Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Forecasting techniques”

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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Neutrino Properties with Ground-based Millimeter-wavelength Line Intensity Mapping

Line intensity mapping (LIM) is emerging as a powerful technique to map the cosmic large-scale structure and to probe cosmology over a wide range of redshifts and spatial scales. We perform Fisher forecasts to determine the optimal design of wide-field ground-based millimeter-wavelength LIM surveys for constraining properties of neutrinos and light relics. We consider measuring the auto-power spectra of several CO rotational lines (from J = 2–1 to J = 6–5) and the [C II ] fine-structure line in the redshift range of 0.25 < z < 12. We study the constraints with and without interloper lines as a source of noise in our analysis, and for several one-parameter and multiparameter extensions of ΛCDM. We show that LIM surveys deployable this decade, in combination with existing cosmic microwave background (CMB; primary) data, could achieve order-of-magnitude improvements over Planck constraints on N eff and M ν . Compared to next-generation CMB and galaxy surveys, a LIM experiment of this scale could achieve bounds that are a factor of ~3 better than those forecasted for surveys such as EUCLID (galaxy clustering), and potentially exceed the constraining power of CMB-S4 by a factor of ~1.5 and ~3 for N eff and M ν , respectively. Finally, we show that the forecasted constraints are not substantially affected when enlarging the parameter space, and additionally demonstrate that such a survey could also be used to measure ΛCDM parameters and the dark energy equation of state exquisitely well.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1981

The seasonal-cycle climate model

The seasonal cycle run which will become the control run for the comparison with runs utilizing codes and parameterizations developed by outside investigators is discussed. The climate model currently exists in two parallel versions: one running on the Amdahl and the other running on the CYBER 203. These two versions are as nearly identical as machine capability and the requirement for high speed performance will allow. Developmental changes are made on the Amdahl/CMS version for ease of testing and rapidity of turnaround. The changes are subsequently incorporated into the CYBER 203 version using vectorization techniques where speed improvement can be realized. The 400 day seasonal cycle run serves as a control run for both medium and long range climate forecasts alsensitivity studies.

Marx, L.↗
NASA NTRS1991

Fitting dynamic models to the Geosat sea level observations in the tropical Pacific Ocean. I - A free wave model

Free, equatorially trapped sinusoidal wave solutions to a linear model on an equatorial beta plane are used to fit the Geosat altimetric sea level observations in the tropical Pacific Ocean. The Kalman filter technique is used to estimate the wave amplitude and phase from the data. The estimation is performed at each time step by combining the model forecast with the observation in an optimal fashion utilizing the respective error covariances. The model error covariance is determined such that the performance of the model forecast is optimized. It is found that the dominant observed features can be described qualitatively by basin-scale Kelvin waves and the first meridional-mode Rossby waves. Quantitatively, however, only 23 percent of the signal variance can be accounted for by this simple model.

Fu, Lee-Lueng↗
NASA NTRS2018

Climate Reanalysis: Progress and Future Prospects

Reanalysis is the process whereby an unchanging data assimilation system is used to provide a consistent reprocessing of observations, typically spanning an extended segment of the historical data record. The process relies on an underlying model to combine often-disparate observations in a physically consistent manner, enabling production of gridded data sets for a broad range of applications including the study of historical weather events, preparation of climatologies, business sector development and, more recently, climate monitoring. Over the last few decades, several generations of reanalyses of the global atmosphere have been produced by various operational and research centers, focusing more or less on the period of regular conventional and satellite observations beginning in the mid to late twentieth century. There have also been successful efforts to extend atmospheric reanalyses back to the late nineteenth and early twentieth centuries, using mostly surface observations. Much progress has resulted from (and contributed to) advancements in numerical weather prediction, especially improved models and data assimilation techniques, increased computing capacity, the availability of new observation types and efforts to recover and improve the quality of historical ones. The recent extension of forecast systems that allow integrated modeling of meteorological, oceanic, land surface, and chemical variables provide the basic elements for coupled data assimilation. This has opened the door to the development of a new generation of coupled reanalyses of the Earth system, or integrated Earth system analyses (IESA). Evidence so far suggests that this approach can improve the analysis of currently uncoupled components of the Earth system, especially at their interface, and lead to increased predictability. However, extensive analysis coupling as envisioned for IESA, while progressing, still presents significant challenges. These include model biases that can be exacerbated when coupled, component systems with different physical characteristics and different spatial and temporal scales, and component observations in different media with different spatial and temporal frequencies and different latencies. Quantification of uncertainty in reanalyses is also a critical challenge and is important for expanding their utility as a tool for climate change assessment. This talk provides a brief overview of the progress of reanalysis development during recent decades, and describes remaining challenges in the progression toward coupled Earth system reanalyses.

IES↗
NASA NTRS2020

Ensemble PM2.5 Forecasting during the 2018 Camp Fire Event Using the HYSPLIT Transport and Dispersion Model

Biomass burning releases a vast amount of aerosols into the atmosphere, often leading to severe air quality and health problems. Prediction of the air quality effects from biomass burning emissions is challenging due to uncertainties in fire emission, plume rise calculation, and other model inputs/processes. Ensemble forecasting is increasingly used to represent model uncertainties. In this paper, an ensemble forecast was conducted to predict surface PM2.5 during the 2018 California Camp Fire event using the NOAA HYSPLIT dispersion model at 0.1- degree horizontal resolution. Different combinations of four satellite-based fire emission datasets (FEER, FLAMBE, GBBEPx and GFAS), two plume rise schemes (Briggs and Sofiev), various meteorology inputs and model setup options were used to create the forecast ensemble, for a total of 112 experiments. The performance of each ensemble member and the ensemble mean were evaluated using ground-based observations, with four statistical metrics and an overall rank. The ensemble spread of the 112 members reached 1000 μg/m3, highlighting the large uncertainty in wildfire forecast. The ensemble mean displayed the best performance. Each fire emission product contributed to one or more members among the top ten performers, revealing the forecasting dependence on both the quality of fire emissions data and model representation of emission, transport and removal processes. In addition, an ensemble size reduction technique was introduced. With the help of this technique, the ensemble size was reduced from 112 to 28 members and still produced an ensemble mean that yielded comparable or even better performance to that of the full ensemble.

Biomass Burning↗
NASA NTRSDate not supplied

GMAO OSSE Framework in Support of PBL Mission Science

The planetary boundary layer is the bottom layer of the troposphere where most of human activities take place. Atmospheric pollutants are capped by the temperature inversion layer in PBL. The thickness of PBL, ranging from tens of meter to several kilometer, affects pollutant dispersion and air quality. The thickness of PBL typically shows a strong diurnal cycle but it is difficult to model and predict its change because complicated dynamic and thermodynamic processes involved with air-surface exchange of temperature and moisture, convective mixing, surface friction, topography, advection and radiation heating and cooling influence the PBL height. Various parameterization schemes for PBL were developed but the underlying processes of PBL are neither clearly understood nor represented in NWP models and adding large uncertainty into weather and climate predictions. There are no systematic global observations to provide information on thermodynamic structure of PBL and efforts to explore new spaceborne instruments and measurement techniques are growing. This study aims to develop a PBL OSSE framework leveraging existing GMAO’s OSSE system that utilizes GEOS data assimilation and global forecast model (1) to evaluate existing and potential new observation types for the PBL structure analysis and prediction and (2) to test sensitivity of PBL parameterization schemes to PBL forecasts.

Min-Jeong Kim↗
NASA NTRS2002

Visualizing 2D Probability Distributions from Satellite Image-Derived Data

Creating maps of biophysical and geophysical variables using Earth Observing System (EOS) satellite image data is an important component of Earth science. These 2D maps have a single value at every location and standard techniques are used to visualize them. Current tools fall short, however, when it is necessary to describe a distribution of values at each location. Distributions may represent a frequency of occurrence over time, frequency of occurrence from multiple runs of an ensemble forecast or possible values from an uncertainty model. 'Distribution data sets' are described, then a case study is presented to visualize such 2D distributions. Distribution data sets are different from multivariate data sets in the sense that the values are for a single variable instead of multiple variables. Our case study data consists of multiple realizations of percent forest cover, generated using a geostatistical technique that combines ground measurements and satellite imagery to model uncertainty about forest cover. We present several approaches for analyzing and visualizing such data sets. The first is a pixel-wise analysis of the probability density functions for the 2D image while the second is an analysis of features identified within the image. Such pixel-wise and feature-wise views will give Earth scientists a more complete understanding of distribution data sets.

Kao, David↗
NASA NTRS2003

Application of Land Surface Data Assimilation to Simulations of Sea Breeze Circulations

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite- observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The sea/land breeze is a well-documented mesoscale circulation that affects many coastal areas of the world including the northern Gulf Coast of the United States. The focus of this paper is to examine how the satellite assimilation technique impacts the simulation of a sea breeze circulation observed along the Mississippi/Alabama coast in the spring of 2001. The technique is implemented within the PSUNCAR MM5 V3-5 and applied at spatial resolutions of 12- and 4-km. It is recognized that even 4-km grid spacing is too coarse to explicitly resolve the detailed, mesoscale structure of sea breezes. Nevertheless, the model can forecast certain characteristics of the observed sea breeze including a thermally direct circulation that results from differential low-level heating across the land-sea interface. Our intent is to determine the sensitivity of the circulation to the differential land surface forcing produced via the assimilation of GOES skin temperature tendencies. Results will be quantified through statistical verification techniques.

Mackaro, Scott↗
DOE OSTI2023

Multi-decadal variation of ENSO forecast skill since the late 1800s

Diagnosing El Niño-Southern Oscillation (ENSO) predictability within operational forecast models is hindered by computational expense and the need for initialization with three-dimensional fields generated by global data assimilation. We instead examine multi-year ENSO predictability since the late 1800s using the model-analog technique, which has neither limitation. We first draw global coupled model states from pre-industrial control simulations, from the Coupled Model Intercomparison Project Phase 6, that are chosen to initially match observed monthly sea surface temperature and height anomalies in the Tropics. Their subsequent 36-month model evolution are the hindcasts, whose 20th century ENSO skill is comparable to twice-yearly hindcasts generated by a state-of-the-art European operational forecasting system. Despite the so-called spring predictability barrier, present throughout the record, there is substantial second-year ENSO skill, especially after 1960. Overall, ENSO exhibited notably high values of both amplitude and skill towards the end of the 19th century, and again in recent decades.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2003

Assimilation of Precipitation Information Retrieved from Passive Microwave Sensors

Estimates of surface precipitation and the associated vertical latent heating structure can be retrieved from space-borne passive microwave sensors such as the TRMM Microwave Imager (TMI) and Special Sensor Microwave/Imager (SSM/I). Studies have shown that assimilation of microwave rain rates can improve the quality of global analysis and forecast. Numerical weather prediction (NWP) centers are beginning to make operational use of these data. In the next few years, there will be a gradual increase in microwave rain products available from operational and research satellites, culminating to a target constellation of 9 satellites to provide global rain measurements every 3 hours with the proposed Global Precipitation Measurement (GPM) mission to be launched around 2007. Realizing the full potential of these observations in data assimilation will require continued advances in retrieval algorithms, assimilation techniques, and model physics. I present results from two research efforts at NASA GSFC. The first is on the assimilation of tropical precipitation using temperature/moisture tendency corrections within a continuous variational assimilation framework to improve analyses and forecasts produced by the Goddard Earth Observing System (GEOS) global data assimilation system. Results show that assimilation of TMI and SSM/I rain rates improves not only precipitation and moisture, but also the related clouds, radiation energy fluxes, and large-scale circulations in GEOS analyses. The improved analyses also yield better short-range forecasts, quantitative precipitation forecast (QPF) threat scores, and storm track predictions. The second part consists of results from exploratory experiments on variational assimilation of convective and stratiform latent heating profiles within the general framework of model parameter estimation as a way to identify systematic model errors and optimize physical paremeterization schemes in global forecast and climate models.

Hou, Arthur Y.↗
DOE OSTI2021

Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks

Integrating photovoltaics (PV) into electricity grids is challenged by potentially large fluctuations in power generation. In recent years, sky image-based PV output prediction using convolutional neural networks (CNNs) has emerged as a promising approach to forecasting fluctuations. A key challenge is imbalanced sky image datasets: because of the geography of solar PV system installations, sky image datasets are often rich in sunny condition data but deficient in cloudy condition data. This imbalance contrasts with the fact that model errors are dominated by cloudy condition performance. In this study, we attempt to remedy this by exploring the enrichment and augmentation of an imbalanced sky images dataset for two PV output prediction tasks: nowcasting (predicting concurrent PV output) and forecasting (predicting 15-minute-ahead future PV output). We empirically examine the efficacy of using different resampling and data augmentation approaches to create a rebalanced dataset for model development. A three-stage greedy search is used to determine the optimal resampling approach, data augmentation techniques and over-sampling rate. The results show that for the nowcast problem, resampling and data augmentation can effectively enhance the model performance, reducing overall root mean squared error (RMSE) by an average of 4%, or a 15 std. (standard deviation) of improvement compared to the variability of the baseline model. In contrast, the treatment RMSE for the forecast problem nearly always overlaps the baseline performance at the ± 2 std. level. The optimal resampling approach expands on the original dataset by over-sampling the minority cloudy data, with the best results from large over-sampling rate (e.g., 4 ~ 6 times over-sampling of cloudy images).

14 SOLAR ENERGY↗
NASA NTRS2002

Land Surface Data Assimilation and the Northern Gulf Coast Land/Sea Breeze

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The sea/land breeze is a well-documented mesoscale circulation that affects many coastal areas of the world including the northern Gulf Coast of the United States. The focus of this paper is to examine how the satellite assimilation technique impacts the simulation of a sea breeze circulation observed along the Mississippi/Alabama coast in the spring of 2001. The technique is implemented within the PSU/NCAR MM5 V3-4 and applied on a 4-km domain for this particular application. It is recognized that a 4-km grid spacing is too coarse to explicitly resolve the detailed, mesoscale structure of sea breezes. Nevertheless, the model can forecast certain characteristics of the observed sea breeze including a thermally direct circulation that results from differential low-level heating across the land-sea interface. Our intent is to determine the sensitivity of the circulation to the differential land surface forcing produced via the assimilation of GOES skin temperature tendencies. Results will be quantified through statistical verification techniques.

Lapenta, William M.↗
NASA NTRS1981

Forecasting of Clear Air Turbulence using a Diagnostic Richardson Number Tendency formulation

The results of several case studies of Clear Air Turbulence (CAT) using the Diagnostic Richardson Number Tendency (DRT) formulation are highlighted. The performance of this technique in resolving regions of documented CAT encounters is encouraging. Its operational adaptability seems particularly attractive in that the input data can be supplied by the currently operational Rawinsonde system. A CAT index is calculated deterministically, sensing synoptic-scale changes in static stability and vertical wind shear conducive for supporting meso-scale CAT layers. This index reveals volumes of the troposphere which act as 'source regions' of Kelvin-Helmholtz instabilities. The results suggest that these regions are particularly efficient with respect to the synoptic/meso-scale energetical coupling necessary for supporting significantly turbulent layers. The output highlights specific regions of the atmosphere which can be interpreted operationally in terms of CAT-encounter probabilities.

Keller, J. L.↗
NASA NTRS1987

Forecast of an exceptionally large even-numbered solar cycle

Using the 'dynamo theory' method to predict solar activity, an accurate prediction was made for solar cycle 21 by Schatten et al. (1978). Using the same dynamo technique for solar cycle 22, a value for the smoothed sunspot number of 170 + or - 25 is obtained. This large sunspot number is expected to peak in 1990 + or - 1 year. The F(10.7) radio flux is expected to reach a smoothed value of 210 + or - 25 flux units. Since this value is larger than values obtained with prediction schemes based upon 'statistical' and 'periodicity' methods, it provides a useful test for the current methodology, based upon the strength of the sun's polar field near solar minimum. The predicted degree of solar activity is expected to enhance the density and temperature of the earth's thermosphere to values somewhat larger than those found in solar cycle 21. This will have an impact on the orbital lifetime of low altitude satellites.

Schatten, Kenneth H.↗
DOE OSTI2025

Examples of Mission-driven Data Science from Jefferson Lab and ACES

This presentation details mission-driven data science initiatives at Jefferson Lab and the Joint Institute for Advanced Computing on Environmental Studies (ACES). JLab, a U.S. Department of Energy Office of Science national laboratory, operates the Continuous Electron Beam Accelerator Facility (CEBAF), and is the lead institute for the new High Performance Data Facility (HPDF) Hub. The Joint Institute for ACES brings together interdisciplinary teams in health informatics, climate modeling, computer science, and physics to address environmental challenges, including flood modeling. The Hampton Roads region, particularly Norfolk and Virginia Beach, faces increasing flood risks, motivating the need for rapid, reliable, and risk-aware decision support. ACES’s flooding work has a focus on uncertainty quantification (UQ) and machine learning (ML) for coastal flood management. The work is motivated by the increasing vulnerability of communities such as Norfolk and Virginia Beach, Virginia, to frequent coastal flooding events, and the need for rapid, reliable decision support. The research develops computationally efficient ML surrogate models to forecast water levels and flooding risk. A central theme is the quantification and calibration of predictive uncertainty, especially for out-of-distribution (OOD) scenarios, using techniques such as Monte Carlo Dropout, Deep Ensembles, Gaussian Processes, and Deep Quantile Regression (DQR). The study demonstrates that distance-aware UQ is critical for reliable scientific AI, particularly in high-dimensional, safety-critical, and real-time applications.

McSpadden, Diana [Thomas Jefferson National Accele↗
NASA NTRS1980

Operational Applications of Satellite Snowcover Observations

The history of remote sensing of snow cover is reviewed and the following topics are covered: various techniques for interpreting LANDSAT and NOAA satellite data; the status of future systems for continuing snow hydrology applications; the use of snow cover observations in streamflow forecasts by Applications Systems Verification and Transfer participants and selected foreign investigators; and the benefits of using satellite snow cover data in runoff prediction.

Rango, A.↗
NASA NTRS1990

Estimating the cost of major ongoing cost plus hardware development programs

Approaches are developed for forecasting the cost of major hardware development programs while these programs are in the design and development C/D phase. Three approaches are developed: a schedule assessment technique for bottom-line summary cost estimation, a detailed cost estimation approach, and an intermediate cost element analysis procedure. The schedule assessment technique was developed using historical cost/schedule performance data.

Bush, J. C.↗
NASA NTRS2005

Spatial Scaling of Snow Observations and Microwave Emission Modeling During CLPX and Appropriate Satellite Sensor Resolution

Accurate estimates of snow water equivalent and other properties play an important role in weather, natural hazard, and hydrological forecasting and climate modeling over a range of scales in space and time. Remote sensing-derived estimates have traditionally been of the "snapshot" type, but techniques involving models with assimilation are also being explored. In both cases, forward emission models are useful to understand the observed passive microwave signatures and developing retrieval algorithms. However, mismatches between passive microwave sensor resolutions and the scales of processes controlling subpixel heterogeneity can affect the accuracy of the estimates. Improving the spatial resolution of new passive microwave satellite sensors is a major desire in order to (literally) resolve such subpixel heterogeneity, but limited spacecraft and mission resources impose severe constraints and tradeoffs. In order to maximize science return while mitigating risk for a satellite concept, it is essential to understand the scaling behavior of snow in terms of what the sensor sees (brightness temperature) as well as in terms of the actual variability of snow. NASA's Cold Land Processes Experiment-1 (CLPX-1: Colorado, 2002 and 2003) was designed to provide data to measure these scaling behaviors for varying snow conditions in areas with forested, alpine, and meadow/pasture land cover. We will use observations from CLPX-1 ground, airborne, and satellite passive microwave sensors to examine and evaluate the scaling behavior of observed and modeled brightness temperatures and observed and retrieved snow parameters across scales from meters to 10's of kilometers. The conclusions will provide direct examples of the appropriate spatial sampling scales of new sensors for snow remote sensing. The analyses will also illustrate the effects and spatial scales of the underlying phenomena (e.g., land cover) that control subpixel heterogeneity.

Kim, Edward J.↗