Construction and Application of a Regional Kilometer-Scale Carbon Source and Sink Assimilation Inversion System(CCMVS-R)

CO_(2)is one of the most important greenhouse gases(GHGs)in the earth’s atmosphere.Since the industrial era,anthropogenic activities have emitted excessive quantities of GHGs into the atmosphere,resulting in climate warming since the 1950s and leading to an increased frequency of extreme weather and climate events.In 2020,China committed to striving for carbon neutrality by 2060.This commitment and China’s consequent actions will result in significant changes in global and regional anthropogenic carbon emissions and therefore require timely,comprehensive,and objective monitoring and verification support(MVS)systems.The MVS approach relies on the top-down assimilation and inversion of atmospheric CO_(2)concentrations,as recommended by the Intergovernmental Panel on Climate Change(IPCC)Inventory Guidelines in 2019.However,the regional high-resolution assimilation and inversion method is still in its initial stage of development.Here,we have constructed an inverse system for carbon sources and sinks at the kilometer level by coupling proper orthogonal decomposition(POD)with four-dimensional variational(4DVar)data assimilation based on the weather research and forecasting-greenhouse gas(WRF-GHG)model.Our China Carbon Monito ring and Verification Support at the Regional level(CCMVS-R)system can continuously assimilate information on atmospheric CO_(2)and other related information and realize the inversion of regional and local anthropogenic carbon emissions and natural terrestrial ecosystem carbon exchange.Atmospheric CO_(2)data were collected from six ground-based monito ring sites in Shanxi Province,China to verify the inversion effect of regio nal anthropogenic carbon emissions by setting ideal and real experiments using a two-layer nesting method(at 27 and 9 km).The uncertainty of the simulated atmospheric CO_(2)decreased significantly,with a root-mean-square error of CO_(2)concentration values between the ideal value and the simulated after assimilation was close to 0.The total anthropogenic carbon emissions in Shanxi Province in 2019 from the assimilated inversions were approximately 28.6%(17%-38%)higher than the mean of five emission inventories using the bottomup method,showing that the top-down CCMVS-R system can obtain more comprehensive information on anthropogenic carbon emissions.

Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI 3DVar, EnKF, and Hybrid En3DVar for the Analysis and Short-Term Forecast of a Supercell Storm Case

Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interpolation ensemble Kalman filter(GSI-EnKF) framework were previously developed and tested with a mesoscale convective system(MCS) case. In this study, such capabilities are further developed to assimilate GOES GLM FED data within the GSI ensemble-variational(EnVar) hybrid data assimilation(DA) framework. The results of assimilating the GLM FED data using 3DVar, and pure En3DVar(PEn3DVar, using 100% ensemble covariance and no static covariance) are compared with those of EnKF/DfEnKF for a supercell storm case. The focus of this study is to validate the correctness and evaluate the performance of the new implementation rather than comparing the performance of FED DA among different DA schemes. Only the results of 3DVar and pEn3DVar are examined and compared with EnKF/DfEnKF. Assimilation of a single FED observation shows that the magnitude and horizontal extent of the analysis increments from PEn3DVar are generally larger than from EnKF, which is mainly caused by using different localization strategies in EnFK/DfEnKF and PEn3DVar as well as the integration limits of the graupel mass in the observation operator. Overall, the forecast performance of PEn3DVar is comparable to EnKF/DfEnKF, suggesting correct implementation.

The effect of glutathione on glucosinolate biosynthesis through the sulfur assimilation pathway in pakchoi associated with the growth conditions

Glucosinolates(GSLs) are a group of nitrogen-and sulfur-containing secondary metabolites, synthesized primarily in members of the Brassicaceae family, that play an important role in food flavor, plant antimicrobial activity, resistance to insect attack, stress tolerance, and human anti-cancer effects. As a sulfur-containing compound, glutathione has a strong connection with GSLs biosynthesis as a sulfur donor or redox system, and exists in reduced(glutathione;GSH) and oxidized(glutathione disulfide;GSSG) forms. However, the mechanism of GSH regulating GSLs biosynthesis remainds unclear. Hence, the exogenous therapy to pakchoi under normal growth condition and sulfur deficiency condition were conducted in this work to explore the relevant mechanism. The results showed that exogenous application of buthionine sulfoximine, an inhibitor of GSH synthesis, decreased the transcript levels of GSLs synthesis-related genes and transcription factors, as well as sulfur assimilation-related genes under the normal growth condition. Application of exogenous GSH inhibited the expression of GSLs synthesis-and sulfur assimilation-related genes under the normal condition, while the GSLs biosynthesis and the sulfur assimilation pathway were activated by exogenous application of GSH when the content of GSH in vivo of plants decreased owing to sulfur deficiency. Moreover,exogenous application of GSSG increased the transcript levels of GSLs synthesis-and sulfur assimilation-related genes under the normal growth condition and under sulfur deficiency. The present work provides new insights into the molecular mechanisms of GSLs biosynthesis underlying glutathione regulation.

Application of the finite analytic numerical method to a flowdependent variational data assimilation

An anisotropic diffusion filter can be used to model a flow-dependent background error covariance matrix,which can be achieved by solving the advection-diffusion equation.Because of the directionality of the advection term,the discrete method needs to be chosen very carefully.The finite analytic method is an alternative scheme to solve the advection-diffusion equation.As a combination of analytical and numerical methods,it not only has high calculation accuracy but also holds the characteristic of the auto upwind.To demonstrate its ability,the one-dimensional steady and unsteady advection-diffusion equation numerical examples are respectively solved by the finite analytic method.The more widely used upwind difference method is used as a control approach.The result indicates that the finite analytic method has higher accuracy than the upwind difference method.For the two-dimensional case,the finite analytic method still has a better performance.In the three-dimensional variational assimilation experiment,the finite analytic method can effectively improve analysis field accuracy,and its effect is significantly better than the upwind difference and the central difference method.Moreover,it is still a more effective solution method in the strong flow region where the advective-diffusion filter performs most prominently.

Progress and future prospects of decadal prediction and data assimilation:A review

年代际预测,也称为“近期气候预测”,旨在预测未来1-10年内的气候变化,是气候预测和气候变化研究领域的一个新关注点.它位于季节至年际预测和长期气候变化预测之间,结合了初值问题和外部强迫问题的两个方面.年代际预测的核心技术在于用于模式初始化的同化方法的准确性和效率,其目标是为模式提供准确的初始条件,其中包含观测到的气候系统内部变率,年代际预测的初始化通常涉及在耦合框架内同化海洋观测,其中观测到的信号通过耦合过程传递到其他分量,如大气和海冰.然而,最近的研究越来越关注在海洋-大气耦合模式中探索耦合数据同化(CDA),有人认为CDA有潜力显著提高年代际预测技巧.本文综合评述了该领域的三个方面的研究现状:初始化方法,年代际气候预测的可预测性和预测技巧,以及年代际预测的未来发展和挑战.

Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems:A Review

Data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.Typical applications span from computational fluid dynamics(CFD)to geoscience and climate systems.Recently,much effort has been given in combining DA,UQ and machine learning(ML)techniques.These research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive guide.This paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of applications.This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems.Therefore,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice versa.Some exciting perspectives of this rapidly developing research field are also discussed.Index Terms-Data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

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 of the AGRI exceeds 500 mm,compared to a maximum value of 532.6 mm measured by the national meteorological stations,and that the location of the maximum precipitation is consistent with the observations.The results for the short periods of intense precipitation processes are that the simulation of the location and intensity of the 3-h cumulative precipitation is also relatively accurate.The analysis increment shows that the main difference between the two sets of assimilation experiments is over the ocean due to the additional ocean observations provided by FY-4A,which compensates for the lack of ocean observations.The assimilation of satellite data adjusts the vertical and horizontal wind fields over the ocean by adjusting the atmospheric temperature and humidity,which ultimately results in a narrower and stronger WV transport path to the center of heavy precipitation in Zhengzhou in the lower troposphere.Conversely,the WV convergence and upward motion in the control experiment are more dispersed;therefore,the precipitation centers are also correspondingly more dispersed.

A data assimilation-based forecast model of outer radiation belt electron fluxes

Because radiation belt electrons can pose a potential threat to the safety of satellites orbiting in space,it is of great importance to develop a reliable model that can predict the highly dynamic variations in outer radiation belt electron fluxes.In the present study,we develop a forecast model of radiation belt electron fluxes based on the data assimilation method,in terms of Van Allen Probe measurements combined with three-dimensional radiation belt numerical simulations.Our forecast model can cover the entire outer radiation belt with a high temporal resolution(1 hour)and a spatial resolution of 0.25 L over a wide range of both electron energy(0.1-5.0 MeV)and pitch angle(5°-90°).On the basis of this model,we forecast hourly electron fluxes for the next 1,2,and 3 days during an intense geomagnetic storm and evaluate the corresponding prediction performance.Our model can reasonably predict the stormtime evolution of radiation belt electrons with high prediction efficiency(up to~0.8-1).The best prediction performance is found for~0.3-3 MeV electrons at L=~3.25-4.5,which extends to higher L and lower energies with increasing pitch angle.Our results demonstrate that the forecast model developed can be a powerful tool to predict the spatiotemporal changes in outer radiation belt electron fluxes,and the model has both scientific significance and practical implications.

Assimilation of FY-3D MWTS-Ⅱ Radiance with 3D Precipitation Detection and the Impacts on Typhoon Forecasts

Precipitation detection is an essential step in radiance assimilation because the uncertainties in precipitation would affect the radiative transfer calculation and observation errors.The traditional precipitation detection method for microwave only detects clouds and precipitation horizontally,without considering the three-dimensional distribution of clouds.Extending precipitation detection from 2D to 3D is expected to bring more useful information to the data assimilation without using the all-sky approach.In this study,the 3D precipitation detection method is adopted to assimilate Microwave Temperature Sounder-2(MWTS-Ⅱ)onboard the Fengyun-3D,which can dynamically detect the channels above precipitating clouds by considering the near-real-time cloud parameters.Cycling data assimilation and forecasting experiments for Typhoons Lekima(2019)and Mitag(2019)are carried out.Compared with the control experiment,the quantity of assimilated data with the 3D precipitation detection increases by approximately 23%.The quality of the additional MWTS-Ⅱradiance data is close to the clear-sky data.The case studies show that the average root-mean-square errors(RMSE)of prognostic variables are reduced by 1.7%in the upper troposphere,leading to an average reduction of4.53%in typhoon track forecasts.The detailed diagnoses of Typhoon Lekima(2019)further show that the additional MWTS-Ⅱradiances brought by the 3D precipitation detection facilitate portraying a more reasonable circulation situation,thus providing more precise structures.This paper preliminarily proves that 3D precipitation detection has potential added value for increasing satellite data utilization and improving typhoon forecasts.

Increasing nitrogen absorption and assimilation ability under mixed NO_(3)^(–)and NH_(4)^(+) supply is a driver to promote growth of maize seedlings

Compared with sole nitrate (NO_(3)^(-)) or sole ammonium (NH_(4)^(+)) supply,mixed nitrogen (N) supply may promote growth of maize seedlings.Previous study suggested that mixed N supply not only increased photosynthesis rate,but also enhanced leaf growth by increasing auxin synthesis to build a large sink for C and N utilization.However,whether this process depends on N absorption is unknown.Here,maize seedlings were grown hydroponically with three N forms (NO_(3)^(-)only,75/25 NO_(3)^(-)/NH_(4)^(+) and NH_(4)^(+) only).The study results suggested that maize growth rate and N content of shoots under mixed N supply was little different to that under sole NO_(3)^(-)supply at 0–3 d,but was higher than under sole NO_(3)^(-)supply at 6–9 d.^(15)N influx rate under mixed N supply was greater than under sole NO_(3)^(-) or NH_(4)^(+) supply at 6–9 d,although NO_(3)^(-) and NH_(4)^(+) influx under mixed N supply were reduced compared to sole NO_(3)^(-) and NH_(4)^(+) supply,respectively.qRT-PCR determination suggested that the increased N absorption under mixed N supply may be related to the higher expression of NO_(3)^(-) transporters in roots,such as ZmNRT1.1A,ZmNRT1.1B,ZmNRT1.1C,ZmNRT1.2 and ZmNRT1.3,or NH_(4)^(+) absorption transporters,such as Zm AMT1.1A,especially the latter.Furthermore,plants had higher nitrate reductase (NR)glutamine synthase (GS) activity and amino acid content under mixed N supply than when under sole NO_(3)^(-) supply.The experiments with inhibitors of NR reductase and GS synthase further confirmed that N assimilation ability under mixed N supply was necessary to promote maize growth,especially for the reduction of NO_(3)^(-) by NR reductase.This research suggested that the increased processes of NO_(3)^(-)and NH_(4)^(+) assimilation by improving N-absorption ability of roots under mixed N supply may be the main driving force to increase maize growth.

Reconstructing urban wind flows for urban air mobility using reduced-order data assimilation

Advancements in uncrewed aircrafts and communications technologies have led to a wave of interest and investment in unmanned aircraft systems(UASs)and urban air mobility(UAM)vehicles over the past decade.To support this emerging aviation application,concepts for UAS/UAM traffic management(UTM)systems have been explored.Accurately characterizing and predicting the microscale weather conditions,winds in particular,will be critical to safe and efficient operations of the small UASs/UAM aircrafts within the UTM.This study implements a reduced order data assimilation approach to reduce discrepancies between the predicted urban wind speed with computational fluid dynamics(CFD)Reynolds-averaged Navier Stokes(RANS)model with real-world,limited and sparse observations.The developed data assimilation system is UrbanDA.These observations are simulated using a large eddy simulation(LES).The data assimilation approach is based on the time-independent variational framework and uses space reduction to reduce the memory cost of the process.This approach leads to error reduction throughout the simulated domain and the reconstructed field is different than the initial guess by ingesting wind speeds at sensor locations and hence taking into account flow unsteadiness in a time when only the mean flow quantities are resolved.Different locations where wind sensors can be installed are discussed in terms of their impact on the resulting wind field.It is shown that near-wall locations,near turbulence generation areas with high wind speeds have the highest impact.Approximating the model error with its principal mode provides a better agreement with the truth and the hazardous areas for UAS navigation increases by more than 10%as wind hazards resulting from buildings wakes are better simulated through this process.

The effect of model errors in ensemble sequential assimilation of geomagnetic field

Earth’s magnetic field is generated in the fluid outer core through the dynamo process.Over the last decade,data assimilation has been used to retrieve the core dynamics and predict the evolution of the geomagnetic field.The presence of model errors in the geomagnetic data assimilation is inevitable because current numerical geodynamo models are still far from realistic core dynamics.In this paper,we investigate the effect of model errors in geomagnetic data assimilation based on ensemble Kalman filter(EnKF).We construct two dynamo models with different control parameters but exhibiting similar force balance and magnetic morphology at the core surface.We then use one dynamo model to generate synthetic observations and the other as the forward model in EnKF.Our test experiments show that the EnKF approach with the pre-setting model errors can nevertheless recover large-scale core surface flow and make a rough short-term(5-year)prediction.However,the data assimilation in the presence of model errors cannot keep improving the core state even though new observations are available.Motivated by the planned Macao Science Satellite-1,which is expected to provide improved internal geomagnetic field model,we also perform a test experiment using synthetic observations up to spherical harmonic degree l=18.Our results indicate that high-resolution observations are crucial in reconstructing small scale flow.

Prediction of landslide block movement based on Kalman filtering data assimilation method

Compared with the study of single point motion of landslides,studying landslide block movement based on data from multiple monitoring points is of great significance for improving the accurate identification of landslide deformation.Based on the study of landslide block,this paper regarded the landslide block as a rigid body in particle swarm optimization algorithm.The monitoring data were organized to achieve the optimal state of landslide block,and the 6-degree of freedom pose of the landslide block was calculated after the regularization.Based on the characteristics of data from multiple monitoring points of landslide blocks,a prediction equation for the motion state of landslide blocks was established.By using Kalman filtering data assimilation method,the parameters of prediction equation for landslide block motion state were adjusted to achieve the optimal prediction.This paper took the Baishuihe landslide in the Three Gorges reservoir area as the research object.Based on the block segmentation of the landslide,the monitoring data of the Baishuihe landslide block were organized,6-degree of freedom pose of block B was calculated,and the Kalman filtering data assimilation method was used to predict the landslide block movement.The research results showed that the proposed prediction method of the landslide movement state has good prediction accuracy and meets the expected goal.This paper provides a new research method and thinking angle to study the motion state of landslide block.

我国大气电学研究的最新进展

大气电学主要研究地球大气和近地空间发生的电学过程及其机理和影响,其核心研究内容是雷电物理和雷暴电学。自1980年代以来,中国大气电学研究不断取得新的进展,特别是近年来,得益于高时间分辨率雷电探测技术的进步,大气电学研究不仅在雷电物理学和雷暴云电荷结构方面取得了重要成果,也在雷电和雷暴对近地空间的影响、强对流天气的雷电特征、以及雷电资料同化和预警预报等方面取得了重要进展。本文从六个方面对近五年来大气电学的主要研究进展进行回顾,包括高精度雷电探测和定位技术、雷电物理过程和机制、雷暴对中上层大气的影响、雷暴云电荷结构的观测和数值模拟、强对流天气的雷电特征与预报、雷电对气候变化的影响与响应等,最后对大气电学未来发展进行展望。

CMA-MESO千米尺度变分同化系统中极小化控制变量的重构

重构GRAPES(Global/Regional Assimilation and Prediction System)全球、区域一体化变分同化系统中的极小化控制变量,提升中、小尺度同化分析能力,为中国气象局业务区域数值预报系统CMA-MESO提供千米尺度适用的同化方案。新方案用纬向风速(u)和经向风速(v)替代原有流函数和势函数作为新的风场控制变量,采用温度和地面气压(T,ps)替代原有非平衡无量纲气压作为新的质量场控制变量,同时不再考虑准地转平衡约束,而是采用连续方程弱约束保证分析平衡。背景误差参数统计和数值试验结果表明,采用重构后的极小化控制变量,观测信息传播更加局地,分析结构更加合理,避免了原方案在中、小尺度应用时存在的虚假相关问题。连续方程弱约束的引入,限制了同化分析中辐合、辐散的不合理增长,帮助新方案在分析更加局地的同时保证分析平衡。为期1个月的连续同化循环和预报试验结果表明,新方案可以减小风场和质量场分析误差,CMAMESO系统地面降水和10 m风场的预报评分显著提升。

对分课堂在本科护理专业解剖学教学中的应用效果

目的 探索在解剖学课程中应用对分课堂教学对护理专业本科生临床思维能力的影响。方法 将2020级护理本科生按照班级随机分为试验组(n=60)和对照组(n=60)。试验组采用对分课堂,对照组采用传统讲授法。比较教学后两组的考核成绩和临床思维能力得分。结果 教学后,试验组平时成绩(t=5.150)和期末理论考试成绩(t=7.060)均高于对照组(P<0.05)。教学实施前两组临床思维能力总分和各维度得分差异无统计学意义(P>0.05);教学后,试验组评判思维能力得分(t=4.557)、系统思维能力得分(t=3.667)、循证思维能力得分(t=5.975)及总分(t=6.809)均高于对照组(P<0.05)。结论 护理专业解剖学课程中应用对分课堂教学效果良好,有利于护理专业学生早期临床思维的培养。

数值预报中气象卫星资料同化前处理技术进展

在数值天气预报变分同化中,利用同化前处理将卫星资料完成有效信息优选、资料拼接和稀疏化、初级通道选择、下边界参数耦合等处理,实现卫星资料同化对数值天气预报业务的正贡献,是决定海量卫星资料同化效率、质量和效果的重要环节。针对多种格式的卫星资料,中国气象局研发标准格式的高时效卫星资料拼接等技术,有效减小整轨卫星资料时间滞后对数值天气预报业务的负面影响。对于风云气象卫星资料,将云和降水检测、资料质量分析等处理置于同化前处理中,实现多光谱资料融合的同化预质量控制,保证了风云卫星微波温度探测资料和红外高光谱资料的同化正贡献。利用统一资料格式对预处理卫星资料进行再处理,拓展针对卫星成像和主动探测资料的处理,将卫星资料同化的部分质量控制功能置于卫星资料同化前处理中,是风云卫星资料同化前处理技术发展的重要趋势。

一种近实时全球电离层数据同化和预报系统的构建与实现

电离层天气变化正成为目前空间天气预报最重要的内容之一,建立一个可靠的、精确的电离层特征参量现报和预报系统对空间科学研究及军民用无线电信息系统保障均具有重要价值。基于国际GNSS服务组织(International GNSS Service,IGS)的地基GNSS和全球电离层无线电观测站(Global Ionospheric Radio Observatory,GIRO)数字测高仪的实时数据,以国际参考电离层(International Reference Ionosphere,IRI)模型为背景模型,采用高斯-马尔可夫-限带卡尔曼滤波同化技术,结合超大规模矩阵稀疏存储与处理方法,在云计算平台上构建完成了近实时全球电离层数据同化和预报系统(near-Real-Time Global Ionospheric Data AssiMilation and forecasting system,RT-GIDAM)。该系统具备了全球电离层TEC和电子密度的近实时(延时约5 min)、较高空间(5°×2.5°)和时间分辨率(15 min)的同化和预报功能,可为空间物理研究及相关无线电系统应用提供数据支撑。

往返平飘式探空在CMA-MESO三维变分中的同化及对模式预报的影响

往返平飘式探空通过一次探空气球施放实现“上升段-平飘段-下降段”三段观测,其下降段能实现在06时(世界时,下同)和18时自动垂直加密观测大气,具备提升区域高分辨率快速同化循环预报系统在06时和18时的预报技巧潜力。为了实现往返平飘式探空在区域高分辨率模式中的同化,分析其对预报的影响,初步提出了“选取模式层最接近观测”的垂直稀疏化方法来预处理资料,深入分析了稀疏化对同化效果的影响,论证了资料垂直稀疏化对于同化应用的必要性;在此基础上,开展了为期1个月的批量同化影响试验,着重分析了往返平飘式探空在长江中下游区域的组网观测对CMA-MESO模式预报技巧的影响。稀疏化敏感性试验结果表明,同化不稀疏化的往返平飘式探空相比同化传统业务探空,分析和预报误差显著增加,降水预报评分也显著降低,相反,“选取最接近模式层”数据的垂直稀疏化方案能提高模式的分析和预报技巧,表明往返平飘式探空同化前必须进行垂直稀疏化。批量同化试验结果表明,在冷启动时刻(00时和12时,为常规探空释放时刻),同化往返平飘式探空(上升段)相对同化传统业务探空,分析误差和预报误差变化较小。但在暖启动时刻(03、06、09、15、18、21时,无常规探空释放时刻),增加往返平飘式探空下降段数据,相比控制试验,分析场精度提高了约0.4%。此外,0~12 h累计降水预报的ETS评分变化较小,但12~24 h累计降水预报在0.1、1.0、5.0、10.0、25.0 mm量级降水ETS评分提高了约0.5%,在50.0 mm量级的降水ETS评分提高了约2.3%。总体而言,同化往返平飘式探空对于区域高分辨率快速同化循环预报系统在暖启动时刻的降水预报技巧有正贡献。

多源极轨卫星微波温度计资料实时区域同化系统

基于中尺度数值预报模式WRF和WRFDA同化系统,实现多源极轨卫星微波温度计资料实时区域同化,并对同化产品进行评估和应用。2018年同化试验结果表明:通过质量控制和偏差订正,AMSU-A资料第5~9通道亮温观测增量O-B(观测值O和背景场的正演辐射模拟值B的差值)的标准差有效降低,同化后各通道亮温分析残差O-A(观测值O和分析场的正演辐射模拟值A的差值)的标准差有效降低。同化预报产品被应用在暴雨强对流个例和台风个例中,取得良好效果。