Update README.md

Adding processing steps to the data downloaded from the CDS

README.md

@@ -136,7 +136,27 @@ The data samples used for training corresponds to the period from 1985 and 2013
 
 ### Preprocessing
 
-More information needed
+The preprocessing of climate datasets ERA5 and CERRA, extracted from the Climate Data Store (CDS), is a critical step before their utilization in training models. 
+This paper delineates the series of preprocessing steps undertaken to homogenize these datasets into a common format conducive for the ensuing experiment. The steps 
+include unit standardization, coordinate system rectification, and grid interpolation. The rationale and methodologies employed in each step are discussed comprehensively,
+setting a robust foundation for the subsequent training procedure.
+
+1. Unit Standardization:
+A preliminary step in the preprocessing pipeline involved the standardization of units across both datasets.
+This was imperative to ensure a uniform unit system, facilitating a seamless integration of the datasets in later stages.
+The units in both datasets were scrutinized and amended to adhere to a common unit system, thereby eliminating any discrepancies that could hinder the analysis.
+
+2. Coordinate System Rectification:
+The coordinate system of the datasets was rectified to ensure a coherent representation of geographical information. 
+Specifically, the coordinates and dimensions were renamed to a standardized format with longitude (lon) and latitude (lat) as designated names. 
+The longitude values were adjusted to range from -180 to 180 instead of the initial 0 to 360 range, while latitude values were ordered in ascending order, 
+thereby aligning with conventional geographical coordinate systems.
+
+3. Grid Interpolation:
+The ERA5 dataset is structured on a regular grid with a spatial resolution of 0.25º, whereas the CERRA dataset inhabits a curvilinear grid with a Lambert Conformal
+projection of higher spatial resolution (0.05º). To overcome this disparity, a grid interpolation procedure was initiated.
+This step was crucial to align the datasets onto a common regular grid (with different spatial resolution), thereby ensuring consistency in spatial representation.
+The interpolation transformed the CERRA dataset to match the regular grid structure of the ERA5 dataset, thus harmonizing the spatial resolution across both datasets.
 
 ### Speeds, Sizes, Times