# 07: NDarrays - Raster time series analysis, temporal aggregation, regression in time, PCA/EOF analysis

UW Geospatial Data Analysis  
CEE467/CEWA567  
David Shean, Eric Gagliano, Quinn Brencher

## Overview
In modules 4 and 6 we used rioxarray to read and analyze rasters which were represented as xarray DataArray objects. Module 7 introduces advanced capabilities of Xarray for handling multidimensional geospatial data. In particular, we will work with N-dimensional arrays (NDarrays) that incorporate a time dimension alongside spatial coordinates, enabling time series analysis of raster data.

## Reading and Tutorials
Please take some time to review the following material, and come with questions on topics that are unclear, so we can discuss together. 

### xarray
* [Why Xarray?](https://docs.xarray.dev/en/stable/getting-started-guide/why-xarray.html)
* [Quick overview](https://docs.xarray.dev/en/stable/getting-started-guide/quick-overview.html)
* [Learning Xarray fundamentals](https://tutorial.xarray.dev/overview/fundamental-path/README.html)

### ERA5 climate reanalysis data
* [What is climate reanalysis?](https://climate.copernicus.eu/climate-reanalysis)
* [ERA5 reanalysis](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5)

### Cartopy
* [Xarray maps with Cartopy](https://docs.xarray.dev/en/stable/user-guide/plotting.html#maps)
* [Maps with Catopy](https://rabernat.github.io/research_computing_2018/maps-with-cartopy.html)