Ocean satellite data assimilation experiments in FIO-ESM using ensemble adjustment Kalman filter

Using Ensemble Adjustment Kalman Filter(EAKF), two types of ocean satellite datasets were assimilated into the First Institute of Oceanography Earth System Model(FIO-ESM), v1.0. One control experiment without data assimilation and four assimilation experiments were conducted. All the experiments were ensemble runs for 1-year period and each ensemble started from different initial conditions. One assimilation experiment was designed to assimilate sea level anomaly(SLA); another, to assimilate sea surface temperature(SST); and the other two assimilation experiments were designed to assimilate both SLA and SST but in different orders. To examine the effects of data assimilation, all the results were compared with an objective analysis dataset of EN3. Different from the ocean model without coupling, the momentum and heat fluxes were calculated via air-sea coupling in FIO-ESM, which makes the relations among variables closer to the reality. The outputs after the assimilation of satellite data were improved on the whole, especially at depth shallower than 1000 m. The effects due to the assimilation of different kinds of satellite datasets were somewhat different. The improvement due to SST assimilation was greater near the surface, while the improvement due to SLA assimilation was relatively great in the subsurface. The results after the assimilation of both SLA and SST were much better than those only assimilated one kind of dataset, but the difference due to the assimilation order of the two kinds of datasets was not significant.

An ensemble adjustment Kalman filter study for Argo data

An ensemble adjustment Kalman filter system is developed to assimilate Argo profiles into the Northwest Pacific MASNUM wave-circulation coupled model, which is based on the Princeton Ocean Model (POM). This model was recoded in FORTRAN-90 style, and some new data types were defined to improve the efficiency of system design and execution. This system is arranged for parallel computing by using UNIX shell scripts: it is easier with single models running separately with the required information exchanged through input/output files. Tests are carried out to check the performance of the system: one for checking the ensemble spread and another for the performance of assimilation of the Argo data in 2005. The first experiment shows that the assimilation system performs well. The comparison with the Satellite derived sea surface temperature (SST) shows that modeled SST errors are reduced after assimilation; at the same time, the spatial correlation between the simulated SST anomalies and the satellite data is improved because of Argo assimilation. Furthermore, the temporal evolution/trend of SST becomes much better than those results without data assimilation. The comparison against GTSPP profiles shows that the improvement is not only in the upper layers of ocean, but also in the deeper layers. All these results suggest that this system is potentially capable of reconstructing oceanic data sets that are of high quality and are temporally and spatially continuous.

FY-3A SATELLITE MICROWAVE DATA ASSIMILATION EXPERIMENTS IN TROPICAL CYCLONE FORECAST

China's new generation of polar-orbiting meteorological satellite FY-3A was successfully launched on May 26,2008,carrying microwave sounding devices which had similar performance to ATOVS of NOAA series.In order to study the application of microwave sounding data in numerical prediction of typhoons and to improve typhoon forecasting,we assimilated data directly for numerical forecasting of the track and intensity of the 2009 typhoon Morakot(0908)based on the WRF-3DVar system.Results showed that the initial fields of the numerical model due to direct assimilation of FY-3A microwave sounding data was improved much more than that due to assimilation of conventional observations alone,and the improvement was especially significant over the ocean,which is always without conventional observations.The model initial fields were more reasonable in reflecting the initial situation of typhoon circulation as well as temperature and humidity conditions,and typhoon central position at sea was also adjusted.Through direct 3DVar assimilation of FY-3A microwave data,the regional mesoscale model improves the forecasting of typhoon track.Therefore,the FY-3A microwave data could efficiently improve the numerical prediction of typhoons.

A baroclinic typhoon model with a moving multi-nested grid andvariational adjustment initializationII. Forecast experiments

A 3-dimensional baroclinic typhoon model with a moving multi-nested grid and its initialization are described first. Prediction results are improved by using a simple but effective data assimilation method in which the initial field is adjusted by the sixth hour's typhoon report and the weak-constraint variational principle. Finally someforecast examples made by this typhoon model are given.

Application of Lightning Data Assimilation to Numerical Forecast of Super Typhoon Haiyan (2013)

Previous observations from World Wide Lightning Location Network(WWLLN) and satellites have shown that typhoon-related lightning data have a potential to improve the forecast of typhoon intensity. The current study was aimed at investigating whether assimilating TC lightning data in numerical models can play such a role. For the case of Super Typhoon Haiyan in 2013, the lightning data assimilation(LDA) was realized in the Weather Research and Forecasting(WRF) model, and the impact of LDA on numerical prediction of Haiyan’s intensity was evaluated.Lightning data from WWLLN were used to adjust the model’s relative humidity(RH) based on the method developed by Dixon et al.(2016). The adjusted RH was output as a pseudo sounding observation, which was then assimilated into the WRF system by using the three-dimensional variational(3DVAR) method in the cycling mode at 1-h intervals. Sensitivity experiments showed that, for Super Typhoon Haiyan(2013), which was characterized by a high proportion of the inner-core(within 100 km from the typhoon center) lightning, assimilation of the inner-core lightning data significantly improved its intensity forecast, while assimilation of the lightning data in the rainbands(100–500 km from the typhoon center) led to no obvious improvement. The improvement became more evident with the increase in LDA cycles, and at least three or four LDA cycles were needed to achieve obvious intensity forecast improvement. Overall, the improvement in the intensity forecast by assimilation of the inner-core lightning data could be maintained for about 48 h. However, it should be noted that the LDA method in this study may have a negative effect when the simulated typhoon is stronger than the observed, since the LDA method cannot suppress the spurious convection.

A New Infrared Atmospheric Sounding Interferometer Channel Selection and Assessment of Its Impact on Met Office NWP Forecasts

A new set of Infrared Atmospheric Sounding Interferometer （IASI） channels was re-selected from 314 EUMETSAT channels. In selecting channels, we calculated the impact of the individually added channel on the improvement in the analysis outputs from a one-dimensional variational analysis （1D-Var） for the Unified Model （UM） data assimilation system at the Met Office, using the channel score index （CSI） as a figure of merit. Then, 200 channels were selected in order by counting each individual channel＇s CSI contribution. Compared with the operationally used 183 channels for the UM at the Met Office, the new set shares 149 channels, while the other 51 channels are new. Also examined is the selection from the entropy reduction method with the same 1D-Var approach, Results suggest that channel selection can be made in a more objective fashion using the proposed CSI method. This is because the most important channels can be selected across the whole IASI observation spectrum. In the experimental trial runs using the UM global assimilation system, the new channels had an overall neutral impact in terms of improvement in forecasts, as compared with results from the operational channels. However, upper-tropospheric moist biases shown in the control run with operational channels were significantly reduced in the experimental trial with the newly selected channels. The reduction of moist biases was mainly due to the additional water vapor channels, which are sensitive to the upper-tropospheric water vapor.

Preliminary Study on Direct Assimilation of Cloud-affected Satellite Microwave Brightness Temperatures

Direct assimilation of cloud-affected microwave brightness temperatures from AMSU-A into the GSI three-dimensional variational （3D-Var） assimilation system is preliminarily studied in this paper. A combination of cloud microphysics param- eters retrieved by the 1D-Var algorithm （including vertical profiles of cloud liquid water content, ice water content, and rain water content） and atmospheric state parameters from objective analysis fields of an NWP model are used as background fields. Three cloud microphysics parameters （cloud liquid water content, ice water content, and rain water content） are ap- plied to the control variable. Typhoon Halong （2014） is selected as an example. The results show that direct assimilation of cloud-affected AMSU-A observations can effectively adjust the structure of large-scale temperature, humidity and wind anal- ysis fields due to the assimilation of more AMSU-A observations in typhoon cloudy areas, especially typhoon spiral cloud belts. These adjustments, with temperatures increasing and humidities decreasing in the movement direction of the typhoon, bring the forecasted typhoon moving direction closer to its real path. The assimilation of cloud-affected satellite microwave brightness temperatures can provide better analysis fields that are more similar to the actual situation. Furthermore, typhoon prediction accuracy is improved using these assimilation analysis fields as the initial forecast fields in NWP models.

Impact of Assimilation of Radiosonde and UAV Observations from the Southern Ocean in the Polar WRF Model

Weather forecasting in the Southern Ocean and Antarctica is a challenge above all due to the rarity of observations to be assimilated in numerical weather prediction(NWP)models.As observations are expensive and logistically challenging,it is important to evaluate the benefit that additional observations could bring to NWP.Atmospheric soundings applying unmanned aerial vehicles(UAVs)have a large potential to supplement conventional radiosonde sounding observations.Here,we applied UAV and radiosonde sounding observations from an RV Polarstern cruise in the ice-covered Weddell Sea in austral winter 2013 to evaluate the impact of their assimilation in the Polar version of the Weather Research and Forecasting(Polar WRF)model.Our experiments revealed small to moderate impacts of radiosonde and UAV data assimilation.In any case,the assimilation of sounding data from both radiosondes and UAVs improved the analyses of air temperature,wind speed,and humidity at the observation site for most of the time.Further,the impact on the results of 5-day-long Polar WRF experiments was often felt over distances of at least 300 km from the observation site.All experiments succeeded in capturing the main features of the evolution of near-surface variables,but the effects of data assimilation varied between different cases.Due to the limited vertical extent of the UAV observations,the impact of their assimilation was limited to the lowermost 1?2-km layer,and assimilation of radiosonde data was more beneficial for modeled sea level pressure and near-surface wind speed.

Fourth-Order Predictive Modelling: I. General-Purpose Closed-Form Fourth-Order Moments-Constrained MaxEnt Distribution

This work (in two parts) will present a novel predictive modeling methodology aimed at obtaining “best-estimate results with reduced uncertainties” for the first four moments (mean values, covariance, skewness and kurtosis) of the optimally predicted distribution of model results and calibrated model parameters, by combining fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters. Underlying the construction of this fourth-order predictive modeling methodology is the “maximum entropy principle” which is initially used to obtain a novel closed-form expression of the (moments-constrained) fourth-order Maximum Entropy (MaxEnt) probability distribution constructed from the first four moments (means, covariances, skewness, kurtosis), which are assumed to be known, of an otherwise unknown distribution of a high-dimensional multivariate uncertain quantity of interest. This fourth-order MaxEnt distribution provides optimal compatibility of the available information while simultaneously ensuring minimal spurious information content, yielding an estimate of a probability density with the highest uncertainty among all densities satisfying the known moment constraints. Since this novel generic fourth-order MaxEnt distribution is of interest in its own right for applications in addition to predictive modeling, its construction is presented separately, in this first part of a two-part work. The fourth-order predictive modeling methodology that will be constructed by particularizing this generic fourth-order MaxEnt distribution will be presented in the accompanying work (Part-2).

Second-Order MaxEnt Predictive Modelling Methodology. I: Deterministically Incorporated Computational Model (2nd-BERRU-PMD)

This work presents a comprehensive second-order predictive modeling (PM) methodology designated by the acronym 2nd-BERRU-PMD. The attribute “2nd” indicates that this methodology incorporates second-order uncertainties (means and covariances) and second-order sensitivities of computed model responses to model parameters. The acronym BERRU stands for “Best- Estimate Results with Reduced Uncertainties” and the last letter (“D”) in the acronym indicates “deterministic,” referring to the deterministic inclusion of the computational model responses. The 2nd-BERRU-PMD methodology is fundamentally based on the maximum entropy (MaxEnt) principle. This principle is in contradistinction to the fundamental principle that underlies the extant data assimilation and/or adjustment procedures which minimize in a least-square sense a subjective user-defined functional which is meant to represent the discrepancies between measured and computed model responses. It is shown that the 2nd-BERRU-PMD methodology generalizes and extends current data assimilation and/or data adjustment procedures while overcoming the fundamental limitations of these procedures. In the accompanying work (Part II), the alternative framework for developing the “second- order MaxEnt predictive modelling methodology” is presented by incorporating probabilistically (as opposed to “deterministically”) the computed model responses.

Second-Order MaxEnt Predictive Modelling Methodology. II: Probabilistically Incorporated Computational Model (2nd-BERRU-PMP)

This work presents a comprehensive second-order predictive modeling (PM) methodology based on the maximum entropy (MaxEnt) principle for obtaining best-estimate mean values and correlations for model responses and parameters. This methodology is designated by the acronym 2nd-BERRU-PMP, where the attribute “2nd” indicates that this methodology incorporates second- order uncertainties (means and covariances) and second (and higher) order sensitivities of computed model responses to model parameters. The acronym BERRU stands for “Best-Estimate Results with Reduced Uncertainties” and the last letter (“P”) in the acronym indicates “probabilistic,” referring to the MaxEnt probabilistic inclusion of the computational model responses. This is in contradistinction to the 2nd-BERRU-PMD methodology, which deterministically combines the computed model responses with the experimental information, as presented in the accompanying work (Part I). Although both the 2nd-BERRU-PMP and the 2nd-BERRU-PMD methodologies yield expressions that include second (and higher) order sensitivities of responses to model parameters, the respective expressions for the predicted responses, for the calibrated predicted parameters and for their predicted uncertainties (covariances), are not identical to each other. Nevertheless, the results predicted by both the 2nd-BERRU-PMP and the 2nd-BERRU-PMD methodologies encompass, as particular cases, the results produced by the extant data assimilation and data adjustment procedures, which rely on the minimization, in a least-square sense, of a user-defined functional meant to represent the discrepancies between measured and computed model responses.

Fourth-Order Predictive Modelling: II. 4th-BERRU-PM Methodology for Combining Measurements with Computations to Obtain Best-Estimate Results with Reduced Uncertainties

This work presents a comprehensive fourth-order predictive modeling (PM) methodology that uses the MaxEnt principle to incorporate fourth-order moments (means, covariances, skewness, kurtosis) of model parameters, computed and measured model responses, as well as fourth (and higher) order sensitivities of computed model responses to model parameters. This new methodology is designated by the acronym 4th-BERRU-PM, which stands for “fourth-order best-estimate results with reduced uncertainties.” The results predicted by the 4th-BERRU-PM incorporates, as particular cases, the results previously predicted by the second-order predictive modeling methodology 2nd-BERRU-PM, and vastly generalizes the results produced by extant data assimilation and data adjustment procedures.

往返平飘式探空观测系统对CMA-MESO的影响研究

往返平飘式探空观测是我国研发的一种新型高空观测技术,除了具备与传统探空观测一致的上升段大气垂直廓线观测能力,同时还增加了平飘段和下降段的大气探测,自动实现了探测廓线的时空加密。利用ERA5再分析资料作为“真值”,利用往返平飘式探空模拟仿真系统构造了往返式探空模拟观测,基于CMA-MESO区域模式和3D-Var同化系统进行了观测系统模拟试验(Observing System Simulation Experiments,OSSEs)。数值试验结果表明:相比传统单次上升段探空观测,往返平飘式探空在全国组网的情况下,其增加的下降段模拟探空观测,能够有效提高CMA-MESO的降水预报技巧,不同降水量级的ETS评分提高约2%~5%,同时改进要素场(温、湿场和风场)的预报,改进率约为2%~5%。此外,典型天气个例分析结果表明,增加往返平飘式探空观测能够改善模式初值偏差,从而更准确地模拟降水分布。该文的研究结论为往返平飘式探空的未来科学布局和应用提供了理论支撑。

Direct assimilation of satellite radiance data in GRAPES variational assimilation system

Variational method is capable of dealing with observations that have a complicated nonlinear relation with model variables representative of the atmospheric state, and so make it possible to directly assimilate such measured variables as satellite radiance, which have a nonlinear relation with the model variables. Assimilation of any type of observations requires a corresponding observation operator, which establishes a specific mapping from the space of the model state to the space of observation. This paper presents in detail how the direct assimilation of real satellite radiance data is implemented in the GRAPES-3DVar analysis system. It focuses on all the components of the observation operator for direct assimilation of real satellite radiance data, including a spatial interpolation operator that transforms variables from model grid points to observation locations, a physical transformation from model variables to observed elements with different choices of model variables, and a data quality control. Assimilation experiments, using satellite radiances such as NOAA17 AMSU-A and AMSU-B (Advanced Microwave Sounding Unit), are carried out with two different schemes. The results from these experiments can be physically understood and clearly reflect a rational effect of direct assimilation of satellite radiance data in GRAPES-3DVar analysis system.

基于实测数据的声速剖面延拓和同化应用研究

海水中声速剖面的建立是研究水下声场传播规律的基础之一,基于实测声速剖面数据对历史声速剖面数据库进行延拓和同化。首先,对某次海洋实验实测数据进行预处理,剔除数据中异常点,并根据拟合法将实测数据与历史网格对齐;其次,利用融合法对历史数据进行同化,得到同化后的声速剖面;最后,根据观测值和同化值的均方根误差进行实验验证,结果符合预期。

ATOVS辐射率资料的直接变分同化试验研究

文中采用增量三维变分 ( 3D Var)同化方法 ,对先进的微波探测装置 (AMSU A)探测的辐射亮温资料与常规探空资料在MM5中尺度数值预报模式中的直接同化和预报进行对比试验研究。结果表明 ,同化AMSU A辐射亮温资料对中高层温度分析场的影响最明显 ,对MM5模式的温度和水汽混合比预报有总体上的正效应 。

FY-3A微波资料在“莫拉克”台风预报中的同化试验

我国新一代极轨气象卫星FY-3A于2008年5月26日发射成功,携带的微波垂直探测仪与NOAA系列卫星的ATOVS性能相似。为研究微波垂直探测仪资料在台风数值预报中的作用,实现我国FY-3A卫星的微波探测资料的直接同化,达到改进台风预报的目的,利用FY-3A微波探测资料,以WRF-3DVar系统为基础,针对2009年第8号台风"莫拉克"路径和强度预报,开展数值预报直接同化技术研究。试验结果表明,直接同化FY-3A微波资料对数值模式初始场改进要优于仅仅同化常规观测资料,对缺乏观测资料的海洋上改进尤为明显,模式初始场更加合理地反映海上台风环流形势以及温湿条件,海上台风的模式初始位置也得到了校正;经过FY-3A微波资料三维变分直接同化后,区域中尺度模式对台风路径预报效果具有积极的改善作用。

L波段分钟数据在WRF模式中的变分同化应用试验

为探讨蕴含气球漂移信息的高垂直分辨率L波段分钟探空数据在同化预报中的应用及影响,在初步讨论其基本特征的基础上,采用廓线和逐点两种同化方式,开展单站、降水个例及批量分钟探空同化试验,并与常规探空同化试验对比,得到以下主要结论:分钟数据可补充常规探空无法解析的垂直信息并提供气球漂移精确定位数据;同化分钟探空可有效增加观测在分析中的权重,使分析场更接近实况,逐点较廓线同化的结果差异主要在对流层上层;同化整个预报区域的分钟数据对模式的分析和预报性能有一定影响,漂移造成的风场同化差异在200 hPa及我国北方高空急流位置更明显;整体而言同化分钟数据可一定程度提升模式初始场(高层风场和低层水汽场)品质,主要改善对流层高空风和大量级降水预报性能。

加密探空资料在华南暴雨数值预报的应用试验

应用逐时同化预报系统CHAF进行加密探空资料同化试验,比较了同化加密探空资料与仅同化常规探空资料所得同化分析场和模式预报结果的差异。同化加密探空资料所得分析场水汽的均方根误差减小,水汽条件和大气环流形势与实况更接近。以加密同化探空资料所得分析场为模式初始场进行预报,雨区的分布和降水量级都较控制试验有改善。分析模式预报改善的原因,主要是大气水汽条件与环流模拟的改善以及二者之间很好的配合。

AMSU微波探测资料同化的质量控制方法概述

微波相比红外、可见光等卫星探测方式有能够穿透薄云的优点,同化微波探测资料能明显改进数值预报模式初始场。由于观测算子在云、降水粒子及性质复杂下垫面等因素影响下模拟辐射传输过程不准确,以及资料的观测误差较大等原因,实际同化应用时必须对微波探测资料加以认真筛选。为充分发挥探测资料作用并保证同化分析效果,在同化AMSU微波探测的研究中,很多机构和学者建立了散射指数、降水检测等质量控制方法,用来剔除观测算子不能准确模拟的观测。研究表明,资料同化过程中引入质量控制能起到改善同化效果,提高数值天气预报准确率的作用。但是,对于各种质量控制方法的原理和使用条件目前尚无完整的分析,使得各业务研究单位使用的质量控制方案差别较大。文章针对AMSU微波探测资料同化,在分析同化误差来源的基础上,总结了散射指数、降水概率、下垫面类型检测等质量控制方法,并简单讨论了质量控制的发展方向。