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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

State-of-the-Art Techniques for Large-Scale Stochastic Unit Commitment

Recent advances in deterministic unit commitment, both formulaic and algorithmic, along with modern algorithmic approaches for stochastic programming, have enabled the solution of stochastic unit commitment problems with hundreds of scenarios on large-scale transmission networks. In this presentation, we will give an overview of these methods, including lazy transmission constraint generation, lower-bounding techniques, and heuristics, all of which can be executed in concert with customized decomposition approaches for optimization under uncertainty. We demonstrate the effectiveness of these techniques on the TAMU Texas7K synthetic transmission network, leveraging realistic high-resolution forecasts based on NREL renewable resource availability data. The software leveraged for these demonstrations is available via the open-source software packages EGRET (for electrical grid optimization) and mpi-sppy (for optimization under uncertainty).

27 ARPA - Advanced Research Projects Agency-Energy↗
NASA NTRS2012

An OSSE on Mesoscale Model Assimilation of Simulated HIRAD-Observed Hurricane Surface Winds

The hazards of landfalling hurricanes are well known, but progress on improving the intensity forecasts of these deadly storms at landfall has been slow. Many cite a lack of high-resolution data sets taken inside the core of a hurricane, and the lack of reliable measurements in extreme conditions near the surface of hurricanes, as possible reasons why even the most state-of-the-art forecasting models cannot seem to forecast intensity changes better. The Hurricane Imaging Radiometer (HIRAD) is a new airborne microwave remote sensor for observing hurricanes, and is operated and researched by NASA Marshall Space Flight Center in partnership with the NOAA Atlantic Oceanographic and Meteorological Laboratory/Hurricane Research Division, the University of Central Florida, the University of Michigan, and the University of Alabama in Huntsville. This instrument?s purpose is to study the wind field of a hurricane, specifically observing surface wind speeds and rain rates, in what has traditionally been the most difficult areas for other instruments to study; the high wind and heavy rain regions. Dr. T. N. Krishnamurti has studied various data assimilation techniques for hurricane and monsoon rain rates, and this study builds off of results obtained from utilizing his style of physical initializations of rainfall observations, but obtaining reliable observations in heavy rain regions has always presented trouble to our research of high-resolution rainfall forecasting. Reliable data from these regions at such a high resolution and wide swath as HIRAD provides is potentially very valuable to mesoscale forecasting of hurricane intensity. This study shows how the data assimilation technique of Ensemble Kalman Filtering (EnKF) in the Weather Research and Forecasting (WRF) model can be used to incorporate wind, and later rain rate, data into a mesoscale model forecast of hurricane intensity. The study makes use of an Observing System Simulation Experiment (OSSE) with a simulated HIRAD dataset sampled during a hurricane and uses EnKF to forecast the track and intensity prediction of the hurricane. Comparisons to truth and error metrics are used to assess the model?s forecast performance.

Albers, Cerese↗
DOE OSTI2024

Evaluating downscaled products with expected hydroclimatic co-variances

Abstract. There has been widespread adoption of downscaled products amongst practitioners and stakeholders to ascertain risk from climate hazards at the local scale (e.g., ∼ 5 km resolution). Such products must nevertheless be consistent with physical laws to be credible and of value to users. Here we evaluate statistically and dynamically downscaled products by examining local co-evolution of downscaled temperature and precipitation during convective and frontal precipitation events (two mechanisms testable with just temperature and precipitation). We find that two widely used statistical downscaling techniques (Localized Constructed Analogs version 2, LOCA2, and Seasonal Trends and Analysis of Residuals Empirical Statistical Downscaling Model, STAR-ESDM) generally preserve expected co-variances during convective precipitation events over the historical and future projected intervals as compared to European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) and two observation-based data products (Livneh and nClimGrid-Daily). However, both techniques dampen future intensification of frontal precipitation that is otherwise robustly captured in global climate models (i.e., prior to downscaling) and with process-based dynamical downscaling across five different regional climate models. In the case of LOCA2, this leads to appreciable underestimation of future frontal precipitation event intensity. This study is one of the first to quantify a likely ramification of the stationarity assumption underlying statistical downscaling methods and identify a phenomenon where projections of future change diverge depending on data production method employed. Finally, our work proposes expected co-variances during convective and frontal precipitation as useful evaluation diagnostics that can be universally applied to a wide range of statistically downscaled products.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Comparative Validation of Realtime Solar Wind Forecasting Using the UCSD Heliospheric Tomography Model

The University of California, San Diego 3D Heliospheric Tomography Model reconstructs the evolution of heliospheric structures, and can make forecasts of solar wind density and velocity up to 72 hours in the future. The latest model version, installed and running in realtime at the Community Coordinated Modeling Center(CCMC), analyzes scintillations of meter wavelength radio point sources recorded by the Solar-Terrestrial Environment Laboratory(STELab) together with realtime measurements of solar wind speed and density recorded by the Advanced Composition Explorer(ACE) Solar Wind Electron Proton Alpha Monitor(SWEPAM).The solution is reconstructed using tomographic techniques and a simple kinematic wind model. Since installation, the CCMC has been recording the model forecasts and comparing them with ACE measurements, and with forecasts made using other heliospheric models hosted by the CCMC. We report the preliminary results of this validation work and comparison with alternative models.

MacNeice, Peter↗
NASA NTRS2009

SPoRT - An End-to-End R2O Activity

Established in 2002 to demonstrate the weather and forecasting application of real-time EOS measurements, the Short-term Prediction Research and Transition (SPoRT) program has grown to be an end-to-end research to operations activity focused on the use of advanced NASA modeling and data assimilation approaches, nowcasting techniques, and unique high-resolution multispectral observational data applications from EOS satellites to improve short-term weather forecasts on a regional and local scale. SPoRT currently partners with several universities and other government agencies for access to real-time data and products, and works collaboratively with them and operational end users at 13 WFOs to develop and test the new products and capabilities in a "test-bed" mode. The test-bed simulates key aspects of the operational environment without putting constraints on the forecaster workload. Products and capabilities which show utility in the test-bed environment are then transitioned experimentally into the operational environment for further evaluation and assessment. SPoRT focuses on a suite of data and products from MODIS, AMSR-E, and AIRS on the NASA Terra and Aqua satellites, and total lightning measurements from ground-based networks. Some of the observations are assimilated into or used with various versions of the WRF model to provide supplemental forecast guidance to operational end users. SPoRT is enhancing partnerships with NOAA / NESDIS for new product development and data access to exploit the remote sensing capabilities of instruments on the NPOESS satellites to address short term weather forecasting problems. The VIIRS and CrIS instruments on the NPP and follow-on NPOESS satellites provide similar observing capabilities to the MODIS and AIRS instruments on Terra and Aqua. SPoRT will be transitioning existing and new capabilities into the AWIIPS II environment to continue the continuity of its activities.

Jedlovec, Gary J.↗
NASA NTRS1999

Real-Time Assimilation of Goes-Derived Products into A Mesoscale Model and It's Impact on Short-Term (06-36hr) Forecasts from 17 October 1998 through the Present

As the parameterizations of surface energy budgets in regional models have become more complete physically, models have the potential to be much more realistic in simulations of coupling between surface radiation, hydrology, and surface energy transfer. Realizing the importance of properly specifying the surface energy budget, many institutions are using land-surface models to represent the lower boundary forcing associated with biophysical processes and soil hydrology. However, the added degrees of freedom due to inclusion of such land-surface schemes require the specification of additional parameters within the model system such as vegetative resistances, green vegetation fraction, leaf area index, soil physical and hydraulic characteristics, stream flow, runoff, and the vertical distribution of soil moisture. Spatial heterogeneity of these parameters makes correct specification problematic since measurements are not routinely available. A technique has been developed for assimilating GOES-IR skin temperature tendencies, solar insolation, and surface albedo into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. The technique has been successfully employed in a number of mesoscale models in case-study mode. We have taken the next step and developed a study to determine if assimilating these types of data into mesoscale models in real-time can improve short-term (648h) forecasts of temperature, relative humidity, and QPF on a daily basis over relatively large regions. Therefore, an operational modeling/assimilation system has been developed at the GHCC during the past summer that allows us to produce simulations out to 48 hours in a timely manor. The PSU/NCAR MM5 is used in a nested configuration with a 25 km grid covering the southeastern third of the US. The model has been on-line since 1 July 1998 and forecast products are posted on our web site. The satellite algorithms that generate data to be assimilated came on-line 17 October 1998. Quantitative assessment of the forecast quality is performed via traditional verification statistics. In addition, invaluable qualitative information is obtained through close collaboration with several NWSFO's who are using the MM5 products in real-time on a daily basis. The assimilation technique has been applied in an off-line mode since 17 October. Results based on bulk statistical verification of surface meteorology over the entire Southeastern US show that assimilating the GOES-derived land surface tendencies and solar radiation results in a significant reduction of the shelter air temperature and RH bias on a daily basis. In fact, the assimilation technique has produced improved temperature and RH forecasts for 97% of the 100 simulations performed to date. Work is currently underway to determine the sensitivity of the assimilation procedure to the availability of satellite data, length of assimilation period, model initialization, and synoptic-scale meteorological conditions. In addition, results from a detailed energy budget analysis using the Early Eta, our operational MM5, and the assimilation runs will help us to better understand the satellite assimilation the land-surface energy budge. Research during the spring-summer of 1999 will focus on the impact of the assimilation technique during the warm season where it is hypothesized that it can have a positive impact on QPF during conditions of weak synoptic-scale forcing.

Lapenta, William M.↗
DOE OSTI2020

Non-autoregressive time-series methods for stable parametric reduced-order models

Advection-dominated dynamical systems, characterized by partial differential equations, are found in applications ranging from weather forecasting to engineering design where accuracy and robustness are crucial. There has been significant interest in the use of techniques borrowed from machine learning to reduce the computational expense and/or improve the accuracy of predictions for these systems. These rely on the identification of a basis that reduces the dimensionality of the problem and the subsequent use of time series and sequential learning methods to forecast the evolution of the reduced state. Often, however, machine-learned predictions after reduced-basis projection are plagued by issues of stability stemming from incomplete capture of multiscale processes as well as due to error growth for long forecast durations. To address these issues, we have developed a non-autoregressive time series approach for predicting linear reduced-basis time histories of forward models. In particular, we demonstrate that non-autoregressive counterparts of sequential learning methods such as long short-term memory (LSTM) considerably improve the stability of machine-learned reduced-order models. Further, we evaluate our approach on the inviscid shallow water equations and show that a non-autoregressive variant of the standard LSTM approach that is bidirectional in the principal component directions obtains the best accuracy for recreating the nonlinear dynamics of partial observations. Moreover-and critical for many applications of these surrogates-inference times are reduced by three orders of magnitude using our approach, compared with both the equation-based Galerkin projection method and the standard LSTM approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Local-thermal-gradient and large-scale-circulation impacts on turbine-height wind speed forecasting over the Columbia River Basin

Abstract. We investigate the sensitivity of turbine-height wind speed forecast to initial condition (IC) uncertainties over the Columbia River Gorge (CRG) and Columbia River Basin (CRB) for two typical weather phenomena, i.e., local-thermal-gradient-induced marine air intrusion and a cold frontal passage. Four types of turbine-height wind forecast anomalies and their associated IC uncertainties related to local thermal gradients and large-scale circulations are identified using the self-organizing map (SOM) technique. The four SOM types are categorized into two patterns, each accounting for half of the ensemble members. The first pattern corresponds to IC uncertainties that alter the wind forecast through a modulating weather system, which produces the strongest wind anomalies in the CRG and CRB. In the second pattern, the moderate uncertainties in local thermal gradient and large-scale circulation jointly contribute to wind forecast anomaly. We analyze the cross section of wind and temperature anomalies through the gorge to explore the evolution of vertical features of each SOM type. The turbine-height wind anomalies induced by large-scale IC uncertainties are more concentrated near the front. In contrast, turbine-height wind anomalies induced by the local IC thermal uncertainties are found above the surface thermal anomalies. Moreover, the wind forecast accuracy in the CRG and CRB is limited by IC uncertainties in a few specific regions, e.g., the 2 m temperature within the basin and large-scale circulation over the northeast Pacific around 140∘ W.

17 WIND ENERGY↗
DOE OSTI2020

PyDDA: A Pythonic Direct Data Assimilation Framework for Wind Retrievals

This software assimilates data from an arbitrary number of weather radars together with other spatial wind fields (eg numerical weather forecasting model data) in order to retrieve high resolution three dimensional wind fields. PyDDA uses NumPy and SciPy’s optimization techniques combined with the Python Atmospheric Radiation Measurement (ARM) Radar Toolkit (Py-ART) in order to create wind fields using the 3D variational technique (3DVAR). PyDDA is hosted and distributed on GitHub at https://github.com/openradar/PyDDA. PyDDA has the potential to be used by the atmospheric science community to develop high resolution wind retrievals from radar networks. These retrievals can be used for the evaluation of numerical weather forecasting models and plume modelling. This paper shows how wind fields from 2 NEXt generation RADar (NEXRAD) WSR-88D radars and the High Resolution Rapid Refresh can be assimilated together using PyDDA to create a high resolution wind field inside Hurricane Florence.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

Cloud Statistics for NASA Climate Change Studies

The Principal Investigator participated in two field experiments and developed a global data set on cirrus cloud frequency and optical depth to aid the development of numerical models of climate. Four papers were published under this grant. The accomplishments are summarized: (1) In SUCCESS (SUbsonic aircraft: Contrail & Cloud Effects Special Study) the Principal Investigator aided weather forecasters in the start of the field program. A paper also was published on the clouds studied in SUCCESS and the use of the satellite stereographic technique to distinguish cloud forms and heights of clouds. (2) In SHEBA (Surface Heat Budget in the Arctic) FIRE/ACE (Arctic Cloud Experiment) the Principal Investigator provided daily weather and cloud forecasts for four research aircraft crews, NASA's ER-2, UCAR's C-130, University of Washington's Convert 580, and the Canadian Atmospheric Environment Service's Convert 580. Approximately 105 forecasts were written. The Principal Investigator also made daily weather summaries with calculations of air trajectories for 54 flight days in the experiment. The trajectories show where the air sampled during the flights came from and will be used in future publications to discuss the origin and history of the air and clouds sampled by the aircraft. A paper discussing how well the FIRE/ACE data represent normal climatic conditions in the arctic is being prepared. (3) The Principal Investigator's web page became the source of information for weather forecasting by the scientists on the SHEBA ship. (4) Global Cirrus frequency and optical depth is a continuing analysis of global cloud cover and frequency distribution are being made from the NOAA polar orbiting weather satellites. This analysis is sensitive to cirrus clouds because of the radiative channels used. During this grant three papers were published which describe cloud frequencies, their optical properties and compare the Wisconsin FM Cloud Analysis to other global cloud data such as the International Satellite Cloud Climatology Program (ISCCP) and the Stratospheric Aerosol and Gas Experiment (SAGE). A summary of eight years of HIRS data will be published in late 1998. Important information from this study are: 1) cirrus clouds cover most of the earth, 2) they are found about 40% of the time globally, 3) in the tropics cirrus cloud frequencies are even higher, from 80-100%, 4) there is slight evidence that cirnis cloud cover is increasing in the northern hemisphere at about 0.5% per year, and 5) cirrus clouds have an average infrared transmittance of about 40% of the terrestrial radiation. (5) Global Cloud Frequency Statistics published on the Principal Investigator's web page have been used in the planning of the future CRYSTAL experiment and have been used for refinements of a global numerical model operated at the Colorado State University.

Wylie, Donald P.↗
DOE OSTI2025

Peeking into the next decade in large-scale structure cosmology with its Effective Field Theory

After the successful full-shape analyses of BOSS data using the Effective Field Theory of Large-Scale Structure, we investigate what upcoming galaxy surveys might achieve. We introduce a “perturbativity prior” that ensures that loop terms are as large as theoretically expected, which is effective in the case of a large number of EFT parameters. After validating our technique by comparison with already-performed analyses of BOSS data, we provide Fisher forecasts using the one-loop prediction for power spectrum and bispectrum for two benchmark surveys: DESI and MegaMapper. We find overall great improvements on the cosmological parameters. In particular, we find that MegaMapper (DESI) should obtain at least a 12σ (2σ) evidence for non-vanishing neutrino masses, bound the curvature Ω k to 0.0012 (0.012), and primordial inflationary non-Gaussianities as follows: f NL loc. to ± 0.26 (3.3), f NL eq. to ±16 (92), f NL orth. to ± 4.2 (27). Such measurements would provide much insight on the theory of Inflation. We investigate the limiting factor of shot noise and ignorance of the EFT parameters.

cosmological parameters from LSS↗
DOE OSTI2020

Technical note: Deep learning for creating surrogate models of precipitation in Earth system models

Abstract. We investigate techniques for using deep neural networks to produce surrogatemodels for short-term climate forecasts. A convolutional neural network istrained on 97 years of monthly precipitation output from the 1pctCO2 run (theCO 2 concentration increases by 1 % per year) simulated by the second-generation Canadian Earth System Model (CanESM2). The neural network clearly outperforms a persistence forecast anddoes not show substantially degraded performance even when the forecast lengthis extended to 120 months. The model is prone to underpredicting precipitationin areas characterized by intense precipitation events. Scheduled sampling(forcing the model to gradually use its own past predictions rather than groundtruth) is essential for avoiding amplification of early forecasting errors.However, the use of scheduled sampling also necessitates preforecasting(generating forecasts prior to the first forecast date) to obtain adequateperformance for the first few prediction time steps. We document the trainingprocedures and hyperparameter optimization process for researchers who wish toextend the use of neural networks in developing surrogate models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

'Emerging technologies for the changing global market' - Prioritization methodology for chemical replacement

This project served to define an appropriate methodology for effective prioritization of technology efforts required to develop replacement technologies mandated by imposed and forecast legislation. The methodology used is a semiquantitative approach derived from quality function deployment techniques (QFD Matrix). This methodology aims to weight the full environmental, cost, safety, reliability, and programmatic implications of replacement technology development to allow appropriate identification of viable candidates and programmatic alternatives. The results will be implemented as a guideline for consideration for current NASA propulsion systems.

Cruit, Wendy↗
NASA NTRS1993

Prioritization Methodology for Chemical Replacement

This project serves to define an appropriate methodology for effective prioritization of efforts required to develop replacement technologies mandated by imposed and forecast legislation. The methodology used is a semiquantitative approach derived from quality function deployment techniques (QFD Matrix). This methodology aims to weigh the full environmental, cost, safety, reliability, and programmatic implications of replacement technology development to allow appropriate identification of viable candidates and programmatic alternatives. The results are being implemented as a guideline for consideration for current NASA propulsion systems.

Cruit, W.↗
NASA NTRS1995

Technical report series on global modeling and data assimilation. Volume 4: Documentation of the Goddard Earth Observing System (GEOS) data assimilation system, version 1

This report describes the analysis component of the Goddard Earth Observing System, Data Assimilation System, Version 1 (GEOS-1 DAS). The general features of the data assimilation system are outlined, followed by a thorough description of the statistical interpolation algorithm, including specification of error covariances and quality control of observations. We conclude with a discussion of the current status of development of the GEOS data assimilation system. The main components of GEOS-1 DAS are an atmospheric general circulation model and an Optimal Interpolation algorithm. The system is cycled using the Incremental Analysis Update (IAU) technique in which analysis increments are introduced as time independent forcing terms in a forecast model integration. The system is capable of producing dynamically balanced states without the explicit use of initialization, as well as a time-continuous representation of non- observables such as precipitation and radiational fluxes. This version of the data assimilation system was used in the five-year reanalysis project completed in April 1994 by Goddard's Data Assimilation Office (DAO) Data from this reanalysis are available from the Goddard Distributed Active Center (DAAC), which is part of NASA's Earth Observing System Data and Information System (EOSDIS). For information on how to obtain these data sets, contact the Goddard DAAC at (301) 286-3209, EMAIL daac@gsfc.nasa.gov.

Suarez, Max J.↗
NASA NTRS1983

Use of a personal computer for dynamical engineering illustrations in a classroom and over an instructional TV network

A personal computer has been used to illustrate physical phenomena and problem solution techniques in engineering classes. According to student evaluations, instruction of concepts was greatly improved through the use of these illustrations. This paper describes the class of phenomena that can be effectively illustrated, the techniques used to create these illustrations, and the techniques used to display the illustrations in regular classrooms and over an instructional TV network. The features of a personal computer required to apply these techniques are listed. The capabilities of some present personal computers are discussed and a forecast of the capabilities of future personal computers is presented.

Watson, V. R.↗
NASA NTRS2001

Inter-Comparison of GOES-8 Imager and Sounder Skin Temperature Retrievals

Skin temperature (ST) retrievals derived from geostationary satellite observations have both high temporal and spatial resolutions and are therefore useful for applications such as assimilation into mesoscale forecast models, nowcasting, and diagnostic studies. Our retrieval method uses a Physical Split Window technique requiring at least two channels within the longwave infrared window. On current GOES satellites, including GOES-11, there are two Imager channels within the required spectral interval. However, beginning with the GOES-M satellite the 12-um channel will be removed, leaving only one longwave channel. The Sounder instrument will continue to have three channels within the longwave window, and therefore ST retrievals will be derived from Sounder measurements. This research compares retrievals from the two instruments and evaluates the effects of the spatial resolution and sensor calibration differences on the retrievals. Both Imager and Sounder retrievals are compared to ground-truth data to evaluate the overall accuracy of the technique. An analysis of GOES-8 and GOES-11 intercomparisons is also presented.

Haines, Stephanie L.↗