UMBC GESTAR II
Permanent URI for this collectionhttp://hdl.handle.net/11603/24116
n December 2021, GESTAR II partnered with NASA Goddard Space Flight Center’s Earth Science Division to advance Earth science and Goddard’s leadership by providing a competitive environment to hire and retain high-quality scientists who are on track to be leaders at NASA, in academia and in industry. GESTAR II exemplifies the power of mentorship, embracing a career development strategy that only a university research center can provide. In GESTAR II, early-career researchers and students can build outstanding resumes, launching them to become the Earth science leaders of tomorrow.
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Item Remote Sensing of Ice Cloud Properties With Millimeter and Submillimeter-Wave Polarimetry(IEEE, 2024-11-20) Wu, Dong L.; Gong, Jie; Deal, William R.; Gaines, Willian; Cooke, Caitlyn M.; De Amici, Giovanni; Pantina, Peter; Liu, Yuli; Yang, Ping; Eriksson, Patrick; Bennartz, RalfIce clouds are poorly constrained in current global climate and weather models and have been used as a tuning parameter in the models to balance radiation budget at the top of atmosphere and precipitation at the surface. Sub-millimeter-wave (Submm) remote sensing can fill the sensitivity gap of cloud ice observations between visible/infrared (VIS/IR) and microwave (MW) frequencies. The added value from submm-wave bands has been recognized for achieving a better understanding of cloud, convection and precipitation (CCP) processes. Recent satellite observations at microwave frequencies showed promising results that additional information on cloud microphysical properties (e.g., ice particle shape and orientation) can be inferred from V-pol and H-pol radiances. Motivated by the added value from cloud polarimeters, a compact SWIRP (Submm-Wave and Long-Wave InfraRed Polarimeter) was developed under NASA's Instrument Incubator Program (IIP) to reduce instrument size, weight, power (SWaP) for future Earth science missions. Low-SWaP sensors like SWIRP will allow the cost-effective implementation of a distributed observing system to study fast cloud processes with the needed spatiotemporal sampling.Item Interactive Assessment of Variances of High-Resolution Model Features in Digital Twin Simulations(ACM, 2024-11-22) Kulkarni, Chhaya; Privé, Nikki; Janeja, VandanaPrior to the deployment of expensive instruments into orbit, spatio-temporal digital twin systems modeling the whole earth are used to study the efficacy of these instruments. However, we need to make sure that the simulated instruments have realistic characteristics (to reflect the physics of the atmosphere and limits of the instrument itself) in order for the results of the digital twin to be robust and usable. If these simulations are done accurately, the instrument can be deployed, leading to more accurate weather forecasts and climate research. This demonstration system validates the simulations, specifically the realism of remotely sensed observations. The digital twin system is a low-cost way to improve instrument design used in meteorological and climatological research. The primary goal is to show how atmospheric data can improve the development and validation of new observational systems for meteorology and climate science. We have developed an interactive variability study system that uses a dynamic platform to visualize, assess, and grasp complex atmospheric dynamics. The dashboard is built using Python for backend operations and integrates tools such as the Streamlit framework for quick web application development and the Folium library for advanced geospatial visualizations. This dashboard acts as a bridge between advanced atmospheric modeling and spatio-temporal digital twin applications, showcasing the substantial benefits of integrating comprehensive model outputs into the simulation of observational systems.Item In-flight characterization of the Hyper-Angular Rainbow Polarimeter (HARP2) on the NASA PACE mission(SPIE, 2024-11-20) McBride, Brent; Sienkiewicz, Noah; Xu, Xiaoguang; Puthukkudy, Anin; Fernandez-Borda, Roberto; Martins, J. VanderleiThe Hyper-Angular Rainbow Polarimeter (HARP2) is a novel wide-field of view imaging polarimeter instrument on the recently-launched NASA Plankton Aerosol Cloud ocean Ecosystem (PACE) mission. Since launch on February 8 2024, HARP2 has taken over 6 months of global Earth data. In order for this data to meet scientific quality standards, we must ensure that it is as accurate as possible and over long periods of time. We use well-characterized Earth targets, such as Saharan deserts, as well as regular views of the Sun and dark frames to trend our on-orbit calibration. In this work, we discuss the preliminary performance trends derived from these activities and how well they compare with the HARP2 prelaunch calibration.Item First results and on-orbit performance of the Hyper-Angular Rainbow Polarimeter (HARP2) on the PACE satellite(SPIE, 2024-11-20) Martins, J. Vanderlei; Fernandez-Borda, Roberto; Puthukkudy, Anin; Xu, Xiaoguang; Sienkiewicz, Noah; Smith, Rachel; McBride, Brent; Dubovik, Oleg; Remer, LorraineThe Hyper-Angular Rainbow Polarimeter-2 (HARP2) was launched on board the Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission, in February 2024, for the global measurement of aerosol and cloud properties as well as to provide atmospheric correction over the footprint of the Ocean Color Instrument (OCI). HARP2 is designed to collect data over a wide field of view in the cross-track direction (+/-47deg) allowing for global coverage in about two days, as well as an even wider field of view in the along-track direction (+/-54deg) providing measurements over a wide range of scattering angles. HARP2 samples 10 angles at 440, 550, and 870nm focusing on aerosol and surface retrievals, and up to 60 angles at 670nm for the hyper-angular retrieval of cloud microphysical properties. The HARP2 instrument collects three nearly identical images with linear polarizers aligned at 0°, 45°, and 90° that can be converted to push-broom images of the I, Q, and U Stokes parameters for each angle, and each wavelength. The HARP2 technology was first demonstrated with the HARP CubeSat satellite which collected a limited dataset for 2 years from 2020 to 2022. HARP2 extends these measurements to a full global coverage in two days, seven days a week.Item OreSat 0.5: next-generation small satellite for global cirrus cloud detection and mapping(SPIE, 2024-11-20) McBride, Brent; Smith, Rachel E.; Greenberg, Andrew; Dixon, Scott; Muller, Jan-Peter; Martins, J. VanderleiThe OreSat 0.5 is a novel small satellite developed in collaboration between Portland State University in Portland, Oregon, USA, the University of Maryland, Baltimore County, in Baltimore, MD, USA, and the Mullard Space Science Laboratory at University College London, Surrey, UK. OreSat 0.5 will demonstrate global cirrus cloud detection and mapping from a compact, low-cost platform. In this work, we preview the OreSat 0.5 mission and demonstrate the calibration and science behind its primary payload, the Cirrus Flux Camera (CFC). The CFC is a three-channel shortwave infrared radiometer (870, 1390, 1590 nm bands). Flux ratios between its three bands will be used to differentiate ice versus water and noncloud signals. Along-track and Across-track pointing up to ±45° will allow retrievals of heights and winds of the cirrus cloud tops. We discuss a preliminary pre-launch calibration of CFC and plans to expand upon and maintain this calibration vicariously on-orbit and through proxy sources. OreSat 0.5 launched to space on August 16 2024 and first light data is expected by Q4 2024.Item Analysis of the Influence of Clear-Sky Fluxes on the Cloud-Type Mean Cloud Radiative Effects in the Tropical Convectively Active Regions With CERES Satellite Data(AGU, 2024-11-20) Xu, Kuan-Man; Sun, Moguo; Zhou, YapingCloud radiative effects (CREs) and cloud-type mean CREs depend upon how clear-sky fluxes are computed over a large area: those of the immediate environment of clouds or the regional mean clear-sky fluxes. Five convectively active regions in the Tropics, two over land (Africa and Amazon) and three over ocean (eastern and western Pacific and Atlantic), are selected to understand the influence of immediate environment of clouds on CREs. Fluxes derived from 19 years of high-resolution CERES satellite data, categorized by cloud type, are utilized. The cloud types are classified based on the joint cloud top pressure and cloud optical depth distribution. For the entire tropical region, differences in cloud-type mean CRE with regional mean and immediate environment clear skies range from −7.8 to 10.7 Wm⁻² for shortwave (SW), 2.9 to 15.8 Wm⁻² for longwave (LW), and 6.1 to 17.9 Wm⁻² for net, respectively. The oceanic and Amazonia regions have negative (positive) SW (LW) CRE differences, typically 2–6 Wm⁻² in SW but 7–10 Wm⁻² in LW, whereas Africa has positive SW and LW CRE differences (typically 20–30 Wm⁻², up to 40–50 Wm⁻²). The influence of immediate environment reduces the regionally averaged, that is, cloud-type mean CREs weighted by cloud fractions, SW cloud cooling, and LW cloud warming in four of the five regions except for Africa. For Africa, it increases the SW cloud cooling and greatly reduces the LW cloud warming, resulting in net cloud cooling as in other regions instead of warming. The implications of these findings for observational and modeling studies are discussed.Item Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations(EGU, 2024-11-20) Liu, Yuli; Adams, Ian S.The submillimeter-wave radiometer operating in the along-track scanning mode continuously collects brightness temperature (TB) data over a two-dimensional (2D) cloud cross-section as the platform moves forward. TB observations from multiple positions and viewing angles show great promise in better constraining the 2D cloud microphysical properties compared to single-angle observations. In this study, we develop two types of tomographic reconstruction algorithms to retrieve 2D ice water content (IWC) profiles using multi-angle TB observations. The one-dimensional (1D) tomographic algorithm performs 1D retrievals beam by beam using each TB observation at a specific position and angle to derive cloud properties along the propagation path. It then integrates the 1D retrieval results to construct 2D cloud distributions. The 2D tomographic algorithm directly constrains the 2D cloud microphysical properties using multi-angle scanning TB observations. Starting with an initial assumption, the algorithm iteratively refines the 2D cloud microphysical quantities by minimizing discrepancies between TB simulations and observations under prior constraints. Both tomographic algorithms are developed based on a hybrid of Bayesian Monte Carlo Integration (MCI) and Optimal Estimation Method (OEM). A simulation experiment is conducted to evaluate the performance of two tomographic reconstruction algorithms. The experiment demonstrates stable convergence of both tomographic methods, with the 2D tomographic algorithm exhibiting superior performance. The experiment results highlight the significant advantage of using multi-angle observations to constrain 2D cloud structure. Compared to nadir-only retrievals, the tomographic technique provides a detailed reconstruction of ice clouds’ inner structure with high spatial resolution. Also, the technique significantly improves retrieval accuracy by correcting systematic biases and reducing the derivation of retrieval errors. Furthermore, the tomography technique effectively increases detection sensitivity for small ice cloud particles.Item Photonic Integrated Circuits (PICs) in Space: The Hyperspectral Microwave Photonic Instrument (HyMPI)(NTRS, 2024-06-05) Torres, Victor; Stephen, Mark; Gambini, Fabrizio; Mohammed, Priscilla; Blaisdell, John; Zhou, Yaping; Piepmeier, Jeffrey; Gambacorta, Antonia; Swap, Robert; Adams, Ian; Shahroudi, Narges; Rosenberg Robert; MacKinnon, James; Kotsakis, AlexanderItem The West-Coast Hyperspectral Microwave Sensor Intensive Experiment (WHyMSIE): A Prototype for A PBL Mission of Missions(NTRS, 2024-05-20) Gambacorta, Antonia; Kotsakis, Alexander; Kroodsma, Rachael; MacKinnon, James; Zhou, Yaping; Shahroudi, Narges; Gong, Jie; Moradi, Isaac; Santanello, Joseph; Piepmeier, Jeff; Stephen, Mark; Gambini, Fabrizio; Mohammed, Priscilla; Fritts, Matt; McLinden, Matt; Adams, Ian; Platnick, Steve; Oreopoulos, Lazaros; Swap, RobertWe present an overview of the 2024 West-Coast Hyperspectral Microwave Sensor Intensive Experiment(WHyMSIE). WHyMSIE is a joint NASA-NOAA multi-sensor airborne experiment, embracing passive and active sensors from the Program of Record (PoR) along with novel technology funded through the NASA ESTO Instrument Incubation Program. At the core of this effort is the demonstration of the Conical Scanning Millimeter-wave Imaging Radiometer Hyperspectral (CoSMIR-H) instrument, a PBL DSI funded effort to develop hyperspectral sounding capability in the thermal microwave domain finalized to improved temperature and water vapor soundings in the Earth’s Planetary Boundary Layer (PBL). An overview of the field campaign design, instrument payload and validation plan is presented here.Item Multiphase sulfur chemistry facilitates particle growth in a cold and dark urban environment(Royal Society of Chemistry, 2024-11-15) Mao, Jingqiu; Bali, Kunal; Campbell, James Raemond; Robinson, Ellis Shipley; DeCarlo, Peter F.; Ijaz, Amna; Temime-Roussel, Brice; Barbara, D'Anna; Ketcherside, Damien; Yokelson, Robert J.; Hu, Lu; Cesler-Maloney, Meeta; Simpson, William; Guo, Fangzhou; Flynn, James; St. Clair, Jason; Nenes, Athanasios; Weber, RodneySulfate comprises an average of 20% of the ambient PM2.5 mass during the winter months in Fairbanks, based on 24-hour filter measurements. During the ALPACA 2022 field campaign (Jan 15th-Feb 28th of 2022), we deployed two aerosol mass spectrometers (AMS) and one aerosol chemical speciation monitor (ACSM) at three urban sites, combined with Scanning Mobility Particle Sizers (SMPS), to examine the evolution of aerosol composition and size distribution at a sub-hourly time scale. During an intense pollution episode with ambient temperature between -25 and -35°C, all three instruments (two AMS and one ACSM) recorded a sharp increase in sulfate mass, ranging from 5 to 40 μg/m³ within a few hours. This increase contributed up to half of the observed rise in ambient PM2.5 mass concentration and coincided with a substantial shift in the number distribution from particle sizes less than 100 nm diameter (Dp < 100 nm) to larger particles (Dp > 100 nm) with little increase in number concentration. The corresponding increase in the volume concentration and distribution shift to larger particle size suggests the secondary formation of sulfate and organic aerosol onto pre-existing aerosols. Comparing AMS-sulfate (all sulfur species) to inorganic sulfate measured by online particle-into-liquid sampler−ion chromatography (PILS-IC), we find roughly 80% of sulfate increase was due to organic sulfur, consistent with the observation of mass spectral signatures in the AMS of organosulfur compounds. The rapid formation of sulfate appears to coincide with spikes in ambient aldehyde concentrations (formaldehyde and acetaldehyde) and an increase in S(IV) in ambient PM2.5. This likely results from multiphase chemistry, where hydroxymethanesulfonate (HMS) and other aldehyde-S(IV) adducts are formed through reactions between aldehydes and SO2 in deliquesced aerosols. We estimate that all S(IV) species, including HMS, contribute an average of 30% to aerosol sulfur, with a dominant fraction occurring during rapid sulfate increase events. Our work highlights the crucial role of controlling aldehydes to mitigate severe air pollution events in Fairbanks and may apply to other urban areas. It also emphasizes the significance of multiphase chemistry in driving particle growth from Aitken mode to accumulation mode, a key step for aerosol-cloud interactions.Item Continental freshwater discharge influences sea surface salinity variability near world’s megadeltas(PNAS, 2024-12-03) Khadim, Fahad K.; Getirana, Augusto; Bindlish, Rajat; Kumar Biswas, Nishan; Nie, Wanshu; Lahmers, Timothy M.; Kumar, Sujay V.Sea surface salinity (SSS) is a key parameter in the thermohaline circulation of global oceans. Near the megadeltas, inland streamflow through large catchments plays a crucial role in mediating salinity. While some regional studies have investigated how SSS is impacted through water cycle and climate components, a global scale quantification of inland streamflow contribution on SSS variability is lacking. Here, we utilized remote sensing and observation-driven datasets to quantify the statistical associations between SSS and streamflow (Qbasin) at 48 megadeltas worldwide. This study uncovers a robust negative association between Qbasin and SSS, with correlation coefficients R less than -0.6 for seasonal data found in 26 of the 48 megadeltas, and less than -0.4 for deseasoned data in 21 megadeltas. The anticorrelation relationship is more pronounced in large deltas, particularly near tropical climates and in river-influenced deltas. The study also underscores the significant roles of climate, morphological, and anthropogenic stratification in impacting the natural influence of freshwater discharge on SSS. By highlighting the interconnected impacts of alterations in terrestrial water cycle upstream and SSS, this work contributes to enhancing our understanding of global ocean and climate circulation patterns and in tackling environmental issues pertaining to marine ecosystems.Item Tutorial on Causal Inference with Spatiotemporal Data(ACM, 2024-11-04) Ali, Sahara; Wang, JianwuSpatiotemporal data, which captures how variables evolve across space and time, is ubiquitous in fields such as environmental science, epidemiology, and urban planning. However, identifying causal relationships in these datasets is challenging due to the presence of spatial dependencies, temporal autocorrelation, and confounding factors. This tutorial provides a comprehensive introduction to spatiotemporal causal inference, offering both theoretical foundations and practical guidance for researchers and practitioners. We explore key concepts such as causal inference frameworks, the impact of confounding in spatiotemporal settings, and the challenges posed by spatial and temporal dependencies. The paper covers synthetic spatiotemporal benchmark data generation, widely used spatiotemporal causal inference techniques, including regression-based, propensity score-based, and deep learning-based methods, and demonstrates their application using synthetic datasets. Through step-by-step examples, readers will gain a clear understanding of how to address common challenges and apply causal inference techniques to spatiotemporal data. This tutorial serves as a valuable resource for those looking to improve the rigor and reliability of their causal analyses in spatiotemporal contexts.Item Evolution of Reactive Organic Compounds and Their Potential Health Risk in Wildfire Smoke(ACS, 2024-10-22) Pye, Havala O. T.; Xu, Lu; Henderson, Barron H.; Pagonis, Demetrios; Campuzano-Jost, Pedro; Guo, Hongyu; Jimenez, Jose L.; Allen, Christine; Skipper, T. Nash; Halliday, Hannah S.; Murphy, Benjamin N.; D’Ambro, Emma L.; Wennberg, Paul O.; Place, Bryan K.; Wiser, Forwood C.; McNeill, V. Faye; Apel, Eric C.; Blake, Donald R.; Coggon, Matthew M.; Crounse, John D.; Gilman, Jessica B.; Gkatzelis, Georgios I.; Hanisco, Thomas F.; Huey, L. Gregory; Katich, Joseph M.; Lamplugh, Aaron; Lindaas, Jakob; Peischl, Jeff; St Clair, Jason; Warneke, Carsten; Wolfe, Glenn; Womack, CarolineWildfires are an increasing source of emissions into the air, with health effects modulated by the abundance and toxicity of individual species. In this work, we estimate reactive organic compounds (ROC) in western U.S. wildland forest fire smoke using a combination of observations from the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign and predictions from the Community Multiscale Air Quality (CMAQ) model. Standard emission inventory methods capture 40–45% of the estimated ROC mass emitted, with estimates of primary organic aerosol particularly low (5–8×). Downwind, gas-phase species abundances in molar units reflect the production of fragmentation products such as formaldehyde and methanol. Mass-based units emphasize larger compounds, which tend to be unidentified at an individual species level, are less volatile, and are typically not measured in the gas phase. Fire emissions are estimated to total 1250 ± 60 g·C of ROC per kg·C of CO, implying as much carbon is emitted as ROC as is emitted as CO. Particulate ROC has the potential to dominate the cancer and noncancer risk of long-term exposure to inhaled smoke, and better constraining these estimates will require information on the toxicity of particulate ROC from forest fires.Item Accelerating Subglacial Bed Topography Prediction in Greenland: A Performance Evaluation of Spark-Optimized Machine Learning Models(2024) Cham, Mostafa; Tabassum, Tartela; Shakeri, Ehsan; Wang, JianwuItem Landslide Hazard Is Projected to Increase Across High Mountain Asia(AGU, 2024-10-03) Stanley, Thomas; Soobitsky, Rachel B.; Amatya, Pukar; Kirschbaum, Dalia B.High Mountain Asia has long been known as a hotspot for landslide risk, and studies have suggested that landslide hazard is likely to increase in this region over the coming decades. Extreme precipitation may become more frequent, with a nonlinear response relative to increasing global temperatures. However, these changes are geographically varied. This article maps probable changes to landslide hazard, as shown by a landslide hazard indicator (LHI) derived from downscaled precipitation and temperature. In order to capture the nonlinear response of slopes to extreme precipitation, a simple machine-learning model was trained on a database of landslides across High Mountain Asia to develop a regional LHI. This model was applied to statistically downscaled data from the 30 members of the Seamless System for Prediction and Earth System Research large ensembles to produce a range of possible outcomes under the Shared Socioeconomic Pathways 2-4.5 and 5-8.5. The LHI reveals that landslide hazard will increase in most parts of High Mountain Asia. Absolute increases will be highest in already hazardous areas such as the Central Himalaya, but relative change is greatest on the Tibetan Plateau. Even in regions where landslide hazard declines by year 2100, it will increase prior to the mid-century mark. However, the seasonal cycle of landslide occurrence will not change greatly across High Mountain Asia. Although substantial uncertainty remains in these projections, the overall direction of change seems reliable. These findings highlight the importance of continued analysis to inform disaster risk reduction strategies for stakeholders across High Mountain Asia.Item AI to the rescue: how to enhance disaster early warnings with tech tools(Nature, 2024-10-01) Kuglitsch, Monique M.; Cox, Jon; Luterbacher, Jürg; Jamoussi, Bilel; Xoplaki, Elena; Thummarukudy, Muralee; Radwan, Golestan Sally; Yasukawa, Soichiro; McClain, Shanna N.; Albayrak, Arif; Oehmen, David; Ward, ThomasArtificial intelligence can help to reduce the impacts of natural hazards, but robust international standards are needed to ensure best practice.Item Spectral correlation in MODIS water-leaving reflectance retrieval uncertainty(Optica, 2024-01-15) Zhang, Minwei; Ibrahim, Amir; Franz, Bryan A.; Sayer, Andrew; Werdell, P. Jeremy; McKinna, Lachlan I.Spectral remote sensing reflectance, Rᵣₛ(λ) (sr⁻¹), is the fundamental quantity used to derive a host of bio-optical and biogeochemical properties of the water column from satellite ocean color measurements. Estimation of uncertainty in those derived geophysical products is therefore dependent on knowledge of the uncertainty in satellite-retrieved Rᵣₛ. Furthermore, since the associated algorithms require Rᵣₛ at multiple spectral bands, the spectral (i.e., band-to-band) error covariance in Rᵣₛ is needed to accurately estimate the uncertainty in those derived properties. This study establishes a derivative-based approach for propagating instrument random noise, instrument systematic uncertainty, and forward model uncertainty into Rᵣₛ, as retrieved using NASA’s multiple-scattering epsilon (MSEPS) atmospheric correction algorithm, to generate pixel-level error covariance in Rᵣₛ. The approach is applied to measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite and verified using Monte Carlo (MC) analysis. We also make use of this full spectral error covariance in Rᵣₛ to calculate uncertainty in phytoplankton pigment chlorophyll-a concentration (chlₐ, mg/m³) and diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kₔ(490), m⁻¹). Accounting for the error covariance in Rᵣₛ generally reduces the estimated relative uncertainty in chlₐ by ~1-2% (absolute value) in waters with chlₐ < 0.25 mg/m³ where the color index (CI) algorithm is used. The reduction is ~5-10% in waters with chlₐ > 0.35 mg/m³ where the blue-green ratio (OCX) algorithm is used. Such reduction can be higher than 30% in some regions. For Kₔ(490), the reduction by error covariance is generally ~2%, but can be higher than 20% in some regions. The error covariance in Rᵣₛ is further verified through forward-calculating chlₐ from MODIS-retrieved and in situ Rᵣₛ and comparing estimated uncertainty with observed differences. An 8-day global composite of propagated uncertainty shows that the goal of 35% uncertainty in chlₐ can be achieved over deep ocean waters (chlₐ ≤ 0.1 mg/m³). While the derivative-based approach generates reasonable error covariance in Rᵣₛ, some assumptions should be updated as our knowledge improves. These include the inter-band error correlation in top-of-atmosphere reflectance, and uncertainties in the calibration of MODIS 869 nm band, in ancillary data, and in the in situ data used for system vicarious calibration.Item VIIRS Version 2 Deep Blue Aerosol Products(AGU, 2024) Lee, Jaehwa; Hsu, N. Christina; Kim, Woogyung V.; Sayer, Andrew; Tsay, Si-CheeNASA's Deep Blue aerosol project has developed global aerosol data records using consistent retrieval algorithms applied to various satellite sensors. The primary components of these data records are derived from the series of Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (SNPP) and the National Oceanic and Atmospheric Administration or NOAA-20+ satellites as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), among others. These instruments provide over 23 years of measurements with similar radiometric characteristics for aerosol retrievals. The algorithms used for the initial Version 1 SNPP VIIRS data set were based on the MODIS Collection 6.1 Deep Blue algorithm over land and Satellite Ocean Aerosol Retrieval (SOAR) algorithm over water. For VIIRS Version 2 data reprocessing, major updates have been made to the algorithm suite, including better accounting for effects of surface pressure, improved determination of surface reflectance, and the inclusion of fine-mode aerosol optical models to better represent anthropogenic aerosols over land. Cross-calibration gain factors are derived for the NOAA-20 VIIRS measurements to be consistent with the SNPP VIIRS, which allows the use of a unified algorithm package for both instruments. Comparisons against AERONET observations indicate that the Version 2 AOD data from SNPP VIIRS are significantly better than the Version 1 counterpart over land and slightly degraded over water in exchange for better spatial coverage. The AOD data from SNPP and NOAA-20 VIIRS are comparable, indicating that cross-calibration enables the creation of consistent aerosol data records using the series of VIIRS sensors.Item GESTAR II Center Awarded $47 Million Extension On Cooperative Agree With NASA Goddard Space Flight Center(UMBC News, 2024-09-26) Fraser, Adriana; Demond, MarlaynaThe UMBC-led Goddard Earth Science Technology and Research (GESTAR II) center has been awarded a two-year, $47 million extension to continue its cooperative agreement with the NASA Goddard Space Flight Center.Item Relationship between the sub-micron fraction (SMF) and fine-mode fraction (FMF) in the context of AERONET retrievals(EGU, 2023-03-03) O'Neill, Norman T.; Ranjbar, Keyvan; Ivănescu, Liviu; Eck, Thomas; Reid, Jeffrey S.; Giles, David M.; Pérez-Ramírez, Daniel; Chaubey, Jai PrakashThe sub-micron (SM) aerosol optical depth (AOD) is an optical separation based on the fraction of particles below a specified cutoff radius of the particle size distribution (PSD) at a given particle radius. It is fundamentally different from spectrally separated FM (fine-mode) AOD. We present a simple (AOD-normalized) SM fraction versus FM fraction (SMF vs. FMF) linear equation that explains the well-recognized empirical result of SMF generally being greater than the FMF. The AERONET inversion (AERinv) products (combined inputs of spectral AOD and sky radiance) and the spectral deconvolution algorithm (SDA) products (input of AOD spectra) enable, respectively, an empirical SMF vs. FMF comparison at similar (columnar) remote sensing scales across a variety of aerosol types. SMF (AERinv-derived) vs. FMF (SDA-derived) behavior is primarily dependent on the relative truncated portion (ε꜀) of the coarse-mode (CM) AOD associated with the cutoff portion of the CM PSD and, to a second order, the cutoff FM PSD and FM AOD (εբ). The SMF vs. FMF equation largely explains the SMF vs. FMF behavior of the AERinv vs. SDA products as a function of PSD cutoff radius (“inflection point”) across an ensemble of AERONET sites and aerosol types (urban-industrial, biomass burning, dust, maritime and a mixed class of Arctic aerosols). The overarching dynamic was that the linear SMF vs. FMF relation pivots clockwise about the approximate (SMF, FMF) singularity of (1, 1) in a “linearly inverse” fashion (slope and intercept of approximately 1-ε꜀ and ε꜀) with increasing cutoff radius. SMF vs. FMF slopes and intercepts derived from AERinv and SDA retrievals confirmed the general domination of ε꜀ over εբ in controlling that dynamic. A more general conclusion is the apparent confirmation that the optical impact of truncating modal (whole) PSD features can be detected by an SMF vs. FMF analysis.