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NASA NTRS2020

Development of a Daily GRACE Mascon Solution for Terrestrial Water Storage

The Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On missions have provided a global history of terrestrial water storage changes since 2002. Traditional GRACE products resolve monthly time-variable gravity at spatial resolutions of 300-500 km, with many recent efforts focusing on regularized mass concentration (mascon) solutions to better resolve signals spatially. However, monthly resolution inhibits the applicability of GRACE to investigations of sub-monthly signals. This study presents a new daily mass change solution estimated as deviations from the NASA Goddard Space Flight Center (GSFC) converged monthly mascon product and quantifies the fundamental trade-off between temporal and spatial resolution in GRACE-only solutions. As an iteration of the GSFC product, this daily solution represents a mixing of monthly information at higher relative spatial resolution with daily information at lower relative spatial resolution, resolving each temporal scale at the best resolution achievable without requiring hydrological model dependencies in the estimation. The resolution of the daily mascons is 300-400 km at high latitudes and 600-1,000 km lower, depending on each daily orbital track and the proximity of each mascon to constraint region boundaries (e.g., coastlines). Through simulations and model comparisons, we demonstrate that daily signals over areas larger than 400,000 km(2) are recoverable, with basins larger than 800,000 km(2) exhibiting strong signal recovery relative to leakage. This analysis establishes baselines for daily signal recovery from GRACE in the context of longer time scale solutions, characterizes leakage inherent in daily GRACE information, and creates new opportunities for applying GRACE to investigations of sub-monthly signals.

M. J. Croteau↗

NASA NTRS2021

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

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

Lu Zhou↗

NASA NTRSDate not supplied

Ice Accretion Roughness Variations on a Hybrid CRM65-Midspan Wing Model

Ice accretion roughness measurements were performed in the Icing Research Tunnel (IRT) at NASA Glenn Research Center for the Hybrid CRM65-Midspan model in a range of icing conditions. The Hybrid CRM65-Midspan model was chosen for this investigation because 1) the model exhibits high sweep relative to models previously explored in the roughness investigations, 2) the model has leading edge characteristics similar to wing shapes currently used in mid-size commercial airliners, and 3) the sweep and thickness ratios relate better to hybrid lifting body designs for N+2 and N+3 vehicles than other models available. The investigation consisted of multiple sets of tests which focused on 1) 0-angle of attack cases replicating the conditions employed by Anderson et al. (1998) using both Appendix C and SLD cloud conditions, 2) cases based on the “Max Scallop” case by Broeren et al. (2016) and a “High Temperature” case with cloud properties similar to the “Max Scallop” case. Additional tests were performed 1) based on the “Max Scallop” case with variations in freestream static temperature and 2) using test section speeds near 10,000-hold flight conditions. The point clouds were characterized using the approach of McClain and Kreeger (2013) for the ice roughness variations and using the approach of McClain (2016) for the mean ice thickness variations. The resulting roughness and mean thickness variations generally follow the temporal scaling previously identified using on airfoil models without sweep, but the collapse of the time progression profiles is not as tight as found for past measurements on models without sweep. LEWICE and modified panel-method predictions were used explore spatial roughness variations and to compare to the roughness correlations developed by McClain et al. (2021) for the “Max Scallop” cases.

Icing↗

NASA NTRS2021

Quantifying Uncertainties in Nighttime Light Retrievals From Suomi-NPP and NOAA-20 VIIRS Day/Night Band Data

Satellite observations of nighttime lights (NTL) from Suomi-NPP and NOAA-20 VIIRS Day/Night Band data have been widely used to estimate human activities. Long-term changes such as urban development and abrupt short-term changes such as power outages have been monitored from temporal NTL acquired by satellites. While high temporal NTL variation has been found across NTL data of varying temporal scale (e.g., daily, monthly, and annual composites), the sources of measurement error and uncertainty are poorly understood. This paper quantifies the sources of VIIRS-derived NTL uncertainty due to view-illumination geometry, surface Bidirectional Reflectance Distribution Function (BRDF)/albedo, and the effects of snow cover, lunar irradiance, aerosol loading, cloud mask, vegetation, geometry, and ephemeral artifacts (e.g., the Aurora Borealis). Based on this current assessment of NASA Black Marble retrievals (VNP46, Collection V001), we found that angular and atmospheric effects dominate retrieval uncertainty. Errors introduced by upstream data inputs (e.g., a coarser nighttime snow cover flag and misclassification errors in the existing VIIRS nighttime cloud mask) were also found to impact retrieval quality. Despite these challenges, a consistent daily NTL time series record can be routinely generated from top-of-atmosphere VNP46 radiances. Key recommendations include: (1) the use of lunar-BRDF adjusted and atmospherically corrected NTL (i.e., as identified as high-quality retrievals in the VNP46 QA fields), (2) development and improvement to the VIIRS snow cover and cloud masks algorithms to accurately reflect NTL retrieval conditions, (3) characterizing seasonal variations in NTL due to vegetation and snow, (4) reducing geometric effects due to the spatial mismatch of gridded pixel and observation footprint, (5) employing angularly-consistent NTL observations from multiple VIIRS instruments (i.e., Suomi-NPP and NOAA-20) to reduce pixel-based uncertainties and address persistent data gaps, and (6) being mindful of surface-reflected radiance from aurora events at mid-to-high latitudes.

Zhuosen Wang↗

NASA NTRS2021

An Operational Overview of the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) Northeast Pacific Field Deployment

The goal of the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) field campaign is to develop a predictive understanding of the export, fate, and carbon cycle impacts of global ocean net primary production. To accomplish this goal, observations of export flux pathways, plankton community composition, food web processes, and optical, physical, and biogeochemical (BGC) properties are needed over a range of ecosystem states. Here we introduce the first EXPORTS field deployment to Ocean Station Papa in the Northeast Pacific Ocean during summer of 2018, providing context for other papers in this special collection. The experiment was conducted with two ships: a Process Ship, focused on ecological rates, BGC fluxes, temporal changes in food web, and BGC and optical properties, that followed an instrumented Lagrangian float; and a Survey Ship that sampled BGC and optical properties in spatial patterns around the Process Ship. An array of autonomous underwater assets provided measurements over a range of spatial and temporal scales, and partnering programs and remote sensing observations provided additional observational context. The oceanographic setting was typical of late-summer conditions at Ocean Station Papa: a shallow mixed layer, strong vertical and weak horizontal gradients in hydrographic properties, sluggish sub-inertial currents, elevated macronutrient concentrations and low phytoplankton abundances. Although nutrient concentrations were consistent with previous observations, mixed layer chlorophyll was lower than typically observed, resulting in a deeper euphotic zone. Analyses of surface layer temperature and salinity found three distinct surface water types, allowing for diagnosis of whether observed changes were spatial or temporal.The 2018 EXPORTS field deployment is among the most comprehensive biological pump studies ever conducted. A second deployment to the North Atlantic Ocean occurred in spring 2021, which will be followed by focused work on data synthesis and modeling using the entire EXPORTS data set.

Biological pump↗

NASA NTRS2022

Evaluation of Retrieving Chlorophyll a Concentration and Colored Dissolved Organic Matter Absorption from Satellite Ocean Color Remote Sensing in the Coastal Waters of Hokkaido, Japan

Coastal ecosystems are important in biogeochemical cycles, because they provide many pathways for chemical elements and compounds to flow between the physical environment and living organisms. The spatial and temporal scales of the biotic and abiotic interactions establish the inherent dynamics of the coastal zone and the requirement for high-resolution investigations. Using match-up data collected in Hokkaido coastal waters, the study herein evaluated the performance of deriving chlorophyll (Chl) a concentration and colored dissolved organic matter (CDOM) absorption (a(sub CDOM)) as a function of wavelength (λ) from satellite observations. The Japanese Aerospace Exploration Agency (JAXA) Second generation GLobal Imager (SGLI), which has 250 m spatial resolution, was compared with other satellite ocean color (OC) sensors. Our results show that the standard SGLI OC4 algorithm with the 530 nm band provided the best performance for Chl a retrievals, and the end-member analysis (EMA) technique improved the estimation of a(sub CDOM)(λ) . Overall differences between in situ radiometric data and satellite retrievals suggest that additional challenges remain, especially in the ultraviolet and blue spectral domains which are useful for studying CDOM and harmful algae blooms in coastal waters. To fulfill applications wherein high-quality remote sensing data are required, the improvement of the atmospheric correction is a likely research area where additional accomplishment will be beneficial for satellite observations of optically complex coastal waters.

SGLI/GCOM-C↗

NASA NTRS2022

Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City

The surface urban heat island (SUHI) effect is among the major environmental issues encountered in urban regions. To better predict the dynamics of the SUHI and its impacts on extreme heat events, an accurate characterization of the surface energy balance in urban regions is needed. However, the ability to improve understanding of the surface energy balance is limited by the heterogeneity of surfaces in urban areas. This study aims to enhance the understanding of the urban surface energy budget through an innovation in the use of land surface temperature (LST) observations from remote sensing satellites. A LST database with 5–min temporal and 30–m spatial resolution is developed by spatial downscaling of the Geostationary Operational Environmental Satellites—R (GOES–R) series LST product over New York City (NYC). The new downscaling method, known as the Spatial Downscaling Method (SDM), benefits from the fine spatial resolution of Landsat–8 and high temporal resolution of GOES–R, and considers the temporal variation in LST for each land cover type separately. Preliminary results show that the SDM can reproduce the temporal and spatial variability of LST over NYC reasonably well and the downscaled LST has a spatial root mean square error (RMSE) of the order of 2 K as compared to the independent Landsat–8 observations. The SDM shows smaller RMSE of 1.93 K over the tree canopy land cover, whereas RMSE is 2.19 K for built–up areas. The overall results indicate that the SDM has potential to estimate LST at finer spatial and temporal scales over urban regions.

land surface temperature↗

NASA NTRS2022

Opportunistic Experiments to Constrain Aerosol Effective Radiative Forcing

Aerosol-cloud interactions (ACI) are considered to be the most uncertain driver of present-day radiative forcing due to human activities. The non-linearity of cloud-state changes to aerosol perturbations make it challenging to attribute causality in observed relationships of aerosol radiative forcing. Using correlations to infer causality can be challenging when meteorological variability also drives both aerosol and cloud changes independently. Natural and anthropogenic aerosol perturbations from well defined sources provide ‘opportunistic experiments’ (also known as natural experiments) to investigate ACI in cases where causality may be more confidently inferred. These perturbations cover a wide range of locations and spatio-temporal scales, including point sources such as volcanic eruptions or industrial sources, plumes from biomass burning or forest fires, and tracks from individual ships or shipping corridors.We review the different experimental conditions and conduct a synthesis of the available satellite data sets and field campaigns to place these opportunistic experiments on a common footing, facilitating new insights and a clearer understanding of key uncertainties in aerosol radiative forcing. Cloud albedo perturbations are strongly sensitive to background meteorological conditions. Strong liquid water path increases due to aerosol perturbations are largely ruled out by averaging across experiments. Opportunistic experiments have significantly improved process-level understanding of ACI, but it remains unclear how reliably the relationships found can be scaled to the global level, thus demonstrating a need for deeper investigation in order to improve assessments of aerosol radiative forcing and climate change.

Matthew W Christensen↗

NASA NTRSDate not supplied

Assessing Uncertainties and Variability 34 Years of Surface Radiative Fluxes Using GEWEX SRB Release4-IP

The NASA/GEWEX Surface Radiation Budget (SRB) project produces 3-hrly shortwave and longwave surface and top of atmosphere radiative fluxes for the 1983-near present time period. The new Release 4 Integrated Product (IP) uses the newly recalibrated and processed ISCCP HXS product as its primary input for cloud and radiance data, replacing ISCCP DX with a ninefold increase in pixel count (10 km instead of 30 km). This first version retains a 1°x1° resolution enabling intercomparison against previous versions and other data sets such as CERES, but spans 34 years from July 1983 through June 2017 and was announced by Kummerow et al. 2019 (GEWEX News). This new IP product also uses an atmospheric temperature and moisture dataset known as nnHIRS and other parameters such as near surface and skin temperatures from SeaFlux and LandFlux data sets. In addition to the input data improvements, several important algorithm improvements have been made since Release 3. These include recalculated SW atmospheric transmissivities and reflectivities, updated ocean and snow/ice albedos, and variable total solar irradiance consistent with SORCE measurements. The LW code has been updated to improve the optical property treatment for clouds and aerosols are included in this version. Radiative treatment of ice clouds is also improved in the LW. The variable aerosol optical properties for the SW and LW are specified using a detailed aerosol history from the Max Planck Institute Aerosol Climatology (MAC). Here we present an assessment of the LW radiative fluxes and the uncertainty of those fluxes relative to the various inputs to surface SW/LW flux measurements from BSRN and PMEL buoys measurements. We review the validation of the SW and LW fluxes and then in terms of time series and then assess the products in terms of their long-term variability of the surface SW and LW net fluxes compared to multiple other data products including atmospheric reanalysis products. The comparisons of radiative estimates to observations are performed at various temporal scales and aimed at investigation of agreement at longer time averages but accessing potential change in diurnal magnitude and daily variability. Utilizing this uncertainty information, to access long-term variability of surface radiation components at selected region and global scales, considering satellite sampling/calibration “artifacts” as necessary. At the longer time scales, the net SW and net LW the TOA and surface have implications toward closure of the energy budgets at the surface, we assess these compared to other studies on energy budget closure for the same selected global and regional scales.

Paul W Stackhouse↗

NASA NTRSDate not supplied

Contribution to Solar Brightness of Small-Size Magnetic Elements

Irradiance variability is mostly driven by surface magnetism, each magnetic feature contributing in a different manner. The contribution of small-size magnetic elements observed ubiquitously at high-spatial resolution on the solar photosphere, is still debated, as such features are mostly unresolved on full-disk images employed to model irradiance variability. Understanding the contribution to solar brightness of small-size magnetic elements, especially in quiet regions, will help understanding irradiance variability on the decadal and longer temporal scales, which, in turn, are fundamental to understand the role of the Sun on the Earth’s climate. We present a preliminary study of the brightness of small-size magnetic elements using high-spatial resolution spectro-polarimetric observations acquired with the National Science Foundations Daniel K. Inouye Solar Telescope (DKIST) during Commissioning Phase 1. We focus on the Intensity contrast vs magnetic field relation, which is a fundamental observable in irradiance studies. Previous studies mostly focused on the FeI 630.1/630.2 nm spectral range and so to contextualize our results we focus here on this spectral region as well. By comparing DKIST results with MURaM magneto-hydrodynamic simulations, and with previous results published in the literature, we conclude that our results are in agreement with studies conducted on HINODE observations. We plan to extend the analysis to UV and IR ViSP observations and VBI blue continuum in order to better constrain the quiet sun contribution to spectral irradiance variations.

SMD↗

NASA NTRSDate not supplied

Comparing Low-Cost Sensors with Ground-Based and Airborne In-Situ and Column Observations of NO2 and HCHO during the FRAPPE Field Campaign in Colorado, USA

Even in the presence of more reliable air quality tools, low-cost sensors have the benefit of recording data on highly localized spatial and temporal scales, allowing for multiple measurements within a single satellite pixel and on pixel boundaries. However, they are less accurate than their regulatory-grade counterparts, requiring regular co-locations with accepted instruments to ensure their validity. Thus, the addition of low-cost sensors to a field campaign – where reference-grade air quality instruments are abundant – not only provides ample opportunities for low-cost sensor co-location and calibration, but also allows the low-cost instruments to be used for sub-pixel validation, covering more surface area than the regulatory instruments alone with a network of sensors. During the summer of 2014, our low-cost sensor network was deployed during the Front Range Air Pollution and Photochemistry Experiment (FRAPPÉ) campaign conducted to sample the composition of air at and above ground level in northeastern Colorado, USA. The low-cost sensor platform included a suite of gas-phase sensors, notably NO2 and two generalized volatile organic compound (VOC) sensors, which were leveraged together to quantify speciated hydrocarbons such as formaldehyde. These key pollutants were chosen for their impacts on human health and climate change, as well as their inclusion on the TEMPO satellite launching this year. Airborne campaign measurements included slant column optical observations of formaldehyde (HCHO), nitrogen dioxide (NO2), and ozone (O3). Myriad additional in-situ instruments described chemical composition up to approximately 5 km above surface level. Ground-based instrumentation included both stationary and mobile regulatory-grade instruments, which were used for sensor calibration. Machine learning techniques such as artificial neural networks (ANNs) were used to match the low-cost signals to that of the reference-grade instruments. Here, we compare calibrated low-cost sensor data collected at ground level in a variety of locations along Colorado’s Front Range to various data sources from the FRAPPÉ campaign to better understand how well airborne and regulatory ground-based measurements can be extrapolated to other locations. Further, as the slant column measurements act as satellite simulators, we explore how low-cost instruments can be used for satellite validation purposes. Comparisons among these different data types also have important implications in data fusion.

Kristen Okorn↗

NASA NTRS2022

How Well do Multisatellite Products Capture the Space-Time Dynamics of Precipitation? Part II: Building an Error Model Through Spectral System Identification

Satellite precipitation products, as all quantitative estimates, come with some inherent degree of uncertainty. To associate a quantitative value of the uncertainty to each individual estimate, error modeling is necessary. Most of the error models proposed so far compute the uncertainty as a function of precipitation intensity only, and only at one specific spatio-temporal scale. We propose a spectral error model which accounts for the neighboring space-time dynamics of precipitation into the uncertainty quantification. Systematic distortions of the precipitation signal and random errors are characterized distinctively in every frequency-wavenumber band in the Fourier domain, to accurately characterize error across scales. The systematic distortions are represented as a deterministic space-time linear filtering term. The random errors are represented as a non-stationary additive noise. The spectral error model is applied to the IMERG multi satellite precipitation product and its parameters are estimated empirically through a system identification approach using the GV-MRMS gauge-radar measurements as reference (“truth”) over the eastern United States. The filtering term is found to be essentially low-pass. While traditional error models attribute most of the error variance to random errors, it is found here that the systematic filtering term explains 48% of the error variance at the native resolution of IMERG. This fact confirms that, at high resolution, filtering effects in satellite precipitation products cannot be ignored, and that the error cannot be represented as a purely random additive or multiplicative term. An important consequence is that precipitation estimates derived from totally different sources shall not be expected to automatically have statistically independent errors.

Precipitation↗

NASA NTRS2022

Dayside Transient Phenomena and Their Impact on the Magnetosphere and Ionosphere

Dayside transients, such as hot flow anomalies, foreshock bubbles, magnetosheath jets, flux transfer events, and surface waves, are frequently observed upstream from the bow shock, in the magnetosheath, and at the magnetopause. They play a significant role in the solar wind-magnetosphere-ionosphere coupling. Foreshock transient phenomena, associated with variations in the solar wind dynamic pressure, deform the magnetopause, and in turn generates field-aligned currents (FACs) connected to the auroral ionosphere. Solar wind dynamic pressure variations and transient phenomena at the dayside magnetopause drive magnetospheric ultra low frequency (ULF) waves, which can play an important role in the dynamics of Earth’s radiation belts. These transient phenomena and their geoeffects have been investigated using coordinated in-situ spacecraft observations, spacecraft-borne imagers, ground-based observations, and numerical simulations. Cluster, THEMIS, Geotail, and MMS multi-mission observations allow us to track the motion and time evolution of transient phenomena at different spatial and temporal scales in detail, whereas ground-based experiments can observe the ionospheric projections of transient magnetopause phenomena such as waves on the magnetopause driven by hot flow anomalies or flux transfer events produced by bursty reconnection across their full longitudinal and latitudinal extent. Magnetohydrodynamics (MHD), hybrid, and particle-in-cell (PIC) simulations are powerful tools to simulate the dayside transient phenomena. This paper provides a comprehensive review of the present understanding of dayside transient phenomena at Earth and other planets, their geoeffects, and outstanding questions.

Bow shock↗

NASA NTRS2023

Ocean Carbon From Space: Current Status and Priorities for the Next Decade

The ocean plays a central role in modulating the Earth’s carbon cycle. Monitoring how the ocean carbon cycle is changing is fundamental to managing climate change. Satellite remote sensing is currently our best tool for viewing the ocean surface globally and systematically, at high spatial and temporal resolutions, and the past few decades have seen an exponential growth in studies utilising satellite data for ocean carbon research. Satellite-based observations must be combined with in-situ observations and models, to obtain a comprehensive view of ocean carbon pools and fluxes. To help prioritise future research in this area, a workshop was organised that assembled leading experts working on the topic, from around the world, including remote-sensing scientists, field scientists and modellers, with the goal to articulate a collective view of the current status of ocean carbon research, identify gaps in knowledge, and formulate a scientific roadmap for the next decade, with an emphasis on evaluating where satellite remote sensing may contribute. A total of 449 scientists and stakeholders participated (with balanced gender representation), from North and South America, Europe, Asia, Africa, and Oceania. Sessions targeted both inorganic and organic pools of carbon in the ocean, in both dissolved and particulate form, as well as major fluxes of carbon between reservoirs (e.g., primary production) and at interfaces (e.g., air-sea and land–ocean). Extreme events, blue carbon and carbon budgeting were also key topics discussed. Emerging priorities identified include: expanding the networks and quality of in-situ observations; improved satellite retrievals; improved uncertainty quantification; improved understanding of vertical distributions; integration with models; improved techniques to bridge spatial and temporal scales of the different data sources; and improved fundamental understanding of the ocean carbon cycle, and of the interactions among pools of carbon and light. We also report on priorities for the specific pools and fluxes studied, and highlight issues and concerns that arose during discussions, such as the need to consider the environmental impact of satellites or space activities; the role satellites can play in monitoring ocean carbon dioxide removal approaches; economic valuation of the satellite based information; to consider how satellites can contribute to monitoring cycles of other important climatically-relevant compounds and elements; to promote diversity and inclusivity in ocean carbon research; to bring together communities working on different aspects of planetary carbon; maximising use of international bodies; to follow an open science approach; to explore new and innovative ways to remotely monitor ocean carbon; and to harness quantum computing. Overall, this paper provides a comprehensive scientific roadmap for the next decade on how satellite remote sensing could help monitor the ocean carbon cycle, and its links to the other domains, such as terrestrial and atmosphere.

oceanography↗

NASA NTRSDate not supplied

Linking Individual Variation in Movement Behavior to Infection Dynamics in Highly Mobile Species

Animal movements are linked to infectious disease dynamics in wildlife through multiple mechanisms across various spatial and temporal scales. For example, infections can impede the movement of infected individuals, but infected hosts can also disperse infectious agents as they move. Individuals inevitably vary in their responses to infections and their roles in determining infection dynamics, which means that considering variation among individuals – whether across species, across time, or within populations – is important for understanding movement-disease relationships and their implications for epidemiology. Here, I draw on studies from multiple systems to examine how variation in movement behavior links to infectious disease epidemiology. For example, a comparative study of habitat selection across multiple waterfowl species highlights that diel behavioral patterns, rather than individual or seasonal variation, is the most important driver of the risk of overlap between waterfowl and poultry farms, and therefore for the risk of avian influenza spillover. Together, these studies show that individual traits interact with the environment to influence the roles of individuals for infectious disease ecology.

Claire Stewart Teitelbaum↗

NASA NTRS2023

PACE Technical Report Series, Volume 11: The PACE Postlaunch Airborne eXperiment (PACE-PAX)

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE; https://pace.gsfc.nasa.gov) mission is NASA's latest and greatest effort to study the ocean-atmosphere system and build upon the legacy of previous satellite missions that have revolutionized our understanding of our home planet. By using advanced radiometric and polarimetric instruments to measure ocean color and key atmospheric variables, PACE aims to provide new insights into the complex interactions between the ocean, atmosphere, and biosphere. This information will be critical for improving our ability to model and predict changes in the Earth's climate, as well as to manage and conserve our planet's valuable natural resources. Performance assessments and science data product validation is a critical process for any Earth Science space-borne mission, as it involves assessing and verifying the quality of the data products generated by the mission while it is in orbit, as well as assigning uncertainties to these retrievals. More specifically, validation efforts are essential for assessing the accuracy of instrument data and the derived science products across varied spatial and temporal scales and ensuring their reliability and consistency with the mission's scientific goals and objectives. The overall plan for validation of PACE data is described in “PACE Science Data Product Validation Plan” (see, e.g., https://pace.gsfc.nasa.gov), and among range of post-launch activities, it describes the need and requirements for a post-launch airborne field campaign in support of PACE validation. This volume outlines the plans and requirements of the PACE Postlaunch Airborne eXperiment (PACE-PAX). The main goal of PACE-PAX is to gather validation and assessment data for the PACE mission, using a variety of airborne and ground-based assets. These assets will be coordinated to obtain complementary measurements of key ocean and atmospheric variables, such as ocean color, aerosols, and clouds. By collecting data from multiple sources, PACE-PAX will help to validate and refine the data products generated by the PACE mission, as well as to identify any potential errors or biases in the measurements. This will be essential for ensuring the accuracy and reliability of the PACE data products, and for maximizing the scientific value of the mission.

Kirk D Knobelspiesse↗

NASA NTRS2023

A Nowcasting Approach for Low Earth Orbit Hyperspectral Infrared Soundings Within the Convective Environment

Low Earth orbit (LEO) hyper-spectral infrared (IR) sounders have significant yet untapped potential for characterizing thermodynamic environments of convective initiation and ongoing convection. While LEO soundings are of value to weather forecasters, the temporal resolution needed to resolve the rapidly evolving thermodynamics of the convective environment is limited. We have developed a novel nowcasting methodology to extend snapshots of LEO soundings forward in time up to six hours to create a product available within National Weather Service systems for user assessment. Our methodology is based on parcel forward-trajectory calculations from the satellite observing time to generate future soundings of temperature (T) and specific humidity (q) at regularly gridded intervals in space and time. The soundings are based on NOAA-Unique Combined Atmospheric Processing System (NUCAPS) retrievals from the Suomi NPP and NOAA-20 satellite platforms. The tendencies of derived convective available potential energy (CAPE) and convective inhibition (CIN) are evaluated against gridded, hourly accumulated rainfall obtained from the Multi-Radar Multi-Sensor (MRMS) observations for 24 hand-selected cases over the Contiguous United States. Areas with forecast increases in CAPE (reduced CIN) are shown to be associated with areas of precipitation. The increases in CAPE and decreases in CIN are largest for areas that have the heaviest precipitation and are statistically significant compared to areas without precipitation. These results imply that adiabatic parcel advection of LEO satellite sounding snapshots forward in time are capable of identifying convective initiation over an expanded temporal scale compared to soundings used only during the LEO satellite overpass time.

North America↗

NASA NTRSDate not supplied

The Third Flight of the Marshall Grazing Incidence X-Ray Spectrometer (MaGIXS-3)

The Marshall Grazing Incidence X-ray Spectrometer (MaGIXS) is the first X-ray slitless imaging spectrograph sounding rocket instrument designed to observe spectrally dispersed soft X-ray images of the solar corona over a wide field-of-view. During the first flight of MaGIXS (MaGIXS-1), occurred on 30 July 2021, several emission lines from coronal structures including X-ray bright points were observed. Further, MaGIXS-1 analysis also demonstrated the successful inversion of overlappograms using robust unfolding algorithms. Given the demonstrated success of MaGIXS-1, the second flight of the instrument with a simplified optical design, MaGIXS-2, is scheduled for 2024 to observe high temperature diagnostic emission lines. Results from MaGIXS-1 discovered dominant missing emission lines near 15A, arising from relatively cool plasma that peaks around 2 MK. This wavelength region hosts several closely spaced satellite lines of Fe XVII, Fe XVI and Fe XV ions, which are expected to be enhanced at lower temperatures and are currently unmodeled in the CHIANTI atomic database. This wavelength region offers one of the most unique diagnostics to measure electron temperature, as well as signatures of equilibrium state of the plasma in active regions, which has not been studied so far. The goal of MaGIXS-3 mission is the to determine coronal heating parameters, such as the spatial and temporal properties of coronal heating events, by measuring discriminating observations, such as electron temperature, effective temperature, density, abundance, and departures from thermal equilibrium of the plasma, by observing the Sun in the SXR wavelength range. To meet this goal, the MaGIXS instrument will be upgraded with a new X-ray telescope mirror that will provide higher spatial and spectral resolution and throughput, allowing for spectral lines to be observed at the relevant spatial and temporal scales. In addition, MaGIXS-3 will also carry The Resolving Inversion Context X-ray Spectrometer (TRICXS), a high dispersion Bragg crystal spectrometer to spectrally resolve the lines near 14.9 to 15.9A, critical to unlock the full diagnostic potential of this wavelength range. Here we will present the preliminary concept design of MaGIXS-3 and discuss the potential observations.

X-ray Imaging↗