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Interoperability between Earth Observations and Earth Science Models: Bridging the Gap

This session will discuss the challenges and solutions for integrating Earth observation data with Earth science models, exploring topics such as data diversity, interoperability standards, and geospatial workflows.

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Interoperability between Earth Observations and Earth Science Models: Bridging the Gap

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  1. Interoperability between Earth observations and Earth science modelsSession IntroductionESIP 2016 Winter Meeting, January 6-8, 2016 Liping Di Center for Spatial Information Science and Systems (CSISS) George Mason University (GMU) 4400 University Drive, MSN 6E1 Fairfax, VA 22030 ldi@gmu.edu http://csiss.gmu.edu

  2. Introduction • Earth science model (ESM) is the major tool to study the Earth system. • Earth observation (EO) through sensors is the most important way to collect Earth science data, which describe the current status of the Earth. • In-situ sensor • Remote sensor • Models typically use the high-level products derived from raw EO data • initializing the models • constraining the model external forcings • calibrating the model parameters • verifying and validating (V&V) model performance Page 2

  3. Diversities in EO data and ESMs • EO data are very diverse • collecting sensors • Intended purpose • data formats • projections • access methods • spatial/temporal resolutions and coverage • metadata, quality, documentation, and user support • Numerous ESMs in different Earth science domains • Number of disciplines • Type of models • Parameters needed and parameterization schema • Gridding schema • Data ingest capability

  4. Issues related to use EO data in ESMs • difficulty to find and obtain the needed data from geographically distributed data sources, • data not in ready-to-ingest form, e.g., incompatible format, projection, and resolution among data from different sources and between the data from external sources and the in-house analysis system used by scientists, • the unavailability of the needed data products, e.g., further process the low-level data into higher level customized products are often needed before an application can use them • lack of or inadequate computing resources (both software and hardware) to handle the large volume of data.

  5. Use Case Scenarios • The needed data product exists at a data source in the form exactly matching the ESM requirement. • find at which data source the data product is located, retrieve the data product, and present the product to the requester. • The needed data product exists at a data source but in a form different from the form needed by the ESM • find where the data is located, retrieve the data product, and apply a set of data preprocessing functions to the data in sequence to make the data in the ESM-needed form. • The needed data product doesn’t exist in any data sources • Determine processing functions and input data needed to generate the requested product, the locations of needed processing functions and the input data, and methods to access the functions and data • apply the functions to data to generate the needed product on the flying

  6. Technologies • Interoperability standards and specifications • Standard-based data access technologies • Web processing services • Geospatial processing models (GPM), workflow, product virtualization • Workflow engine

  7. Today’s talk • OGC activities and standards for model-sensor interoperability and the Model Web (George Percivall) • Needs for Earth observational data from coastal oceanographic modeling perspective (Haosheng Huang) • Realize effortlessly feeding FVCOM and CRM with multisource earth observation data: two applications of CyberConnector (Ziheng Sun) • Geospatial workflows for reusing standard Web services (Eugene Yu)

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