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NASA NTRSDate not supplied

FluxSat: Long-term Earth Science Data Record (ESDR) for Terrestrial Gross Primary Production (GPP) based on satellite data calibrated with eddy covariance data

Gross primary production (GPP), the amount of carbon dioxide (CO 2 ) assimilated by plants through photosynthesis, is one of the most variable and uncertain components of the global carbon cycle. Global GPP has been estimated with a number of process-based models, data-driven, and hybrid approaches. Dynamic global vegetation models (DGVMs), driven by observed environmental changes, are used for global carbon budget assessments and long-term (climate) prediction. Benchmarking these and other models globally with data-driven GPP estimates is critical for understanding the land sink and ensuring accurate forecasts of the carbon cycle. In addition, global data-driven GPP estimates are crucial for studies of interannual variability, including trends that are linked to mechanisms with large uncertainties, such as the indirect CO 2 fertilization effect related to greening. In response to a community need for a GPP data set that well captures spatio-temporal variability, we developed FluxSat, a data-driven approach that optimizes the use of satellite reflectance data from the NASA MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, calibrated using ground-based eddy covariance (EC) data. We are enhancing (spatially, higher resolution) and extending FluxSat (in time, with additional sensors) to create a high quality long term GPP Earth System Data Record (ESDR) for use in model benchmarking, carbon cycle modeling, and studies of trends and interannual variability. Our team’s objectives are to: 1. Update and document the current MODIS FluxSat GPP (daily, 0.05o and 0.5o resolutions) products with latest available MODIS and EC data sets; 2. Extend FluxSat GPP record forward in time with the Visible Infrared Imaging Radiometer Suite (VIIRS) on operational weather satellites going forward; 3. Extend FluxSat GPP record backward in time using the Advanced Very High Resolution Radiometer (AVHRR) on weather satellites dating back to 1981; 4. Provide higher spatial resolution MODIS and VIIRS GPP (0.0083o). 5. Thoroughly evaluate all FluxSat products with independent data; and 6. Create a homogenized long-term GPP record spanning 40+ years. We will discuss plans for this long-term data set that is supported through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program.

gross Primary Production↗

NASA NTRSDate not supplied

Monitoring Trends in Air Quality During a Drought: Case Study to Improve Public Health Response to Drought Threats

Drought has been an area of distinct concern in the Pacific Northwest since the mid-2010s. Recent studies have documented a correlation between air quality and drought in the United States, which is often observed in increased aerosols including airborne particulate matter (PM). This study partnered with the Oregon Health Authority and Washington State Department of Health to evaluate trends in air quality in the Pacific Northwest during the evolution of drought conditions using aerosol optical depth (AOD) observations collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua satellites. These satellite data were analyzed in conjunction with ground-based PM2.5 and PM10 data sourced from the Environmental Protection Agency (EPA) Air Quality System’s network of ground-based monitors and the Standardized Precipitation Evapotranspiration Index drought index. Based on recommendations by local health departments, this study examined air quality trends between 2015 and 2022 in 12 counties within Oregon and Washington that reflected diversity in population density, drought exposure, rural and urban status, and data availability from EPA AQS monitors. The findings from this study provided supplemental information to the ongoing efforts by community health stakeholders to study the public health impacts of drought and support public health responses to future drought events.

Taylor West↗

NASA NTRS2023

Public Health Data Applications Using the CDC Tracking Network: Augmenting Environmental Hazard Information with Lower-latency NASA Data

Exposure to environmental hazards is an important determinant of health, and the frequency and severity of exposures is expected to be impacted by climate change. Through a partnership with the U.S. National Aeronautics and Space Administration, the U.S. Centers for Disease Control and Prevention’s National Environmental Public Health Tracking Network is integrating timely observations and model data of priority environmental hazards into its publicly accessible Data Explorer (https://ephtracking.cdc.gov/DataExplorer/). Newly integrated datasets over the contiguous U.S. (CONUS) include: daily 5-day forecasts of air quality based on the Goddard Earth Observing System Composition Forecast (GEOS-CF), daily historical (1980-present) concentrations of speciated PM2.5 based on the Modern Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), and Moderate Resolution Imaging Spectroradiometer (MODIS) daily near real-time maps of flooding (MCDWD). Data integrated into the CDC Tracking Network are broadly intended to improve community health through action by informing both research and early warning activities, including (1) describing temporal and spatial trends in disease and potential environmental exposures, (2) identifying populations most affected, (3) generating hypotheses about associations between health and environmental exposures, and (4) developing, guiding, and assessing environmental public health policies and interventions aimed at reducing or eliminating health outcomes associated with environmental factors.

air quality↗

NASA NTRSDate not supplied

Improving Radiative Fluxes for the CERES FluxByCldTyp Data Product Using Deep Neural Network

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Preliminary results show significant LW improvement.

Sun, Moguo↗

NASA NTRSDate not supplied

Mapping Aerosol Lidar Ratios Over Ocean Using Constrained Retrievals and A Global Aerosol Model

The current NASA Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) algorithms assign one lidar ratio (i.e., extinction-to-backscatter ratio; LR) value globally for each of the seven tropospheric aerosol types. In a future data products release, the CALIPSO project aims to improve these algorithms through the development of regional and seasonal LR climatologies. In this work, aerosol LRs are inferred through Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) backscatter profiles constrained by collocated aerosol optical depth (AOD) from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data. This analysis is subsampled for those profiles that are cloud-free and contain only one CALIOP-classified aerosol type (e.g., marine). The CALIOP profiles are then collocated with aerosol volume fractions obtained through Goddard Chemistry Aerosol Radiation and Transport (GOCART) model simulations. In this presentation, we show that the twelve-year (June 2006-August 2018) mean spatial distributions of inferred aerosol LRs for CALIOP-classified marine aerosols correspond inversely with patterns of GOCART sea salt volume fraction (SSVF). For example, smaller SSVFs (< 65%) and larger LRs (> 55 sr), are found near land masses. This is indicative of the influence of over-land aerosols (e.g., pollution and biomass burning smoke). In the remote oceans (i.e., regions likely less impacted by non-sea salt aerosols), larger SSVFs (> 95%) and smaller LRs (< 25 sr) are found. The developed relationship between the GOCART SSVFs and MODIS AOD constrained LRs (polynomial fit intersect values of ~56 sr for SSVF of 0% and ~21 sr for SSVF of 100%) is used to produce model-assisted climatological LR maps on seasonal scales. Additionally, we show maps of inferred LRs from constrained retrievals using the CALIPSO Ocean Derived Column Optical Depth (ODCOD) product and comparisons with those from the MODIS analyses. The technique demonstrated in this study not only benefits CALIPSO in the near-term, but similar methods can be applied to future spaceborne elastic backscatter lidars with collocated passive sensors, such as those associated with the upcoming NASA Atmosphere Observing System (AOS).

Travis Toth↗

NASA NTRS2023

Highly Resolved Mapping of NO 2 Vertical Column Densities From GeoTASO Measurements Over a Megacity and Industrial Area During the KORUS-AQ Campaign

The Korea–United States Air Quality (KORUS-AQ) campaign is a joint study between the United States National Aeronautics and Space Administration (NASA) and the South Korea National Institute of Environmental Research (NIER) to monitor megacity and transboundary air pollution around the Korean Peninsula using airborne and ground-based measurements. Here, tropospheric nitrogen dioxide (NO 2 ) slant column density (SCD) measurements were retrieved from Geostationary Trace and Aerosol Sensor Optimization (GeoTASO) L1B data during the KORUS-AQ campaign (2 May to 10 June 2016). The retrieved SCDs were converted to tropospheric vertical column densities using the air mass factor (AMF) obtained from a radiative transfer calculation with trace gas profiles and aerosol property inputs simulated with the Community Multiscale Air Quality (CMAQ) model and surface reflectance data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). For the first time, we examine highly resolved (250 m × 250 m resolution) tropospheric NO 2 over the Seoul and Busan metropolitan regions and the industrial region of Anmyeon. We reveal that the maximum NO 2 vertical column densities (VCDs) were 4.94×10 16 and 1.46×10 17 molec. cm −2 at 09:00 and 15:00 LT over Seoul, respectively, 6.86×10 16 and 4.89×10 16 molec. cm −2 in the morning and afternoon over Busan, respectively, and 1.64×10 16 molec. cm −2 over Anmyeon. The VCDs retrieved from the GeoTASO airborne instrument were correlated with those obtained from the Ozone Monitoring Instrument (OMI) (r=0.48), NASA's Pandora Spectrometer System (r=0.91), and NO 2 mixing ratios obtained from in situ measurements (r=0.07 in the morning, r=0.26 in the afternoon over the Seoul, and r>0.56 over Busan). Based on our results, GeoTASO is useful for identifying NO 2 hotspots and their spatial distribution in highly populated cities and industrial areas.

Gyo-Hwang Choo↗

NASA NTRSDate not supplied

Progress Towards a Common CERES Cloud Mask Algorithm for MODIS and VIIRS: Evaluation using CALIOP Data

For over two decades The Clouds and the Earth’s Radiant Energy System (CERES) project has endeavored to produce a long-term global climate data record for detecting changes in the Earth’s radiation budget and to improve understanding of how clouds contribute to those changes. CERES incorporates cloud information derived from passive narrowband satellite imaging radiometers, and over the course of the project different instruments have contributed to this effort, namely the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS).Cloud properties have been derived from MODIS on the Aqua satellite for CERES since 2002,and VIIRS on the NOAA-20 satellite launched in 2017 will continue the cloud record once Aqua-MODIS reaches the end of its operational lifetime. However, MODIS and VIIRS have different spectral capabilities and characteristics which complicates our ability to seamlessly transition the record from MODIS to VIIRS. This study evaluates some of the recent algorithm modifications incorporated by the CERES Cloud Working Group towards developing a unified cloud mask algorithm for MODIS and VIIRS that utilizes a set of spectral bands common to both instruments. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data products are used to evaluate MODIS and VIIRS regional cloud fraction estimates and identify areas where improvements can be made. The cloud mask algorithm relies on computed estimates of cloud-free top-of-atmosphere radiances to differentiate cloudy and cloud-free imager pixels. The unified cloud mask algorithm currently under development uses a reduced number of spectral bands common to both MODIS and VIIRS and so relies on the accuracy of these simulated cloud-free radiances more heavily than predecessor algorithms, i.e., the CERES Edition 4 cloud mask, which use as much spectral information as available on each imager with less consideration for cross-platform consistency. Our validation strategy using CALIOP data is also described. The CALIOP observations are critical for assessing cloud detection accuracies and for independently confirming cloud-free conditions which enables a more robust evaluation of the simulated cloud-free radiances for biases due to factors such as view angle and the spectral dependence of water vapor absorption. These effects are known to differ for the two satellite instruments. Comparisons of MODIS and VIIRS cloud fractions are presented in context with estimates from CALIOP and from the CERES Edition 4 cloud mask algorithm to gauge current progress in developing accurate and consistent MODIS and VIIRS cloud properties for CERES.

CERES↗

NASA NTRSDate not supplied

A Machine Learning Approach to Determine Surface Radiative Fluxes based on CERES Observations

The Clouds and Earth’s Radiant Energy System (CERES) projects provides satellite-based observations of the radiative fluxes and clouds systems. CERES climate quality data products typically take several months of calibration and validation before release to the public. An alternative data product, Fast Longwave and Shortwave radiative Flux (FLASHFlux), was created to provide data to the applied sciences and educational users. FLASHFlux provides Top-of-Atmosphere radiative fluxes, Clouds properties, and parameterized surface radiative fluxes within four days for footprint (Level 2) data. We investigate the use of Artificial Neural Network (ANN) using MODerate resolution Imaging Spectroradiometer (MODIS) derived clouds properties and meteorology from the Global Assimilation and Meteorology Office (GMAO) scaled to the CERES footprint from the CERES Clouds Radiative Swath (CRS) data product to compute surface radiative fluxes. We test ANN produce fluxes against surface fluxes produced from the Fu-Liou model used in CRS and the Langley Parameterized Shortwave Algorithm (LPSA) and Langley Parameterized Longwave Algorithm (LPLA) used in FLASHFlux. We also validated each model with ground-based observations. Furthermore, we investigate Leave-One-Feature-Out Importance (LOFO) to evaluate the significance of each feature in our training and provide insight for future models. Advances in machine learning, along with increases in computational capabilities and available data allow us to estimate effects of unresolved processes in our climate without direct modeling. This work evaluates the ability to create accurate data-driven models to supplement or replace current models that estimate surface radiative fluxes.

Climatology↗

NASA NTRSDate not supplied

A14H-04 Analysis of Simulated and Observed Trends in Global Surface PM2.5 and Aerosol Optical Properties from 1958 to 2018 Using the NASA GEOSCCM

Modeling of long-term trends of aerosols and their properties is important for constraining aerosol-climate forcing, and for characterizing changes in particulate matter pollution speciation and exposure. Here we study global and regional long-term trends in surface fine particulate matter (PM ) and aerosol optical properties for 60 years from 1958 to 2018, using simulations performed with the NASA Goddard Earth Observing System Chemistry Climate Model (GEOSCCM), and evaluate the model hindcast with observations for the last three decades. Comparing the modeled aerosols with a diverse set of observations helps interpret observed and simulated trends, and serves as a benchmark for future GEOSCCM developments and input datasets improvements. We first characterize modeled global and regional temporal changes in surface PM and its components, aerosol optical depth (AOD) and single scattering albedo (SSA) and we interpret their link with emissions drivers. We then compare modeled surface PM with ground-based observations from monitoring networks and with global reconstructed PM datasets from observations-model data fusion. Total AOD is compared with long-term satellite measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) and measurements from the ground-based Aerosol Robotic Network (AERONET). Additional aerosol optical properties, such as absorption and scattering coefficients, are evaluated with ground-based observations from the Global Atmosphere Watch (GAW) records.

aerosols↗

NASA NTRSDate not supplied

Improving Modeled Snow Mass in GEOS with Snow Cover Fraction Assimilation

The NASA Goddard Earth Observing System (GEOS) Forward Processing (FP) global weather analysis often considerably underestimates near-surface air temperature during the spring snow melt season, which can be caused by excessive simulated snow cover. To mitigate such errors, we developed an empirical snow cover fraction (SCF) analysis to assimilate SCF observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) into the GEOS Catchment land surface model. Results obtained with a land-only version of the system indicate that the SCF analysis reduces errors in snow mass and snow depth by ~10% when compared to in situ measurements from the Snow Telemetry (SNOTEL), Canadian historical Snow Water Equivalent dataset (CanSWE), and Global Historical Climatology Network - Daily (GHCN-Daily) networks.

Rolf Reichle↗

NASA NTRS2023

Thermal Infrared Dust Optical Depth and Coarse-Mode Effective Diameter Over Oceans Retrieved From Collocated MODIS and CALIOP Observations

In this study, we developed a novel algorithm based on the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) observations and dust vertical profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) to simultaneously retrieve dust aerosol optical depth at 10 µm (DAOD10μm) and the coarse-mode dust effective diameter (Deff) over global oceans. The accuracy of the Deff retrieval is assessed by comparing the dust lognormal volume particle size distribution (PSD) corresponding to retrieved Deff with the in-situ measured dust PSDs from the AER-D, SAMUM-2 and SALTRACE field campaigns through case studies. The new DAOD10μm retrievals were evaluated first through comparisons with the collocated DAOD10.6μm retrieved from the combined Imaging Infrared Radiometer (IIR) and CALIOP observations from our previous study (Zheng et al. 2022). The pixel-to-pixel comparison of the two DAOD retrievals indicates a good agreement (R~0.7) and a significant reduction of (~50%) retrieval uncertainties largely thanks to the better constraint on dust size. In a climatological comparison, the seasonal and regional (5°×2°) mean DAOD10um retrievals based on our combined MODIS and CALIOP method are in good agreement with the two independent Infrared Atmospheric Sounding Interferometer (IASI) products over three dust transport regions (i.e., North Atlantic (NA; R = 0.9), Indian Ocean (IO; R = 0.8) and North Pacific (NP; R = 0.7)). Using the new retrievals from 2013 to 2017, we performed a climatological analysis of coarse mode dust Deff over global oceans. We found that dust Deff over IO and NP are up to 20% smaller than that over NA. Over NA in summer, we found a ~50% reduction of the number of retrievals with Deff > 5 μm from 15°W to 35°W and a stable trend of Deff average at 4.4 μm from 35°W throughout the Caribbean Sea (90°W). Over NP in spring, only ~5% of retrieved pixels with Deff > 5 μm are found from 150°E to 180°, while the mean Deff remains stable at 4.0 μm throughout eastern NP. To our best knowledge, this study is the first to retrieve both DAOD and coarse-mode dust particle size over global oceans for multiple years. This retrieval dataset provides insightful information for evaluating dust long-wave radiative effects and coarse mode dust particle size in models.

dust optical depth↗

NASA NTRS2023

Calibration of the SNPP and NOAA 20 VIIRS Sensors for Continuity of the MODIS Climate Data Records

Accurate long-term sensor calibration and periodic re-processing to ensure consistency and continuity of atmospheric, land and ocean geophysical retrievals from space within the mission period and across different missions is a major requirement of climate data records. In this work, we applied the Multi-Angle Implementation of Atmospheric Correction (MAIAC)-based vicarious calibration technique over Libya-4 desert site to perform calibration analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. For both VIIRS sensors we characterized residual linear calibration trends and cross-calibrated both sensors to MODerate resolution Imaging Spectroradiometer (MODIS) Aqua regarded as a calibration standard. The relative spectral response (RSR) differences were accounted for using the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) hyperspectral surface reflectance data. Our results agree with independent vicarious calibration results of both the MODIS/VIIRS Characterization Support Team as well as the CERES Imager and Geostationary Calibration Group within estimated uncertainty of 1–2%. Analysis of MAIAC geophysical products with the new calibration shows a high level of agreement of MAIAC aerosol, surface reflectance and NDVI records between MODIS and VIIRS. Excluding high aerosol optical depth (AOD), all three sensors agree in AOD with mean difference (MD) less than 0.01 and residual mean squared difference rmsd ∼ 0.04. Spectral geometrically normalized surface reflectance agrees within rmsd of 0.003–0.005 in the visible and 0.01–0.012 at longer wavelengths. The residual surface reflectance differences are fully explained by differences in spectral filter functions. Finally, difference in NDVI is characterized by rmsd ∼ 0.02 and MD less than 0.003 for NDVI based on VIIRS imagery bands I1/I2 and less than 0.01 for NDVI based on VIIRS radiometric bands M5/M7. In practical sense, these numbers indicate consistency and continuity in MAIAC records ensuring the smooth transition from MODIS to VIIRS.

MAIAC↗

NASA NTRS2024

Spectral Correlation in MODIS Water-Leaving Reflectance Retrieval Uncertainty

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 R rs . Furthermore, since the associated algorithms require R rs at multiple spectral bands, the spectral (i.e., band-to-band)error covariance in R rs 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 R rs as retrieved using NASA’s multiple-scattering epsilon (MSEPS) atmospheric correction algorithm, to generate pixel-level error covariance in R rs . 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 R rs to calculate uncertainty in phytoplankton pigment chlorophyll-a concentration (chl a , mg/m 3 ) and diffuse attenuation coefficient of downwelling irradiance at 490 nm (K d (490), m -1 ). Accounting for the error covariance in R rs generally reduces the estimated relative uncertainty in chl a by ∼1-2% (absolute value) in waters with chl a < 0.25 mg/m 3 where the color index (CI) algorithm is used. The reduction is ∼5-10% in waters with chl a > 0.35 mg/m 3 where the blue-green ratio (OCX) algorithm is used. Such reduction can be higher than 30% in some regions. For K d (490), the reduction by error covariance is generally ∼2%, but can be higher than 20% in some regions. The error covariance in R rs is further verified through forward-calculating chl a from MODIS-retrieved and in situ R rs and comparing estimated uncertainty with observed differences. An 8-day global composite of propagated uncertainty shows that the goal of 35% uncertainty in chl a can be achieved over deep ocean waters (chl a ≤ 0.1 mg/m3). While the derivative-based approach generates reasonable error covariance in R rs 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.

Ocean color↗

NASA NTRSDate not supplied

A Review on Sensor Spatial Resolution and A Proposal on Grid Pixel Sizing

Sensor spatial resolution (SSR) is a determining factor in developing remote sensing instruments for earth observations. The SSR is defined as the full width at half maximum (FWHM) of sensor point/line spread function (PSF/LSF). This presentation first discusses large variations in image absolute SSR and SSR ratios to ground sampling distance (GSD) or grid pixel size from multiple instruments including AVHRR (Advanced Very-High Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite), WorldView and Planet. When the physics based SSR is more than twice the nominal grid pixel size, a more appropriate grid pixel size is proposed.

Guoqing (Gary) Lin↗

NASA NTRSDate not supplied

Creating Near Real-time Hourly VIIRS Radiance Mosaic over Polar Regions from SatCORPS Global Cloud Composite (GCC)

While polar orbiting satellites are essential in providing the needed observations over polar regions for data assimilation, weather diagnoses/forecasting, and flight guidance and situation awareness in airborne field campaigns, there is limited temporal coverage. Often, polar mosaics are created by overlaying multiple satellite imagers on top of each other which can lead to transition lines and discontinuities in the data products. To help ease some of these shortcomings, we utilize the NASA Langley Satellite ClOud and Radiation Property retrieval System (SatCORPS) Mosaic algorithm for optimally fusing and merging selected imager radiances from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard the Suomi National polar-orbiting Partnership (SNPP), Joint Polar Satellite System (JPSS-1), and JPSS-2 satellites, and the MODerate resolution Imaging Spectroradiometer (MODIS) instruments onboard AQUA and TERRA satellites. Since multiple satellites overpasses and instruments are used in creating hourly 3-km composites over the polar regions, selection of the best satellite data for each 3-km composite pixel is based on aggregated ranking of satellite resolution, time, viewing zenith angle, and distance from terminator. This ranking system provides a smoother transition and improves data discontinuity in the merged output resulting in a more seamless polar region dataset. We will discuss the use of this near real-time dataset to support polar airborne field campaigns and SatCORPS satellite-derived Global Cloud Composite (GCC) generation.

Satellite composites↗

NASA NTRS2024

VIIRS Version 2 Deep Blue Aerosol Products

NASA'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.

Deep Blue↗

NASA NTRS2024

Parameterizing Spectral Surface Reflectance Relationships for the Dark Target Aerosol Algorithm Applied to A Geostationary Imager

Originally developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) in polar, sun-synchronous low-earth orbit (LEO), the Dark Target (DT) aerosol retrieval algorithm relies on the assumption of a Surface Reflectance Parameterization (SRP) over land surfaces. Specifically for vegetated and dark-soiled surfaces, values of surface reflectance in blue and red visible-wavelength bands are assumed to be nearly linearly related to each other and to the value in a shortwave infrared (SWIR) wavelength band. This SRP also includes dependencies on scattering angle and a normalized difference vegetation index computed from two SWIR bands (NDVISWIR). As the DT retrieval algorithm is being ported to new sensors to continue and expand the aerosol data record, we assess whether the MODIS-assumed SRP can be used for these sensors. Here, we specifically assess SRP for the Advanced Baseline Imager (ABI) aboard, the Geostationary Operational Environmental Satellite (GOES)-16/East (ABIE). First, we find that using MODIS-based SRP leads to higher biases and artificial diurnal signatures in aerosol optical depth (AOD)retrievals from ABIE. The primary reason appears to be that geostationary orbit (GEO) encounters an entirely different set of observation geometry than does LEO, primarily with regards to solar angles coupled with fixed view angles. Therefore, we have developed a new SRP for GEO that draws the angular shape of the surface bidirectional reflectance. We also introduce modifications to the parametrization of both red-SWIR and blue-red spectral relationships to include additional information.The revised Red-SWIR SRP includes solar zenith angle, NDVISWIR, and land-type percentage from an ancillary database. The blue-red SRP adds dependencies on the scattering angle and NDVISWIR. The new SRPs improve the AOD retrieval of ABIE in terms of overall less bias and mitigation of the overestimation around local noon. The average bias of DT AOD compared to AERONET AOD shows a reduction from 0.08 to 0.03, while the bias of local solar noon decreases from 0.12 to 0.03.The agreement between DT and AERONET AOD is established through regression slope of 1.06 and y-intercept of 0.01with correlation coefficient is0.74. By using the new SRP, the percentage of data falling within the expected error range (±0.05 + 15%) is notably risen from 54% to 78%.

Mijin Kim↗

NASA NTRSDate not supplied

Impacts of Aerosol and Cloud Variability on Satellite-Derived All-skies Direct Aerosol Radiative Effects (DARE) over the North and Southeast Atlantic

The Top-of-Atmosphere (TOA) Shortwave (SW) Direct Aerosol Radiative Effect (DARE) in all-sky conditions (i.e., aerosols in clear skies and aerosols above and below all types of clouds) is the global change in upwelling radiative flux due to aerosols. It is one of the strongest indicators of global climate change due to aerosols. SW DARE at TOA depends on the Earth’s surface albedo, cloud fraction, cloud optical properties, and aerosol optical properties, which are all challenging to accurately characterize from space. The overarching goals of our project are to provide state-of-the-art observational all-sky TOA SW DARE, along with guidance on which aerosol and/or cloud properties are the most important to measure, and at which spatio-temporal scales, for accurate DARE observations. We compute all-sky DARE based on state-of-the-art cloud and aerosol retrieval algorithms from CALIOP (Cloud–Aerosol Lidar with Orthogonal Polarization) and MODIS (Moderate Resolution Imaging spectroradiometer) satellite sensors, as well as aerosol intensive properties from MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) simulations over three specific regions of the Atlantic Ocean from 2012 to 2016. In this symposium, we present a characterization of the cloud and aerosol optical and physical properties and the observational TOA SW DARE along three individual satellite tracks in the Southeast Atlantic region -- on September 18 and 20, 2016 and August 13, 2017. We then quantify the impact of assuming homogeneous cloud or aerosol fields in space-based TOA SW DARE calculations. And, finally, the resulting space-based all-skies aerosol vertical distribution and DARE calculations are evaluated using remote sensing observations from coincident suborbital flights from the NASA ORACLES (the ObseRvations of Aerosols above CLouds and their intEractionS, ORACLES field campaign) field campaign. The NASA Atmospheric Observing System (AOS) mission addresses the NASA Aerosol, Cloud, Convection and Precipitation (ACCP) designated observables and proposes, as one of its aerosol objectives, to reduce uncertainties in estimates of global mean all-sky SW DARE at TOA. Well characterizing clouds, aerosol vertical distributions, aerosol types and associated all-skies DARE over the Atlantic Ocean will inform the AOS community on where, when, how, and how often the satellite retrievals should be performed to estimate DARE and reduce all-skies DARE uncertainties most accurately. These comprehensive characterizations will also identify the key regions and times when the AOS (or other ACCP-related) suborbital missions should be conducted to evaluate and improve the AOS space-based observations and retrievals.

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