Using Earth Observations to Assess Water Quality and Inform Decision-making in Big Cypress National Reserve, Florida
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Sarah Swiersz↗
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Application of ESE Data and Tools to Air Quality Management: Services for Helping the Air Quality Community use ESE Data (SHAirED)
The goal of this REASoN applications and technology project is to deliver and use Earth Science Enterprise (ESE) data and tools in support of air quality management. Its scope falls within the domain of air quality management and aims to develop a federated air quality information sharing network that includes data from NASA, EPA, US States and others. Project goals were achieved through a access of satellite and ground observation data, web services information technology, interoperability standards, and air quality community collaboration. In contributing to a network of NASA ESE data in support of particulate air quality management, the project will develop access to distributed data, build Web infrastructure, and create tools for data processing and analysis. The key technologies used in the project include emerging web services for developing self describing and modular data access and processing tools, and service oriented architecture for chaining web services together to assemble customized air quality management applications. The technology and tools required for this project were developed within DataFed.net, a shared infrastructure that supports collaborative atmospheric data sharing and processing web services. Much of the collaboration was facilitated through community interactions through the Federation of Earth Science Information Partners (ESIP) Air Quality Workgroup. The main activities during the project that successfully advanced DataFed, enabled air quality applications and established community-oriented infrastructures were: develop access to distributed data (surface and satellite), build Web infrastructure to support data access, processing and analysis create tools for data processing and analysis foster air quality community collaboration and interoperability.
Falke, Stefan↗
Parametric Analysis of Surveillance Quality and Level and Quality of Intent Information and Their Impact on Conflict Detection Performance
A loss-of-separation (LOS) is said to occur when two aircraft are spatially too close to one another. A LOS is the fundamental unsafe event to be avoided in air traffic management and conflict detection (CD) is the function that attempts to predict these LOS events. In general, the effectiveness of conflict detection relates to the overall safety and performance of an air traffic management concept. An abstract, parametric analysis was conducted to investigate the impact of surveillance quality, level of intent information, and quality of intent information on conflict detection performance. The data collected in this analysis can be used to estimate the conflict detection performance under alternative future scenarios or alternative allocations of the conflict detection function, based on the quality of the surveillance and intent information under those conditions.Alternatively, this data could also be used to estimate the surveillance and intent information quality required to achieve some desired CD performance as part of the design of a new separation assurance system.
Guerreiro, Nelson M.↗
FAIR-ness Assessment of NASA’s Earth Observation System Data and Information System (EOSDIS)
This presentation addresses the challenge of evaluating a multi-disciplinary institutional network of data repositories in operation since 1994 against the relatively recent criteria that constitute FAIR (Findable, Accessible, Interoperable, Reusable) data. NASA’s Earth Observation System Data and Information System (EOSDIS), with its 12 discipline-based Distributed Active Archive Centers (DAACs), preceded the definition and popularization of FAIR by over two decades. An assessment is very useful to describe how well the FAIR principles are met and to identify any improvements needed. In 2020, A “self-assessment” of EOSDIS and DAACs was performed by the ESDIS Project staff and the DAACs from the points of view of human actionability and machine actionability. More recently, a draft of a Science Mission Directorate (SMP) Program Directive (SPD-41a) has been released by NASA Headquarters for comment, where it is recommended that all SMD-funded data should follow the FAIR principles. This presentation is timely to initiate community discussion within the Information Quality Cluster (IQC) of the Earth Science Information Partners (ESIP) and help strategize and develop implementation guidelines for EOSDIS and DAACs to conform to FAIR principles.
Remote sensing↗
NASA GEOS Composition Forecast System, GEOS-CF
NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution analysis and forecasts for weather, aerosols, and air quality. Since 2019, the NASA Global Earth Observing System (GEOS) model provides global near-real-time historical estimates and daily 5-day forecasts of atmospheric composition to the public at unprecedented horizontal resolution of 0.25 degrees (~25 km) from the surface up to the lower mesosphere. This composition forecast system (“GEOS-CF”) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module to deliver detailed analysis of a wide range of air pollutants, including the policy-relevant species such as ozone, carbon monoxide, nitrogen oxides, sulfur dioxide and fine particulate matter (PM2.5). The GEOS-CF is a tool for scientists and the public health community. This presentation will cover 1) an overview of the GEOS-CF modeling framework and data/visualization access, 2) examples of current and future applications to support NASA missions (e.g., a priori for trace gas retrievals by TEMPO, ground-based instrument teams and field campaigns), and 3) research and development activities as the GEOS-CF system continues to evolve to include multi-constituent data assimilation, near-real time emission adjustment estimates, down-scaling methods to urban-scale, and data access on Google Earth Engine, Amazon Web Services, and other platforms to integrate our state-of-the-science air quality information onto platforms used by stakeholders, air quality managers, and the public.
K. Emma Knowland↗
Global-to-local air quality forecasts using the NASA GEOS Composition Forecast System
Since 2019, the NASA Global Earth Observing System (GEOS) model has been used to generate global, near-real-time estimates and daily five-day forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (~25 km) from the surface up to the lower mesosphere. This composition forecast system (“GEOS-CF”) combines the GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module to deliver detailed analysis of a wide range of air pollutants, including the policy-relevant species such as ozone, carbon monoxide, nitrogen oxides, sulfur dioxide and fine particulate matter (PM2.5). Because GEOS-CF includes atmospheric levels up through the stratosphere, this system has been leveraged to support the Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite mission and provide stratospheric intrusion alerts to ground-based monitoring stations. We will present recent advances to GEOS-CF which target increased computational efficiency and accuracy. These include the incorporation of simplified chemistry mechanisms to accelerate model forecasts, use of model-observation data fusion techniques to provide highly localized forecasts, and assimilation of satellite observations to produce more accurate model analyses. We further discuss our attempts to make these tools publicly available on platforms outside the NASA domain, such as Google Earth Engine and Amazon Web Services with the goal to facilitate the integration of state-of-the-science air quality information onto platforms used by stakeholders, air quality managers, and the public.
Emma Knowland↗
Evaluating a Priori Ozone Profile Information Used in TEMPO Tropospheric Ozone Retrievals
Ozone (O3) is a greenhouse gas and toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is primarily conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address these limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product.TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME, GOME-2, and OMI. This algorithm uses a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB) O3 climatology). It has been shown that satellite O3 retrievals are sensitive to a priori O3 profiles and covariance matrices. During this work we investigate the climatological data to be used in TEMPO algorithms (TB O3) and simulated data from the NASA GMAO Goddard Earth Observing System (GEOS-5) Forward Processing (FP) near-real-time (NRT) model products. These two data products will be evaluated with ground-based lidar data from the Tropospheric Ozone Lidar Network (TOLNet) at various locations of the US. This study evaluates the TB climatology, GEOS-5 climatology, and 3-hourly GEOS-5 data compared to lower tropospheric observations to demonstrate the accuracy of a priori information to potentially be used in TEMPO O3 algorithms. Here we present our initial analysis and the theoretical impact on TEMPO retrievals in the lower troposphere.
TEMPO↗
Evaluating A Priori Ozone Profile Information Used in TEMPO Tropospheric Ozone Retrievals
Ozone (O3) is a greenhouse gas and toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is primarily conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address these limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product. TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME, GOME-2, and OMI. This algorithm uses a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB) O3 climatology). It has been shown that satellite O3 retrievals are sensitive to a priori O3 profiles and covariance matrices. During this work we investigate the climatological data to be used in TEMPO algorithms (TB O3) and simulated data from the NASA GMAO Goddard Earth Observing System (GEOS-5) Forward Processing (FP) near-real-time (NRT) model products. These two data products will be evaluated with ground-based lidar data from the Tropospheric Ozone Lidar Network (TOLNet) at various locations of the US. This study evaluates the TB climatology, GEOS-5 climatology, and 3-hourly GEOS-5 data compared to lower tropospheric observations to demonstrate the accuracy of a priori information to potentially be used in TEMPO O3 algorithms. Here we present our initial analysis and the theoretical impact on TEMPO retrievals in the lower troposphere.
ozone↗
CARETS: A prototype regional environmental information system. Volume 7: Land use information and air quality planning
The pilot national land use information system developed by the U.S. Geological Survey in the Central Atlantic Regional Ecological Test Site project has provided an improved technique for estimating emissions, diffusion, and impact patterns of sulfur dioxide (S0 2 ) and particulate matter. Implementation of plans to control air quality requires land use information, which, until this time, has been inadequate. The pilot system, however, provided data for updating information on the sources of point and area emissions of S0 2 and particulate matter affecting the Norfolk--Portsmouth area of Virginia for the 1971-72 winter (Dec.-Jan.-Feb.) and the annual 1972 period, and for a future annual period--1985. This emission information is used as input to the Air Quality Display Model of the Environmental Protection Agency to obtain diffusion and impact patterns for the three periods previously mentioned. The results are: (1) During the 1971-72 winter, estimated SO 2 amounts over an area with a SW-NE-axis in the central section of Norfolk exceeded both primary and secondary levels; (2) future annual levels of SO 2 , estimated by anticipated residential development and point-source changes, are not expected to cause serious deterioration of the region's present air quality; and (3) for the 1971-72 winter and annual 1972 period the diffusion results showed that both primary and secondary standards for particulate matter are regularly exceeded in central Norfolk and Portsmouth. In additions on the bas is of current control programs, the 1985 levels of particulate matter are expected to exceed the presently established secondary air quality standards throughout central Norfolk and Portsmouth and in certain areas of Virginia Beach. The land use information can be used to estimate emissions for inputs to diffusion models and to interpret the implications of diffusion patterns for: (1) Implementing various control strategies (2) selecting sites of air sampling stations, and (3) predicting the effects that proposed changes in land use might have on emission patterns and air quality.
Remote sensing↗
Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine
The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO2. The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.
Callum Wayman↗
Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine
The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO 2 . The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.
Callum Wayman↗
Tracking and Establishing Provenance of Earth Science Datasets: A NASA-based Example
Information quality is of paramount importance to science. Accurate, scientifically vetted and statistically meaningful and, ideally, reproducible information engenders scientific trust and research opportunities. Therefore, so-called Highly Influential Scientific Assessments (HISA) such as the U.S. Third National Climate Assessment undergo a very rigorous process to ensure transparency and credibility. As an activity to support the transparency of such reports, the U.S. Global Change Research Program has developed the Global Change Information System (GCIS). Specifically related to the transparency of NCA3, a recent activity was carried out to trace the provenance as completely as possible for all figures in the NCA3 report that predominantly used NASA data. This paper discusses lessons learned from this activity that trace the provenance of NASA figures in a major HISA-class pdf report.
Ramapriyan, Hampapuram K.↗
Land use change detection with LANDSAT-2 data for monitoring and predicting regional water quality degradation
The author has identified the following significant results. Comparison between LANDSAT 1 and 2 imagery of Arkansas provided evidence of significant land use changes during the 1972-75 time period. Analysis of Arkansas historical water quality information has shown conclusively that whereas point source pollution generally can be detected by use of water quality data collected by state and federal agencies, sampling methodologies for nonpoint source contamination attributable to surface runoff are totally inadequate. The expensive undertaking of monitoring all nonpoint sources for numerous watersheds can be lessened by implementing LANDSAT change detection analyses.
Macdonald, H.↗
Landsat change detection can aid in water quality monitoring
Comparison between Landsat-1 and -2 imagery of Arkansas provided evidence of significant land use changes during the 1972-75 time period. Analysis of Arkansas historical water quality information has shown conclusively that whereas point source pollution generally can be detected by use of water quality data collected by state and federal agencies, sampling methodologies for nonpoint source contamination attributable to surface runoff are totally inadequate. The expensive undertaking of monitoring all nonpoint sources for numerous watersheds can be lessened by implementing Landsat change detection analyses.
Macdonald, H. C.↗
A quality control monitoring system for satellite telemetry data information systems
Quality control monitoring system for satellite telemetry data information systems
Karras, T. J.↗
Challenges in Development of Online Visualization and Analysis Tools for Satellite Data
Over the years, various online visualization and analysis tools have been developed to facilitate satellite data access and help scientific users around the world to conduct research and develop applications (e.g., data product evaluation, what-if questions, etc.). For those who are new to satellite data products, using them can be a daunting task due to many obstacles in data processing such as data formats, complex data structures, special software packages, unfamiliar terminology, etc., especially when one is not sure whether a dataset is suitable for his/er research project. Even for experienced users, developing software for data processing and analysis can be a costly and time-consuming task. Online visualization tools can overcome many of these difficulties and allow users to focus on scientific questions. For example, Giovanni (the Geospatial Interactive Online Visualization and Analysis Infrastructure, https://giovanni.gsfc.nasa.gov), developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), allows access over 1900 satellite and model variables in 82 measurement groups of 8 disciplines without downloading data and software. Main features include basic functions for data analysis and visualization, data provenance, output data in different formats (ASCII, NetCDF, GeoTIFF), and more. Over the years, ~1700 peer-reviewed publications in different disciplines have been benefited from Giovanni in research activities (e.g. initial investigation, what-if questions, product evaluation). Despite the success of online visualization and analysis tools, challenges and new opportunities still exist and more can be done with new requirements and technology. Examples are: a) how to increase the efficiency of dataset search by enhancing intuitive aspects; b) how to facilitate interdisciplinary research; c) how to provide data quality information; d) how to engage users to participate in data quality assessment; and more. NASA Earth Observing System Data and Information System (EOSDIS) satellite-based data products are processed at various levels ranging from Level 0 to Level 4. While most users use data products at higher levels (Level-3 and 4), products at lower levels are still important for case studies, algorithm development, ground validation, etc. In this presentation, we will use Giovanni as an example to present and discuss challenges and near-future opportunities for satellite data online visualization and analysis tools.
Liu, Zhong↗
A design for a ground-based data management system
An initial design for a ground-based data management system which includes intelligent data abstraction and cataloging is described. The large quantity of data on some current and future NASA missions leads to significant problems in providing scientists with quick access to relevant data. Human screening of data for potential relevance to a particular study is time-consuming and costly. Intelligent databases can provide automatic screening when given relevent scientific parameters and constraints. The data management system would provide, at a minimum, information of availability of the range of data, the type available, specific time periods covered together with data quality information, and related sources of data. The system would inform the user about the primary types of screening, analysis, and methods of presentation available to the user. The system would then aid the user with performing the desired tasks, in such a way that the user need only specify the scientific parameters and objectives, and not worry about specific details for running a particular program. The design contains modules for data abstraction, catalog plan abstraction, a user-friendly interface, and expert systems for data handling, data evaluation, and application analysis. The emphasis is on developing general facilities for data representation, description, analysis, and presentation that will be easily used by scientists directly, thus bypassing the knowledge acquisition bottleneck. Expert system technology is used for many different aspects of the data management system, including the direct user interface, the interface to the data analysis routines, and the analysis of instrument status.
Lambird, Barbara A.↗
A Quality Screening Service for Remote Sensing Data
NASA provides a wide variety of Earth-observing satellite data products to a diverse community. These data are annotated with quality information in a variety of ways, with the result that many users struggle to understand how to properly account for quality when dealing with satellite data. To address this issue, a Data Quality Screening Service (DQSS) is being implemented for a number of datasets. The DQSS will enable users to obtain data files in which low-quality pixels have been filtered out, based either on quality criteria recommended by the science team or on the user s particular quality criteria. The objective is to increase proper utilization of this critical quality data in science data analysis of satellite data products.
Lynnes, Christopher↗