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In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4 km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter’s observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories. 相似文献
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Assimilation of Hourly Surface Observations with the Canadian High-Resolution Ensemble Kalman Filter
An hourly-cycling ensemble Kalman filter (EnKF) working at 2.5?km horizontal grid spacing is implemented over southern Ontario (Canada) to assimilate Meteorological Terminal Aviation Routine Weather Reports (METARs) in addition to the observations assimilated operationally at the Canadian Meteorological Centre. This high-resolution EnKF (HREnKF) system employs ensemble land analyses and perturbed roughness length to prevent an ensemble spread that is too small near the surface. The HREnKF then performs continuously for a four-day period, from which twelve-hour ensemble forecasts are launched every six hours. The impact on analyses and short-term forecasts of assimilating METAR data is given special attention.It is shown that using ensemble land surface analyses increases near-surface ensemble spreads for temperature and specific humidity. Perturbing roughness length enlarges the spread for surface wind. Given sufficient ensemble spread, the four-day case study shows that the near-surface model state is brought closer to surface observations during the cycling process. The impact of assimilating surface data can also be seen at higher levels by using aircraft reports for verification. The ensemble forecast verification suggests that METAR data assimilation improves ensemble forecasts of air temperature and dewpoint near the surface up to a lead time of six hours or even longer. However, only minor improvement is found in surface wind forecasts. 相似文献
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文章首先对GIS中5种常用的空间插值方法原理进行了详细阐述,采用交叉验证法来对比分析不同插值方法的精度。应用反距离权重法、克里金法、自然邻域法、样条函数法、趋势面法5种常用的空间插值方法对兖矿集团济三煤矿1308工作面开采沉陷预计数据进行插值,对比分析了各种插值方法的精度,并对插值的误差影响因素进行了实验分析。结果表明:5种常用的空间插值方法中,克里金插值和样条函数插值精度最高,自然邻域插值和反距离权重插值精度居中,趋势面插值精度最低;对插值结果造成影响的因素与沉陷预计点的数量和密度有关,沉陷预计点的密度越大、数量越多,空间插值结果的精度就越高。研究成果可为矿山开采沉陷预计插值方法的选取提供借鉴。 相似文献
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Microseismic events enhancement and detection in sensor arrays using autocorrelation‐based filtering 下载免费PDF全文
Entao Liu Lijun Zhu Anupama Govinda Raj James H. McClellan Abdullatif Al‐Shuhail SanLinn I. Kaka Naveed Iqbal 《Geophysical Prospecting》2017,65(6):1496-1509
Passive microseismic data are commonly buried in noise, which presents a significant challenge for signal detection and recovery. For recordings from a surface sensor array where each trace contains a time‐delayed arrival from the event, we propose an autocorrelation‐based stacking method that designs a denoising filter from all the traces, as well as a multi‐channel detection scheme. This approach circumvents the issue of time aligning the traces prior to stacking because every trace's autocorrelation is centred at zero in the lag domain. The effect of white noise is concentrated near zero lag; thus, the filter design requires a predictable adjustment of the zero‐lag value. Truncation of the autocorrelation is employed to smooth the impulse response of the denoising filter. In order to extend the applicability of the algorithm, we also propose a noise prewhitening scheme that addresses cases with coloured noise. The simplicity and robustness of this method are validated with synthetic and real seismic traces. 相似文献
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对湟源地震台周边各种因素所致的地震记录干扰情况进行了分析,认为干扰源主要有采石场爆破、灌溉水渠、小河、车辆干扰及各种天气及固有干扰等。根据这些干扰源的频谱特性,采用MATLAB语言,设计、编制了不同类型的滤波器,在震相识别上发挥了很大作用。 相似文献
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????????????д?????????????????????????ζ?????????(UPF)?????????????????????????μ???????????ζ?????????(AR??UPF)???÷??????÷???????????????????????????????UKF??????????????????????Э????????????????????????????????·????????Ч?????????????????????????????? 相似文献