Assimilation of the FY-4A AGRI Clear-Sky Radiance Data in a Regional Numerical Model and Its Impact on the Forecast of the “21·7” Henan Extremely Persistent Heavy Rainfall

Assimilation of the Advanced Geostationary Radiance Imager(AGRI) clear-sky radiance in a regional model is performed. The forecasting effectiveness of the assimilation of two water vapor(WV) channels with conventional observations for the “21·7” Henan extremely heavy rainfall is analyzed and compared with a baseline test that assimilates only conventional observations in this study. The results show that the 24-h cumulative precipitation forecast by the assimilation experiment with the addition ...     

Data Assimilation Method Based on the Constraints of Confidence Region

The ensemble Kalman filter(En KF) is a distinguished data assimilation method that is widely used and studied in various fields including methodology and oceanography. However, due to the limited sample size or imprecise dynamics model, it is usually easy for the forecast error variance to be underestimated, which further leads to the phenomenon of filter divergence.Additionally, the assimilation results of the initial stage are poor if the initial condition settings differ greatly from the true initial state. To address these problems, the variance inflation procedure is usually adopted. In this paper, we propose a new method based on the constraints of a confidence region constructed by the observations, called En CR, to estimate the inflation parameter of the forecast error variance of the En KF method. In the new method, the state estimate is more robust to both the inaccurate forecast models and initial condition settings. The new method is compared with other adaptive data assimilation methods in the Lorenz-63 and Lorenz-96 models under various model parameter settings. The simulation results show that the new method performs better than the competing methods.     

Improved Land Use and Leaf Area Index Enhances WRF-3DVAR Satellite Radiance Assimilation: A Case Study Focusing on Rainfall Simulation in the Shule River Basin during July 2013

The application of satellite radiance assimilation can improve the simulation of precipitation by numerical weather prediction models. However, substantial quantities of satellite data, especially those derived from low-level(surface-sensitive)channels, are rejected for use because of the difficulty in realistically modeling land surface emissivity and energy budgets.Here, we used an improved land use and leaf area index(LAI) dataset in the WRF-3 DVAR assimilation system to explore the benefit of using improved quality of land surface information to improve rainfall simulation for the Shule River Basin in the northeastern Tibetan Plateau as a case study. The results for July 2013 show that, for low-level channels(e.g., channel 3),the underestimation of brightness temperature in the original simulation was largely removed by more realistic land surface information. In addition, more satellite data could be utilized in the assimilation because the realistic land use and LAI data allowed more satellite radiance data to pass the deviation test and get used by the assimilation, which resulted in improved initial driving fields and better simulation in terms of temperature, relative humidity, vertical convection, and cumulative precipitation.     

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.     

Geology and geochemical evaluation of Taftan Volcano, Sistan and Baluchestan Province, southeast of Iran

At a glance of its stratighraphy, the Taftan Volcano can be classified as three groups： pre-, syn- and post-volcanic deposits. The pre-volcanic deposits consist mostly of flysch facies and colored mélange complex. The syn-volcanic deposits are mainly the product of the Taftan Volcano which is mostly composed of pyroclastic and lava flows from the main body of this volcano. The post-volcanic deposits are mostly epiclastic and reworked materials from the Taftan Volcano due to its erosion and weathering. Major and trace elements, and Sr/Rb isotopic compositions determined on whole-rock samples from the Taftan Volcano showed that the volcano was formed at the continental margin. The whole-rock isotopic composition of the Taftan Volcano showed a feature of strong enrichment with ^87Sr/^86Sr=0.705326-0.705921. Geochronological samples of the Tartan Volcano determined by the ^40K/^40Ar method gave an age range of 6.95±0.72 to 0.71±0.03 Ma. The rare-earth element patterns are characterized by high LREE and nearly strongly linear patterns for MREE to HREE, suggesting that distinctive minerals such as olivine and pyroxene crystallized in the early magmatic stage and then were involved in reaction between the lower crust and residual magma. The integrated isotope and trace element systematics and tectonic structure beneath the Taftan Volcano suggested the lower-crust assimilation by the primary magma. The primary magma had generated from a heterogeneous mantle source and a secondary petrogenetical process. This magma could have been affected by the subduction of the Oman Sea undemeath the continental Eurasia plate.     

Evaluation of ocean data assimilation in CAS-ESM-C: Constraining the SST field

A weakly coupled assimilation system, in which SST observations are assimilated into a coupled climate model(CASESM-C) through an ensemble optimal interpolation scheme, was established. This system is a useful tool for historical climate simulation, showing substantial advantages, including maintaining the atmospheric feedback, and keeping the oceanic fields from drifting far away from the observation, among others. During the coupled model integration, the bias of both surface and subsurface oceanic fields in the analysis can be reduced compared to unassimilated fields. Based on 30 model years of output from the system, the climatology and interannual variability of the climate system were evaluated. The results showed that the system can reasonably reproduce the climatological global precipitation and SLP, but it still suffers from the double ITCZ problem. Besides, the ENSO footprint, which is revealed by ENSO-related surface air temperature, geopotential height and precipitation during El Ni ?no evolution, is basically reproduced by the system. The system can also simulate the observed SST–rainfall relationships well on both interannual and intraseasonal timescales in the western North Pacific region, in which atmospheric feedback is crucial for climate simulation.     

Testing a four-dimensional variational data assimilation method using an improved intermediate coupled model for ENSO analysis and prediction

A four-dimensional variational (4D-Var) data assimilation method is implemented in an improved intermediate coupled model (ICM) of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The “observation” of the SST anomaly, which is sampled from a “truth” model simulation that takes default parameter values and has Gaussian noise added, is directly assimilated into the assimilation model with its parameters set erroneously. Results show that 4D-Var effectively reduces the error of ENSO analysis and therefore improves the prediction skill of ENSO events compared with the non-assimilation case. These results provide a promising way for the ICM to achieve better real-time ENSO prediction.     

温湿统计平衡约束关系对GRAPES全球湿度分析的作用

为改进GRAPES全球三维变分同化系统(GRAPES-3DVar)的湿度分析,借鉴Hólm等(2002)的思想,在背景误差协方差结构中引入湿度与温度的统计平衡约束关系。通过扣除湿度变化中与温度有关的平衡部分获取非平衡拟相对湿度,并引入非线性对称变换对其做标准化处理,将处理后的变量作为新的湿度控制变量。统计结果表明,温湿统计平衡约束主要出现在中高纬度对流层中层相对湿度大于80%的区域,与大尺度抬升凝结加热有关;新的湿度控制变量能满足无偏、高斯分布特征。单点理想观测试验结果表明,新的湿度分析具备了流依赖特征,并能有效地抑制负水汽与超饱和水汽的出现。同化循环与预报试验结果表明,新方案给出的湿度分析的偏差和均方根误差均有所减小。而针对降水预报的检验结果表明,引入新方案后的0.1-10 mm降水预报,在ETS评分没有显著降低的情况下,BIAS评分更靠近1,降水空报有所减缓。然而60-84 h的25 mm以上的降水漏报现象更为明显,表明湿度同化分析方案还有改进空间。通过引入温湿统计平衡约束关系,完善了GRAPES-3DVar分析框架,为全球湿度分析的持续改进奠定了坚实基础。      

Development and Application of the Chinese Global Operational Oceanography Forecasting System

Chinese Global operational Oceanography Forecasting System (CGOFS) is configured in three levels of nested grids from global ocean, open ocean to offshore. This global operational oceanography forecasting system architecture is firstly bulit in China by the National Marine Environmental Forecasting Center (NMEFC). It has been put into operational forecasting at NMEFC, providing real-time forecasting of multi-scale ocean current, temperature, salinity, wave, sea surface wind, etc. All the ocean forecasting products are released in many ways and made available through the online, realizing full-range coverage in resolution from hundreds kilometer to several kilometer. The CGOFS includes 8 subsystems: global sea-surface wind numerical forecasting subsystem, global ocean circulation numerical forecasting subsystem, global ocean wave numerical forecasting subsystem, global tide and tidal current forecasting subsystem, Indian Ocean marine environment numerical forecasting subsystem, polar sea ice numerical forecasting subsystem, refined marine environment numerical forecasting for China’s surrounding waters,and integration management subsystem for operational support service of the CGOFS. Operational applications of the CGOFS are closely connected with China’s economic-social development and military security needs. For example, the CGOFS palys a crucial role in environmental forecasting for Chinese research vessel and icebreaker Xuelong, MH370 Searching, submersible “Jiaolong” exploration and nuclear contaminant transport from Fukushima Daiichi nuclear power plant, providing important scientific support for developing an ocean power, protecting national maritime rights, ensuring marine safety and coping with ocean problems in emergency.     

Application of the distributed parameter filter to predict simulated tidal induced shallow water flow

Distributed parameter filtering theory is employed for estimating the state variables and associated error covariances of a dynamical distributed system under highly random tidal and meteorological influences. The stochastic-deterministic mathematical model of the physical system under study consists of the shallow water equations described by the momentum and continuity equations in which the external forces such as Coriolis force, wind friction, and atmospheric pressure are considered. White Gaussian noises in the system and measurement equations are used to account for the inherent stochasticity of the system. By using an optimal distributed parameter filter, the information provided by the stochastic dynamical model and the noisy measurements taken from the actual system are combined to obtain an optimal estimate of the state of the system, which in turn is used as the initial condition for the prediction procedure. The approach followed here has numerical approximation carried out at the end, which means that the numerical discretization is performed in the filtering equations, and not in the equations modelling the system. Therefore, the continuous distributed nature of the original system is maintained as long as possible and the propagation of modelling errors in the problem is minimized. The appropriateness of the distributed parameter filter is demonstrated in an application involving the prediction of storm surges in the North Sea. The results confirm excellent filter performance with considerable improvement with respect to the deterministic prediction.