Innovative Approaches in Coastal Cloud Modeling

The rising costs of weather events have been shadowed by increases in High Performance Computational (HPC) modeling in an effort to predict and mitigate these events. Scaling up this effort presents new challenges, but it also provides opportunities to enhance and implement innovative workflows - such as event-driven forecasting, carbon cost tracking, and citizen science feedback. This presentation explores innovative strategies to improve coastal modeling that are being examined in the Coastal Modeling Cloud Sandbox developed by NOAA's US Integrated Ocean Observing System.

Public cloud providers offer scalable computing resources on demand, which helps address gaps in acquisition cycles for new HPCs. This Flexibility is particularly useful for handling the variable demands of weather modeling, such as during extreme weather events when computational needs spike. Further, the cloud platform better facilitates event-driven modeling, which provides updated predictions in response to real-time changes. Power-efficient HPC systems use resources more effectively, which can enhance overall performance and allow for more computational tasks to be handled within the same energy budget. The ability to track the carbon costs of computational expenditures can help reduce carbon emissions and minimize the environmental impact.

Lastly, the explosion of IoT devices around the world presents an unprecedented opportunity for citizen science to benefit modelers by collecting data where there are gaps in traditional collection methods. Local observers often operate in areas where traditional data collection infrastructure is limited or non-existent, and they can provide valuable data for regions that are otherwise underrepresented in existing datasets. Mobile apps and online platforms enable people to provide real-time data on the ground. The widespread participation of citizen scientists can generate large volumes of data, which can enhance the richness and detail of models. This expanded dataset can improve the accuracy and robustness of models.

These computational changes represent a great opportunity not only for scientific advancement but also to save lives and protect property.