Now showing items 1-2 of 2

    • Self‐Validating Deep Learning for Recovering Terrestrial Water Storage From Gravity and Altimetry Measurements 

      Irrgang, ChristopherORCIDiD; Saynisch‐Wagner, Jan; Dill, RobertORCIDiD; Boergens, EvaORCIDiD; Thomas, Maik (Geophysical Research Letters, 2020-08-28)
      Quantifying and monitoring terrestrial water storage (TWS) is an essential task for understanding the Earth's hydrosphere cycle, its susceptibility to climate change, and concurrent impacts for ecosystems, agriculture, and ...
    • WeatherBench: A Benchmark Data Set for Data-Driven Weather Forecasting 

      Rasp, StephanORCIDiD; Dueben, Peter D.ORCIDiD; Scher, SebastianORCIDiD; Weyn, Jonathan A.ORCIDiD; Mouatadid, SoukaynaORCIDiD; Thuerey, NilsORCIDiD (Journal of Advances in Modeling Earth Systems, 2020)
      Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains. A natural question to ask is whether data-driven methods could also be used to predict global weather ...