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DOE OSTI2022

Quantifying Heavy Precipitation throughout the Entire Tropical Cyclone Life Cycle

Abstract Tropical cyclones (TCs) and their associated precipitation can have devastating impacts on the areas affected, with outcomes ranging from mudslides to inland flash flooding. Previous studies have used a fixed radius around the TC to isolate storm-related precipitation. One previous study instead used a dynamic radius of 8 m s −1 winds, but the wind field of the TC can deteriorate or shift quickly after landfall or the onset of extratropical transition (ET). This study uses a dynamical radius derived from the 500-hPa geopotential height in and around the TC to define TC- and post-tropical cyclone (PTC)-related heavy precipitation, allowing for the analysis of precipitation with tropical origins after the official demise of the original TC. Climatologies are constructed, indicating a maximum in TC- and PTC-related heavy precipitation in the west North Pacific and a secondary maximum in the east North Pacific. PTC-related heavy precipitation accounts for as much as 40% of the annual heavy precipitation in the northwest portion of the west North Pacific basin and 3.13% of heavy precipitation globally. We observe that the major hurricane stage contributes on average 2.6% of the global TC- and PTC-related precipitation, while the less intense but more common tropical storm stages of the TC life cycle contribute 85.7% of this observed precipitation. This analysis framework can be further extended to assess model biases and climate projections of TC and PTC precipitation.

Meteorology & Atmospheric Sciences↗

DOE OSTI2021

Polarimetric Signatures in Landfalling Tropical Cyclones

Polarimetric radar observations from the NEXRAD WSR-88D operational radar network in the contiguous United States, routinely available since 2013, are used to reveal three prominent microphysical signatures in landfalling tropical cyclones: 1) hydrometeor size sorting within the eyewall convection, 2) vertical displacement of the melting layer within the inner core, and 3) dendritic growth layers within stratiform regions of the inner core. Size sorting signatures within eyewall convection are observed with greater frequency and prominence in more intense hurricanes, and are observed predominantly within the deep-layer environmental wind shear vector-relative quadrants that harbor the greatest frequency of deep convection (i.e., downshear and left-of-shear). Melting-layer displacements are shown that exceed 1 km in altitude compared to melting-layer altitudes in outer rainbands and are complemented by analyses of archived dropsonde data. Dendritic growth and attendant snow aggregation signatures in the inner core are found to occur more often when echo-top altitudes are low (≤10 km MSL), nearer the -15°C isotherm commonly associated with dendritic growth. These signatures, uniquely observed by polarimetric radar, provide greater insight into the physical structure and thermodynamic characteristics of tropical cyclones, which are important for improving rainfall estimation and the representation of tropical cyclones in numerical models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Deep Learning Experiments for Tropical Cyclone Intensity Forecasts

Reducing tropical cyclone (TC) intensity forecast errors is a challenging task that has interested the operational forecasting and research community for decades. To address this, we developed a deep learning (DL)-based multilayer perceptron (MLP) TC intensity prediction model. The model was trained using the global Statistical Hurricane Intensity Prediction Scheme (SHIPS) predictors to forecast the change in TC maximum wind speed for the Atlantic basin. In the first experiment, a 24-h forecast period was considered. To overcome sample size limitations, we adopted a leave one year out (LOYO) testing scheme, where a model is trained using data from all years except one and then evaluated on the year that is left out. When tested on 2010–18 operational data using the LOYO scheme, the MLP outperformed other statistical–dynamical models by 9%–20%. Additional independent tests in 2019 and 2020 were conducted to simulate real-time operational forecasts, where the MLP model again outperformed the statistical–dynamical models by 5%–22% and achieved comparable results as HWFI. The MLP model also correctly predicted more rapid intensification events than all the four operational TC intensity models compared. In the second experiment, we developed a lightweight MLP for 6-h intensity predictions. When coupled with a synthetic TC track model, the lightweight MLP generated realistic TC intensity distribution in the Atlantic basin. Therefore, the MLP-based approach has the potential to improve operational TC intensity forecasts, and will also be a viable option for generating synthetic TCs for climate studies.

58 GEOSCIENCES↗

DOE OSTI2020

Large-scale inflatable structures for tunnel protection: a review of the Resilient Tunnel Plug project

The protection of underground civil infrastructure continues to be a high priority for transportation and transit security agencies. In particular, rail transit tunnels running under bodies of water are susceptible to disruptions due to flooding caused by extraordinary climatic events such as hurricanes or other events resulting from human activities. Several events have taken place in the past decades that have demonstrated the need to mitigate vulnerabilities or, at least, minimize the consequences of catastrophic events. Although it is impossible to prevent all situations that can lead to flooding, damage can be substantially decreased by reducing the area affected by the event. To minimize the effects of an event, a possible approach is to compartmentalize the tunnel system by creating temporary barriers that can contain the propagation of flooding until a more permanent solution can be implemented. One way to create a temporary barrier is by the deployment of a large-scale inflatable structure, also known as an inflatable plug. In such an application, the inflatable structure is prepared for placement, either permanently or temporally, and maintained ready for deployment, inflation, and pressurization when needed. The internal plug pressure imparts a normal force against the tunnel wall surface with the friction between the plug and tunnel surfaces opposing axial movement of the plug. The sealing effectiveness depends on the ability of the inflatable structure to self-deploy and fit, without human intervention, to the intricacies of the perimeter of the conduit being sealed. Primary design constraints include having the plug stowed away from the dynamic envelope of the trains and being able to withhold the pressure of the flooding water. This work presents a compilation of the main aspects of the activities completed for the development of large-scale inflatable structures as part of the Resilient Tunnel Plug (RTP) Project. The main test results and lessons learned are presented to demonstrate the viability of implementing large-scale inflatable plugs for the containment of flooding in rail tunnels systems. Over 400 coupon and specimen tests, 200 reduced scale tests, and 100 full-scale tests were conducted to demonstrate the efficacy of the design of different prototypes over a 10-year research and development project. The culmination of the work was 12 large-scale flooding demonstrations where the inflatable tunnel plug was shown able to be deployed remotely and withstand a simulated flooding event.

42 ENGINEERING↗

DOE OSTI2025

Multiclass Classification Using Bayesian Multivariate Adaptive Regression Splines

We present a new Bayesian model for the problem of multiclass classification. In this model, the probabilities of class membership of a given observation are determined by the mean of a latent Gaussian distribution. The mean functions of this latent distribution consist of combinations of highly flexible basis functions of the inputs: multivariate adaptive regression splines (MARS), first developed for multiple regression. We use reversible jump Markov chain Monte Carlo to make inference on the classification model, including the number of basis functions. We compare the probabilistic classification performance of our proposed approach to existing methods on simulated and benchmark data, and compare uncertainty estimates on simulated data. Our proposed method compares favorably with existing Bayesian and frequentist multiclass classification methods in out-of-sample probabilistic classification, and uncertainty estimation of these probabilistic classifications. We examine the fit of the proposed method to a data set of hurricane storm surge levels near Delaware Bay, US, and conclude that sea level rise is a key contributor to damage delivered by storm surge.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Coarse Woody Debris Decomposition Assessment Tool: Model validation and application

Coarse woody debris (CWD) is a significant component of the forest biomass pool; hence a model is warranted to predict CWD decomposition and its role in forest carbon (C) and nutrient cycling under varying management and climatic conditions. A process-based model, CWDDAT (Coarse Woody Debris Decomposition Assessment Tool) was calibrated and validated using data from the FACE (Free Air Carbon Dioxide Enrichment) Wood Decomposition Experiment utilizing pine ( Pinus taeda ), aspen ( Populous tremuloides ) and birch ( Betula papyrifera ) on nine Experimental Forests (EF) covering a range of climate, hydrology, and soil conditions across the continental USA. The model predictions were evaluated against measured FACE log mass loss over 6 years. Four widely applied metrics of model performance demonstrated that the CWDDAT model can accurately predict CWD decomposition. The R 2 (squared Pearson’s correlation coefficient) between the simulation and measurement was 0.80 for the model calibration and 0.82 for the model validation ( P <0.01). The predicted mean mass loss from all logs was 5.4% lower than the measured mass loss and 1.4% lower than the calculated loss. The model was also used to assess the decomposition of mixed pine-hardwood CWD produced by Hurricane Hugo in 1989 on the Santee Experimental Forest in South Carolina, USA. The simulation reflected rapid CWD decomposition of the forest in this subtropical setting. The predicted dissolved organic carbon (DOC) derived from the CWD decomposition and incorporated into the mineral soil averaged 1.01 g C m -2 y -1 over the 30 years. The main agents for CWD mass loss were fungi (72.0%) and termites (24.5%), the remainder was attributed to a mix of other wood decomposers. These findings demonstrate the applicability of CWDDAT for large-scale assessments of CWD dynamics, and fine-scale considerations regarding the fate of CWD carbon.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

UAS Edge Computing of Energy Infrastructure Damage Assessment

Energy infrastructure assessments are needed within 72 hours of natural disasters, and previous data collection methods have proven too slow. We demonstrate a scalable end-to-end solution using a prototype unmanned aerial system that performs on-the-edge detection, classification (i.e., damaged or undamaged), and geo-location of utility poles. The prototype is suitable for disaster response because it requires no local communication infrastructure and is capable of autonomous missions. Collections before, during, and after Hurricane Ida in 2021 were used to test the system. The system delivered an F1 score of 0.65 operating with a 2.7 s/frame processing speed with the YOLOv5 large model and an F1 score of 0.55 with a 0.48 s/frame with the YOLOv5 small model. Geo-location uncertainty in the bottom half of the frame was ~8 m, mostly driven by error in camera pointing measurement. With additional training data to improve performance and detect additional types of features, a fleet of similar drones could autonomously collect actionable post-disaster data.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Raw seedling and sapling census data collected from June 2015 - April 2019 within a tropical wet forest in Puerto Rico

This data package contains data used to evaluate the effects of experimental warming (4˚C above ambient), drought, and hurricane disturbance on species richness, diversity, and composition of understory plant communities across ontogeny at the Tropical Responses to Altered Climate Experiment (TRACE) in Luquillo, Puerto Rico. The raw data in this package include a file for the species composition matrix of woody seedlings (>10 cm and <20 cm height; TRACE_composition_seedling.csv) and saplings (>20 cm height; TRACE_composition_sapling.csv) within the six TRACE plots, collected during six census dates from June 2015 to April 2019. Also included is also a file with the species names associated with the species codes found in the composition files (TRACE_species_codes.csv), and a data dictionary file describing the columns in the composition files (data_dictionary_composition_files.csv). All data files are in csv format, with the exception of the metadata which is in xml format.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Data for Regier et al. (2025), "Short-term experimental flooding impacts soil biogeochemistry but not aboveground vegetation in a coastal forest"

Rising sea levels and intensifying storms increase flooding pressure on coastal forests, but the mechanisms that drive coastal forest mortality remain unclear. This study used an ecosystem-scale manipulation (TEMPEST, Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments) to simulate hurricane-level flooding of a coastal forest and explore the individual and interactive impacts of inundation and salinity. This dataset comprises the results reported by Regier et al. (2025) in their paper "Short-term experimental flooding impacts soil biogeochemistry but not aboveground vegetation in a coastal forest." It consists of measurements of:- Belowground conductance- Soil dissolved oxygen and redox- Soil and tree greenhouse gas fluxes- Leaf photosynthesis, stomatal conductance, and intercellular carbon dioxide- Sap flux- Soil volumetric water content and electrical conductivityAll files are plain-text CSV (comma separated value) and no special software is required to read them.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

AmeriFlux MX-PMm Puerto Morelos mangrove

This is the AmeriFlux version of the carbon flux data for the site MX-PMm Puerto Morelos mangrove. Site Description - The tower was located at the “Dr. Alfredo Barrera Marín” Botanical Garden. The surface monitored is covered with basin mangrove (sporadically flooded wetland) that grows in a fringe parallel to the northeastern coast (ca. 1 km) of the Yucatan Peninsula. The dominant species are Rhizophora mangle and Conocarpus erectus, which grow intertwined and relatively stunted (max tree height is 5m). Under the influence of trade winds and frequent stroms. The vegetation is regenerating after complete defoliation due to hurricane Wilma (cat. 4) passing in October 2005. The site is sporadically flooded by rainfall soil saturation excess; there are no marine surface water inputs but ground water level fluctuations show tidal signals. The terrain of the monitored surface is flat, while the area downwind of the tower presents a slope which corresponds to an ancient coast line. There is extensive urban development in the coastal dune along the coast mainly for tourism use (Mayan Riviera).

Alvarado-Barrientos, Ma. Susana↗

DOE OSTI2022

AmeriFlux FLUXNET-1F US-Ha1 Harvard Forest EMS Tower (HFR1)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Ha1 Harvard Forest EMS Tower (HFR1). This is the FLUXNET version of the carbon flux data for the site US-Ha1 Harvard Forest EMS Tower (HFR1) produced by applying the standard ONEFlux (1F) software. Site Description - The Harvard Forest tower is on land owned by Harvard University. The site is designated as an LTER site. Most of the surrounding area was cleared for agrigulture during European settlement in 1600-1700. The site has been regrowing since before 1900 (based on tree ring chronologies) and is now predominantly red oak and red maple, with patches of mature hemlock stand and individual white pine. Overstory trees were uprooted by hurricane in 1938. Climate measurements have been made at Harvard Forest since 1964.

Munger, J. William↗

DOE OSTI2022

AmeriFlux FLUXNET-1F US-xBR NEON Bartlett Experimental Forest (BART)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xBR NEON Bartlett Experimental Forest (BART). This is the FLUXNET version of the carbon flux data for the site US-xBR NEON Bartlett Experimental Forest (BART) produced by applying the standard ONEFlux (1F) software. Site Description - The Bartlett Experimental Forest is an actively managed forest; managed portions (30%) reflect a range of forest patch sizes and structural distributions. The Bartlett forest has a history of logging dating from colonial times through the beginning of the 20th century. Approximately 70% of the land area has remained uncut since the early 1900s. Natural disturbances include late 19th century fire, beech scale-Nectria complex (beech bark disease) beginning in the 1940s, severe wind disturbance resulting from hurricanes in 1938 and 1954 and a damaging ice storm in 1998.

Network), NEON (National Ecological Observatory↗

DOE OSTI2026

AmeriFlux FLUXNET-1F MX-PMm Puerto Morelos mangrove

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site MX-PMm Puerto Morelos mangrove. This is the FLUXNET version of the carbon flux data for the site MX-PMm Puerto Morelos mangrove produced by applying the standard ONEFlux (1F) software. Site Description - The tower was located at the “Dr. Alfredo Barrera Marín” Botanical Garden. The surface monitored is covered with basin mangrove (sporadically flooded wetland) that grows in a fringe parallel to the northeastern coast (ca. 1 km) of the Yucatan Peninsula. The dominant species are Rhizophora mangle and Conocarpus erectus, which grow intertwined and relatively stunted (max tree height is 5m). Under the influence of trade winds and frequent stroms. The vegetation is regenerating after complete defoliation due to hurricane Wilma (cat. 4) passing in October 2005. The site is sporadically flooded by rainfall soil saturation excess; there are no marine surface water inputs but ground water level fluctuations show tidal signals. The terrain of the monitored surface is flat, while the area downwind of the tower presents a slope which corresponds to an ancient coast line. There is extensive urban development in the coastal dune along the coast mainly for tourism use (Mayan Riviera).

Alvarado-Barrientos, Ma. Susana [Instituto de Ecol↗

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

Mid-Atlantic Bight Wave Hindcast To Support DOE Lidar Buoy Deployments: Model Validation

This study presents a shelf scale wave hindcast for the Mid-Atlantic Bight to provide accurate wave data to complement the measured data gathered by two DOE lidar buoy deployments off the coasts of Virginia and New Jersey. PNNL developed a ~2 km resolution model based on Wavewatch III and forced with analyzed winds and ocean currents; and executed a four-year hindcast for the period from January 2014 to December 2017. The model results are compared well against 16 wave-measuring buoys and data derived from six satellite-borne radar altimeters. In addition, the model results for two storm events that generated large waves, Hurricane Hermine and the January 2016 Blizzard, are discussed in detail.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Financing Microgrids in the Federal Sector

Recent high-impact events such as hurricanes and wildfires highlight the need for energy system resilience. One potential impact of these events is the loss of utility grid power to federal sites, negatively affecting the ability to carry out their missions. Depending on the need to sustain critical functions, it may be important to continue operations during an outage of the electric grid, or at least have power restored quickly after the event. One approach to addressing this need is through a microgrid. Microgrids can provide a more reliable solution than the traditional approach of using a backup generator at individual buildings. A microgrid can enhance a site’s electrical power system during normal operations, as well as provide reliable backup power to critical loads when electric utility power is interrupted. Many times, certain components of a microgrid (and potentially the entire system) can be justified economically and paid for out of energy savings or avoided costs. In cases where savings can cover the project costs, one of several privately financed procurement mechanisms may be used to implement the project. Procurement options available to all federal agencies include utility energy services contracts (UESC), energy savings performance contracts (ESPC), and utility services contracts (USC); other mechanisms such as a power purchase agreement (PPA), enhanced use lease (EUL), and utility privatization (UP) may also be useful to agencies that have the authority to use them. Implementing a microgrid in a comprehensive effort that also includes energy efficiency can provide an important savings component, while reducing power requirements for critical loads, thus reducing the cost of the microgrid and associated distributed energy resources (DER). However, it can be difficult to achieve enough savings and avoided costs to cover the entire microgrid system, so leveraging these avoided costs in combination with appropriated funds may be necessary to implement the full project. When a site is deciding if a microgrid is the best solution, activities early in the process include identifying critical loads and existing DERs, conversations with the local electric utility, and implementation options that work best for the agency and site. This paper explores procurement options and agreements that may be useful to federal agencies interested in implementing microgrids at their sites. Microgrid complexities, implementation considerations, and suggestions to get started are also discussed. This information will be most useful to agencies when they are exploring electric energy system resilience options and/or considering different procurement options to meet their needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Measurement Uncertainty in One-of-a-kind Experiments

A golden standard in science is to repeat an experimental measurement multiple times and calculate the measured value with experimental uncertainty by following well developed statistical procedures. For various reasons – cost, technical difficulty, international treaties, ethics of dealing with human or animal subjects, ecology - many important experiments and observations can not be repeated. Astronomy, earthquakes, hurricanes produce data from one-of-a-kind events. When information is not available in any other way, it should not be dismissed as qualitative, anecdotal evidence only. Analyzed in a mathematically rigorous way, it produces quantitative experimental data. Analysis of data from one-of-a-kind event differs from analysis of repeated experiments’ data. For repeated experiments, the experimental error includes a range of true values generated by repetitions of the experiment, and measurement uncertainty caused by detectors. They are independent. Repetitions of any experiment, as similar as achievable, always have built-in differences resulting in a range of the true values rather than in a single value. Measurement uncertainty depends on the measurement system only. Digital measurements have very small uncertainty, frequently smaller than the range of true experimental values resulting from built-in differences in the experiment repetitions. When data from one–of–a kind experiment are analyzed, only the measurement uncertainty can be reported.

42 ENGINEERING↗

DOE OSTI2020

Financing Microgrids in the Federal Sector

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗