A Modular Software Framework for Compression of Structured Climate Data

Through the introduction of next-generation models the climate sciences have experienced a breakthrough in high-resolution simulations. In the past, the bottleneck was the numerical complexity of the models, nowadays it is the required storage space for the model output. One way to tackle the data storage challenge is through data compression. In this article we introduce a modular framework for the compression of structured climate data. Our modular framework supports the creation of individual predictors, which can be customised and adjusted to the data at hand. We provide a framework for creating interfaces and customising components, which are building blocks of individualised compression modules that are optimised for particular applications. Furthermore, the framework provides additional features such as the execution of benchmarks and validity tests for sequential as well as parallel execution of compression algorithms

Recurrence Based Similarity Identification of Climate Data

Climate change has become a challenging and emerging research problem in many research related areas. One of the key parameters in analyzing climate change is to analyze temperature variations in different regions. The temperature variation in a region is periodic within the interval. Temperature variations, though periodic in nature, may vary from one region to another and such variations are mainly dependent on the location and altitude of the region and also on other factors like the nearness of sea and vegetation. In this paper, we analyze such periodic variations using recurrence plot (RP), cross recurrence plot (CRP), recurrence rate (RR), and correlation of probability of recurrence (CPR) methods to find similarities of periodic variations between and within climatic regions and to identify their connectivity trend. First, we test the correctness of our method by applying it on voice and heart rate data and then experimentation is performed on synthetic climate data of nine regions in the United States and eight regions in China. Finally, the accuracy of our approach is validated on both real and synthetic datasets and demonstrated using ANOVA, Kruskal–Wallis, and z-statistics significance tests