Reanalysis Ensemble Service for Multi-Source Climate Data Intercomparison
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Solution Overview
Problem
Current data analytics platforms lack the capability to efficiently operate over multiple climate reanalysis collections and perform intercomparisons between analysis results, limiting their utility in climate variability studies and applications.
Innovation Solution
The Reanalysis Ensemble Service provides a system that converts disparate climate reanalysis datasets into a common format, using a data analytics platform with conversion utilities, a service interface, and a services library to perform high-performance operations and dynamic data object creation, enabling access to multiple reanalysis collections and intercomparison analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a data analytics platform operates on a single reanalysis dataset, then the platform structure is simple and easier to maintain, but it cannot perform intercomparison analytics between multiple reanalysis collections
Solution Approach 1:
The system segments the handling of different reanalysis datasets by introducing separate conversion utilities for each dataset type (MERRA-2, ERA-Interim, CFSR, JRA-55). Each utility is responsible for converting its specific dataset format to the common internal format, allowing the core platform to remain unchanged while supporting multiple data sources.
Solution Approach 2:
The patent introduces a common internal data format as an intermediary layer between various reanalysis dataset formats and the analytics engine. This intermediary format enables seamless conversion and comparison operations without requiring the platform to directly handle each source format, thus maintaining platform simplicity while achieving multi-dataset capability.
2Adaptability or versatility
If conversion utilities are added to support multiple reanalysis datasets, then intercomparison capability is improved, but the conversion process becomes more complex and time-consuming
Solution Approach 1:
The system performs preliminary conversion of reanalysis datasets from their native formats to a common internal format before analytics operations. This pre-conversion approach enables efficient intercomparison operations without repeated format transformations, reducing overall processing time for multi-dataset analyses.
3Productivity
If the platform provides comprehensive operations on multiple reanalysis collections, then the utility for climate variability studies is improved, but the computational resources and processing time increase
Solution Approach 1:
The patent merges multiple reanalysis datasets into a unified common format within the platform, enabling simultaneous processing and comparison operations. This consolidation allows the system to leverage shared computational resources and perform integrated analytics across datasets, improving productivity while optimizing resource utilization compared to separate processing approaches.
Data Source
AI summary
A reanalysis ensemble service includes a plurality of conversion utilities, each conversion utility configured to convert a specific one of a plurality of disparate climate reanalysis datasets from different sources to common format files that are temporally and spatially registered, a data analytics platform for storing and operating on the different sourced common format files, a service interface for mapping service requests to analytic operations performed on the different sourced common format files by the data analytics platform, and a services library that dynamically creates data objects from one or more of the different sourced common format files in response to the analytic operations, and delivers the data objects to the service interface.