Surface Meteorological Station - ARL 10m, Boardman - Raw Data
The dataset provides 1-minute measurements of basic meteorological variables.
17 WIND ENERGY↗
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Surface Meteorological Station - ARL 10m, Boardman - Reviewed Data
This dataset provides 1-minute measurements of basic meteorological variables.
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Effects of in-situ and reanalysis climate data on estimation of cropland gross primary production using the Vegetation Photosynthesis Model
Satellite-based Production Efficiency Models (PEMs) often require meteorological reanalysis data such as the North America Regional Reanalysis (NARR) by the National Centers for Environmental Prediction (NCEP) as model inputs to simulate Gross Primary Production (GPP) at regional and global scales. This study first evaluated the accuracies of air temperature (TNARR) and downward shortwave radiation (RNARR) of the NARR by comparing with in-situ meteorological measurements at 37 AmeriFlux non-crop eddy flux sites, then used one PEM – the Vegetation Photosynthesis Model (VPM) to simulate 8-day mean GPP (GPPVPM) at seven AmeriFlux crop sites, and investigated the uncertainties in GPPVPM from climate inputs as compared with eddy covariance-based GPP (GPPEC). Results showed that TNARR agreed well with in-situ measurements; RNARR, however, was positively biased. An empirical linear correction was applied to RNARR, and significantly reduced the relative error of RNARR by ~25% for crop site-years. Overall, GPPVPM calculated from the in-situ (GPPVPM(EC)), original (GPPVPM(NARR)) and adjusted NARR (GPPVPM(adjNARR)) climate data tracked the seasonality of GPPEC well, albeit with different degrees of biases. GPPVPM(EC) showed a good match with GPPEC for maize (Zea mays L.), but was slightly underestimated for soybean (Glycine max L.). Replacing the in-situ climate data with the NARR resulted in a significant overestimation of GPPVPM(NARR) (18.4/29.6% for irrigated/rainfed maize and 12.7/12.5% for irrigated/rainfed soybean). GPPVPM(adjNARR) showed a good agreement with GPPVPM(EC) for both crops due to the reduction in the bias of RNARR. The results imply that the bias of RNARR introduced significant uncertainties into the PEM-based GPP estimates, suggesting that more accurate surface radiation datasets are needed to estimate primary production of terrestrial ecosystems at regional and global scales.
AmeriFlux↗
NIRP Core Software Suite v. 1.0
The NIRP Core Software Suite is a core set of code that supports multiple applications. It includes miscellaneous base code for data objects, mathematic equations, and user interface components; and the framework includes several fully-developed software applications that exist as stand-alone tools to compliment other applications. The stand-alone tools are described below. Analyst Manager: An application to manage contact information for people (analysts) that use the software products. This information is often included in generated reports and may be used to identify the owners of calculations. Radionuclide Viewer: An application for viewing the DCFPAK radiological data. Compliments the Mixture Manager tool. Mixture Manager: An application to create and manage radionuclides mixtures that are commonly used in other applications. High Explosive Manager: An application to manage explosives and their properties. Chart Viewer: An application to view charts of data (e.g. meteorology charts). Other applications may use this framework to create charts specific to their data needs.
Whitener, Dustin Heath↗
Meteorological Observations Available for the State of Utah
The National Weather Service’s Meteorological Assimilation Data Ingest System (MADIS) contains a large number of station networks of surface and upper air meteorological observations for the state of Utah. In addition to MADIS, observations from individual station networks may also be available. It has been confirmed that LLNL has access to the data sources listed below.
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Surface Meteorological Station McKinleyville, CA
These data are collected as part of an observational database developed to support the floating offshore wind research and industry under the ORACLE project funded by DOE WETO. The surface meteorological data are collected by NOAA (https://psl.noaa.gov/data/obs/datadisplay), and here only data within the state of California are part of the database.
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Detecting outliers in streaming time series data from ARM distributed sensors
The Atmospheric Radiation Measurement (ARM) Data Center at ORNL collects data from a number of permanent and mobile facilities around the globe. The data is then ingested to create high level scientific products. High frequency streaming measurements from sensors and radar instruments at ARM sites require high degree of accuracy to enable rigorous study of atmospheric processes. Outliers in collected data are common due to instrument failure or extreme weather events. Thus, it is critical to identify and flag them. We employed multiple univariate, multivariate and time series techniques for outlier detection methods and studied their effectiveness. First, we examined Pearson correlation coefficient which is used to measure the pairwise correlations between variables. Singular Spectrum Analysis (SSA) was applied to detect outliers by removing the anticipated annual and seasonal cycles from the signal to accentuate anomalies. K-means was applied for multivariate examination of data from collection of sensors to identify any deviation from expected and known patterns and identify abnormal observation. The Pearson correlation coefficient, SSA and K-means methods were later combined together in a framework to detect outliers through a range of checks. We applied the developed method to data from meteorological sensors at ARM Southern Great Plains site and validated against existing database of known data quality issues.
Lu, Yuping↗
Develop Wake Mitigation Strategy (CRADA CRD-17-00693 Final Report)
Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. In an autonomous wind farm, enabling cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving an autonomous wind farm is to develop algorithms that provide necessary information to ensure reliable, robust, and efficient operation of wind turbines in a wind plant using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. In this work consensus control is applied in a hybrid analysis to data from an existing wind farm to demonstrate the benefit of consensus control.
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Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification
Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE
denoising autoencoder↗
Data from: "Moisture rivals temperature in limiting photosynthesis by trees establishing beyond their cold-edge range limit under ambient and warmed conditions"
This archive contains data files that were used to draw conclusions in “Moisture rivals temperature in limiting photosynthesis by trees establishing beyond their cold-edge range limit under ambient and warmed conditions”, by Moyes et al., 2015. All field research was completed in common garden plots set up as part of the Alpine Treeline Warming Experiment (ATWE) on Niwot Ridge, Colorado, USA.There are two main data file formats in this archive: comma-separated values (.csv), and Microsoft Excel (.xls and .xlsx). .xlsx files can be read using Microsoft Excel and Google Sheets, and .csv files can be read using any simple text editor program, such as TextEdit (Mac) and Notepad (Windows). This .pdf data user’s guide can be read using Adobe Acrobat Reader, or any other compatible software. Seedling photographs and their corresponding leaf area-processed images are available in .jpg/.JPG image format, and can be opened using Preview (Mac) and Photos (Windows). To provide additional spatial context, two types of geospatial files are also published in this data package: ESRI shapefiles (.shp) and .kml files. Shapefiles are compatible with any GIS software able to read the file type (such as QGIS or ESRI’s ArcGIS suite), and .kml files can be opened with Google Earth or Google Maps. Figures 3 and 4 in the publication contain data from Moyes et al. 2013. This publication is cited in the References section in this archive, and data files can be accessed via the Alpine Treeline Warming Experiment project portal on ESS-DIVE. ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Climate change is altering plant species distributions globally, and warming is expected to promote uphill shifts in mountain trees. However, at many cold-edge range limits, such as alpine treelines in the western United States, tree establishment may be colimited by low temperature and low moisture, making recruitment patterns with warming difficult to predict.- We measured response functions linking carbon (C) assimilation and temperature- and moisture-related microclimatic factors for limber pine (Pinus flexilis) seedlings growing in a heating × watering experiment within and above the alpine treeline. We then extrapolated these response functions using observed microclimate conditions to estimate the net effects of warming and associated soil drying on C assimilation across an entire growing season.- Moisture and temperature limitations were each estimated to reduce potential growing season C gain from a theoretical upper limit by 15–30% (c. 50% combined). Warming above current treeline conditions provided relatively little benefit to modeled net assimilation, whereas assimilation was sensitive to either wetter or drier conditions.- Summer precipitation may be at least as important as temperature in constraining C gain by establishing subalpine trees at and above current alpine treelines as seasonally dry subalpine and alpine ecosystems continue to warm.
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The Case for Custom TMYs: Examples Using the NSRDB
This presentation provides an overview of custom typical meteorological year data sets using the National Solar Radiation Database.
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Evaluating the Accuracy of Various Irradiance Models in Detecting Soiling of Irradiance Sensors: Preprint
We evaluate the feasibility of using various clear-sky models or purchased satellite data for estimating the soiling of a reference cell irradiance sensor. We find results to be more accurate for models that consider local meteorological conditions. We conclude that given the data sets considered, and depending on the requirements of the data analyst, choosing to use purchased satellite irradiance data from Solargis to estimate the soiling of a reference cell sensor tends to yield more accurate results, although there are instances where a clear-sky model performs better. The SOLIS clear-sky model in PVLIB with variable Pwat provided useful soiling results, implying that the general method of using a clear-sky model with local meteorological data may provide a low-cost tool for detecting soiling of irradiance sensors. irradiance data from Solargis to estimate the soiling of a reference cell sensor tends to yield more accurate results, although there are instances where a clear-sky model performs better. The SOLIS clear-sky model in PVLIB with variable Pwat provided useful soiling results, implying that the general method of using a clear-sky model with local meteorological data may provide a low-cost tool for detecting soiling of irradiance sensors.
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Spatial Interpolation of Air Pollutant and Meteorological Variables in Central Amazonia
The Amazon Rainforest is highlighted by the global community both for its extensive vegetation cover that constantly suffers the effects of anthropic action and for its substantial biodiversity. This dataset presents data of meteorological variables from the Amazon Rainforest region with a spatial resolution of 0.001° in latitude and longitude, resulting from an interpolation process. The original data were obtained from the GoAmazon 2014/5 project, in the Atmospheric Radiation Measurement (ARM) repository, and then processed through mathematical and statistical methods. The dataset presented here can be used in experiments in the field of Data Science, such as training models for predicting climate variables or modeling the distribution of species.
54 ENVIRONMENTAL SCIENCES↗
Watershed Workflow
Watershed Workflow aims to lower the bar on leveraging open data products in hyperresolution watershed hydrologic models. Integrated, distributed hydrologic models of watersheds require large amounts and complexity of data including meteorological forcing datasets; land cover datasets; watershed geometry, hydrogography, and elevation datasets; and subsurface soil and rock structural datasets. Increasingly open data products meeting this need are available through U.S. governmental and other agencies via APIs and other electronic means, but are not available in the same format, through the same API, or in the same coordinate system as one another, and present other challenges in manipulations across scales. This product automates the discovery, download, curation, and integration of these types of datasets into a common format for use with hydrologic models.
Coon, EthanT [Oak Ridge National Laboratory] (0000↗
High-Resolution Meteorology with Climate Change Impacts from Global Climate Model Data Using Generative Machine Learning
As renewable energy generation increases, the impacts of weather and climate on energy generation and demand become critical to the reliability of the energy system. However, these impacts are often overlooked. Global climate models (GCMs) can be used to understand possible changes to our climate, but their coarse resolution makes them difficult to use in energy system modelling. Here we present open-source generative machine learning methods that produce meteorological data at a nominal spatial resolution of 4 km at an hourly frequency based on inputs from 100 km daily-average GCM data. These methods run 40 times faster than traditional downscaling methods and produce data that have high-resolution spatial and temporal attributes similar to historical datasets. We demonstrate that these methods can be used to downscale projected changes in wind, solar and temperature variables across multiple GCMs including projections for more frequent low-wind and high-temperature events in the Eastern United States.
climate change↗
Integrating New Arctic Plant Functional Types in a Land Surface Model Using Above- and Belowground Field Observations: Modeling Archive
This Modeling Archive is in support of an NGEE Arctic publication "Integrating New Arctic Plant Functional Types in a Land Surface Model Using Above- and Belowground Field Observations". We used field observations of biomass and leaf traits (Salmon et al., 2019b) across a gradient of plant communities on the Seward Peninsula in northwest Alaska (Breen et al., 2020; Iversen et al., 2017) to replace the original ELM configuration with nine, arctic-specific PFTs. Original arctic PFTs in the model were (1) broadleaf deciduous boreal shrub and (2) C3 arctic grass. New PFTs were (1) lichen, (2) bryophyte, (3) evergreen dwarf shrub, (4) deciduous dwarf shrub, (5) deciduous low shrub, (6) deciduous low to tall shrub, (7) alder shrub, (8) forb, and (9) graminoid. New PFTs were parameterized and evaluated using site-level trait and biomass measurements (Salmon et al., 2019b), root C:N ratios and rooting depth patterns (Salmon et al., 2019c), and root biomass (Salmon et al., 2020). This archive contains forcing data and model output from ELM simulations conducted at the Kougarok Hillslope site (Kougarok Road Mile Marker 64). Simulations were conducted for the Kougarok Hillslope site using meteorological driving data from the Scenarios Network for Alaska and Arctic Planning (SNAP) downscaled climate projection dataset using NCAR-CCSM forcing (Bieniek et al., 2020 dataset; Walsh et al., 2018). The archive includes three versions of model simulations representing a progression from the original model configuration to an Arctic-specific configuration (see Output section below). Simulation 1 used grid cell data from a global E3SM configuration, including the fractional area of E3SM PFTs assigned to the grid cell containing the Kougarok Hillslope site in global simulations. This simulation did not distinguish between different plant communities on the Kougarok Hillslope, but instead used a single point simulation to represent the entire area. Simulation 2 used default E3SM PFT definitions combined with adjusted depth to bedrock for plant communities with shallow rocky layers to represent the role of abiotic soil factors in driving site differences (based on depth measurements of Iversen et al., 2019a), and adjusted the relative areas of the broadleaf deciduous boreal shrub and C3 arctic grass PFTs to reflect the observed spatial coverage of shrub and non-shrub PFTs across Kougarok Hillslope plant communities. Simulation 3 used the new arctic PFT definitions and parameterizations based on the vegetation types present at the site, along with the adjusted soil depths used in Simulation 2. Included are *.pdf, *.nc (NetCDF), *tar.gz, *.py, and *.bash files. We would like to thank Mary's Igloo Native Corporation for allowing us to perform this research on their land and giving us the opportunity to share this research with their community. Thanks to Shawn Serbin and Alistair Rogers for helpful discussions. Holly Vander Stel, Joanne Childs, and Stan Wullschleger helped with collection of field data. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
54 ENVIRONMENTAL SCIENCES↗
Integrated Hourly Meteorological Database of 20 Meteorological Stations (1981-2022) for Watershed Function SFA Hydrological Modeling
This dataset contains (a) a script “R_met_integrated_for_modeling.R”, and (b) associated input CSV files: 3 CSV files per location to create a 5-variable integrated meteorological dataset file (air temperature, precipitation, wind speed, relative humidity, and solar radiation) for 19 meteorological stations and 1 location within Trail Creek from the modeling team within the East River Community Observatory as part of the Watershed Function Scientific Focus Area (SFA). As meteorological forcings varied across the watershed, a high-frequency database is needed to ensure consistency in the data analysis and modeling. We evaluated several data sources, including gridded meteorological products and field data from meteorological stations. We determined that our modeling efforts required multiple data sources to meet all their needs. As output, this dataset contains (c) a single CSV data file (*_1981-2022.csv) for each location (20 CSV output files total) containing hourly time series data for 1981 to 2022 and (d) five PNG files of time series and density plots for each variable per location (100 PNG files). Detailed location metadata is contained within the Integrated_Met_Database_Locations.csv file for each point location included within this dataset, obtained from Varadharajan et al., 2023 doi:10.15485/1660962. This dataset also includes (e) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and (f) a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. Review the (g) ReadMe_Integrated_Met_Database.pdf file for additional details on the script, methods, and structure of the dataset.The script integrates Northwest Alliance for Computational Science and Engineering’s PRISM gridded data product, National Oceanic and Atmospheric Administration’s NCEP-NCAR Reanalysis 1 gridded data product (through the `RCNEP` R package, Kemp et al., doi:10.32614/CRAN.package.RNCEP), and analytical-based calculations. Further, this script downscales the input data into hourly frequency, which is necessary for the modeling efforts.
54 ENVIRONMENTAL SCIENCES↗
Assessing the impacts of assimilating IASI and MOPITT CO retrievals using CESM-CAM-chem and DART
We report on the results and evaluation with independent measurements from assimilating both MOPITT (Measurements Of Pollution In The Troposphere) and IASI (Infrared Atmospheric Sounding Interferometer) retrieved profiles into the Community Earth System Model (CESM). We used the Data Assimilation Research Testbed ensemble Kalman filter technique, with the full atmospheric chemistry CESM component Community Atmospheric Model with Chemistry. We first discuss the methodology and evaluation of the current data assimilation system with coupled meteorology and chemistry data assimilation. The various capabilities of MOPITT and IASI retrievals are highlighted, with specific attention to instrument vertical sensitivity and coverage and how these impact the analyses. MOPITT and IASI CO retrievals mostly constrain the CO fields close to the main anthropogenic, biogenic, and biomass burning CO sources. In the case of IASI CO assimilation, we also observe constraints on CO far from the sources. During the simulation time period (June and July 2008), CO assimilation of both instruments strongly improves the atmospheric CO state as compared to independent observations, with the higher spatial coverage of IASI providing better results on the global scale. Yet, the enhanced sensitivity of multispectral MOPITT observations to near surface CO over the main source regions provides synergistic effects at regional scales.
54 ENVIRONMENTAL SCIENCES↗