Post-Launch Calibration Support for VIIRS Onboard NASA NPP Spacecraft

The NPP Instrument Calibration Support Element (NICSE) is one of the elements within the NASA NPP Science Data Segment (SDS). The primary responsibility of NICSE is to independently monitor and evaluate on-orbit radiometric and geometric performance of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument and to validate its Sensor Data Record (SDR) [1]. The NICSE interacts and works closely with other SDS Product Evaluation and Analysis Tools Elements (PEATE) and the NPP Science Team (ST) and supports their on-orbit data product calibration and validation efforts. The NICSE also works closely with the NPP Instrument Calibration Support Team (NICST) during sensor pre-launch testing in ambient and thermal vacuum environment [2]. This paper provides an overview of NICSE VIIRS sensor post-launch calibration support with a focus on the use of sensor on-board calibrators (OBC) for the radiometric calibration and characterization. It presents the current status of NICSE post-launch radiometric calibration tool development effort based on its design requirement

Development of an intelligent interface for adding spatial objects to a knowledge-based geographic information system

Earth Scientists lack adequate tools for quantifying complex relationships between existing data layers and studying and modeling the dynamic interactions of these data layers. There is a need for an earth systems tool to manipulate multi-layered, heterogeneous data sets that are spatially indexed, such as sensor imagery and maps, easily and intelligently in a single system. The system can access and manipulate data from multiple sensor sources, maps, and from a learned object hierarchy using an advanced knowledge-based geographical information system. A prototype Knowledge-Based Geographic Information System (KBGIS) was recently constructed. Many of the system internals are well developed, but the system lacks an adequate user interface. A methodology is described for developing an intelligent user interface and extending KBGIS to interconnect with existing NASA systems, such as imagery from the Land Analysis System (LAS), atmospheric data in Common Data Format (CDF), and visualization of complex data with the National Space Science Data Center Graphics System. This would allow NASA to quickly explore the utility of such a system, given the ability to transfer data in and out of KBGIS easily. The use and maintenance of the object hierarchies as polymorphic data types brings, to data management, a while new set of problems and issues, few of which have been explored above the prototype level

Addressing and Presenting Quality of Satellite Data via Web-Based Services

With the recent attention to climate change and proliferation of remote-sensing data utilization, climate model and various environmental monitoring and protection applications have begun to increasingly rely on satellite measurements. Research application users seek good quality satellite data, with uncertainties and biases provided for each data point. However, different communities address remote sensing quality issues rather inconsistently and differently. We describe our attempt to systematically characterize, capture, and provision quality and uncertainty information as it applies to the NASA MODIS Aerosol Optical Depth data product. In particular, we note the semantic differences in quality/bias/uncertainty at the pixel, granule, product, and record levels. We outline various factors contributing to uncertainty or error budget; errors. Web-based science analysis and processing tools allow users to access, analyze, and generate visualizations of data while alleviating users from having directly managing complex data processing operations. These tools provide value by streamlining the data analysis process, but usually shield users from details of the data processing steps, algorithm assumptions, caveats, etc. Correct interpretation of the final analysis requires user understanding of how data has been generated and processed and what potential biases, anomalies, or errors may have been introduced. By providing services that leverage data lineage provenance and domain-expertise, expert systems can be built to aid the user in understanding data sources, processing, and the suitability for use of products generated by the tools. We describe our experiences developing a semantic, provenance-aware, expert-knowledge advisory system applied to NASA Giovanni web-based Earth science data analysis tool as part of the ESTO AIST-funded Multi-sensor Data Synergy Advisor project

A world of smartphone experiments with the app phyphox

SMARTPHONES AS MEASUREMENT DEVICES The concept of the app phyphox is based on the simple idea that smartphones and tablets come with a plethora of sensors, which can be used for data acquisition in science education. Phyphox was developed at the RWTH Aachen University for this purpose and presents itself as an open source tool with many options to customise data sources, data analysis and data presentation, while not overwhelming students with these options while they use their own devices to discover the world. Experimentation with device sensors There are many situations in which these readily available measurement devices can enhance science education. These range from classical educational experiments that can be reproduced with household items (radial acceleration in a salad spinner), over casually discovering the world around us (determine the speed of an elevator with the pressure sensor) to projects on technical applications (build a Pitot tube based on this pressure sensor). DIY-Sensors with Arduino and MicroPython Beyond these typical experiments, phyphox can be used in modern microcontroller-based projects. Smartphone sensors can easily be combined with cheap external sensors using an Arduino or MicroPython library for phyphox. This allows us to combine the visualisation capabilities of the phone with the wide choice of sensors of DIY electronics and is accessible even to programming beginners. Collaborative experiments for large audiences While these examples are suitable on the scale of typical school classes, the connectivity of smartphones allows us to scale experimental data acquisition to large audiences. Automated data collection and analysis allow for entire lecture halls to participate in live experiments during a lecture and even worldwide experiments to determine Earth’s axial tilt have been demonstrated. FURTHER READING Sebastian Staacks, Simon Hütz, Heidrun Heinke, Christoph Stampfer. (2018). Advanced tools for smartphone-based experiments: phyphox. Physics Education, 53(4), 045009. https://doi.org/10.1088/1361-6552/aac05e Sebastian Staacks, Dominik Dorsel, Simon Hütz, Frank Stallmach, Tobias Splith, Heidrun Heinke, Christoph Stampfer. (2022). Collaborative smartphone experiments for large audiences with phyphox. European Journal of Physics, 43(5), 055702. https://doi.org/10.1088/1361-6404/ac783

Community-Based Services that Facilitate Interoperability and Intercomparison of Precipitation Datasets from Multiple Sources

Over the past 12 years, large volumes of precipitation data have been generated from space-based observatories (e.g., TRMM), merging of data products (e.g., gridded 3B42), models (e.g., GMAO), climatologies (e.g., Chang SSM/I derived rain indices), field campaigns, and ground-based measuring stations. The science research, applications, and education communities have greatly benefited from the unrestricted availability of these data from the Goddard Earth Sciences Data and Information Services Center (GES DISC) and, in particular, the services tailored toward precipitation data access and usability. In addition, tools and services that are responsive to the expressed evolving needs of the precipitation data user communities have been developed at the Precipitation Data and Information Services Center (PDISC) (http://disc.gsfc.nasa.gov/precipitation or google NASA PDISC), located at the GES DISC, to provide users with quick data exploration and access capabilities. In recent years, data management and access services have become increasingly sophisticated, such that they now afford researchers, particularly those interested in multi-data set science analysis and/or data validation, the ability to homogenize data sets, in order to apply multi-variant, comparison, and evaluation functions. Included in these services is the ability to capture data quality and data provenance. These interoperability services can be directly applied to future data sets, such as those from the Global Precipitation Measurement (GPM) mission. This presentation describes the data sets and services at the PDISC that are currently used by precipitation science and applications researchers, and which will be enhanced in preparation for GPM and associated multi-sensor data research. Specifically, the GES-DISC Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) will be illustrated. Giovanni enables scientific exploration of Earth science data without researchers having to perform the complicated data access and match-up processes. In addition, PDISC tool and service capabilities being adapted for GPM data will be described, including the Google-like Mirador data search and access engine; semantic technology to help manage large amounts of multi-sensor data and their relationships; data access through various Web services (e.g., OPeNDAP, GDS, WMS, WCS); conversion to various formats (e.g., netCDF, HDF, KML (for Google Earth)); visualization and analysis of Level 2 data profiles and maps; parameter and spatial subsetting; time and temporal aggregation; regridding; data version control and provenance; continuous archive verification; and expertise in data-related standards and interoperability. The goal of providing these services is to further the progress towards a common framework by which data analysis/validation can be more easily accomplished

Applying principles of metrology to historical Earth observations from satellites

Approaches from metrology can assist Earth Observation (EO) practitioners to develop quantitative characterisation of uncertainty in EO data. This is necessary for the credibility of statements based on Earth observations in relation to topics of public concern, particularly climate and environmental change. This paper presents the application of metrological uncertainty analysis to historical Earth observations from satellites, and is intended to aid mutual understanding of metrology and EO. The nature of satellite observations is summarised for different EO data processing levels, and key metrological nomenclature and principles for uncertainty characterisation are reviewed. We then address metrological approaches to developing estimates of uncertainty that are traceable from the satellite sensor, through levels of data processing, to products describing the evolution of the geophysical state of the Earth. EO radiances have errors with complex error correlation structures that are significant when performing common higher-level transformations of EO imagery. Principles of measurement-function-centred uncertainty analysis are described that apply sequentially to each EO data processing level. Practical tools for organising and traceably documenting uncertainty analysis are presented. We illustrate these principles and tools with examples including some specific sources of error seen in EO satellite data as well as with an example of the estimation of sea surface temperature from satellite infra-red imagery. This includes a simulation-based estimate for the error distribution of clear-sky infra-red brightness temperature (BT) in which calibration uncertainty and digitisation are found to dominate. The propagation of these errors to sea surface temperature is then presented, illustrating the relevance of the approach to derivation of EO-based climate datasets. We conclude with a discussion arguing that there is broad scope and need for improvement in EO practice as a measurement science. EO practitioners and metrologists willing to extend and adapt their disciplinary knowledge to meet this need can make valuable contributions to EO

Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary).

In May 2017, a two-day workshop was held in Los Angeles (California, U.S.A.) to gather practitioners who work with low-cost sensors used to make air quality measurements. The community of practice included individuals from academia, industry, non-profit groups, community-based organizations, and regulatory agencies. The group gathered to share knowledge developed from a variety of pilot projects in hopes of advancing the collective knowledge about how best to use low-cost air quality sensors. Panel discussion topics included: (1) best practices for deployment and calibration of low-cost sensor systems, (2) data standardization efforts and database design, (3) advances in sensor calibration, data management, and data analysis and visualization, and (4) lessons learned from research/community partnerships to encourage purposeful use of sensors and create change/action. Panel discussions summarized knowledge advances and project successes while also highlighting the questions, unresolved issues, and technological limitations that still remain within the low-cost air quality sensor arena