基于集合卡尔曼滤波的土壤水分同化试验

集合卡尔曼滤波是由大气数据同化发展的新的顺序同化算法,它利用蒙特卡罗方法计算背景场的误差协方差矩阵,克服了卡尔曼滤波需要线性化的模型算子和观测算子的难点。我们发展了一个基于集合卡尔曼滤波和简单生物圈模型(SiB2,Simple Biosphere Model)的单点陆面数据同化方案。利用1998年7月6日至8月9日青藏高原GAME-Tibet实验区MS3608站点的观测数据进行了同化试验。结果表明,利用集合卡尔曼滤波的数据同化方法可以明显地提高表层、根区、深层土壤水分的估算精度。     

Assessing the Performance of Separate Bias Kalman Filter in Correcting the Model Bias for Estimation of Soil Moisture Profiles

The performance of separate bias Kalman filter (SepKF) in correcting the model bias for the improvement of soil moisture profiles is evaluated by assimilating the near-surface soil moisture observations into a land surface model (LSM). First, an observing system simulation experiment (OSSE) is carried out, where the true soil moisture is known, two types of model bias (i.e., constant and sinusoidal) are specified, and the bias error covariance matrix is assumed to be proportional to the model forecast error covariance matrix with a ratio λ. Second, a real assimilation experiment is carried out with measurements at a site over Northwest China. In the OSSE, the soil moisture estimation with the SepKF is improved compared with ensemble Kalman filter (EnKF) without the bias filter, because SepKF can properly correct the model bias, especially in the situation with a large model bias. However, the performance of SepKF becomes slightly worse if the constant model bias increases or temporal variability of the sinusoidal model bias becomes large. It is suggested that the ratio λ should be increased (decreased) in order to improve the soil moisture estimation if temporal variability of the sinusoidal model bias becomes high (low). Finally, the assimilation experiment with real observations also shows that SepKF can further improve the estimation of soil moisture profiles compared with EnKF without the bias correction.     

集合卡尔曼滤波同化探空资料的数值试验

应用集合卡尔曼滤波（Ensemble Kalman Filter;EnKF）方法,同化了2005年7月一次暴雨过程的探空观测资料,并用非静力中尺度模式MM5进行数值模拟试验。结果表明：在理想模式的假设下,即假设真实模拟和所产生的集合用的是同一个模式并有相同的初始误差,EnKF方法同化的分析结果较好。如果不运用EnKF方法同化探空观测资料,则集合预报结果和不加扰动的单个数值预报结果都没有EnKF方法同化过的好。     

A ＂Dressed＂ Ensemble Kalman Filter Using the Hybrid Coordinate Ocean Model in the Pacific

The computational cost required by the Ensemble Kalman Filter (EnKF) is much larger than that of some simpler assimilation schemes, such as Optimal Interpolation (OI) or three-dimension variational assimilation (3DVAR). Ensemble optimal interpolation (EnOI), a crudely simplified implementation of EnKF, is sometimes used as a substitute in some oceanic applications and requires much less computational time than EnKF. In this paper, to compromise between computational cost and dynamic covariance, we use the idea of ``dressing' a small size dynamical ensemble with a larger number of static ensembles in order to form an approximate dynamic covariance. The term ``dressing' means that a dynamical ensemble seed from model runs is perturbed by adding the anomalies of some static ensembles. This dressing EnKF (DrEnKF for short) scheme is tested in assimilation of real altimetry data in the Pacific using the HYbrid Coordinate Ocean Model (HYCOM) over a four-year period. Ten dynamical ensemble seeds are each dressed by 10 static ensemble members selected from a 100-member static ensemble. Results are compared to two EnKF assimilation runs that use 10 and 100 dynamical ensemble members. Both temperature and salinity fields from the DrEnKF and the EnKF are compared to observations from Argo floats and an OI SST dataset. The results show that the DrEnKF and the 100-member EnKF yield similar root mean square errors (RMSE) at every model level. Error covariance matrices from the DrEnKF and the 100-member EnKF are also compared and show good agreement.     

不同模式误差方案在集合Kalman滤波土壤湿度同化中的比较试验

本文主要目的是探讨不同模式误差方案在土壤湿度同化中的性能。基于集合Kalman滤波同化方法和AVIM (Atmosphere-Vegetation Interaction Model) 陆面模式, 利用理想试验对膨胀因子方案 (Covariance Inflation, 简称CI)、 直接随机扰动方案 (Direct Random Disturbance, 简称DRD)、 误差源扰动方案 (Source Random Disturbance, 简称SRD) 等3种模式误差方案的同化效果进行了比较, 讨论了各方案在不同观测误差、 观测层数、 观测间隔情况下的同化性能。试验结果表明在观测误差估计完全准确的情况下, 3种方案都能获得较好的同化效果, 并且SRD方案相对于真值的均方根误差最小。当观测误差估计不准确时, SRD方案的同化效果仍能基本得以保持, 而CI和DRD方案则对观测误差估计更为敏感, 同化效果下降明显。当同化多层观测时, CI和DRD方案由于难以保持不同层观测之间的匹配关系, 同化结果反而变差, 而SRD方案能有效协调同化多层观测, 增加观测层后同化结果有了进一步的改善。当观测时间间隔较大时, CI和DRD方案的同化效果显著下降; 而SRD方案由于包含了一定的误差订正功能, 在观测稀疏时仍能保持较好的同化效果。     

Application of ATOVS Radiance-Bias Correction to Typhoon Track Prediction with Ensemble Kalman Filter Data Assimilation

In this paper, firstly, the bias between observed radiances from the Advanced TIROS-N Operational Vertical Sounder (ATOVS) and those simulated from a model first-guess are corrected. After bias correction, the observed minus calculated (O-B) radiances of most channels were reduced closer to zero, with peak values in each channel shifted towards zero, and the distribution of O-B closer to a Gaussian distribution than without bias correction. Secondly, ATOVS radiance data with and without bias correction are assimilated directly with an Ensemble Kalman Filter (EnKF) data assimilation system, which are then adopted as the initial fields in the forecast model T106L19 to simulate Typhoon Prapiroon (2006) during the period 2-4 August 2006. The prediction results show that the assimilation of ATOVS radiance data with bias correction has a significant and positive impact upon the prediction of the typhoon’s track and intensity, although the results are not perfect.     

基于集合卡尔曼滤波的土壤温湿度同化试验

以通用陆面模式CLM 3.0(Community Land Model 3.0)为模型算子,基于集合卡尔曼滤波(Ensemble Kalman Filter,En KF)发展了一个土壤温湿度同化系统,主要用于改进模式对土壤温湿度和地表水热通量的模拟精度,并考察集合样本数、同化频率及不同观测量的组合对同化效果的影响。该系统同化了FLUXNET两个站点(阿柔和Bondville)不同土壤深度、不同时间频率的土壤温度和湿度数据。通过对阿柔站不同集合样本数的设计,综合考虑计算成本和计算精度,最终将集合样本数设置为40。通过分析三种同化方案对同化频率的敏感性得出,同化土壤温度最为敏感,同时同化土壤温湿度次之,同化土壤湿度最不敏感。对于阿柔站点,同化系统对不同土壤深度温度和湿度的模拟精度均能提高90%,潜热通量的均方根误差由94.0 W·m~(-2)降为46.3 W·m~(-2),感热通量均方根误差由55.9 W·m~(-2)降为24.6 W·m~(-2)。Bondville站点浅层土壤温度的改进在30%左右,深层土壤温度改进达到60%,对土壤湿度的改进均在70%以上,潜热通量和感热通量的均方根误差分别从57.4 W·m~(-2)和54.4 W·m~(-2)降为51.0 W·m~(-2)和42.5 W·m~(-2)。试验结果表明,同化站点土壤温湿度数据对土壤水热状况及通量的模拟改进非常有效,同时也验证了同化土壤水分遥感产品的可行性和必要性。     

基于集合Kalman滤波数据同化的热带气旋路径集合预报研究

构建了一个基于集合Kalman滤波数据同化的热带气旋集合预报系统,通过积云参数化方案和边界层参数化方案的9个不同组合,采用MM5模式进行了不同时间的短时预报。对预报结果使用“镜像法”得到18个初始成员,为同化提供初始背景集合。将人造台风作为观测场,同化后的结果作为集合预报的初值,通过不同参数组合的MM5模式进行集合预报。对2003～2004年16个台风个例的分析表明,初始成员产生方法能够对热带气旋的要素场、中心强度和位置进行合理扰动。同化结果使台风强度得到加强,结构更接近实际。基于同化的集合路径预报结果要优于未同化的集合预报。使用“镜像法”增加集合成员提高了预报准确度,路径预报误差在48小时和72小时分别低于200 km和250 km。     

A Multimodel Ensemble-based Kalman Filter for the Retrieval of Soil Moisture Profiles

With the combination of three land surface models (LSMs) and the ensemble Kalman filter (EnKF), a multimodel EnKF is proposed in which the multimodel background superensemble error covariance matrix is estimated by two different algorithms: the Simple Model Average (SMA) and the Weighted Average Method (WAM). The two algorithms are tested and compared in terms of their abilities to retrieve the true soil moisture profile by respectively assimilating both synthetically-generated and actual near-surface soil moisture measurements. The results from the synthetic experiment show that the performances of the SMA and WAM algorithms were quite different. The SMA algorithm did not help to improve the estimates of soil moisture at the deep layers, although its performance was not the worst when compared with the results from the single-model EnKF. On the contrary, the results from the WAM algorithm were better than those from any single-model EnKF. The tested results from assimilating the field measurements show that the performance of the two multimodel EnKF algorithms was very stable compared with the single-model EnKF. Although comparisons could only be made at three shallow layers, on average, the performance of the WAM algorithm was still slightly better than that of the SMA algorithm. As a result, the WAM algorithm should be adopted to approximate the multimodel background superensemble error covariance and hence used to estimate soil moisture states at the relatively deep layers.     

不同下垫面水分与能量传输模式

首先从简单生物圈模式（Simple Biosphere Model） 的物理模型出发,对其控制方程进行了修改。土壤温度使用求解热传导方程得到,土壤湿度使用了水汽扩散方程、Darcy的水流方程同时求解得到,目的是使简单生物圈模式既能在有植被下垫面使用,又能扩展到考虑水汽运动的沙漠地区使用。为了检验修改后的模式能适用于计算不同下垫面的地气之间水分和能量交换,选择了草原、森林和沙漠三种类型,在4个不同的实验点得到的资料,进行了单点模式（OFF LINE）检验。结果表明,修改后的简单生物圈模式模式可使用于不同下垫面地气之间水分和能量交换,尤其是解决了沙漠地区水热传输的模拟问题。     

Ensemble Square Root Filter Assimilation of Near-Surface Soil Moisture and Reference-Level Observations into a Coupled Land Surface-Boundary Layer Model

The potential for using the ensemble square root filter data assimilation technique to estimate soil moisture profiles, surface heat fluxes, and the state of the planetary boundary layer （PBL） is explored. An observing system simulation experiment is designed to mimic the assimilation of near-surface soil moisture observations （θo ） and in-situ measurements of 2-m temperature （To ）, 2-m specific humidity （Qo ）, and 10-m horizontal winds [Vo =（Uo , Vo ）]. The background forecasts are generated by a one-dimensional coupled land surface-boundary layer model （CLS-BLM） with soil, surface-layer and PBL parameterization schemes similar to those used in the Weather Research and Forecasting （WRF） model. Soil moisture, surface heat fluxes, and the state of the PBL evolve on different characteristic timescales, so the minimum assimilation time intervals required for skillful estimates of each target component are different. Correct estimates of the soil moisture profile are obtained effectively when a 6-h update time interval is used, while skillful estimates of surface fluxes and the PBL state require more frequent updates. The CLS-BLM requires a shorter assimilation time interval to correctly estimate the soil moisture profile than previously indicated by experiments using an off-line land surface model （LSM）. Results from assimilating different subsets of observations show that θo makes a larger contribution to soil moisture estimates, while To , θo , and Vo are more important for estimates of surface heat fluxes and the PBL state. It is therefore necessary to combine these variables to accurately estimate the states of both the land surface and the PBL. Experimentation with different prescribed observational errors shows that the assimilation system is more sensitive to increases in observational errors than to reductions in observational errors.     

HBFNEnKF混合同化方法设计及检验

基于前后张驰逼近（Back and Forth Nudging,简称BFN）和集合卡尔曼滤波（EnKF）方法,构建了一种新的同化方法HBFNEnKF（Hybrid Back and Forth Nudging EnKF）混合同化方法,并将此同化系统分别与通道浅水模式（shallow water model）和全球浅水模式对接,检验了HBFNEnKF同化方法的有效性。同时,对比了集合均方根滤波（EnSRF）、HNEnKF (Hybrid Nudging EnKF)、HBFNEnKF三种方法在有误差模式中的同化效果。试验结果表明:HBFNEnKF同化方法保留了HNEnKF方法的同化连续性,解决了EnKF同化不连续不平滑的问题,同时还有着更快的收敛速度;当采用单变量分析试验时,HBFNEnKF方法的优势最为明显,表明HBFNEnKF能够较好地保持不同模式变量间的平衡。此外,增量场尺度分析结果表明:相比EnSRF,HBFNEnKF在大尺度范围有更好的同化效果,同时能够避免在中小尺度范围内出现大量的虚假增量。     

基于AMSR-E土壤湿度产品的LIS同化试验

由陆面信息系统 (Land Information System, 简称LIS) 通过NOAH陆面过程模型使用集合卡尔曼滤波开展AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System) 土壤湿度同化试验,得到2003年中国区域垂直深度为4层、水平空间分辨率为0.25°×0.25°的土壤湿度试验数据。使用农业气象观测站土壤相对湿度和国家生态系统野外科学观测研究站土壤体积含水量对试验结果进行检验,结果表明:同化过程整体上提高了陆面模型的模拟精度,草地生态系统模拟精度高于作物和森林生态系统;有效的同化过程依赖于AMSR-E土壤湿度的准确性;模拟出的土壤湿度空间分布特征与实际相符。同化试验得到的时空相对连续且精度相对准确的土壤湿度数据是气候变化和干旱监测的重要数据基础。     

ETKF初值扰动方法中真实观测及扰动调节因子研究

目前国家气象中心业务GRAPES区域集合预报系统中集合变换卡尔曼滤波（ETKF）方法采用的是模拟观测信息，为进一步完善ETKF方法，拟对ETKF初值扰动通过引入真实探空观测资料，使扰动场能够代表真实观测的不确定信息，改善区域集合预报技巧。真实观测资料的引入会使得每日的观测数目和分布发生变化，这对ETKF方法而言可能会引起扰动振幅的不稳定，因此在引入真实观测资料的基础上设计了新的扰动振幅调节因子，通过格点空间中离散度和均方根误差关系来对初值扰动振幅进行自适应调整。从初值扰动结构、概率预报技巧以及降水预报效果等方面对比分析了基于模拟观测、真实观测以及真实观测结合新型调节因子的ETKF方案的差异，结果表明:真实探空资料能够有效应用于GRAPES区域集合预报系统中，真实观测资料与模拟观测资料相比较为稀疏，可以获得更大量级的初值扰动振幅；真实观测资料有助于提高区域集合的离散度，但对集合预报准确度以及概率预报结果的提高有限，对于降水预报效果提高也有限；新型的扰动振幅调节因子可以有效获得稳定的初值扰动振幅，并保持ETKF扰动结构，真实观测资料与扰动振幅自适应调节因子相结合，可以有效提高区域集合的概率预报结果，并有效提高降水预报效果。     

Evaluating the Capabilities of Soil Enthalpy,Soil Moisture and Soil Temperature in Predicting Seasonal Precipitation

Soil enthalpy(H) contains the combined effects of both soil moisture(w) and soil temperature(T) in the land surface hydrothermal process. In this study, the sensitivities of H to w and T are investigated using the multi-linear regression method.Results indicate that T generally makes positive contributions to H, while w exhibits different(positive or negative) impacts due to soil ice effects. For example, w negatively contributes to H if soil contains more ice; however, after soil ice melts,w exerts positive contributions. In particular, due to lower w interannual variabilities in the deep soil layer(i.e., the fifth layer), H is more sensitive to T than to w. Moreover, to compare the potential capabilities of H, w and T in precipitation(P) prediction, the Huanghe–Huaihe Basin(HHB) and Southeast China(SEC), with similar sensitivities of H to w and T,are selected. Analyses show that, despite similar spatial distributions of H–P and T –P correlation coefficients, the former values are always higher than the latter ones. Furthermore, H provides the most effective signals for P prediction over HHB and SEC, i.e., a significant leading correlation between May H and early summer(June) P. In summary, H, which integrates the effects of T and w as an independent variable, has greater capabilities in monitoring land surface heating and improving seasonal P prediction relative to individual land surface factors(e.g., T and w).     

基于Radarsat-2 SAR数据反演定西裸露地表土壤水分

利用Radarsat-2 SAR数据和定西地区野外土钻法及WET仪器观测的土壤水分数据,分析了同极化后向散射系数与不同土层深度土壤水分之间的关系,采用交叉极化(VV/VH)组合模型反演土壤水分并进行对比验证。结果表明:水平、垂直同极化后向散射系数均与10~20 cm土壤含水量相关性最好,相关系数R均为0.74;受地表粗糙度和土壤质地等影响,同极化后向散射系数与0~10 cm土壤水分相关性均较低。交叉极化组合模型的反演值与10~20 cm实测土壤水分相关性较高,R值达0.75,而与0~10 cm和20~30 cm实测值的相关性较低(R值分别为0.47和0.52),但均通过α=0.05的显著性检验;WET仪器实测0~6 cm土壤水分经校正后与反演值的相关系数为0.46(通过α=0.01的显著性检验),校正后的结果有效提高了WET仪器测量精度。交叉极化组合模型可用于裸露地表土壤水分的反演,更适用于提取10~20 cm土壤含水量信息。     

基于大气降水的华北地区土壤湿度预测模型

基于引入随机变量的机理性模型方法,利用华北地区2000—2008年气象台站观测数据,以大气降水为随机变量,并考虑其延迟效应,利用回归方法建立了预测时效为1旬的土壤相对湿度预测模型。利用预测率和干旱等级预报精度两个评价指标,结合2009年土壤湿度实际观测数据,验证了预测模型预报率均在90%以上,绝大部分站点的干旱等级预报精度均在70%以上,得出该预测模型在华北地区应用的合理性,从而建立了一套客观、动态的土壤湿度预测方法,有利于及时掌握农田旱情程度和分布,主动采取防旱、抗旱应对措施。     

基于FDR的土壤水分探测系统与应用

根据中国气象局对农业气象观测的要求,采用频域反射技术(Frequency Domain Reflectometry,FDR)设计开发了一套土壤水分探测系统。系统共包括传感器测量、数据采集和远程数据管理等几个部分。FDR测量原理建立在传输线理论和谐振电路的基础上。数据采集部分充分考虑了信号的抗干扰能力以及纠错措施。系统采用无线通信模式将测量数据传送到中心数据库,并使用互联网将数据发送到各个用户。运行结果表明该系统能够连续稳定地测量,经过订正之后的数据与烘干法测量结果接近,误差小于2%,满足农业气象观测的需要。     

基于概念模型的麦田土壤水分动态模拟研究

农田土壤水分模拟是农业用水管理的重要依据。以根区土体水量平衡方程为依据,考虑根区下界面水分通量,构建了农田土壤水分变化模拟模型,该模型由作物蒸散量模型、根区下界面水分通量模型以及水量平衡方程等组成。采用山西水利职业技术学院试验基地2007年和2008年2个年度冬小麦试验资料,确定了模型参数。结果表明,土壤储水量模拟计算值与实测值有较好的一致性,其相关系数达到0.9555;F检验结果达到极显著水平,所建立的麦田土壤水分动态模型可用于作物蒸散量、根区下界面水分通量和田间土壤水分的模拟计算;计算精度平均达到3%～11%。表明该模型可较好地描述农田士壤水分转化过程。     

基于吉林省观测土壤水分的WOFOST模型模拟研究

利用吉林省白城站试验数据进行模型参数调整,通过独立的观测资料对生育期、叶面积指数、地上部分各器官生物量进行模拟验证与评价。以白城站和榆树站代表吉林省西部玉米种植区和中部黄金玉米带参数,利用农业气象观测站发育期资料、气象资料和经过质量控制后的逐日土壤水分自动站观测数据进行模拟。为了提高WOFOST模型模拟精度,将由模型通过降水量计算的土壤体积含水量替换为实测土壤水分计算的体积含水量,采用替换后的土壤体积含水量参与模型下一步运算,以此来模拟2001—2016年春玉米穗生物量变化状况,构建玉米土壤体积含水量改善率(PD)指标,来表征降水驱动和土壤水分驱动对作物模型模拟结果的影响。结果表明:(1)模型对白城站春玉米生育期、叶面积、地上部分总生物量和叶生物量较准确,而穗生物量模拟效果一般。(2)从代表站白城来看,穗生物量模拟值与降水量存在明显正相关,降水偏少的年份土壤模拟效果明显优于降水驱动。(3)从区域来看,以盐碱土为主的地区或降水量偏少的年型下土壤水分驱动效果优于降水驱动;在以黑土为主的区域或降水偏多的年型下,两者模拟效果基本接近。(4)总体来说,利用观测土壤水分替换降水量参与模型能够显著提高模型模拟精度。