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This dataset contains raw data from the WFIP3 RHOD site supplementary meteorological sensors: T/RH and barometer; 1-sec average. The supplementary meteorological suite was added to PNNL Surface Flux Station to provide independent measurements of air temperature, relative humidity, and atmospheric pressure.
This dataset contains raw data from the WFIP3 RHOD site supplementary meteorological sensors: T/RH and barometer; 1-sec average. The supplementary meteorological suite was added to PNNL Surface Flux Station to provide independent measurements of air temperature, relative humidity, and atmospheric pressure.
Los Alamos National Laboratory (LANL) operates seven mesa-top instrumented meteorology towers: Technical Area (TA) 6, TA-49, TA-53, TA-54, TA-63, TA-54B, and TA-16B. An additional instrumented tower is located in Mortandad Canyon (TA-5 MDCN), and there is a rain gauge at North Community (NCOM), located within the town of Los Alamos. The 10 meter (m) towers at TA-63, TA- 54B, and TA-16B were installed in 2021, and will be included in the 2023 data completeness/quality report. A description of the meteorology monitoring network, prior to the installation of the TA-63, TA-54B, and TA-16B is found in Dewart and Boggs (2014). Four of the mesa-top towers (e.g., TA-6, TA-49, TA-53, and TA-54) are instrumented at the 1.2 m, 11.5 m, 23 m, and 46 m levels. In addition, the TA-6 tower is instrumented at the 92 m level. The TA-5 MDCN tower is 10 m in height and is instrumented at 1.2 m and 10 m. Data are collected and averaged every 15 minutes. Range checking is performed on each measurement every 15 minutes; data that are beyond normal ranges are eliminated from the data set and replaced by a code for missing data. In addition, data are reviewed weekly by qualified meteorologists to identify bad data not identified by the range checking technique. The data steward eliminates these data from the data set and replaces them with a code for missing data. The instrument technicians also review that data and schedule instrument replacement, as required. All instruments are calibrated at a frequency that meets the criteria identified in ANSI/ANS-3.11-2015. Data completeness is determined by the number of total 15-minute records available versus the number of possible measurements for the entire year. As a rule, the meteorologists do not attempt to estimate data that are eliminated as bad data. Original datalogger records, including bad data, can be recalled from program archival storage.
Los Alamos National Laboratory (LANL) operates seven mesa-top instrumented meteorology towers: Technical Area (TA) 6, TA-49, TA-53, TA-54, TA-63, TA-54B, and TA-16B. An additional instrumented tower is located in Mortandad Canyon (TA-5 MDCN), and there is a rain gauge at North Community (NCOM), located within the town of Los Alamos. The 10 meter (m) towers at TA-63, TA-54B, and TA-16B have been in testing since they were installed in 2021, and will be included in a future data completeness report. A description of the meteorology monitoring network, prior to the installation of the TA-63, TA-54B, and TA-16B is found in Dewart and Boggs (2014). Four of the mesa-top towers (e.g., TA-6, TA-49, TA-53, and TA-54) are instrumented at the 1.2 m, 11.5 m, 23 m, and 46 m levels. In addition, the TA-6 tower is instrumented at the 92 m level. The TA-5 MDCN tower is 10 m in height and is instrumented at 1.2 m and 10 m. Data are collected and averaged every 15 minutes. Range checking is performed on each measurement every 15 minutes; data that are beyond normal ranges are eliminated from the data set and replaced by a code for missing data. In addition, data are reviewed weekly by qualified meteorologists to identify bad data not identified by the range checking technique. The data steward eliminates these data from the data set and replaces them with a code for missing data. The instrument technicians also review that data and schedule instrument replacement, as required. All instruments are calibrated at a frequency that meets the criteria identified in ANSI/ANS-3.11-2015. Data completeness is determined by the number of total 15-minute records available versus the number of possible measurements for the entire year. As a rule, the meteorologists do not attempt to estimate data that are eliminated as bad data. Original datalogger records, including bad data, can be recalled from program archival storage.
Los Alamos National Laboratory (LANL) operates four mesa-top meteorology towers: Technical Area (TA) 06, TA-49, TA-53, and TA-54. An additional tower is located in Mortandad Canyon (TA-5 MDCN), and a rain gauge at North Community (NCOM). A description of the meteorology monitoring network is found in Dewart and Boggs (2014). Mesa-top towers are instrumented at 1.2 meters (m), 11.5 m, 23 m, and 46 m. In addition, TA-06 is instrumented at 92 m. The TA-5 MDCN tower is 10 m in height and is instrumented at 1.2 m and 10 m. Data are collected every 15 minutes. Range checking is done on each measurement every 15 minutes; data that are beyond normal ranges are eliminated from the data set and replaced by a code for missing data. In addition, data are reviewed weekly by meteorologists to identify bad data not identified by range checking. The data steward eliminates these data from the data set and replaces them with a code for missing data. The instrument technicians also review that data and schedule instrument replacement as required. Data completeness is determined by the number of total 15-minute records available versus the total number of possible measurements for the entire year. As a rule, the meteorologists do not attempt to estimate data that are eliminated as bad data. Original datalogger records, containing bad data, can be recalled from program archival storage.
Los Alamos National Laboratory (LANL) operates seven mesa-top instrumented meteorology towers: Technical Area (TA) 6, TA-49, TA-53, TA-54, TA-63, TA-54B, and TA-16B. An additional instrumented tower is located in Mortandad Canyon (TA-5 MDCN), and there is a rain gauge at North Community (NCOM), located within the town of Los Alamos. The 10 meter (m) towers at TA-63, TA54B, and TA-16B were installed in 2021, and will be included in the 2022 data completeness/quality report.
A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.
Abstract An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land surface models. Recently, airborne observations (e.g., lidar) have emerged as a promising method to accurately quantify SWE at high resolutions (scales of ∼100 m and finer). However, the frequency of these observations is very low, typically once or twice per season in the Rocky Mountains of Colorado. Here, we present a machine learning framework that is based on random forests to model temporally sparse lidar-derived SWE, enabling estimation of SWE at unmapped time points. We approximated the physical processes governing snow accumulation and melt as well as snow characteristics by obtaining 15 different variables from gridded estimates of precipitation, temperature, surface reflectance, elevation, and canopy. Results showed that, in the Rocky Mountains of Colorado, our framework is capable of modeling SWE with a higher accuracy when compared with estimates generated by the Snow Data Assimilation System (SNODAS). The mean value of the coefficient of determination R 2 using our approach was 0.57, and the root-mean-square error (RMSE) was 13 cm, which was a significant improvement over SNODAS (mean R 2 = 0.13; RMSE = 20 cm). We explored the relative importance of the input variables and observed that, at the spatial resolution of 800 m, meteorological variables are more important drivers of predictive accuracy than surface variables that characterize the properties of snow on the ground. This research provides a framework to expand the applicability of lidar-derived SWE to unmapped time points. Significance Statement Snowpack is the main source of freshwater for close to 2 billion people globally and needs to be estimated accurately. Mountainous snowpack is highly variable and is challenging to quantify. Recently, lidar technology has been employed to observe snow in great detail, but it is costly and can only be used sparingly. To counter that, we use machine learning to estimate snowpack when lidar data are not available. We approximate the processes that govern snowpack by incorporating meteorological and satellite data. We found that variables associated with precipitation and temperature have more predictive power than variables that characterize snowpack properties. Our work helps to improve snowpack estimation, which is critical for sustainable management of water resources.
This dataset, collected from Duke Forest Free Air CO2 Enrichment (FACE) – Forest-Atmosphere Carbon Transfer and Storage (FACTS-I) experiment, includes variables describing the meteorological conditions above canopy, within canopy, and soil depending on the variable. The Duke FACE experiment was located in a loblolly pine (Pinus taeda L.) plantation established in 1983. Naturally regenerated broadleaved species including sweetgum (Liquidambar styraciflua L.) and tulip poplar (Liriodendron tulipifera L.), mostly in the overstory, and winged elm (Ulmus alata Michx.) and red maple (Acer rubrum L.) were common in the understory. The FACE experiment commenced with two plots (plots 7-8) in 1994 (Oren et al. 2001), with six additional plots (plots 1-6) coming online on 27 August 1996. The CO2 enrichment was terminated on 31 October 2010 and post-enrichment data collection continued through 2012. Complete fertilization was applied annually to half of plots 7-8 from 1998 to 2004. The nutrient addition experiment expanded to half of plot 1-6 with a common protocol of N-fertilization in 2005. N-fertilization continued until 2012. The data range varied by sensor availability. A summary of information about variable name and data range can be found in the ‘FileDescription_[variable_name].txt’ files.
This report summarizes data completeness by tower and by instrument for 2019 and compares that data with the Los Alamos National Laboratory (LANL) and the ANSI/ANS-3.11-2015 standard. This report is designed to make data users aware of data completeness and any data quality issues. LANL meteorology monitoring goals include 95% completeness for all measurements. The ANS 2015 standard requires 90% completeness for all measurements. This report documents instrument/tower issues as they impact data completeness.
Historically, power has flowed from large power plants to customers. Increasing penetration of distributed energy resources such as solar power from rooftop photovoltaic has made the distribution network a two-way-street with power being generated at the customer level. The incorporation of renewables introduces additional uncertainty and variability into the power grid. Distribution network operation studies are being adapted to include renewables; however, such studies require high quality solar irradiance data that adequately reflect realistic meteorological variability. Data from satellite-based products are spatially complete, but temporally coarse, whereas solar irradiances exhibit high frequency variation at very fine timescales. We propose a new stochastic method for temporally downscaling global horizontal irradiance (GHI) to 1 min resolution, but we do not consider the spatial aspect due to limited availability of the in situ irradiance measurements. Solar irradiance's first and second-order structures vary diurnally and seasonally, and our model adapts to such nonstationarity. Empirical irradiance data exhibits highly non-Gaussian behavior; we develop a nonstationary and non-Gaussian moving average model that is shown to capture realistic solar variability at multiple timescales. We also propose a new estimation scheme based on Cholesky factors of empirical autocovariance matrices, bypassing difficult and inaccessible likelihood-based approaches. The model is demonstrated for a case study of three locations that are located in diverse climates through the United States. The model is compared against competitors from the literature and is shown to provide better uncertainty and variability quantification on testing data.
Associated with the Meteorological Tower Network Expansion Pilot Project by the Environmental Protection and Compliance (EPC) Compliance Programs (CP) Meteorology and Air Quality (MAQ) team, the purpose of this technical report is to establish requisite justification for the siting of three 10-meter high instrumented meteorological towers at three new locations on the Los Alamos National Laboratory (LANL) site. These towers and their associated instrumentation are similar in scope to the existing 10-meter meteorological tower that is collecting meteorological data in Mortandad Canyon (MDCN). The conclusions and recommendations in this technical report will inform LANL management, EPC-CP MAQ staff, and other LANL divisions of the criteria employed for the selection of the final location for siting these three new towers. Meeting such comprehensive criteria will ensure that all data acquired will be spatially representative for meeting the diverse Safety Basis (SB), emergency management, and operations organizations programmatic needs and analytical data input requirements to its codes.
This data package includes 6 csv files that include Community Land Model (CLM) simulations for three Fluxnet sites (i.e., US-NR1, US-GLE and US-VCM) and three SNOTEL sites (i.e., ER-BT, ER-SP and ER-PK) in the Upper Colorado River Basin area. Data inputs include meteorological forcing data (obtained from Fluxnet and SNOTEL databases) and CLM-simulated evapotranspiration (ET) data from 2005 to 2016. These datasets support the development of subseasonal regime approach based on statistics driven Hidden Markov Model to assess intra- and inter-annual variability of ET, which improves our understanding of climate change's impacts on water resources and ecosystem dynamics in the Colorado River Basin.
This dataset contains raw data from the WFIP3 site NANT UTD Alphasense N3 OPC.
This dataset contains raw data from the WFIP3 site NANT UTD Alphasense N3 OPC colocated with the UTD 200S scanning lidar.
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