...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of hydrometeorology >The Impact of Rainfall Error Characterization on the Estimation of Soil Moisture Fields in a Land Data Assimilation System
【24h】

The Impact of Rainfall Error Characterization on the Estimation of Soil Moisture Fields in a Land Data Assimilation System

机译：土地数据同化系统中降雨误差特征对土壤水分场估算的影响

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This study presents a numerical experiment to assess the impact of satellite rainfall error structure on the efficiency of assimilating near-surface soil moisture observations. Specifically, the study contrasts a multidimensional satellite rainfall error model (SREM2D) to a simpler rainfall error model (CTRL) currently used to generate rainfall ensembles as part of the ensemble-based land data assimilation system developed at the NASA Global Modeling and Assimilation Office. The study is conducted in the Oklahoma region using rainfall data from a NOAA multisatellite global rainfall product [the Climate Prediction Center (CPC) morphing technique (CMORPH)] and the National Weather Service rain gauge–calibrated radar rainfall product [Weather Surveillance Radar-1988 Doppler (WSR-88D)] representing the "uncertain" and "reference" model rainfall forcing, respectively. Soilmoisture simulations using theCatchment land surfacemodel (CLSM), obtained by forcing the model with reference rainfall, are randomly perturbed to represent satellite retrieval uncertainty, and assimilated into CLSM as synthetic near-surface soil moisture observations. The assimilation estimates show improved performance metrics, exhibiting higher anomaly correlation coefficients (e.g., ;0.79 and ;0.90 in the SREM2Dnonassimilation and assimilation experiments for root zone soil moisture, respectively) and lower rootmean- square errors (e.g.,;0.034 m~3 m~(-3) and ;0.024 m~3 m~(-3) in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively). The more elaborate rainfall errormodel in the assimilation systemleads to slightly improved assimilation estimates. In particular, the relative enhancement due toSREM2D over CTRL is larger for root zone soil moisture and in wetter rainfall conditions.
机译：这项研究提出了一个数值实验，以评估卫星降雨误差结构对吸收近地表土壤水分观测值的效率的影响。具体而言，该研究将多维卫星降雨误差模型（SREM2D）与当前用于生成降雨集合的更简单的降雨误差模型（CTRL）进行了对比，作为美国国家航空航天局全球建模和同化办公室开发的基于集合的土地数据同化系统的一部分。这项研究是在俄克拉荷马州地区使用NOAA多卫星全球降雨产品[气候预测中心（CPC）变形技术（CMORPH）]和国家气象局雨量计校准的雷达降雨产品[Weather Surveillance Radar-1988]的降雨数据进行的。多普勒（WSR-88D）]分别代表“不确定”模型和“参考”模型降雨强迫。通过使用参考降雨强迫模型获得的流域土地表面模型（CLSM）进行土壤水分模拟，将其随机扰动来表示卫星反演的不确定性，并将其作为合成的近地表土壤水分观测值吸收到CLSM中。同化估计显示出改进的性能指标，表现出更高的异常相关系数（例如，在SREM2D非同化和同化实验中，根区土壤水分分别为; 0.79和; 0.90）和更低的均方根误差（例如; 0.034 m〜3 m根区土壤水分的SREM2D非同化和同化实验分别为〜（-3）和; 0.024 m〜3 m〜（-3））。同化系统中更精细的降雨误差模型导致同化估计值略有改善。特别是，对于根区土壤水分和潮湿的降雨条件，SREM2D相对于CTRL的相对增强更大。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号