type algorithm for GOES Geostationary Lightning Mapper (GLM) observations that identifies convective/nonconvective regions in thunderstorms based on signatures of interactions with nonconvective charge structures in the lightning flash data. The GLM sensor permits a rapid (20 s) update cycle over the combined GOES-16–GOES-17 domain across all hours of the day. Storm regions that do not produce lightning will not be classified by our algorithm, however. The GLM cloud-type product is intended to provide situational awareness of electrified nonconvective clouds and to complement other cloud-type retrievals by providing a contemporary assessment tied to lightning physics. We propose that a future combined ABI–GLM cloud-type algorithm would be a valuable product that could draw from the strengths of each instrument and approach.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Summertime Continental Shallow Cumulus Cloud Detection Using GOES‐16 Satellite and Ground‐Based Ceilometer at North Alabama

Abstract Accurate simulations of boundary layer cloud processes remain challenging in Earth system modeling. Observations are essential to evaluate and improve models of such processes. This study introduces a comprehensive validation framework for a satellite‐based detection algorithm of continental shallow cumulus (ShCu) clouds during the daytime, which was initially developed using ground‐based observations of stereo cameras at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site (J. Tian et al., 2021, https://doi.org/10.3390/rs13122309 , 2022, https://doi.org/10.1029/2021gl097070 ). To validate this algorithm, the framework employs ground‐based ceilometer measurements from North Alabama (NA) where ShCu populations are prevalent. This study first generates clear‐sky surface reflectance maps at NA and identifies ShCu pixels with a detection threshold using Geostationary Operational Environmental Satellite (GOES) reflectance data. The obtained cloud fractions (CFs) are then compared against CFs from a ground‐based ceilometer, considering factors such as observed area differences, satellite parallax issue, and systematic biases. We found that with a detection threshold (∆R) of 0.05, the ShCu detection algorithm is effective for NA, enabling the reproduction of hourly ShCu CFs using GOES. Our framework is straightforward and easily repeatable to evaluate the effectiveness of a ∆R threshold for detecting ShCu clouds in various geographic regions where ceilometers are deployed. This satellite detection of ShCu provides a crucial regional context for ground‐based measurements, facilitating the tracking of convection initiation and its coupling with land surface conditions. Integrating localized ground‐based and regional satellite data will enhance our ability to conduct thorough studies of cloud morphology and land‐atmosphere interactions in North Alabama.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Physics-guided machine learning for improved accuracy of the National Solar Radiation Database

The National Solar Radiation Database (NSRDB) provides high-resolution spatiotemporal solar irradiance data for the entire globe. The NSRDB uses a two-step Physical Solar Model (PSM) to compute the effects of clouds and other atmospheric variables on the solar radiation reaching the surface of the Earth. Physical and optical cloud properties are fundamental inputs to the PSM and are derived from the National Oceanic and Atmospheric Administration's Geostationary Operational Environmental Satellites. This paper describes recent improvements to the NSRDB driven by physics-guided machine learning methods for cloud property retrieval. The impacts of these new methods on the NSRDB irradiance data are validated using an extensive set of ground measurement sites, showing significant improvement for all sites. We report on average, the mean absolute percentage error for global horizontal irradiance and direct normal irradiance show reductions of 2.16 and 3.95 percentage points respectively for all daylight conditions, 5.92 and 17.39 percentage points respectively for cloudy conditions, and 9.00 and 22.59 percentage points respectively for gap-filled cloudy conditions. These new methods will help improve the quality and accuracy of the irradiance and cloud data in the NSRDB.

14 SOLAR ENERGY↗
DOE OSTI2022

Linking Synoptic Patterns to Cloud Properties and Local Circulations Over Southeastern Texas

This report classifies meteorological regimes in the southeastern Texas region to identify environmental conditions that favor sea-breeze induced convection. The classification is accomplished using a Self-Organizing Map (SOM) approach. We applied SOM to 10 years of 700-hPa geopotential height anomalies during the summer months from reanalysis data to distinguish three dominant synoptic regimes, with a continuum of transitional states between those. The primary regimes include: (a) a pre-trough regime associated with a synoptic trough, (b) a post-trough regime with upper-level northerly flow, and (c) an anticyclonic regime within the westward extent of the Bermuda High. We project the data from the Geostationary Operational Environmental Satellite and the Next Generation Weather Radar system onto each SOM node to investigate the characteristics of cloud and precipitation properties in different regimes. When southeastern Texas is positioned to the southwest quadrant of a maritime high pressure system, an increased cloud frequency is observed over the region during the afternoon hours due to significant moisture advection. A confluence of synoptic southerly flow and sea-breeze circulation commonly occurs in this regime. When a high pressure system is over southeastern Texas, the area is dominated by large-scale subsidence with weak pressure gradients and moderate precipitable water vapor. This weak synoptic forcing is favorable for the formation of a sea-breeze circulation. This is confirmed by an enhanced onshore flow and a decreased temperature at the surface in the early afternoon, as well as a sharp increase in radar echo top height.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Resolve the Clear-Sky Continuous Diurnal Cycle of High-Resolution ECOSTRESS Evapotranspiration and Land Surface Temperature

Although numerous evapotranspiration (ET) estimates have been developed from remote sensing measurements, a diurnally resolved product with high spatial resolution (<100 m) is still lacking, which is critically needed for agricultural and ecological monitoring. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides, for the first time, ET at 70 m resolution with diurnal samplings, enabling a step toward filling this data gap. This study developed the first framework to resolve the full diurnal cycle of ET and Land Surface Temperature (LST) from sporadic ECOSTRESS measurements. We first constructed 70 m diurnal LST utilizing a diurnal temperature cycle (DTC) model that fused ECOSTRESS and Geostationary Operational Environmental Satellite (GOES) LST. Next, we derived 70 m diurnal ET from the diurnally resolved LST, along with ancillary meteorological and reflectance data sets, using the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm. This framework was tested in two regions: A relatively homogeneous semiarid grassland landscape (centered at the US-Seg Ameriflux site) and a heterogeneous humid cropland landscape (centered at US-ARM). Our diurnally resolved LST and ET successfully reproduced the spatial patterns in the native ECOSTRESS measurements during overpasses for both landscapes (LST: r > 0.97, RMSE ~2°C; ET: r > 0.99, and RMSE = 1–7 W m -2 ). The constructed time series also well captured the temporal variability of the in situ measurements (especially under clear-sky conditions) at both US-Seg and US-ARM, albeit performance varying with availability and quality of ECOSTRESS samples. This study sets the stage for testing and applying our framework to broader climates, biomes, and landscapes toward eventually generating diurnally resolved 70 m global operational LST/ET products to enhance ecological/agricultural applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Survey of Thunderstorms That Produce Megaflashes Across the Americas

We previously observed that long-horizontal lightning flashes exceeding 100 km in length, known as “megaflashes,” occur preferentially in certain thunderstorms. In this study, we develop a cluster feature approach for automatically documenting the evolutions of thunderstorm systems from continuous lightning observations provided by the Geostationary Lightning Mapper (GLM) on NOAA's Geostationary Operational Environmental Satellites (GOES). We apply this methodology to GOES-16 GLM observations from 2018 to mid-2022 to improve our understanding of megaflash-producing storms. We find that megaflashes occur in long-lived (median: 14 hr) storms that grow to exceptional sizes (median: 11,984 km 2 ) while they propagate across long distances (median: 622 km) compared to ordinary storms. The first megaflashes are typically produced within 15 min of the storm reaching its peak intensity and extent. However, most megaflashes occur ≥13 hr after the initial megaflash activity, and are sufficiently close to convection to suggest initiation in the thunderstorm core (where GLM has difficulty detecting faint early light sources from megaflashes). Megaflashes generated outside of convection are rare, accounting for 2.7% of the sample using a 50 km convective distance threshold, but also tend to be larger than normal megaflashes, possibly due to having direct access to electrified stratiform clouds through which megaflashes propagate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

SCOPE: Spectral cloud optical property estimation using real-time GOES-R longwave imagery

The output of ground-based, solar power generation systems is strongly dependent on cloud cover, which is the main contributor to solar power variability and uncertainty. Cloud optical properties are typically over-simplified in forecasting applications due to the lack of realtime, accurate estimates. In this work, we introduce a method, the Spectral Cloud Optical Property Estimation (SCOPE), for estimating cloud optical properties directly from high-resolution (5-min, 2 km) imagery from Geostationary Operational Environmental Satellite (GOES)-R, which is the newest generation of the GOES system. The SCOPE method couples a two-stream, spectrally resolved radiative model with the longwave GOES-R sensor output to simultaneously estimate the cloud optical depth, cloud top height, and cloud thickness during both day and night at 5-min intervals. The accuracy of SCOPE is evaluated using one year (2018) of downwelling longwave (DLW) radiation measurements from the Surface Radiation Budget Network, which consists of seven sites spread across climatically diverse regions of the contiguous United States. During daytime clear-sky periods, SCOPE predicts DLW within instrument uncertainty (10 W m -2 ) for four of the seven locations, with the remaining locations yielding errors of the order of 11.2, 17.7, and 20.2 W m -2 . For daytime cloudy-sky, daytime all-sky (clear or cloudy), and nighttime all-sky periods, SCOPE achieves root mean square error values of 23.0–34.5 W m -2 for all seven locations. These results, together with the low-latency of the method (1 s per sample), show that SCOPE provides a viable solution to real-time, accurate estimation of cloud optical properties for both day and night.

42 ENGINEERING↗
DOE OSTI2022

Correction and calibration of atmospheric impact observations in GOES GLM data

The Earth's atmosphere is impacted daily by both meteoroids and artificial objects. Calibrated observations of the emitted light at sufficiently high sampling rates can enable or improve the estimation of impactor attributes such as size, cohesion, trajectory, and composition, but are difficult to obtain owing to the unpredictability, brevity, and high dynamic (brightness) range of impacts. Ground-based camera systems have successfully monitored small regions of the atmosphere at video frame rates and with limited radiometric capabilities, but most impacts occur over the 70% of the Earth's surface covered by water and are therefore missed by these networks. The Geostationary Lightning Mapper (GLM) instruments aboard Geostationary Operational Environmental Satellites 16 and 17 provide near-hemispherical coverage at 500 frames per second. These data have been shown to contain the signatures of many independently confirmed impacts, often from both viewing angles simultaneously, and constitute an observational resource that is currently unparalleled in the public domain. NASA's Asteroid Threat Assessment Project has implemented an automated impact detection pipeline that processes data from GLM daily. Given a detected impact, the GLM data contain a wealth of information for use in quantitative follow-up analyses. However, impact events differ from lightning in ways that violate key assumptions built into GLM's design. The result is that GLM's onboard processing introduces errors into pixel observations of impact events and the calibrated energies near the periphery of the detector may be substantially overestimated. We present methods for mitigating these and other issues to produce a data product more suitable for impact analyses than the existing GLM lightning product.

58 GEOSCIENCES↗
DOE OSTI2021

CFTrack v.1.0

SAND2021-13952 O Cloud Feature Tracker (CFTrack) is an application for following low-cloud motion in Geostationary Operational Environmental Satellite (GOES) R-series imagery. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Larson, Kelsie↗
DOE OSTI2022

New WMO Certified Megaflash Lightning Extremes for Flash Distance (768 km) and Duration (17.01 seconds) Recorded from Space

Initial global extremes in lightning duration and horizontal distance were established in 2017 by an international panel of atmospheric lightning scientists and engineers assembled by the WMO. The subsequent launch of NOAA’s latest GOES-16/17 satellites with their Geostationary Lightning Mappers (GLMs) enabled extreme lightning to be monitored continuously over the western hemisphere up to 55⁰ latitude for the first time. Consequently, the former lightning extremes were more than doubled in 2019 to 709 km for distance and 16.730 s for duration. Continued detection and analysis of lightning “megaflashes” has now revealed two flashes that even exceed those 2019 records. As part of the ongoing work of the WMO in detection and documentation of global weather extremes, an international WMO evaluation committee was created to critically adjudicate these two GLM megaflash cases as new records for extreme lightning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Hazards Posed by Mesoscale Lightning Megaflashes

Lightning megaflashes extending over >100-km distances have been observed by the Geostationary Lightning Mappers (GLMs) on NOAA’s R-series Geostationary Operational Environmental Satellites (GOES). The hazards posed by megaflashes are unclear, however, because of limitations in the GLM data. We address these by reprocessing GOES-16 GLM measurements from 1 January 2018 to 15 January 2020 and integrating them with Earth Networks Global Lightning Network (ENGLN) observations. ENGLN verified 194 880 GLM megaflashes as natural lightning. Of these, 127 479 flashes occurred following the October 2018 GLM software update that standardized GLM timing. Reprocessed GLM/ENGLN lightning maps from these postupdate cases provide a comprehensive view of how individual megaflashes evolve. This megaflash dataset is used to generate statistics that describe their hazards. The average megaflash produces 5–7 cloud-to-ground (CG) strokes that are spread across 40%–50% of the flash extent. As flash extent increases beyond 100 km, megaflashes become concentrated in key hot-spot regions in North and South America while the number of CG and intracloud events per flash and the overall peak current increase. CGs in the larger megaflashes occur over 80% of the flash extent measured by GLM, and the majority contain regions where the megaflash is the only lightning activity in the preceding hour. These statistics demonstrate that there is no safe location below an electrified cloud that is producing megaflashes, and current lightning safety guidance is not always sufficient to mitigate megaflash hazards.

54 ENVIRONMENTAL SCIENCES↗