When we talk about scientific data, the term is kind of a catch-all that really refers to three data formats commonly used in the scientific community: NetCDF, HDF, and GRIB. If a normal raster is a grid where each block is pixel that has a value, these data formats have at least one more dimension (such as a vertical or time dimension) and can store data for many attributes at a given location. If this needs clarification, just ask in the comments and I’ll figure out another way to explain this stuff.
Anyway, there’s a ton that you can do with these kinds of datasets. The raster team has been working to make ArcGIS compatible to read and analyse this type of data within our mosaic dataset. To get you started, check out this package of goodies on ArcGIS Online that has step by step instructions for:
- Analyzing categorical data from MODIS
- Visualizing vegetation
- Displaying an RGB composite
- Using a QA/QC band to create masks
- Extracting variables from modeled weather datasets such as GLDAS and NLDAS
- Aggregating hourly or daily data into monthly totals
- Deriving the heat index and wind chill from temperature, wind and humidity data
- Displaying ocean currents as vectors (No, not a shapefile… nope, not disease vectors either…but something with a magnitude and a direction. OK, fine. It’s an arrow.)
Also, I would be remiss if I didn’t mention our github repo. This is the place to go to if you want to create custom raster functions. If you can script in Python, you can pretty much do anything (like, you could probably build and launch a satellite in python if you actually know what you’re doing). Anyway, at the repo you can learn all about the basics of what functions can and cannot do, and how they work really well for the kinds of datasets I’m talking about here.