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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Nowcasting in the GPM Era

The Global Precipitation Measurement (GPM) Mission is an international satellite mission to unify and advance global precipitation measurements from a constellation of dedicated and operational microwave sensors. The GPM concept centers on the deployment of a Core Spacecraft in a non-Sun-synchronous orbit at 65deg inclination carrying a dual-frequency precipitation radar (DPR) and a multi-frequency passive microwave radiometer (GMI) with high-frequency capabilities to serve as a precipitation physics observatory and calibration standard for the constellation radiometers. The baseline GPM constellation is envisioned to comprise conical-scanning microwave imagers (e.g., GMI, SSMIS, AMSR, MIS, MADRAS, GPM-Brazil) augmented with cross-track microwave temperaturelhumidity sounders (e.g., MHS, ATMS) over land. In addition to the Core Satellite, the GPM Mission will contribute a second GMI to be flown in a low-inclination (approx.40deg) non-Sun-synchronous orbit to improve near real-time monitoring of hurricanes. GPM is a science mission with integrated applications goals aimed at (1) advancing the knowledge of the global waterlenergy cycle variability and freshwater availability and (2) improving weather, climate, and hydrological prediction capabilities through more accurate and frequent measurements of global precipitation. The GPM Mission is currently a partnership between NASA and the Japan Aerospace Exploration Agency (JAXA), with opportunities for additional partners in satellite constellation and ground validation activities. Within the framework of the inter-governmental Group ob Earth Observations (GEO) and Global Earth Observation System of Systems (GEOSS), GPM has been identified as a cornerstone for the Precipitation Constellation (PC) being developed under the auspices of Committee of Earth Observation Satellites (CEOS). The GPM Core Observatory is scheduled for launch in 201 3, followed by the launch of the GPM Low-Inclination Observatory in 2014. An overview of the GPM mission status, measurement capabilities, ground validation plans, and anticipated contributions to scientific research and societal applications with a special emphasis on nowcasting will be presented.

Hou, Arthur Y.↗
NASA NTRS2008

The Global Precipitation Measurement (GPM) Mission: Overview and Status

The Global Precipitation Measurement (GPM) Mission is an international satellite mission to unify and advance global precipitation measurements from a constellation of dedicated and operational microwave sensors. The GPM concept centers on the deployment of a Core SpacecraR in a non-Sun-synchronous orbit at 65 deg. inclination carrying a dual-frequency precipitation radar (DPR) and a multi-frequency passive microwave radiometer (GMI) with high-frequency capabilities to serve as a precipitation physics observatory and calibration standard for the constellation radiometers. The baseline GPM constellation is envisioned to comprise conical-scanning microwave imagers (e.g., GMI, SSMIS, AMSR, MIS, MADRAS, GPM-Brazil) augmented with cross-track microwave temperaturethumidity sounders (e.g., MHS, ATMS) over land. In addition to the Core Satellite, the GPM Mission will contribute a second GMI to be flown in a low-inclination (approximately 40 deg.) non-Sun-synchronous orbit to improve near-realtime monitoring of hurricanes. GPM is a science mission with integrated applications goals aimed at (1) advancing the knowledge of the global watertenergy cycle variability and freshwater availability and (2) improving weather, climate, and hydrological prediction capabilities through more accurate and frequent measurements of global precipitation. The GPM Mission is currently a partnership between NASA and the Japan Aerospace Exploration Agency (JAXA), with opportunities for additional partners in satellite constellation and ground validation activities. Within the framework of the inter-governmental Group ob Earth Observations (GEO) and Global Earth Observation System of Systems (GEOSS), GPM has been identified as a cornerstone for the Precipitation Constellation (PC) being developed under the auspices of Committee of Earth Observation Satellites (CEOS). The GPM Core Observatory is scheduled for launch in 2013, followed by the launch of the GPM Low-Inclination Observatory in 2014. An overview of the GPM mission status, instrument capabilities, ground validation plans, and anticipated scientific and societal benefits will be presented.

Hou, Arthur Y.↗
NASA NTRS2008

Nowcasting in the GPM Era

The Global Precipitation Measurement (GPM) Mission is an international satellite mission to unify and advance global precipitation measurements from a constellation of dedicated and operational microwave sensors. The GPM concept centers on the deployment of a Core Spacecraft in a non-Sun-synchronous orbit at 65' inclination carrying a dual-frequency precipitation radar (DPR) and a multi-frequency passive microwave radiometer (GMI) with high-frequency capabilities to serve as a precipitation physics observatory and calibration standard for the constellation radiometers. The baseline GPM constellation is envisioned to comprise conical-scanning microwave imagers (e.g., GMI, SSMIS, AMSR, MIS, MADRAS, GPM-Brazil) augmented with cross-track microwave temperaturelhumidity sounders (e.g., MHS, ATMS) over land. In addition to the Core Satellite, the GPM Mission will contribute a second GMI to be flown in a low-inclination (-40') non-Sun-synchronous orbit to improve near real-time monitoring of hurricanes. GPM is a science mission with integrated applications goals aimed at (1) advancing the knowledge of the global waterlenergy cycle variability and freshwater availability and (2) improving weather, climate, and hydrological prediction capabilities through more accurate and frequent measurements of global precipitation. The GPM Mission is currently a partnership between NASA and the Japan Aerospace Exploration Agency (JAXA), with opportunities for additional partners in satellite constellation and ground validation activities. Within the framework of the inter-governmental Group ob Earth Observations (GEO) and Global Earth Observation System of Systems (GEOSS), GPM has been identified as a cornerstone for the Precipitation Constellation (PC) being developed under the auspices of Committee of Earth Observation Satellites (CEOS). The GPM Core Observatory is scheduled for launch in 201 3, followed by the launch of the GPM Low-Inclination Observatory in 2014. An overview of the GPM mission status, measurement capabilities, ground validation plans, and anticipated contributions to scientific research and societal applications with a special emphasis on nowcasting will be presented.

Hou, Arthur Y.↗
NASA NTRS2011

GPM Mission Overview

The Global Precipitation Measurement (GPM) Mission is an international satellite mission to unify and advance precipitation measurements from a constellation of research and operational sensors to provide "next-generation" precipitation products. Relative to current global rainfall products, GPM data products will be characterized by: (1) more accurate instantaneous precipitation measurements (especially for light rain and cold-season solid/snow precipitation), (2) more frequent sampling by an expanded constellation of microwave radiometers that include operational humidity sounders over land, (3) inter-calibrated microwave brightness temperatures from constellation radiometers within a unified framework, and (4) physical-based precipitation retrievals from constellation radiometers using a common a priori cloud hydrometeor database derived from GPM Core sensor measurements. The cornerstone of the GPM mission is the deployment of a Core Observatory in a unique 65 degree non-Sun-synchronous orbit to serve as a physics observatory and a calibration reference to improve precipitation measurements by a constellation of dedicated and operational passive microwave sensors. The Core Observatory will carry a KulKa-band Dual-frequency Precipitation Radar (DPR) and a multi-channel (10-183 GHz) GPM Microwave Radiometer (GMI). The combined use ofDPR and GMI measurements will place greater constraints on possible solutions to radiometer retrievals to improve the accuracy and consistency of precipitation retrievals from all constellation radiometers. As a science mission with integrated application goals, GPM is designed to (1) advance precipitation measurement capability from space through combined use of active and passive microwave sensors, (2) advance the knowledge of the global water/energy cycle and freshwater availability through better description of the space-time variability of global precipitation, and (3) improve weather, climate, and hydrological prediction capabilities through more accurate and frequent measurements of instantaneous precipitation rates and time-integrated rainfall accumulation. An overview of the GPM mission concept and science activities in the United States, together with an update on international collaborations in radiometer intercalibration and ground validation, will be presented.

Hou, Arthur Y.↗
NASA NTRS2020

Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershedin the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’sLAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

Adnan Rajib↗
NASA NTRS2002

The Use of Remote Sensing for Monitoring, Prediction, and Management of Hydrologic, Agricultural, and Ecological Processes in the Northern Great Plains

The NASA-EPSCoR program in South Dakota is focused on the enhancement of NASA-related research in earth system science and corresponding infrastructure development to support this theme. Hence, the program has adopted a strategy that keys on research projects that: a) establish quantitative links between geospatial information technologies and fundamental climatic and ecosystem processes in the Northern Great Plains (NGP) and b) develop and use coupled modeling tools, which can be initialized by data from combined satellite and surface measurements, to provide reliable predictions and management guidance for hydrologic, agricultural, and ecological systems of the NGP. Building a partnership network that includes both internal and external team members is recognized as an essential element of the SD NASA-EPSCoR program. Hence, promoting and tracking such linkages along with their relevant programmatic consequences are used as one metric to assess the program's progress and success. This annual report first summarizes general activities and accomplishments, and then provides progress narratives for the two separate, yet related research projects that are essential components of the SD NASA-EPSCoR program.

Farwell, Sherry O.↗
NASA NTRS2022

A New Snow Module Improves Predictions of the Isotope-Enabled MAIDENiso Forest Growth Model

The representation of snow processes in forest growth models is necessary to accurately predict the hydrological cycle in boreal ecosystems and the isotopic signature of soil water extracted by trees, photosynthates and tree-ring cellulose. Yet, most process-based models do not include a snow module; consequently, their simulations may be biased in cold environments. Here, we modified the MAIDENiso model to incorporate a new snow module that simulates snow accumulation, melting and sublimation, as well as thermal exchanges driving freezing and thawing of the snow and the soil. We tested these implementations in two sites in eastern and western Canada for black spruce (Picea mariana (Mill.) B.S.P.) and white spruce (Picea glauca (Moench) Voss) forests, respectively. The new snow module improves the skills of the model to predict components of the hydrological cycle. The MAIDENiso model is now able to reproduce the spring discharge peak and to simulate stable oxygen isotopes in tree-ring cellulose more realistically than in the original snow-free version of the model. The new implementation also results in simulations with a higher contribution from the source water on the oxygen isotopic composition of the simulated cellulose, leading to more accurate estimates of cellulose isotopic composition. Future work may include the development of inverse modelling with this new version of MAIDENiso to produce robust reconstructions of the hydrological cycle and isotope processes in cold environments.

Snow processes↗
DOE OSTI2021

Integrating AI with physics-based hydrological models and observations for insightinto changing climate and anthropogenic impacts

Focal Areas: Advanced computational methods that integrate AI, physics, and observations to provide predictive landscape hydrological modeling over large areas (regional, continental, worldwide) while incorporating increasingly available high-resolution data from drones, lidar and satellite. Science Challenge: Landscape data is available at finer scales than can be used in physics-based hydrological (PBH) models for regional or continental terrestrial water modeling. Thus, we throw away observable detail to achieve computability. We argue that integration of AI with PBH models and observed data can be used to provide upscaling for predictive models that are computable, retain physical conservation properties, and represent the fine-scale features that affect complex flow physics through both natural and urban environments. Developing such next-generation capabilities requires outside-the-box thinking that melds the different approaches of AI modeling, PBH modeling, and observation across multiple scales from local drones to satellites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Monitoring and modeling hydrologic conditions in Ukraine for hydropower generation

Study region: The Dnieper and Dniester Rivers of Ukraine. Study focus: The ongoing conflict in Ukraine has caused disruptions to electricity generation, of which hydroelectric sources contribute approximately 9 % to the country’s needs. With the takeover of the Zaporizhzhia nuclear power plant by enemy forces, the loss of the Kakhovka hydroelectric dam, and the future impacts of the conflict on electricity generation unclear, it may be valuable for the Ukrainian government to better understand how it could leverage hydroelectric power sources in the near future. Unfortunately, measurements of river discharge throughout Ukraine ceased data collection in the late 1980’s to early 1990’s. To address this data gap, we developed a protocol that combined satellite-based time-series measurements of river width at seven locations throughout Ukraine from 2013 to 2023 with reanalysis data, climate-model predictions, and hydrologic models to both provide a means of monitoring a proxy for near-real-time discharge and also predict near-term (i.e., 2023–2030) hydrologic patterns for the region. New hydrological insights for the region: We ran new algorithms on 144 WorldView-2 and WorldView-3 satellite images to map rivers and extract width, one of which was validated against river gauge data located along the same river but in a neighboring country. Hydrologic models using two climate scenarios found minimal change in annual discharge at all sites, but magnitude and timing of peak discharge showed a moderate trend. The results suggest that hydropower is underutilized in Ukraine.

13 HYDRO ENERGY↗
DOE OSTI2025

Coupled Hydrological-Thermal-Biogeochemical Modeling for Predicting Arctic Carbon Emissions (CH4PACE)

The Arctic, with nearly 1,700 billion metric tons of carbon stored in its soils, is experiencing significant warming trends that raise concerns about the potential release of greenhouse gases, particularly methane. This report investigates the conversion of carbon stored in Arctic soils and permafrost into methane and its subsequent atmospheric release over the coming decades. Recent data indicate a rise in global methane concentrations, highlighting the importance of understanding the mechanisms of methane generation under anaerobic conditions prevalent in thawing permafrost. Our study employs a multi-faceted approach that integrates fieldwork, benchtop experiments, and numerical simulations to explore the subsurface hydrological-thermal-biogeochemical systems influencing methane emissions. We focus on the relationship between microbial activity in decomposing organic matter under oxygen-limited conditions and the transport of methane out of these saturated wetland soils. Key hydrological parameters, including permeability, porosity, and thermal conductivity, were measured on soil samples from Fairbanks, Alaska, and used to parameterize PFLOTRAN, a coupled hydrological-thermal-reactive transport simulator. Our model enables prediction of the evolution of the active layer and its impact on methane production and release. Our findings underscore the critical need for a mechanistic understanding of methane dynamics in permafrost environments to improve predictions of methane emissions in Earth systems models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Rethink hydrologic modeling framework with AI integrating multi-processes across scales

The proposed work is to develop a new hydrologic modeling framework that leverages vast amount earth and human system observational data, AI technologies, and data-driven information flows to improve predictability of hydrologic system that involves hydrologic, terrestrial, and biogeochemical processes and their interactions with the human and atmosphere systems.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

Hydrologic Baseline Conditions and Projected Trends for Kennedy Space Center and the Cape Canaveral Barrier Island Complex

The purpose of this document is the creation of a comprehensive collection of data, information and knowledge on the eco-hydrologic setting, and current and predicted (2030 -2080) hydrologic conditions that will directly and indirectly influence the Kennedy Space Center (KSC) industrial and ecological system. Focus is on identification of existing data sources (web based, published, in-house) and identification of future possible needs. The document will aid in assessment of risks to the National Aeronautics and Space Administration (NASA) and stakeholder missions associated with changing local, regional and global conditions. This living document will allow for rapid updates as new data and improved spatial data (LiDAR elevations, land use, land cover, runoff coefficients, etc.) are accumulated by the Environmental Management Branch (EMB), stakeholders, and supporting staff from the NASA Environmental and Medical Contract (NEMCON) currently managed by Herndon Solutions Group LLC. (HSG).The report summarizes and visualizes hydrologic conditions supporting long-term trends monitoring, management and regulatory compliance needs, climate risk mitigation, and achievement of KSC and stakeholder’s sustainability goals. Future utility may extend to implementation actions of the KSC future development plan, KSC updates to future KSC Environmental Resource Documents (ERD), and the regional multi-agency Indian River Lagoon Health Initiative.

Carlton Hall↗
NASA NTRS1973

Application of remote sensing to hydrology

Streamflow forecasting and hydrologic modelling are considered in a feasibility assessment of using the data produced by remote observation from space and/or aircraft to reduce the time and expense normally involved in achieving the ability to predict the hydrological behavior of an ungaged watershed. Existing watershed models are described, and both stochastic and parametric techniques are discussed towards the selection of a suitable simulation model. Technical progress and applications are reported and recommendations are made for additional research.

Ambaruch, R.↗
NASA NTRS2018

Influence of Precipitation Forcing Uncertainty on Hydrological Simulations with the NASA South Asia Land Data Assimilation System

Accurate meteorological estimates are critical for process-based hydrological simulationand prediction. This presents a significant challenge in mountainous Asia where in situmeteorological stations are limited and major river basins cross international borders. In thiscontext, remotely sensed and model-derived meteorological estimates are often necessary inputsfor distributed hydrological analysis. However, these datasets are difficult to evaluate on accountof limited access to ground data. In this case, the implications of uncertainty associated withprecipitation forcing for hydrological simulations is explored by driving the South Asia Land DataAssimilation System (South Asia LDAS) using a range of meteorological forcing products.MERRA2, GDAS, and CHIRPS produce a wide range of estimates for rainfall, which causes awidespread simulated streamflow and evapotranspiration. A combination of satellite-derived andlimited in situ data are applied to evaluate model simulations and, by extension, to constrain theestimates of precipitation. The results show that available gridded precipitation estimates based onin situ data may systematically underestimate precipitation in mountainous regions and thatperformance of gridded satellite-derived or modeled precipitation estimates varies systematicallyacross the region. Since no station-based data or product including station data is satisfactoryeverywhere, our results suggest that the evaluation of the hydrological simulation of streamflowand ET can be used as an indirect evaluation of precipitation forcing based on ground-basedproducts or in-situ data. South Asia LDAS produces reasonable evapotranspiration and streamflowwhen forced with appropriate meteorological forcing and the choice of meteorological forcingshould be made based on the geographical location as well as on the purpose of the simulations.

South Asia land data assimilation system (South As↗
DOE OSTI2021

A Fire Community Observatory: Interdisciplinary, AI-informed Post-Fire Rapid Response for Improved Water Cycle Science at Watershed Scale

Wildfire is an ecological disturbance that disrupts the hydrological cycle. In the past few years, a record number of multiple-and-compounding fires have occurred across urban-wildland gradients in the Western United States. Changes to watershed hydrological partitioning in response to fires (infiltration, runoff, evapotranspiration) presents unprecedented challenges to “Water-in-the-West” through negative impacts to water supply and its quality, and is a direct threat to downstream communities, groundwater, and drinking water supply infrastructure. While much work is being done to advance Artificial Intelligence and Machine Learning (AI/ML) use during fires for emergency response (i.e. predict fire movement, direct evacuations), significant potential exists to use AI/ML to address three scientific grand challenges that are rarely addressed in a convergent science context: 1) how to enhance the potential resiliency of a landscape before fire(s), 2) how to cost-effectively and optimally monitor watershed changes after fires, and 3) how to predict future hydrological and biogeochemical trajectories in fire-impacted watershed given climate change. This whitepaper addresses DOE Focal Area 1: Data acquisition and assimilation enabled by machine learning, AI, and advanced methods.

58 GEOSCIENCES↗
DOE OSTI2022

Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics

Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.

58 GEOSCIENCES↗
DOE OSTI2021

A Spatiotemporal Sequence Forecasting Platform to Advance the Prediction of Changing Spatiotemporal Patterns of CO 2 Concentration by Incorporating Human Activity and Hydrological Extremes

Focal Areas (2): Predictive modeling using AI techniques and AI-derived model components. It describes the use of AI and other tools to design a prediction system comprising a hierarchy of models. Specifically, this white paper focuses on the development of a collaborative deep learning platform for evaluating and predicting spatiotemporal relationships between the hydrological and carbon cycles and includes capabilities for considering biogenic and anthropogenic inputs to these systems.

54 ENVIRONMENTAL SCIENCES↗