评估集合卡曼滤波反演土壤湿度廓线的性能

doi:10.11867/j.issn.1001-8166.2010.04.0400

地球科学进展 ›› 2010, Vol. 25 ›› Issue (4): 400 -407. doi: 10.11867/j.issn.1001-8166.2010.04.0400

评估集合卡曼滤波反演土壤湿度廓线的性能

苟浩锋 ^1,2,刘彦华 ¹,张述文 ^1*,李得勤 ¹

1.兰州大学大气科学学院，甘肃省干旱气候变化与减灾重点实验室, 甘肃兰州 730000;2.兰州市气象局，甘肃兰州 730020

收稿日期:2009-10-29 修回日期:2010-01-15 出版日期:2010-04-10
通讯作者: 张述文（1966-)，男，河南固始人，教授，主要从事陆面过程、数据同化算法研究. E-mail:zhangsw@lzu.edu.cn
基金资助:
国家自然科学基金项目“同化多源观测估算土壤湿度廓线”(编号：40775065);公益性行业（气象）科研专项“WRF-EnSRF四维同化业务预报系统关键技术研究”（编号：GYHY200806029）共同资助. 

Assessing the Performance of the Ensemble Kalman Filter for Soil Moisture Profile Retrieval

Gou Haofeng ^1,2,Liu Yanhua ¹, Zhang Shuwen ¹,Li Deqin ¹

1. Key Laboratory of Arid Climate Change and Reducing Disaster of Gansu Province, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China;2.Lanzhou Meteorological Bureau, Lanzhou 730020, China

Received:2009-10-29 Revised:2010-01-15 Online:2010-04-10 Published:2010-04-10
Contact: shuwen zhang E-mail:zhangsw@lzu.edu.cn
Supported by:
苟浩锋（1979），男，甘肃庆阳人，硕士研究生，主要从事陆面过程同化算法应用的研究.E-mail:gouhf07@lzu.cn

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集合卡曼滤波由于易于使用而被广泛地应用到陆面数据同化研究中，它是建立在模型为线性、误差为正态分布的假设上，而实际土壤湿度方程是高度非线性的，并且当土壤过干或过湿时会发生样本偏斜。为了全面评估它在同化表层土壤湿度观测来反演土壤湿度廓线的性能，特引入不需要上述假设的采样重要性重采样粒子滤波，比较非线性和偏斜性对同化算法的影响。结果显示：不管是小样本还是大样本，集合卡曼滤波都能快速、准确地逼近样本均值，而粒子滤波只有在大样本时才能缓慢地趋近|此外，集合卡曼滤波的粒子边缘概率密度及其偏度和峰度与粒子滤波完全不同，前者粒子虽不完全满足正态分布，但始终为单峰状态，而后者粒子随同化推进经历了单峰到双峰再到单峰的变化。 

关键词: 集合卡曼滤波, 粒子滤波, 土壤湿度, Richards方程

The ensemble Kalman filter is an easy to use, flexible, and efficient data assimilation algorithm widely used in Land Surface Data Assimilation System. It bases on the normality approximation of model error and observational error as well as the linearity assumption of the soil moisture errors between the near-surface observation and other deep layers. However, the soil moisture equation is highly nonlinear and also soil moisture can be highly skewed toward the wet or dry ends. To evaluate the effects of these approximations and the performance of the ensemble kalman filter (EnKF) in estimating soil moisture profile based on the near-surface soil moisture measurements, the results from the EnKF are compared with those obtained from a sequential importance resampling (SIR) particle filter that is one of nonlinear filters . The comparative results show: the EnKF can quickly and accurately obtain the exact soil moisture profile regardless of the small number of ensemble members used (40) or the large number of ensemble members used (800); however, the SIR needs the large number of replicates required to accurately represent the variable conditional probability densities. The near-surface soil moisture forecast densities, the skewness and kurtosis and obtained from the EnKF are completely different from those from the SIR filter; the densities from the EnKF is only one modal during the total assimilation time window but those from the SIR experiences from one mode to two modes and again to one mode process.

Key words: Ensemble Kalman filter, Sampling importance resampling, Soil moisture, Richards equation

中图分类号:

P461.4

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