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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Feasibility of Using Radar for Characterizing and Tracking Plumes

With the enhanced capabilities of NEXRAD radars has come an increased body of research focused on non-weather artifacts in the radar signal. Radar data has been collected and evaluated for a range of plume-generating events. Events such as wildfires, measuring and tracking ash from volcanic eruptions, and tracking plumes from events such as the SpaceX rocket explosion in 2016 have been investigated to determine the useful measurements available in radar data. For decision makers interested in radar use in nuclear forensics or during an emergency response, the usefulness of radar for attempting to provide some answers early in an event is clear. Currently emplaced operational radars provide high spatial and temporal resolution data within minutes of an event. This is not to say radar observations would replace current methods used by decision makers; however, supplementing decision making with radar data can only enhance and better inform the decision process. This report builds on two previous reports submitted in November 2016 and May 2017. This feasibility study demonstrates that radar can be used to improve decision making in the early phases of a plume-generating event by decreasing uncertainty as well as improving source-term inputs in F&T models. Radar coverage exists today in capabilities such as NOAA’s NEXRAD weather surveillance radars and Terminal Doppler Weather Radars. Improvement could be realized quickly by (1) improving awareness of existing radar products and incorporating them into decision making and modeling efforts, (2) evaluating and improving radar coverage around key urban areas and critical infrastructure, and (3) improving radar algorithms to optimize for non-meteorological events such as plumes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

6th Annual Earth System Grid Federation Face-to-Face Conference Report, December 5–9, 2016, Washington, D.C.

Since its inception in the late 1990s, the Earth System Grid Federation (ESGF) has evolved significantly as a system. As technology changed and its user base grew larger and more diverse, ESGF gradually developed into the state-of-the-art federated system it is today. Due to the enormity of its scope and the urgency of community needs, many features had to be tested in the real world during the expansion process. The rollout of Version 2.4 in 2016 culminated a series of releases and user feedback initiatives that began in 2013. During this 3-year period, ESGF hosted user group meetings to better understand how the system was being used; this information was then leveraged to begin Version 3.0 planning and preparations. The user meetings were collectively led and organized by the U.S. Department of Energy (DOE), the Infrastructure for the European Network for Earth System Modelling (IS-ENES), the U.S. National Aeronautics and Space Administration (NASA), the Australian National Computational Infrastructure (NCI), the U.S. National Oceanic and Atmospheric Administration (NOAA), and the U.S. National Science Foundation (NSF). The information emerging from the user group meetings influenced requirements, development, and operations. In 2015, representatives from a significant fraction of projects that use ESGF to disseminate and analyze data attended the Fifth Annual ESGF Face-to-Face (F2F) Conference (DOI: 10.2172/1253685). Attendees provided important feedback regarding current and future community data use cases. Discussions focused on maintaining essential operations while developing new and improved software to handle ever-increasing data variety, complexity, velocity, and volume—a task accomplished by the entire consortium.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

High-Resolution Coupling and Initialization to Improve Predictability and Predictions in Climate Models Workshop, September 30 – October 2, 2015

The goals of the workshop were 1) to enhance interactions and communications between the climate-prediction and the climate modeling and projection communities, 2) to summarize and synthesize the current status of the research and also document the challenges in initialized high-resolution simulations in both communities, and 3) to identify the criteria for establishing a multi-model experimental framework to optimally address major pressing questions in the context of available computing resources. The two-and-a-half-day workshop was hosted September 30 to October 2, 2015, at the NOAA Center for Weather and Climate Prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

The Quasi-Biennial Oscillation in Model-Simulated and Observed Equatorial Temperature of the Lower Stratosphere

In an otherwise thorough survey of metrics for models that simulate the Quasi-Biennial Oscillation, Schenzinger et al. say “there is no comparable observational dataset” to compare with model-simulated stratospheric temperatures (Geosci. Model Dev. 10, 2157-2168, 2017). They compare model temperatures only with reanalysis datasets, which come from numerical weather prediction models and therefore are not direct observations. But stratospheric brightness temperatures have been directly observed by microwave radiometers on board a series of satellites since 1979. Christy and Drouilhet (J. Climate 7, 106-120, 1994) found a QBO signal in 1979-1992 data from the Microwave Sounding Unit’s lower stratospheric Channel 4 (with peak weighting function at the 75hPa pressure level and half-power values at 120 and 40hPa, about 20±5 km altitude). To detect the QBO signal, Christy and Drouilhet used a latitude weighting that matches the QBO’s anti-correlation between the Equator and ±15°. Fernandez et al. also found a QBO signal in 1979-2000 MSU data, using EOF analysis (J. Climate 17, 3934-3946, 2004). Recently, Christy and Covey extended Christy and Drouilhet’s analysis through 2017 (“Stratospheric Temperature,” Section 2b5 in State of the Climate in 2017. Bull. Amer. Meteor. Soc. 99, S150–S152; Figure 2.10c). The QBO signal was again clearly visible. Although temperature trends for the lower atmosphere are sensitive to inter-satellite calibration and other processing details, giving rise to some controversy, data for the lower stratosphere are more robust. Christy and Covey’s Figure 2.9 shows that data processed separately by the National Oceanic and Atmospheric Administration (NOAA) and by Remote Sensing Systems (RSS) gives a QBO index that is virtually identical to that of Christy’s group at the University of Alabama at Huntsville (UAH).

58 GEOSCIENCES↗
DOE OSTI2019

Filter Aerosol Measurements for ARM

The goal of this study was to determine the best filter media for use in ARM PSAPs following the manufacturer discontinuation of the current filter media. The study comprised by multiple experiments to assess four potential replacement filter medium relative to the Pallflex E-70, which is being discontinued, and is documented in ARM ServiceNow under ENG0003846. The experiments laid out in the document ‘180504 Filter Aerosol Measurements for ARM.docx’ and several subsequent tests suggest that the Pallflex Emfab media is the best candidate for ongoing ARM filter-based absorbance measurements. Other media were ruled out based on particle-capture efficiency for small particles. In tests with Nigrosin, EMFAB exceeded all other media. Tests were done measuring reported absorbance for all media in Particle Soot Absorption Photometers (PSAPs) operated in parallel sampling under several conditions: ambient air, 3 sizes of black-dyed PSL (Poly Styrene Latex) spheres, and Nigrosin particles. These tests indicate a different factor of reported absorbance for each media relative to the original Pallflex E70 filter. The factor for the Azumi media was consistent with unpublished work by Ogren (NOAA). The tests described here were not exhaustive and were complicated by time-varying differences among the PSAPs used. However, it is concluded that they are sufficient to justify ARM switching to Emfab filter media for future ARM PSAP measurements. The remaining store of E70 will be reserved to quantitatively characterize differences between Emfab and E70 (and Azumi?) in both laboratory and field side-by-side tests as put forth in a succeeding ENG. This future work will empirically identify correction factors applicable to Emfab and quantify the uncertainty in comparing the historical E70 measurements with ongoing Emfab measurements. Comparison of PSAP measurements with an independent absolute ‘reference’ for aerosol absorbance, i.e., PhotoThermal Interferometry or difference measurements of Cavity Attenuated Phase Shift extinction (CAPS) minus Nephelometry scattering would be valuable but exceed the scope of ARM Infrastructure. While the characterization of correction factors is required for maintaining and improving accuracy in future absorption measurements, it was beyond the scope of the current study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Filter Aerosol Measurements for ARM

The goal of this study was to determine the best filter media for use in Atmospheric Radiation Measurement (ARM) particle soot absorption photometers (PSAPs) following the manufacturer discontinuation of the current filter media. The study comprised multiple experiments to assess four potential replacement filter medium relative to the Pallflex E-70, which is being discontinued, and is documented in ARM’s ServiceNow system under ENG0003846. The experiments laid out in the document ‘180504 Filter Aerosol Measurements for ARM.docx’ and several subsequent tests suggest that the Pallflex Emfab media is the best candidate for ongoing ARM filter-based absorbance measurements. Other media were ruled out based on particle capture efficiency for small particles. In tests with Nigrosin, Emfab exceeded all other media. Tests were done measuring reported absorbance for all media in PSAPs operated in parallel sampling under several conditions: ambient air, three sizes of black-dyed PSL (polystyrene latex) spheres, and Nigrosin particles. These tests indicate a different factor of reported absorbance for each media relative to the original Pallflex E70 filter. The factor for the Azumi media was consistent with unpublished work by Ogren (National Oceanic and Atmospheric Administration [NOAA]). The tests described here were not exhaustive and were complicated by time-varying differences among the PSAPs used. However, it is concluded that they are sufficient to justify ARM switching to Emfab filter media for future ARM PSAP measurements. The remaining store of E70 will be reserved to quantitatively characterize differences between Emfab and E70 (and Azumi?) in both laboratory and field side-by-side tests as put forth in a succeeding ENG. This future work will empirically identify correction factors applicable to Emfab and quantify the uncertainty in comparing the historical E70 measurements with ongoing Emfab measurements. Comparison of PSAP measurements with an independent absolute ‘reference’ for aerosol absorbance, i.e., photo thermal interferometry or difference measurements of cavity attenuated phase shift extinction (CAPS) minus nephelometry scattering, would be valuable but exceed the scope of ARM Infrastructure. While the characterization of correction factors is required for maintaining and improving accuracy in future absorption measurements, it was beyond the scope of the current study.

aerosol observing system↗
DOE OSTI2019

Winds of Change: Assessing Wind Energy Efficiency in Complex Terrain

In 2015, the Department of Energy (DOE) and the National Oceanic and Atmospheric Administration (NOAA) initiated a four-year study, the Second Wind Forecast Improvement Project (WFIP2), which aims to improve the accuracy of meteorological models that forecast wind speed in complex terrain for applications in wind energy (Wilczak et al.2019). As a part of WFIP2, a dataset was collected in the Columbian River and Basin regions of Oregon and Washington which led to significant wind forecast model improvements (Kenyon et al.2018). Similar to the observations performed in Oregon and Washington, Light Detection and Ranging (LiDAR) systems were deployed at three observation points at the Lawrence Livermore National Laboratory Site 300 Experimental Test Site as part of WFIP2. Site 300, located in the rolling hills approximately ten kilometers from the Altamont Pass Wind Resource Area, represents a second complex terrain location for validating and evaluating the model improvements made using the original Columbian River dataset. The three LiDARsystems continuously collected wind speed and direction data at heights ranging from 10 meters to 150 meters. The data were used to better understand how wind flow is modified over hills and valleys in the Altamont Pass Wind Resource Area and to determine the optimal hub height for new turbine installations in the region. We observed five popular turbine models that are installed in the Altamont Wind Resource Area Using the collected data and comparisons of existing turbine models in the area, the most efficient turbine model for the Altamont Wind ResourceArea based on power output was determined to be the Mitsubishi 1.0MW. Future work related to this research includes further comparing turbine models based on factors including overall cost and effects on wildlife as well as determining optimal spacing between turbine units.

17 WIND ENERGY↗
DOE OSTI2016

Measurements and Modeling of Black-Carbon Aerosols in the Arctic

We have made the first continuous measurements of black carbon in Barrow, Alaska at the ARM aerosol-observing site at the NOAA Barrow Observatory using a Single-Particle Soot Photometer (SP2). These data demonstrate that BC particles are extremely small, and a majority of the particles (by number density) are smaller than 0.5 fg, the lower limit of reliability of the SP2. We developed the first numerical model capable of quantitatively reproducing the laser-induced incandescence (LII) and scattering signals produced by the SP2, the industry-standard BC instrument. Our model reproduces the SP2 signal temporally and spectrally and demonstrates that the current SP2 optical design allows substantial contamination of LII on the scattering signal. We ran CAM5-SE in nudged mode, i.e., by constraining the transport used in the model with meteorological data. The results demonstrate the problem observed previously of under-predicting BC at high latitudes. The cause of the discrepancy is currently unknown, but we suspect that it is associated with scavenging and rainout mechanisms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Impacts of Future Climate-Driven Runoff on Six County WWT Facilities in New Jersey

The occurrence of extreme storms and associated flood hazards has increased in most regions in recent decades (Groisman et al. 2005, Alexander et al. 2006, Hegerl et al. 2011, Min et al. 2011). Extensive data on weather extremes since 1910 kept by the National Oceanic and Atmospheric Administration (NOAA)’s National Climatic Data Center have shown an increasing trend (reaching more than 20%) in U.S. one-day extreme precipitation events. This trend is expected to continue in the future, with likely intensification of heavy precipitation events (Kunkel et al. 2010, Westra et al. 2013). Recently, Argonne has performed dynamic downscaling from global climate model (GCM) projections with high-resolution regional climate model, Weather Research and Forecasting (WRF) and projected future climate conditions for the middle of century (2035-2064). For this project, we incorporated our future climate projections into our hydrological modeling to evaluate potential effects of changing climate on streamflow near the six county WWT facilities in New Jersey to improve our understanding of climate-driven precipitation impacts and use this information to inform the potential risk of precipitation-affected infrastructures. The information for the future projected hydrological flow discharges was also provided to support a storm surge study. The study was expanded later to quantify likelihood of river discharges near WWT facilities with a statistical approach and informed the flood risk contributed by the river discharges.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Inventory and Probabilistic Function of Natural Hazards in the United States: July 19, 2018 - September 18, 2018

The U.S. Department of Energy's National Renewable Energy Laboratory (NREL) developed a standardized risk assessment process that considers the location-specific vulnerabilities and threats (climate-related as well as human-caused) at a site to generate a ranked list of organizational risks and associated mitigation options for reducing those risks. To support this effort, the Southern Climate Impacts Planning Program (SCIPP), a National Oceanic and Atmospheric (NOAA) Regional Integrated Sciences and Assessments (RISA) team, was selected to provide climate science expertise to develop a comprehensive inventory and probabilistic function of natural hazards (e.g., hurricanes, tornadoes, flooding, and sea level rise) for the entire United States by region. The inventory of climate-related hazards and the associated likelihood scores are incorporated into a companion risk assessment tool enabling standardized comparisons of risk at sites across the country.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Initial Atmospheric Transport of Particles

A new version of the Initial Atmospheric Transport (IAT) model has been developed. This report fully characterizes the new model. IAT is designed to model heat release into the atmosphere that contains contaminated particles. The heat release forms a buoyant puff or plume. Buoyancy is quickly dissipated in 10's to 100's of seconds through drag and entrainment of ambient air. The final location of contaminant particles in the atmosphere after dissipation of abnormal heat is an important input to long term transportation codes such as the National Oceanic and Atmospheric Administration's (NOAA) Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) code. Ultimately the settling of contaminants after long term transport provides a basis for estimating health consequences. In comparison to the old version, the new model includes explicit simulation of particles, newly formulated equations of motion for a rising puff, and a new plume model. The plume model releases many instances of a single puff along a single trajectory in a way that balances energy released over a longer period than would be appropriate for a single puff. The puff model has additions for vorticity, turbulence, virtual mass, drag, and entrainment. The particle model leads to direct interfacing between IAT and the long-term transport code HYSPLIT. Previously, particle locations were given a pattern based on IAT's puff trajectory with no particle simulation. Now, particles are explicitly simulated, and 3-D spatial distributions of particles are output based on final positions. Ballistic particles are thrown outward at high velocity from explosive events and tend to hit the ground before the end of the simulation. Non-ballistic particles tend to get pulled into the rising puff s ring of vorticity with some escaping to ambient conditions as buoyant energy dissipates. A new model verification and validation effort was made for the new plume model. The Wallops Island Solid Rocket Propellant (SRP) fire plume was extracted to a 3-D transient dataset. After calibration, the model performs better than previous single puff attempts. Even so, error levels in the process used to estimate location and ambient weather conditions approach 30%. This leads to the conclusion that unplanned photogrammetry evaluations of the IAT model are useful but not sufficiently accurate to quantify model parameters. Like previous attempts, the vertical motion entrainment parameters come out much too high in comparison to values for other studies in the literature. It is suspected that a new model of shear entrainment would alleviate this problem. Unfortunately, IAT already has more calibration parameters than the data comparisons thus far can resolve with statistical certainty. This underscores the need to verify and validate the IAT model using detailed CFD studies that allow ideal statistical comparison with the IAT model for hundreds of cases. Even so, validation by comparison to carefully planned experiments (i.e. with acceptable error levels) is also necessary because the dynamics of entrainment are at a sub-grid level for CFD. Though better validation and verification are most important, many new enhancements are still envisioned for future IAT development. These include particle rain out due to condensation and agglomeration of water, direct connection to the North American Mesoscale (NAM) weather dataset, dynamic weather conditions, including ambient particle entrainment into the puff, and detrainment at the puff to ambient mixing interface. In general, including detailed aerosol dynamics is the greatest coming challenge which will require an entire new sub-model in IAT. This is very important if contaminant particles change in such a way that they increase or decrease in their potential harm to the environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Workshop on the Initialization of High-Resolution Earth System Models

From weather to climate timescales, weeks to decades, societal needs require detailed predictions of the atmosphere, ocean, and land-surface to facilitate informed planning. Central to this scientific endeavor is the ability to accurately initialize the earth system models (ESMs), since frequently the skill in forecasts, predictions, and projections depends on the quality of the initialization. However, initialization of high-resolution ESMs remains an outstanding challenge, particularly for the climate modeling community where initialization has received relatively less attention and where longer-term information is needed. This need, after a review of previous workshop outcomes, led the U.S. Department of Energy (DOE) and National Oceanic and Atmospheric Administration (NOAA) to host a workshop that focused exclusively on the opportunities that data assimilation (DA) and, specifically, coupled data assimilation (CDA) might provide to the ESM enterprise with particular attention toward initialization. The workshop was held April 9-10, 2018, at the Rockville Hilton in Rockville, Maryland, with 41 participants. It was composed of seven sessions, each with a distinct topical theme that related to the broad challenge of initializing high-resolution ESMs. The first session was used to frame the workshop challenge. Two of the sessions (2 and 7) focused the current state of the art of CDA within laboratory and operational centers at both the short-term, subseasonal and long-term, climate time scales. Three of the sessions (3, 4, and 5) addressed new algorithmic approaches to CDA at high resolution. Session 6 focused on the computational science solutions for the management of large input/output streams required to carry out the assimilation procedure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM). Final Report

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM) is a proposed system that will enable data from an infinite number of diverse sources to be organized and accessed from anywhere using any handheld or other computer device. The approach offers a powerful roadmap for the creation and integration of a unified knowledge base of an entire ecosystem, including its many geophysical, geographical, social, political, agricultural, energy, transportation, and cyber aspects. The resulting aggregation of data has the potential to generate an informational universe of unprecedented size that has never before been possible due to the prohibitive costs, managerial complexity, and technical barriers associated with ever-changing exponential-growth data flows. We envision that DREAM will accelerate discovery by enabling climate researchers, among other types of researchers, to manage, analyze, and visualize data from earth-scale measurements and simulations. DREAM’s success will be built on proven components that leverage existing services and resources. A key building block for DREAM will be the ESGF, chaired by Dean N. Williams. Expanding on the existing ESGF, the project will ensure that the access, storage, movement, and analysis of the large quantities of data that are processed and produced by diverse science projects can be dynamically distributed with proper resource management. Much of the Office of Science data is currently generated by multiple stand-alone facilities. DREAM can collect data accumulated from these facilities and incorporate it into a fully integrated network accessible from anywhere in the world. The result is a completely new paradigm shift for data management, analysis, and visualization enabling researchers to: Manage their calculations, data, tools, and research results; Ensure that all data are sharable, reproducible and (re)usable—accompanied by appropriate metadata describing its provenance, syntax, and semantics at creation; Advance application performance by selectively adapting APIs and services in response to scientific requirements and architectural complexities; and Provide scalable interactive resource management—navigate data and metadata at multiple levels, provide architecture-aware data integration, analysis and visualization tools. We will engage closely with DOE, NASA, and NOAA science groups working at the leading edge of computing. These engagements—in domains such as biology, climate, and hydrology—will allow us to advance disciplinary science goals and inform our development of technologies that can accelerate discovery across DOE more broadly. We will advertise and promote our technologies via dedicated workshops, tutorials, and sessions at conferences, stand-alone events with broad inter-disciplinary invitation, and engagements with leadership facilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

General Analysis of Data Collected from DOE Lidar Buoy Deployments Off Virginia and New Jersey

Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinel lidar buoys for the U.S. Department of Energy’s Wind Energy Technologies. The purpose of these buoys is to collect hub-height winds and supporting meteorological and oceanographic information to facilitate the development of offshore wind energy in the U.S. In general, each buoy is deployed for a year or more at a given location in order to capture at least a full annual cycle of weather conditions. The initial deployment for one buoy was off the coast of Virginia beginning in 2015, and the other buoy was first deployed off the coast of New Jersey beginning in 2016. Over the last two years, PNNL has had an opportunity to analyze the data collected during these first two deployments. This report describes a substantial analysis of data collected by the two lidar buoys operated off the coasts of Virginia and New Jersey. The centerpiece instrument for each buoy as deployed off Virginia and New Jersey is a lidar system, which is designed to measure the horizontal wind vector from approximately 40 m to 200 m above the sea surface with vertical resolution of 40 m. Since the initial deployments, the original lidars have been replaced with more powerful Leosphere 866 v2 systems. The analyses in this report will apply to the original Vindicator systems. In addition to the wind profiles from the lidars, the buoys collect near-surface measurements of wind speed and direction, air temperature, relative humidity, barometric pressure, and solar irradiance. Oceanographic variables measured include the two-dimensional wave spectrum, water temperature and conductivity, and ocean current vectors to a depth of 90 m. An assessment of overall data recovery and a basic analysis of the data collected was provided in a previous report This report substantially extends that analysis. The various sections describe the development and application of an inertial measurement unit (IMU) data recovery scheme for the New Jersey deployment; a climatological analysis of winds at hub height and at the surface together with thermodynamic variables measured at the surface for the full deployment periods; an analysis of oceanographic observations describe sea state; the development of a refinement for NOAA’s WaveWatch III model to allow its application to near-shore areas; a basic climatology of ocean currents observed from the buoys; an analysis of observed winds in the framework of Monin-Obukhov Similarity Theory; and the development and evaluation of techniques to extract turbulence intensity and turbulence kinetic energy from the lidars. The analyses contained in this report provide a great deal of new information about offshore conditions on the U.S. East Coast. In addition, the experience gained will inform both configurations and analysis of data from future deployments of these lidar buoy systems.

17 WIND ENERGY↗
DOE OSTI2020

Interdisciplinary Workshop on the Impacts of Land Use and Land Management on Earth System Evolution, Biogeochemical Cycles, Extremes, and Inter-Sectoral Dynamics (Final Report)

The Aspen Global Change Institute (AGCI) convened a week-long interdisciplinary science workshop from September 15-20, 2019 entitled, “Impacts of Land Use and Land Management on Earth System Evolution, Biogeochemical Cycles, Extremes, and Inter-Sectoral Dynamics.” The purpose of the workshop was to advance research on the impacts of land use and land management on climate, carbon emissions, and extremes, as well as the impact of land-use change on important societal issues such as food, water, and energy security. The workshop was co-chaired by Kate Calvin (PNNL), George Hurtt (University of Maryland), and Dave Lawrence (National Center for Atmospheric Research). Co-funders of the workshop included NASA, NOAA, and travel support for European colleagues through CRESCENDO. This workshop brought together 31 professionals with expertise across biogeochemistry, climate impacts, mitigation, land use datasets, and societal impacts to coordinate on upcoming research and publications on the role of biogeochemical and biophysical properties of the Earth surface and their implications for climate, as conducted through the Land Use model Intercomparison Project (LUMIP), which is part of Phase 6 of the Coupled Model Intercomparison Project (CMIP6). A primary outcome of the meeting was the definition and planning of a series of LUMIP papers that will rely on the LUMIP and CMIP6 simulations. By the end of the meeting more than twenty papers and paper lead authors had been identified and scoped out. Around ten of these papers were submitted by the December 31, 2019 IPCC deadline.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Assessment of Model Hub Height Wind Speed Performance Using DOE Lidar Buoy Data

As the offshore wind industry continues to gain momentum both in the U.S. and globally, reanalysis products remain essential tools for the lifecycle of a wind farm. From project siting and production estimates to determining construction and maintenance windows, reanalysis products play an important role. These regional and global long-term models also serve as inputs to higher resolution models. With such wide-ranging impacts on the offshore wind industry, the need for validation of reanalysis products is strong, particularly at typical turbine hub heights. Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinelTM buoys for the U.S. Department of Energy (DOE) in order to collect meteorological and oceanographic data, including hub height wind speed, in areas of interest for offshore wind development. This work uses DOE buoy observations off the coasts of New Jersey and Virginia to evaluate the performance of Modern Era Retrospective Analysis for Research and Applications-2 (MERRA-2), the North American Regional Reanalysis (NARR), the European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th generation (ERA5), and the National Oceanic and Atmospheric Administration (NOAA) assimilation system Rapid Refresh (RAP). Biases and degrees of correspondence are determined for each reanalysis product in order to provide insights on the performance and uncertainty for long-term wind resource characterization. With the baseline metrics determined, this work proceeds with an investigation into the sources of large deviations between the modeled and observed hub height wind speeds during the East Coast lidar buoy deployments. Consistent sources of reanalysis model error include stably stratified flow conditions, high wind shear, flow parallel to the coastline, and tropical and winter storms. For the near shore New Jersey location, offshore winds (i.e. blowing from land to water) tend to be associated with model overestimation of observed hub height wind speed, while winds parallel to the coastline are correlated with model underestimation of wind speed. Similarly, near the Virginia buoy, during southerly winds (parallel to the coastline) models underestimate the observed wind speed due to coastal upwelling. Reanalysis model wind speeds underestimate the hub height winds with increasing stable atmospheric stratification. Reanalysis model biases are seen when the observed wind speeds approach or exceed typical turbine cut-out speeds (around 20 m s-1). Large model errors during winter and tropical storm events, high wind speed events, and ramp events are often the result of wind speed timing and magnitude offsets. Overall, ERA5 provides the most successful representation of observed offshore hub height wind speeds at the U.S. East Coast buoy locations and is therefore best suited as an input boundary condition for model case studies. ERA5 performs well for a variety of atmospheric phenomena, including storms and sea breezes, however RAP, the next most successful reanalysis model, performs best in capturing the frequency of ramp events.

17 WIND ENERGY↗
DOE OSTI2019

When the Rain Stops: Drought on Subseasonal and Longer Timescales

The Aspen Global Change Institute hosted a technical science workshop, “When the Rain Stops: Drought on Subseasonal and Longer Timescales,” on September 9-14, 2018 in Aspen, CO. Gerald Meehl (NCAR), Roger Pulwarty (NOAA), and Angeline Pendergrass (NCAR) served as co-chairs for the workshop. The meeting included the participation of 25 scientists for a total of 125 participant days. The workshop resulted in agreement on definitions for both operational and research/prediction uses. Workshop participants are in the process of testing these definitions within their work on drought modeling, and impacts assessment, with the intention that these definitions can then be evaluated for CMIP6 (and incorporated into ESMValTool), as well as for the IPCC AR6 from observations, models, and impacts. Findings from the workshop and follow on experiments are in the process of being summarized and submitted for publication in a high profile peer-reviewed journal.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Fast response High Altitude Laser Optical Sensor (HALOS) for atmospheric water and temperature measurements

Advanced instrumentation and sensing methods are the key factor in the determination of many climate science research problems. In this Phase I STTR a laser-based diagnostic system for atmospheric temperature and water concentrations measurements was developed, this system, HALOS – High Altitude Laser Optical Sensor, employs a mid-infrared (MIR) quantum cascade laser (QCL) of the distributed feedback variety (DFB) powered by a 100 kHz rep rate ramp modulated current profile which enables spectral profiling. Measurements made during Phase I showed that HALOS can surpass the available hygrometers refresh rates while offering superior accuracy (<4%) from the low Frost Point (Tfrost) of -110°C to dew points (Tdew) at 35°C while being able to encompass sub ppm concentration at altitude and supersaturated conditions at ground level. Readings from HALOS include temperature sensitivity on the order of 0.2°C and absolute humidity sensitivity (AH) on the order of 0.010 μmol/m 3 (0.18 mg/m 3 ) at a data output rate exceeding 1 kHz. HALOS can be easily integrable in existing Department of Energy (DOE) or National Oceanic and Atmospheric Administration (NOAA) conventional or remote operated aircraft having either a fixed or rotary wing.

54 ENVIRONMENTAL SCIENCES↗