Introduction

The EISCAT-3D radar, https://eiscat3d.se/, is a next-generation incoherent scatter radar system operated by the EISCAT association. It is a world-leading infrastructure using the incoherent scatter technique to study the atmosphere in the Fenno-Scandinavian Arctic and to investigate how the Earth's high latitude atmosphere is coupled to space.

The Sanya Incoherent Scatter radar (SYISR) is also a next-generation incoherent scatter radar system under design and construction by the Institute of Geology and Geophysics, Chinese Academy of Sciences (IGGCAS). It will use the incoherent scatter technique to study the atmosphere at the low latitude in the East Asia region and to investigate how the Earth's low latitude atmosphere is coupled to space.

Both radars aim to establish a system of distributed phased array radars that enable comprehensive three-dimensional vector observations of the atmosphere and ionosphere. The use of new radar technology, combined with the latest digital signal processing, will achieve ten times higher temporal and spatial resolution than obtained by present radars while simultaneously offering, for the first time, continuous measurement capabilities. Both systems will allow the study of atmospheric phenomena at both large and small scales unreachable by the present systems. The new system will be implemented for a wide range of users and applications. The continuous data coverage will facilitate the inclusion of detailed incoherent scatter radar data into climate and Earth system modelling.

Challenges

The challenge of EISCAT-3D and SYISR data management system is how to handle large-scale experimental data that will be massively generated at great speeds and volumes. During its first operation stage in 2018, EISCAT-3D will produce 5PB data per year, and the total data volume will rise up to 40PB per year in its full operations stage in 2023. While for SYISR, during its first operation stage in 2021, SYISR will produce 1PB data per year, and the total data volume will rise up to 3PB per year in its full operations stage in 2023.

In this case, the EISCAT and SYISR radar data fusion and computing may require further technical supports from the GOSC Initiative within the following aspects:

1. Secure check-in services for accessing cross-border cloud services
2. DIRAC for job submission
3. Radar data storage and federated processing
4. On-demand data movement

Engagement

Platform and semantic interoperability are two key issues to be addressed in EISCAT-3D & SYISR radar data cooperation. Under the umbrella of the GOSC initiative, joint activities for EISCAT and SYISR may include:

1. (Meta)data federation: Metadata searchable via an integrated portal, with data stored in the EISCAT repository and the SYISR repository. Such a search portal can be based on the EISCAT Portal (by either configuring the EISCAT Portal to be able to access the SYISR metadata, or by hosting a customized instance of the EISCAT Portal in the CNIC cloud) and configuring it to be able to access the SYISR and EISCAT metadata.
2. Federated processing: After discovering relevant EISCAT and SYISR data, the user could launch analysis jobs in the cloud to process those data. These jobs would run in the European or the Chinese clouds depending on the user’s affiliation (e.g., Chinese users would access the CNIC portal and run jobs in the CNIC cloud, others would use the EISCAT portal and run jobs in the EGI/EISCAT cloud). Other clouds could also be included when necessary.
3. On-demand data movement: If the data must be moved, the user would be able to move data from EISCAT and SYISR into a single cloud site and perform online analysis with both sets.
4. Collaborations on necessary training and activities for SYISR and EISCAT community outreach.

Deliverables

After the implementation of the five-year GOSC program, the anticipated outputs from the EISCAT-3D and SYISR Case Study may focus on the following aspects:

1. Exploration of technical solutions for EISCAT and SYISR (Meta)data federation.
2. Deployment of GOSC technical solutions supporting the cloud federated data processing and on-demand data movement.
3. Lessons and good practices for analogous research infrastructures in OSCs.