Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “time series forecasting”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Southwest Water Resources: Monitoring Surface Water Extents of Remote Stock Ponds in the Southwestern United States Using Earth Observing Systems for Enhanced Water Resources Management

Due to increasingly frequent and severe drought conditions in the southwestern US, land managers and livestock producers need to monitor stock ponds with increasing regularity. The ability to assess stock pond water levels with Earth observing satellite systems would enhance monitoring efforts of partners at the US Forest Service, Arizona Department of Game and Fish, and the Diablo Trust. This study employed Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) to monitor surface water extent for hundreds of critical stock ponds in Arizona. Using methods adapted from previously developed image processing workflows, this project conducted a time-series analysis to capture seasonal and interannual variations in surface water area between 2013 to 2021. In addition, end users can monitor the surface water extent of stock ponds through the developed Google Earth Engine software tool called Surface Water Identification and Forecasting Tool (SWIFT). SWIFT incorporates the Automated Water Extraction Index, Modified Normalized Difference Water Index, and Tasseled Cap-Wetness Index for optical imagery and the incidence angle, VV and VH polarization bands for Sentinel-1 imagery to detect small water bodies in the study area with an overall accuracy range of 88-93%. These tools will empower our partners to monitor the extents of water in their stock ponds remotely, enabling them to develop data-informed and sustainable management solutions for decades to come.

Rainey Aberle↗
NASA NTRS2011

30 WS North Base Wind Study

The 30 Weather Squadron (30 WS) is concerned about strong winds observed at their northern towers without advance warning. They state that terrain influences along the extreme northern fringes of Vandenberg Air Force Base (VAFB) make it difficult for forecasters to issue timely and accurate high wind warnings for northeasterly wind events. These events tend to occur during the winter or early spring when they are under the influence of the Great Basin high pressure weather regime. The Launch Weather Officers (LWOs) have seen these rapid wind increases in the current northern Towers 60, 70 and 71 in excess of their 35 kt operational warning threshold. For this task, the 30 WS requested the Applied Meteorology Unit (AMU) analyze data from days when these towers reported winds in excess of 35 kt and determine if there were any precursors in the observations that would allow the LWOs to better forecast and warn their operational customers for these wind events. The 30 WS provided wind tower data for the cool season (October - March) from the period January 2004-March 20 IO. The AMU decoded and evaluated the wind tower data for 66 days identified by the 30 WS as having high-wind events. Out of the 66 event days, only 30 had wind speed observations of > or =35 kt from at least one of the three northern towers. The AMU analyzed surface and upper air charts to determine the synoptic conditions for each event day along with tower peak wind speed and direction time series and wind rose charts for all 30 event days. The analysis revealed a trend on all event days in which the tower winds shifted to the northeast for a period of time before the first recorded > or =35 kt wind speed. The time periods for the 30 event days ranged from 20 minutes to several hours, with a median value of 110 minutes. This trend, if monitored, could give the 30 WS forecasters a precursor to assist in issuing an operational warning before a high wind event occurs. The AMU recommends developing a high-wind alert capability for VAFB using a local mesoscale model to forecast these wind events. The model should incorporate all of the VAFB local data sets and have a forecast capability of between 2 to 24 hours. Such a model would allow the meteorologists at VAFB to alert the operational customers of high wind events in a timely manner so protective action could be taken.

Wheeler, Mark↗
NASA NTRS1975

Policy issues and data communications for NASA earth observation missions until 1985

The series of LANDSAT sensors with the highest potential data rates of the missions were examined. An examination of LANDSAT imagery uses shows that relatively few require transmission of the full resolution data on a repetitive quasi real time basis. Accuracy of global crop size forecasting can possibly be improved through information derived from LANDSAT imagery. A current forecasting experiment uses the imagery for crop area estimation only, yield being derived from other data sources.

Corte, A. B.↗
NASA NTRS2021

Assateague Island National Seashore Ecological Forecasting: Characterizing Nearshore Suspended Sediments and Landcover Change Relative to Sediment Bypassing and Catastrophic Events

Assateague Island is located off the coast of Maryland and Virginia and serves as a home to sensitive species and habitats. However, infrastructure development disrupted the natural sediment transport processes of the barrier island, which accelerated erosion of the island’s shoreline. To counteract this, the United States Army Corps of Engineers (USACE) initiated semiannual sediment bypassing operations in 2004. Over time, financial constraints limited the amount of sediment deposition possible, leading to concerns over navigational issues in nearby channels and the possibility of the operations not providing their intended benefits. To address these issues, NASA DEVELOP partnered with the National Park Service NPS and USACE. The team performed time series analyses of nearshore suspended sediment from 2004-2020 and landcover change from 2006-2018 with satellite imagery from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), and Sentinel-2 MultiSpectral Instrument (MSI). The sediment transport analyses showed that suspended sediment levels are seasonally dependent. Meanwhile, historical land cover trends included a net increase in unconsolidated shore and a net decrease in open water. Land cover change was then forecast to 2021, 2031, and 2046 using the IDRISI TerrSet Land Change Modeler. The model predicted the most drastic land cover changes in the southern portion and the least on the eastern foreshore of the island. As a result, this project allows our partners to understand the impact of sediment bypassing operations more fully and make better-informed decisions regarding the island’s management.

M Colin Marvin↗
NASA NTRS2015

Using Dynamically Downscaled Climate Model Outputs to Inform Projections of Extreme Precipitation Events

Many of the storms that generate damaging floods are caused by locally intense, sub-daily precipitation, yet the spatial and temporal resolution of the most widely available climate model outputs are both too coarse to simulate these events. Thus there is often a disconnect between the nature of the events that cause damaging floods and the models used to project how climate change might influence their magnitude. This could be a particular problem when developing scenarios to inform future storm water management options under future climate scenarios. In this study we sought to close this gap, using sub-daily outputs from the Weather Research and Forecasting model (WRF) from each of the nine climate regions in the United States. Specifically, we asked 1) whether WRF outputs projected consistent patterns of change for sub-daily and daily precipitation extremes; and 2) whether this dynamically downscaled model projected different magnitudes of change for 3-hourly vs 24-hourly extreme events. We extracted annual maximum values for 3-hour through 24-hour precipitation totals from an 11-year time series of hindcast (1995-2005) and mid-century (2045-2055) climate, and calculated the direction and magnitude of change for 3-hour and 24-hour extreme events over this timeframe. The model results project that the magnitude of both 3-hour and 24-hour events will increase over most regions of the United States, but there was no clear or consistent difference in the relative magnitudes of change for sub-daily vs daily events.

Climate models↗
NASA NTRS2019

Atmospheric Chemistry Modeling and Air Quality Forecasting Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS1994

Data assimilation and model evaluation experiment datasets

The Institute for Naval Oceanography, in cooperation with Naval Research Laboratories and universities, executed the Data Assimilation and Model Evaluation Experiment (DAMEE) for the Gulf Stream region during fiscal years 1991-1993. Enormous effort has gone into the preparation of several high-quality and consistent datasets for model initialization and verification. This paper describes the preparation process, the temporal and spatial scopes, the contents, the structure, etc., of these datasets. The goal of DAMEE and the need of data for the four phases of experiment are briefly stated. The preparation of DAMEE datasets consisted of a series of processes: (1) collection of observational data; (2) analysis and interpretation; (3) interpolation using the Optimum Thermal Interpolation System package; (4) quality control and re-analysis; and (5) data archiving and software documentation. The data products from these processes included a time series of 3D fields of temperature and salinity, 2D fields of surface dynamic height and mixed-layer depth, analysis of the Gulf Stream and rings system, and bathythermograph profiles. To date, these are the most detailed and high-quality data for mesoscale ocean modeling, data assimilation, and forecasting research. Feedback from ocean modeling groups who tested this data was incorporated into its refinement. Suggestions for DAMEE data usages include (1) ocean modeling and data assimilation studies, (2) diagnosis and theoretical studies, and (3) comparisons with locally detailed observations.

Lai, Chung-Cheng A.↗
NASA NTRS2020

California & Oregon Ecological Forecasting: Detecting and Forecasting Fog Occurrence, Frequency, and Change to Support Coast Redwood (Sequoia sempervirens) Habitat Assessments

Fog and low clouds play an important role in providing moisture to coastal ecosystems. Coast redwood (Sequoia sempervirens) forests are currently distributed along a narrow strip of coastline in California and Oregon and rely on the presence of marine fog for moisture availability during the dry season (June-October). Recent time series analyses presented an uncertain future of fog frequency; however, a decline in fog presence may have adverse effects on the coast redwood habitat. To support Save the Redwoods League, a non-profit organization dedicated to coast redwood forest management, the team analyzed hourly fog data from the Geospatial Operational Environmental Satellite 17 (GOES-17) Advanced Baseline Imager (ABI) and daily cloud cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite. To explore present day fog longevity, GOES-17 was utilized to map the number of fog hours per day for the 2019 and 2020 dry seasons. The MODIS cloud flag was used to map the presence or absence of daily fog, which was summarized to create a monthly fog frequency dataset and identify trends in fog presence between 2000-2020. Both datasets were used as inputs into the random forest machine learning algorithm to identify climatic drivers of fog presence and longevity over the landscape. The present-day models suggested that daily temperature difference is a driving force behind fog presence and longevity. Trends in fog presence from 2000-2020 indicated great interannual variability. Finally, fog presence was modeled under a 2080 climate projection to shed light on the future of fog presence under a projected warmer climate. Model results projected an overall decline in fog presence during the dry season in the 2080s. Decreased fog presence as a result of increased temperature difference under a warmer climate remains to be a topic of investigation as to the impact on future redwood habitat suitability.

DEVELOP Project Summary↗
NASA NTRS2020

California & Oregon Ecological Forecasting: Detecting and Forecasting Fog Occurrence, Frequency, and Change to Inform Coast Redwood (Sequoia sempervirens) Habitat Assessments

Fog and low clouds play an important role in providing moisture to coastal ecosystems. Coast redwood (Sequoia sempervirens) forests are currently distributed along a narrow strip of coastline in California and Oregon and rely on the presence of marine fog for moisture availability during the dry season (June-October). Recent time series analyses presented an uncertain future of fog frequency; however, a decline in fog presence may have adverse effects on the coast redwood habitat. To complement ongoing work by Save the Redwoods League, a non-profit organization dedicated to coast redwood forest management, the team analyzed hourly fog data from the Geospatial Operational Environmental Satellite 17 (GOES-17) Advanced Baseline Imager (ABI) and daily cloud cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite. To explore present day fog longevity, GOES-17 was utilized to map the number of fog hours per day for the 2019 and 2020 dry seasons. The MODIS cloud flag was used to map the presence or absence of daily fog, which was summarized to create a monthly fog frequency dataset and identify trends in fog presence between 2000-2020. Both datasets were used as inputs into the random forest machine learning algorithm to identify climatic drivers of fog presence and longevity over the landscape. The present-day models suggested that daily temperature difference is a driving force behind fog presence and longevity. Trends in fog presence from 2000-2020 indicated great interannual variability. Finally, fog presence was modeled under a 2080 climate projection to shed light on the future of fog presence under a projected warmer climate. Model results projected an overall decline in fog presence during the dry season in the 2080s. Decreased fog presence as a result of increased temperature difference under a warmer climate remains to be a topic of investigation as to the impact on future redwood habitat suitability.

DEVELOP Technical Paper↗
NASA NTRS2009

Monitoring Coastal Marshes for Persistent Saltwater Intrusion

Primary goal: Provide resource managers with remote sensing products that support ecosystem forecasting models requiring salinity and inundation data. Work supports the habitat-switching modules in the Coastal Louisiana Ecosystem Assessment and Restoration (CLEAR) model, which provides scientific evaluation for restoration management (Visser et al., 2008). Ongoing work to validate flooding with radar (NWRC/USGS) and enhance persistence estimates through "fusion" of MODIS and Landsat time series (ROSES A.28 Gulf of Mexico). Additional work will also investigate relationship between saltwater dielectric constant and radar returns (Radarsat) (ROSES A.28 Gulf of Mexico).

Kalcic, Maria↗
NASA NTRSDate not supplied

Anticipating Hazard Impacts through Capacity Building and Co-development

Improving forecast accuracy, extending lead time, understanding hazard susceptibility, and integrating exposure and vulnerability data to generate impact-based forecasts are critical in mitigating disaster impacts. These efforts enable anticipatory action through enhancing the efficacy of early warning systems to assess potential multi-dimensional hazard impacts. In response to this need, SERVIR has co-developed a series of hazard services providing vital information from national to regional scales. This poster showcases examples of geospatial services co-developed with partners through a capacity building approach, supporting the establishment of hazard early warning / early action systems. These services integrate multidimensional vulnerability and exposure data to comprehensively assess impacts. Furthermore, these services demonstrate how co-development and capacity building can advance the development of impact-based forecasts and multi-hazards services. By prioritizing collaboration, human insights, local knowledge, capacity building, and employing applied science approaches in geospatial service development, this work has helped create inclusive and customized solutions. These solutions are tailored to meet the needs of local communities and are readily adaptable into decision-making processes.

weather↗
NASA NTRS1992

Adaptive filtering and prediction of the Southern Oscillation index

Singular spectrum analysis (SSA), a variant of principal component analysis, is applied to a time series of the Southern Oscillation index (SOI). The analysis filters out variability unrelated to the Southern Oscillation and separates the high-frequency, 2- to 3-year variability, including the quasi-biennial oscillation, from the lower-frequency 4- to 6-year El Nino cycle. The maximum entropy method (MEM) is applied to forecasting the prefiltered SOI. Prediction based on MEM-associated autoregresive models has useful skill for 30-36 months. A 1993-1994 La Nina event is predicted based on data through February 1992.

Keppenne, Christian L.↗
NASA NTRSDate not supplied

Coastal Land Change Due to Tectonic Processes and Implications for Relative Sea-Level Rise in the Samoan Islands

Of the major coastal land change mechanisms responsible for relative sea-level change, tectonic subsidence is generally quoted as ranging from < mm/yr to 1 cm/yr. However, we documented coseismic and ongoing post-earthquake surface displacements from continuous GPS and tide gauge/altimetry data that indicated rapid subsidence on two of the major Samoan Islands of 12 - 20 cm during and following the 8.1 2009 Tonga-Samoa earthquake. Earlier results and our modeling of GRACE-derived gravimetric data provided a preliminary forecast of future relative sea-level rise through rapid land subsidence [Han et al., 2019]. Of course these numerical forecasts of time-dependent deformation are only as good as our input observations and our assumed rheological models. As part of our current NASA Earth Surface and Interior study, we are obtaining a wider range of data to constrain and test alternate models of ongoing postseismic deformation across American Samoa and Upolu, Samoa: (1) times series of altimetry plus tide gauge data processed to complement the cGPS data available to provide high-temporal resolution, point measurements of uplift/subsidence, (2) InSAR derived observations of surface deformation across the highly vegetated Samoan Islands, (3) evaluating and using NASA satellite lidar data (ICESat-I & ICESat-II, GEDI) for fusion with multi-source topographic data sets and for estimating topographic change on the decadal time scale. We are evaluating and using these new observations to better understand and separate out local, island-wide, and multi-island subsidence patterns and to evaluate the high impact of rising sea-level in a tectonically active region.

Jeanne Sauber-Rosenberg↗
NASA NTRS1995

TOGA COARE Satellite data summaries available on the World Wide Web

Satellite data summary images and analysis plots from the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE), which were initially prepared in the field at the Honiara Operations Center, are now available on the Internet via World Wide Web browsers such as Mosaic. These satellite data summaries consist of products derived from the Japanese Geosynchronous Meteorological Satellite IR data: a time-size series of the distribution of contiguous cold cloudiness areas, weekly percent high cloudiness (PHC) maps, and a five-month time-longitudinal diagram illustrating the zonal motion of large areas of cold cloudiness. The weekly PHC maps are overlaid with weekly mean 850-hPa wind calculated from the European Centre for Medium-Range Weather Forecasts (ECMWF) global analysis field and can be viewed as an animation loop. These satellite summaries provide an overview of spatial and temporal variabilities of the cloud population and a large-scale context for studies concerning specific processes of various components of TOGA COARE.

Chen, S. S.↗
NASA NTRS2001

A Regional Model Study of Synoptic Features Over West Africa

Synoptic weather features over West Africa were studied in simulations by the regional simulation model (RM) at the NASA/Goddard Institute for Space Studies. These pioneering simulations represent the beginning of an effort to adapt regional models for weather and climate prediction over West Africa. The RM uses a cartesian grid with 50 km horizontal resolution and fifteen vertical levels. An ensemble of four simulations was forced with lateral boundary conditions from ECMWF global analyses for the period 8-22 August 1988. The simulated mid-tropospheric circulation includes the skillful development and movement of several African wave disturbances. Wavelet analysis of mid-tropospheric winds detected a dominant periodicity of about 4 days and a secondary periodicity of 5-8 days. Spatial distributions of RM precipitation and precipitation time series were validated against daily rain gauge measurements and ISCCP satellite infrared cloud imagery. The time-space distribution of simulated precipitation was made more realistic by combining the ECMWR initial conditions with a 24-hr spin-up of the moisture field and also by damping high frequency gravity waves by dynamic initialization. Model precipitation "forecasts" over the Central Sahel were correlated with observations for about three days, but reinitializing with observed data on day 5 resulted in a dramatic improvement in the precipitation validation over the remaining 9 days. Results imply that information via the lateral boundary conditions is not always sufficient to minimize departures between simulated and actual precipitation patterns for more than several days. In addition, there was some evidence that the new initialization may increase the simulations' sensitivity to the quality of lateral boundary conditions.

Druyan, Leonard M.↗
NASA NTRS1994

The short-term prediction of universal time and length of day using atmospheric angular momentum

The ability to predict short-term variations in the Earth's rotation has gained importance in recent years owing to more precise spacecraft tracking requirements. Universal time (UT1), that component of the Earth's orientation corresponding to the rotation angle, can be measured by number of high-precision space geodetic techniques. A Kalman filter developed at the Jet Propulsion Laboratory (JPL) optimally combines these different data sets and generates a smoothed times series and a set of predictions for UT1, as well as for additional Earth orientation components. These UT1 predictions utilize an empirically derived random walk stochastic model for the length of the day (LOD) and require frequent and up-to-date measurements of either UT1 or LOD to keep errors from quickly accumulating. Recent studies have shown that LOD variations are correlated with changes in the Earth's axial atmospheric angular momentum (AAM) over timescales of several years down to as little as 8 days. AAM estimates and forecasts out to 10 days are routinely available from meteorological analysis centers; these data can supplement geodetic measurements to improve the short-term prediction of LOD and have therefore been incorporated as independent data types in the JPL Kalman filter. We find that AAM and, to a lesser extent, AAM forecast data are extremely helpful in generating accurate near-real-time estimates of UT1 and LOD and in improving short-term predictions of these quantities out to about 10 days.

Freedman, A. P.↗
NASA NTRS2001

Troposphere-Stratosphere Dynamic Coupling Under Strong and Weak Polar Vortex Conditions

The relationship between Northern Hemisphere (NH) tropospheric and stratospheric wave-like anomalies of spherical zonal wave number (ZWN) 1 is studied by applying Canonical Correlation Analysis (CCA). A lag-correlation technique is used with 10-day lowpass filtered daily time series of 50- and 500-hPa geopotential heights. Generally stratospheric circulation is determined by ultralong tropospheric planetary waves. During winter seasons characterized either by any anomalously strong or weak polar winter vortex different propagation characteristics for waves of ZWN 1 are observed. The non-linear perspective of the results have implications for medium range weather forecast and climate sensitivity experiments.

Perlwitz, Judith↗
NASA NTRS2012

Community Coordinated Modeling Center: Addressing Needs of Operational Space Weather Forecasting

Models are key elements of space weather forecasting. The Community Coordinated Modeling Center (CCMC, http://ccmc.gsfc.nasa.gov) hosts a broad range of state-of-the-art space weather models and enables access to complex models through an unmatched automated web-based runs-on-request system. Model output comparisons with observational data carried out by a large number of CCMC users open an unprecedented mechanism for extensive model testing and broad community feedback on model performance. The CCMC also evaluates model's prediction ability as an unbiased broker and supports operational model selections. The CCMC is organizing and leading a series of community-wide projects aiming to evaluate the current state of space weather modeling, to address challenges of model-data comparisons, and to define metrics for various user s needs and requirements. Many of CCMC models are continuously running in real-time. Over the years the CCMC acquired the unique experience in developing and maintaining real-time systems. CCMC staff expertise and trusted relations with model owners enable to keep up to date with rapid advances in model development. The information gleaned from the real-time calculations is tailored to specific mission needs. Model forecasts combined with data streams from NASA and other missions are integrated into an innovative configurable data analysis and dissemination system (http://iswa.gsfc.nasa.gov) that is accessible world-wide. The talk will review the latest progress and discuss opportunities for addressing operational space weather needs in innovative and collaborative ways.

Kuznetsova, M.↗