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NASA NTRSDate not supplied

Advancing Satellite-Constrained Modeled Air-Sea CO 2 Fluxes With a Focus on the Strength of the Southern Ocean Carbon Sink

Challenge and Motivation: The ocean plays a critical role in mitigating climate change by removing approximately a quarter of annual anthropogenic CO 2 emissions from the atmosphere. Model-based estimates point to the Southern Ocean as a key marine region, responsible for approximately 40 % of the anthropogenic carbon uptake by the global ocean. However, the contemporary strength of the Southern Ocean carbon sink has recently come into question. On the one hand, airborne-based observations of atmospheric CO 2 gradients indicate that the Southern Ocean represents a strong net sink of atmospheric CO 2 , consistent in magnitude with atmospheric inversion estimates and surface-ocean partial pressure of CO 2 (pCO 2 )-based products. On the other hand, estimates of pCO 2 based on in situ pH measurements taken by biogeochemical profiling floats yield strong wintertime outgassing fluxes that greatly reduce the Southern Ocean’s annually integrated CO 2 uptake. This uncertainty in the strength of the Southern Ocean air-sea CO 2 flux and its role in the global carbon cycle hinders our ability to constrain global carbon fluxes, one of the major goals of NASA’s Carbon Monitoring System (CMS). Opportunity: The NASA Ocean Biogeochemical Model (NOBM) produces near-global pCO 2 and air-sea CO 2 flux estimates that are currently included into the NASA’s Goddard Earth Observing System (GEOS) models in support of the CMS effort to monitor global carbon fluxes. The NOBM assimilates ocean color data to improve the representation of biogeochemical fluxes and overcome spatial and temporal gaps in the space-based retrievals. Here, we propose to advance the satellite-constrained flux estimates by investigating the uncertainties in the Southern Ocean air-sea CO 2 flux produced by the NOBM, and assess the value that remote sensing ocean color data can have in providing improved estimates of carbon fluxes in the ocean. Our proposed work includes the delivery of refined in situ float-based carbon fluxes to serve as a constraint on the model-based estimates. Taking advantage of the model’s integration of satellite ocean color data to represent multiple phytoplankton groups, we propose to deliver maps of biogenic carbon export specific to each modeled phytoplankton type and investigate the role of ecological plankton complexity in regulating marine carbon uptake and export. Goals: (a) Delivery of seasonally-adjusted float-based Southern Ocean air-sea CO 2 fluxes: We will produce updated and improved float-based air-sea CO 2 fluxes that will serve as a bias-reduced float-based constraint to evaluate our model-based estimates of the NOBM. (b) Investigation of uncertainties in Southern Ocean air-sea CO 2 flux from the NOBM: Modeled air-sea carbon fluxes will be evaluated against the updated float product as well as ship- and airborne-based data to identify uncertainties and potential model deficiencies. (c) Delivery of model-based carbon export partitioning by phytoplankton functional types (PFTs): We will produce depth-resolved maps of particulate organic export production integrated for all phytoplankton groups and allocated to each individual PFT in the model. The expected significance of this goal is to quantify the role that the functional-oriented diversity in phytoplankton groups represented in the NOBM plays in regulating air-sea CO 2 fluxes in the Southern Ocean.

Lionel A. Arteaga↗

NASA NTRS1984

Monitoring the Thickness of Coal-Conversion Slag

Technique adapts analogous ocean-floor-mapping technology. Existing ocean floor acoustic technology adapted for real-time monitoring of thickness and viscosity of flowing slag in coal-conversion processing.

Walsh, J. V.↗

NASA NTRSDate not supplied

Global Ocean Mass Change Derived From Atmospheric Reanalyses

Barystatic sea level change is a significant climate variable and a gauge for water exchanges between land, atmosphere, and ocean, with consequential relevance to stakeholders. The monitoring and attribution of globally averaged sea level variations represents substantial challenges to the climate community. Atmospheric reanalyses provide a level of monitoring for elements of the climate system that are in communication with the ocean, including the atmosphere, terrestrial land, and the surfaces of glaciated land. Global atmospheric reanalyses comprise model output that is statistically adjusted to available observations. Potentially, reanalyses offer a climatic context to observed changes in water mass distribution. Improvement in the representation of the water cycle and in water mass conservation have been stated goals of reanalysis projects. Here, we assess the capability of atmospheric reanalyses to represent variations in the global water budget from the perspective of ocean mass balance. The purpose of this work is to provide a detailed evaluation of current atmospheric reanalyses and their ability to reproduce spatial and temporal variations in atmospheric and terrestrial water storage. A second goal is to identify and characterize recent apparent trends in the water balance. We examine the ERA5, MERRA-2, and JRA-3Q reanalyses with a focus on the radio-occultation observing period from 2007 to the present. For terrestrial water storage we also compare with ERA5-Land, a surface reanalysis. The primary validation is from the NASA/DLR Gravity Recovery and Climate Experiment (GRACE) and its follow-on, GRACE-FO. The Atmospheric Infrared Sounder (AIRS) onboard NASA's Aqua satellite is also used for evaluation of the atmospheric moisture budget. Comparisons reveal strengths and weaknesses in the reanalyses of each component of the climate system. Reanalyses agree closely on atmospheric moisture storage trends, although mean values differ. Reanalyses show significant differences in land water storage. They also apply large corrections to the land water budget. Finally, reanalyses have differing abilities in representing the surface mass balance over glaciated land. Using the observed gravimetry record, changes in the underlying glaciated land ice are examined as a residual.

Richard Cullather↗

NASA NTRS1983

Satellite-Derived Sea Surface Temperature: Workshop 1

Satellite measurements of sea surface temperature are now possible using a variety of sensors. The present accuracies of these methods are in the range of 0.5 to 2.0 C. This makes them potentially useful for synoptic studies of ocean currents and for global monitoring of climatological anomalies. To improve confidence in the satellite data, objective evaluations of sensor accuracies are necessary, and the conditions under which these accuracies degrade need to be understood. The Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 satellite was studied. Sea surface temperatures, derived from November 1979 SMMR data, were compared globally against ship measurements and climatology, using facilities of the JPL Pilot Ocean Data System. Methods for improved data analysis and plans for additional workshops to incorporate data from other sensors were discussed.

Njoku, E. G.↗

NASA NTRS2002

A National Program for Analysis of the Climate System

Perhaps the single greatest roadblock to fundamental advances in our understanding of climate variability and climate change is the lack of robust and unbiased long-term global observations of the climate system. Such observations are critical for the identification and diagnosis of climate variations, and provide the constraints necessary for developing and validating climate models. The first generation of reanalysis efforts, by using fixed analysis systems, eliminated the artificial climate signals that occurred in analyses generated at the operational numerical weather prediction centers. These datasets are now widely used by the scientific community in a variety of applications including atmosphere-ocean interactions, seasonal prediction, climate monitoring, the hydrological cycle, and a host of regional and other diagnostic studies. These reanalyses, however, had problems that made them sub-optimal or even unusable for some applications. Perhaps the most serious problem for climate applications was that, while the assimilation system remained fixed, changes in the observing systems did produce spurious changes in the perceived climate. The first generation reanalysis products also exposed problems with physical consistency of the products and the accurate representation of physical processes in the climate system. Examples are bias in the estimates of ocean surface fluxes, and inadequate representation of polar hydrology. In this talk, I will describe some initial plans for a national program on reananlysis. The program is envisioned to be part of an on-going activity to maintain, improve, and reprocess our record of climate observations. I will discuss various issues affecting the quality of reanalyses, with a special focus on those relevant to the ocean.

Schubert, Siegfried↗

NASA NTRS2023

Retrieval of Aerosol Optical Properties From GOCI-II Observations: Continuation of Long-Term Geostationary Aerosol Monitoring Over East Asia

Since the Geostationary Ocean Color Imager (GOCI) was successfully launched in 2010, the GOCI Yonsei aerosol retrieval (YAER) algorithm has been continuously updated to retrieve hourly aerosol optical properties. GOCI-II has 4 more channels including UV, finer spatial resolution (250 m), and daily full disk coverage as compared to GOCI, and was launched in February 2020, onboard the GEO-KOMPSAT-2B (GK-2B) satellite. In this study, we extended the YAER algorithm to GOCI-II data based on its improved performance in many aspects and present the first results of aerosol optical properties retrieved from GOCI-II data. Utilizing the overlapping period between the GOCI-II and GOCI in geostationary Earth orbit, we present GOCI-II aerosol retrievals for high aerosol-loading cases over East Asia and show that these have a consistent spatial distribution with those from GOCI. Furthermore, GOCI-II provides AOD at an even higher spatial resolution, revealing finer changes in aerosol concentrations. Validation results for one year data show that the GOCI-II AOD has a correlation coefficient of 0.83 and a ratio within the expected error (EE) of 59.4 % when compared with the aerosol robotic network (AERONET) data. We compared statistical metrics for the GOCI and GOCI-II AODs to assess the consistency between the two datasets. In addition, it was found that there is a strong correlation between the two datasets from the comparison of gridded GOCI and GOCI-II AOD products. It is expected that data from GOCI-II will continue long-term aerosol records with high accuracy that can be used to address air-quality issues over East Asia.

AOD↗

NASA NTRS1993

An experimental study of microwave scattering from rain- and wind-roughened seas

This paper investigates radar cross-section (RCS) characteristics of rain- and wind-roughened sea-surfaces. We conducted experiments in laboratory wind-wave tanks using artificial rain. The study includes light rain rates, light wind speeds, and combinations of these. A 36 Ghz scatterometer was operated at 30 deg incidence angle and with vertical polarization. RCS data were obtained not only with the scatterometer pointing up-wind but also as a function of azimuthal angle. We use a scatterometer rain and wind model SRWM-1, which relates the total average RCS in storms to the sum of the average RCS due to rain plus the average RCS due to wind. Implications of the study for operational monitoring of wind in rainy oceanic areas by satellite-borne instruments is discussed.

Bliven, L. F.↗

NASA NTRS1992

Instrument configurations for the restructured Earth Observing System (EOS)

The Earth Observing System (EOS), the major NASA contribution to the U.S. Global Change Research Program, consists of a series of satellites with sensors designed to measure the crucial variables to monitor processes on the land, ocean, and atmosphere, a Data and Information System (EOSDIS) to analyze geophysical and biological products and distribute them to investigators, and a scientific research program. EOSDIS began in 1991 with organization of existing data sets, with the goal of a useful, accessible system in 1994. The launch of the first medium-size EOS platform will occur in 1998, with launches every 18 to 24 months and replacements every 5 years to provide a 15-year series of reliable scientific products. The selection of the instruments that comprise the payloads is designed to address the highest priority science/policy questions, as identified by the inter-agency Committee on Earth and Environmental Sciences (CEES) and the Intergovernmental Panel on Climate Change (IPCC).

Dozier, Jeff↗

NASA NTRS2017

In-Flight Performance of the Ozone Monitoring Instrument

The Dutch-Finnish Ozone Monitoring Instrument (OMI) is an imaging spectrograph flying on NASA's EOS Aura satellite since 15 July 2004. OMI is primarily used to map trace-gas concentrations in the Earth's atmosphere, obtaining mid-resolution (0.4-0.6 nm) ultraviolet-visible (UV- VIS; 264-504 nm) spectra at multiple (30-60) simultaneous fields of view. Assessed via various approaches that include monitoring of radiances from selected ocean, land ice and cloud areas, as well as measurements of line profiles in the solar spectra, the instrument shows low optical degradation and high wavelength stability over the mission lifetime. In the regions relatively free from the slowly unraveling "row anomaly" (RA) the OMI irradiances have degraded by 3- 8 %, while radiances have changed by 1-2 %. The long-term wavelength calibration of the instrument remains stable to 0.005-0.020 nm.

ultraviolet↗

NASA NTRS2017

The Earth Observing System (EOS)

The Earth Observing System is a US National Aeronautics and Space Administration (NASA) program consisting of a science segment, a data system, and a space segment made up of a series of polar-orbiting and mid-inclination satellites for long-term monitoring of the Earth as an integrated system, including observations of the land surface, biosphere, atmosphere, cryosphere, and oceans. Initially conceived in the mid-late 1980s, it was implemented as a series of large "flagship" missions and smaller focused satellites, often in partnership with instruments and sometimes spacecraft from other nations. In 1984 NASA formed several instrument panels to develop concepts for “facility instruments” that would provide comprehensive support to the scientific and applications’ communities, each with a supporting science team competitively selected via peer-review. An Announcement of Opportunity was released in 1988 and proposals were solicited for three types of proposals: (i) instrument investigations that would include the provision of instrumentation for flight on a polar platform, including non-Earth science payloads requiring polar orbits (PI instruments), (ii) research facility instrument team member and team leader investigations for facility instruments, and (iii) interdisciplinary science investigations to provide data analysis and modeling, preparing to use data acquired from the space platforms. The initial selections made in 1989 included support for 24 instrument investigations, 6 research facility instrument investigations, and 28 interdisciplinary investigations. Although considerable changes were made over the years in the structure and orbital configuration of the various instruments and platforms, a total of 10 different missions were eventually flown, as shown in Fig. 1. These included three multiinstrument flagship platforms known as Terra (launched in 1999), Aqua (launched in 2002), and Aura (launched in 2004), where the Terra mission emphasis was primarily on land processes, Aqua on the hydrological cycle, and Aura on atmospheric chemistry. In addition to these flagship missions the EOS missions included Landsat 7 (land cover classification and change), QuikScat (wind speed and direction over the oceans), Jason-1 (ocean surface topography and sea level), ACRIMSAT (total solar irradiance (TSI)), SAGE III (stratospheric aerosol and ozone levels), ICESat (ice, cloud, and land elevation), and SORCE (Solar Radiation and Climate Experiment; total and spectral solar irradiance). In addition EOS supported CERES (an Earth radiation budget sensor) and LIS (a lightning sensor), that flew as part of the NASA/JAXA Tropical Rainfall Measuring Mission (1997), and SeaWinds (wind speed and direction), that flew as part of the short-lived ADEOS II (Midori II, 2002) mission of JAXA (Japan Aerospace Exploration Agency). The EOS contributions are too numerous to present in detail in this article, but can be viewed for convenience as a means of obtaining global measurements (with appropriate characteristics) of the 24 variables and variable groups listed in Table 1. This table was created by the EOS community in the late 1990s as a way to succinctly indicate which instruments on the various EOS platforms were expected to contribute to each of the specified variables in primary (bold), secondary (bold italics), or contributing (roman) roles. All of the science data products produced by these space missions are available without charge throughout the world, and are thus highly valued for their many contributions to applications such as air quality, fires, land cover change, and the state of the oceans, as well as for science and monitoring of the state of the Earth system and its changes over time. These and other international satellites have played a crucial role in understanding and documenting global change, including (i) global surface temperature and atmospheric temperature and water vapor profiles, (ii) glacial retreat, (iii) sea ice extent and change, (iv) solar radiation into and out of the Earth-atmosphere system, (v) atmospheric aerosol and cloud properties, (vi) sources and sinks of

ICESat↗

NASA NTRS2022

The Future Intensification of the North Atlantic Winter Storm Track: The Key Role of Dynamic Ocean Coupling

Climate models project an intensification of the wintertime North Atlantic storm track, over its downstream region, by the end of this century. Previous studies have suggested that ocean-atmosphere coupling plays a key role in this intensification, but the precise role of the different components of the coupling has not been explored and quantified. In this paper, using a hierarchy of ocean coupling experiments, we isolate and quantify the respective roles of thermodynamic (changes in surface heat fluxes) and dynamic (changes in ocean heat flux convergence) ocean coupling in the projected intensification of North Atlantic transient eddy kinetic energy (TEKE). We show that dynamic coupling accounts for nearly all of the future TEKE strengthening as it overcomes the much smaller effect of surface heat flux changes to weaken the TEKE. We further show that by reducing the Arctic amplification in the North Atlantic, ocean heat flux convergence increases the meridional temperature gradient aloft, causing a larger eddy growth rate, and resulting in the strengthening of North Atlantic TEKE. Our results stress the importance of better monitoring and investigating the changes in ocean heat transport, for improving climate change adaptation strategies.

North Atlantic winter storms↗

NASA NTRS2012

Comparative Analysis of Upper Ocean Heat Content Variability from Ensemble Operational Ocean Analyses

Upper ocean heat content (HC) is one of the key indicators of climate variability on many time-scales extending from seasonal to interannual to long-term climate trends. For example, HC in the tropical Pacific provides information on thermocline anomalies that is critical for the longlead forecast skill of ENSO. Since HC variability is also associated with SST variability, a better understanding and monitoring of HC variability can help us understand and forecast SST variability associated with ENSO and other modes such as Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO), Tropical Atlantic Variability (TAV) and Atlantic Multidecadal Oscillation (AMO). An accurate ocean initialization of HC anomalies in coupled climate models could also contribute to skill in decadal climate prediction. Errors, and/or uncertainties, in the estimation of HC variability can be affected by many factors including uncertainties in surface forcings, ocean model biases, and deficiencies in data assimilation schemes. Changes in observing systems can also leave an imprint on the estimated variability. The availability of multiple operational ocean analyses (ORA) that are routinely produced by operational and research centers around the world provides an opportunity to assess uncertainties in HC analyses, to help identify gaps in observing systems as they impact the quality of ORAs and therefore climate model forecasts. A comparison of ORAs also gives an opportunity to identify deficiencies in data assimilation schemes, and can be used as a basis for development of real-time multi-model ensemble HC monitoring products. The OceanObs09 Conference called for an intercomparison of ORAs and use of ORAs for global ocean monitoring. As a follow up, we intercompared HC variations from ten ORAs -- two objective analyses based on in-situ data only and eight model analyses based on ocean data assimilation systems. The mean, annual cycle, interannual variability and longterm trend of HC have been analyzed

Xue, Yan↗

NASA NTRS1999

GPS Altimetry

The advent of satellite altimetry has greatly improved our ability to observe global ocean circulation. However, the swath of a single, nadir-viewing satellite altimeter is only a few km and the track spacing is several hundred km to resolve the two-dimensional structure of ocean eddies. Our goal is to increase spatial and temporal coverage by monitoring Global Positioning System (GPS) signals reflected from the ocean. A constellation of spacecraft would each carry a GPS receiver capable of recording 8 reflections simultaneously. The reflections are well distributed in azimuth and elevation and can be tracked continuously while the satellite is in view, and another is then acquired, as illustrated below. The diagram depicts a new approach at altimetry measurements where ocean surface reflected GPS signals are simultaneously tracked and processed in a GPS flight receiver in space. The reflected GPS signals from the ocean must be compared precisely with the direct GPS signals in order to infer the characteristics of the ocean from the combined data set. Understanding the features and accuracy of GPS altimetry measurement is crucial to establishing its suitability for oceanography. Preliminary work has enabled us to theoretically model the signal output of the correlator for a variety of system parameters such as wind speed (sea roughness), receiver height, incidence angle, receiver range and Doppler filter bandwidth and antenna gain. Expected signal-to-noise ratio has been estimated from which we have inferred, to a first approximation, the basic receiver gain requirements for a space-based altimeter and the expected range raw error. In 1998, work on a different task led to the extraction of the first reflected GPS signal observed from a spaceborne receiver during the 1995 Space Transportation System-68 (STS-68) Shuttle Radar Laboratory-2 (SRL-2) high resolution synthetic aperture radar mission. Good comparisons with our signal models have been obtained. Having established that only modest signal-to-noise ratios are obtainable unless very high gain antennas are used, we expect that single measurements might not provide an estimate of sea state parameters as accurate as that obtainable with traditional remote sensing instruments, if costs are limited. Therefore spatial and temporal averaging of many measurements is required. Since the receiving satellite tracks do not repeat, measurements in a given area will be used to refine the local solution as a function of time, and will define the spatial resolution. In order to make this instrument viable in space, we need to detect and process many scattered signals. In 1998 we made a first step at understanding what configurations of antenna gain and orientations capture the largest number of viable signals to be used in the subsequent spatial/temporal averaging process. Additional information is contained in the original.

Lichten, Stephen M.↗

NASA NTRS2019

Satellite Salinity Observing System: Recent Discoveries and the Way Forward

Advances in L-band microwave satellite radiometry in the past decade, pioneered by ESA's SMOS and NASA's Aquarius and SMAP missions, have demonstrated an unprecedented capability to observe global sea surface salinity (SSS) from space. Measurements from these missions are the only means to probe the very-near surface salinity (top cm), providing a unique monitoring capability for the interfacial exchanges of water between the atmosphere and the upper-ocean, and delivering a wealth of information on various salinity processes in the ocean, linkages with the climate and water cycle, including land-sea connections, and providing constraints for ocean prediction models. The satellite SSS data are complimentary to the existing in situ systems such as Argo that provide accurate depiction of large-scale salinity variability in the open ocean but under-sample mesoscale variability, coastal oceans and marginal seas, and energetic regions such as boundary currents and fronts. In particular, salinity remote sensing has proven valuable to systematically monitor the open oceans as well as coastal regions up to approximately 40 km from the coasts. This is critical to addressing societally relevant topics, such as land-sea linkages, coastal-open ocean exchanges, research in the carbon cycle, near-surface mixing, and air-sea exchange of gas and mass. In this paper, we provide a community perspective on the major achievements of satellite SSS for the aforementioned topics, the unique capability of satellite salinity observing system and its complementarity with other platforms, uncertainty characteristics of satellite SSS, and measurement versus sampling errors in relation to in situ salinity measurements. We also discuss the need for technological innovations to improve the accuracy, resolution, and coverage of satellite SSS, and the way forward to both continue and enhance salinity remote sensing as part of the integrated Earth Observing System in order to address societal needs.

Vinogradova, Nadya↗

NASA NTRS2003

Bio-Optical and Remote Sensing Observations in Chesapeake Bay

The high temporal and spatial resolution of satellite ocean color observations will prove invaluable for monitoring the health of coastal ecosystems where physical and biological variability demands sampling scales beyond that possible by ship. However, ocean color remote sensing of Case 2 waters is a challenging undertaking due to the optical complexity of the water. The focus of this SIMBIOS support has been to provide in situ optical measurements from Chesapeake Bay (CB) and adjacent mid-Atlantic bight (MAB) waters for use in algorithm development and validation efforts to improve the satellite retrieval of chlorophyll (chl a) in Case 2 waters. CB provides a valuable site for validation of data from ocean color sensors for a number of reasons. First, the physical dimensions of the Bay (> 6,500 km2) make retrievals from satellites with a spatial resolution of approx. 1 km (i.e., SeaWiFS) or less (i.e., MODIS) reasonable for most of the ecosystem. Second, CB is highly influenced by freshwater flow from major rivers, making it a classic Case 2 water body with significant concentrations of chlorophyll, particulates and chromophoric dissolved organic matter (CDOM) that highly impact the shape of reflectance spectra.

Harding, Lawrence W., Jr.↗

NASA NTRS2004

Evaluation and Validation of Case 2 Algorithms in Chesapeake Bay

The high temporal and spatial resolution of satellite ocean color observations will prove invaluable for monitoring the health of coastal ecosystems where physical and biological variability demands sampling scales beyond that possible by ship. However, ocean color remote sensing of Case 2 waters is a challenging undertaking due to the optical complexity of the water. The focus of this SIMBIOS support has been to provide in situ optical measurements form Chesapeake Bay (CB) and adjacent mid-Atlantic bight (MAB) waters for use in algorithm development and validation efforts to improve the satellite retrieval of chlorophyll (chl a) in Case 2 waters. CB provides a valuable site for validation of data from ocean color sensors for a number of reasons. First, the physical dimensions of the Bay (greater than 6,500 square kilometers) make retrievals from satellites with a spatial resolution of approximately 1 kilometer (i.e., SeaWiFS) or less (i.e., MODIS) reasonable for most of the ecosystem. Second, CB is highly influenced by freshwater flow from major rivers, making it a classic Case 2 water body with significant concentrations of chlorophyll, particulates and chromophoric dissolved organic matter (CDOM) that highly impact the shape of reflectance spectra. Finally, past and ongoing research efforts provided an expensive data set of optical observations that support the goal of this project.

Harding, Lawrence W., Jr.↗

NASA NTRS2025

PACE Water Resources: Demonstrating the Use of NASA's PACE Hyperspectral Ocean Color Instrument Data for Enhanced Coastal Management

This project developed tools to support the future use of Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) hyperspectral imagery in water resource monitoring and research by NASA DEVELOP teams and members of the PACE applications community. We sought to address a need for support in processing and visualizing hyperspectral PACE Ocean Color Instrument (OCI) data among researchers and decision-makers working in coastal water quality management and harmful algal bloom (HAB) monitoring. To supplement the day of simulated PACE imagery available, we used Aqua MODIS earth observations with Level 3 processing from March 2022 to build a Python graphical user interface (GUI) for visualizing ocean biogeochemical parameters relevant to the early detection and monitoring of HABs. We used simulated PACE OCI Level 2 data derived from the Python Top of Atmosphere Simulation Tool (PyTOAST) to build Jupyter Notebooks for band subset and selection. The Level 3 PACE Viewer components support users with quick visualizations as well as the creation of geoTIFFs and time-series. The Level 2 Jupyter Notebooks address users’ concerns over the volume and complexity of hyperspectral imagery. The PACE Viewer is useful for visual inspection and netCDF data processing but should not be used for geospatial analysis. Once PACE launches, this tool will alleviate the technical burdens of working with hyperspectral data and support the early detection and monitoring of HABs using PACE satellite imagery.

Python Top of Atmosphere Simulation Tool↗

NASA NTRS2019

The NASA OBPG 2020 On-Orbit Calibration of SNPP VIIRS for Ocean Color Applications

The NASA Ocean Biology Processing Group (OBPG) has continued monitoring the SNPP VIIRS on-orbit calibration for bands M1-M11 over its mission to optimize the calibration for ocean color applications. The OBPG has recently implemented several changes to the calibration scheme: using solar-derived f-factors to detrend the lunar observations; using long-term exponentials of time as basis vectors (along with libration angles) for radiometric fits to any resulting lunar temporal drifts; deriving gain adjustments to the solar f-factors from these exponentials; and deriving gain adjustments due to modulated RSRs outside of the solar/lunar calibration using TOA reference spectra. These calibration changes minimize the impact of uncertainties in any one component of the calibration on the derived f-factors. The final f-factors incorporate VIIRS solar diffuser measurements, h-factor BRDF corrections, lunar-derived gains, and modulated RSR gains. The combined BRDF corrections, lunar gain adjustments, and mRSR gain adjustments define effective h-factors for each band. The improvements in the on-orbit calibration are validated by evaluation of globally-derived anomaly plots of remote sensing reflectance for the ocean color bands. The ultimate goal of the OBPG calibration effort is incorporation of a consistent SNPP VIIRS ocean color data set into the NASA multi-mission ocean color climate data record.

VIIRS↗