Number formats, error mitigation, and scope for 16‐bit arithmetics in weather and climate modeling analyzed with a shallow water model
Journal of Advances in Modeling Earth Systems American Geophysical Union 12:10 (2020) e2020MS002246
Abstract:
The need for high‐precision calculations with 64‐bit or 32‐bit floating‐point arithmetic for weather and climate models is questioned. Lower‐precision numbers can accelerate simulations and are increasingly supported by modern computing hardware. This paper investigates the potential of 16‐bit arithmetic when applied within a shallow water model that serves as a medium complexity weather or climate application. There are several 16‐bit number formats that can potentially be used (IEEE half precision, BFloat16, posits, integer, and fixed‐point). It is evident that a simple change to 16‐bit arithmetic will not be possible for complex weather and climate applications as it will degrade model results by intolerable rounding errors that cause a stalling of model dynamics or model instabilities. However, if the posit number format is used as an alternative to the standard floating‐point numbers, the model degradation can be significantly reduced. Furthermore, mitigation methods, such as rescaling, reordering, and mixed precision, are available to make model simulations resilient against a precision reduction. If mitigation methods are applied, 16‐bit floating‐point arithmetic can be used successfully within the shallow water model. The results show the potential of 16‐bit formats for at least parts of complex weather and climate models where rounding errors would be entirely masked by initial condition, model, or discretization error.The 2020 Climate Informatics Hackathon: Generating Nighttime Satellite Imagery from Infrared Observations
Association for Computing Machinery (ACM) (2020) 134-138
Constraint on precipitation response to climate change by combination of atmospheric energy and water budgets
npj Climate and Atmospheric Science Springer Nature 3 (2020) 34
Abstract:
Global mean precipitation is expected to increase with increasing temperatures, a process which is fairly well understood. In contrast, local precipitation changes, which are key for society and ecosystems, demonstrate a large spread in predictions by climate models, can be of both signs and have much larger magnitude than the global mean change. Previously, two top-down approaches to constrain precipitation changes were proposed, using either the atmospheric water or energy budget. Here, using an ensemble of 27 climate models, we study the relative importance of these two budgetary constraints and present analysis of the spatial scales at which they hold. We show that specific geographical locations are more constrained by either one of the budgets and that the combination of water and energy budgets provides a significantly stronger constraint on the spatial scale of precipitation changes under anthropogenic climate change (on average about 3000 km, above which changes in precipitation approach the global mean change). These results could also provide an objective way to define the scale of ‘regional’ climate change.Map Generation from Large Scale Incomplete and Inaccurate Data Labels
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining ACM (2020) 2514-2522
Quantifying the sensitivity of aerosol optical properties to the parameterizations of physico-chemical processes during the 2010 Russian wildfires and heatwave
Atmospheric Chemistry and Physics Copernicus GmbH 20:16 (2020) 9679-9700