Atmospheric scientists often need to waste time on non-science tasks: installing software libraries, making models compile and run without bugs, preparing model input data, or even setting up a Linux server.

Those technical tasks are getting more and more challenging – as atmospheric models evolve to incorporate more scientific understandings and better computational technologies, they also need more complicated software, more computing power, and much more data.

Cloud computing can largely alleviate those problems. The goal of this project is to allow researchers to fully focus on scientific analysis, not fighting with software and hardware problems.

https://acmg.seas.harvard.edu/geos/geos_overview.html

GEOS-Chem currently have 30 TB of GEOS-FP/MERRA2 meteorological input data. With a bandwidth of 1 MB/s, it takes two weeks to download a 1-TB subset and a year to download the full 30 TB. To set up a high-resolution nested simulation, one often needs to spend long time getting the corresponding meteorological fields. GCHP can ingest global high-resolution data and will further push the data size to increase.

The new paradigm to solve this big data challenge is to “move compute to data”, i.e. perform computing directly in the cloud environment where data is already available. (also see Massive Earth observation data). AWS has agreed to host all GEOS-Chem input data for free under the Public Data Set Program. By having all the data already available in the cloud environment, you can perform simulations over any periods with any configurations.