Most existing online processing and analytics systems for climate studies only support fixed user interface with predefined functions. These systems are often not scalable to handle massive climate data that could easily accumulate terabytes daily. To address the major limitations of existing online systems for climate studies, this paper presents a scalable online visual analytic system, known as SOVAS, to balance both usability and flexibility. SOVAS, enabled by a set of key techniques, supports large-scale climate data analytics and knowledge discovery in a scalable and sharable environment.
SOVAS Website: https://gidbusc.github.io/SCOVAS
Li, Z., Huang, Q., Jiang, Y., & Hu, F. (2020). SOVAS: a scalable online visual analytic system for big climate data analysis. International Journal of Geographical Information Science, 34(6), 1188-1209.
Six types of User Defined Spatiotemporal Functions (UDSFs) and their syntax/usage examples
Query analysis examples
SOVAS generates a unique identifier for each query. Thus, each query can be shared and retrieved with a unique URL. The query examples explained in the manuscript can be accessed with following links.
- Extreme heat for a city
- Extreme heat map
- Anomaly analysis
- Correlation analysis
- Multi-variables visualization
- Zonal statistics: Meteorological measurements (daily precipitation) using US state boundary as zone input
- Zonal statistics: MERRA data (hourly land surface temperature) using US climate division as zone input.
- Explore the hourly local variability of the July land surface temperature by aggregating ten-year MERRA land data from 2001 to 2010.
- Analyze the global land surface temperature anomalies in Jun-Jul-Aug (Northern Hemisphere summer) for each year between 1980 and 2015. This analysis illustrates how to build a query to obtain the local z-score (anomaly) maps. You can modify the spatiotemporal criteria and variable to create a new analysis.