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NASA NTRSDate not supplied

NASA GLOBE CLOUD GAZE: Creating Data Quality Flags for Citizen Science Cloud Observations Matched to NASA Satellite Data

The GLOBE Program, NASA’s largest and longest lasting citizen science program about the Earth, has been collecting cloud observations matched to multiple satellite data daily. The program’s cloud protocol is historically the most popular protocol as your eyes are the only instruments you need to collect observations of the sky. This dataset includes over 3,300,000 cloud observations with variables like total cloud cover, cloud type and opacity that are collocated to the nearest overpass times of geostationary satellites (GOES-15, GOES-16, GOES-17, Meteosat-8, Meteosat-11, or Himawari-8), or to Clouds and the Earth’s Radiant Energy System (CERES) instruments onboard Aqua and Terra, or the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. In order to increase the usability of this dataset, the Community science project Leveraging Online and User Data through GLOBE And Zooniverse Engagement (CLOUD GAZE) has been developed to generate data quality flags of these ground-up and top-down perspectives of sky and clouds. Recently funded through NASA’s Citizen Science for Earth Systems Program, CLOUD GAZE has partnered with the Zooniverse online platform to obtain reference data and image tagging of sky photographs collected through The GLOBE Program’s clouds protocol. This paper will present the GLOBE Clouds dataset matched to NASA satellite data, integration of CLOUD GAZE to develop data quality flags, and research applications of the dataset (includes ground-up and top-down perspective comparisons, ground observations of dust storms and smoke plumes, and cloud observations in polar regions). The paper will also present on techniques and recommendations for classroom use and for community engagement, particularly for those looking to online resources.

Marilé Colón Robles↗
NASA NTRSDate not supplied

Observations of Small Debris from the Cosmos 1408 Anti-Satellite Test using the HUSIR and Goldstone Radars

On 15 November 2021, Russia conducted a direct-ascent anti-satellite (ASAT) test against its Cosmos 1408 satellite, which had been in orbit since 1982. The test produced at least 1500 fragments trackable by the U.S. Space Surveillance Network (SSN). The test is a significant event because the resulting cloud has the potential to endanger the International Space Station and other satellites in low Earth orbit (LEO). For almost 30 years, the NASA Orbital Debris Program Office (ODPO) has used the Haystack Ultrawideband Satellite Imaging Radar (HUSIR), operated by the Massachusetts Institute of Technology’s Lincoln Laboratory to perform statistical measurements of debris in LEO too small to be tracked by the SSN, nominally down to 5.5 mm at 1000 km altitude. The ODPO also utilizes the Goldstone Orbital Debris Radar (Goldstone), operated by NASA’s Jet Propulsion Laboratory, to characterize the small debris environment in LEO down to approximately 3 mm at 1000 km altitude. To characterize the small debris component of the Cosmos 1408 ASAT test, a series of observation campaigns were conducted with the HUSIR and Goldstone radars. This paper discusses the observation planning, including beam overlap analysis needed for Goldstone bistatic operation, using a model of the debris cloud produced with the NASA Standard Satellite Breakup Model. A description of the radars and the data processing techniques used to analyze the data are also discussed. Finally, results of the measurement campaigns including cumulative count rate versus size and surface area flux versus altitude and inclination are presented

James Murray↗
NASA NTRSDate not supplied

Comparing Satellite Measurements of Volcanic SO2 Mass from OMI, OMPS and TROPOMI

Sulfur dioxide (SO2) is a major air pollutant that contributes to acid rain and aerosol formation (e.g., sulfates), adversely affects the environment and human health, and explosive volcanic SO2 emissions can impact climate. The majority of SO2 emissions are related to anthropogenic processes (e.g., fossil fuel burning, metal ore smelting operations), although natural processes such as volcanic eruptions and degassing also play an important role as anthropogenic SO2 emissions continue to decline. Generally, the most interest in volcanoes occurs during major eruptions. We will focus on comparing volcanic SO2 outgassing that occurs on an almost daily basis from lesser known volcanoes using satellite data. At NASA’s Global Sulfur Dioxide Monitoring Home page (https://so2.gsfc.nasa.gov/), we have been posting daily SO2 maps from 40 volcanic and industrial regions around the world using measurements from three satellite instruments; the Ozone Monitoring Instrument (OMI) onboard NASA’s Earth Observing System Aura satellite, the Ozone Monitoring and Profiler Suite (OMPS) onboard the NASA-NOAA Suomi National Polar-orbiting Partnership (NPP) satellite, and the TROPOspheric Monitoring Instrument (TROPOMI) onboard the ESA/Copernicus Sentinel-5 Precursor satellite. These instruments in low Earth sun-synchronous polar orbits with 1:30-2:00 pm equator crossing local time provide daily SO2 maps at different spatial resolutions: 13 x 24 km2, 50 x 50 km2 and 5.5 x 3.5 km2 for OMI, OMPS and TROPOMI respectively. Data from OMI are available since October 2004 (partial coverage since 2008), from OMPS since 2012 and from TROPOMI since 2018. We will present comparative SO2 mass time-series (see Hunga-Tonga plot) and statistical analyses of recent eruptions that have data from all the instruments.

SO2↗
NASA NTRSDate not supplied

Conjunction Assessment and Deconfliction Paradigm for Co-Located Satellite Constellations With on-Spacecraft “Autonomous” Flight Dynamics Control

Satellite constellations that employ on-spacecraft flight dynamics and conjunction assessment (CA) approaches face particular challenges. First, they must arrange to obtain and upload to the constellation satellite close approach information, such as Conjunction Data Messages, so that their on-board systems will have the data they need to make informed CA decisions. Second, given current latencies in obtaining CA screening information, such systems often cannot obtain dedicated screenings of their proposed maneuvers before they are executed. Third, they usually cannot engage in real time with owner/operators (O/Os) with whom they may come into conjunction, relying instead on pre-arranged courses of action that may or may not fit a given situation well. Despite these difficulties, it is nonetheless possible, as operators such as SpaceX have demonstrated, to operate safely using on-board CA calculations, in the presence of a largely collaborative operating environment. The situation breaks down, however, when two constellations that operate with such a paradigm are placed in the same orbital region and will experience conjunctions with each other. Each may have a vague idea of where the other’s satellites are, or perhaps even a somewhat precise idea if they exchange O/O ephemerides, but neither will know the other’s more immediate maneuver intentions, especially for situations in which they are in a high-risk conjunction with each other and a mitigation maneuver is required. As there is no formalized way for these automated systems to ascertain each other’s intentions for this type of conjunction, it is quite possible that each spacecraft may choose a maneuver that results in the two objects’ colliding. An opportunity to develop a solution to this problem was presented by the NASA “Starling” project, a small experimental constellation developed to demonstrate autonomous satellite control and constellation reconfiguration and for which a launch was obtained that, serendipitously, will place this constellation close to the SpaceX Starlink constellation. Because the NASA constellation is experimental, an extended mission part of the mission has been negotiated to implement and test mechanisms for resolving the problem of co-located constellations with on-board flight dynamics control. For the last eighteen months, a consortium among NASA Ames (the Starling satellite providers), NASA CARA (the Agency organization responsible for conjunction assessment), Space X, and the Department of Commerce (in observer status) has met regularly to assemble a concept of operations for a robust solution to this problem and has developed a prototype ground node that will serve as a needed CA screening and information dispensary. The approach and methods, as well as the active employment of the developed ground node, will be tested both in simulation before actual use and then in actual spacecraft operations, which are anticipated to begin in January 2024. This paper will describe the co-located autonomously-controlled constellation problem and the developed solution in detail, outlining what ground node (i.e., government) infrastructure and features are required, what particular capabilities are needed by the constellations themselves in order to make use of this deconfliction paradigm, and the migration path forward to allow such a capability set to be incorporated by the Department of Commerce’s space traffic coordination system.

Conjunction Assessment↗
NASA NTRS2023

Assimilation of Blended Satellite Soil Moisture Data Products to Further Improve Noah-MP Model Skills

Microwave satellite remote sensing has enabled observations of soil moisture (SM) at the global scale, and multiple SM data products have been developed in the past decades. However, single-sensor-based measurements are insufficient for continuous spatiotemporal coverage. In the context of its climate program, the Climate Change Initiative, the European Space Agency (ESA) has developed robust, long term, global scale, multi instrument satellite derived time series of climate data record for key component of the climate system, including soil moisture (CCI), while the Soil Moisture Operational Product System (SMOPS) was specifically developed by National Oceanic and Atmospheric Administration (NOAA) to offer the real time blended SM datasets through merging all available individual products. Before combining, all individual SM data ingested into both SMOPS and CCI blended products are scaled to Global Land Data Assimilation System (GLDAS) 0-10 cm SM climatology. Benefiting from land surface model evolution and the availability of high-quality forcing data, GLDAS has become more comprehensive to track SM changes and dynamic trends. The development of GLDAS and the scaling procedure in CCI and SMOPS leave an open scientific and operational question: do the blended satellite SM data products have added value comparing to the GLDAS product? This study clearly reveals that both CCI and SMOPS can provide the reliable SM observations with independent information, although their climatology matches well with GLDAS. Relative to assimilation of GLDAS 0-10 cm SM data, Noah-MP model can be further improved by assimilating the blended satellite SM observations with respect to the quality-controlled in situ measurements. The strong consistency of results presented in this paper proves that the blended satellite SM data products are more useful than the GLDAS product in terms of improving Noah-MP model performance.

Jifu Yin↗
NASA NTRSDate not supplied

Pre-Launch Performance Trending of Joint Polar Satellite System (JPSS) Advanced Technology Microwave Sounder (ATMS)

The Advanced Technology Microwave Sounder (ATMS) microwave radiometer instrument is utilized on-board NOAA’s Joint Polar Satellite System (JPSS) fleet of spacecraft to perform temperature and water vapor soundings of Earth’s atmosphere. Consisting of 22 channels over a frequency range from 22 to 183 GHz, ATMS provides high-impact observations for numerical weather prediction (NWP) models. A general description of the ATMS instrument is discussed in [1]. There are five ATMS flight units in the polar-orbiting JPSS program; three are currently on-orbit and two are pending launch. The first ATMS was flown on the Suomi National Polar-orbiting Partnership (SNPP) satellite, launched in 2011. The second ATMS was launched on the NOAA-20 (previously JPSS-1) satellite in 2017. The third ATMS was launched on the NOAA-21 (previously JPSS-2) satellite in 2022. The SNPP and NOAA-20 ATMS units are operational. The NOAA-21 ATMS unit is completing on-orbit commissioning and checkout, having achieved provisional maturity status in December 2022 with validated maturity expected in May 2023 [2]. The fourth and fifth ATMS units are planned for the JPSS-3 (launch ~2028) and JPSS-4 (launch ~2032) satellites [3]. This paper will focus on trending performance characteristics of each JPSS ATMS build from the pre-launch activities. Pre-launch trending of some parameters have been previously published up to the JPSS-3 mission [4][5]. This paper differs from and expands the scope of the prior work as it will include all five of the JPSS ATMS builds. The entire suite of JPSS ATMS units have completed their pre-ship instrument-level I&T and verification activities. These activities include a radiometric performance characterization of each instrument. In addition to comparing the performance across builds, the performance will also be compared to requirements and specifications where applicable. The on-orbit performance of the launched units will be excluded from the scope of this paper in order to focus on evaluations that are common across all builds. The post-launch performance of SNPP ATMS is detailed in [1][6][7][8]. The post-launch performance of NOAA-20 ATMS is detailed in [6][8][9]. The pre-launch characterization of the ATMS occurs at both subassembly-level and instrument-level testing. The antenna subsystem is tested at Northrop Grumman’s Compact Antenna Test Range (CATR) in Azusa, CA [9]. This testing characterizes the antenna pattern and the pointing performance of the scan drive mechanism and antenna subsystem. Trended parameters from this evaluation will include beam pointing accuracy, beamwidth, and beam efficiency. These parameters are captured in each instrument’s Calibration Data Book [10]. Instrument-level radiometric performance evaluation is primarily done during thermal vacuum (TVAC) calibration testing at Northrop Grumman’s Azusa, CA facility [9]. A general description of the calibration activities is presented in [1][9]. The testing involves inferring a scene target brightness temperature (TB) and comparing it to the actual scene TB while the instrument is at flight-like temperature and pressure environmental conditions. Trended parameters from this activity will include Noise Equivalent Delta Temperature (NEDT), nonlinearity, radiometric accuracy, gain stability, striping, and inter-channel noise correlation. The trending evaluation will allow for a direct comparison of the ATMS performance across builds. The paper will highlight observed performance improvements.

Edward J Kim↗
NASA NTRSDate not supplied

Improving Ocean Reanalyses and ENSO Forecasts By Assimilation of Rain-Corrected Satellite Sea Surface Salinity Using the GMOA S2s Forecast System

The ENSO phenomenon has a significant global socio-economic impact and has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of satellite altimetry and subsurface temperature and salinity from (mostly) Argo help improve the initialization of the thermocline, while satellite SST aids in constraining surface heat-fluxes, leading to improved coupled system sub-seasonal to seasonal forecasts. However, few studies have focused on improving the near-surface density and mixing through satellite sea surface salinity (SSS) assimilation. The few ocean models that assimilate satellite SSS, bias correct to normalize towards the near-surface Argo data for expediency. This assumption is likely inadequate in rainy regions, where buoyant water forms a fresh surface lens. In previous work, we showed that adjusting SSS to bulk salinity (Sb) using the Rain Impact Model (RIM) of Santos-Garcia et al., 2014 improves the near-surface density and mixed layer depth, leading to deeper thermocline and improved NINO3.4 SST forecasts. We now utilize the Soil Moisture and Ocean Salinity rain-corrected (SMOS_RC) SSS, available in SMOS-CATDS products, to represent Sb more accurately at the first model layer (e.g., 5 m). Rather than a diffusivity model as RIM, SMOS_RC uses a statistical correction dependent on Integrated Multi-satellitE Retrievals for GPM (IMERG) rain rates, established on observed SMOS SSS decreases related to Sb in the presence of rain (Supply et al., 2020). For all experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF scheme (Penny et al., 2013). One reanalysis additionally assimilates SMOS SSS data as is, and a separate reanalysis assimilates SMOS_RC. We assess the impact on near-surface and subsurface dynamics by validating against observations and explore how SSS assimilation (SMOS vs SMOS_RC) impacts ENSO forecasts using the NASA GMAO Sub-seasonal to Seasonal coupled forecast system (S2S-v3, Molod et al. 2020). We show that improved estimates of density and near-surface mixing led to more accurate coupled air/sea interaction and better ENSO forecasts. The increased SSS, resulting from the removal of the instantaneous rain effect, modifies the ocean state by enhancing mixing and deepening the thermocline.

Veronica Ruiz Xomchuk↗
NASA NTRSDate not supplied

Synergy of Satellite Radiation, Precipitation, and Other Meteorological Variable Observations for Global Mean Sea Surface Turbulent Heat Flux Estimation

Sea surface turbulent heat flux is one of the key components in global freshwater and energy balances and plays an important role in atmospheric dynamics, thermodynamics and general circulation. This turbulent heat flux is dominantly decided by sea surface latent heat release with some contribution from sensible heat exchange. The flux and its anomaly could significantly affect ocean heat storage and ocean circulation. They are part of and have great impacts on climate variability. Though the turbulent heat flux is extremely important for the climate and weather systems, only very limited ship and buoy observations of the flux are available over open oceans. There are no global, operational, direct measurements of this crucial meteorological variable. Global estimates are, basically, indirectly calculated from bulk turbulent flux parameterization with a combination of satellite column water vapor, sea surface water temperature, and wind speed observations. Critical parameters such as sea surface air temperature and humidity are estimated from empirical relations of theses variables with the water vapor and sea surface water temperature, respectively. Lacking accurate knowledge on surface air temperature and humidity, uncertainties in the parameterization and potential changes in the non-linear turbulent processes with long-term climate variations could cause large errors in estimated long-term turbulent fluxes from the indirect method as shown in current global sea surface turbulent flux datasets. This study uses synergized data of satellite global radiation, precipitation, and other meteorological variable observations to estimate sea surface turbulent heat fluxes. The radiation observations are made by the satellite Clouds and the Earth’s Radiant Energy System (CERES) sensors, while the global precipitation data is from NASA’s satellite Global Precipitation Climatology Project (GPCP). Other data includes satellite sea surface water temperature and wind speed observations. These datasets are obtained from a wide range of space sensors from passive to active instruments and from visible and near infrared to thermal infrared and microwave spectral sounders. One significant feature of these datasets is that they have multi-decades long climate records. Top-of-atmosphere (TOA) radiation and its anomaly represents the net heat energy input to the climate system, and the oceanic precipitation and ocean-land moisture transport can be used to quantify sea surface latent heat release. Based on the synergized datasets and the principle of global water and energy balances, global mean turbulent heat fluxes are estimated. The turbulent heat anomalies for the first two decades of the 21st century are, then, obtained mainly from global CERES radiation and GPCP precipitation anomalies, along with CERES derived ocean-land heat transports. Analysis indicates that the uncertainties in the estimated turbulent flux anomalies may be reduced considerably. The results suggest a strong needs in synchronized and synergized observations of atmospheric radiation, precipitation, and oceanic meteorological variables for long-term climate studies.

Bing Lin↗
NASA NTRSDate not supplied

Use of Beacon Satellites for Efficient Uplink Transmission for Free-Space Quantum Entanglement Distribution

Efficient transmission of optical beams from ground to space is important for free-space entanglement distribution in a dual-uplink architecture in which photons from entanglement sources at two ground locations interact in an optical Bell-state measurement implemented on a satellite. Efficient transmission requires large transmitting apertures to minimize diffraction losses and high-performance adaptive optics to overcome the effects of atmospheric turbulence. Achieving good atmospheric compensation for beams transmitted to satellites is challenging because the need to point ahead of the satellite introduces anisoplanatism error that limits the effectiveness of the adaptive optics. An attractive approach for solving the point-ahead problem is to position a separate small satellite at the point-ahead location to provide a beacon source for the adaptive optics. This paper presents analysis of the requirements for the beacon source(s) for a dual-uplink entanglement-distribution demonstration. A relative performance metric is defined and evaluated for multiple beacon configurations, from a single, fixed beacon serving both ground sites to a dedicated beacon for each site continually adjusted to be at the correct point-ahead distance from the target satellite.

quantum↗
NASA NTRSDate not supplied

Enhancing Air Quality Applications in the Hindu Kush-Himalayan Region Using Satellite, Model, and Machine Learning Techniques

Air pollution in the Hindu Kush Himalayan (HKH) region of South Asia is a severe issue, as increases in emissions over the past two decades have degraded air quality (AQ) across the region, which poses major threats to human health, the ecosystem, climate, and agriculture. A diversity of anthropogenic and natural emission sources including transportation, power plants, industries, open biomass burning of crop residue, forest fires, cooking and heating fires, and dust storms contribute to unhealthy AQ and transboundary pollution issues in the region. Further complicating matters is the importance of meteorology and terrain on AQ, especially in the Kathmandu Valley where extreme haze episodes frequently develop from the atmospherically stable weather conditions during the winter monsoon. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with machine learning techniques to develop a comprehensive toolkit for enhancing AQ monitoring and forecasting in HKH. The toolkit incorporates new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager (AMI), which provide unprecedented resolution on aerosols and trace gases, including nitrogen dioxide (NO 2 ), formaldehyde (CH2O), sulfur dioxide (SO 2 ), carbon monoxide (CO), and ozone (O 3 ), and aerosol optical depth (AOD). Value-added products [e.g., Particulate matter with diameters less than 2.5 micrometers (PM2.5)] are developed from the suite of satellite observations to further improve AQ monitoring capabilities in the region. The satellite products are also used to assimilate a high-resolution chemical transport model tailored for the HKH region, which is providing daily, 54-hour AQ forecasts with horizontal grid spacings of 12- and 4-km. This presentation will provide an overview of the suite of satellite- and model-based products in the AQ toolkit and application and performance of the toolkit for AQ monitoring and forecasting in HKH.

Air Quality↗
NASA NTRS2023

Spectral and Spatial Dependencies in the Validation of Satellite-Based Aerosol Optical Depth from the Geostationary Ocean Color Imager Using the Aerosol Robotic Network

The regional and global scale of aerosols in the atmosphere can be quantified using the aerosol optical depth (AOD) retrieved from satellite observations. To obtain reliable satellite AODs, conducting consistent validations and refining retrieval algorithms are crucial. AODs and Ångström exponents (AEs) measured with the aerosol robotic network (AERONET) are considered as the ground truth for satellite validations. AERONET AEs are used to collocate the wavelength of the AERONET AODs to those of the satellite AODs when there is a discordancy in their wavelengths. However, numerous validation studies have proposed different strategies by applying the AERONET AODs and AEs, and spatiotemporal collocation criteria. This study examined the impact of the wavelength and spatial collocation radius variations by comparing AODs at 550 nm derived from the geostationary ocean color imager (GOCI) with those obtained from the AERONET for the year 2016. The estimated AERONET AODs at 550 nm varied from 5.18% to 11.73% depending on the selection of AOD and AE, and the spatial collocation radii from 0 to 40 km, respectively. The longer the collocation radius and the higher the AODs, the greater the variability observed in the validation results. Overall, the selection of the spatial collocation radius had a stronger impact on the variability in the validation results obtained compared to the selection of the wavelength. The variability was also found in seasonal analysis. Therefore, it is recommended to carefully select the data wavelength and spatial collocation radius, consider seasonal effects, and provide this information when validating satellite AODs using AERONET.

GOCI↗
NASA NTRSDate not supplied

Deriving Essential Climate Variable Data from Multiple Satellite Remote Sensors Using a Consistent Fingerprinting Method

Hyperspectral observations from satellite-based sensors provide high information content for the Earth’s atmospheric and surface properties. Traditionally, long-term climate products are derived by performing spatial and temporal averaging of level-2 satellite products. It is a time-consuming process to generate level-2 data products since modern hyperspectral satellite sensors have millions of observations each day with thousands of spectral channels for each observation. Additionally, differences in level-2 retrieval algorithms can lead to errors in the climate products when fusing data from different satellite sensors. We have developed a radiometrically consistent spectral fingerprinting method, which overcomes the above-mentioned shortcomings, to derive climate change signals from multiple satellite sensors using spatiotemporally averaged level-1 data. We have applied this method to Atmospheric Infrared Sounder (AIRS) and Cross-track Infrared Sounder (CrIS) data and generated decade-long climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. A key component to this work is a set of observational-based radiative kernels produced from CrIS level-1 data using a single field of view (SFOV) optimal estimation retrieval algorithm. Only limited CrIS level-1 data (e.g., 1-2 years of data) are needed to the derive radiative kernels. Our Principal Component-based Radiative Model (PCRTM) enables us to perform SFOV retrievals under all sky conditions and provides radiative kernels (including those for clouds) needed by the spectral fingerprinting method. In this presentation, we will describe the basic methodology, the details of the algorithm, and results from NASA Aqua AIRS and Suomi-NPP CrIS data. The method can be applied to study future hyperspectral remote sensors such as CLARREO (Climate Absolute Radiance and Refractivity Observatory) Pathfinder (CPF), Tropospheric Emissions: Monitoring of Pollution (TEMPO), Surface Biology and Geology (SBG), Aerosol and Cloud, Convection and Precipitation (ACCP).

Xu Liu↗
NASA NTRS2023

The Impact of New Accelerometer Transplant Data (ACH) on GRACE Follow-On Along-Orbit Inter-Satellite Laser Ranging Observations and Monthly Time-Variable Gravity and Mascon Solutions

GRACE-D accelerometer data show significant bias jumps since one month after the launch of the GRACE Follow-On (GRACE-FO) satellites in May 2018, making them inapplicable for correcting GRACE-FO products for non-gravitational accelerations. The GRACE-FO Science Data System (SDS) compensated this issue by transplanting the GRACE-C accelerometer data toward that of GRACE-D. Recently, GRACE-FO SDS implemented an updated transplant method, used in the latest release of GRACE-FO data. Here, we examine the impact of updated accelerometer transplant data (ACH) on GRACE-FO measurements at all levels: (a) Level-1B inter-satellite laser ranging residuals measured along satellite orbit, (b) Level-2 time-variable gravity solutions from all SDS centers (JPL, CSR, and GFZ), and (c) Level-3 mascon solutions. We show that inter-satellite laser ranging residuals are modified at low frequencies below 1 mHz, affecting the along-orbit analysis of large-scale time-variable gravity signals. When mapped into monthly Level-2 spherical harmonic coefficients of geopotential, the low-frequency change in inter-satellite ranging residuals leads to substantial improvement of coefficients associated with resonant orders (i.e., 15, 30, 45, etc.) and C 30 . We also present an improved SLR-derived C 30 which significantly improves the agreement with updated GRACE-FO C 30 at seasonal and interannual timescales. Moreover, we demonstrate the noise reduction in mass change estimates from new GRACE-FO Level-2 data over oceans, Greenland, and Antarctica for all SDS solutions. GRACE-FO mascon solutions show a moderate change in the updated release. Our comprehensive analyses demonstrate high-quality estimates of non-gravitational accelerations by the updated transplant method, resulting in more accurate GRACE-FO time-variable gravity and mass change observations.

Laser ranging interferometer↗
NASA NTRSDate not supplied

Geodetic Reference Instrument Transponder for Small Satellites

The local ties between Satellite Laser Ranging stations and other collocated geodetic stations are an important component of the realization of the International Terrestrial Reference Frame but the survey accuracies are limited due to the inaccessibility of the instrument reference points. The NASA Geodetic Reference Instrument Transponder for Small Satellites (GRITSS) technology demonstration mission is designed to overcome this limitation by using a small satellite as a space-based reference point for tying together the measurement points of collocated geodetic stations. The GRITSS instrument upconverts the Global Positioning System (GPS) signals received at the satellite in real-time to S and X band and transmits them to a VLBI Global Observing System (VGOS) antenna ground station. The measurement observables do not require the satellite to be in view of more than one VLBI station at a time. The GRITSS demonstration mission utilizes a 12-UL CubeSat in a Low Earth, Sun Synchronous orbit. Tracking by the global ILRS network throughout the life of the mission is critical to establishing the space-based reference along with the precision orbit determination required by the measurement concept. Due to limitations in spacecraft power and other constraints, the initial phase of the mission will only broadcast the GRITSS VLBI signals twice a day to each of the NASA VGOS stations in Maryland, Hawaii, and Texas, but other international VGOS stations may be added during the second half of the mission. An overview of the GRITSS mission and measurement concept will be presented.

Stephen M Merkowitz↗
NASA NTRSDate not supplied

Communication and Social Science in the Satellite Needs Working Group (SNWG) Assessment

Every two years, NASA conducts an in-depth assessment of the satellite Earth observation data needs of U.S. federal civilian agencies submitted through the Satellite Needs Working Group (SNWG) survey. The SNWG assessment occurs at the nexus of science and people: in the 2022 assessment, over 100 scientists across NASA, NOAA, and USGS were organized to interview over 165 end users at 29 agencies about their unique satellite needs, brainstorm a range of solutions to meet those needs, and communicate back to agencies about resources available for meeting their needs. The innovative approaches to communication, organization, and team make-up that will be described in this talk are vital to the success of the SNWG assessment. As the first major step in evaluating a satellite need, the tri-agency assessment team interviews the agency SMEs who submitted the survey to understand how satellite data could help inform their decision-making process or enable them to fulfill their key responsibilities. In preparation, NASA utilizes social scientists to provide training to all assessment participants on how to hold a discovery-centered interview, including starting with a purpose, creating a welcoming space, exploring all aspects and edges of the need, and brainstorming possible solutions to meet the need. After the interviews, assessment participants propose and review solutions across all thematic areas, seeking those that will help multiple agencies. During the selection process, agencies expected to benefit from a new solution have an opportunity to provide feedback on the proposed activity and are invited to co-design the solution with NASA, should it be implemented. The organization of needs and solutions takes place in Asana, a workflow management tool adapted for the SNWG assessment, and the Report Generation Tool (RGT) enables assessment teams to collaboratively write reports that are returned to each agency with information on current and upcoming resources that help meet their needs.

Katrina Virts↗
NASA NTRSDate not supplied

Enhancing Air Quality Applications in the Hindu Kush-Himalayan Region Using Satellite, Model, and Machine Learning Techniques

Air pollution in the Hindu Kush Himalayan (HKH) region of South Asia is a severe issue, as increases in emissions over the past two decades have degraded air quality (AQ) across the region, which poses major threats to human health, the ecosystem, climate, and agriculture. A diversity of anthropogenic and natural emission sources including transportation, power plants, industries, open biomass burning of crop residue, forest fires, cooking and heating fires, and dust storms contribute to unhealthy AQ and transboundary pollution issues in the region. Further complicating matters is the importance of meteorology and terrain on AQ, especially in the Kathmandu Valley where extreme haze episodes frequently develop from the atmospherically stable weather conditions during the winter monsoon. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with machine learning techniques to develop a comprehensive toolkit for enhancing AQ monitoring and forecasting in HKH. The toolkit incorporates new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager (AMI), which provide unprecedented resolution on aerosols and trace gases, including nitrogen dioxide, formaldehyde, sulfur dioxide, carbon monoxide, and ozone, and aerosol optical depth. Value-added products, such as level 4 PM2.5 products, are developed from the suite of satellite observations to further improve AQ monitoring capabilities in the region. The satellite products are also used to assimilate a high-resolution chemical transport model tailored for the HKH region, which is providing daily, 54-hour AQ forecasts with horizontal grid spacings of 12- and 4-km. This presentation will provide an overview of the suite of satellite- and model-based products in the AQ toolkit and application and performance of the toolkit for AQ monitoring and forecasting in HKH.

Forecasting↗
DOE OSTI2020

Systems and methods for augmenting a satellite based navigation system with alternative position and timing data

Various technologies pertaining to integrating a positioning or timing solution from an alternative position, navigation, or timing (PNT) system into a satellite-based-navigation system are described herein. The PNT system generates a position/timing (P/T) solution that indicates a current position or current time of the PNT system. The P/T solution is provided as input to a simulation of a satellite constellation of the satellite-based-navigation system, and a constellation signal is generated based on the simulation and the P/T solution. The constellation signal is formatted according to a navigation signal format of the satellite-based-navigation system such that when the constellation signal is provided to a receiver of the satellite-based-navigation system, the receiver computes a P/T solution that indicates the current position or the current time identified by the PNT system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Characterizing Spatial and Temporal Variability of California’s Coastal Upwelling Using a Satellite-Derived CUTI Index

Ocean upwelling off California is a wind-driven coastal process with complex interactions between spatial wind patterns and sea surface temperatures. Large-scale spatial and temporal upwelling characterizations are traditionally based on numerical models, while quantifying upwelling with direct observations is not straightforward. To complement model-based upwelling characterizations, here we use satellite estimates of winds, sea surface temperatures, and ocean currents along the California coast to calculate the coastal upwelling transport index (CUTI), originally calculated from a numerical ocean model. The CUTI framework accounts for both Ekman and geostrophic offshore-directed ocean transport, the latter of which is often neglected in satellite-based upwelling assessments. The approach yields the CUTI over a 30-yr period at a daily temporal and 0.25° spatial resolution to analyze upwelling patterns, with focus on potential Morro Bay and Humboldt wind energy areas. Morro Bay (CUTI median of 0.63 m 2 s −1 ) exhibits stronger, more consistent upwelling throughout the year, whereas Humboldt (CUTI median of 0.45 m 2 s −1 ) exhibits a pronounced seasonal cycle with strongest upwelling in the summer and occasional downwelling in winter. The cluster analysis of 1993–2022 satellite maps of CUTI identifies four characteristic spatial upwelling patterns along the coast, corresponding to distinct spatial distributions of sea surface temperature anomalies, Ekman transport, and geostrophic ocean water transport. This dataset provides an observation-based foundation for characterizing natural coastal upwelling off the California coast, showing good agreement with the original model CUTI while being based on satellite data and offering increased spatial resolution.

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