SEARCH · Search NASA

Results for “data gap”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 667 records · Page 37

All sources NASA NTRS DOE OSTI

NASA NTRS1989

Infrared/submillimeter optical properties data base

The general goal was to build a data base containing optical properties, such as reflectance, transmittance, refractive index, in the far infrared to submillimeter wavelength region. This data base would be limited to selected crystalline materials and temperature between 300 and 2 K. The selected materials were: lithium, lead, and strontium; the bromides of potassium and thallium; the carbides of silicone and tungsten; and the materials of KRS5, KRS6, diamond, and sapphire. Last summer, barium fluoride was selected as prototype material for building the data base. This summer the literature search, preparation of the data for barium fluoride was completed. In addition the literature search for data related to the compounds mentioned was completed. The current status is that barium fluoride is in a form suitable for a NASA internal publication. The papers containing the data on the other materials were xeroxed and they are ready to be reduced. On the reverse side, the top figure is a sample combination of data for the index of refraction at 300 K. The lower figure shows the transmittance vs wavelength at 300 and 80 K. These figures are a sample of many which were developed. Since barium fluoride was studied more than most of the materials listed above, it is clear that additional measurements should be made to fill in the gaps present on both temperature and wavelength data.

Alley, Phillip W.↗

DOE OSTI2026

Topsoil bulk geochemical compositions - An updated harmonized global dataset

Mineral weathering is a key biogeochemical process because of the capacity of minerals to stabilize organic matter. However, predicting soil weathering status across large spatial areas still isn’t possible due to a lack of global data and theoretical frameworks. To address this knowledge gap, multiple global datasets of bulk topsoil geochemical compositions have been harmonized using R. These datasets document topsoil bulk geochemical compositions across five continents (n = ~16,000 observations). Source data for these observations include the EuroGEOSurveys Geochemical Baseline Database (FOREGS), the US Geological Survey National Geochemical Database (NASGLP), the Geochemical Atlas of Australia (GAA), the US Geological Survey Alaska Geochemical Database (AGD84), the National Cooperative Soil Survey (NCSS), the European Geochemical Mapping of Agricultural Soil (GEMAS), Ecorespira-Amazon (ERA), the New Zealand Geochemical Baseline Survey (NZ_GBS), and the African Soil Information Service (AFSIS). Major elements observed include Aluminum (Al), Calcium (Ca), Iron (Fe), Potassium (K), Magnesium (Mg), Sodium (Na), Titanium (Ti), Manganese (Mn), Phosphorus (P), Carbon (C), and Sulfur (S). This data package includes the harmonized dataset itself, and the R scripts necessary to harmonize these datasets, in addition to metadata that describes all columns, files, and databases used in this project. Methods & Sampling Step 1 – Databases of geochemical data identified This study aimed to leverage existing measurements of topsoil geochemical data. Databases were first identified and deemed appropriate for inclusion if they were measuring soils and performed these measurements on the <2mm soil fraction. Databases such as NCSS and AGD84 needed more post processing to include in the database and this was done using the NCSS_datamerge_031626 R file and Alaska_USGSmerge_031626 R file, respectively. Step 2 – Database harmonization Once appropriate databases were identified, they were harmonized for ease of analysis using the R script Database_Harmonization_031826. This included removing columns from original datasets that would not be used in analysis (removed columns are noted in the code). Then, data cleaning procedures specific to each dataset were undertaken. This includes standardizing columns to include units and adding metadata columns regarding procedures for analyzing specific elements. Functions for standardizing measurements and units are outline in R files: calculate element_mg_kg_031626, calculate_oxide_wt_perc_031626, change_oxide_caps_031626, and conv_2_numeric_031626. This also included adding a unique identifier for each sample to identify it with its respective database (see CD_ID in data dictionary). Geographic information: Data reflect a compilation of datasets collected globally. Geographic areas covered by each of the datasets include: - EuroGEOSurveys Geochemical Baseline Database (FOREGS) - European continent - North American Soil Geochemical Landscapes (NASGLP) - continental United States and limited parts of Canada (see database key for more details) - National Geochemical Survey of Australia (GAA) - Australia - Alaska geochemical database (AGDB4) - Alaska - National Cooperative Soil Survey (NCSS) - Global measurements, but concentrated in the continental United States - Geochemical data for arable land and land under permanent grass cover in continental Europe (GEMAS) - continental Europe - Ecorespira-Amazon (ERA) - Geochemical data from the Amazon basin - Geochemical baseline data for New Zealand (NZGBS) - New Zealand - Geochemical data collected across continental Africa (AfSIS) - Measurements across Africa

EARTH SCIENCE > LAND SURFACE > SOILS↗

DOE OSTI2025

Ubiquity and Causes of Soil Water Preferential Flow Across 17 Ecoregions

Abstract Preferential flow (PF) in soil causes the rapid transport of water, nutrients, and contaminants into the subsurface, influencing groundwater recharge and streamflow. Data scarcity has hindered the quantification of PF occurrence and the identification of its drivers across diverse ecoregions. We address this gap by analyzing high‐frequency, multi‐depth soil moisture data across 17 ecoregions in the USA, using ∼1,500 sensors at 40 sites. We discovered that PF is widespread, with sites experiencing PF in up to 60% of rainfall events ≥2 mm. Multiple approaches consistently show that PF is more likely to occur with increased peak rainfall intensity, finer textured material, low soil moisture variability, humid climate, and higher net primary productivity. This suggests that PF patterns could shift with projected climate changes, increasing uncertainty in predictions of groundwater recharge, water quality, and streamflow generation. Plain Language Summary Water can bypass part of the soil's matrix through a process called preferential flow (PF). This quick transport of water through the soil brings with it nutrients and contaminants and eventually makes it to groundwater and streams. To ensure ample amounts of good quality groundwater and surface water we need to understand when and where PF occurs. We inferred when PF occurred at 40 different sites across 17 ecoregions in the USA using soil moisture and rainfall data. We found that PF happened at all sites and in up to 60% of rainfall events ≥2 mm. Preferential flow was most likely at sites with high rainfall intensities, high clay content in soils, low variability in soil moisture, and high vegetation productivity. As rainfall intensities are predicted to increase due to climate change and vegetation becomes more productive, PF occurrence becomes more important for predicting groundwater recharge, water quality, and streamflow generation. Key Points Preferential flow (PF) is ubiquitous across the USA and occurs in up to 60% of all rainfall events ≥2 mm Rainfall intensity, soil texture, and antecedent soil moisture emerge as critical in generating PF across diverse ecoregions Two different PF detection approaches show similar relationships between key drivers and occurrence of PF

Li, Bonan↗

DOE OSTI2024

Modeling of the metal–insulator transition temperature in alio-valently doped VO 2 through symbolic regression

The correlated semiconductor vanadium dioxide (VO 2 ) exhibits an insulator–metal transition (IMT) near room temperature, which is of interest in various device applications. Precise IMT temperature control is crucial to determine the use cases across technologies such as thermochromic windows, actuators for robots or neuronal oscillators. Doping the cation or anion sites can modulate the IMT by several tens of degrees and control hysteresis. However, modeling the effects of control parameters (e.g., doping concentration, type of dopants) is challenging due to complex experimental procedures and limited data, hindering the use of traditional data-driven machine learning approaches. Symbolic regression (SR) can bridge this gap by identifying nonlinear expressions connecting key input parameters to target properties, even with small data sets. In this work, we develop SR models to capture the IMT trends in VO 2 influenced by different dopant parameters. Using experimental data from the literature, our study reveals a dual nature of the IMT temperature with varying tungsten (W) doping concentrations. The symbolic model captures data trends and accounts for experimental variability, providing a complementary approach to first-principles calculations. Our feature-driven analysis across a broader class of dopants informs selectivity and provides qualitative insights into tuning phase transition properties valuable for neuromorphic computing and thermochromic windows.

36 MATERIALS SCIENCE↗

DOE OSTI2025

AI in Astrophysics: Tackling Domain Shift, Model Robustness and Uncertainty

Artificial Intelligence (AI) is revolutionizing physics research from probing the large-scale structure of the Universe to modeling subatomic interactions and fundamental forces. Yet, a major challenge persists: AI models trained on simulations or old experiment / astronomical survey often perform poorly when applied to new data exposing issues of dataset (domain) shift, model robustness, and uncertainty in predictions. This talk will introduce common challenges in applying AI across domains and present solutions based on domain adaptation a set of techniques designed to improve model generalization under domain shift. We will cover foundational ideas, practical strategies, and current research frontiers in this area. Through examples in astrophysics, we'll explore how domain adaptation can help bridge the gap between synthetic and real-world data, improve trust in model outputs, and advance scientific discovery.

Ciprijanvoic, Aleksandra [Fermilab] (ORCID:0000000↗

NASA NTRS2020

Cloud-Computing and Machine Learning in Support of Country-Level Land Cover and Ecosystem Extent Mapping in Liberia and Gabon

Liberia and Gabon joined the Gaborone Declaration for Sustainability in Africa (GDSA), established in 2012, with the goal of incorporating the value of nature intonational decision making by estimating the multiple services obtained from ecosystems using the natural capital accounting framework. In this study, we produced 30-m resolution 10 classes land cover maps for the 2015 epoch for Liberia and Gabon using the Google Earth Engine (GEE) cloud platform to support the ongoing natural capital accounting efforts in these nations. We pro-pose an integrated method of pixel-based classification using Landsat 8 data, the Random Forest(RF) classifier and ancillary data to produce high quality land cover products to fit abroad range of applications, including natural capital accounting. Our approach focuses on a pre-classification filtering (Masking Phase) based on spectral signature and ancillary data to reduce the number of pixels prone to be misclassified; therefore, increasing the quality of the final product. The proposed approach yields an overall accuracy of 83% and 81% for Liberia and Gabon, respectively, out performing prior land cover products for these countries in both thematic content and accuracy. Our approach, while relatively simple and highly replicable, was able to produce high quality land cover products to fill an observational gap in up to date land cover data at national scale for Liberia and Gabon.

Celio de Sousa↗

DOE OSTI2025

Shift Happens: Building Robust AI Models with Domain Adaptation

Artificial Intelligence (AI) is revolutionizing physics research—from probing the large-scale structure of the Universe to modeling subatomic interactions and fundamental forces. Yet, a major challenge persists: AI models trained on simulations or old experiment / astronomical survey often perform poorly when applied to new data—exposing issues of dataset (domain) shift, model robustness, and uncertainty in predictions. This summer school session will introduce students to common challenges in applying AI across domains and present solutions based on domain adaptation—a set of techniques designed to improve model generalization under domain shift. We will cover foundational ideas, practical strategies, and current research frontiers in this area. Through examples in astrophysics, we'll explore how domain adaptation can help bridge the gap between synthetic and real-world data, improve trust in model outputs, and advance scientific discovery. The concepts discussed are broadly applicable across physics and other scientific disciplines, making this a valuable topic for anyone interested in building robust, transferable AI models for science.

Ciprijanovic, A. [Fermilab] (ORCID:000000031281719↗

NASA NTRS2017

Saffire: A Novel Approach to Study of Spacecraft Fire Safety Using Un-Manned Spacecraft

The Saffire Experiment represents the first test of spacecraft fires at a scale relevant to real spacecraft. Although large-scale fire tests on Earth are conducted for every type of habitable structure (buildings, planes, trains, automobiles, ships and mines) they had never been attempted in a space experiment for obvious reasons of practicality and safety. This is despite the fact that fire is a catastrophic hazard for spaceflight where the crew has very limited or no escape options. The spread and growth of a fire, combined with its interactions with the vehicle cannot be expected to scale linearly from small-scale test data, and so there is a substantial gap in our ability to predict the behavior of spacecraft fire and also its impact on the spacecraft habitability and operability. The experiments were performed aboard the Cygnus vehicle, a large unmanned resupply spacecraft to the International Space Station (ISS). The experiment was installed in the Cygnus vehicle, and remained dormant during docked operations. Once the vehicle was reloaded with ISS trash, it unberthed and moved to a separate orbit. The Saffire experiment was initiated by ground command and downlinked the data before the vehicle re-entered into the earth's atmosphere. The downloaded test data show resolved the question of whether flame growth will achieve a steady size and demonstrated that the size of the enclosure surrounding the sample has more impact on the flame spread than anticipated.

microgravity↗

DOE OSTI2024

A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data and the improvement in practical applications. However, many of these models prioritize high utility performance, such as accuracy, with a lack of privacy consideration, which is a major concern in modern society where privacy attacks are rampant. To address this issue, researchers have started to develop privacy-preserving GNNs. Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain. In this survey, we aim to address this gap by summarizing the attacks on graph data according to the targeted information, categorizing the privacy preservation techniques in GNNs, and reviewing the datasets and applications that could be used for analyzing/solving privacy issues in GNNs. We also outline potential directions for future research in order to build better privacy-preserving GNNs.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2009

Human Research Program Human Health Countermeasures Element Sensorimotor Risk Standing Review Panel (SRP) Final Report

The Sensorimotor Risk Standing Review Panel (SRP) met at the NASA Johnson Space Center on October 4-6, 2009 to discuss the areas of future research targeted by the Human Health Countermeasures (HHC) Element of the Human Research Program (HRP). Using evidence-based knowledge as a background for risks, NASA had identified gaps in knowledge to address those risks. Ongoing and proposed tasks were presented to address the gaps. The charge to the Sensorimotor Risk SRP was to review the gaps, evaluate whether the tasks addressed these gaps and to make recommendations to NASA s HRP Science Management Office regarding the SRP's review. The SRP was requested to evaluate the practicality of the proposed efforts in light of the realistic demands placed on the HRP. In short, all tasks presented in the Integrated Research Plan (IRP) should address specific risks related to the challenges faced by the astronauts as a result of prolonged exposure to microgravity. All tasks proposed to fill the gaps in knowledge should provide applied, translational data necessary to address the specific risks. Several presentations were made to the SRP during the site visit and the SRP spent sufficient time to address the panel charge, either as a group or in separate sessions. The SRP made a final debriefing to the HRP Program Scientist. Taking the evidence and the risk as givens, the SRP reached the following conclusions: 1) the panel is very supportive of and endorses the present activities of the Sensorimotor Risk; and the panel is likewise supportive of the gaps and associated tasks in the IRP; 2) overall, the tasks addressed the gaps in the IRP; 3) there were some gaps and tasks that merit further enhancement and some new gaps/tasks that the SRP recommends.

Peterson, Barry↗

DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2014

Constraining the Structure of the Transition Disk HD 135344B (SAO 206462) by Simultaneous Modeling of Multiwavelength Gas and Dust Observations

Context: Constraining the gas and dust disk structure of transition disks, particularly in the inner dust cavity, is a crucial step toward understanding the link between them and planet formation. HD 135344B is an accreting (pre-)transition disk that displays the CO 4.7 micrometer emission extending tens of AU inside its 30 AU dust cavity. Aims: We constrain HD 135344B's disk structure from multi-instrument gas and dust observations. Methods: We used the dust radiative transfer code MCFOST and the thermochemical code ProDiMo to derive the disk structure from the simultaneous modeling of the spectral energy distribution (SED), VLT/CRIRES CO P(10) 4.75 Micrometers, Herschel/PACS [O(sub I)] 63 Micrometers, Spitzer/IRS, and JCMT CO-12 J = 3−2 spectra, VLTI/PIONIER H-band visibilities, and constraints from (sub-)mm continuum interferometry and near-IR imaging. Results: We found a disk model able to describe the current gas and dust observations simultaneously. This disk has the following structure. (1) To simultaneously reproduce the SED, the near-IR interferometry data, and the CO ro-vibrational emission, refractory grains (we suggest carbon) are present inside the silicate sublimation radius (0.08 is less than R less than 0.2 AU). (2) The dust cavity (R is less than 30 AU) is filled with gas, the surface density of the gas inside the cavity must increase with radius to fit the CO ro-vibrational line profile, a small gap of a few AU in the gas distribution is compatible with current data, and a large gap of tens of AU in the gas does not appear likely. (4) The gas-to-dust ratio inside the cavity is >100 to account for the 870 Micrometers continuum upper limit and the CO P(10) line flux. (5) The gas-to-dust ratio in the outer disk (30 is less than R less than 200 AU) is less than 10 to simultaneously describe the [O(sub I)] 63 Micrometers line flux and the CO P(10) line profile. (6) In the outer disk, most of the gas and dust mass should be located in the midplane, and a significant fraction of the dust should be in large grains. Conclusions: Simultaneous modeling of the gas and dust is required to break the model degeneracies and constrain the disk structure. An increasing gas surface density with radius in the inner cavity echoes the effect of a migrating Jovian planet in the disk structure. The low gas mass (a few Jupiter masses) throughout the HD 135344B disk supports the idea that it is an evolved disk that has already lost a large portion of its mass.

inner dust cavity↗

NASA NTRS1991

Data compression for near Earth and deep space to Earth transmission

Key issues of data compression for near Earth and deep space to Earth transmission discussion group are briefly presented. Specific recommendations as made by the group are as follows: (1) since data compression is a cost effective way to improve communications and storage capacity, NASA should use lossless data compression wherever possible; (2) NASA should conduct experiments and studies on the value and effectiveness of lossy data compression; (3) NASA should develop and select approaches to high ratio compression of operational data such as voice and video; (4) NASA should develop data compression integrated circuits for a few key approaches identified in the preceding recommendation; (5) NASA should examine new data compression approaches such as combining source and channel encoding, where high payoff gaps are identified in currently available schemes; and (6) users and developers of data compression technologies should be in closer communication within NASA and with academia, industry, and other government agencies.

Erickson, Daniel E.↗

NASA NTRS2016

Design and Analysis of Map Relative Localization for Access to Hazardous Landing Sites on Mars

Human and robotic planetary lander missions require accurate surface relative position knowledge to land near science targets or next to pre-deployed assets. In the absence of GPS, accurate position estimates can be obtained by automatically matching sensor data collected during descent to an on-board map. The Lander Vision System (LVS) that is being developed for Mars landing applications generates landmark matches in descent imagery and combines these with inertial data to estimate vehicle position, velocity and attitude. This paper describes recent LVS design work focused on making the map relative localization algorithms robust to challenging environmental conditions like bland terrain, appearance differences between the map and image and initial input state errors. Improved results are shown using data from a recent LVS field test campaign. This paper also fills a gap in analysis to date by assessing the performance of the LVS with data sets containing significant vertical motion including a complete data set from the Mars Science Laboratory mission, a Mars landing simulation, and field test data taken over multiple altitudes above the same scene. Accurate and robust performance is achieved for all data sets indicating that vertical motion does not play a significant role in position estimation performance.

Lander Vision System↗

NASA NTRS2019

Human Performance Contributions to Safety in Commercial Aviation

In the commercial aviation domain, large volumes of data are collected and analyzed on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that contribute to routine successful outcomes, safety management and system design decisions are based on a small sample of non- representative safety data. Analysis of aviation accident data suggests that human error is implicated in up to 80% of accidents, which has been used to justify future visions for aviation in which the roles of human operators are greatly diminished or eliminated in the interest of creating a safer aviation system. However, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant and largely unrecognized risk when making policy decisions about human roles and responsibilities. Opportunities exist to leverage the vast amount of data that has already been collected, or could be easily obtained, to increase our understanding of human contributions to “things going right” in commercial aviation. The principal focus of this assessment was to identify current gaps and explore methods for identifying human “success” data generated by the aviation system, from personnel and within the supporting infrastructure.

Holbrook, Jon B.↗

NASA NTRS2011

The SEEDS of Planet Formation: Observations of Transitional Disks

As part of its 5-year study, the Strategic Exploration of Exoplanets and Disk Systems (SEEDS) has already observed a number of YSOs with circumstellar disks, including 13 0.5-8 Myr old A-M stars with indications that they host wide gaps or central cavities in their circumstellar disks in millimeter or far-IR observations, or from deficits in warm dust thermal emission. For 8 of the disks, the 0.15" inner working angle of HiCIAO+A0188 samples material in the millimeter or mid-IR identified cavity. In one case we report detection of a previously unrecognized wide gap. For the remaining 4 stars, the SEEDS data sample the outer disk: in 3 cases, we present the first NIR imagery of the disks. The data for the youngest sample members 1-2 Myr) closely resemble coeval primordial disks. After approximately 3 Myr, the transitional disks show a wealth of structure including spiral features, rings, divots, and in some cases, largely cleared gaps in the disks which are not seen in coeval primordial disks. Some of these structural features are predicted consequences of Jovian-mass planets having formed in the disk, while others are novel features. We discuss the implications for massive planet formation timescales and mechanisms.

Grady, Carol A.↗

NASA NTRS2011

The SEEDS of Planet Formation: Observations of Transitional Disks

As part of its 5-year study, the Strategic Exploration of Exoplanets and Disk Systems (SEEDS) has already observed a number of YSOs with circumstellar disks, including 13 0.5- 8 Myr old A-M stars with indications that they host wide gaps or central cavities in their circumstellar disks in millimeter or far-IR observations, or from deficits in warm dust thermal emission. For 8 of the disks, the 0.15" inner working angle of HiCIAO+A0188 samples material in the millimeter or mid-IR identified cavity. In one case we reprt detection of a previously unrecognized wide gap. For the remaining 4 stars, the SEEDS data sample the outer disk: in 3 cases, we present the first NIR imagery of the disks. The data for the youngest sample members (less than 1-2 Myr) closely resemble coeval primordial disks. After approximately 3 Myr, the transitional disks show a wealth of structure including spiral features, rings, divots, and in some cases, largely cleared gaps in the disks which are not seen in coeval primordial disks. Some of these structural features are predicted consequences of lovianmass planets having formed in the disk, while others are novel features. We discuss the implications for massive planet formation timescales and mechanisms.

Grady, Carol↗

NASA NTRS2024

Evaluation of Vertical Patterns in Chlorophyll-A Derived From A Data Assimilating Model of Satellite-Based Ocean Color

Satellite-based sensors of ocean color have become the primary tool to infer changes in surface chlorophyll, while BGC-Argo floats are now filling the information gap at depth. Here we use BGC-Argo data to assess depth-resolved information on chlorophyll-a derived from an ocean biogeochemical model constrained by the assimilation of surface ocean color remote sensing. The data-assimilating model replicates well the general seasonality and meridional gradients in surface and depth-resolved chlorophyll-a inferred from the float array in the Southern Ocean. On average, the model tends to overestimate float-based chlorophyll, particularly at times and locations of high productivity such as the beginning of the spring bloom, subtropical deep chlorophyll maxima, and non-iron limited regions of the Southern Ocean. The highest model RMSE in the upper 50 m with respect to the float array is of 0.6 mg Chl m −3 , which should allow the detection of seasonal changes in float-based biomass (varying between 0.01 and >1 mg Chl m −3 ) but might hinder the identification of subtle changes in chlorophyll at narrow local scales. Both model and float profiling data show good agreement with in situ data from station ALOHA, with model estimates showing a slight accuracy edge in inferring depth-resolved observations. Uncertainties in float bio-optical estimates impede their use as a reliable benchmark for validation, but the general qualitative agreement between model and float data provides confidence in the ability of model to replicate biogeochemical features below the surface, where data is not directly constrained by the assimilation of satellite ocean color.

Lionel A Quintero↗