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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology

Abstract. Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of vegetation phenology represented in land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and high-temporal-resolution canopy greenness observations through digital repeat photography by the PhenoCam network. We compare a multiple linear regression model, a no-memory-effect LSTM model and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates across 50 sites and 317 site years during 2009–2018, covering deciduous broadleaf forests, evergreen needleleaf forests and grasslands. Results show that the deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than the no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957 and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands). We also find that the full-memory-effect LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the full-memory-effect LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that (1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and (2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach.

Geology↗
DOE OSTI2022

Inductive predictions of hydrologic events using a Long Short-Term Memory network and the Soil and Water Assessment Tool

We present machine learning methods to predict hydrologic features such as streamflow and soil moisture from spatially and temporally varying hydrological and meteorological data. Here, we used a temporal reduction technique to reduce computation and memory requirements and trained a Long Short-Term Memory (LSTM) network to predict soil moisture and streamflow over multiple watersheds. We show LSTM networks can be trained in a fraction of the time required by complex process-based and attention-based models such as Soil and Water Assessment Tool (SWAT) and GeoMAN without sacrificing accuracy. We also demonstrate that outside data - sourced from a watershed other than the target - can be used to train LSTM to comparable or even superior prediction accuracy. The success of LSTM in such spatially-inductive settings shows hydrologic features can be predicted with minimal prior knowledge of the watershed in question. Finally, we make all methodologies of this work publicly available as an end-to-end software pipeline that facilitates rapid prototyping of hydrologic learners.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Los Alamos Climatology 2021 Update

The Los Alamos National Laboratory (LANL or the Laboratory) operates a Meteorological Monitoring (Met) Program to support LANL emergency response, engineering designs, environmental compliance, environmental assessments, safety evaluations, weather forecasting, environmental monitoring, research programs, and environmental restoration. Weather data has been collected in Los Alamos since 1910. A 30-year averaging period is termed by the National Weather Service (NWS) as a climatic normal, and these statistics are updated every 10 years to represent the current climatic normal conditions. Previous climate statistics are provided in Bowen (1990) for the 1961–1990 averaging period and Dewart et al. (2017) for the 1981–2010 averaging period. No climate statistics were developed for the 1971-2000 averaging period. This report provides climate statistics for the current 30-year 1991–2020 averaging period.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Probabilistic Hazard Assessment for Tornadoes, Straight-Line Wind, and Extreme Precipitation at the Savannah River Site

Recent data sets for three meteorological phenomena with the potential to inflict damage on SRS facilities – tornadoes, straight-line winds, and heavy precipitation – are analyzed using appropriate statistical techniques to estimate the occurrence probabilities for these events in the future. Summaries of the results for DOE-mandated return periods and comparisons to similar calculations performed in 2013 by Werth et al. (W2013) are given. Using tornado statistics for i) the combined states of Georgia and South Carolina, and ii) a 2⁰ square area surrounding SRS, we calculated the probability per year of any location at SRS being struck by a tornado (the ‘strike’ probability) and the probability that any point will experience winds above set thresholds. The strike probability was calculated to be 7.04E-4 (1 chance in 1420) per year and tornadic wind speeds for DOE mandated return periods of 50,000 years (corresponding to wind design category 3 (WDC-3), and 125,000 years (meeting WDC-4) (USDOE, 2016) were estimated to be 132 mph and 147 mph, respectively. By contrast, default tornado wind speeds taken from ANSI/ANS-2.3-2011 are somewhat higher: 161 mph for return periods of 50,000 years and 173 mph every 125,000 years (ANS, 2011). Although the ANS and the SRS evaluation used the same basic model (Ramsdell and Rishel, 2007), the region defined in ANS 2.3 that encompasses the SRS also includes areas of the Great Plains and lower Midwest, regions with much higher occurrence frequencies of strong tornadoes. The SRS straight-line wind values associated with various return periods were calculated by fitting existing wind data to a GEV1 distribution and extrapolating the values for any return period from the tail of that function. For the DOE mandated return periods, we expect straight-line winds of 117 mph every 2500 years (the required WDC-3 standard) and 125 mph every 6250 years (WDC-4) at any point within the SRS. These values are similar to those from the ANS-2.3-2011 report, which has wind speeds of 125mph and 133 mph for return periods of 2500 years and 6250 years, respectively. For extreme precipitation, we compared the fits of two different theoretical extreme-value distributions and applied the one that fit the data best for each of several accumulation periods. The DOE mandated 6-hr accumulated rainfall for return periods of 10,000 years (corresponding to precipitation design category 3 (PDC-3) and 25,000 years (PDC-4) were estimated as 9.1 inches and 10.1 inches, respectively. For the 24-hr rainfall return periods of 10,000 years and 25,000 years, total rainfall estimates were 12.02 inches and 13.17 inches, respectively, higher than comparable values provided in the W2013 report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Growth modeling to evaluate alternative cultivation strategies to enhance national microalgal biomass production

In this paper, we present a model-based assessment of alternative cultivation strategies on algal biomass production within the conterminous United States. Our assessment focuses on two basic cultivation strategies: 1) seasonal rotation of three representative freshwater algal strains that are well suited for warm weather, cold weather, and all-season diverse weather conditions, respectively; and 2) variation between three open pond water depths (15cm, 20cm, and 25cm). The enhanced Biomass Assessment Tool (BAT) is applied on a site-specific, hourly basis at 5,832 North American Land Data Assimilation System Phase 2 1/8° meteorological model grid cells over a 30-year period (1980-2009) to evaluate the operational strategies. Recognizing that resource management decisions may also consider scales beyond the individual site, we also conduct a regional assessment of focused on 7 representative climate zones in the conterminous United States. Results demonstrate that the spatial variability of algal productivity is largely affected by the strain-specific growth response to light and temperature that vary significantly by climate zone and latitude. Therefore, with a limited set of alternative cultivation strategies, the BAT identified the best performing combination of cultivation strategies on a site-specific basis that considerably enhances national annual biomass productivity. Specifically, the appropriate choice of seasonal strain rotation is able to significantly dampen climate-driven seasonal and spatial variability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Two Large-Scale Meteorological Patterns are Associated with Short-Duration Dry Spells in the Northeastern United States

Large-scale meteorological pattern (LSMP)–based analysis is used in a novel way to understand meteorological conditions before and during short-duration dry spells over the northeastern United States. These LSMPs are useful to assess models and select better-performing models for future projections. Dry-spell events are identified from histograms of consecutive dry days below a daily precipitation threshold. Events lasting 12 days or longer, which correspond to ~10% of dry-spell events, are examined. The 500-hPa stream-function anomaly fields for the first 12 days of each event are time averaged, and k -means clustering is applied to isolate the dry-spell-related LSMPs. The first cluster has a strong low pressure anomaly over the Atlantic Ocean, southeast of the region, and is more common in winter and spring. The second cluster has strong high pressure over east-central North America and is most common during autumn. Over the region, both clusters have negative specific humidity anomalies, negative integrated vapor transport from the north, and subsidence associated with a midlatitude jet stream dipole structure that reinforces upper-level convergence. Subsidence is supported by cold-air advection in the first cluster and the location on the east side of the lower-level high pressure in the second cluster. Extratropical cyclone storm tracks are generally shifted southward of the region during the dry spells. Individual events lie on a continuum between two distinct clusters. These clusters have similar local, but different remote, properties. Although dry spells occur with greater frequency during drought months, most dry spells occur during nondrought months. Significance Statement: This study examines the large-scale weather patterns and meteorological conditions associated with dry-spell events lasting at least 2 weeks while affecting the northeastern United States. A statistical approach groups events together on the basis of similar atmospheric features. We find two distinct sets of patterns that we call large-scale meteorological patterns. These patterns reduce moisture, foster localized sinking, and shift the storm track southward along the Atlantic seaboard, all of which reduce precipitation. Besides greater understanding, knowing the meteorological patterns during short-term dryness in the region provides an important tool to assess how well atmospheric models reproduce these specific patterns. More dry spells occur in non-drought months than in drought months, which means that dry spells can occur without preexisting drought conditions.

58 GEOSCIENCES↗
DOE OSTI2020

Requirements and Conceptual Design of Off-gas Systems for the Reprocessing of Metallic Fuels

An assessment has been conducted to determine how key regulations regarding volatile radionuclide emissions to the atmosphere may apply to the off-gas streams associated with electrochemical reprocessing. The scope of this assessment was based upon a generic electrochemical reprocessing scheme with a throughput rate of 200 MTIHM/y applied to metallic fuel discharged from a sodium fast reactor (SFR), but the findings are able to be translated to other advanced nuclear scenarios as merited. Air dispersion modeling was performed using the EPA CAP-88 model and evaluated the uncontrolled decontamination factors (DFs) that would be required to achieve regulatory compliance with the dose-based limits set forth by EPA regulation 40 CFR 190.10(a). These DFs were compared to those required by fuel cycle–based limits set forth by EPA regulation 40 CFR 190.10(b). Two theoretical sites with disparate climatological conditions were selected for air dispersion modeling (Idaho and Tennessee). The radionuclides modeled included 3 H, 85 Kr, 129 I, and selected alpha-emitting transuranic isotopes (referred to here as 239 Pu-TRU <1y ). It was found that the fuel cycle-based limits in 40 CFR 190.10(b) are most restrictive for 85 Kr and 239 Pu-TRU <1y , with DFs of 3 and 6.1E+09, respectively. The dose-based limit as derived from 40 CFR 190.10(a) could require mitigation of tritium in some scenarios, with an estimated DF of about 3 for the reference scenarios. The fuel cycle-based limit for 129 I resulted in a DF of about 240 for the reference scenario. The need for iodine mitigation based on dose to the public depended upon the physical form of iodine as either particulate or vapor-phase species. Emission of iodine from the facility as a vapor necessitated DFs of about 2 but emission as a particulate would require DFs >6,000 to meet thyroid dose-based limits. Effects of physical form on needed iodine mitigation are significant, but the understanding of speciation of iodine both during electrochemical reprocessing and after release to the atmosphere is limited. The electrochemical processing unit operations were evaluated to identify potential release points for the volatile radionuclides and to assess the potential for retention of the radionuclides within the process (thus decreasing the need for mitigation). Mitigation strategies for 3 H, 85 Kr, 129 I, and 239 Pu-TRU <1y were identified. In all cases, there are reasonably achievable pathways to regulatory compliance, although in some cases additional R&D is merited to verify the chemical speciation of these isotopes and to develop and demonstrate potential treatment technologies for this application. Whether or not additional off-gas controls (beyond common operations such as HEPA filtration and oxygen and moisture control) are needed for any of these regulated or volatile radionuclides depends on the (a) type of facility (NRC-regulated or DOE), (b) used fuel process rate, (c) used fuel burnup and composition, (d) speciation and retention of volatile radionuclides in the process and in the cell gas cleanup system, (e) site-specific parameters such as location, meteorology, stack height, and site boundaries, and (f) levels of conservatism and safety factors used in assessing compliance to air emissions regulations. Performance of this assessment revealed several areas where information is lacking or additional research is required in order to better determine if or what kinds of off-gas control might be needed. First, and most significantly, the understanding of the chemical speciation and physical form and partitioning of iodine during electrochemical processing operations is lacking and prevents the ability to accurately assess the potential iodine mitigation requirements. Future research in this area should be multifaceted and include thermodynamic modeling of iodine speciation in different process steps, experiments to quantify the kinetics of vapor-phase and melt-phase transitions, bench-scale experiments to determine the potential chemical and physical form of iodine emissions from the electrorefining process, and verification of iodine behavior with experiments utilizing operational facilities. Similarly, an improved understanding of iodine behavior in the environment after release from the facility stack will be required to refine dose estimations, as particulate and vapor-phase emissions result in significantly different doses to the MEI.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

A Comparison of Adverse Meteorology Inputs for Emergency Management and Safety Basis Dispersion Modeling Applications at SRS

The MELCOR Accident Consequence Calculation System (MACCS), used for safety basis accident analyses at SRS, implements adjustment factors to consider the increased vertical turbulence associated with local surface roughness and the increased lateral turbulence associated with time-based plume meander. The two models used for SRS emergency planning radiological consequence assessment, i.e., Hotspot and Puff/Plume, currently do not have an option to input or apply diffusion adjustments for meander or surface roughness. Adverse meteorology conditions at SRS have been determined as E stability class and 1.3 meters per second wind speed for ground level releases. This is the input used for emergency planning models. Contrastingly safety basis analyses use a different input, representing adverse meteorology values as F stability class and wind speeds of 1.3 meters per second (for a ground level release). This results in an apparent discrepancy between the two approaches. In this report we document the differing modeling assumptions, discuss the logic behind each modeling approach used to represent local roughness and plume meander conditions, and numerically compare dispersion coefficients and concentration estimates obtained from each set of model inputs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Albedo susceptibility of northeastern Pacific stratocumulus: the role of covarying meteorological conditions

Abstract. Quantification of the radiative adjustment of marine low clouds to aerosol perturbations, regionally and globally, remains the largest source of uncertainty in assessing current and future climate. One of the important steps towards quantifying the role of aerosol in modifying cloud radiative properties is to quantify the susceptibility of cloud albedo and liquid water path (LWP) to perturbations in cloud droplet number concentration (Nd). We use 10 years of spaceborne observations from the polar-orbiting Aqua satellite to quantify the albedo susceptibility of marine low clouds to Nd perturbations over the northeast (NE) Pacific stratocumulus (Sc) region. Mutual information analysis reveals a dominating control of cloud state (e.g., LWP and Nd) on low-cloud albedo susceptibility, relative to the meteorological states that drive these cloud states. Through a LWP–Nd space decomposition of albedo susceptibilities, we show clear separation among susceptibility regimes (brightening or darkening), consistent with previously established mechanisms through which aerosol modulates cloud properties. These regimes include (i) thin non-precipitating clouds (LWP < 55 g m−2) that exhibit brightening (occurring 37 % of the time), corresponding to the Twomey effect; (ii) thicker non-precipitating clouds, corresponding to entrainment-driven negative LWP adjustments that manifest as a darkening regime (36 % of the time); and (iii) another brightening regime (22 % of the time) consisting of mostly precipitating clouds, corresponding to precipitation-suppression LWP positive adjustments. Overall, we find an annual-mean regional low-cloud brightening potential of 20.8±2.68 W m−2 ln(Nd)−1, despite an overall negative LWP adjustment for non-precipitating marine stratocumulus, owing to the high occurrence of the Twomey–brightening regime. Over the NE Pacific, clear seasonal covariabilities among meteorological factors related to the large-scale circulation are found to play an important role in grouping conditions favorable for each susceptibility regime. When considering the covarying meteorological conditions, our results indicate that for the northeastern Pacific stratocumulus, clouds that exhibit the strongest brightening potential occur most frequently within shallow marine boundary layers over a cool ocean surface with a stable atmosphere and a dry free troposphere above. Clouds that exhibit a darkening potential associated with negative LWP adjustments occur most frequently within deep marine boundary layers in which the atmospheric instability and the ocean surface are not strong and warm enough to produce frequent precipitation. Cloud brightening associated with warm-rain suppression is found to preferably occur either under unstable atmospheric conditions or humid free-tropospheric conditions that co-occur with a warm ocean surface.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Comparison of Projections for a Short-Term Release, CAP88 vs NARAC

The purpose of this study was to compare the projected dose using different plume models, evaluating a short duration release of tritium to the 16 compliance sectors and 4 additional points of interest using local meteorology. This report is written as guidance to the decision makers when reviewing this specific Gaussian plume model intended for radiological dose assessment and regulatory compliance, called CAP88 1 , as compared to a more-complex model designed for emergency response. The emergency response model, NARAC 2 , can be used to supplement the CAP88 compliance model evaluations, since NARAC is intended for use in situations where releases are shorter in duration and have increased complexity in terrain and meteorology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Sea surface warming and ocean-to-atmosphere feedback driven by large-scale offshore wind farms under seasonally stratified conditions

Offshore wind farms may induce changes in the upper ocean and near-surface atmosphere through coupled ocean-atmosphere feedbacks. Yet, the role of air-sea interactions mediated by offshore wind farms remains poorly understood. Using fully coupled ocean-atmosphere-wave model simulations for seasonally stratified conditions along the US East Coast, we show that simulated cumulative reductions in wind stress due to large-scale wind farm clusters lead to sea surface warming of 0.3° to 0.4°C and a shallower mixed layer. This warming drives upward heat fluxes, destabilizing the atmospheric boundary layer and enhancing wind stress, which partially offsets wake-induced wind deficits. These wake-ocean interactions influence near-surface meteorology and air-sea fluxes, suggesting that a coupled modeling approach may be necessary for assessing potential oceanographic impacts of offshore wind developments. However, ocean coupling exerts limited influence on winds at turbine-relevant heights or within downstream wakes, resulting in minimal impact on long-term energy. These findings suggest that models without ocean coupling may be adequate for wind energy applications.

17 WIND ENERGY↗
DOE OSTI2025

Contrasting Trends in Colorado Fire Weather Index from Reanalysis and Observations

Recent wildfires in Colorado raise the question of whether rising global temperatures have increased fire weather occurrences in Colorado. The U.S. National Weather Service defines fire weather as when “forecast weather conditions will result in a significant threat for the ignition and/or spread of wildfires.” We use two datasets to address the question: “How has the occurrence of fire weather changed in Colorado?” Using 22 years of observed weather conditions from a meteorological tower at the National Renewable Energy Laboratory and 67 years of ERA5 reanalysis data, we assess changing trends in Colorado fire weather as defined by hot, dry, and windy conditions. Additionally, we explore if the difference in recorded wind speeds between observational data and reanalysis data can be explained by differences in spatial and temporal resolution and what are the implications in the context of quantifying fire weather occurrences. The observational data are limited in temporal extent and spatial representativeness, but they capture exact real-world conditions at a location in complex terrain. The reanalysis data are available for an extended period of time and for the entire state, but the data are of relatively coarse spatial and temporal resolution and may fail to capture extremes. To quantify fire risk, we calculate the hot–dry–windy index (HDWI), which relies on wind speed and vapor pressure deficit. No statistically significant trend in the HDWI appears in the observational dataset. However, according to the reanalysis data, strong increasing trends in HDWI values emerge across all of Colorado. This apparent conflict between observational and reanalysis data suggests that reanalysis data may not be representative. Further, more long-term observational datasets are required to assess fire risk.

17 WIND ENERGY↗
DOE OSTI2026

Alaska Meteorology, Energy, and Transmission (MET) Toolkit

The Alaska MET (Meteorology, Energy, and Transmission) Toolkit is the National Laboratory of the Rockies' (NLR) new flagship atmospheric dataset, designed to support comprehensive long-term planning and operations across the entire power sector. Serving as the regional counterpart to CONUS-wide HRRR MET Toolkit, this dataset provides a comprehensive, high-fidelity meteorological record covering Alaska.The Alaska MET Toolkit is delivered at an hourly resolution on a standardized 2-km horizontal grid. This dataset is repackaged from the National Oceanic and Atmospheric Administration's (NOAA) operational High-Resolution Rapid Refresh for Alaska (HRRR-AK) forecasts. Spanning from 2019 to 2025, it overcomes the technical barriers of native weather models by providing spatial regridding from the native 3-km HRRR-AK horizontal resolution to a 2-km grid, temporal gap-filling, and vertical interpolation at key energy-relevant heights. By delivering highly accurate, validation-backed data across a comprehensive suite of atmospheric variables - including temperature, pressure, humidity, and wind characteristics - the Alaska MET Toolkit provides a highly accessible and strictly standardized foundation for modern power system modeling.

17 WIND ENERGY↗
DOE OSTI2024

Development of typical solar years and typical wind years for efficient assessment of renewable energy systems across the U.S.

Weather data plays a critical role in renewable energy analysis. Compared to using multiple Actual Meteorological Years, simulations using a single typical year require significantly fewer computational resources. Previous efforts to create typical weather datasets for renewable energy analysis either lack justified or optimized strategies for selecting and weighting different weather parameters or are limited to a few specific locations. Here, in this study, we developed a dataset comprising Typical Solar Years (TSYs) and Typical Wind Years (TWYs) for over 2000 locations across the U.S., based on data from NASA's POWER project. The strategies for creating TSYs and TWYs were optimized based on the simulated outputs of various PV systems and wind turbines in 16 representative cities. This dataset provides an efficient means for the rapid evaluation and optimization of renewable energy systems throughout the entire U.S. Additionally, the optimal strategies identified in this study can be directly applied to create near-optimal TSYs and TWYs for most locations worldwide. However, readers can also employ the optimization approach presented in this work to develop optimal strategies tailored for particular regions.

NASA POWER↗
DOE OSTI2023

Hyperspectral Infrared Observations of Arctic Snow, Sea Ice, and Non-Frozen Ocean from the RV Polarstern during the MOSAiC Expedition October 2019 to September 2020

This study highlights hyperspectral infrared observations from the Marine-Atmospheric Emitted Radiance Interferometer (M-AERI) collected as part of the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) deployment on the icebreaker RV Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. The ARM M-AERI directly measures the infrared radiance emission spectrum between 520 cm –1 and 3000 cm –1 (19.2–3.3 μm) at 0.5 cm –1 spectral resolution. These ship-based observations provide a valuable set of radiance data for the modeling of snow/ice infrared emission as well as validation data for the assessment of satellite soundings. Remote sensing using hyperspectral infrared observations provides valuable information on sea surface properties (skin temperature and infrared emissivity), near-surface air temperature, and temperature lapse rate in the lowest kilometer. Comparison of the M-AERI observations with those from the DOE ARM meteorological tower and downlooking infrared thermometer are generally in good agreement with some notable differences. Operational satellite soundings from the NOAA-20 satellite were also assessed using ARM radiosondes launched from the RV Polarstern and measurements of the infrared snow surface emission from the M-AERI showing reasonable agreement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Tools for Assessing Performance (TAP) Project: FY2023 Quarter 2 Report

LANL personnel presented TAP-sponsored work at the 103 rd Annual Meeting of the American Meteorological Society, which was held in Denver, CO in January 2023. There were four different presentations related to TAP-sponsored work that were given by LANL staff and studentsat this meeting: (1) Gloeckler, T., M. Nelson, H. Shah, M. Deshler, L. Wedell, R. Linn, N. Duboc, S. Pol, P. Bieringer, A. Annunzio, B. Martin, and H. Tinnesand, 2023. Testing Validity and Limitations of a Low-Order Diffusive Wake Model. 15 th Symposium on the Urban Environment, January 8-12, 2023, Denver, CO, U.S.A. (2) Nelson, M., H. Shah, T. Gloeckler, M. Deshler, L. Wedell, A. Ortega, R. Linn, N. Duboc, P. Conry, S. Pol, and H. Tinnesand, 2023. The Isolated Building Wake Experiment. Paper 1A.1. 24 th Symposium on Boundary Layers and Turbulence, January 8-12, 2023. Denver, CO, U.S.A. (3) Shah, H., M. Nelson, S. Pol, R. Linn, N. Duboc, and H. Tinnesand, 2023. Detailed Measurement of a Building Wake Under Real-World Atmospheric Conditions. Poster 102. 24 th Symposium on Boundary Layers and Turbulence, January 8-12, 2023, Denver, CO, U.S.A. (4) Wedell, L., M. Nelson, H. Shah, T. Gloeckler, M. Deshler, R. Linn, S. Pol, H. Tinnesand, 2023. The Isolated Building Wake Experiment: Using a Rotating Building to Mulitply the Number of Wake Measurement Locations without Moving the Instruments. Paper 1A.4A. 24 th Symposium on Boundary Layers and Turbulence, January 8-12, 2023, Denver, CO, U.S.A.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Statistically Resolved Planetary Boundary Layer Height Diurnal Variability Using Spaceborne Lidar Data

The Planetary Boundary Layer Height (PBLH) significantly impacts weather, climate, and air quality. Understanding the global diurnal variation of the PBLH is particularly challenging due to the necessity of extensive observations and suitable retrieval algorithms that can adapt to diverse thermodynamic and dynamic conditions. This study utilized data from the Cloud-Aerosol Transport System (CATS) to analyze the diurnal variation of PBLH in both continental and marine regions. By leveraging CATS data and a modified version of the Different Thermo-Dynamics Stability (DTDS) algorithm, along with machine learning denoising, the study determined the diurnal variation of the PBLH in continental mid-latitude and marine regions. The CATS DTDS-PBLH closely matches ground-based lidar and radiosonde measurements at the continental sites, with correlation coefficients above 0.6 and well-aligned diurnal variability, although slightly overestimated at nighttime. In contrast, PBLH at the marine site was consistently overestimated due to the viewing geometry of CATS and complex cloud structures. The study emphasizes the importance of integrating meteorological data with lidar signals for accurate and robust PBLH estimations, which are essential for effective boundary layer assessment from satellite observations.

54 ENVIRONMENTAL SCIENCES↗