A ten time smaller version of CPC global daily precipitation dataset for parallel distributed processing in Matlab and R

Abstract

A ten times smaller version of CPC Global Unified Gauge-Based Daily Precipitation Dataset (1979–2021) is released and described in this paper. Its usability is tested and proved, firstly, by illustrating that the transition to the derived smaller dataset is a case of Pearson correlation transitivity starting with the scale of global yearly data and, secondly, by using the original correlation performance criterion that the original dataset satisfies relative to the set of global actual measurements on the record. Subsequently, the daily, weekly, and monthly data cases are considered and discussed. The dataset is (re)structured for parallel processing in Matlab and R from the level of global daily data. Considering the above arguments and its reduced size, the derived dataset is appropriate to be used with Matlab and R as a replacement for the original dataset, especially for the case when much faster exploratory master-slave parallel and distributed Matlab and/or R tasks will run over locally distributed data on the slaves.