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At least 1,549 records · Page 86

Geometric calibration of the Multi-Angle Imager for Aerosols (MAIA)

The Multi-Angle Imager for Aerosols (MAIA) instrument is currently scheduled for launch into polar Earth orbitin 2022. MAIA uses a single camera on a 2-axis gimbal for multi-angle viewing. The camera focal plane consistsof one visible and one SWIR detector, comprising a total of 14 wavelength channels from the UV to SWIR.Three of the channels are polarimetric to enhance the camera's sensitivity to airborne particle type. The MAIAtelescope is a standard four-mirror anastigmat and has signi?cant distortion of the ?eld-of-view. Each channelin the camera comprises one or two rows of pixels, and each row has a total of 1216 pixels. This talk will reviewthe tests undertaken to measure pixel positions in the focal plane with respect to their corresponding angles inobject space, i.e. the pointing angle of each pixel. This measurement is necessary to achieve good geo-locationof ground control points, a precursor to the successful registration of the 14 channels to each other.

Bruegge, Carol J.↗

Ice Sheet Melt Water Profile Mapping Using Multi-frequency Microwave Radiometry

For understanding englacial hydrology and its impact on ice sheet mass balance, observations of the liquid water content (LWC) within the ice sheets are needed. Earlier studies have shown the complementary nature of multi-frequency microwave radiometer measurements to detect subsurface LWC distribution in addition to surface LWC, which is critical for understanding the seasonal melt dynamics of ice sheets. In this study, we used 1.4 GHz brightness temperature (TB) measurements from the NASA Soil Moisture Active Passive (SMAP) satellite, and 6.9, 10.7, 18.9, and 36.5 GHz TB measurements from the JAXA Global Change Observation Mission-Water Shizuku (GCOM-W) satellite to investigate the multi-frequency response at pan-Greenland scale. The melt indications derived at different frequencies show trends consistent with persistent seasonal subsurface melt water and delayed subsurface refreezing of the seasonal melt water. The result suggests that the seasonal subsurface persistent melt water occurrences that are not captured by the high-frequency retrievals are both temporally and spatially very significant.

Burgin, Mariko↗

Multi-Resolution Elevation Mapping and Safe Landing Site Detection with Applications to Planetary Rotorcraft

In this paper, we propose a resource-efficient approach to provide an autonomous UAV with an on-board perception method to detect safe, hazard-free landing sites during flights over complex 3D terrain. We aggregate 3D measurements acquired from a sequence of monocular images by a Structure-from-Motion approach into a local, robot-centric, multi-resolution elevation map of the overflown terrain, which fuses depth measurements according to their lateral surface resolution (pixel-footprint) in a probabilistic framework based on the concept of dynamic Level of Detail. Map aggregation only requires depth maps and the associated poses, which are obtained from an on-board Visual Odometry algorithm. An efficient landing site detection method then exploits the features of the underlying multi-resolution map to detect safe landing sites based on the slope, roughness, and quality of the reconstructed terrain surface. The evaluation of the performance of the mapping and landing site detection modules are analyzed independently and jointly in simulated and realworld experiments in order to establish the efficacy of the proposed approach.

Brockers, Roland↗

The GEOS Neural Network Retrieval (NNR) for Multi-spectral AOD

One of the difficulties in data assimilation is the need for multi-sensor data merging that can account for temporal and spatial biases between satellite sensors. In the Goddard Earth Observing System Model Version 5 (GEOS-5) aerosol data assimilation system, a neural network retrieval (NNR) is used as a mapping between satellite observed top of the atmosphere (TOA) reflectance and AOD, which is the target variable that is assimilated in the model. By training observations of TOA reflectance from multiple sensors to map to a common AOD dataset (in this case AOD observed by the ground based Aerosol Robotic Network, AERONET), we are able to create a global, homogenous, satellite data record of AOD from multiple sensors. In this presentation, I will present recent updates to the GEOS-5 NNR for estimation of spectral AOD from MODIS and VIIRS, and the potential for multi-channel AOD assimilation to provide constraints on aerosol composition.

Patricia Castellanos↗

The Impact and Estimation of Uncertainty Correlation for Multi-Angle Polarimetric Remote Sensing of Aerosols and Ocean Color

Multi-angle polarimetric (MAP) measurements contain rich information for characterization of aerosol microphysical and optical properties that can be used to improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere-ocean system, although uncertainty correlation among measurements is generally ignored due to lack of knowledge on its strength and characterization. In this work, we provide a practical framework to evaluate the impact of the angular uncertainty correlation from retrieval results and a method to estimate correlation strength from retrieval fitting residuals. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on neural network forward models, is used to conduct the retrievals and uncertainty quantification. In addition, we also discuss a flexible approach to include a correlated uncertainty model in the retrieval algorithm. The impact of angular correlation on retrieval uncertainties is discussed based on synthetic AirHARP and HARP2 measurements using a Monte Carlo uncertainty estimation method. Correlation properties are estimated using auto-correlation functions based on the fitting residuals from both synthetic AirHARP and HARP2 data and real AirHARP measurement, with the resulting angular correlation parameters found to be larger than 0.9 and 0.8 for reflectance and DoLP, respectively, which correspond to correlation angles of 10° and 5°. Although this study focuses on angular correlation from HARP instruments, the methodology to study and quantify uncertainty correlation is also applicable to other instruments with angular, spectral, or spatial correlations, and can help inform laboratory calibration and characterization of the instrument uncertainty structure.

PACE↗

Multi-Angle Imager for Aerosols (MAIA) spectral and radiometric calibration

The Multi-angle Imaging SpectroRadiometer (MISR) was launched as part of NASA’s Terra satellite suite of instruments. Still operational, it makes use of observations from multiple view angles to retrieve aerosol abundance and particle properties. The Multi-Angle Imager for Aerosols (MAIA) instrument improves on this heritage by incorporating polarimetry and an expanded spectral range. Combining these data with surface measurements, the relationship between pollution and human health will be explored. MAIA has just completed camera testing, building on the experience from MISR. Spectral calibration now makes use of a double subtractive monochromator, built with the intent to allow the exit slit output to be uniform in spectral content. For radiometric testing, hardware upgrades have included adding UV lamps to the 1.65 cm (65") integrating sphere, use of a NIST-traceable sphere to establish absolute radiances, and the addition of a UV transfer spectrometer to support characterization of the sphere output from 300 to 2500 nm. During camera build, Newton’s rings were observed in the detector Quantum Efficiency (QE) measurements. This is due to etaloning within the detector itself. Etaloning was also evident in the spectral and radiometric characterizations performed on the completed camera. Spectral metrics, including center wavelength and width, are presented here using a moment’s analysis. This better represents the band properties, particularly in bands where fringing is observed, as compared to a full-width at half-maximum determination. It is believed that the MAIA camera has been carefully characterized, and meet its spectral and radiometric requirements.

Harten, Gerard Van↗

Polarimetric calibration of the Multi-Angle Imager for Aerosols (MAIA)

NASA’s Multi-Angle Imager for Aerosols (MAIA) mission, under development at the Jet Propulsion Laboratory, is designed to study the adverse health effects of different types of particulate air pollution. Planned for launch in 2023 for a 3-year mission, the MAIA satellite instrument will focus on a selected set of metropolitan target areas, where air quality monitors and health data are available. Aerosol concentration and speciation are inferred from multi-angle measurements of backscattered sunlight in 14 spectral bands from 350-2200 nm, with bands near 442, 645 and 1040 nm measuring the degree (DoLP) and angle of linear polarization (AoLP) in addition to radiance. The pushbroom camera has a ~240-km cross-track field of view with a nadir resolution of ~200 m, and is mounted onto a biaxial gimbal to provide along-track view angles within ±60°, to extend the field of regard to ±48°, and to view the instrument’s onboard calibrator (OBC) and dark target. The OBC consists of a sunlit transmissive diffuser, followed by 12 polarizers at different orientations. MAIA’s polarimetry is implemented using miniature wire grid polarizers on the focal plane array, and dual photoelastic modulators (PEMs) and achromatic quarter-wave plates to rapidly rotate the polarization. The resulting ~26-Hz intensity modulation encodes the linearly polarized and total radiance in each pixel, leaving the DoLP and AoLP insensitive to gain calibration. We report on the polarimetric calibration of the MAIA camera using a vacuum-compatible polarization state generator, consisting of a 1600W Xenon lamp, 12-inch integrating sphere, and rotating high-extinction polarizer. Mueller-matrix-based calibration coefficients for each detector pixel are derived from measurements at multiple polarizer angles, and are used to correct the measurements for instrumental polarization aberrations. Prior to flight, the calibrated MAIA camera is panned across the OBC to characterize its output, using uniform illumination with an irradiance similar to the Sun.

Werne, Thomas↗

Single and Multi-Node Modeling of Direct, Submerged, and Self-Pressurization of A Cryogenic Propellant Tank Using Nodal Tools

The pressurization of cryogenic propellant tanks will always be an important process so long as cryogenic liquids are being considered as fuel sources or used for other in-space applications. Pressure control of the tank ullage is necessary for managing propellant flowrates to an engine or a receiver tank, and modeling of the process is used to predict the pressurant requirements and the amount of propellant boiloff. Direct ullage pressurization is the more traditional approach to tank pressurization, as the physics are straight-forward, and ample test (flight) data have been collected and analyzed over the past several decades. Submerged injection pressurization is an alternate method for tank pressurization and has been shown to reduce pressurant requirements, subcool the propellant, and reduce the risk of ullage collapse. Additionally, the pressurant gas entering the ullage is usually much colder when using the submerged pressurization approach, resulting in reduced propellant boiloff. These benefits are at the expense of vaporizing a small percentage of the propellent. Both tank pressurization methods are viable options for current and future space missions, and it is important to have the capability of analyzing the tank ullage conditions for both approaches. Our previous work has demonstrated the development of a Generalized Fluid System Simulation Program (GFSSP) model, which contains a thermodynamic equilibrium heat and mass transfer subroutine capable of effectively analyzing both direct and submerged pressurization systems [1-2]. This subroutine has most recently been enhanced to include the non-equilibrium effect of pressurant dissolution into the propellant. To date the ullage has always been represented as a single node, and although the simulated single-node temperatures have good comparison with the volume-averaged ullage temperatures computed from test data, the physics of the thermal stratification in the ullage were never captured, and adjustment factors in the model were required. The purpose of this paper is to introduce the development of a multi-node ullage model using GFSSP and to discuss the improvements of the simulated ullage temperature distribution and its resulting effects on ullage heat transfer processes. Test data from the Cryogenic Propellant Storage and Transfer Engineering Developmental Unit (CPST EDU) was used for model validation. For additional comparison, a Thermal Desktop (TD) model was also developed to analyze the CPST EDU direct ullage pressurization tests using both a single node and multi-node approach. The model includes the direct pressurant line, vent line, fill/drain line, and a TD FloCAD Compartment. The TD FloCAD Compartment is employed to represent the liquid and ullage as single volumes inside the tank, to include a liquid/vapor interface, and to generate network level objects such as lumps (analogous to nodes in GFSSP), paths, and ties between the fluid and thermal elements. An established heat load on the model tank was leveraged from a pre-existing higher-fidelity model correlated to CPST EDU test data.

pressurization↗

Space Algae-2: Preflight Testing for A Long-Duration, Multi-Omics Analysis of Arthrospira Platensis

The cyanobacteria Arthrospira platensis NIES-39, commonly known as spirulina, could provide a fresh supply of nutrients for crew on long-duration spaceflight missions. Spirulina is a readily digestible food that is high in protein with all essential amino acids as well as significant levels of B vitamins, antioxidants, and anti-inflammatory metabolites. Spaceflight has multiple abiotic stressors such as increased ionizing radiation and microgravity, which causes a lack of convective mixing. These environmental conditions may impact productivity, nutritional composition, and in long-duration propagation, spaceflight stress may impact the genetic stability of spirulina cultures. We are developing an International Space Station experiment to continuously culture A. platensis for six months. Multi-omics profiling will be used to monitor for changes in the genome, transcriptome, proteome, and metabolome to determine if A. platensis is a suitable nutritional supplement on long-duration missions. During preflight testing we developed a protocol for inoculated liquid cultures to survive a 10-week storage period prior to photo-incubation. The bioreactor bag, temperature, and lighting conditions that support a 14-day growth cycle between passages were also determined. Media testing identified minimal salts supporting robust growth that can be stored in liquid or dry form. A simple filtration method was developed to dewater cultures and harvest biomass for frozen sample return. We optimized a cryopreservation method to enable return of live cells for isolation of individual A. platensis clones. The concept of operations for Space Algae-2 developed from these test results as well as progress on multi-omics analysis methods will be presented.

Algae↗

NASA GeneLab Multi-study Visualization Portal

NASA GeneLab has helped advance the field of Space Biology by providing a public repository where researchers can store, share, analyze and visualize the results of space flight related omics experiments. The GeneLab data visualization portal allows any user, regardless of bioinformatics knowledge or access to computational resources, to interact with the experimental data, draw their own conclusions, and gain insights about the effects of space on living systems. These tools help democratize scientific research and foster the NASA Open Science initiative. The new multi-study feature of the GeneLab visualization platform allows users to mine study metadata from RNA sequencing (RNA-seq) experiments to identify samples of interest by filtering datasets based on organism, tissue, assay technology type, and/or factor. Once samples are selected from multiple datasets, users can combine and normalize the sample data, then utilize the visualization displays, including Principal Component Analysis (PCA) plots, to assess sample distributions. Finally, users can perform differential gene expression analysis on the combined data and visualize the results through PCA plots, Volcano plots, Pair plots, Heatmap, Ideogram and Gene Set Enrichment Analysis. All user-generated results and visualizations will be available for download. Here, we present a biological study using samples from multiple GeneLab RNA-seq datasets and analyzed using the multi-study visualization platform to demonstrate inter- and intra-study variability, as well as commonly differentially expressed genes between spaceflight and ground control conditions across datasets. This new feature opens a wide range of possibilities and opportunities for further development including combining other assay technology types and integration with batch effect correction techniques and machine learning applications. Overall, this tool allows users to increase the statistical power of individual experiments, validate hypothesis, identify patterns, and opens the door to new and exciting research.

space biology↗

Comparison of Dust Optical Depth From Multi-Sensor Products and MONARCH (Multiscale Online Non-hydrostatic AtmospheRe CHemistry) Dust Reanalysis Over North Africa, the Middle East, and Europe

Aerosol reanalysis datasets are model-based, observationally constrained, continuous 3D aerosol fields with a relatively high temporal frequency that can be used to assess aerosol variations and trends, climate effects, and impacts on socioeconomic sectors, such as health. Here we compare and assess the recently published MONARCH (Multiscale Online Non-hydrostatic AtmospheRe CHemistry) high-resolution regional desert dust reanalysis over northern Africa, the Middle East, and Europe (NAMEE) with a combination of ground-based observations and space-based dust retrievals and products. In particular, we compare the total and coarse dust optical depth (DOD) from the new reanalysis with DOD products derived from MODIS (MODerate resolution Imaging Spectroradiometer), MISR (Multi-angle Imaging SpectroRadiometer), and IASI (Infrared Atmospheric Sounding Interferometer) spaceborne instruments. Despite the larger uncertainties, satellite-based datasets provide a better geographical coverage than ground-based observations, and the use of different retrievals and products allows at least partially overcoming some single-product weaknesses in the comparison. Nevertheless, limitations and uncertainties due to the type of sensor, its operating principle, its sensitivity, its temporal and spatial resolution, and the methodology for retrieving or further deriving dust products are factors that bias the reanalysis assessment. We, therefore, also use ground-based DOD observations provided by 238 stations of the AERONET (AErosol RObotic NETwork) located within the NAMEE region as a reference evaluation dataset. In particular, prior to the reanalysis assessment, the satellite datasets were evaluated against AERONET, showing moderate underestimations in the vicinities of dust sources and downwind regions, whereas small or significant overestimations, depending on the dataset, can be found in the remote regions. Taking these results into consideration, the MONARCH reanalysis assessment shows that total and coarse-DOD simulations are consistent with satellite- and ground-based data, qualitatively capturing the major dust sources in the area in addition to the dust transport patterns. Moreover, the MONARCH reanalysis reproduces the seasonal dust cycle, identifying the increased dust activity that occurred in the NAMEE region during spring and summer. The quantitative comparison between the MONARCH reanalysis DOD and satellite multi-sensor products shows that the reanalysis tends to slightly overestimate the desert dust that is emitted from the source regions and underestimate the transported dust over the outflow regions, implying that the model's removal of dust particles from the atmosphere, through deposition processes, is too effective. More specifically, small positive biases are found over the Sahara desert (0.04) and negative biases over the Atlantic Ocean and the Arabian Sea (−0.04), which constitute the main pathways of the long-range dust transport. Considering the DOD values recorded on average there, such discrepancies can be considered low, as the low relative bias in the Sahara desert (< 50 %) and over the adjacent maritime regions (< 100 %) certifies. Similarly, over areas with intense dust activity, the linear correlation coefficient between the MONARCH reanalysis simulations and the ensemble of the satellite products is significantly high for both total and coarse DOD, reaching 0.8 over the Middle East, the Atlantic Ocean, and the Arabian Sea and exceeding it over the African continent. Moreover, the low relative biases and high correlations are associated with regions for which large numbers of observations are available, thus allowing for robust reanalysis assessment.

Michail Mytilinaios↗

Precipitation over the U.S. Coastal Land/Water Using Gauge-Corrected Multi-Radar/Multi-Sensor System and Three Satellite Products

The weather and climate over the coastal regions have received increasing attention because of substantial population growth, the rising sea level, and extreme weather. Satellite remote sensing provides global precipitation estimates (including coastal land/ocean). While these datasets have been extensively evaluated over land, they have rarely been assessed over coastal ocean. As precipitation radars cover both coastal land and ocean, we used the Multi-Radar/Multi-Sensor System (MRMS) gauge-corrected precipitation product from 2018 to 2020 to evaluate three widely used satellite-based precipitation products over the U.S. coastal land versus the ocean (and the water over the Great Lakes). These products included the Integrated Multi-satellite Retrievals for GPM (IMERG), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Climate Prediction Center Morphing technique (CMORPH). The MRMS data showed a precipitation climatology difference between the coastal land and the ocean that was higher in the winter and lower in the summer and autumn. IMERG and CMORPH performed best over land and water, respectively, while PERSIANN was the most consistent in its performance over land versus water. Heavy precipitation was overestimated by the three products, with larger overestimates over water than over land. These results were not affected by the MRMS uncertainties due to the gauge correction or by the use of different versions.

coastal precipitation↗

Hypervelocity Impact Characterization on Hubble Space Telescope Multi-Layer Insulation

Multi-layer insulation (MLI) is a common material used on rocket bodies and spacecraft to provide thermal regulation. This material is typically attached on the surface of the object in orbit and is thus exposed to the space environment and to micrometeoroid and orbital debris (MMOD) impacts. Occasionally, these exposed surfaces are returned and used by NASA’s Orbital Debris Program Office (ODPO) for analysis. When used to assess impacts on surfaces over time, materials like MLI provide an opportunity for in-situ measurements to support the Orbital Debris Engineering Model (ORDEM) development and validation. During the 2009 Hubble Space Telescope (HST) Serving Mission, the MLI blankets covering the HST electronics bays 5, 8, and 10 were removed and subsequently analyzed by the ODPO in 2010. The bay 5 blanket was used in concert with ground-based hypervelocity impact tests on single layer MLI to validate the ORDEM 3.1 computer model of the OD environment, but further research was required to properly differentiate the impact features attributed to MM or OD. The ORDEM 3.1 validation effort identified several areas of further study to improve model validation and understanding of the space environment. Among these topics are the effects of underlying MLI layers on first layer impact feature characteristics, the effects of space weathering and associated damage to the surface on impact feature characteristics, and the transition from perforating to cratering regions. To better understand hypervelocity impact damage and long-term degradation of MLI surfaces, further hypervelocity impact testing was proposed and completed from 2020 to 2022. This paper discusses test conditions and parameters with resultant impact hole analyses of single layer versus multi-layered samples, space-exposed samples versus pristine, equivalent samples, and perforation versus cratering criteria in the difficult transition region. Damage equations, with associated uncertainties, are estimated from the test parameters and presented. The results of a feasibility study are described, in which the scanning electron microscopy/electron dispersive x-ray was used to examine the MLI layers to differentiate MM and OD impacts. Finally, the extension of HST MLI outcomes and analysis methodology as it applies to future returned MLI samples is discussed.

Melissa A Murray↗

Space Algae-2: Preflight Testing for A Long-Duration, Multi-Omics Analysis of Arthrospira Platensis

The cyanobacteria Arthrospira platensis NIES-39, commonly known as spirulina, could provide a fresh supply of nutrients for crew on long-duration spaceflight missions. Spirulina is a readily digestible food that is high in protein with all essential amino acids as well as significant levels of B vitamins, antioxidants, and anti-inflammatory metabolites. Spaceflight has multiple abiotic stressors such as increased ionizing radiation and microgravity, which causes a lack of convective mixing. These environmental conditions may impact productivity, nutritional composition, and in long-duration propagation, spaceflight stress may impact the genetic stability of spirulina cultures. We are developing an International Space Station experiment to continuously culture A. platensis for six months. Multi-omics profiling will be used to monitor for changes in the genome, transcriptome, proteome, and metabolome to determine if A. platensis is a suitable nutritional supplement on long-duration missions. During preflight testing we developed a protocol for inoculated liquid cultures to survive a 10-week storage period prior to photo-incubation. The bioreactor bag, temperature, and lighting conditions that support a 14-day growth cycle between passages were also determined. Media testing identified minimal salts supporting robust growth that can be stored in liquid or dry form. A simple filtration method was developed to dewater cultures and harvest biomass for frozen sample return. We optimized a cryopreservation method to enable return of live cells for isolation of individual A. platensis clones. The concept of operations for Space Algae-2 developed from these test results as well as progress on multi-omics analysis methods will be presented.

Algae↗

HelioSwarm: Transfer Trajectory and P/2 Lunar Resonant Orbit Design for a Multi-Satellite Observatory

The HelioSwarm mission will fly a multi-satellite observatory in a P/2 lunar resonant orbit to study plasma turbulence in the solar wind. Lunar resonant orbits enable long-term access to high Earth altitudes. The relative dynamics of a multi-satellite observatory introduce novel considerations for lunar resonant orbit design. Additional maneuvers in the post lunar encounter transfer enable heightened control of the lunar resonant orbit initial state compared to previous missions. Adjusting the mission orbit’s initial conditions enables refinement of the orbit design based on requirements imposed by the relative dynamics of the observatory. refinement of the orbit design based on requirements imposed by the relative dynamics of the observatory.

cislunar↗

Leveraging Multi-Primary PS-InSAR Configurations for the Robust Estimation of Coastal Subsidence

InSAR is a key technique used to constrain contributions of diverse processes to coastal subsidence, also known as vertical land motion (VLM). However, coastal environments can pose major challenges for InSAR due to natural disturbances that degrade interferogram quality. We describe a new multi-primary pairing strategy for persistent scatterer InSAR (PS-InSAR) to estimate subsidence in challenging coastal environments. Our method retains only consistent PS candidates across multiprimary substacks and solves for redundant velocity observations using SVD-based inversion, similar to the conventional small baseline subset (SBAS) method. Through simulations and a case study comparing with single-primary PS-InSAR and conventional SBAS techniques, we show that our pairing strategy reduces temporal and spatial uncertainty in subsidence estimates in the presence of strong but temporary decorrelation loss, even with increased distance from the reference point. Moreover, our method visibly dampens time-series variation and decreases standard error in our time series fit by nearly 2x in our case study. Thus, we find that implementing a multi-primary PS-InSAR configuration is a simple method of increasing the robustness of VLM estimates in challenging coastal environments.

Persistent scatterer InSAR↗

Breaking Wave Field Statistics With A Multi-Layer Model

The statistics of breaking wave fields are characterised within a novel multi-layer framework, which generalises the single-layer Saint-Venant system into a multi-layer and non-hydrostatic formulation of the Navier–Stokes equations. We simulate an ensemble of phase-resolved surface wave fields in physical space, where strong nonlinearities, including directional wave breaking and the subsequent highly rotational flow motion, are modelled, without surface overturning. We extract the kinematics of wave breaking by identifying breaking fronts and their speed, for freely evolving wave fields initialised with typical wind wave spectra. The Λ(c) distribution, defined as the length of breaking fronts (per unit area) moving with speed c to c + dc following Phillips (J. Fluid Mech., vol. 156, 1985, pp. 505–531), is reported for a broad range of conditions. We recover the Λ(c) ∝ c−6 scaling without wind forcing for sufficiently steep wave fields. A scaling of Λ(c) based solely on the root-mean-square slope and peak wave phase speed is shown to describe the modelled breaking distributions well. The modelled breaking distributions are in good agreement with field measurements and the proposed scaling can be applied successfully to the observational data sets. The present work paves the way for simulations of the turbulent upper ocean directly coupled to a realistic breaking wave dynamics, including Langmuir turbulence, and other sub-mesoscale processes.

Jiarong Wu↗