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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Accurate and Timely Forecasts of Geologic Carbon Storage using Machine Learning Methods

Carbon capture and storage is one strategy to reduce greenhouse gas emissions. One approach to storing the captured CO2 is to inject it into deep saline aquifers. However, dynamics of the injected CO2 plume is uncertain and the potential for leakage back to the atmosphere must be assessed. Thus, accurate and timely forecasts of CO2 storage via real-time measurements integration becomes very crucial. This study proposes a learning-based, inverse-free prediction method that can accurately and rapidly forecast CO2 movement and distribution with uncertainty quantification based on limited simulation and observation data. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO2 storage site operators with an effective tool for real-time decision making.

Lu, Dan↗
NASA NTRSDate not supplied

Sub-City-Scale Air Quality Forecasts Combining Models, Satellites, and Surface Measures

Poor air quality is a global major concern, especially in cities with their higher emissions and large numbers of exposed people. Air quality monitoring has traditionally relied on ground-based measurements from a few accurate but expensive regulatory-grade monitors, leading to limited spatial data coverage. More recently, these have been supplemented with other data sources, including satellite observations of pollutants, atmospheric chemistry model simulations, and low-cost monitors allowing for denser spatial data collection at the expense of lower accuracy compared to regulatory-grade monitors. Each of these air quality data sources have their own benefits and drawbacks, and so there is an opportunity to combine these data together while respecting their relative strengths and weaknesses in order to generate a more comprehensive and detailed picture of local air quality. I will present our current work towards such a combination, with a focus on producing higher spatial resolution estimates and near-term forecasts of Nitrogen Dioxide in urban areas in the United States. Our method combines data from the GEOS Composition Forecasting (GEOS-CF) model system, TROPOMI tropospheric NO2 satellite data products, and ground measurements from the EPA regulatory monitoring network using a combination of simple intuitive relationships and machine learning techniques. I will show the performance of this proposed method in several urban areas in the United States, comparing it with baseline approaches using single data sources separately. Overall, we find that combining these disparate datasets together leads to more accurate air quality forecasts in the target areas than is possible using each data source separately.

Air quality↗
NASA NTRSDate not supplied

Assessing the Viability of Using GEOS-Forecast Product for Landslides Forecasting: A Step Toward Early Warning System

Landslides across the globe are mostly triggered by extreme rainfall events affecting infrastructure, transportation and livelihoods. The risks are rarely quantified due to lack of data, analytical skills and limited modeling techniques. Knowledge of local to global scale landslide risks provides communities and national agencies the ability to adapt disaster management practices to mitigate and recover from these hazards. In order to minimize the risks and improve characterization of community resilience to landslides, it is vital to have reliable information about the factors triggering landslides such as rainfall, well ahead in time. Forecasting potential landslide activity and impacts can be achieved through reliable precipitation forecast models. However, it is challenging because of the temporal and spatial variability of precipitation, an important factor in triggering landslides. Evaluation of the precipitation field, associated errors, and sampling uncertainties is integral for development of efficient and reliable landslide forecasting and early warning system. This study develops a methodology to assess the viability of using a precipitation field provided by a global model and its potential integration in the landslide forecasting system. The study focuses on the comparison between the IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Mission) and GEOS (NASA Goddard Earth Observing System)-Forecast product over contiguous United States (CONUS). GEOS model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The framework is tested on the GEOS-Forecast Model initialized at 00 UTC using daily IMERG early product as reference using both categorical and continuous statistics. The categorical statistics includes the probability of detection (POD), success ratio (SR), critical success index (CSI), and the hit bias. Continuous statistics such as correlation, normalized standard deviation, and root-mean-square error are also evaluated. Overall, GEOS-Forecast precipitation field over the analysis period (~1 year) show underestimation with respect to IMERG early for the daily accumulated rainfall. However, the probability distribution function and cumulative distribution function of both show similar patterns. In terms of correlations, POD, SR, CSI, hit bias, the performance varies with respect to the rainfall threshold used.

Sana Khan↗
DOE OSTI2022

Projected U.S. drought extremes through the twenty-first century with vapor pressure deficit

Global warming is expected to enhance drought extremes in the United States throughout the twenty-first century. Projecting these changes can be complex in regions with large variability in atmospheric and soil moisture on small spatial scales. Vapor Pressure Deficit (VPD) is a valuable measure of evaporative demand as moisture moves from the surface into the atmosphere and a dynamic measure of drought. Here, VPD is used to identify short-term drought with the Standardized VPD Drought Index (SVDI); and used to characterize future extreme droughts using grid dependent stationary and non-stationary generalized extreme value (GEV) models, and a random sampling technique is developed to quantify multimodel uncertainties. The GEV analysis was performed with projections using the Weather Research and Forecasting model, downscaled from three Global Climate Models based on the Representative Concentration Pathway 8.5 for present, mid-century and late-century. Results show the VPD based index (SVDI) accurately identifies the timing and magnitude short-term droughts, and extreme VPD is increasing across the United States and by the end of the twenty-first century. The number of days VPD is above 9 kPa increases by 10 days along California’s coastline, 30–40 days in the northwest and Midwest, and 100 days in California’s Central Valley.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A composite likelihood approach for inference under photometric redshift uncertainty

ABSTRACT Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.

(cosmology:) large-scale structure of Universe↗
DOE OSTI2025

Machine learning-driven predictive resource management in complex science workflows

Here, the collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1973

Technology requirements for communication satellites in the 1980's

The key technology requirements are defined for meeting the forecasted demands for communication satellite services in the 1985 to 1995 time frame. Evaluation is made of needs for services and technical and functional requirements for providing services. The future growth capabilities of the terrestrial telephone network, cable television, and satellite networks are forecasted. The impact of spacecraft technology and booster performance and costs upon communication satellite costs are analyzed. Systems analysis techniques are used to determine functional requirements and the sensitivities of technology improvements for reducing the costs of meeting requirements. Recommended development plans and funding levels are presented, as well as the possible cost saving for communications satellites in the post 1985 era.

Burtt, J. E.↗
NASA NTRS1980

Very short range forecasts of visibility and ceiling

The development of methods for the short range forecasting of visibility and ceiling conditions is discussed. Short range forecasts of one hour or less (5 or 30 minutes), immediately after a series of local observations can be expected to be more accurate and reliable than any forecast of more than one hour. These forecasts can be accomplished by the operational implementation of fully automated aviation observation systems and the utilization of statistical techniques such as the Generalized Equivalent Markov model.

Hilsenrod, A.↗
NASA NTRS1981

Cabin ozone and tropopause definition

A method to estimate ozone profiles is based on knowing the total ozone which may come from any observational system available such as Dobson photometers that are available from NMC forecasts and used operationally in flight planning. From this an ozone profile at selected pressure levels is obtained. The technique was based on five years of ozonesonde data. So far, models have been developed for winter, spring, and fall.

Belmont, A. D.↗
NASA NTRS1991

A Variational Assimilation Method for Satellite and Conventional Data: a Revised Basic Model 2B

A variational objective analysis technique that modifies observations of temperature, height, and wind on the cyclone scale to satisfy the five 'primitive' model forecast equations is presented. This analysis method overcomes all of the problems that hindered previous versions, such as over-determination, time consistency, solution method, and constraint decoupling. A preliminary evaluation of the method shows that it converges rapidly, the divergent part of the wind is strongly coupled in the solution, fields of height and temperature are well-preserved, and derivative quantities such as vorticity and divergence are improved. Problem areas are systematic increases in the horizontal velocity components, and large magnitudes of the local tendencies of the horizontal velocity components. The preliminary evaluation makes note of these problems but detailed evaluations required to determine the origin of these problems await future research.

Achtemeier, Gary L.↗
NASA NTRS2012

Receivers Gather Data for Climate, Weather Prediction

Signals from global positioning system (GPS) satellites are now being used for more than just location and navigation information. By looking at the radio waves from GPS satellites, a technology developed at NASA s Jet Propulsion Laboratory (JPL) not only precisely calculates its position, but can also use a technique known as radio occultation to help scientists study the Earth s atmosphere and gravity field to improve weather forecasts, monitor climate change, and enhance space weather research. The University Corporation for Atmospheric Research (UCAR), a nonprofit group of universities in Boulder, Colorado, compares radio occultation to the appearance of a pencil when viewed though a glass of water. The water molecules change the path of visible light waves so that the pencil appears bent, just like molecules in the air bend GPS radio signals as they pass through (or are occulted by) the atmosphere. Through measurements of the amount of bending in the signals, scientists can construct detailed images of the ionosphere (the energetic upper part of the atmosphere) and also gather information about atmospheric density, pressure, temperature, and moisture. Once collected, this data can be input into weather forecasting and climate models for weather prediction and climate studies. Traditionally, such information is obtained through the use of weather balloons. In 1998, JPL started developing a new class of GPS space science receivers, called Black Jack, that could take precise measurements of how GPS signals are distorted or delayed along their way to the receiver. By 2006, the first demonstration of a GPS radio occultation constellation was launched through a collaboration among Taiwan s National Science Council and National Space Organization, the U.S. National Science Foundation, NASA, the National Oceanic and Atmospheric Administration (NOAA), and other Federal entities. Called the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), JPL was responsible for designing COSMIC s primary instrument - based on its revolutionary Black Jack receiver.

Source record↗
NASA NTRS2012

Applications of NASA and NOAA Satellite Observations by NASA's Short-term Prediction Research and Transition (SPoRT) Center in Response to Natural Disasters

NASA s Short-term Prediction Research and Transition (SPoRT) Center supports the transition of unique NASA and NOAA research activities to the operational weather forecasting community. SPoRT emphasizes real-time analysis and prediction out to 48 hours. SPoRT partners with NOAA s National Weather Service (NWS) Weather Forecast Offices (WFOs) and National Centers to improve current products, demonstrate future satellite capabilities and explore new data assimilation techniques. Recently, the SPoRT Center has been involved in several activities related to disaster response, in collaboration with NOAA s National Weather Service, NASA s Applied Sciences Disasters Program, and other partners.

Molthan, Andrew L.↗
NASA NTRSDate not supplied

Assimilation of microwave satellite observations over the rainbands of tropical cyclones

The potential of microwave observations in improving the weather forecasts is limited by the accuracy of all-sky radiative transfer calculations. We introduce a novel Bayesian MonteCarlo technique to improve the assimilation of microwave observations over the rainbands of tropical cyclones. The BMCI technique eliminates the need for a forward model in the data assimilation system. The technique includes three steps, (i) generating a comprehensive dataset using in-situ cloud measurements and atmospheric profiles, (ii) generating synthetic ATMS observations from the training dataset, and (iii) using real observations to estimate the geophysical variables over the rainbands of tropical cyclones. The retrieved profiles of temperature, relative humidity, and cloud water content as well assurface information such as SST were then assimilated into the model. The results show that assimilating the BMCI retrievals can influence the dynamical features of the cyclone, including a stronger warm core, a symmetric eye, and vertically aligned wind columns.

Monte Carlo↗
NASA NTRSDate not supplied

Sea Surface Air Pressure Retrieval with MBARS

Global meteorological observations are essential in initialization and assimilation of numerical weather prediction (NWP) models. Many key weather variables such as temperature, humidity, and wind can be relatively well observed by in-situ and remote techniques from surface, suborbital and orbital platforms. However, sea surface air pressure cannot be well-sampled. Over open oceans, the pressure can only be observed by very limited buoys, ships, and oceanic platforms. There is no operational remote sensing method available for the crucial dynamic variable of the Earth’s climate and weather systems. Advanced techniques for global sea level pressure (SLP) observations are urgently needed for improving NWP models, particularly for improvement of severe weather forecasts. Recent studies on the space and airborne Microwave BArometric Radar and Sounder (MBARS) systems provide a great potential to fill the SLP observational gap. The systems studied have three channels with center frequencies at 65.5, 67.76 and 70 GHz. These channels are designed to operate at atmospheric oxygen (O2) absorption bands for column O2 mass measurements. When sea surface returns of spectrally closely-spaced paired channels are measured, surface reflection and atmospheric attenuation other than O2 are very similar, but the difference in O2 absorption is substantial. Thus, the differential absorption loss of this frequency pair leads to measures of atmospheric O2 amount. For the residual absorption loss caused by other agents, a third frequency having the same spectral space to the weak absorption frequency of the first pair can be used, which forms the second pair and produces the same residual as the first pair. Removal of the residual of this second pair from the first pair can further mitigate environmental impacts. Thus, this 3-channel approach will provide precise O2 amount retrievals. Since oxygen is well mixed in the atmosphere, the column air mass or SLP can be obtained. Currently, MBARS is rapidly maturing its advanced technology. An upcoming airborne field flight campaign is planned in late 2024. The 3-channel approach will be use in SLP retrievals. This MBARS team will discuss the physics-based SLP retrieval technique and report latest progresses in this upcoming AGU annual meeting.

O2 differential absorption↗
DOE OSTI2025

Forecasting Dark Matter Subhalo Constraints from Stellar Streams using Implicit Likelihood Inference

The evidence for dark matter (DM) remains compelling, although attempts to understand its particle nature remain inconclusive. One promising method to study DM is detecting DM subhalos through their gravitational interactions with stellar streams. In this study, we apply Neural Posterior Estimation (NPE) to constrain subhalo interaction parameters, including mass, scale radius, velocity, and encounter geometry, from stellar stream kinematics. We generate particle spray simulations based on the Lagrange Cloud stripping technique, focusing on the ATLAS-Aliqa Uma stream as a test case. We train multiple NPE models across multiple observational scenarios, quantifying how kinematic completeness affects inference and forecasting constraints from upcoming surveys including LSST, 4MOST, and 10-year Gaia data. Our results demonstrate that NPE can produce accurate and well-calibrated posteriors. In the idealized case with full 6D coordinates, we achieve subhalo mass uncertainties of 15-20% for a $10^7 \, \mathrm{M_\odot}$ subhalo, with 5D coordinates (excluding radial velocities) achieving similar performance. Under realistic observational conditions, mass uncertainties range from 50% (present-day) to 20-40% (future scenarios), with comparable performance between the photometric-only LSST sample and a smaller sample that includes Gaia proper motions and 4MOST radial velocities. Most notably, we find that velocity bimodality emerges when phase space is poorly sampled, whether due to missing kinematic information or limited stellar tracers. Combining large photometric samples with targeted spectroscopic follow-up can effectively resolves this degeneracy. These results demonstrate the power of implicit likelihood inference for optimizing stellar stream observational strategies and forecasting DM subhalo constraints from upcoming surveys.

Nguyen, Tri [Northwestern U. (main); SkAI, Chicago↗
NASA NTRS1981

Airline flight planning - The weather connection

The history of airline flight planning is briefly reviewed. Over half a century ago, when scheduled airline services began, weather data were almost nonexistent. By the early 1950's a reliable synoptic network provided upper air reports. The next 15 years saw a rapid growth in commercial aviation, and airlines introduced computer techniques to flight planning. The 1970's saw the development of weather satellites. The current state of flight planning activities is analyzed. It is found that accurate flight planning will require meteorological information on a finer scale than can be provided by a synoptic forecast. Opportunities for a new approach are examined, giving attention to the available options, a mesoscale numerical weather prediction model, limited area fine mesh models, man-computer interactive display systems, the use of interactive techniques with the present upper air data base, and the implementation of interactive techniques.

Steinberg, R.↗
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↗
NASA NTRS1986

Monitoring and predicting tropical cyclone movement using geosynchronous satellite remote sensing techniques

Data collected on special hurricane days by the VISSR Atmospheric Sounder (VAS) instrument flown on a satellite in 1981 and 1982 were studied for their usefulness in forecasting motion of hurricanes. The retrieved constant pressure heights for the 500-mb surface provided the basis for reasonable forecasts of 24 hour hurricane motion. The conclusions are illustrated with examples from Hurricane Harvey (1981) and Hurricane Irene (1981). Recommendations are made for future use of the VAS type instruments for tropical cyclone forecasting.

Dubach, L.↗