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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The Potential Benefits of Handling Mixture Statistics via a Bi-Gaussian EnKF: Tests With All-Sky Satellite Infrared Radiances

The meteorological characteristics of cloudy atmospheric columns can be very different from their clear counterparts. Thus, when a forecast ensemble is uncertain about the presence/absence of clouds at a specific atmospheric column (i.e., some members are clear while others are cloudy), that column's ensemble statistics will contain a mixture of clear and cloudy statistics. Such mixtures are inconsistent with the ensemble data assimilation algorithms currently used in numerical weather prediction. Hence, ensemble data assimilation algorithms that can handle such mixtures can potentially outperform currently used algorithms. In this study, we demonstrate the potential benefits of addressing such mixtures through a bi-Gaussian extension of the ensemble Kalman filter (BGEnKF). The BGEnKF is compared against the commonly used ensemble Kalman filter (EnKF) using perfect model observing system simulated experiments (OSSEs) with a realistic weather model (the Weather Research and Forecast model). Synthetic all-sky infrared radiance observations are assimilated in this study. In these OSSEs, the BGEnKF outperforms the EnKF in terms of the horizontal wind components, temperature, specific humidity, and simulated upper tropospheric water vapor channel infrared brightness temperatures. This study is one of the first to demonstrate the potential of a Gaussian mixture model EnKF with a realistic weather model. Our results thus motivate future research toward improving numerical Earth system predictions though explicitly handling mixture statistics.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1998

Distortion Representation of Forecast Errors for Model Skill Assessment and Objective Analysis

We proposed a novel characterization of errors for numerical weather predictions. A general distortion representation allows for the displacement and amplification or bias correction of forecast anomalies. Characterizing and decomposing forecast error in this way has several important applications, including the model assessment application and the objective analysis application. In this project, we have focused on the assessment application, restricted to a realistic but univariate 2-dimensional situation. Specifically, we study the forecast errors of the sea level pressure (SLP), the 500 hPa geopotential height, and the 315 K potential vorticity fields for forecasts of the short and medium range. The forecasts are generated by the Goddard Earth Observing System (GEOS) data assimilation system with and without ERS-1 scatterometer data. A great deal of novel work has been accomplished under the current contract. In broad terms, we have developed and tested an efficient algorithm for determining distortions. The algorithm and constraints are now ready for application to larger data sets to be used to determine the statistics of the distortion as outlined above, and to be applied in data analysis by using GEOS water vapor imagery to correct short-term forecast errors.

Hoffman, Ross N.↗
NASA NTRS2002

Leveraging Improvements in Precipitation Measuring from GPM Mission to Achieve Prediction Improvements in Climate, Weather and Hydrometeorology

The main scientific goal of the GPM mission, currently planned for start in the 2007 time frame, is to investigate important scientific problems arising within the context of global and regional water cycles. These problems cut across a hierarchy of scales and include climate-water cycle interactions, techniques for improving weather and climate predictions, and better methods for combining observed precipitation with hydrometeorological prediction models for applications to hazardous flood-producing storms, seasonal flood/draught conditions, and fresh water resource assessments. The GPM mission will expand the scope of precipitation measurement through the use of a constellation of some 9 satellites, one of which will be an advanced TRMM-like "core" satellite carrying a dual-frequency Ku-Ka band precipitation radar and an advanced, multifrequency passive microwave radiometer with vertical-horizontal polarization discrimination. The other constellation members will include new dedicated satellites and co-existing Operational/research satellites carrying similar (but not identical) passive microwave radiometers. The goal of the constellation is to achieve approximately 3-hour sampling at any spot on the globe. The constellation's orbit architecture will consist of a mix of sun-synchronous and non-sun-synchronous satellites with the core satellite providing measurements of cloud-precipitation microphysical processes plus calibration-quality rainrate retrievals to be used with the other retrieval information to ensure bias-free constellation coverage. GPM is organized internationally, currently involving a partnership between NASA in the US and the National Space Development Agency in Japan. Additionally, the program is actively pursuing agreements with other international partners and domestic scientific agencies and institutions, as well as participation by individual scientists from academia, government, and the private sector to fulfill mission goals and to pave the way for what ultimately is expected to become an internationally-organized operational global precipitation observing system. Notably, the broad societal applications of GPM are reflected in the United Nation s identification of this mission as a foremost candidate for its Peaceful Uses of Space Program. In this presentation, an overview of the GPM mission design will be presented, followed by an explanation of its scientific agenda as an outgrowth of making improvements in rain retrieval accuracy, microphysics dexterity, sampling frequency, and global coverage. All of these improvements offer new means to observe variability in precipitation and water cycle fluxes and to achieve improved predictability of weather, climate, and hydrometeorology. Specifically, the scientific agenda of GPM has been designed to leverage the measurement improvements to improve prognostic model performance, particularly quantitative precipitation forecasting and its linked phenomena at short, intermediate, and extended time scales. The talk will address how GPM measurements will enable better detection of accelerations and decelerations in regional and global water cycle processes and their relationship to climate variability, better impacts of precipitation data assimilation on numerical weather prediction and global climate reanalysis, and better performance from basin scale hydrometeorological models for short and long term flood-drought forecasting and seasonal fresh water resource assessment. Improved hydrometeorological forecasting will be possible by using continuous global precipitation observations to obtain better closure in water budgets and to generate more realistic forcing of the models themselves to achieve more accurate estimates of interception, infiltration, evaporation/transpiration fluxes, storage, and runoff.

Smith, Eric A.↗
DOE OSTI2018

Forecasting Responses of a Northern Peatland Carbon Cycle to Elevated CO 2 and a Gradient of Experimental Warming

The ability to forecast ecological carbon cycling is imperative to land management in a world where past carbon fluxes are no longer a clear guide in the Anthropocene. However, carbon–flux forecasting has not been practiced routinely like numerical weather prediction. This study explored (1) the relative contributions of model forcing data and parameters to uncertainty in forecasting flux– versus pool–based carbon cycle variables and (2) the time points when temperature and CO 2 treatments may cause statistically detectable differences in those variables. We developed an online forecasting workflow (Ecological Platform for Assimilation of Data (EcoPAD)), which facilitates iterative data–model integration. EcoPAD automates data transfer from sensor networks, data assimilation, and ecological forecasting. We used the Spruce and Peatland Responses Under Changing Experiments data collected from 2011 to 2014 to constrain the parameters in the Terrestrial Ecosystem Model, forecast carbon cycle responses to elevated CO 2 and a gradient of warming from 2015 to 2024, and specify uncertainties in the model output. Our results showed that data assimilation substantially reduces forecasting uncertainties. Interestingly, we found that the stochasticity of future external forcing contributed more to the uncertainty of forecasting future dynamics of C flux–related variables than model parameters. However, the parameter uncertainty primarily contributes to the uncertainty in forecasting C pool–related response variables. Given the uncertainties in forecasting carbon fluxes and pools, our analysis showed that statistically different responses of fast–turnover pools to various CO 2 and warming treatments were observed sooner than slow–turnover pools. In conclusion, our study has identified the sources of uncertainties in model prediction and thus leads to improve ecological carbon cycling forecasts in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods

We describe and release a comprehensive solar irradiance, imaging, and forecasting dataset. Our goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, and Numerical Weather Prediction forecasts. We also include sample codes of baseline models for benchmarking of more elaborated models.

Remote sensing↗
DOE OSTI2023

Constraining chaos: Enforcing dynamical invariants in the training of reservoir computers

Drawing on ergodic theory, we introduce a novel training method for machine learning based forecasting methods for chaotic dynamical systems. The training enforces dynamical invariants—such as the Lyapunov exponent spectrum and the fractal dimension—in the systems of interest, enabling longer and more stable forecasts when operating with limited data. The technique is demonstrated in detail using reservoir computing, a specific kind of recurrent neural network. Finally, results are given for the Lorenz 1996 chaotic dynamical system and a spectral quasi-geostrophic model of the atmosphere, both typical test cases for numerical weather prediction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Diagnostics for Stationary Planetary-scale Waves

The largest waves in Earth’s atmosphere are nearly stationary and thus appear in monthly means of pressure and temperature. But they evolve slowly with time, forming a link between weather and climate. For example, droughts in California are associated with persistent high pressure off the west coast of North America that directs storms poleward; the high pressure areas, in turn, are often linked to climate shifts in the western tropical Pacific (e.g. Trenberth 1988, Molteni et al. 2015, Scaife et al. 2017). With numerical models of weather and climate, a necessary condition for believing predictions of the future is that the models accurately simulate a background unperturbed climatology. Therefore the PCMDI is establishing a standard set of performance metrics for climate models that compares observations with simulations of key space-time fields (Gleckler et al. 2016). This technical report proposes an additional metric involving quasi-stationary, planetary-scale waves in atmospheric geopotential Φ.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

A Deep Learning Approach to Fast Radiative Transfer

Due to the sheer volume of data, leveraging satellite instrument observations effectively in a data assimilation context for numerical weather prediction or for remote sensing requires a radiative transfer model as an observation operator that is both fast and accurate at the same time. Physics-based line-by-line radiative transfer (RT) models fulfil the requirement for accuracy, but are too slow and too costly in computational terms for operational applications. Therefore, fast methods were developed to be able to perform fast RT calculations using techniques such as spectral sampling or pre-computed look-up tables. The operational fast models currently calculate the absorption and scattering coefficients from the pre-computed regression coefficients and atmospheric state and cloud profiles. As a novel solution to this problem, this work investigates a deep learning approach to replace the regression coefficients in the fast RT models. A selection of hidden-layer neural network configurations is trained against atmospheric transmittance profile data computed by an accurate line-by-line model and their performance is evaluated and their advantages and disadvantages are discussed.

Machine learning↗
NASA NTRSDate not supplied

Impacts of Assimilating Infrared Sounders from Geostationary Orbit

A set of observing system simulation experiments (OSSEs) was used to assess the impact of assimilating hyperspectral infrared (IR) radiances from geostationary platforms. This was done in preparation for the proposed National Oceanic and Atmospheric Administration (NOAA) Geostationary eXtended Observations (GeoXO) Sounder (GXS), expected to launch in the 2030s, using the National Aeronautics and Space Administration (NASA) Global Modeling and Assimilation Office (GMAO) OSSE framework. From a numerical weather prediction (NWP) perspective, a global “ring” of geostationary IR sounders was found to improve both the analysis and forecasts and provide beneficial impacts as measured by the forecast sensitivity observation impact (FSOI) metric.

Geostationary orbit↗
DOE OSTI2016

Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California Independent System Operator’s (ISO) load forecasts by integrating behind-the-meter (BTM) PV forecasts. The three, new, state-of-the-art solar irradiance forecasting models included: the infrared (IR) satellite-based cloud motion vector (CMV) model; the WRF-SolarCA model and variants; and the Optimized Deep Machine Learning (ODML)-training model. The first two forecasting models targeted known weaknesses in current operational solar forecasts. They were benchmarked against existing operational numerical weather prediction (NWP) forecasts, visible satellite CMV forecasts, and measured PV plant power production. IR CMV, WRF-SolarCA, and ODML-training forecasting models all improved the forecast to a significant degree. Improvements varied depending on time of day, cloudiness index, and geographic location. The fourth objective was to demonstrate that the California ISO’s load forecasts could be improved by integrating BTM PV forecasts. This objective represented the project’s most exciting and applicable gains. Operational BTM forecasts consisting of 200,000+ individual rooftop PV forecasts were delivered into the California ISO’s real-time automated load forecasting (ALFS) environment. They were then evaluated side-by-side with operational load forecasts with no BTM-treatment. Overall, ALFS-BTM day-ahead (DA) forecasts performed better than baseline ALFS forecasts when compared to actual load data. Specifically, ALFS-BTM DA forecasts were observed to have the largest reduction of error during the afternoon on cloudy days. Shorter term 30 minute-ahead ALFS-BTM forecasts were shown to have less error under all sky conditions, especially during the morning time periods when traditional load forecasts often experience their largest uncertainties. This work culminated in a GO decision being made by the California ISO to include zonal BTM forecasts into its operational load forecasting system. The California ISO’s Manager of Short Term Forecasting, Jim Blatchford, summarized the research performed in this project with the following quote: “The behind-the-meter (BTM) California ISO region forecasting research performed by Clean Power Research and sponsored by the Department of Energy’s SUNRISE program was an opportunity to verify value and demonstrate improved load forecast capability. In 2016, the California ISO will be incorporating the BTM forecast into the Hour Ahead and Day Ahead load models to look for improvements in the overall load forecast accuracy as BTM PV capacity continues to grow.”

14 SOLAR ENERGY↗
NASA NTRS2011

Numerical Model Sensitivity to Heterogeneous Satellite Derived Vegetation Roughness

The sensitivity of a mesoscale weather prediction model to a 1 km satellite-based vegetation roughness initialization is investigated for a domain within the south central United States. Three different roughness databases are employed: i) a control or standard lookup table roughness that is a function only of land cover type, ii) a spatially heterogeneous roughness database, specific to the domain, that was previously derived using a physically based procedure and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, and iii) a MODIS climatologic roughness database that like (i) is a function only of land cover type, but possesses domain specific mean values from (ii). The model used is the Weather Research and Forecast Model (WRF) coupled to the Community Land Model within the Land Information System (LIS). For each simulation, a statistical comparison is made between modeled results and ground observations within a domain including Oklahoma, Eastern Arkansas, and Northwest Louisiana during a 4-day period within IHOP 2002. Sensitivity analysis compares the impact the three roughness initializations on time-series temperature, precipitation probability of detection (POD), average wind speed, boundary layer height, and turbulent kinetic energy (TKE). Overall, the results indicate that, for the current investigation, replacement of the standard look-up table values with the satellite-derived values statistically improves model performance for most observed variables. Such natural roughness heterogeneity enhances the surface wind speed, PBL height and TKE production up to 10 percent, with a lesser effect over grassland, and greater effect over mixed land cover domains.

Jasinski, Michael↗
NASA NTRS2005

A Mechanism for the Loading-Unloading Substorm Cycle Missing in MHD Global Magnetospheric Simulation Models

Loading and consequent unloading of magnetic flux is an essential element of the substorm cycle in Earth's magnetotail. We are unaware of an available global MHD magnetospheric simulation model that includes a loading- unloading cycle in its behavior. Given the central role that MHD models presently play in the development of our understanding of magnetospheric dynamics, and given the present plans for the central role that these models will play in ongoing space weather prediction programs, it is clear that this failure must be corrected. A 2-dimensional numerical driven current-sheet model has been developed that incorporates an idealized current- driven instability with a resistive MHD system. Under steady loading, the model exhibits a global loading- unloading cycle. The specific mechanism for producing the loading-unloading cycle will be discussed. It will be shown that scale-free avalanching of electromagnetic energy through the model, from loading to unloading, is carried by repetitive bursts of localized reconnection. Each burst leads, somewhat later, to a field configuration that is capable of exciting a reconnection burst again. This process repeats itself in an intermittent manner while the total field energy in the system falls. At the end of an unloading interval, the total field energy is reduced to well below that necessary to initiate the next unloading event and, thus, a loading-unloading cycle results. It will be shown that, in this model, it is the topology of bursty localized reconnection that is responsible for the appearance of the loading-unloading cycle.

Klimas, A. J.↗
NASA NTRS2022

An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space

The task of quantifying spatial and temporal variations in terrestrial water, energy, and vegetation conditions is challenging due to the significant complexity and heterogeneity of these conditions, all of which are impacted by climate change and anthropogenic activities. To address this challenge, Earth Observations (EOs) of the land and their utilization within data assimilation (DA) systems are vital. Satellite EOs are particularly relevant, as they offer quasi-global coverage, are non-intrusive, and provide uniformity, rapid measurements, and continuity. The past three decades have seen unprecedented growth in the number and variety of land remote sensing technologies launched by space agencies and commercial companies around the world. There have also been significant developments in land modeling and DA systems to provide tools that can exploit these measurements. Despite these advances, several important gaps remain in current land DA research and applications. This paper discusses these gaps, particularly in the context of using DA to improve model states for short-term numerical weather and sub-seasonal to seasonal predictions. We outline an agenda for land DA priorities so that the next generation of land DA systems will be better poised to take advantage of the significant current and anticipated shifts and advancements in remote sensing, modeling, computational technologies, and hardware resources.

land surface↗
NASA NTRS1980

The compatible balancing approach to initialization, and four-dimensional data assimilation

A unified theoretical approach to both the four-dimensional assimilation of asynoptic data and the initialization problem is attempted. This approach relies on the derivation of certain relationships between geopotential tendencies and tendencies of the horizontal velocity field in primitive-equation models of atmospheric flow. The approach is worked out and analyzed in detail for some simple barotropic models. Certain independent results of numerical experiments for the time-continuous assimilation of real asynoptic meteorological data into a complex, baroclinic weather prediction model are discussed in the context of the present approach. Tentative inferences are drawn for practical assimilation procedures.

Ghil, M.↗
DOE OSTI2019

Description of the ARM Large-Scale Forcing Data from the Constrained Variational Analysis (VARANAL) Version 2

This technical report represents an update of the previous technical report written by Zhang et al. (2001a) (available at http://www.arm.gov/publications/tech_reports/arm-tr-005.pdf), which described the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility constrained variational analysis (VARANAL) that is used to develop the large-scale forcing data for driving single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation models (LES). The VARANAL algorithm was originally developed by Zhang and Lin (1997) and Zhang et al. (2001b) at Stony Brook University and was migrated to the Lawrence Livermore National Laboratory (LLNL) as the ARM operational objective analysis system in May 1999. Since then, the algorithm has been evolved with time along with the availability of new observations and techniques to meet various modeling needs. Major updates include: (1) The method used to develop multi-year continuous forcing data (Xie et al. 2004), (2) The incorporation of eddy correlation flux measurement system (ECOR) turbulent fluxes into the analysis (Tang et al. 2019), and (3) Improvements to the workflow (e.g., implementing part of the code into the ARM Data Integrator [ADI]) to increase efficiency. The ARM large-scale forcing data have been widely used for SCM/CRM/LES to understand and improve physical processes in models. Zhang et al. (2016) has provided a comprehensive review of the SCM concept, early efforts to derive forcing data for SCM studies, efforts of the ARM constrained variational analysis, and previous SCM studies using ARM cases. This technical report focuses on the constrained variational analysis algorithm and the introduction of the ARM VARANAL products.

58 GEOSCIENCES↗
NASA NTRS2014

Evaluating the Impact of Aerosols on Numerical Weather Predictions

This presentation focused on proposed Aerosol related case studies, and details were given about participating centers and modeling systems, as well as webpage and analysis tools. Preliminary results were given with regards to a case study on the dust storm over Egypt and another case study on the extreme pollution event in China.

Aerosol↗