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The WindCube v2 was co-located with Site 300’s 52-m-tall meteorological tower so that measurements below 40 m could also be observed. The meteorological tower has three measurement levels: 10 m, 23 m, and 52 m. Wind speed was measured with a cup anemometer; wind direction with a wind vane. Data are available as 15-minute averages.
The main objective of this study is to compare data provided by Database of NASA with available ground data for regions covered by national meteorological net NASA estimates that their measurements of average daily solar radiation have a root-mean-square deviation RMSD error of 35 W/m{sup 2} (roughly 20% inaccuracy). Unfortunately valid data from meteorological stations for regions of interest are quite rare in Albania. In these cases, use of Solar Radiation Database of NASA would be a satisfactory solution for different case studies. Using a statistical method allows to determine most probable margins between to sources of data. Comparison of mean insulation data provided by NASA with ground data of mean insulation provided by meteorological stations show that ground data for mean insolation results, in all cases, to be underestimated compared with data provided by Database of NASA. Converting factor is 1.149.
This dataset consists of annual CSV files containing multiple sources of modeled, hourly wind speeds and generation. For complete information about this dataset, including validation of modeled generation versus recorded generation, please see the Scientific Data article: Millstein, D., Jeong, S., Ancell, A., & Wiser, R. (2023). A database of hourly wind speed and modeled generation for US wind plants based on three meteorological models. Scientific Data, 10(1), 883. https://doi.org/10.1038/s41597-023-02804-w
Variable capacity heat pumps represent the next wave of technology for heat pumps. In this report, the performance of two variable capacity heat pumps (HPs) is compared to that of a single or two stage baseline system. The units were installed in two existing research houses located in Knoxville, TN. These houses were instrumented to collect energy use and temperature data while both the baseline systems and variable capacity systems were installed. The homes had computer controlled simulated occupancy, which provided consistent schedules for hot water use and lighting. The temperature control and energy use of the systems were compared during both the heating and cooling seasons. Multiple linear regression models were used along with TMY3 data for Knoxville, TN in order to normalize the effect that the outdoor air temperature has on energy use. This enables a prediction of each system's energy use over a year with the same weather. The first system was a multi-split system consisting of 8 indoor units and a single outdoor unit. This system replaced a 16 SEER single stage HP with a zoning system, which served as the baseline. Data was collected on the baseline system from August 2009 to December 2010 and on the multi-split system from January 2011 to January 2012. Soon after the installation of the multi-split system, some of the smaller rooms began over-conditioning. This was determined to be caused by a small amount of continuous refrigerant flow to all of the indoor units when the outdoor unit was running regardless of whether they were calling for heat. This, coupled with the fact that the indoor fans run continuously, was providing enough heat in some rooms to exceed the set point. In order to address this, the indoor fans were disabled when not actively heating per the manufacturer's recommendation. Based on the measured data, the multi-split system was predicted to use 40% more energy in the heating season and 16% more energy in the cooling season than the baseline system, for the typical meteorological year weather data. The AHRI ratings indicated that the baseline system would perform slightly better than the multi-split system, but not by as large of a margin as seen in this study. The multi-split system was able to maintain more consistent temperature throughout the house than the baseline system, but it did allow relative humidity levels to increase above 60% in the summer. The second system was a split system with an inverter driven compressor and a single ducted air handler. This unit replaced a 16 SEER two stage HP with a zoning system. Data was collected on the baseline system from July 2009 to November 2010 and on the ducted inverter system from December 2010 to January 2012. The ducted inverter system did not offer a zone controller, so it functioned as a single zone system. Due to this fact, the registers had to be manually adjusted in order to better maintain consistent temperatures between the two levels of the house. The predicted heating season energy use for the ducted inverter system, based on the measured energy use, was 30% less than that of the baseline system for the typical meteorological year. However, the baseline system was unable to operate in its high stage due to a wiring issue with the zone controller. This resulted in additional resistance heat use during the winter and therefore higher energy use than would be expected in a properly performing unit. The AHRI ratings would indicate that the baseline system would use less energy than the ducted inverter system, which is opposite to the results of this study. During the cooling season, the ducted inverter system was predicted to use 23% more energy than the baseline system during the typical meteorological year. This is also opposite of the results expected by comparing the AHRI ratings. After a detailed comparison of the ducted inverter system's power use compared to that of a recently installed identical system at a retro-fit study house, there is concern that the unit is not operating as intended. The power use and cycles indicate that the unit is performing more like a single stage unit than a variable capacity unit. Analysis of the data indicates that a change in operating behavior occurred during a service call shortly after the installation of the unit. The logbook only indicates that refrigerant charge was added, but does not indicate any other change. This is being investigated further. While the energy comparison results of these two variable capacity heat pumps is generally underwhelming, it is difficult to draw any hard conclusions about the maximum attainable efficiency of these units when optimally installed. Both units appear to have undesirable conditions associated with the installation or operation, which could have had an adverse effect on their energy use.
Buildings consume more than one third of the world?s total primary energy. Weather plays a unique and significant role as it directly affects the thermal loads and thus energy performance of buildings. The traditional simulated energy performance using Typical Meteorological Year (TMY) weather data represents the building performance for a typical year, but not necessarily the average or typical long-term performance as buildings with different energy systems and designs respond differently to weather changes. Furthermore, the single-year TMY simulations do not provide a range of results that capture yearly variations due to changing weather, which is important for building energy management, and for performing risk assessments of energy efficiency investments. This paper employs large-scale building simulation (a total of 3162 runs) to study the weather impact on peak electricity demand and energy use with the 30-year (1980 to 2009) Actual Meteorological Year (AMY) weather data for three types of office buildings at two design efficiency levels, across all 17 ASHRAE climate zones. The simulated results using the AMY data are compared to those from the TMY3 data to determine and analyze the differences. Besides further demonstration, as done by other studies, that actual weather has a significant impact on both the peak electricity demand and energy use of buildings, the main findings from the current study include: 1) annual weather variation has a greater impact on the peak electricity demand than it does on energy use in buildings; 2) the simulated energy use using the TMY3 weather data is not necessarily representative of the average energy use over a long period, and the TMY3 results can be significantly higher or lower than those from the AMY data; 3) the weather impact is greater for buildings in colder climates than warmer climates; 4) the weather impact on the medium-sized office building was the greatest, followed by the large office and then the small office; and 5) simulated energy savings and peak demand reduction by energy conservation measures using the TMY3 weather data can be significantly underestimated or overestimated. It is crucial to run multi-decade simulations with AMY weather data to fully assess the impact of weather on the long-term performance of buildings, and to evaluate the energy savings potential of energy conservation measures for new and existing buildings from a life cycle perspective.
The NASA Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE) conducted 162 joint flights with two aircraft over the northwest Atlantic to study aerosol–cloud interactions (ACIs), which represent the largest uncertainty in estimating total anthropogenic radiative forcing. The combination of a high-flying King Air and low-flying HU-25 Falcon, equipped with remote sensing and in situ instruments, characterized trace gases, aerosol particles, clouds, and meteorological variables with data collected nearly simultaneously below, within, and above marine boundary layer (MBL) clouds. Flights spanning warm and cold seasons across 3 years (2020–22) provided a broad range of conditions associated with aerosol particles, cloud properties (including particle size and phase), and meteorology, ideally suited for robust ACI calculations and assessing how well models simulate a wide range of MBL clouds from stratiform to cumulus. ACTIVATE data suggest that drivers of cloud droplet number concentration N d , including aerosol particles and MBL dynamics, vary between winter and summer months with a stronger potential to convert aerosol particles into cloud droplets in winter. Models of varying complexity not only highlight some skills in simulating winter and summer cloud types but also identify challenges that still need to be addressed such as treatment of turbulence, wet scavenging, and mesoscale organization. Remote sensing advances range from new retrieval methods for N d , cloud phase classification, vertically resolved aerosol and cloud condensation nuclei number concentration, and ocean surface wind speed. This work describes these scientific and technological advances along with efforts in outreach and open data science.
In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon.
In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon.
In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon.
In support of the Wind Forecasting Improvement Project, Pacific Northwest National Laboratory (PNNL) deployed surface meteorological stations in Oregon.
As countries continue to implement sustainable and renewable energy goals, the need for affordable low-carbon technologies, including those related to offshore wind energy, is accelerating. The U.S. federal government recognizes the environmental and economic benefits of offshore wind development and is taking the necessary steps to overcome critical challenges facing the industry to realize these benefits. The U.S. Department of Energy (DOE) is investing in buoy-mounted lidar systems to facilitate offshore measurement campaigns that will advance our understanding of the offshore environment and provide the observational data needed for model validation, particularly at hub height where offshore observations are particularly lacking. On behalf of the DOE, Pacific Northwest National Laboratory manages a Lidar Buoy Program that facilitates meteorological and oceanographic data collection using validated methods to support the U.S. offshore wind industry. Since being acquired in 2014, two DOE lidar buoys have been deployed on the U.S. east and west coasts; and their data represent the first publicly available multi-seasonal hub height data to be collected in U.S. waters. In addition, the buoys have undergone performance testing, significant upgrades, and a lidar validation campaign to ensure the accuracy and reliability of the lidar data needed to support wind resource characterization and model validation. The Lidar Buoy Program is providing valuable offshore data to the wind energy community, while focusing data collection on areas of acknowledged high priority.
While climate models have evolved over time to produce high fidelity and high resolution climate forecasts, visualization and analysis of the output of the model simulations has been limited, typically constrained to single dimensional charts for visualization and basic aggregate statistics for analytics. Same is true for the large troves of observational data available from meteorological stations all over the world. For richer understanding of climate and the impact of climate change, one needs computational tools that allow researchers, policymakers, and general public, to interact with the climate data. In this paper, we describe, webGlobe, a browser based GIS framework for interacting with climate data, and other datasets available in similar format. webGlobe is a unique resource that allows unprecedented access to climate data through a browser. The framework also allows for deploying machine learning based analytical applications on the climate data without putting computational burden on the client. Instead, webGlobe uses a client-server framework, where the server, deployed on a cloud infrastructure, allows for dynamic allocation of resources for running compute-intensive applications. The capabilities of the framework will be discussed in context of a use case: identifying extreme events from real and simulated climate data using a Gaussian process based change detection algorithm.
A fully coupled meteorology-chemistry model (WRF-Chem, the Weather Research and Forecasting model coupled with chemistry) has been configured to conduct quasi-global simulation for 5 years (2010–2014) and evaluated with multiple observation data sets for the first time. The evaluation focuses on the simulation over the trans-Pacific transport region using various reanalysis and observational data sets for meteorological fields and aerosol properties. The simulation generally captures the overall spatial and seasonal variability of satellite retrieved aerosol optical depth (AOD) and absorbing AOD (AAOD) over the Pacific that is determined by the outflow of pollutants and dust and the emissions of marine aerosols. The assessment of simulated extinction Ångström exponent (EAE) indicates that the model generally reproduces the variability of aerosol size distributions as seen by satellites. In addition, the vertical profile of aerosol extinction and its seasonality over the Pacific are also well simulated. The difference between the simulation and satellite retrievals can be mainly attributed to model biases in estimating marine aerosol emissions as well as the satellite sampling and retrieval uncertainties. Compared with the surface measurements over the western USA, the model reasonably simulates the observed magnitude and seasonality of dust, sulfate, and nitrate surface concentrations, but significantly underestimates the peak surface concentrations of carbonaceous aerosol likely due to model biases in the spatial and temporal variability of biomass burning emissions and secondary organic aerosol (SOA) production. A sensitivity simulation shows that the trans-Pacific transported dust, sulfate, and nitrate can make significant contribution to surface concentrations over the rural areas of the western USA, while the peaks of carbonaceous aerosol surface concentrations are dominated by the North American emissions. Both the retrievals and simulation show small interannual variability of aerosol characteristics for 2010–2014 averaged over three Pacific sub-regions. Furthermore, the evaluation in this study demonstrates that the WRF-Chem quasi-global simulation can be used for investigating trans-Pacific transport of aerosols and providing reasonable inflow chemical boundaries for the western USA, allowing one to further understand the impact of transported pollutants on the regional air quality and climate with high-resolution nested regional modeling.
Abstract. The wind turbine design process requires performing thousands of simulations for a wide range of inflow and control conditions, which necessitates computationally efficient yet time-accurate models, especially when considering wind farm settings. To this end, FAST.Farm is a dynamic-wake-meandering-based mid-fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. This work is an extension of a study that addressed constructing a diurnal cycle evolution based on experimental data (Quon, 2024). Here, this inflow is used to validate the turbine structural and wake-meandering response between experimental data, FAST.Farm simulation results, and high-fidelity large-eddy simulation results from the coupled Simulator fOr Wind Farm Applications (SOWFA)–OpenFAST tool. The validation occurs within the nocturnal stable boundary layer when corresponding meteorological and turbine data are available. To this end, we compared the load results from FAST.Farm and SOWFA–OpenFAST to multi-turbine measurements from a subset of a full-scale wind farm. Computational predictions of blade-root and tower-base bending loads are compared to 10 min statistics of strain gauge measurements during 3.5 h of the evolving stable boundary layer, generally with good agreement. This time period coincided with an active wake-steering campaign of an upstream turbine, resulting in time-varying yaw positions of all turbines. Wake meandering was also compared between the computational solutions, generally with excellent agreement. Simulations were based on a high-fidelity precursor constructed from inflow measurements and using state-of-the-art mesoscale-to-microscale coupling.
In the United States, forty-eight (48) percent of the residential end-use energy consumption is spent on space heating and air conditioning. Reducing envelope-generated heating and cooling loads through application of phase change material (PCM)-enhanced building envelopes can facilitate maximizing the energy efficiency of buildings. Combined experimental testing and numerical modeling of PCM-enhanced envelope components are two important aspects of the evaluation of their energy benefits. An innovative phase change material (nano-PCM) was developed with PCM encapsulated with expanded graphite (interconnected) nanosheets, which is highly conductive for enhanced thermal storage and energy distribution, and is shape-stable for convenient incorporation into lightweight building components. A wall with cellulose cavity insulation and prototype PCM-enhanced interior wallboards was built and tested in a natural exposure test (NET) facility in a hot-humid climate location. The test wall contained PCM wallboards and regular gypsum wallboard, for a side-by-side annual comparison study. Further, numerical modeling of the walls containing the nano-PCM wallboard was performed to determine its actual impact on wall-generated heating and cooling loads. The model was first validated using experimental data, and then used for annual simulations using Typical Meteorological Year (TMY3) weather data. This article presents the measured performance and numerical analysis evaluating the energy-saving potential of the nano-PCM-enhanced wallboard.
A promising approach to increasing the energy efficiency of buildings is the implementation of a phase change material (PCM) in the building envelope. Numerous studies over the last two decades have reported the energy saving potential of PCMs in building envelopes, but their wide application has been inhibited, in part, by their high cost. This article describes a novel PCM made of naturally occurring fatty acids/glycerides trapped into high density polyethylene (HDPE) pellets and its performance in a building envelope application. The PCM-HDPE pellets were mixed with cellulose insulation and then added to an exterior wall of a test building in a hot and humid climate, and tested over a period of several months, To demonstrate the efficacy of the PCM-enhanced cellulose insulation in reducing the building envelope heat gains and losses, side-by-side comparison was performed with another wall section filled with cellulose-only insulation. Further, numerical modeling of the test wall was performed to determine the actual impact of the PCM-HDPE pellets on wall-generated heating and cooling loads and the associated electricity consumption. The model was first validated using experimental data and then used for annual simulations using typical meteorological year (TMY3) weather data. Furthermore, this article presents the experimental data and numerical analyses showing the energy-saving potential of the new PCM.
Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and validated representation of hourly load profiles in the U.S. commercial and residential building stock. The End-Use Load Profiles project website includes links to data viewers for this new dataset. For documentation of dataset validation, model calibration, and uncertainty quantification, see Wilson et al. (2022). These data were first created around 2012 as a byproduct of various analyses of solar photovoltaics and solar water heating (see references below for are two examples). This dataset contains several errors and limitations. It is recommended that users of this dataset transition to the updated version of the dataset posted in the resources. This dataset contains weather data, commercial load profile data, and residential load profile data. Weather The Typical Meteorological Year 3 (TMY3) provides one year of hourly data for around 1,000 locations. The TMY weather represents 30-year normals, which are typical weather conditions over a 30-year period. Commercial The commercial load profiles included are the 16 ASHRAE 90.1-2004 DOE Commercial Prototype Models simulated in all TMY3 locations, with building insulation levels changing based on ASHRAE 90.1-2004 requirements in each climate zone. The folder names within each resource represent the weather station location of the profiles, whereas the file names represent the building type and the representative city for the ASHRAE climate zone that was used to determine code compliance insulation levels. As indicated by the file names, all building models represent construction that complied with the ASHRAE 90.1-2004 building energy code requirements. No older or newer vintages of buildings are represented. Residential The BASE residential load profiles are five EnergyPlus models (one per climate region) representing 2009 IECC construction single-family detached homes simulated in all TMY3 locations. No older or newer vintages of buildings are represented. Each of the five climate regions include only one heating fuel type; electric heating is only found in the Hot-Humid climate. Air conditioning is not found in the Marine climate region. One major issue with the residential profiles is that for each of the five climate zones, certain location-specific algorithms from one city were applied to entire climate zones. For example, in the Hot-Humid files, the heating season calculated for Tampa, FL (December 1 - March 31) was unknowingly applied to all other locations in the Hot-Humid zone, which restricts heating operation outside of those days (for example, heating is disabled in Dallas, TX during cold weather in November). This causes the heating energy to be artificially low in colder parts of that climate zone, and conversely the cooling season restriction leads to artificially low cooling energy use in hotter parts of each climate zone. Additionally, the ground temperatures for the representative city were used across the entire climate zone. This affects water heating energy use (because inlet cold water temperature depends on ground temperature) and heating/cooling energy use (because of ground heat transfer through foundation walls and floors). Representative cities were Tampa, FL (Hot-Humid), El Paso, TX (Mixed-Dry/Hot-Dry), Memphis, TN (Mixed-Humid), Arcata, CA (Marine), and Billings, MT (Cold/Very-Cold). The residential dataset includes a HIGH building load profile that was intended to provide a rough approximation of older home vintages, but it combines poor thermal insulation with larger house size, tighter thermostat setpoints, and less efficient HVAC equipment. Conversely, the LOW building combines excellent thermal insulation with smaller house size, wider thermostat setpoints, and more efficient HVAC equipment. However, it is not known how well these ...
The lack of consistent, accurate information on evapotranspiration (ET) and consumptive use of water by irrigated agriculture is one of the most important data gaps for water managers in the western United States (U.S.) and other arid agricultural regions globally. The ability to easily access information on ET is central to improving water budgets across the West, advancing the use of data-driven irrigation management strategies, and expanding incentive-driven conservation programs. Recent advances in remote sensing of ET have led to the development of multiple approaches for field-scale ET mapping that have been used for local and regional water resource management applications by U.S. state and federal agencies. The OpenET project is a community-driven effort that is building upon these advances to develop an operational system for generating and distributing ET data at a field scale using an ensemble of six well-established satellite-based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web-based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Here we describe the OpenET framework, including the models used in the ensemble, the satellite, meteorological, and ancillary data inputs to the system, and the OpenET data visualization and access tools. We also summarize an extensive intercomparison and accuracy assessment conducted using ground measurements of ET from 139 flux tower sites instrumented with open path eddy covariance systems. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite-driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within ±8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET.