Test Data for the implementation of the SPAR TC Matframework

This dataset contains daily precipitation data over India from 2007 to 2022, organized as triplets for multi-scale analysis. Each triplet consists of three datasets—X, Y, and Z—representing fine, medium, and coarse spatial resolutions, respectively. Dataset X is the fine-scale dataset derived from the SM2RAIN product, which estimates rainfall using active remote sensing based on soil moisture inversion. It has a spatial resolution of 0.1° × 0.1°. Dataset Y is the medium-scale dataset obtained from ERA5 reanalysis, representing model-based precipitation estimates, also at a resolution of 0.1° × 0.1°. Dataset Z is the coarse-scale dataset derived from the CHIRPS product, which combines passive satellite observations with ground station data, and has a resolution of 0.25° × 0.25°. The dataset will be used to estimate scale-dependent error variances for the triplets using the SPAR‑TC (Spatially Representative Triple Collocation) approach. SPAR-TC enables the estimation of error variances for each dataset in a triplet without requiring a ground truth reference, making it suitable for evaluating remotely sensed and model-based precipitation products.