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NASA NTRSDate not supplied

Two Decades of Atmospheric and Surface Temperature, Water Vapor, and Cloud Trends Derived from Satellite Remote Sensors

Satellite remote sensor such as Atmospheric Infrared Sounder (AIRS), Cross-track Infrared Sounder (CrIS), and Infrared Atmospheric Sounding Interferometer (IASI) provide high-quality atmospheric temperature, water, vapor, and greenhouse gas vertical profiles. Additionally, they provide atmospheric cloud properties, surface emissivity, and surface skin temperatures. We have developed two algorithms which can consistently derive these products from multiple IR sounders. The first one is a Single Field-of-view Sounder Atmospheric Product (SIFSAP) algorithm and the second one is a Climate Fingerprinting Sounder Product (ClimFiSP) algorithm. Compared to current operational AIRS and CrIS Level-2 (L2) algorithms, which perform one retrieval for each 3 by 3 field of views (FOVs) using a cloud-clearing approach, the SiFSAP algorithm, on the other hand, performs one retrieval for each FOV using an all-sky optimal estimation approach. The SiFSAP algorithm retrieves all the above-mentioned atmosphere and surface properties simultaneously including cloud properties with 3-time higher spatial resolution and 9-times more products. Climate products are typically derived by performing spatial and temporal averaging of L2 products. It is a time-consuming process to generate L2 data products since AIRS, CrIS, and IASI have millions of observations each day with thousands of spectral channels for each observation. Additionally, differences in L2 retrieval algorithms for different satellite sensors can lead to errors in the climate products. Our ClimFiSP algorithm, which performs retrievals from spatiotemporally averaged L1 hyperspectral radiances directly, will be orders of magnitude faster than traditional method. he ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides accurate data climate data fusion products from multiple satellite sensors. We have applied this method to both AIRS and CrIS (on SNPP and on NOAA 20) data and generated two decades climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. Both SiFSAP and ClimFiSP will be available at NASA GES DISC data center for public access.

remote sensing↗
NASA NTRSDate not supplied

Mapping National Forest Aboveground Biomass in Mexico By Integrating GEDI and Landsat Times Series Data

Mexico is one of the countries with great potential for the UN's Reducing Emissions from Deforestation and Forest Degradation (REDD+) program, a key nature-based solution for the forest sector. To monitor carbon stock changes, there is a growing demand for unbiased Monitoring Reporting Verification (MRV) systems to facilitate effective forest management and climate change mitigation strategies. Remote sensing-based national aboveground biomass density (AGBD) estimation over Mexico is scarce and often limited to one-time static mapping, leading to spatiotemporal inconsistency in inputs. As an effort under NASA's Carbon Monitoring System (CMS) program, we have developed a remote sensing-based approach to create consistent historical AGBD maps of Mexico using multi-stream remote sensing data, including spaceborne lidar GEDI and long-term Landsat time series, as well as topographic information. We employ the continuous change detection and classification (CCDC) algorithm for temporal modeling of Landsat surface reflectance, followed by the inference of forest AGBD using a random forest machine learning algorithm with the temporal information of land surface dynamics extracted by the CCDC as input. GEDI provides unprecedented forest structure and AGBD sampling datasets for model training and validation practices. In this presentation, we share the progress made in developing a spatially explicit mapping of historical AGBD changes associated with land surface changes and post-disturbance landscapes.

Taejin Park↗
NASA NTRS2023

Extending Aquatic Spectral Information with the First Radiometric IR-B Field Observations

Planetary radiometric observations enable remote sensing of biogeochemical parameters to describe spatiotemporal variability in aquatic ecosystems. For approximately the last half century, the science of aquatic radiometry has established a knowledge base using primarily, but not exclusively, visible wavelengths. Scientific subdisciplines supporting aquatic radiometry have evolved hardware, software, and procedures to maximize competency for exploiting visible wavelength information. This perspective culminates with the science requirement that visible spectral resolution must be continually increased to extract more information. Other sources of information, meanwhile, remain underexploited, particularly information from nonvisible wavelengths. Herein, absolute radiometry is used to evaluate spectral limits for deriving and exploiting aquatic data products, specifically the normalized water-leaving radiance, Γ(λ)⁠, and its derivative products. Radiometric observations presented herein are quality assured for individual wavebands, and spectral verification is conducted by analyzing celestial radiometric results, comparing agreement of above- and in-water observations at applicable wavelengths, and evaluating consistency with bio-optical models and optical theory. The results presented include the first absolute radiometric field observations of Γ(λ) within the IR-B spectral domain (i.e. spanning 1400–3000 nm), which indicate that IR-B signals confer greater and more variable flux than formerly ascribed. Black-pixel processing, a routine correction in satellite and in situ aquatic radiometry wherein a spectrum is offset corrected relative to a nonvisible waveband (often IR-B or a shorter legacy waveband) set to a null value, is shown to degrade aquatic spectra and derived biogeochemical parameters.

aquatic optics↗
NASA NTRSDate not supplied

Prediction of Broadband Blade-Wake Back-Scatter Noise from a Hovering Ideally Twisted Rotor using OVERFLOW2-ANOPP2

This work illustrates the use of a hybrid RANS/LES framework for the prediction of broadband noise via integral formulations from an ideally twisted hovering rotor. Four simulations were conducted, which investigated the effect of different turbulence modeling and spatiotemporal resolutions. The aerodynamic performance and aeroacoustics predicted by these four computational cases were compared against data acquired in the Small Hover Anechoic Chamber at the NASA Langley Research Center and against previous simulations conducted using the lattice-Boltzmann solver, PowerFLOW. The simulation case with the finest spatial resolution had the most favorable aerodynamic performance and broadband noise comparison with measured data; however, the simulation case utilizing the SST-DDES turbulence model predicted the most accurate tonal noise signature. Aerodynamic flowfield visualizations identified near-wake trailing edge vortex shedding, perpendicular blade-vortex interactions, and tip vortex formation, which are known to cause broadband noise. The fluid dynamics associated with a new broadband noise source, blade-wake back-scatter, were also identified. Lastly, a broadband noise source visualization technique was utilized to confirm the presence of blade-wake back-scatter as a dominant broadband noise source.

blade-wake back-scatter↗
NASA NTRSDate not supplied

Open-Source Science-led Development of the AOS Mission Science Data System (SDS)

The Earth System Observatory (ESO) Atmosphere Observing System (AOS) mission will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The AOS Science Data System (SDS) will be a system of systems developed within the Cloud to manage the research and operational processing of AOS mission orbital and suborbital sensors and curate these data for reprocessing (e.g., in near real-time or by collection) and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage. Further, AOS SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The AOS mission follows NASA’s lead in making a commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the AOS SDS system components will be developed with open-source concepts including components of SDS itself as well as AOS mission algorithms. Further, the AOS SDS assumes the role to lead and facilitate OSS activities for the AOS mission. This presentation describes the framework of the AOS SDS and its integral part in facilitating OSS within the AOS mission.

David M. Giles↗
NASA NTRSDate not supplied

Enhancing Long-Term Trend Simulation of OH Through the Synergy of Model Simulations and Aura Ozone Monitoring Instrument (OMI) NO 2 and HCHO Retrievals

During the last few years, tremendous progress has been made to develop an efficient parameterization module using agile machine learning techniques. The aim of this module is to provide dynamic response of the tropospheric hydroxyl radical (OH) to its major drivers, including trace gases, aerosols, clouds, and meteorology. This module, named ECCOH (pronounced “echo”) and implemented in NASA’s GEOS-5 global model, offers an unrealized opportunity to unravel the convoluted response of OH to its underlying drivers while approaching the accuracy of full-chemistry without incurring excessive computational costs, making it suitable for climate models. However, the accurate representation of OH in ECCOH poses challenges due to the lack of representation of some of its critical inputs such as the abundance of NO 2 and HCHO concentrations. As such, we leverage the well-characterized satellite observations of NO2 and HCHO columns from Aura OMI to enhance their representation in ECCOH using an optimal interpolation method for the time period of 2005 - present. We show how the inclusion of OMI information can affect the spatiotemporal variability and long-term trends of OH, CO, and CH 4 across the globe. Additionally, we underscore the necessity of obtaining high-fidelity information regarding tropospheric ozone from the southern hemisphere from space, a region currently lacking full verification in models, posing a challenge to get a reasonable amount of chemical sink for CH 4 .

OH↗
NASA NTRSDate not supplied

SAGE III/ISS Validation Methods

Spatiotemporal collocation of measurements between instruments is an essential yet challenging component of the data validation process. Traditional methods of collocation may simply consider length of time and physical distance between measurements. However, the distribution of atmospheric constituents may change rapidly over time and space, so care must be taken to ensure that matched measurements represent the same environment and will not introduce unnecessary bias. More advanced collocation methods may consider additional physical parameters to minimize inappropriate matches with large differences caused by local circulation. We report results of SAGE III/ISS validation with ground-based measurements using advanced collocation methods, and assessments are performed on the quality and presence of trends in both datasets.

Mary Cate McKee↗
NASA NTRS2023

The Statistical Emulators of GGCMI Phase 2: Responses of Year-to-Year Variation of Crop Yield to CO 2 , Temperature, Water and Nitrogen Perturbations

Understanding the impact of climate change on year-to-year variation of crop yield is critical to global food stability and security. While crop model emulators are believed to be lightweight tools to replace the models, few emulators have been developed to capture such interannual variation of crop yield in response to climate variability. In this study, we developed a statistical emulator with a machine learning algorithm to reproduce the response of year-to-year variation of four crop yields to CO 2 (C), temperature (T), water (W), and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2. The emulators were able to explain more than 52 % of the variance of simulated yield and performed well in capturing the year-to-year variation of global average and gridded crop yield over current croplands in the baseline. With the changes in CO 2 –temperature–water–nitrogen (CTWN) perturbations, the emulators could reproduce the year-to-year variation of crop yield well over most current cropland. The variation of R and the mean absolute error was small under the single CTWN perturbations and dual-factor perturbations. These emulators thus provide statistical response surfaces of yield, including both its mean and interannual variability, to climate factors. They could facilitate spatiotemporal downscaling of crop model simulation, projecting the changes in crop yield variability in the future and serving as a lightweight tool for multi-model ensemble simulation. The emulators enhanced the flexibility of crop yield estimates and expanded the application of large-ensemble simulations of crop yield under climate change.

Climate change↗
NASA NTRSDate not supplied

Geochemical Insights Into Volcanic and Lithospheric Evolution of Mars

Introduction: The Martian lithosphere plays a pivotal role in volcanic processes, altering surface features, climate shifts, and the planet's early habitability. Crucially, the rigid lithosphere significantly influences Mars' long-term thermal conditions. Studying various geological eons’ volcanic compositions unveils how the lithosphere has evolved, even though understanding the early Noachian volcanic chemistry remains challenging due to weathering and resurfacing. In this study, newly discovered Noachian volcanic terranes[1-2] along with Hesperian and Amazonian volcanic terranes[3] are considered(Figure 1), to infer the evolution of the Martian lithosphere. Figure 1:Mars topographic map derived from MOLA-HRSC data [4] highlights the study regions including the Noachian volcanic terranes, Arabia Terra (AT), Thaumasia Minor (TM), and the Southwest Margin of Icaria Planum (SWIP)[5].Additionally, the Hesperian volcanic terranes such as Syrtis Major (SM), Hesperia Planum (HP), and, the Amazonian volcanic terranes encompass Elysium Mons (EM), Alba Patera (AP), and Olympus Mons (OM) are marked. Methods and Datasets: We use the most recent elemental mass fraction maps derived from gamma spectroscopy data from the Mars Odyssey 2001mission [6]to ascertain the bulk composition of the Martian landscape. Utilizing the updated geochemical provinces dataset of Mars[7], we consider bulk chemistry variations within our study regions. The maps, at a resolution of 50×50, encompass elements like Al, Ca, Fe, Si, K, Th, H2O, Cl, and S, detected through Gamma-Ray Spectroscopy (GRS) with decimeter scale depth sensitivity[6].Additional major and minor elements like Mg, Na, Ti, P, and Mn, using mass balance methods [8]serve as inputs for melting and crystallization simulations. We employ the pMELTS model with FMQ-3to FMQ oxygen fugacity to estimate the Pressure(P)and melting degree(F)in various eons of volcanic terranes. We have also used conventional thermobarometric calculations to determine their formation P-T conditions, using silica activity and olivine-melt Mg-exchange thermometry[9-10]. Results: Chemical Composition of Studied Volcanic Terranes: To assess formation conditions, we have investigated if GRS-measured compositions signify primary igneous processes by evaluating weathering through the Chemical Index of Alteration (CIA), K/Th ratios, and ternary plots for aqueous alteration. Consistent K/Th ratios and low CIA (<50) across volcanic regions suggest minimal large-scale weathering, affirming well-preserved igneous material extending at decimeter depths. In addition, the calculated bulk compositions align with Martian mantle equilibrium, confirming studied volcanic terranes-Arabia Terra, Thaumasia Minor, and SWIP -as primary or less altered compositions, reinforcing their representativeness at a regional GRS scale on Mars. We have also estimated the bulk compositions of Hesperian and Amazonian volcanic terranes shown in Figure 1. All volcanic terranes represent the primary or minimally altered composition. Temporal Evolution of Martian Volcanic Terranes: Our results show P-T conditions ranging from 1.3-1.6 GPa and 1350-1390°C for Noachian terranes, while Hesperian volcanic terranes vary from 1.6-1.7 GPa and 1370-1395°C, and Amazonian volcanic terranes range between 1.9-2.8 GPa and 1380-1415°C. Our analyses correspond to lithospheric thicknesses/ depth of melting for Noachian which ranges from110-135 km, 120-235 km for Hesperian terranes, and 160-235 km for Amazonian terranes. Partial melting percentages (F) range from 8-12% for Noachian, 10-11% for Hesperian, and 10-12% for Amazonian. We have also calculated mantle potential temperatures (Tp) to show variations across epochs(Figure 2). Heat flux estimation shows variations from 51-65 mW/m2 for Noachian, 38-45 mW/m2for Hesperian, and 27-39 mW/m2for Amazonian terranes, implying temporal changes in lithospheric thickness and heat flow. Comparison of Noachian volcanic terranes with Hesperian and Amazonian reveals variations in lithospheric thickness and heat flux, indicating potential implications for Martian surface conditions, including volcanic activity, climate evolution, and early habitability. Figure 2: P-T diagram displays formation conditions of studied Martian volcanic regions using GRS data, alongside Noachian-age surface basalt samples from Gusev, Gale, and Jezero. Dashed lines represent Martian mantle potential temperatures, calculated by adding the latent heat of fusion to the equilibrium temperature at which melt and solid mantle coexist and subtracting the Martian adiabatic gradient corresponding to the depth of melt. The solid line shows the Martian mantle solidus [11].Discussion& Implication: The study explores Martian volcanic activity across eons. Uniform lithospheric thickness from the Noachian to Hesperian eons suggests persistent weak plumes until Hesperian, influencing heat flow variations. Noachian's high heat flux, likely due to concentrated radioactive elements, shows spatial heterogeneity, indicating early Mars' differentiated mantle. Volcanic outgassing plays avital role in the formation of the Martian climate, likely sustained water on the surface, influencing warm-wet or cold-dry conditions. Hesperian's favorable lithospheric heat flow (45 mW/m2) supports extended water existence, resembling Earth's conditions. Whereas, Amazonian's diminished heat flux (25 mW/m2) corresponds to water loss and arid climate. Hesperian's similar lithospheric thickness to mid-Noachian implies prolonged water retention, influencing climate and potential for astrobiological research, expanding perspectives for future Martian missions. Future work: As volcanism was active throughout its history. The emergence of Mars' extensive volcanic regions, known as large igneous provinces, which are linked to intermittent volcanic activity driven by mantle plumes, poses challenges to understanding the scale and persistence of these phenomena in Martian convection models. Therefore, understanding the regional-scale changes in eruptive processes within Martian volcanic provinces over time remains unclear and crucial for deciphering the evolution of the Martian interior without Earth-like plate tectonics. We will continue this work with petrological and thermoelastic analyses to study the compositional and spatiotemporal evolution of the Elysium Volcanic Province.

A Rani↗
NASA NTRSDate not supplied

Evaluating Diurnal Ozone Emissions: A Comparative Analysis of DSCOVR EPIC, PANDORA, and TOLNet Data for Space-Based Monitoring Assessment: A Case Study by ASDC

Stratospheric ozone occurs naturally in the upper atmosphere, forming a protective layer that shields us from the sun's harmful ultraviolet rays. Tropospheric ozone is not emitted directly into the air but is created by chemical reactions between nitrogen oxides (NOx) and volatile organic compounds (VOC). This reaction happens when pollutants emitted by cars, power plants, industrial boilers, refineries, chemical plants, and other sources chemically react in sunlight [1]. Therefore, increased levels of tropospheric ozone indicate the presence of pollutants in the air. While daily anthropogenic activity causes an increase of ground-level ozone, variation of stratospheric ozone changes happens much slower, so that variation of the total ozone column reflects the variation of the tropospheric column due to natural, e.g., wildfires and anthropogenic air pollution. Both total and tropospheric ozone column products are used in this study. The reflectance spectra measured by the Earth Polychromatic Imaging Camera (EPIC) instrument aboard the Deep Space Climate Observatory (DSCOVR) spacecraft are compared with a set of radiative transfer-derived lookup tables for the EPIC filter transmission functions and a wide range of ozone values to retrieve ozone with a maximum resolution of 18 km at the sub-satellite point [2]. EPIC provides total column ozone in level 2 and level 4 products and tropospheric column ozone in level 4 products. Both EPIC Ozone products [2] are available at the Atmospheric Science Data Center (ASDC) at NASA Langley Research Center [3, 4]. Pandora spectrometer instrument measures columnar amounts of trace gases in the atmosphere. These gases (O3, NO2, CH2O) absorb light from the sun at specific wavelengths in the ultraviolet-visible spectrum [5]. Using the theoretical solar spectrum as a reference, Pandora determines trace gas amounts using differential optical absorption spectroscopy (DOAS). Pandora ozone retrievals are available from the Pandonia Global Network [6]. Pandora data from North American major metropolitan areas, New York, NY, Washington DC, Los Angeles, CA, and Mexico City. Tropospheric Ozone Lidar Network (TOLNet) was established in 2012 to provide high spatiotemporal observations of tropospheric ozone to (1) better understand physical processes driving the ozone budget in various meteorological and environmental conditions and (2) validate the tropospheric ozone measurements of space-borne missions [7]. TOLNet data are available at ASDC [8]. While EPIC provides global coverage of ozone retrievals several times daily, temporal resolution may miss some features in daily ozone variations. Ground-based sensors such as Pandora spectrometers and TOLNet lidars provide better temporal resolution while missing continuous spatial coverage. The forthcoming ozone retrieval from the TEMPO mission [9] will provide better spatial and temporal coverage of air quality (including ozone) over North America. This study investigates whether EPIC ozone products can detect diurnal air quality variations and compare ozone temporal development with retrieval by ground-based instruments.

diurnal ozone emissions, DSCOVR EPIC, PANDORA, spa↗
NASA NTRSDate not supplied

Evaluation of Planetary Boundary Layer Structure from NASA Global Modeling and Assimilation Office’s Next Retrospective Analysis Product MERRA-21C

The Planetary Boundary Layer (PBL) is a complex interface between Earth’s surface and the atmosphere with high spatiotemporal variability in its characteristics, and accurate simulation and observation of the PBL has proven to be a challenge. In this study, we evaluate PBL structures from NASA Global Modeling and Assimilation Office (GMAO)’s next retrospective analysis product: the Goddard Earth Observing System Retrospective Analysis of the early 21st Century (GEOS-R21C), with 25-km horizontal resolution. The GEOS-R21C incorporates a wide range of observing systems and various improvements over previous GMAO reanalysis. We compare the PBL thermodynamic structure as well as PBL Height (PBLH) from GEOS-R21C with various PBL observations. For PBLH, definitions are different depending on each observing system. PBLHs derived from radiosonde observations and Global Navigation Satellite System Radio Occultation (GNSS RO) are based on bulk Richardson number and refractivity gradient, respectively. The results of the evaluation of PBL structure from GMAO’s next retrospective analysis product will be presented. In addition, comparison between near-real-time (NRT) commercial and NASA commercial GNSS RO PBLH will also be discussed.

Eun-Gyeong Yang↗
NASA NTRSDate not supplied

Automated Tracking of Shallow Cu Growth Rates on Geostationary Imagery and Linkages to Cloud Organization

Shallow moist convection is ubiquitous throughout the tropics and represents a key player in boundary layer processes. Common tropical cloud features, such as convective rolls and cold pool fronts, form and persist for different periods within environments that support such development. Determining differences in lifecycle amongst these features in varying environments requires viewing their evolution from initiation to decay. Geostationary satellites provide a means to follow the clouds with enhanced spatiotemporal sampling from space. We apply a cloud-tracking tool to study lifecycle properties of shallow cumulus sampled during the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex) field campaign of 2019. The mission conducted airborne and shipborne operations over the West Pacific Ocean, in tandem with Rapid Scan imagery from the Advanced Himawari Imager (AHI) on the Japan Meteorological Agency’s (JMA) Himawari-8 satellite. Shallow cumulus was segmented on AHI 0.5-km visible reflectance and tracked during the daylit hours of several research flights that exhibited ideal atmospheric conditions for tracking. The resulting cloud tracks were collected according to regions containing airborne sampling of individual clouds at various stages of their lifecycles, yielding ensembles of tracks in separate environments. We present an analysis on lifecycle properties extracted from the cloud-track ensembles and their potential connections to cloud organizations observed throughout CAMP2Ex. Each ensemble is evaluated by calculating cloud-layer growth rates and comparing to spatial parameters, including track-achieved area and cloud-top height. The apparent clustering of growth rates for specific ensembles is then compared against overall cloud organizations, such as isolated congestus and cold pool fronts, that are observed with the airborne data. Finally, we consider how such differences in cloud growth appear in the airborne radar observations of intercepted cloud tracks.

Cloud Tracking↗
NASA NTRS2024

Extending the Utility of Space-Borne Snow Water Equivalent Observations Over Vegetated Areas With Data Assimilation

Snow is a vital component of the earth system, yet no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use a fraternal twin observing system simulation experiment, specifically investigating how much snow simulated using widely used models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24° x 37° domain in the western USA and Canada, simulating snow at 250 m resolution and hourly time steps in water year 2019. We perform two data assimilation experiments, including (1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals and (2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that, relative to a nature run, or assumed true simulation of snow evolution, assimilating synthetic SWE observations improved average SWE biases at maximum snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at the time of maximum snow volume were 111 mm and average SWE biases were on the order of 150 %. Here the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River region, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash–Sutcliffe efficiency of runoff improved from −2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability.

Justin Pflug↗
NASA NTRS2023

Optimal Estimation Inversion of Ionospheric Electron Density from GNSS-POD Limb Measurements: Part I-Algorithm and Morphology

GNSS-LEO radio links from Precise Orbital Determination (POD) and Radio Occultation (RO) antennas have been used increasingly in characterizing the global 3D distribution and variability of ionospheric electron density (N e ). In this study, we developed an optimal estimation (OE) method to retrieve N e profiles from the slant total electron content (hTEC) measurements acquired by the GNSS-POD links at negative elevation angles (ε < 0°). Although both OE and onion-peeling (OP) methods use the Abel weighting function in the N e inversion, they are significantly different in terms of performance in the lower ionosphere. The new OE results can overcome the large N e oscillations, sometimes negative values, seen in the OP retrievals in the E-region ionosphere. In the companion paper in this Special Issue, the HmF2 and NmF2 from the OE retrieval are validated against ground-based ionosondes and radar observations, showing generally good agreements in NmF2 from all sites. Nighttime hmF2 measurements tend to agree better than the daytime when the ionosonde heights tend to be slightly lower. The OE algorithm has been applied to all GNSS-POD data acquired from the COSMIC-1 (2006–2019), COSMIC-2 (2019–present), and Spire (2019–present) constellations, showing a consistent ionospheric N e morphology. The unprecedented spatiotemporal sampling of the ionosphere from these constellations now allows a detailed analysis of the frequency–wavenumber spectra for the N e variability at different heights. In the lower ionosphere (~150 km), we found significant spectral power in DE1, DW6, DW4, SW5, and SE4 wave components, in addition to well-known DW1, SW2, and DE3 waves. In the upper ionosphere (~450 km), additional wave components are still present, including DE4, DW4, DW6, SE4, and SW4. The co-existence of eastward- and westward-propagating wave4 components implies the presence of a stationary wave4 (SPW4), as suggested by other earlier studies. Further improvements to the OE method are proposed, including a tomographic inversion technique that leverages the asymmetric sampling about the tangent point associated with GNSS-LEO links.

GNSS constellation↗
NASA NTRSDate not supplied

Remote Sensing of Atmospheric Temperature, Water Vapor, Trace Gases, Cloud, and Surface Properties on Daily and Decadal Time Scales

Hyperspectral IR sounders such as AIRS on Aqua, CrIS on S-NPP, NOAA20 and JPSS-2, IASI on Metop A, B, and C provide high-quality atmospheric temperature, water, vapor, and greenhouse gas vertical profiles. Additionally, they provide atmospheric cloud properties, surface emissivity, and surface skin temperatures. We have developed two algorithms which can consistently derive these products from multiple IR sounders. The first one is a Single Field-of-view Sounder Atmospheric Product (SIFSAP) algorithm and the second one is a Climate Fingerprinting Sounder Product (ClimFiSP) algorithm. The SiFSAP algorithm performs one retrieval for each FOV using an all-sky optimal estimation approach. The core of the SiFSAP algorithm is an accurate and fast Principal Component-based Radiative Transfer Model (PCRTM), which can calculate hyperspectral radiance spectra under both clear and cloudy conditions. The PCRTM was developed in the past decade using consistent reference line-by-line radiative transfer model and spectroscopy for hyperspectral sounders such as AIRS, CrIS, IASI, NAST-I, and S-HIS. The ClimFiSP algorithm, which performs retrievals from spatiotemporally averaged L1 hyperspectral radiances directly, will be orders of magnitude faster than traditional method. he ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides accurate data climate data fusion products from multiple satellite sensors. Both SiFSAP and ClimFiSP will be available at NASA GES DISC data center for public access.

Xu Liu↗
NASA NTRS2024

High-Resolution Mapping of Nitrogen Oxide Emissions in Large US Cities From TROPOMI Retrievals of Tropospheric Nitrogen Dioxide Columns

Satellite-derived spatiotemporal patterns of nitrogen oxide (NO x ) emissions can improve accuracy of emission inventories to better support air quality and climate research and policy studies. In this study, we develop a new method by coupling the chemical transport Model-Independent SATellite-derived Emission estimation Algorithm for Mixed-sources (MISATEAM) with a divergence method to map high-resolution NO x emissions across US cities using TROPOspheric Monitoring Instrument (TROPOMI) tropospheric nitrogen dioxide (NO 2 ) retrievals. The accuracy of the coupled method is validated through application to synthetic NO 2 observations from the NASA-Unified Weather Research and Forecasting (NU-WRF) model, with a horizontal spatial resolution of 4 km × 4 km for 33 large and mid-size US cities. Validation reveals excellent agreement between inferred and NU-WRF-provided emission magnitudes (R= 0.99, normalized mean bias, NMB = −0.01) and a consistent spatial pattern when comparing emissions for individual grid cells (R=0.88 ± 0.06). We then develop a TROPOMI-based database reporting annual emissions for 39 US cities at a horizontal spatial resolution of 0.05° × 0.05° from 2018 to 2021. This database demonstrates a strong correlation (R= 0.90) with the National Emission Inventory (NEI) but reveals some bias (NMB = −0.24). There are noticeable differences in the spatial patterns of emissions in some cities. Our analysis suggests that uncertainties in TROPOMI-based emissions and potential misallocation of emissions and/or missing sources in bottom-up emission inventories both contribute to these differences.

NOx emissions↗
NASA NTRSDate not supplied

Satellite Tracking of Shallow Cumulus during CAMP2Ex: The Aerosol and Organization Connection

Shallow moist convection is ubiquitous throughout the tropics and represents a key player in boundary layer processes. Shallow clouds come in many forms, from scattered, short-lived cumulus to organized features that include cold pool boundaries and lines reaching O(100 km) scale lasting several hours. In this study, we explore how cloud morphology and environmental aerosol affect shallow cumulus lifecycle properties. Measuring such influences requires a broad, detailed view of these cloud fields as they grow and decay. Geostationary satellites provide a means to follow the clouds with enhanced spatiotemporal sampling from space. We apply a cloud-tracking tool to study lifecycle properties of shallow cumulus sampled during the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex) field campaign of 2019. The mission conducted airborne and shipborne operations over the West Pacific Ocean, in tandem with Rapid Scan imagery from the Advanced Himawari Imager (AHI) on the Japan Meteorological Agency’s (JMA) Himawari-8 satellite. We segment shallow cumulus on AHI 0.5-km visible reflectance during the daylit hours of several research flights that exhibited ideal atmospheric conditions for tracking. We collect the resulting cloud tracks by regions containing airborne sampling of the cloud environments and by two specific segmentation techniques: one optimized for small, disorganized cumulus and the other for larger, organized clouds using suitable thresholds for reflectance and blurring. We present an analysis of cloud lifecycle properties using our dual segmentation approach for various sampled environments of CAMP2Ex. A total of 7 research flights (RFs) exhibited ideal conditions for generating cloud-track ensembles under similar thermodynamic and kinematic profiles but with varying aerosol load. We consider lifetime, track-achieved metrics, and ensemble-fitted growth rates to evaluate the aerosol effect. Cloud elongation, a factor in cloud morphology, is also considered for any potential influence in observed shifts in growth rates. The results suggest that aerosol affects cloud lifecycles more significantly for clouds with larger areas and independent of the elongation factor.

Cloud Tracking↗
NASA NTRSDate not supplied

Open-Source Science-Driven Development of the Science Data System (SDS) for Earth System Observatory (ESO) Atmospheric Missions

The NASA Earth System Observatory (ESO) atmospheric missions will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The Science Data System (SDS) will deploy the adaptive processing system (APS) developed within the Cloud to manage the research and operational processing of ESO atmospheric mission orbital and suborbital sensors and curate these data for near real-time and collection reprocessing and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage and distribution. Further, the SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The SDS follows NASA’s commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the SDS system components will be developed with open-source concepts including components of APS itself as well as ESO atmospheric mission algorithms. This presentation describes the framework of the SDS and its integral part in facilitating OSS within the ESO atmospheric missions.

David M. Giles↗