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首页> 外文期刊>Geosciences >Sequential Ensembles Tolerant to Synthetic Aperture Radar (SAR) Soil Moisture Retrieval Errors
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Sequential Ensembles Tolerant to Synthetic Aperture Radar (SAR) Soil Moisture Retrieval Errors

机译：合成孔径雷达（SAR）土壤水分反演误差的顺序集合体

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Due to complicated and undefined systematic errors in satellite observation, data assimilation integrating model states with satellite observations is more complicated than field measurements-based data assimilation at a local scale. In the case of Synthetic Aperture Radar (SAR) soil moisture, the systematic errors arising from uncertainties in roughness conditions are significant and unavoidable, but current satellite bias correction methods do not resolve the problems very well. Thus, apart from the bias correction process of satellite observation, it is important to assess the inherent capability of satellite data assimilation in such sub-optimal but more realistic observational error conditions. To this end, time-evolving sequential ensembles of the Ensemble Kalman Filter (EnKF) is compared with stationary ensemble of the Ensemble Optimal Interpolation (EnOI) scheme that does not evolve the ensembles over time. As the sensitivity analysis demonstrated that the surface roughness is more sensitive to the SAR retrievals than measurement errors, it is a scope of this study to monitor how data assimilation alters the effects of roughness on SAR soil moisture retrievals. In results, two data assimilation schemes all provided intermediate values between SAR overestimation, and model underestimation. However, under the same SAR observational error conditions, the sequential ensembles approached a calibrated model showing the lowest Root Mean Square Error (RMSE), while the stationary ensemble converged towards the SAR observations exhibiting the highest RMSE. As compared to stationary ensembles, sequential ensembles have a better tolerance to SAR retrieval errors. Such inherent nature of EnKF suggests an operational merit as a satellite data assimilation system, due to the limitation of bias correction methods currently available.
机译：由于卫星观测中复杂且不确定的系统误差，将模型状态与卫星观测相集成的数据同化比在本地规模上基于现场测量的数据同化更为复杂。在合成孔径雷达（SAR）土壤湿度的情况下，由粗糙度条件的不确定性引起的系统误差是很大且不可避免的，但是当前的卫星偏差校正方法不能很好地解决问题。因此，除了卫星观测的偏差校正过程外，重要的是在这种次优但更现实的观测误差条件下评估卫星数据同化的固有能力。为此，将集合卡尔曼滤波器（EnKF）随时间变化的顺序集合与集合最佳插值（EnOI）方案的平稳集合进行比较，该集合不会随时间演化。由于敏感性分析表明表面粗糙度对SAR取回比测量误差更敏感，因此监测数据同化如何改变粗糙度对SAR土壤水分取回的影响是本研究的范围。结果，两种数据同化方案都提供了SAR高估与模型低估之间的中间值。但是，在相同的SAR观测误差条件下，顺序合奏采用的校正模型显示出最低的均方根误差（RMSE），而固定的集合向显示最高RMSE的SAR观测收敛。与固定乐团相比，顺序乐团对SAR检索误差有更好的容忍度。由于目前可用的偏差校正方法的局限性，EnKF的这种固有性质暗示了其作为卫星数据同化系统的运行优势。

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