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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Global Estimates of PBL Depth from Space-Borne LIDAR

The planetary boundary layer (PBL) is responsible for communicating the exchange of energy, moisture, momentum, pollutants, and aerosols between the surface and the free atmosphere and is therefore crucial to many studies of the atmosphere. Unfortunately, there have historically been few observations of this important layer due to the complexity involved in its measurement. However, with the advent of more advanced satellites, global measurements of the PBL are now becoming possible. The PBL is often characterized by a high concentration of aerosols within the layer and low level clouds capping it and these are observable from space. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite was launched in 2006 with the intention of observing aerosols and clouds and was the first space-based LIDAR optimized for this purpose. CALIPSO observations are therefore well suited to observing the depth of the PBL. Since it was launched, CALIPSO has been making nearly continuous measurements enabling a global picture of PBL depth. We plan to present a global PBL depth product and how it evolves throughout the year. The product is able to identify deeper PBL depths in the summer hemisphere over land and deeper depths along the northern hemisphere oceanic storm tracks in winter associated with cold air traveling over warm water. Large seasonal cycles are also evident in the subtropical desert locations among other features. In addition, comparisons will be made between several estimates of PBL depth based on turbulent intensity, meteorology profiles, and aerosol profiles from the GEOS5 model.

McGrath-Spangler, Erica lynn↗
NASA NTRS2011

Effect of Thin Cirrus Clouds on Dust Optical Depth Retrievals From MODIS Observations

The effect of thin cirrus clouds in retrieving the dust optical depth from MODIS observations is investigated by using a simplified aerosol retrieval algorithm based on the principles of the Deep Blue aerosol property retrieval method. Specifically, the errors of the retrieved dust optical depth due to thin cirrus contamination are quantified through the comparison of two retrievals by assuming dust-only atmospheres and the counterparts with overlapping mineral dust and thin cirrus clouds. To account for the effect of the polarization state of radiation field on radiance simulation, a vector radiative transfer model is used to generate the lookup tables. In the forward radiative transfer simulations involved in generating the lookup tables, the Rayleigh scattering by atmospheric gaseous molecules and the reflection of the surface assumed to be Lambertian are fully taken into account. Additionally, the spheroid model is utilized to account for the nonsphericity of dust particles In computing their optical properties. For simplicity, the single-scattering albedo, scattering phase matrix, and optical depth are specified a priori for thin cirrus clouds assumed to consist of droxtal ice crystals. The present results indicate that the errors in the retrieved dust optical depths due to the contamination of thin cirrus clouds depend on the scattering angle, underlying surface reflectance, and dust optical depth. Under heavy dusty conditions, the absolute errors are comparable to the predescribed optical depths of thin cirrus clouds.

Feng, Qian↗
NASA NTRS2019

Estimating Alpine Snow Depth by Combining Multi-Frequency Passive Radiance Observations with Ensemble Snowpack Modeling

This paper presents a physically-based snow depth retrieval algorithm adapted for deep mountainous snowpack and airborne multifrequency (10.7, 18.7, 37.0 and 89.0 GHz) passive microwave (PM) radiance observations from a single flight. The algorithm employs a single forecast-analysis cycle of a traditional sequential assimilation scheme. It uses an ensemble of multi-layer snowpack model runs to resolve snow microstructure and melt-refreeze crusts, andmicrowave radiative transfer models to relate snow properties to microwave measurements.Snow depth was retrieved at a 120 m spatial resolution over three 1 km2 Intensive Study Area (ISA) within the Rabbit Ears Meso-Cell Study Area (MSA) from the NASA Cold Land Processes Experiment (CLPX) in Colorado (United States) for one date in February 2003. When evaluated against in situ observations, root mean square error (RMSE) of the snow depth from the assimilation was 13.3 cm for areas with low (<5 %) forest cover, which was a reduction of 48 % in the RMSE compared with the modeled snow depth when the PM observations were not assimilated; indicating a ~5% relative error of the posterior snow depth with respect to the average snow depth (200 cm) measured at these pixels. For pixels with forest cover ranging from 5-15% and 15-30%, results were improved (R2 increased from 0.65 to 0.71 and from 0 to 0.38, respectively) by introducing a forest radiative transfer model during the assimilation.

Airborne Multifrequency↗
NASA NTRS2020

ICESat-2 Meltwater Depth Estimates: Application to Surface Melt on Amery Ice Shelf, East Antarctica

Surface melting occurs during summer on the Antarctic and Greenland ice sheets, but the volume of meltwater stored has been difficult to quantify due to a lack of accurate depth estimates. NASA’s ICESat-2 laser altimeter brings a new capability: photons penetrate water and are reflected from both the water and the underlying ice; the difference provides a depth estimate. ICESat-2 sampled Amery Ice Shelf on 2 January 2019 and showed double returns from surface depressions, indicating meltwater. For four melt features, we compared depth estimates from eight algorithms: six based on ICESat-2 and two from coincident Landsat-8 and Sentinel-2 imagery. All algorithms successfully identified surface water at the same locations. Algorithms based on ICESat-2 produced the most accurate depths; the image-based algorithms underestimated depths (by 30-70%). This implies that ICESat-2 depths can be used to tune image-based algorithms, moving us closer to quantifying stored meltwater volumes across Antarctica and Greenland.

ICESat-2↗
NASA NTRS2021

Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval

In this study, we compare eight recently developed snow depth products over Arctic sea ice, which use satellite observations, modeling, or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance observations and airborne measurements. Large mean snow depth discrepancies are observed over the Atlantic and Canadian Arctic sectors. The differences between climatology and the snow products early in winter could be in part a result of the delaying in Arctic ice formation that reduces early snow accumulation, leading to shallower snowpacks at the start of the freeze-up season. These differences persist through spring despite overall more winter snow accumulation in the reanalysis-based products than in the climatologies. Among the products evaluated, the University of Washington (UW) snow depth product produces the deepest spring (March–April) snowpacks, while the snow product from the Danish Meteorological Institute (DMI) provides the shallowest spring snow depths. Most snow products show significant correlation with snow depths retrieved from Operational IceBridge (OIB) while correlations are quite low against buoy measurements, with no correlation and very low variability from University of Bremen and DMI products. Inconsistencies in reconstructed snow depth among the products, as well as differences between these products and in situ and airborne observations, can be partially attributed to differences in effective footprint and spatial–temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in situ and remote sensing observations.

Lu Zhou↗
NASA NTRSDate not supplied

AMSR-2 Daily Snow Depth Data Product Using a Neural Network Algorithm Trained by Collocated ICESat-2 Measurements

By using diffusion theory and Monte Carlo lidar radiative transfer simulations, Hu 1 et al. (2022b) has derived snow depth from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. Lu 2 et al. (2022,2024) calculated the snow depth by applying the methods to the satellite ICESat-2 lidar measurements over the Arctic sea ice, as well as land surfaces of Northern Hemisphere. In this paper, a neural network (NN) algorithm, employing several channels from AMSR-2 and the humidity vertical profiles GMAO GEOS-IT, is trained to determine snow depth identified by time and geolocation matched 2019 ICESat-2 snow-depth data during winter months over the Arctic sea ice. The trained NN snow-depth was applied to 2014-2020 AMSR-2 clear pixel data, although the algorithms perform reasonably well in thinner clouds. This paper used AMSR-2 data, a passive microwave instrument to generate a wide range of snow depth data, covering extensive spatial areas in the cross-orbit direction.

Neural Network↗
DOE OSTI2021

Pyrolysis temperature and soil depth interactions determine PyC turnover and induced soil organic carbon priming

Pyrogenic organic carbon (PyC) is a complex, heterogeneous class of thermally altered organic substrates, but its dynamics and how its behavior changes with soil depth remain poorly understood. We conducted a laboratory incubation study to investigate the interactive effects of pyrolysis temperature and soil depth on the turnover of PyC compared to its precursor wood and native SOC (NSOC). We incubated dual-labeled (13C and 15N) jack pine pyrogenic organic matter produced at 300 °C (PyC300), 450 °C (PyC450), and their precursor pine wood in a fine-loamy, mixed-conifer forest soil for 745 days. A mixture of surface (0–10 cm) and subsurface (50–70 cm) forest soils, with and without labeled biomass were incubated in the dark at 55% soil water field capacity and 25 °C. Total 13C from PyC and wood mineralized as 13C-CO2 (as % of C added to soil) declined with an increase in pyrolysis temperature as follows: 54 ± 7.7% for wood, 3.1 ± 0.2% for PyC300, and 0.94 ± 0.08% for PyC450. Furthermore, after 2 years, soil depth interacted with pyrolysis temperature to affect C turnover, with total wood C losses significantly declining from 70.6% in surface soils to 37.5% in subsurface soil, while total losses of PyC300 and PyC450 were unaffected by differences between surface and subsurface soils. Wood induced negative priming (i.e., decreased mineralization rates) in surface soil at days 3 and 60, while PyC300 induced positive priming (i.e., increased mineralization rates) in subsurface soil at day 60. After 2 years, unlabeled NSOC losses increased from 9.2 ± 0.8% of NSOC in unamended treatments to 16.5 ± = 2.6% of NSOC with PyC450 additions. Our results suggest that PyC pyrolyzed at a given temperature can mineralize at similar rates between soil depths, and high amounts of PyC450 in subsurface soils can stimulate NSOC losses. These findings indicate that soil depth imposes critical controls on PyC dynamics belowground.

13C-labeling↗
NASA NTRS2017

Potential Basin-Scale Estimates of Arctic Snow Depth with Sea Ice Freeboards from CryoSat-2 and ICESat-2: An Exploratory Analysis

The potential of deriving snow depth estimates using differences in freeboard heights from CryoSat-2 (CS-2) and ICESat-2 (IS-2) is examined. In our analysis, we use lidar freeboard from the Airborne Topographic Mapper (ATM) on Operation IceBridge (OIB) as proxy of IS-2 total (snow plus ice) freeboard. Snow depths are estimates from the OIB snow radar. Differences in height between the total (ATM) and ice (CS-2) freeboards are related to snow depth by the refractive index of the snow layer, which is dependent on snow density. For two years (2014 and 2015), regression of the ATM and CS-2 freeboard differences against OIB snow depth gives correlations of approximately 0.80, estimated eta (sub s) of 1.21, and standard errors of 8 centimeters. The resulting refractive index, eta (sub s), can be compared to that expected of the Arctic snow cover in early spring (1.25 plus or minus 0.05). The expected biases and variability in the regression analysis are discussed. Results suggest that snow depth can be estimated from the freeboard differences. The benefits of adjusting the orbit of CS-2 for providing more optimized overlaps between IS-2 and CS-2 are considered.

Sea ice↗
NASA NTRS2021

Importance of Quantifying the Full-depth Carbon Reservoir of Jamaica Bay Salt Marshes, New York

Constraining uncertainty in the global carbon cycle requires valid assessment of both surface and stored carbon in marine and coastal ecosystems (Blue Carbon) as well as terrestrial carbon (forests, peatlands, and soils) [Pendleton et al., 2012]. Quantifying the global carbon stock of coastal salt marshes, potentially the most efficient carbon-burying ecosystems in the world per area, is a key area of further research in both of these fields Pendleton et al., 2012. One of the largest challenges is that despite the fact that salt marshes often sequester carbon several meters deep, nearly all estimates of salt marsh carbon stocks consider only the upper 1 m of sediment (Windham-Myers et al., 2015) [54]. This is particularly concerning because coastal wetlands are increasingly at risk due to climate change, sea level rise, and anthropogenic disturbance and destruction (Deegan et al., 2012) [15]. Using full-depth measurements from marsh cores, we estimate the carbon stock of five salt marshes in the highly urbanized estuary of Jamaica Bay, New York and argue that partial-depth measurements can underestimate carbon stocks. These estimates use calculated carbon content and probe depth data of these marshes collected between 2000 and 2019, applying this data across the full area of the marsh obtained from satellite imagery. Carbon density measurements are then multiplied by the full-depth volume of the marshes to create an estimate of total carbon stock. In addition to calculating present-day estimates, we compare our carbon stock estimates to historical Jamaica Bay imagery to calculate historical carbon stocks and carbon loss. The carbon stock estimates presented here show a 95% carbon stock loss between 1885 and 2019 in Jamaica Bay and highlight the severe underestimation of carbon stocks without full-depth calculations. These findings have important implications for disappearing salt marshes with regard to the global carbon cycle and the incorporation of belowground carbon into global climate models. The findings are increasingly relevant for advocacy efforts aiming to conserve these marshes with sea level rise.

Tidal wetlands↗
DOE OSTI2024

Depth and microtopography influence microbial biogeochemical processes in a forested peatland

Background and aims: Peat-accumulating wetlands have undulating surfaces of raised areas (hummocks) and depressions (hollows). Hummock-hollow microtopography in relation to the water table influences the distribution of plant species, root density, and microbial community composition, which could in turn alter carbon (C) and nitrogen (N) cycling within peatlands. We used paired hummock and hollow cores from a boreal, forested peatland to assess how microtopography influences peatland microbial function and, in turn, ecosystem C and N cycling. Methods: The peat was analyzed for microbial biomass and potential enzyme activity in 10 cm depth increments relative to the water table, resulting in two increments for hollows and three for hummocks, which has a raised increment above the water table. Results: Across hummocks and hollows, microbial C and N and fungal biomass generally decreased with depth from the peat surface. In contrast, potential enzyme activity often increased with depth, but this varied within enzyme functional groups according to topography, depth, or both. The potential enzyme activity of C-N degrading peptidases, for example, differed across the five topography × depth increments with the lowest rate in the aerated hummocks. Hummocks compose approximately 66% of the land area at our study site and would therefore underestimate C turnover by an average of 25% if solely used to extrapolate patterns across a forested bog. Conclusion: In conclusion, our results suggest that asynchrony in C and N cycling across the undulating surface of forested peatlands impacts our ability to accurately predict biogeochemical cycling across this important ecosystem.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

How deep to dig: effects of web-scraping search depth on hyperlink network analysis of environmental stewardship organizations

Abstract Social network analysis (SNA) tools and concepts are essential for addressing many environmental management and sustainability issues. One method to gather SNA data is to scrape them from environmental organizations’ websites. Web-based research can provide important opportunities to understand environmental governance and policy networks while potentially reducing costs and time when compared to traditional survey and interview methods. A key parameter is ‘search depth,’ i.e., how many connected pages within a website to search for information. Existing research uses a variety of depths and no best practices exist, undermining research quality and case study comparability. We therefore analyze how search depth affects SNA data collection among environmental organizations, if results vary when organizations have different objectives, and how search depth affects social network structure. We find that scraping to a depth of three captures the majority of relevant network data regardless of an organization’s focus. Stakeholder identification (i.e., who is in the network) may require less scraping, but this might under-represent network structure (i.e., who is connected). We also discuss how scraping web-pages of local programs of larger organizations may lead to uncertain results and how our work can combine with mixed methods approaches.

Sayles, Jesse S. (ORCID:0000000218378920)↗
DOE OSTI2025

Advancing the Understanding of Snow Accumulation, Melting, and Associated Thermal Insulation Using Spatially Dense Snow Depth and Temperature Time Series

Snow thermal insulation is a critical factor influencing ground thermal dynamics and associated biogeochemical processes. We analyzed the spatiotemporal variability of snow accumulation, melting, and thermal insulation dynamics using spatially dense, collocated snow depth and ground interface temperature time series over two consecutive years. We demonstrated that considering late‐winter snow depth alone was insufficient to fully capture the complexity in snow and insulation dynamics. The influence of vegetation and topography on snow depth distribution varied over the season, across sites and years. We found that deep snow with a long melting period had a substantial impact on thawing n‐factors. To better predict snow insulation effects, we proposed a new weighted snow depth metric that integrates mean daily snow depth and air temperature throughout the cold season. Our results provide insights for developing space‐time remote sensing products and evaluating the representation of snow and permafrost processes in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Disentangling the Impacts of Microtopography and Shrub Distribution on Snow Depth in a Subarctic Watershed: Toward a Predictive Understanding of Snow Spatial Variability

Snow plays a critical role in carbon cycling, vegetation dynamics, and permafrost hydrology at high latitudes by influencing surface energy exchange. Predicting snow distribution patterns is essential for understanding the evolution of Arctic ecosystems, yet scaling process-level knowledge to landscape predictions remains challenging. Here, we analyze snow depth (2019 and 2022), terrain elevation, and vegetation height from a watershed on the Seward Peninsula, Alaska, to examine how topography and shrubs shape snow redistribution across spatial scales. We find that snow depth is strongly coupled to terrain at scales below ∼60 m but becomes increasingly decoupled at larger scales. The topographic model of snow depth variation, which transforms terrain data to align with these scale-dependent snow patterns, is well correlated with local snow depth variations (linear fit R 2 > 0.5 for 85% of 100-m patches). A machine learning reconstruction of shrub canopy snow trapping reveals a simple exponential relationship between canopy structure and snow accumulation ( R 2 = 0.59), highlighting the combined influence of topography and vegetation on snow distribution. Together, these empirical relationships capture much of the observed snow variability in the watershed ( R 2 = 0.49, root mean square error (RMSE) = 30 cm), though systematic limitations persist in areas of strong scour and at coarser scales where wind-terrain interactions are more complex. These findings provide a framework for more efficient snow depth prediction and offer insights to improve snow-vegetation feedback representation in Earth System Models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Agricultural practices influence soil microbiome assembly and interactions at different depths identified by machine learning

Agricultural practices affect soil microbes which are critical to soil health and sustainable agriculture. To understand prokaryotic and fungal assembly under agricultural practices, we use machine learning-based methods. We show that fertility source is the most pronounced factor for microbial assembly especially for fungi, and its effect decreases with soil depths. Fertility source also shapes microbial co-occurrence patterns revealed by machine learning, leading to fungi-dominated modules sensitive to fertility down to 30 cm depth. Tillage affects soil microbiomes at 0-20 cm depth, enhancing dispersal and stochastic processes but potentially jeopardizing microbial interactions. Cover crop effects are less pronounced and lack depth-dependent patterns. Machine learning reveals that the impact of agricultural practices on microbial communities is multifaceted and highlights the role of fertility source over the soil depth. Machine learning overcomes the linear limitations of traditional methods and offers enhanced insights into the mechanisms underlying microbial assembly and distributions in agriculture soils.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Unraveling the depth-dependent causal dynamics of methanogenesis and methanotrophy in a high-latitude fen peatland

The dynamics of methane (CH 4 ) cycling in high-latitude peatlands through different pathways of methanogenesis and methanotrophy are still poorly understood due to the spatiotemporal complexity of microbial activities and biogeochemical processes. Additionally, long-term in situ measurements within soil columns are limited and associated with large uncertainties in microbial substrates (e.g. dissolved organic carbon, acetate, hydrogen). To better understand CH 4 cycling dynamics, we first applied an advanced biogeochemical model, ecosys , to explicitly simulate methanogenesis, methanotrophy, and CH 4 transport in a high-latitude fen (within the Stordalen Mire, northern Sweden). Next, to explore the vertical heterogeneity in CH 4 cycling, we applied the PCMCI/PCMCI+ causal detection framework with a bootstrap aggregation method to the modeling results, characterizing causal relationships among regulating factors (e.g. temperature, microbial biomass, soil substrate concentrations) through acetoclastic methanogenesis, hydrogenotrophic methanogenesis, and methanotrophy, across three depth intervals (0–10 cm, 10–20 cm, 20–30 cm). Our results indicate that temperature, microbial biomass, and methanogenesis and methanotrophy substrates exhibit significant vertical variations within the soil column. Soil temperature demonstrates strong causal relationships with both biomass and substrate concentrations at the shallower depth (0–10 cm), while these causal relationships decrease significantly at the deeper depth within the two methanogenesis pathways. In contrast, soil substrate concentrations show significantly greater causal relationships with depth, suggesting the substantial influence of substrates on CH 4 cycling. CH 4 production is found to peak in August, while CH 4 oxidation peaks predominantly in October, showing a lag response between production and oxidation. Overall, this research provides important insights into the causal mechanisms modulating CH 4 cycling across different depths, which will improve carbon cycling predictions, and guide the future field measurement strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Optimal Twirling Depth for Classical Shadows in the Presence of Noise

The classical shadows protocol is an efficient strategy for estimating properties of an unknown state p using a small number of state copies and measurements. In its original form, it involves twirling the state with unitaries from some ensemble and measuring the twirled state in a fixed basis. It was recently shown that for computing local properties, optimal sample complexity (copies of the state required) is remarkably achieved for unitaries drawn from shallow depth circuits composed of local entangling gates, as opposed to purely local (zero depth) or global twirling (infinite depth) ensembles. Here, we consider the sample complexity as a function of the depth of the circuit, in the presence of noise. We find that this noise has important implications for determining the optimal twirling ensemble. Under fairly general conditions, we (i) show that any single-site noise can be accounted for using a depolarizing noise channel with an appropriate damping parameter f, (ii) compute thresholds f th at which optimal twirling reduces to local twirling for Pauli operators, (iii) nth order Renyi entropies (n ≥2), and (iv) provide a meaningful upper bound t max on the optimal circuit depth for any finite noise strength f, which applies to observables and entanglement entropy measurements. In conclusion, these thresholds strongly constrain the search for optimal strategies to implement shadow tomography and are easily tailored to the experimental system at hand.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Indications of a Decrease in the Depth of Deep Convective Cores with Increasing Aerosol Concentration during the CACTI Campaign

Abstract An aerosol indirect effect on deep convective cores (DCCs), by which increasing aerosol concentration increases cloud-top height via enhanced latent heating and updraft velocity, has been proposed in many studies. However, the magnitude of this effect remains uncertain due to aerosol measurement limitations, modulation of the effect by meteorological conditions, and difficulties untangling meteorological and aerosol effects on DCCs. The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign in 2018–19 produced concentrated aerosol and cloud observations in a location with frequent DCCs, providing an opportunity to examine the proposed aerosol indirect effect on DCC depth in a rigorous and robust manner. For periods throughout the campaign with well-mixed boundary layers, we analyze relationships that exist between aerosol variables (condensation nuclei concentration > 10 nm, 0.4% cloud condensation nuclei concentration, 55–1000-nm aerosol concentration, and aerosol optical depth) and meteorological variables [level of neutral buoyancy (LNB), convective available potential energy, midlevel relative humidity, and deep-layer vertical wind shear] with the maximum radar-echo-top height and cloud-top temperature (CTT) of DCCs. Meteorological variables such as LNB and deep-layer shear are strongly correlated with DCC depth. LNB is also highly correlated with three of the aerosol variables. After accounting for meteorological correlations, increasing values of the aerosol variables [with the exception of one formulation of aerosol optical depth (AOD)] are generally correlated at a statistically significant level with a warmer CTT of DCCs. Therefore, for the study region and period considered, increasing aerosol concentration is mostly associated with a decrease in DCC depth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Unified Wind-Wave Growth and Spectrum Functions for All Water Depths: Field Observations and Model Results

Abstract Wind-wave development is governed by the fetch- or duration-limited growth principle that is expressed as a pair of similarity functions relating the dimensionless elevation variance (wave energy) and spectral peak frequency to fetch or duration. Combining the pair of similarity functions, the fetch or duration variable can be removed to form a dimensionless function of elevation variance and spectral peak frequency, which is interpreted as the wave energy evolution with wave age. The relationship is initially developed for quasi-neural stability and quasi-steady wind forcing conditions. Further analyses show that the same fetch, duration, and wave-age similarity functions are applicable to unsteady wind forcing conditions, including rapidly accelerating and decelerating mountain gap wind episodes and tropical cyclone (TC) wind fields. Here it is shown that with the dimensionless frequency converted to dimensionless wavenumber using the surface wave dispersion relationship, the same similarity function is applicable in all water depths. Field data collected in shallow to deep waters and mild to TC wind conditions and synthetic data generated by spectrum model computations are assembled to illustrate the applicability. For the simulation work, the finite-depth wind-wave spectrum model and its shoaling function are formulated for variable spectral slopes. Given wind speed, wave age, and water depth, the measured and spectrum-computed significant wave heights and the associated growth parameters are in good agreement in forcing conditions from mild to TC winds and in all depths from deep ocean to shallow lake. Significance Statement This paper presents a growth function and spectrum model to describe wind-wave development in all water depths. Their applicability covers a wide range of wind forcing conditions including steady, accelerating, decelerating, and tropical cyclone events. Support for the unified spectrum model and growth function is presented with field observations and numerical computations.

Hwang, Paul A.↗