Reduction of Used Memory Ensemble Kalman Filtering (RumEnKF): A data assimilation scheme for memory intensive,high performance computing |
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| Institution: | 1. Faculty of Civil Engineering and Geosciences, department of Water Resources Management, Delft University of Technology, Delft, The Netherlands;2. Council for Scientific and Industrial Research, Water Research Institute, Accra, Ghana;1. Department of Architecture and Civil Engineering, University of Bath, Bath, BA2 7AY, United Kingdom;2. Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca'' Foscari Venezia, Italia;1. Arcadis, United States of America;2. Centre for Hydrogeology and Geothermics, University of Neuchâtel, Switzerland;3. Watermark Numerical Computing, Inc., Australia;1. Atmospheric Optics Group (GOA), University of Valladolid, Spain;2. Department of Applied Physics, University of Granada, 18071 Granada, Spain;3. Andalusian Institute for Earth System Research (IISTA-CEAMA), University of Granada, Autonoemous Government of Andalusia, 18006 Granada, Spain;4. Laboratoire d''Optique Atmosphérique, CNRS, Lille 1 University, France;5. Generalized Retrieval of Atmosphere and Surface Properties, SAS, France;6. Cimel Electronique, Paris, France;7. Departamento Ingeniería Eléctrica y Térmica, University of Huelva, Spain;8. Dipartimento di Matematica e Fisica, Università del Salento, Lecce, Italy;9. Izaña Atmospheric Research Center, Meteorological State Agency of Spain (AEMET), Spain;1. Science Systems and Applications, Inc., Lanham, MD 20706, USA;2. Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA;3. V.E. Zuev Institute of Atmospheric Optics, Russian Academy of Sciences, Siberian Branch, Tomsk 634055, Russia;4. Bay Area Environmental Research Institute, Petaluma, CA 94952, USA;5. Earth Science Division, NASA Ames Research Center, Moffett Field, CA 94035, USA |
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| Abstract: | Reduction of Used Memory Ensemble Kalman Filtering (RumEnKF) is introduced as a variant on the Ensemble Kalman Filter (EnKF). RumEnKF differs from EnKF in that it does not store the entire ensemble, but rather only saves the first two moments of the ensemble distribution. In this way, the number of ensemble members that can be calculated is less dependent on available memory, and mainly on available computing power (CPU). RumEnKF is developed to make optimal use of current generation super computer architecture, where the number of available floating point operations (flops) increases more rapidly than the available memory and where inter-node communication can quickly become a bottleneck. RumEnKF reduces the used memory compared to the EnKF when the number of ensemble members is greater than half the number of state variables. In this paper, three simple models are used (auto-regressive, low dimensional Lorenz and high dimensional Lorenz) to show that RumEnKF performs similarly to the EnKF. Furthermore, it is also shown that increasing the ensemble size has a similar impact on the estimation error from the three algorithms. |
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