This paper further explores the estimating and expressing of dynamic balance constraints using statistical methods in GRAPES-3DVAR(Version GM). Unlike the single-level scheme which only considers the coupling between ...This paper further explores the estimating and expressing of dynamic balance constraints using statistical methods in GRAPES-3DVAR(Version GM). Unlike the single-level scheme which only considers the coupling between mass and wind at one level, the multi-level scheme considers the coupling between their vertical profiles and calculates the balanced mass field at each layer using the rotational wind at all model levels. A reformed ridge regression method is used in the new scheme to avoid the multicollinearity problem and reduce the noises caused by unbalanced mesoscale disturbances. The results of numerical experiments show that the new scheme can get more reasonable vertical mass field, reduce the magnitude of the adjustment by the initialization, and improve the potential temperature analysis performance. Furthermore, the results of forecast verification in January(winter) and July(summer) both confirm that the new scheme can significantly improve the temperature forecast accuracy and bring slight positive effects to the pressure and wind forecast.展开更多
利用WRF(Weather research and forecasting)模式及模式模拟的资料,采用Hybrid ETKF-3DVAR(ensemble transform Kalman filter-three-dimensional variational data assimilation)方法同化模拟雷达观测资料。该混合同化方法将集合转换...利用WRF(Weather research and forecasting)模式及模式模拟的资料,采用Hybrid ETKF-3DVAR(ensemble transform Kalman filter-three-dimensional variational data assimilation)方法同化模拟雷达观测资料。该混合同化方法将集合转换卡尔曼滤波(ensemble transform Kalman filter)得到的集合样本扰动通过转换矩阵直接作用到背景场上,利用顺序滤波的思想得到分析扰动场;然后通过增加额外控制变量的方式把"流依赖"的集合协方差信息引入到变分目标函数中去,在3DVAR框架基础下与观测数据进行融合,从而给出分析场的最优估计。试验结果表明,Hybrid ETKF-3DVAR同化方法相比传统3DVAR可以提供更为准确的分析场,Hybrid方法雷达资料初始化模拟的台风涡旋结构与位置比3DVAR更加接近"真实场",对台风路径预报也有明显改进。通过对比Hybrid S试验与Hybrid F试验发现,Hybrid的正效果主要来源于混合背景误差协方差中的"流依赖"信息,集合平均场代替确定性背景场带来的效果并不显著。展开更多
基于WRF模式构建了Hybrid En SRF-En3DVar同化系统,该系统使用En SRF方案直接更新集合扰动。利用构建的同化系统针对台风"桑美"分别进行集合协方差权重敏感性试验和同化雷达不同观测资料的敏感性试验。集合协方差权重敏感性...基于WRF模式构建了Hybrid En SRF-En3DVar同化系统,该系统使用En SRF方案直接更新集合扰动。利用构建的同化系统针对台风"桑美"分别进行集合协方差权重敏感性试验和同化雷达不同观测资料的敏感性试验。集合协方差权重敏感性试验发现:当集合协方差权重分别为0.25、0.5和0.75时,同化效果优于3DVar试验,其中0.75的集合协方差权重试验得到了分析场的最优估计;当集合协方差权重为1.0时,分析场最差。同化雷达不同观测资料的敏感性试验表明,联合同化雷达径向风及反射率能有效改善大气湿度场和风场,但对风场的改善效果不如仅同化雷达径向风好。将En SRF集合扰动更新方案与扰动观测方案综合分析发现,扰动观测方案集合离散度较小,计算代价大,En SRF方案优于扰动观测方案。展开更多
We investigated the impact of tuning the length scale of the background error covariance in the Weather Research and Forecasting （WRF） three-dimensional variational assimilation （3DVAR） system. In particular, we s...We investigated the impact of tuning the length scale of the background error covariance in the Weather Research and Forecasting （WRF） three-dimensional variational assimilation （3DVAR） system. In particular, we studied the effect of this parameter on the assimilation of high-resolution surface data for heavy rainfall forecasts associated with mesoscale convective systems over the Korean Peninsula. In the assimilation of high-resolution surface data, the National Meteorological Center method tended to exaggerate the length scale that determined the shape and extent to which observed information spreads out. In this study, we used the difference between observation and background data to tune the length scale in the assimilation of high-resolution surface data. The resulting assimilation clearly showed that the analysis with the tuned length scale was able to reproduce the small-scale features of the ideal field effectively. We also investigated the effect of a double-iteration method with two different length scales, representing large and small-length scales in the WRF-3DVAR. This method reflected the large and small-scale features of observed information in the model fields. The quantitative accuracy of the precipitation forecast using this double iteration with two different length scales for heavy rainfall was high; results were in good agreement with observations in terms of the maximum rainfall amount and equitable threat scores. The improved forecast in the experiment resulted from the development of well-identified mesoscale convective systems by intensified low-level winds and their consequent convergence near the rainfall area.展开更多
Assimilating satellite radiances into Numerical Weather Prediction(NWP) models has become an important approach to increase the accuracy of numerical weather forecasting. In this study, the assimilation technique sche...Assimilating satellite radiances into Numerical Weather Prediction(NWP) models has become an important approach to increase the accuracy of numerical weather forecasting. In this study, the assimilation technique scheme was employed in NOAA's STMAS(Space-Time Multiscale Analysis System) to assimilate AMSU-A radiances data.Channel selection sensitivity experiments were conducted on assimilated satellite data in the first place. Then, real case analysis of AMSU-A data assimilation was performed. The analysis results showed that, following assimilating of AMSU-A channels 5-11 in STMAS, the objective function quickly converged, and the channel vertical response was consistent with the AMSU-A weighting function distribution, which suggests that the channels can be used in the assimilation of satellite data in STMAS. With the case of the Typhoon Morakot in Taiwan Island in August 2009 as an example, experiments on assimilated and unassimilated AMSU-A radiances data were designed to analyze the impact of the assimilation of satellite data on STMAS. The results demonstrated that assimilation of AMSU-A data provided more accurate prediction of the precipitation region and intensity, and especially, it improved the 0-6h precipitation forecast significantly.展开更多
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 l...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.展开更多
When altimetric data is assimilated, 3DVAR and Ensemble Optimal Interpolation （EnOI） have different ways of projecting the surface information downward. In 3DVAR, it is achieved by minimizing a cost function relatin...When altimetric data is assimilated, 3DVAR and Ensemble Optimal Interpolation （EnOI） have different ways of projecting the surface information downward. In 3DVAR, it is achieved by minimizing a cost function relating the temperature, salinity, and sea level. In EnOI, however, the surface information is propagated to other variables via a stationary ensemble. In this study, the differences between the two methods were compared and their impacts on the simulated variability were evaluated in a tropical Pacific model. Sea level anomalies （SLA） from the TOPEX/Poseidon were assimilated using both methods on data from 1997 to 2001 in a coarse resolution model. Results show that the standard deviation of sea level was improved by both methods, but the EnOI was more effective in the central/eastern Pacific. Meanwhile, the SLA evolution was better reproduced with EnOI than with 3DVAR. Correlations of temperature with the reanalysis data increased with EnOI by 0.1 0.2 above 200 m. In the eastern Pacific below 200 m, the correlations also increased by 0.2. However, the correlations decreased with 3DVAR in many areas. Correlations with the independent TAO profiles were also compared at two locations. While the correlations increased by up to 0.2 at some depths with EnOI, 3DVAR generally reduced the correlations by 0.1 0.3. Though both methods were able to reduce the model-data difference in climatological sense, 3DVAR appears to have degraded the simulated variability, especially during E1 Nifio-Southern Oscillation events. For salinity, similar results were found from the correlations. This tendency should be considered in future SLA assimilations, though the comparisons may vary among different model implementations.展开更多
As part of NOAA＇s ＂Warn-On-Forecast＂ initiative, a convective-scale data assimilation and prediction system was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The system was then eval...As part of NOAA＇s ＂Warn-On-Forecast＂ initiative, a convective-scale data assimilation and prediction system was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The system was then evaluated using retrospective short-range ensemble analyses and probabilistic forecasts of the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA. A 36-member multi-physics ensemble system provided the initial and boundary conditions for a 3-km convective-scale ensemble system. Radial velocity and reflectivity observations from four WSR-88 Ds were assimilated into the ensemble using the ARPS 3DVAR technique. Five data assimilation and forecast experiments were conducted to evaluate the sensitivity of the system to data assimilation frequencies, in-cloud temperature adjustment schemes, and fixed- and mixed-microphysics ensembles. The results indicated that the experiment with 5-min assimilation frequency quickly built up the storm and produced a more accurate analysis compared with the 10-min assimilation frequency experiment. The predicted vertical vorticity from the moist-adiabatic in-cloud temperature adjustment scheme was larger in magnitude than that from the latent heat scheme. Cycled data assimilation yielded good forecasts, where the ensemble probability of high vertical vorticity matched reasonably well with the observed tornado damage path. Overall, the results of the study suggest that the 3DVAR analysis and forecast system can provide reasonable forecasts of tornadic supercell storms.展开更多
基金China Special Fund for Meteorological Research in the Public Interest(GYHY201106008,GYHY201506003)China Meteorological Administration Special Fund for the Development of Numerical Weather Prediction(GRAPES)Research Innovation Program for College Graduates of Jiangsu Province(CXZZ13_0497)
文摘This paper further explores the estimating and expressing of dynamic balance constraints using statistical methods in GRAPES-3DVAR(Version GM). Unlike the single-level scheme which only considers the coupling between mass and wind at one level, the multi-level scheme considers the coupling between their vertical profiles and calculates the balanced mass field at each layer using the rotational wind at all model levels. A reformed ridge regression method is used in the new scheme to avoid the multicollinearity problem and reduce the noises caused by unbalanced mesoscale disturbances. The results of numerical experiments show that the new scheme can get more reasonable vertical mass field, reduce the magnitude of the adjustment by the initialization, and improve the potential temperature analysis performance. Furthermore, the results of forecast verification in January(winter) and July(summer) both confirm that the new scheme can significantly improve the temperature forecast accuracy and bring slight positive effects to the pressure and wind forecast.
基金supported by International S&T Cooperation Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education,Science and Technology(MEST)(2011-00265)the BK21 program of the Korean Government Ministry of Education
文摘We investigated the impact of tuning the length scale of the background error covariance in the Weather Research and Forecasting （WRF） three-dimensional variational assimilation （3DVAR） system. In particular, we studied the effect of this parameter on the assimilation of high-resolution surface data for heavy rainfall forecasts associated with mesoscale convective systems over the Korean Peninsula. In the assimilation of high-resolution surface data, the National Meteorological Center method tended to exaggerate the length scale that determined the shape and extent to which observed information spreads out. In this study, we used the difference between observation and background data to tune the length scale in the assimilation of high-resolution surface data. The resulting assimilation clearly showed that the analysis with the tuned length scale was able to reproduce the small-scale features of the ideal field effectively. We also investigated the effect of a double-iteration method with two different length scales, representing large and small-length scales in the WRF-3DVAR. This method reflected the large and small-scale features of observed information in the model fields. The quantitative accuracy of the precipitation forecast using this double iteration with two different length scales for heavy rainfall was high; results were in good agreement with observations in terms of the maximum rainfall amount and equitable threat scores. The improved forecast in the experiment resulted from the development of well-identified mesoscale convective systems by intensified low-level winds and their consequent convergence near the rainfall area.
基金National Natural Science Foundation of China(41375027,41130960,41275114,41275039)Public Benefit Research Foundation of China Meteorological Administration(GYHY201406001,GYHY201106044)+1 种基金"863"Program(2012AA120903)National Key Research and Development Program of China(2016YFB0502501)
文摘Assimilating satellite radiances into Numerical Weather Prediction(NWP) models has become an important approach to increase the accuracy of numerical weather forecasting. In this study, the assimilation technique scheme was employed in NOAA's STMAS(Space-Time Multiscale Analysis System) to assimilate AMSU-A radiances data.Channel selection sensitivity experiments were conducted on assimilated satellite data in the first place. Then, real case analysis of AMSU-A data assimilation was performed. The analysis results showed that, following assimilating of AMSU-A channels 5-11 in STMAS, the objective function quickly converged, and the channel vertical response was consistent with the AMSU-A weighting function distribution, which suggests that the channels can be used in the assimilation of satellite data in STMAS. With the case of the Typhoon Morakot in Taiwan Island in August 2009 as an example, experiments on assimilated and unassimilated AMSU-A radiances data were designed to analyze the impact of the assimilation of satellite data on STMAS. The results demonstrated that assimilation of AMSU-A data provided more accurate prediction of the precipitation region and intensity, and especially, it improved the 0-6h precipitation forecast significantly.
基金supported by the National Key Research and Development Program of China(Grant No.2016YFA0602701)the National Natural Science Foundation of China(Grant Nos.41721091,41630754,91644225)the Open Program(Grant No.SKLCS-OP-2017-02)from the State Key Laboratory of Cryospheric Science,Northwest Institute of EcoEnvironment and Resources,Chinese Academy of Sciences
文摘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.
基金supportedby National Natural Science Foundation of China(GrantNos.41176014and41075064)the Key Technologies R&D Program of China(Grant No.2011BAC03B02)
文摘When altimetric data is assimilated, 3DVAR and Ensemble Optimal Interpolation （EnOI） have different ways of projecting the surface information downward. In 3DVAR, it is achieved by minimizing a cost function relating the temperature, salinity, and sea level. In EnOI, however, the surface information is propagated to other variables via a stationary ensemble. In this study, the differences between the two methods were compared and their impacts on the simulated variability were evaluated in a tropical Pacific model. Sea level anomalies （SLA） from the TOPEX/Poseidon were assimilated using both methods on data from 1997 to 2001 in a coarse resolution model. Results show that the standard deviation of sea level was improved by both methods, but the EnOI was more effective in the central/eastern Pacific. Meanwhile, the SLA evolution was better reproduced with EnOI than with 3DVAR. Correlations of temperature with the reanalysis data increased with EnOI by 0.1 0.2 above 200 m. In the eastern Pacific below 200 m, the correlations also increased by 0.2. However, the correlations decreased with 3DVAR in many areas. Correlations with the independent TAO profiles were also compared at two locations. While the correlations increased by up to 0.2 at some depths with EnOI, 3DVAR generally reduced the correlations by 0.1 0.3. Though both methods were able to reduce the model-data difference in climatological sense, 3DVAR appears to have degraded the simulated variability, especially during E1 Nifio-Southern Oscillation events. For salinity, similar results were found from the correlations. This tendency should be considered in future SLA assimilations, though the comparisons may vary among different model implementations.
基金provided by the NOAA/Office of Oceanic and Atmospheric Research under the NOAA–University of Oklahoma Cooperative Agreement#NA17RJ1227the U.S.Department of Commerce+2 种基金NSF AGS-1341878the National Natural Science Foundation of China(Project No.41305092)the International S&T Cooperation Program of China(ISTCP)(Grant No.2011DFG23210)
文摘As part of NOAA＇s ＂Warn-On-Forecast＂ initiative, a convective-scale data assimilation and prediction system was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The system was then evaluated using retrospective short-range ensemble analyses and probabilistic forecasts of the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA. A 36-member multi-physics ensemble system provided the initial and boundary conditions for a 3-km convective-scale ensemble system. Radial velocity and reflectivity observations from four WSR-88 Ds were assimilated into the ensemble using the ARPS 3DVAR technique. Five data assimilation and forecast experiments were conducted to evaluate the sensitivity of the system to data assimilation frequencies, in-cloud temperature adjustment schemes, and fixed- and mixed-microphysics ensembles. The results indicated that the experiment with 5-min assimilation frequency quickly built up the storm and produced a more accurate analysis compared with the 10-min assimilation frequency experiment. The predicted vertical vorticity from the moist-adiabatic in-cloud temperature adjustment scheme was larger in magnitude than that from the latent heat scheme. Cycled data assimilation yielded good forecasts, where the ensemble probability of high vertical vorticity matched reasonably well with the observed tornado damage path. Overall, the results of the study suggest that the 3DVAR analysis and forecast system can provide reasonable forecasts of tornadic supercell storms.
