Hydrology is the study of water on and beneath the earth’s surface, and a detailed understanding of the hydrological state is of paramount importance in Norwegian hydropower production.
Statkraft needed help to operationalize a prototype for distributed hydrological forecasting, and hired one of our consultants to help. We ended up reimplementing the whole system, using modern templated C++, and equipping it with a Python interface. The new system, called Shyft, runs several hundred times faster than the old prototype, and is currently used both operationally at Statkraft and as a research tool by hydrologists in academia. Shyft is open source and distributed using the LGPL license.
Eivind has a masters degree in Computational Science from the University of Oslo, completed in 2016. His project investigated a particle system modeling linear elasticity, and accelerating linear algebra computations using GPUs. From the studies leading to his degree he has gained broad knowledge about algorithms, numerical mathematics, and programming in several languages.
Roar has a master’s degree in High Energy Physics from the University of Oslo, completed in 2018. In his thesis he studied two different models of particle production at high energies, one statistical using the thermodynamical equations and one using the properties of a theoretical particle called Pomeron.