System and method for providing automated multi-source data provisioning for a reanalysis ensemble service
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Solution Overview
Problem
Current climate data analytics platforms lack the capability to automatically retrieve, align, and sequence data from multiple disparate sources, perform analytics operations, and deliver results to clients in a timely and efficient manner, especially when dealing with large and varied climate datasets.
Innovation Solution
The development of an extended Reanalysis Ensemble Service that includes a loader services API for data retrieval and conversion, a reanalysis ensemble service API for operational parameter conversion, and a distributed file system for data storage, enabling automatic data collection, alignment, and performance of specified analytics operations, with the ability to update data on demand or scheduled basis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from multiple disparate climate data sources is manually retrieved and processed, then data accuracy and consistency can be maintained, but the time and labor required for data collection and preparation increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically retrieving, validating, and sequencing climate data from multiple sources before analytics operations are requested. The loader services API proactively collects data from disparate sources (MERRA-2, ERA-Interim, CFSR, etc.), converts it to standardized formats, and stores it in the distributed file system, eliminating the need for manual data collection at the time of analysis.
Solution Approach 2:
The system implements self-service through automated data provisioning where the Reanalysis Ensemble Service automatically manages the entire data lifecycle including retrieval from multiple sources, format conversion, validation, sequencing, and storage. The service independently handles data quality assurance through validation operations without requiring manual intervention, thereby maintaining accuracy while reducing time investment.
2Adaptability or versatility
If climate data from multiple disparate sources is collected and processed manually, then comprehensive data coverage can be achieved, but the complexity of data alignment and sequencing increases
Solution Approach 1:
The loader services API implements universality by handling multiple climate data sources (MERRA-2, ERA-Interim, CFSR, JRA-25, JRA-55, etc.) through a single unified interface. The service performs multiple functions including data retrieval, format conversion, validation, and sequencing through standardized operations that work across all disparate sources, thereby achieving comprehensive coverage without proportionally increasing processing complexity.
Solution Approach 2:
The system applies parameter changes by transforming data from various sources into a standardized format with consistent parameters. The conversion utilities modify data structure, temporal resolution, and spatial parameters to align with the Reanalysis Ensemble Service requirements, enabling seamless integration of diverse data sources while managing complexity through systematic parameter standardization.
3Productivity
If automated data retrieval and processing is implemented, then processing efficiency and speed are improved, but the system complexity and infrastructure requirements increase
Solution Approach 1:
The loader services API acts as an intermediary layer between the client applications and the distributed file system containing climate data. It mediates the automated data retrieval process by handling connections to remote platforms, performing conversions, and managing data flow, thereby improving processing efficiency while encapsulating system complexity within the intermediary service rather than exposing it to end users.
4Reliability
If data validation and sequencing operations are performed manually, then data quality can be ensured, but the time required for analytics delivery is extended
Solution Approach 1:
The system implements continuity of useful action by performing validation and sequencing operations continuously as part of the automated data provisioning pipeline. Rather than performing these operations manually after data collection, the loader services API executes validation and sequencing automatically and continuously as data is retrieved and converted, ensuring data quality while minimizing delays in analytics delivery.
Data Source
AI summary
An extended reanalysis ensemble service includes a loader services application program interface configured to receive data parameters for a set of automated multisource data provisioning operations, provide climate source data from one or more disparate climate data collections specified in the data parameters to conversion utilities for transforming the climate source data into flat, serialized block compressed sequence files, and load the sequence files to a distributed file system of the extended reanalysis ensemble service, and a reanalysis ensemble service application program interface configured to receive operational parameters for the set of automated multisource data provisioning operations, convert the operational parameters to one or more methods recognized by a service interface of the extended reanalysis ensemble service to be converted to analytical operations executed by the extended reanalysis ensemble service, and provide results of the one or more analytical operations executed by the extended reanalysis ensemble service to a client.