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This poster details how REopt - NREL's software modeling platform for energy systems integration and optimization - can help to model energy systems. Some benefits of modeling with REopt include optimizing behind the meter storage for cost and resiliency, optimizing lab testing, optimizing dispatch of utility scale storage, and quantifying renewable energy impact on outage survivability.
Abstract. Poor representations of aerosols, clouds, and aerosol–cloud interactions (ACIs) in Earth system models (ESMs) have long been the largest uncertainties in predicting global climate change. Huge efforts have been made to improve the representation of these processes in ESMs, and the key to these efforts is the evaluation of ESM simulations with observations. Most well-established ESM diagnostics packages focus on the climatological features; however, they lack process-level understanding and representations of aerosols, clouds, and ACIs. In this study, we developed the Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package to facilitate the routine evaluation of aerosols, clouds, and ACIs simulated the Energy Exascale Earth System Model (E3SM) from the US Department of Energy (DOE). This paper documents its version 2 functionality (ESMAC Diags v2), which has substantial updates compared with version 1 (Tang et al., 2022a). The simulated aerosol and cloud properties have been extensively compared with in situ and remote-sensing measurements from aircraft, ship, surface, and satellite platforms in ESMAC Diags v2. It currently includes six field campaigns and two permanent sites covering four geographical regions: the eastern North Atlantic, the central US, the northeastern Pacific, and the Southern Ocean. These regions produce frequent liquid- or mixed-phase clouds, with extensive measurements available from the DOE Atmospheric Radiation Measurement user facility and other agencies. ESMAC Diags v2 generates various types of single-variable and multivariable diagnostics, including percentiles, histograms, joint histograms, and heatmaps, to evaluate the model representation of aerosols, clouds, and ACIs. Select examples highlighting the capabilities of ESMAC Diags are shown using E3SM version 2 (E3SMv2). In general, E3SMv2 can reasonably reproduce many observed aerosol and cloud properties, with biases in some variables such as aerosol particle and cloud droplet sizes and number concentrations. The coupling of aerosol and cloud number concentrations may be too strong in E3SMv2, possibly indicating a bias in processes that control aerosol activation. Furthermore, the liquid water path response to a perturbed cloud droplet number concentration behaves differently in E3SMv2 and observations, which warrants further study to improve the cloud microphysics parameterizations in E3SMv2.
A complex rotorcraft model system has been developed by the NASA Langley Research Center and the U.S. Army Air Mobility R&D Laboratory, Langley Directorate, for aerodynamic and acoustic experimental investigations in the NASA Langley V/STOL tunnel. This generalized rotor model system has a powered main rotor, tail rotor, and auxiliary engine capability. It may be configured to represent a variety of rotorcraft configurations. The first investigation was conducted to determine the performance, acoustic, stability and control characteristics of the NASA/Army Rotor Systems Research Aircraft with an articulated rotor. In a second investigation, a quarter-scale AH-1G configuration with a teetering rotor is being represented to determine if a V-tail will improve the directional characteristics. Future programs are planned to investigate advanced rotor blade airfoils for improved performance and acoustic characteristics.
Abstract. The large spatial scale of global Earth system models (ESMs) is often cited as an obstacle to using the output by water resource managers in localized decisions. Recent advances in computing have improved the fidelity of hydrological responses in ESMs through increased connectivity between model components. However, the models are seldom evaluated for their ability to reproduce metrics that are important for and resonate with practitioners or that allow practitioners to situate higher-resolution model outputs within a cascade of uncertainty stemming from different models and scenarios. We draw on the combined experience of the author team and water manager workshop participants to identify salient water management metrics and evaluate whether they are credibly reproduced over the conterminous USA by the Community Earth System Model v2 (CESM2) Large Ensemble. We find that, while the exact values may not match the observations, aspects such as interannual variability can be reproduced by CESM2 for the mean wet day precipitation and length of dry spells. CESM2 also captures the proportion of total annual precipitation that derives from the heaviest rain days in watersheds that are not snow-dominated. Aggregating the 7 d mean daily runoff to two-digit Hydrological Unit Code (HUC2) watersheds also shows that rain-dominated regions capture the timing and interannual variability of annual maximum and minimum flows. We conclude that there is potential for far greater use of large-ensemble ESMs, such as CESM2, in long-range water management decisions to supplement high-resolution regional projections.
This report summarizes discussion and outcomes from the March 2018 workshop, Disturbance and Vegetation Dynamics in Earth System Models, sponsored by the Office of Biological and Environmental Research (BER) within the U.S. Department of Energy Office of Science. The goals of this workshop, held in Gaithersburg, Maryland, were to (1) identify key uncertainties in current dynamic vegetation models limiting the ability to adequately represent vegetation in Earth System Models (ESMs) and (2) identify and prioritize research directions that can improve models, including forest structural change and feedbacks and responses to disturbance. Failure to capture disturbance dynamics and feedbacks limits the utility of ESMs for predictive understanding and application to societally important problems. This workshop considered (a) dynamic processes that significantly affect terrestrial ecosystems and the coupled Earth system and (b) the data constraints and modeling challenges important for future progress. There were three dominant workshop conclusions: vegetation changes, including disturbance- driven changes, are affecting climate and natural resources; these impacts are expected to increase in the future; and, yet, current models have insufficient data and process representations to adequately predict these changes.
The modeling, system identification and controller design aspects of the ASTREX precision space structure are presented in this work. Modeling of ASTREX is performed using NASTRAN, TREETOPS and I-DEAS. The models generated range from simple linear time-invariant models to nonlinear models used for large angle simulations. Identification in both the time and frequency domains are presented. The experimental set up and the results from the identification experiments are included. Finally, controller design for ASTREX is presented. Simulation results using this optimal controller demonstrate the controller performance. Finally the future directions and plans for the facility are addressed.
The many new distributed energy resources being installed at the distribution system level require increased visibility into system operations that will be enabled by distribution system state estimation (DSSE) and situational awareness applications. Reliable and accurate DSSE requires both robust methods for managing the big data provided by smart meters and quality distribution system models. This paper presents intelligent methods for detecting and dealing with missing or inaccurate smart meter data, as well as the ways to process the data for different applications. It also presents an efficient and flexible parameter estimation method based on the voltage drop equation and regression analysis to enhance distribution system model accuracy. Finally, it presents a 3-D graphical user interface for advanced visualization of the system state and events. Moreover, we demonstrate this paper for a university distribution network with the state-of-the-art real-time and historical smart meter data infrastructure.
Setaria viridis is an emerging model system for C 4 grasses. It is closely related to the bioenergy feed stock switchgrass and the grain crop foxtail millet. Recently, the 510 Mb genome of foxtail millet, S. italica, has been sequenced 1,2 and a 25x coverage genome sequence of the weedy relative S. viridis is in progress. S. viridis has a number of characteristics that make it a potentially excellent model genetic system including a rapid generation time, small stature, simple growth requirements, prolific seed production 3 and developed systems for both transient and stable transformation 4 . However, the genetics of S. viridis is largely unexplored, in part, due to the lack of detailed methods for performing crosses. To date, no standard protocol has been adopted that will permit rapid production of seeds from controlled crosses. The protocol presented here is optimized for performing genetic crosses in S. viridis, accession A10.1. We have employed a simple heat treatment with warm water for emasculation after pruning the panicle to retain 20-30 florets and labeling of flowers to eliminate seeds resulting from newly developed flowers after emasculation. After testing a series of heat treatments at permissive temperatures and varying the duration of dipping, we have established an optimum temperature and time range of 48 °C for 3-6 min. By using this method, a minimum of 15 crosses can be performed by a single worker per day and an average of 3-5 outcross progeny per panicle can be recovered. Therefore, an average of 45-75 outcross progeny can be produced by one person in a single day. Broad implementation of this technique will facilitate the development of recombinant inbred line populations of S. viridis X S. viridis or S. viridis X S. italica, mapping mutations through bulk segregant analysis and creating higher order mutants for genetic analysis.
Embodiments of the present subject matter can enable the analysis of signal value errors for system models. In an example, signal value errors can be propagated through the functional blocks of a system model to analyze possible effects as the signal value errors impact incident functional blocks. This propagation of the errors can be applicable to many models of computation including avionics models, synchronous data flow, and Kahn process networks.
Identification of low order equivalent system dynamic models from flight test data was studied. Inputs were pilot control deflections, and outputs were aircraft responses, so the models characterized the total aircraft response including bare airframe and flight control system. Theoretical investigations were conducted and related to results found in the literature. Low order equivalent system modeling techniques using output error and equation error parameter estimation in the frequency domain were developed and validated on simulation data. It was found that some common difficulties encountered in identifying closed loop low order equivalent system models from flight test data could be overcome using the developed techniques. Implications for data requirements and experiment design were discussed. The developed methods were demonstrated using realistic simulation cases, then applied to closed loop flight test data from the NASA F-18 High Alpha Research Vehicle.
Soil and vegetation carbon stocks play a critical role in human-Earth system models. These stocks (denominated as densities in MgC/ha) affect variables such as land use change emissions and also influence land use change pathways under climate forcing scenarios where terrestrial carbon is assigned a carbon price. Here we present reharmonized soil and vegetation carbon densities both at the 5-arcmin resolution grid cell level and also aggregated to 235 water sheds for 4 land use types (Cropland, Grazed land, Urban land and unmanaged vegetation) and 15 unmanaged land cover types. Moreover, we use the distribution of carbon within and across pixels to define statistical "states" of carbon, once again differentiated by land type. These statistical states are used to define a range of possible carbon values that can be used for defining initial conditions of soil and vegetation carbon in human-Earth system models. We implement these data in a state-of-the-art multi sector dynamics model, namely the Global Change Analysis Model (GCAM), and show that these new data improve several land use responses, especially when terrestrial carbon is assigned a carbon price.
Human influence on the hydrologic cycle includes regulation and storage, consumptive use and overall redistribution of water resources in space and time. Representing these processes is essential for applications of earth system models in hydrologic and climate predictions, as well as impact studies at regional to global scales. Emerging large-scale research reservoir models use generic operating rules that are flexible for coupling with earth system models. Those generic operating rules have been successful in reproducing the overall regulated flow at large basin scales. This study investigates the uncertainties of the reservoir models from different implementations of the generic operating rules using the complex multi-objective Columbia River Regulation System in northwestern United States as an example to understand their effects on not only regulated flow but also reservoir storage and fraction of the demand that is met. Numerical experiments are designed to test new generic operating rules that combine storage and releases targets for multi-purpose reservoirs and to compare the use of reservoir usage priorities, withdrawals vs. consumptive demand, as well as natural vs. regulated mean flow for calibrating operating rules. Overall the best performing implementation is the use of the combined priorities (flood control storage targets and irrigation release targets) operating rules calibrated with mean annual natural flow and mean monthly withdrawals. The challenge of not accounting for groundwater withdrawals, or on the contrary, assuming that all remaining demand is met through groundwater extractions, is discussed.
A pressurization system model was developed for a generic bimese Two-Stage-to-orbit Reusable Launch Vehicle using a cross-feed system and operating with densified propellants. The model was based on the pressurization system model for a crossfeed subscale water test article and was validated with test data obtained from the test article. The model consists of the liquid oxygen and liquid hydrogen pressurization models, each made up of two submodels, Booster and Orbiter tank pressurization models. The tanks are controlled within a 0.2-psi band and pressurized on the ground with ambient helium and autogenously in flight with gaseous oxygen and gaseous hydrogen. A 15-psi pressure difference is maintained between the Booster and Orbiter tanks to ensure crossfeed check valve closure before Booster separation. The analysis uses an ascent trajectory generated for a generic bimese vehicle and a tank configuration based on the Space Shuttle External Tank. It determines the flow rates required to pressurize the tanks on the ground and in flight, and demonstrates the model's capability to analyze the pressurization system performance of a full-scale bimese vehicle with densified propellants.
Abstract. The E3SM Diagnostics Package (E3SM Diags) is a modern, Python-based Earth system model (ESM) evaluation tool (with Python module name e3sm_diags), developed to support the Department of Energy (DOE) Energy Exascale Earth System Model (E3SM). E3SM Diags provides a wide suite of tools for evaluating native E3SM output, as well as ESM data on regular latitude–longitude grids, including output from Coupled Model Intercomparison Project (CMIP) class models. E3SM Diags is modeled after the National Center for Atmospheric Research (NCAR) Atmosphere Model Working Group (AMWG, 2022) diagnostics package. In its version 1 release, E3SM Diags included a set of core essential diagnostics to evaluate the mean physical climate from model simulations. As of version 2.7, more process-oriented and phenomenon-based evaluation diagnostics have been implemented, such as analysis of the quasi-biennial oscillation (QBO), the El Niño–Southern Oscillation (ENSO), streamflow, the diurnal cycle of precipitation, tropical cyclones, ozone and aerosol properties. An in situ dataset from DOE's Atmospheric Radiation Measurement (ARM) program has been integrated into the package for evaluating the representation of simulated cloud and precipitation processes. This tool is designed with enough flexibility to allow for the addition of new observational datasets and new diagnostic algorithms. Additional features include customizable figures; streamlined installation, configuration and execution; and multiprocessing for fast computation. The package uses an up-to-date observational data repository maintained by its developers, where recent datasets are added to the repository as they become available. Finally, several applications for the E3SM Diags module were introduced to fit a diverse set of use cases from the scientific community.
Focal Area(s): Focal Area 2: AI for predictive modeling, including AI-derived model components, and AI-enabled applications including parameter optimization, data assimilation, and uncertainty quantification. Focal Area 1: AI-assisted data assimilation using numerical Earth system models. Science Challenge: Earth system models have structural biases that lead to uncertain predictions, and their complexity and expense makes it difficult constraining the models with data or improved physical understanding.
NASA's Constellation Program is embarking on a new era of space exploration, returning to the Moon and beyond. The Constellation architecture will consist of a number of new spacecraft elements, including the Orion crew exploration vehicle, the Altair lunar lander, and the Ares family of launch vehicles. Each of these new spacecraft elements will need an electric power system, and those power systems will need to be designed to fulfill unique mission objectives and to survive the unique environments encountered on a lunar exploration mission. As with any new spacecraft power system development, preliminary design work will rely heavily on analysis to select the proper power technologies, size the power system components, and predict the system performance throughout the required mission profile. Constellation projects have the advantage of leveraging power system modeling developments from other recent programs such as the International Space Station (ISS) and the Mars Exploration Program. These programs have developed mature power system modeling tools, which can be quickly modified to meet the unique needs of Constellation, and thus provide a rapid capability for detailed power system modeling that otherwise would not exist.
The Weather Research and Forecasting (WRF) model is the next generation community mesoscale model designed to enhance collaboration between the research and operational sectors. The NM'S as a whole has begun a transition toward WRF as the mesoscale model of choice to use as a tool in making local forecasts. Currently, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) are running the Advanced Regional Prediction System (AIRPS) Data Analysis System (ADAS) every 15 minutes over the Florida peninsula to produce high-resolution diagnostics supporting their daily operations. In addition, the NWS MLB and SMG have used ADAS to provide initial conditions for short-range forecasts from the ARPS numerical weather prediction (NWP) model. Both NM'S MLB and SMG have derived great benefit from the maturity of ADAS, and would like to use ADAS for providing initial conditions to WRF. In order to assist in this WRF transition effort, the Applied Meteorology Unit (AMU) was tasked to configure and implement an operational version of WRF that uses output from ADAS for the model initial conditions. Both agencies asked the AMU to develop a framework that allows the ADAS initial conditions to be incorporated into the WRF Environmental Modeling System (EMS) software. Developed by the NM'S Science Operations Officer (S00) Science and Training Resource Center (STRC), the EMS is a complete, full physics, NWP package that incorporates dynamical cores from both the National Center for Atmospheric Research's Advanced Research WRF (ARW) and the National Centers for Environmental Prediction's Non-Hydrostatic Mesoscale Model (NMM) into a single end-to-end forecasting system. The EMS performs nearly all pre- and postprocessing and can be run automatically to obtain external grid data for WRF boundary conditions, run the model, and convert the data into a format that can be readily viewed within the Advanced Weather Interactive Processing System. The EMS has also incorporated the WRF Standard Initialization (SI) graphical user interface (GUT), which allows the user to set up the domain, dynamical core, resolution, etc., with ease. In addition to the SI GUT, the EMS contains a number of configuration files with extensive documentation to help the user select the appropriate input parameters for model physics schemes, integration timesteps, etc. Therefore, because of its streamlined capability, it is quite advantageous to configure ADAS to provide initial condition data to the EMS software. One of the biggest potential benefits of configuring ADAS for ingest into the EMS is that the analyses could be used to initialize either the ARW or NMM. Currently, the ARPS/ADAS software has a conversion routine only for the ARW dynamical core. However, since the NIvIM runs about 2.5 times faster than the ARW, it is quite advantageous to be able to run an ADAS/NMM configuration operationally due to the increased efficiency.
Advances in computing capabilities and scientific development have come together to evolve general circulation models into multi-scale Earth system modeling tools. The Goddard Earth Observing System (GEOS) model is one such example of this evolution. The GEOS model is driven by the finite-volume cubed-sphere (FV3) non-hydrostatic dynamical core. Surrounding FV3 is a scale-aware physics package and data assimilation capability permitting multi-scale application of GEOS for sub-seasonal to seasonal climate prediction, medium range weather prediction, and global mesoscale modeling at convection allowing resolutions. GEOS also includes a comprehensive chemistry package representing a range of capabilities from basic chemistry and interactve aerosols and gaseous species, to carbon emissions and uptake, and complex ozone photochemistry. In this study, we apply the GEOS model to study the fidelity of these processes with increasing horizontal resolution and scale-aware processes in GEOS on extreme events. The GEOS model is run for 40-days beginning in August 2016 at three uniform global resolutions of 13-km (c768), 6-km (c1536) and 3-km (c3072) with 72 vertical levels up to 0.01mb. The model physics use the Grell-Freitas scale-aware convection scheme to dynamically reduce the role of parameterized deep convection as resolved scale processes in the model take over at higher resolutions. We will include high-resolution global emissions and fluxes of aerosols and carbon downscaled from recent satellite observations. We will compare these simulations with reanalyses and observations, focusing on rainfall, clouds and radiative forcing at hourly to monthly timescales. We will closely examine the probability distribution of precipitation intensities and radiative properties of clouds on a daily time scale. In addition, we will focus on extreme events, in particular the diurnal cycle of convection over the US and the frequency and physical nature of organized convection and heavy rain events across the globe.