选择合适的插值预测模型对揭示干旱区绿洲地下水与表层土壤特征空间变化特征具有重要意义。根据克里雅绿洲实测地下水(埋深、电导率、水温)与表层土壤(含水率、电导率、土温)数据,系统评价不同空间插值方法(RBF、IDW、Ordinary Kriging...选择合适的插值预测模型对揭示干旱区绿洲地下水与表层土壤特征空间变化特征具有重要意义。根据克里雅绿洲实测地下水(埋深、电导率、水温)与表层土壤(含水率、电导率、土温)数据,系统评价不同空间插值方法(RBF、IDW、Ordinary Kriging)对不同特征预测精度的影响。结果表明:克里雅绿洲区域地下水埋深主要在3 m以下,电导率在5 m S·cm-1以下,温度在15℃以下;表层土壤含水量主要在0.5以下,电导率在2.5 m S·cm-1以下,温度在13℃以下。地下水埋深采用RBF插值的精度较高,电导率采用IDW的精度较高,水温采用RBF的精度较高;表层土壤含水率采用Kriging插值的精度较高,电导率采用RBF的精度较高,土温采用RBF的精度较高;除土壤含水率外,其余指标采用对数转化后插值精度较高。展开更多
The application of ensemble optimal interpolation in wave data assimilation in the South China Sea is presented. A sampling strategy for a stationary ensemble is first discussed. The stationary ensemble is constructed...The application of ensemble optimal interpolation in wave data assimilation in the South China Sea is presented. A sampling strategy for a stationary ensemble is first discussed. The stationary ensemble is constructed by sampling from 24-h-interval significant wave height differences of model outputs over a long period,and is validated with altimeter significant wave height data,indicating that the ensemble errors have nearly the same probability distribution function. The background error covariance fields expressed by the ensemble sampled are anisotropic. Updating the static samples by season,the seasonal characteristics of the correlation coefficient distribution are reflected. Hindcast experiments including assimilation and control runs are conducted for the summer of 2010 in the South China Sea. The effect of ensemble optimal interpolation assimilation on wave hindcasts is validated using different satellite altimeter data(Jason-1 and 2 and ENVISAT) and buoy observations. It is found that the ensemble-optimal-interpolation-based wave assimilation scheme for the South China Sea achieves improvements similar to those of the previous optimal-interpolation-based scheme,indicating that the practical application of this computationally cheap ensemble method is feasible.展开更多
文摘选择合适的插值预测模型对揭示干旱区绿洲地下水与表层土壤特征空间变化特征具有重要意义。根据克里雅绿洲实测地下水(埋深、电导率、水温)与表层土壤(含水率、电导率、土温)数据,系统评价不同空间插值方法(RBF、IDW、Ordinary Kriging)对不同特征预测精度的影响。结果表明:克里雅绿洲区域地下水埋深主要在3 m以下,电导率在5 m S·cm-1以下,温度在15℃以下;表层土壤含水量主要在0.5以下,电导率在2.5 m S·cm-1以下,温度在13℃以下。地下水埋深采用RBF插值的精度较高,电导率采用IDW的精度较高,水温采用RBF的精度较高;表层土壤含水率采用Kriging插值的精度较高,电导率采用RBF的精度较高,土温采用RBF的精度较高;除土壤含水率外,其余指标采用对数转化后插值精度较高。
基金Supported by the National Special Research Fund for Non-Profit Marine Sector(Nos.201005033,201105002)the National Natural Science Foundation of China(No.U1133001)+1 种基金the National High Technology Research and Development Program of China(863 Program)(No.2012AA091801)the NSFC-Shandong Joint Fund for Marine Science Research Centers(No.U1406401)
文摘The application of ensemble optimal interpolation in wave data assimilation in the South China Sea is presented. A sampling strategy for a stationary ensemble is first discussed. The stationary ensemble is constructed by sampling from 24-h-interval significant wave height differences of model outputs over a long period,and is validated with altimeter significant wave height data,indicating that the ensemble errors have nearly the same probability distribution function. The background error covariance fields expressed by the ensemble sampled are anisotropic. Updating the static samples by season,the seasonal characteristics of the correlation coefficient distribution are reflected. Hindcast experiments including assimilation and control runs are conducted for the summer of 2010 in the South China Sea. The effect of ensemble optimal interpolation assimilation on wave hindcasts is validated using different satellite altimeter data(Jason-1 and 2 and ENVISAT) and buoy observations. It is found that the ensemble-optimal-interpolation-based wave assimilation scheme for the South China Sea achieves improvements similar to those of the previous optimal-interpolation-based scheme,indicating that the practical application of this computationally cheap ensemble method is feasible.
