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Implementation of a One-Dimensional Enthalpy Sea-Ice Model in a Simple Pycnocline Prediction Model for Sea-Ice Data Assimilation Studies

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Advances in Atmospheric Sciences 2016年第2期33卷 193-207页

作者： Xinrong WU Shaoqing ZHANG Zhengyu LIU Key Laboratory of Marine Environmental Information Technology State Oceanic AdministrationNational Marine Data and Information Service Geophysical Fluid Dynamics Laboratory National Oceanic and Atmospheric AdministrationPrinceton University Center for Climate Research and Dept.Atmospheric and Oceanic Sciences University of Wisconsin-Madison Laboratory of Ocean–Atmosphere Studies Peking University

To further explore enthalpy-based sea-ice assimilation, a one-dimensional （1D） enthalpy sea-ice model is implemented into a simple pycnocline prediction model. The 1D enthalpy sea-ice model includes the physical processes such as brine expulsion, flushing, and salt diffusion. After being coupled with the atmosphere and ocean components, the enthalpy sea-ice model can be integrated stably and serves as an important modulator of model variability. Results from a twin experiment show that the sea-ice data assimilation in the enthalpy space can produce smaller root-mean-square errors of model variables than the traditional scheme that assimilates the observations of ice concentration, especially for slow-varying states. This study provides some insights into the improvement of sea-ice data assimilation in a coupled general circulation model.

关键词： sea ice enthalpy coupled model data assimilation ensemble kalman filter

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Updating the coal quality parameters in multiple production benches based on combined material measurement： a full case study

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International Journal of Coal Science & Technology 2017年第2期4卷 159-171页

作者： Cansln Yiiksel Jorg Benndorf Matthias Lindig Oliver Lohstrater Resource Engineering Section Department of Geoscienceand Engineering Delft University of Technology Stevinweg1 2628 CN Delft The Netherlands Institute for Mine Surveying and Geodesy Faculty ofGeosciences Geoengineering and Mining University of Technology Bergakademie Freiberg Reiche ZecheFuchsmuhlenweg 9 09599 Freiberg Germany Mitteldeutsche Braunkohlengesellschaft mbH GliJck-Auf-Strage 1 06711 Zeitz Germany

An efficient resource model updating framework concept was proposed aiming for the improvement of raw material quality control and process efficiency in any type of mining operation. The concept integrates sensor data measured online on the production line into the resource or grade/quality control model and continuously provides locally more accurate estimates. The concept has been applied in a lignite field with the aim of identifying local impurities in a coal seam and to improve the prediction of coal quality attributes in neighbouring blocks. A significant improvement was demonstrated which led to better coal quality management. So far, the proposed concept and the application in coal mining was limited to a case where online measurements were unambiguously trackable due to a single extraction face being the point of origin for the material. This contribution presents an extension to the case, where characteristics from blended material, originating from two or three simultaneously operating extraction faces, are measured. The challenge tackled in this contribution is the updating of local coal quality estimates in different production benches based on measurements of a blended material stream. For a practical application of the updating concept, which is based on the ensemble kalman filter, a simple method for generating prior ensemble members based on block geometries defined in the short-term model and the variogram, is discussed. This method allows for a fast, semi-automated and rather simple generation of prior models instead of generating a fully simulated deposit model using conditional simulation in geostatistics. It should foster operational implementation in an industrial environment. The main purpose of this article is to investigate the applicability of the developed framework with a simplified prior resource model. In addition to this any model improvements due to the integration of sensor data obtained by observing a blend of coal from multiple extraction faces is investigated.

关键词： Resource model updating Online sensor ensemble kalman filter Multiple excavator Lignite Real application

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