Assimilating Surface Observations in a Four-Dimensional Variational Doppler Radar Data Assimilation System to Improve the Analysis and Forecast of a Squall Line Case

This paper examines how assimilating surface observations can improve the analysis and forecast ability of a four- dimensional Variational Doppler Radar Analysis System （VDRAS）. Observed surface temperature and winds are assimilated together with radar radial velocity and reflectivity into a convection-permitting model using the VDRAS four-dimensional variational （4DVAR） data assimilation system. A squall-line case observed during a field campaign is selected to investigate the performance of the technique. A single observation experiment shows that assimilating surface observations can influence the analyzed fields in both the horizontal and vertical directions. The surface-based cold pool, divergence and gust front of the squall line are all strengthened through the assimilation of the single surface observation. Three experiments--assimilating radar data only, assimilating radar data with surface data blended in a mesoscale background, and assimilating both radar and surface observations with a 4DVAR cost function--are conducted to examine the impact of the surface data assimilation. Independent surface and wind profiler observations are used for verification. The result shows that the analysis and forecast are improved when surface observations are assimilated in addition to radar observations. It is also shown that the additional surface data can help improve the analysis and forecast at low levels. Surface and low-level features of the squall line-- including the surface warm inflow, cold pool, gust front, and low-level wind--are much closer to the observations after assimilating the surface data in VDRAS.

Improvement of the Surface Pressure Operator in GRAPES and Its Application in Precipitation Forecasting in South China

In this study we investigated the problems involved in assimilating surface pressure in the current global and regional assimilation and prediction system, GRAPES. A new scheme of assimilating surface pressure was proposed, including a new interpolation scheme and a refreshed background covariance. The new scheme takes account of the differences between station elevation and model topography, and it especially deals with stations located at elevations below that of the first model level. Contrast experiments were conducted using both the original and the new assimilation schemes. The influence of the new interpolation scheme and the updated background covariance were investigated. Our results show that the new interpolation scheme utilized more observations and improved the quality of the mass analysis. The background covariance was refreshed using statistics resulting from the technique proposed by Parrish and Derber in 1992. Experiments show that the updated vertical covariance may have a positive influence on the analysis at higher levels of the atmosphere when assimilating surface pressure. This influence may be more significant if the quality of the background field at high levels is poor. A series of assimilation experiments were performed to test the validity of the new scheme. The corresponding simulation experiments were conducted using the analysis of both schemes as initial conditions. The results indicated that the new scheme leads to better forecasting of sea level pressure and precipitation in South China, especially the forecast of moderate and heavv rain.

Assimilating operational SST and sea ice analysis data into an operational circulation model for the coastal seas of China

The prediction of sea surface temperature （SST） is an essential task for an operational ocean circulation model. A sea surface heat flux, an initial temperature field, and boundary conditions directly affect the accuracy of a SST simulation. Here two quick and convenient data assimilation methods are employed to improve the SST simulation in the domain of the Bohai Sea, the Yellow Sea and the East China Sea （BYECS）. One is based on a surface net heat flux correction, named as Qcorrection （QC）, which nudges the flux correction to the model equation; the other is ensemble optimal interpolation （EnOI）, which optimizes the model initial field. Based on such two methods, the SST data obtained from the operational SST and sea ice analysis （OSTIA） system are assimilated into an operational circulation model for the coastal seas of China. The results of the simulated SST based on four experiments, in 2011, have been analyzed. By comparing with the OSTIA SST, the domain averaged root mean square error （RMSE） of the four experiments is 1.74, 1.16, 1.30 and 0.91~C, respectively; the improvements of assimilation experiments Exps 2, 3 and 4 are about 33.3%, 25.3%, and 47.7%, respectively. Although both two methods are effective in assimilating the SST, the EnOI shows more advantages than the QC, and the best result is achieved when the two methods are combined. Comparing with the observational data from coastal buoy stations, show that assimilating the high-resolution satellite SST products can effectively improve the SST prediction skill in coastal regions.

Satellite Monitoring of the Surface Water and Energy Budget in the Central Tibetan Plateau

The water and energy cycle in the Tibetan Plateau is an important component of Monsoon Asia and the global energy and water cycle. Using data at a CEOP （Coordinated Enhanced Observing Period）-Tibet site, this study presents a first-order evaluation on the skill of weather forecasting from GCMs and satellites in producing precipitation and radiation estimates. The satellite data, together with the satellite leaf area index, are then integrated into a land data assimilation system （LDAS-UT） to estimate the soil moisture and surface energy budget on the Plateau. The system directly assimilates the satellite microwave brightness temperature, which is strongly affected by soil moisture but not by cloud layers, into a simple biosphere model. A major feature of this system is a dual-pass assimilation technique, which can auto-calibrate model parameters in one pass and estimate the soil moisture and energy budget in the other pass. The system outputs, including soil moisture, surface temperature, surface energy partition, and the Bowen ratio, are compared with observations, land surface models, the Global Land Data Assimilation System, and four general circulation models. The results show that this satellite data-based system has a high potential for a reliable estimation of the regional surface energy budget on the Plateau.

An evaluation of sea surface height assimilation using along-track and gridded products based on the Regional Ocean Modeling System(ROMS) and the four-dimensional variational data assimilation

Remote sensing products are significant in the data assimilation of an ocean model. Considering the resolution and space coverage of different remote sensing data, two types of sea surface height（SSH） product are employed in the assimilation, including the gridded products from AVISO and the original along-track observations used in the generation. To explore their impact on the assimilation results, an experiment focus on the South China Sea（SCS） is conducted based on the Regional Ocean Modeling System（ROMS） and the four-dimensional variational data assimilation（4 DVAR） technology. The comparison with EN4 data set and Argo profile indicates that, the along-track SSH assimilation result presents to be more accurate than the gridded SSH assimilation, because some noises may have been introduced in the merging process. Moreover, the mesoscale eddy detection capability of the assimilation results is analyzed by a vector geometry–based algorithm. It is verified that, the assimilation of the gridded SSH shows superiority in describing the eddy＇s characteristics, since the complete structure of the ocean surface has been reconstructed by the original data merging.

Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter

The Localized Weighted Ensemble Kalman Filter(LWEnKF)is a new nonlinear/non-Gaussian data assimilation(DA)method that can effectively alleviate the filter degradation problem faced by particle filtering,and it has great prospects for applications in geophysical models.In terms of operational applications,along-track sea surface height(AT-SSH),swath sea surface temperature(S-SST)and in-situ temperature and salinity(T/S)profiles are assimilated using the LWEnKF in the northern South China Sea(SCS).To adapt to the vertical S-coordinates of the Regional Ocean Modelling System(ROMS),a vertical localization radius function is designed for T/S profiles assimilation using the LWEnKF.The results show that the LWEnKF outperforms the local particle filter(LPF)due to the introduction of the Ensemble Kalman Filter(EnKF)as a proposal density;the RMSEs of SSH and SST from the LWEnKF are comparable to the EnKF,but the RMSEs of T/S profiles reduce significantly by approximately 55%for the T profile and 35%for the S profile(relative to the EnKF).As a result,the LWEnKF makes more reasonable predictions of the internal ocean temperature field.In addition,the three-dimensional structures of nonlinear mesoscale eddies are better characterized when using the LWEnKF.

Three-dimensional variational data assimilation of WindSat ocean surface winds for the genesis and forecasting of tropical storm Henri

With available high-resolution ocean surface wind vectors retrieved from the U.S.Naval Research Laboratory's WindSat on Coriolis,the impact of these data on genesis and forecasting of tropical storm Henri is examined using the non-hydrostatic,fifth-generation mesoscale model(MM5) of Pennsylvania State University-National Center for Atmospheric Research plus its newly released three-dimensional variational data assimilation(3DVAR) system.It is shown that the assimilation of the WindSat-retrieved ocean surface wind vectors in the 3DVAR system improves the model initialization fields by introducing a stronger vortex in the lower troposphere.As a result,the model reproduces the storm formation and track reasonably close to the observations.Compared to the experiment without the WindSat surface winds,the WindSat assimilation reduced an error between the model simulated track and observations of more than 80 km and also improved the storm intensity by nearly 2 hPa.It suggests that these data could provide early detection and prediction of tropical storms or hurricanes.

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.

Observation and Numerical Simulations with Radar and Surface Data Assimilation for Heavy Rainfall over Central Korea

This study investigated the impact of multiple-Doppler radar data and surface data assimilation on forecasts of heavy rainfall over the central Korean Peninsula;the Weather Research and Forecasting(WRF) model and its three-dimensional variational data assimilation system(3DVAR) were used for this purpose. During data assimilation,the WRF 3DVAR cycling mode with incremental analysis updates(IAU) was used. A maximum rainfall of 335.0 mm occurred during a 12-h period from 2100 UTC 11 July 2006 to 0900 UTC 12 July 2006.Doppler radar data showed that the heavy rainfall was due to the back-building formation of mesoscale convective systems(MCSs).New convective cells were continuously formed in the upstream region,which was characterized by a strong southwesterly low-level jet(LLJ).The LLJ also facilitated strong convergence due to horizontal wind shear,which resulted in maintenance of the storms.The assimilation of both multiple-Doppler radar and surface data improved the accuracy of precipitation forecasts and had a more positive impact on quantitative forecasting(QPF) than the assimilation of either radar data or surface data only.The back-building characteristic was successfully forecasted when the multiple-Doppler radar data and surface data were assimilated.In data assimilation experiments,the radar data helped forecast the development of convective storms responsible for heavy rainfall,and the surface data contributed to the occurrence of intensified low-level winds.The surface data played a significant role in enhancing the thermal gradient and modulating the planetary boundary layer of the model,which resulted in favorable conditions for convection.

Assimilation of Sea Surface Temperature in a Global Hybrid Coordinate Ocean Model

The Hybrid Coordinate Ocean Model(HYCOM) uses different vertical coordinate choices in different regions. In HYCOM, the prognostic variables include not only the seawater temperature, salinity and current fields, but also the layer thickness. All prognostic variables are usually adjusted in the assimilation when multivariate data assimilation methods are used to assimilate sea surface temperature(SST). This paper investigates the effects of SST assimilation in a global HYCOM model using the Ensemble Optimal Interpolation multivariate assimilation method. Three assimilation experiments are conducted from 2006–08. In the first experiment, all model variables are adjusted during the assimilation process. In the other two experiments, the temperature alone is adjusted in the entire water column and in the mixed layer. For comparison, a control experiment without assimilation is also conducted. The three assimilation experiments yield notable SST improvements over the results of the control experiment. Additionally, the experiments in which all variables are adjusted and the temperature alone in all model layers is adjusted, produce significant negative effects on the subsurface temperature. Also, they yield negative effects on the subsurface salinity because it is associated with temperature and layer thickness. The experiment adjusting the temperature alone in the mixed layer yields positive effects and outperforms the other experiments. The heat content in the upper 300 m and 300–700 m layers further suggests that it yields the best performance among the experiments.

PREDICTION OF ANNUAL FREQUENCY OF AFFECTING TROPICAL CYCLONE USING THE PRODUCTS OF A HYBRID COUPLED AIR-SEA MODEL

Better correlation exists between the activity of tropical cyclones affecting East China and Shanghai and the concurrent signals of SSTA in tropical Pacific. In an attempt to justify this statistic finding, a four-dimensional variational data assimilation system is established to optimize the initial fields of a hybrid air-sea coupled model. The prediction skill of tropical SSTA is improved. Long-term statistical models for predicting annual TC frequency affecting East China area and Shanghai city are developed based on 37-year products of this model and the forecast trials have achieved satisfactory results in 1998 and 1999.

ASSIMILATION OF OBSERVED SURFACE WIND WITH GRAPES

With the advances of numerical weather simulation and reduced data assimilation updating cycle, surface observation data assimilation becomes more and more important in data assimilation systems It is widely accepted that a better data assimilation system should contain the restriction of thermod^amic processes in the surface layer. Therefore, in this paper, a new surface wind observation operator is utilized in Global and Regional Assimilation PrEdiction System_3D-Variance （GRAPES_3D-Var）, with the restriction of thermodynamic process in the planetary boundary layer （PBL）. In order to research the ability of this new surface wind observation operator in assimilation and forecasting, a series of experiments are operated by using the GRAPES model. The main results indicate that this new method of surface wind observation operator has positive impact on the forecast with the GRAPES model.

基于多源数据融合技术的绿洲灌区土壤水分反演

土壤水分是联系农业、生态和水文领域的重要环境变量,而卫星遥感是监测地表土壤水分的重要手段之一。针对微波遥感空间分辨率不足和光学遥感受云雨天气影响的问题,基于Landsat 8和MODIS光学影像、SMAP微波以及CLDAS再分析等多源数据,联合增强型自适应时空融合算法和随机森林模型对土壤水分进行定量反演,获得了绿洲灌区高时空分辨率田块尺度(30 m)土壤水分。结果表明:通过ESTARFM时空融合算法可有效获得日尺度30 m分辨率归一化植被指数(NDVI),融合后的NDVI与原始NDVI空间纹理特征一致,两者的相关系数(R)在0.85以上,均方根误差为0.05~0.08,融合效果较好。基于地表温度、NDVI、增强植被指数、叶面积指数、再分析土壤水分产品多特征参数组合下的随机森林模型反演效果最优,获得的高时空分辨率田块尺度土壤水分能够反映其时空变化,相关系数和均方根误差分别达到0.82和0.037 cm^(3)/cm^(3)。该方法可为灌区灌溉面积识别、旱情监测等提供技术支撑。

联合同化闪电资料和地面常规观测资料对强对流天气预报的影响

以2017年6月16日发生在广东省的一次中尺度对流过程为例,利用WRF模式对中尺度对流系统进行模拟,分析了联合同化闪电和地面常规观测资料较单独同化其中一种资料的改进作用。闪电资料通过WRF-FDDA系统以15 min作为一个闪电累积窗口被连续同化入模式中,地面常规观测资料通过WRFDA-3DVAR系统以1 h间隔循环同化入模式中。结果显示,相比于只同化地面常规观测资料,联合同化试验中闪电资料的引入提高了背景场中上升气流、冷池和阵风锋的准确度;相比于只同化闪电观测资料,地面常规观测资料的引入减小了更大范围的温度、水汽、风场的背景场误差,抑制了部分地区的虚假对流,即两种资料的联合同化整体上提高了对流系统的模拟准确度。预报技巧评分结果显示,联合同化对同化期和预报期的评分也有一定程度的提高。

区域蒸散发遥感模型研究的进展

本文主要介绍了目前常用的几类遥感蒸散发模型,包括基于能量平衡的单层与双层蒸散发模型、彭曼模型以及基于植被指数和地表温度的经验模型等,并对这些模型各自的优缺点分别进行了评述。由于地表水热过程的复杂性,在区域蒸散发模拟中仍存在很多亟待解决的问题,如地表特征参数和地表温度反演中的不确定性,遥感反演在不同时空尺度上扩展的困难以及由此带来的区域验证问题等。因此要提高区域蒸散发反演的精度,必须进一步提高遥感辐射传输模型的精度,降低遥感输入数据和反演参数中存在的不确定性,同时通过不同尺度的田间观测试验加强对地表水热传输机理等方面的研究。此外,遥感和陆面过程模型的结合能有效降低模型和数据中的不确定性,陆面过程同化模型能提供更为可靠的连续的地表水热过程模拟,是一个很有前途的发展方向。

南海TOPEX海面高度资料的混合同化试验

应用普林斯顿海洋模式(POM)，对冬季南海由TOPEX/Poseidon得到的卫星遥感海面高度资料进行混合(blending)同化试验。混合同化的权重系数由以下两者决定：南海POM模式对冬季风强迫产生响应的海面高度场之标准方差；对应期间TOPEX海面高度资料的标准方差。同化结果表明，混合同化方法不失为一种简单而又有效的同化方案。同化得到的南海环流结构与未同化的模式响应场比较可以发现：海面高度资料的同化试验能够有效地修正南海环流的若干大尺度特征，特别是对冬季黑潮入侵南海东北部的动力过程，同化结果有了更准确的描述。同时，另一个重要的修正表现在：同化试验中瞬变的中等尺度涡旋得到加强，体现了南海海洋天气尺度涡被资料同化所“唤醒”(trigger)。这种“唤醒”不仅明显地反映在表层环流场中，对南海次表层动力、热盐结构也有一定的影响。

蒸散发遥感估算方法的研究进展

传统的估算蒸散发的方法虽然能够获取相对准确的均匀下垫面的蒸散发,但大多局限于点或田间尺度,难以反映蒸散发的空间异质性.遥感作为一种高效的空间信息获取和处理手段,被认为是估算陆面蒸散发的有效途径.近30多年来,随着技术的进步,发展了从简单的经验公式到结合陆面过程模型的陆面数据同化系统等基于遥感数据估算蒸散发的方法.本文总结了遥感估算地表蒸散发各种方法的输入数据、假设、原理、优缺点,并概括了遥感估算陆面蒸散发存在的主要问题和发展趋势.

稀土对杉木、杨树幼林生长的影响

对杉木 [Cunninghamia L anceolata (Lamb) Hook]、杨树 (Populuscv.‘Lux’)施用稀土后幼树的根系吸收等指标进行的观测结果表明 ,用不同浓度的稀土处理苗木 ,其单株幼树根系的总吸收面积、活跃吸收面积差异显著 ,根系的比表面差异不显著。杨树以 50 0 mg/kg的稀土溶液浸根时根系的吸收指标最高 ,杉木以 80 0 mg/kg浓度叶面喷施最高。经不同浓度稀土处理后 ,与对照相比 ,杉木、杨树在同时期所测得的高生长量、地径生长量。

适用于云南地区的地面资料同化方案设计及个例分析

利用2000—2007年5～10月云南及周边地区12个探空站08:00和20:00观测资料,分6种不同地形高度类型分析了边界层内基本气象要素间关系,根据分析结果设计了云南地面资料同化方案,并选取两个暴雨个例进行模拟分析。结果表明:探空资料的贡献在引入地面资料后有所调整,在Rug-giero方案中,地面资料的引入在模拟区域初始场中东部地区有明显的升温和增湿,对强降水的模拟改善作用较小;而在云南方案中东部地区的温度和湿度明显降低,叠加的地面测站与模式地形高度差异相关且大致相同,模拟的降水强度和范围在实况降水中心附近明显增加,而在降水强度不明显的区域变化不大,从而使得模拟效果得到了较好的改善,表明云南方案对云南地区强降水的模拟是适用的。

集合卡尔曼滤波数据同化方法改进土壤水分模拟效果

陆面过程模型是连续模拟土壤水分的有效工具,然而输入数据及模型结构本身的不确定性会导致模拟误差在模型运行过程中不断积累。数据同化技术可以考虑模型不确定性,实时修正模型状态变量,进而提高土壤水分的模拟精度。本研究构建集合卡尔曼滤波（En KF,ensemble Kalman filter）数据同化方法,将其集成到水文强化陆面过程模型HELP（hydrologically-enhanced land process）中,对模型中土壤水分及表面温度等状态变量进行优化。模型选取山东位山生态水文观测站2006年的数据进行验证,采用未经同化的模型率定结果作为基准值。结果表明,数据同化后表层、根层、深层土壤水分模拟结果相比基准值均有提高,土壤含水量均方根误差减小30%-50%,证明采用数据同化方法能够有效提高土壤水分的模拟结果。