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NASA NTRS2011

The 2010 AOP Workshop Summary Report

The rationale behind the current workshop, which was hosted by Biospherical Instruments Inc. (BSI), was to update the community and get community input with respect to the following: topics not addressed during the first workshop, specifically the processing of above-water apparent optical property (AOP data) within the Processing of Radiometric Observations of Seawater using Information Technologies (PROSIT) architecture; PROSIT data processing issues that have developed or tasks that have been completed, since the first workshop; and NASA instrumentation developments, both above- and in-water, that are relevant to both workshops and next generation mission planning. The workshop emphasized presentations on new AOP instrumentation, desired and required features for processing above-water measurements of the AOPs of seawater, working group discussions, and a community update for the in-water data processing already present in PROSIT. The six working groups were organized as follows: a) data ingest and data products; b) required and desired features for optically shallow and optically deep waters; c) contamination rejection (clouds), corrections, and data filtering; d) sun photometry and polarimetry; e) instrumentation networks; and f) hyperspectral versus fixed-wavelength sensors. The instrumentation networks working group was intended to provide more detailed information about desired and required features of autonomous sampling systems. Plenary discussions produced a number of recommendations for evolving and documenting PROSIT.

Hooker, Stanford B.↗

NASA NTRS2019

Geonex: Land Surface Monitoring from a New Generation of Geostationary Sensors

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GEONEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GEONEX pipeline has been deployed on Amazon Web Services cloud platform and it currently leverages near-realtime geostationary data hosted in AWS public datasets under a NOAA-AWS agreement. Initial analyses of various products from ABI/AHI sensors suggest that they are comparable to those from MODIS in representing the spatio-temporal dynamics of land conditions. Cloud computing offers a variety of options for deploying the GEONEX pipeline including choice CPUs, storage media, and automation. By making the GEONEX pipeline available on the cloud, we hope to engage a broad community of Earth scientists from around the world in utilizing this new source of data for Earth monitoring.

Nemani, Ramakrishna R.↗

NASA NTRS2019

Earth Observations from Geostationary Satellites

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GeoNEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GeoNEX pipeline has been deployed on Amazon Web Services cloud platform and it currently leverages near-realtime geostationary data hosted in AWS public datasets under a NOAA-AWS agreement. Initial analyses of various products from ABI/AHI sensors suggest that they are comparable to those from MODIS in representing the spatio-temporal dynamics of land conditions. Cloud computing offers a variety of options for deploying the GeoNEX pipeline including choice CPUs, storage media, and automation. By making the GEONEX pipeline available on the cloud, we hope to engage a broad community of Earth scientists from around the world in utilizing this new source of data for Earth monitoring.

Earth↗

NASA NTRS2019

GeoNEX: Land Monitoring from a New Generation of Geostationary Sensors

GeoNEX↗

NASA NTRS2003

Aerosol Measurements by the GLAS Space Borne Lidar

Beginning in February 2003, the Geoscience Laser Altimeter System (GLAS) provides global coverage lidar measurement of the height distribution of aerosol in the atmosphere. The characteristic and value of the unique data will be presented. The instrument is a basic backscatter lidar that operates at two wavelengths, 532 and 1064 nm. The mission data products for atmospheric observations include the calibrated, observed, attenuated backscatter cross section for cloud and aerosol; height detection for multiple cloud layers; planetary boundary layer height; cirrus and aerosol optical depth and the height distribution of aerosol and cloud scattering cross section profiles. The data is expected to significantly enhance knowledge of the distribution, transport and influence of atmospheric aerosol and thin clouds. Initial results from the 1064 nm channel show strong aerosol loading in many regions of the world. From the initial performance and verification experiments, aerosol backscatter cross sections down to sever times 10(exp -6)I/m-sr are detected. In August the 532 nm channel will also provide aerosol profiles but with a projected order of magnitude more sensitivity, and full data products including aerosol optical depths will be generated. Results from these first several months of operation will be presented.

Spinhirne, James D.↗

NASA NTRSDate not supplied

Toward Consistent Long-term Records of Cloud Fraction from MODIS and VIIRS for CERES

In order to produce a long-term and stable climate record of Earth’s energy budget for NASA’s Clouds and Earth’s Radiant Energy System (CERES) project, a consistent cloud fraction record across different measurement platforms is a crucial first step. As Aqua satellite is approaching the end of its operational lifetime, NOAA-20 VIIRS observations on the JPSS-2 spacecraft will be used for cloud detection to continue the long-term Earth energy budget record. Two steps are designed to achieve this goal. The first step is a quick approach that revises the CERES NOAA-20 VIIRS Edition 1 A (CV Ed1A) cloud mask to incorporate Cross-track Infrared Sounder (CrIS) water vapor and CO_2 channels into the VIIRS cloud mask (CV Ed1B) to produce a consistent cloud fraction with CERES MODIS Edition 4 (CM Ed4) to avoid potential discontinuity when Aqua orbit drifts beyond tolerance before the next Edition is completed. The second step is to develop, as part of CERES next edition (Ed5), a unified MODIS and VIIRS cloud mask using common channels to produce consistent cloud products with improved radiative transfer models, refined clear sky predictions, new GMAO reanalysis products, and the latest radiance collections of MODIS and VIIRS data. This poster will present cloud fraction comparison between CV Ed1B and CM Ed4, as well as the preliminary results of ongoing Ed5 including pixel level cloud mask results, monthly global cloud fraction differences between MODIS and VIIRS (consistency), and initial validation using CALIPSO data (accuracy).

CERES↗

NASA NTRS2017

CATS Version 2 Aerosol Feature Detection and Applications for Data Assimilation

The Cloud Aerosol Transport System (CATS) lidar has been operating onboard the International Space Station (ISS) since February 2015 and provides vertical observations of clouds and aerosols using total attenuated backscatter and depolarization measurements. From February March 2015, CATS operated in Mode 1, providing backscatter and depolarization measurements at 532 and 1064 nm. CATS began operation in Mode 2 in March 2015, providing backscatter and depolarization measurements at 1064 nm and has continued to operate to the present in this mode. CATS level 2 products are derived from these measurements, including feature detection, cloud aerosol discrimination, cloud and aerosol typing, and optical properties of cloud and aerosol layers. Here, we present changes to our level 2 algorithms, which were aimed at reducing several biases in our version 1 level 2 data products. These changes will be incorporated into our upcoming version 2 level 2 data release in summer 2017. Additionally, owing to the near real time (NRT) data downlinking capabilities of the ISS, CATS provides expedited NRT data products within 6 hours of observation time. This capability provides a unique opportunity for supporting field campaigns and for developing data assimilation techniques to improve simulated cloud and aerosol vertical distributions in models. We additionally present preliminary work toward assimilating CATS observations into the NASA Goddard Earth Observing System version 5 (GEOS-5) global atmospheric model and data assimilation system.

data assimilation↗

NASA NTRS2006

Assessment of Satellite Surface Radiation Products in Highland Regions with Tibet Instrumental Data

This study presents results of comparisons between instrumental radiation data in the elevated Tibetan Plateau and two global satellite products: the Global Energy and Water Cycle Experiment - Surface Radiation Budget (GEWEX-SRB) and International Satellite Cloud Climatology Project - Flux Data (ISCCP-FD). In general, shortwave radiation (SW) is estimated better by ISCCP-FD while longwave radiation (LW) is estimated better by GEWEX-SRB, but all the radiation components in both products are under-estimated. Severe and systematic errors were found in monthly-mean SRB SW (on plateau-average, -48 W/sq m for downward SW and -18 W/sq m for upward SW) and FD LW (on plateau-average, -37 W/sq m for downward LW and -62 W/sq m for upward LW) for radiation. Errors in monthly-mean diurnal variations are even larger than the monthly mean errors. Though the LW errors can be reduced about 10 W/sq m after a correction for altitude difference between the site and SRB and FD grids, these errors are still higher than that for other regions. The large errors in SRB SW was mainly due to a processing mistake for elevation effect, but the errors in SRB LW was mainly due to significant errors in input data. We suggest reprocessing satellite surface radiation budget data, at least for highland areas like Tibet.

Yang, Kun↗

NASA NTRS2020

Impact of Satellite Microwave Radiance Data Assimilation on GEOS Atmospheric Analysis and Forecasts in Tropics

The NASA Global Modeling and Assimilation Office (GMAO) has developed a system to assimilate all-sky microwave radiance data in the Goddard Earth Observing System (GEOS). The system provides additional constraints on the analysis process near the storm regions and adjusts the geophysical parameters such as precipitation, cloud, moisture, surface pressure, and wind by combining information from microwave radiance measurements and model forecasts in an optimal manner. The system proved that assimilating the all-sky microwave radiance data improve the GEOS atmospheric analyses and forecasts. This all-sky data framework has been included in the GEOS Forward Processing (FP) system and currently assimilating all-sky GMI data in real-time for GEOS global analysis and forecast production at the GMAO. This presentation describes the methodologies to assimilate cloud- and precipitation-affected microwave radiances in GEOS data assimilation system based on hybrid four-dimensional ensemble-variational (4D-EnVar) configuration. In the current operational GEOS-FP system, the addition of all-sky GMI radiances has the largest impact in the Tropics. Specific humidity is significantly improved in the short term (0-72 hours) forecasts. Similar improvements are seen in the Tropical and lower tropospheric temperature and winds. More detailed results on all-sky microwave radiance data impact on GEOS analysis and forecasts are discussed in this presentation.

Kim, Min-Jeong↗

NASA NTRS2017

GES DISC Data Recipes in Jupyter Notebooks

The Earth Science Data and Information System (ESDIS) Project manages twelve Distributed Active Archive Centers (DAACs) which are geographically dispersed across the United States. The DAACs are responsible for ingesting, processing, archiving, and distributing Earth science data produced from various sources (satellites, aircraft, field measurements, etc.). In response to projections of an exponential increase in data production, there has been a recent effort to prototype various DAAC activities in the cloud computing environment. This, in turn, led to the creation of an initiative, called the Cloud Analysis Toolkit to Enable Earth Science (CATEES), to develop a Python software package in order to transition Earth science data processing to the cloud. This project, in particular, supports CATEES and has two primary goals. One, to transition data recipes created by the Goddard Earth Science Data and Information Service Center (GES DISC) into an interactive and educational environment using JupyterNotebooks. Two, to acclimate Earth scientists to cloud computing. To accomplish these goals, we create JupyterNotebooks to compartmentalize the different steps of data analysis and help users obtain and parse data from the command line. We also develop a Docker container, comprised of Jupyter Notebooks, Python dependencies, and command line tools, and configure it into an easy-to-deploy package. The end result is an end-to-end product that simulates the use case of end users working in the cloud computing environment.

discoverability↗

NASA NTRS1986

Measurements of the composition of aerosol component of Venusian atmosphere with Vega 1 lander, preliminary data

Preliminary investigation of mass spectra of gaseous products of pyrolyzed Venusian cloud particles collected and analyzed by the complex device of mass-spectrometer and collector pyrolyzer on board Vega 1 lander revealed the presence of heavy particles in the upper cloud layer. Based on 64 amu peak (SO2+), an estimate of the lower limit of the sulfuric acid aerosol content at the 62 to 54 km heights of approximately 2.0 mg/cu m is obtained. A chlorine line (35 and 37 amu) is also present in the mass spectrum with a lower limit of the chlorine concentration of approximately 0.3 mg/ cu m.

Surkov, Y. A.↗

NASA NTRS2006

Fast Longwave and Shortwave Radiative Flux (FLASHFlux) Products from CERES and MODIS Measurements

The Clouds and the Earth s Radiant Energy Systems (CERES) project is currently producing world-class climatological data products derived from measurements taken aboard the Terra and Aqua spacecrafts (Wielicki et al., 1996). While of exceptional fidelity, these data products require a considerable amount of processing to assure quality and verify accuracy and precision. Obtaining such high quality assurance, however, means that the CERES data is typically released more than six months after the acquisition of the initial measurements. For climate studies, such delays are of little consequence, especially considering the improved quality of the released data products. There are, however, many uses for the CERES data products on a near real-time basis. These include: CERES instrument calibration and subsystem quality checks, CLOUDSAT operations, seasonal predictions, agricultural and ocean assimilations, support of field campaigns, and outreach programs such as S'Cool. The FLASHflux project was envisioned as a conduit whereby CERES data could be provided to the community within a week of the initial measurements, with the trade-off that some degree of fidelity would be exacted to gain speed. In this paper, we will report on some very encouraging initial results from the FLASHflux project in which we compared the FLASHflux instantaneous surface fluxes to the CERES surface-only flux algorithm data products.

Stackhouse, Paul W., Jr.↗

NASA NTRSDate not supplied

The Future of NASA Earth Science in the Commercial Cloud: Challenges and Opportunities

NASA produces a large volume and variety of data products that are used every day to support research, decision making, and education. The widespread use of NASA’s Earth Science data is enabled by NASA’s Earth Science Data System (ESDS) program, which oversees the archiving and distribution of these data and invests in the development of new data systems and tools. However, NASA’s current approach to Earth Science data distribution — based on distributed institutional archives with individual on-premises high-performance computing capabilities — faces some significant challenges, including massive increases in data volume from upcoming missions, a greater need for transdisciplinary science that synthesizes many different kinds of observations, and a push to make science more open, inclusive, and accessible. To address these challenges, NASA is aggressively migrating its Earth Science data and related tools and services into the commercial cloud. Migration of data into the commercial cloud can significantly improve NASA’s existing data system capabilities by (1) providing more flexible options for storage and compute (including rapid, as-needed access to state-of-the-art capabilities); (2) by centralizing and standardizing data access, which gives all of NASA’s institutional data centers access to all of each other’s datasets; and (3) by facilitating “analysis-in-place”, whereby users can bring their own computational workflows and tools to the data rather than having to maintain their own copies of NASA datasets. However, migration to the commercial cloud also poses some significant challenges, including (1) managing costs under a “pay-as-you-go” model; (2) incompatibility with existing tools and data formats with object-based storage and network access; (3) vendor lock-in; (4) challenges with data access for workflows that mix on-premise and cloud computing; and (5) standardization for highly diverse data as is present in NASA’s data archive. I conclude with two examples of recent NASA activities showcasing capabilities enabled by the commercial cloud: An interactive analysis and development platform for analyzing airborne imaging spectroscopy data, and a new collection of tools and services for data discovery, analysis, publication, and data-driven storytelling (Visualization, Exploration, and Data Analysis, VEDA).

Alexey N Shiklomanov↗

DOE OSTI2026

Baltimore Social-Environmental Collaborative (BSEC) Doppler Lidar & Derived Products

This repository contains all processed Doppler‐lidar outputs from the PSU lidar deployed for the Baltimore Social‐Environmental Collaborative (BSEC) project. Vertical Stare Scans (fixed‐beam, vertical profiling): 1 Hz backscatter intensity (m⁻¹ sr⁻¹), signal‐to‐noise ratio (unitless), and Doppler vertical‐velocity (m s⁻¹) on 30 m range gates (and 3 m range gates), stored as CF-compliant NetCDF. Wind Profiles (horizontal‐wind retrieval): daily NetCDF outputs of retrieved horizontal wind speed (m s⁻¹) and direction (degrees), computed from the angled‐scan returns. Profile Statistics (summary statistics on the vertical velocity): 15 min windows (default) of mean, variance, skewness, kurtosis, high-frequency variance, etc., as a function of height; saved as CF-compliant NetCDF files. Boundary Layer Height (BLH) (fuzzy-logic output): 15 min BLH estimates (m), with lower/upper fuzzy bounds (m) and a quality flag (0–4) indicating data status (e.g., no data, good, ran out of signal, below range, cloud-topped). Cloud Base Height (Haar-gradient detection): 10 min estimates of cloud-base height (m). All five product streams are organized by year and date under their own top-level folders (01_Vertical_Stare_Scans/ through 05_Cloud_Height/). Each folder contains a data_ /YYYY/ subdirectory with daily CF-compliant NetCDF outputs (96 windows per day at 15 min intervals). Global attributes in each file include creation history, version (3.0.0), institution, and source. Instrument & MeasurementsThe PSU Doppler Lidar samples aerosol backscatter (m⁻¹ sr⁻¹), signal-to-noise ratio, and radial velocity at ~1 Hz. Vertical stare scans point the beam straight up; after collecting angled scans through multiple elevation angles, the "Wind Profiles" product contains the fully retrieved horizontal wind speed and direction. Data were collected continuously at ~30 m range resolution (and 3 m for the year of 2025), with a typical height ceiling of ~12 km. How to Use Open any NetCDF with Python's xarray, MATLAB, or similar CF-compliant tools. Stare scans and angled-scan retrievals (Wind Profiles) are CF-compliant daily NetCDF files. Profile-Statistics, BLH, and Cloud Height files are daily 15 min (10 min for Cloud Heights) summaries (96 time steps per file). Inspect the included variables (e.g., vertical_velocity_variance, wind_speed, BLH, cloud_base_height) for your analyses. Use the quality flags (BLH_flag, cloud_flag) to filter out poor-quality retrievals. For more information or questions about processing methods, please contact:Nicholas E. Prince ⟨nec5299@psu.edu⟩Penn State Department of Meteorology & Atmospheric Science

Air Quality↗

DOE OSTI2026

Baltimore Social-Environmental Collaborative (BSEC) Doppler Lidar & Derived Products

This repository contains all processed Doppler‐lidar outputs from the PSU lidar deployed for the Baltimore Social‐Environmental Collaborative (BSEC) project. Vertical Stare Scans (fixed‐beam, vertical profiling): 1 Hz backscatter intensity (m⁻¹ sr⁻¹), signal‐to‐noise ratio (unitless), and Doppler vertical‐velocity (m s⁻¹) on 30 m range gates (and 3 m range gates), stored as CF-compliant NetCDF. Wind Profiles (horizontal‐wind retrieval): daily NetCDF outputs of retrieved horizontal wind speed (m s⁻¹) and direction (degrees), computed from the angled‐scan returns. Profile Statistics (summary statistics on the vertical velocity): 15 min windows (default) of mean, variance, skewness, kurtosis, high-frequency variance, etc., as a function of height; saved as CF-compliant NetCDF files. Boundary Layer Height (BLH) (fuzzy-logic output): 15 min BLH estimates (m), with lower/upper fuzzy bounds (m) and a quality flag (0–4) indicating data status (e.g., no data, good, ran out of signal, below range, cloud-topped). Cloud Base Height (Haar-gradient detection): 10 min estimates of cloud-base height (m). All five product streams are organized by year and date under their own top-level folders (01_Vertical_Stare_Scans/ through 05_Cloud_Height/). Each folder contains a data_ /YYYY/ subdirectory with daily CF-compliant NetCDF outputs (96 windows per day at 15 min intervals). Global attributes in each file include creation history, version (3.0.1), institution, and source. Instrument & MeasurementsThe PSU Doppler Lidar samples aerosol backscatter (m⁻¹ sr⁻¹), signal-to-noise ratio, and radial velocity at ~1 Hz. Vertical stare scans point the beam straight up; after collecting angled scans through multiple elevation angles, the "Wind Profiles" product contains the fully retrieved horizontal wind speed and direction. Data were collected continuously at ~30 m range resolution (and 3 m for the year of 2025), with a typical height ceiling of ~12 km. How to Use Open any NetCDF with Python's xarray, MATLAB, or similar CF-compliant tools. Stare scans and angled-scan retrievals (Wind Profiles) are CF-compliant daily NetCDF files. Profile-Statistics, BLH, and Cloud Height files are daily 15 min (10 min for Cloud Heights) summaries (96 time steps per file). Inspect the included variables (e.g., vertical_velocity_variance, wind_speed, BLH, cloud_base_height) for your analyses. Use the quality flags (BLH_flag, cloud_flag) to filter out poor-quality retrievals. For more information or questions about processing methods, please contact:Nicholas E. Prince ⟨nec5299@psu.edu⟩Penn State Department of Meteorology & Atmospheric Science

Air Quality↗

NASA NTRS2011

Improvements in Night-Time Low Cloud Detection and MODIS-Style Cloud Optical Properties from MSG SEVIRI

The MODIS cloud optical properties algorithm (MOD06IMYD06 for Terra and Aqua MODIS, respectively) slated for production in Data Collection 6 has been adapted to execute using available channels on MSG SEVIRI. Available MODIS-style retrievals include IR Window-derived cloud top properties, using the new Collection 6 cloud top properties algorithm, cloud optical thickness from VISINIR bands, cloud effective radius from 1.6 and 3.7Jlm and cloud ice/water path. We also provide pixel-level uncertainty estimate for successful retrievals. It was found that at nighttime the SEVIRI cloud mask tends to report unnaturally low cloud fraction for marine stratocumulus clouds. A correction algorithm that improves detection of such clouds has been developed. We will discuss the improvements to nighttime low cloud detection for SEVIRI and show examples and comparisons with MODIS and CALIPSO. We will also show examples of MODIS-style pixel-level (Level-2) cloud retrievals for SEVIRI with comparisons to MODIS.

Wind, Galina (Gala)↗

NASA NTRSDate not supplied

Spacecraft-Initiated Scheduling of Commercial Communications Services

We propose a software framework enabling a spacecraft in Earth orbit to schedule its own access to space communications services via ground stations and relay satellites. All operations are automated, without a human in the loop, allowing for highly responsive service to meet time-varying needs. We describe a modular software architecture allowing the framework to interface with the scheduling systems of several providers - both government and commercial. Specifically, we present the results of an experiment with Amazon Web Services (AWS) Ground Station, an operational commercial service provider. Proposed software executed scheduling of a ground station antenna with minimal lead time (less than 20 minutes before the start of a pass). During the contact AWS cloud infrastructure was used to demodulate, process, and distribute data products from an orbiting satellite. We believe this work demonstrates the framework's ability to interface with additional providers and potential to integrate more complex decision-making in the future to schedule optimal contacts.

Scheduling↗

NASA NTRS2023

The Calipso Version 4.5 Stratospheric Aerosol Subtyping Algorithm

The accurate classification of aerosol types injected into the stratosphere is important to properly characterize their chemical and radiative impacts within the Earth climate system. The updated stratospheric aerosol subtyping algorithm used in the version 4.5 (V4.5) release of the Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) level 2 data products now delivers more comprehensive and accurate classifications than its predecessor. The original algorithm identified four aerosol subtypes for layers detected above the tropopause: volcanic ash, smoke, sulfate/other, and polar stratospheric aerosol (PSA). In the revised algorithm, sulfates are separately identified as a distinct, homogeneous subtype, and the diffuse, weakly scattering layers previously assigned to the sulfate/other class are recategorized as a fifth “unclassified” subtype. By making two structural changes to the algorithm and revising two thresholds, the V4.5 algorithm improves the ability to discriminate between volcanic ash and smoke from pyrocumulonimbus injections, improves the fidelity of the sulfate subtype, and more accurately reflects the uncertainties inherent in the classification process. The 532 nm lidar ratio for volcanic ash was also revised to a value more consistent with the current state of knowledge. This paper briefly reviews the previous version of the algorithm (V4.1 and V4.2) then fully details the rationale and impact of the V4.5 changes on subtype classification frequency for specific events where the dominant aerosol type is known based on the literature. Classification accuracy is best for volcanic ash due to its characteristically high depolarization ratio. Smoke layers in the stratosphere are also classified with reasonable accuracy, though during the daytime a substantial fraction are misclassified as ash. It is also possible for mixtures of ash and sulfate to be misclassified as smoke. The V4.5 sulfate subtype accuracy is less than that for ash or smoke, with sulfates being misclassified as smoke about one-third of the time. However, because exceptionally tenuous layers are now assigned to the unclassified subtype and the revised algorithm levies more stringent criteria for identifying an aerosol as sulfate, it is more likely that layers labeled as this subtype are in fact sulfate compared to those assigned the sulfate/other classification in the previous data release.

Jason L Tackett↗