This work addresses the problem of estimating the states of nonlinear dynamic systems with sparse observations.We present a hybrid three-dimensional variation（3DVar） and particle piltering（PF） method,which combine...This work addresses the problem of estimating the states of nonlinear dynamic systems with sparse observations.We present a hybrid three-dimensional variation（3DVar） and particle piltering（PF） method,which combines the advantages of 3DVar and particle-based filters.By minimizing the cost function,this approach will produce a better proposal distribution of the state.Afterwards the stochastic resampling step in standard PF can be avoided through a deterministic scheme.The simulation results show that the performance of the new method is superior to the traditional ensemble Kalman filtering（EnKF） and the standard PF,especially in highly nonlinear systems.展开更多
The relationship between the radar reflectivity factor （Z） and the rainfall rate （R） is recalculated based on radar ob- servations from 10 Doppler radars and hourly rainfall measurements at 6529 automatic weather ...The relationship between the radar reflectivity factor （Z） and the rainfall rate （R） is recalculated based on radar ob- servations from 10 Doppler radars and hourly rainfall measurements at 6529 automatic weather stations over the Yangtze-Huaihe River basin. The data were collected by the National 973 Project from June to July 2013 for severe convective weather events. The Z-R relationship is combined with an empirical qr-R relationship to obtain a new Z-qr relationship, which is then used to correct the observational operator for radar reflectivity in the three-dimensional variational （3DVar） data assimilation system of the Weather Research and Forecasting （WRF） model to im-prove the analysis and prediction of severe convective weather over the Yangtze--Huaihe River basin. The perform- ance of the corrected reflectivity operator used in the WRF 3DVar data assimilation system is tested with a heavy rain event that occurred over Jiangsu and Anhui provinces and the surrounding regions on 23 June 2013. It is noted that the observations for this event are not included in the calculation of the Z-R relationship. Three experiments are conducted with the WRF model and its 3DVar system, including a control run without the assimilation of reflectivity data and two assimilation experiments with the original and corrected refleetivity operators. The experimental results show that the assimilation of radar reflectivity data has a positive impact on the rainfall forecast within a few hours with either the original or corrected reflectivity operators, but the corrected reflectivity operator achieves a better per-forrnance on the rainfall forecast than the original operator. The corrected reflectivity operator extends the effective time of radar data assimilation for the prediction of strong reflectivity. The physical variables analyzed with the corrected reflectivity operator present more reasonable mesoscale structures than those obtained with the original re-flectivity operator. This suggests that the new sta展开更多
针对2003年7月5日江淮流域一次暴雨过程，以NCEP／NCAR1°×1°再分析资料为背景场，采用WRF（weather research and forecasting）模式及其三维变分同化系统，对雷达径向风和E-VAP（extended velocity azimuth processing...针对2003年7月5日江淮流域一次暴雨过程，以NCEP／NCAR1°×1°再分析资料为背景场，采用WRF（weather research and forecasting）模式及其三维变分同化系统，对雷达径向风和E-VAP（extended velocity azimuth processing）反演的水平风场进行了直接同化和反演同化试验，结果表明：直接同化雷达径向风资料后，增加了初始风场的中小尺度信息，改善了垂直速度条件，且在风场作用下水汽分布得到改善；反演同化资料虽然能增加初始场的中小尺度信息，但效果不如直接同化明显；无论直接同化还是反演同化都能改善降水预报，但直接同化好于反演同化，且间隔5～6min比30min的直接同化模拟效果好。展开更多
A large number of observational analyses have shown that lightning data can be used to indicate areas of deep convection. It is important to assimilate observed lightning data into numerical models, so that more small...A large number of observational analyses have shown that lightning data can be used to indicate areas of deep convection. It is important to assimilate observed lightning data into numerical models, so that more small-scale information can be incorporated to improve the quality of the initial condition and the subsequent forecasts. In this study, the empirical relationship between flash rate, water vapor mixing ratio, and graupel mixing ratio was used to adjust the model relative humidity, which was then assimilated by using the three-dimensional variational data assimilation system of the Weather Research and Forecasting model in cycling mode at 10-min intervals. To find the appropriate assimilation time-window length that yielded significant improvement in both the initial conditions and subsequent forecasts, four experiments with different assimilation time-window lengths were conducted for a squall line case that occurred on 10 July 2007 in North China. It was found that 60 min was the appropriate assimilation time-window length for this case, and longer assimilation window length was unnecessary since no further improvement was present. Forecasts of 1-h accumulated precipitation during the assimilation period and the subsequent 3-h accumulated precipitation were significantly improved compared with the control experiment without lightning data assimilation. The simulated reflectivity was optimal after 30 min of the forecast, it remained optimal during the following 42 min, and the positive effect from lightning data assimilation began to diminish after 72 min of the forecast. Overall,the improvement from lightning data assimilation can be maintained for about 3 h.展开更多
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 wa...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.展开更多
Assimilation of surface observations including 2-m temperature(T_(2m))in numerical weather prediction(NWP)models remains a challenging problem owing to differences between the elevation of model terrain and that of ac...Assimilation of surface observations including 2-m temperature(T_(2m))in numerical weather prediction(NWP)models remains a challenging problem owing to differences between the elevation of model terrain and that of actual observation stations.NWP results can be improved only if surface observations are assimilated appropriately.In this study,a T_(2m)data assimilation scheme that carefully considers misrepresentation of model and station terrain was established by using the three-dimensional variational data assimilation(3DVAR)system of the China Meteorological Administration mesoscale model(CMA-MESO).The corresponding forward observation operator,tangent linear operator,and adjoint operator for the T_(2m)observations under three terrain mismatch treatments were developed.The T_(2m)data were assimilated in the same method as that adopted for temperature sounding data with additional representative errors,when station terrain was 100 m higher than model terrain;otherwise,the T_(2m)data were assimilated by using the surface similarity theory assimilation operator.Furthermore,if station terrain was lower than model terrain,additional representative errors were stipulated and corrected.Test of a rainfall case showed that the observation innovation and analysis residuals both exhibited Gaussian distribution and that the analysis increment was reasonable.Moreover,it was found that on completion of the data assimilation cycle,T_(2m)data assimilation obviously influenced the temperature,wind,and relative humidity fields throughout the troposphere,with the greatest impact evident in the lower layers,and that both the area and the intensity of rainfall were better forecasted,especially for the first 12hours.Long-term continuous experiments for 2–28 February and 5–20 July 2020,further verified that T_(2m)data assimilation reduced deviations not only in T_(2m)but also in 10-m wind forecasts.More importantly,the precipitation equitable threat scores were improved over the two experimental periods.In summary,this study conf展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No. 41105063)
文摘This work addresses the problem of estimating the states of nonlinear dynamic systems with sparse observations.We present a hybrid three-dimensional variation（3DVar） and particle piltering（PF） method,which combines the advantages of 3DVar and particle-based filters.By minimizing the cost function,this approach will produce a better proposal distribution of the state.Afterwards the stochastic resampling step in standard PF can be avoided through a deterministic scheme.The simulation results show that the performance of the new method is superior to the traditional ensemble Kalman filtering（EnKF） and the standard PF,especially in highly nonlinear systems.
基金Supported by the National(Key)Basic Research and Development(973)Program of China(2013CB430102)National Natural Science Foundation of China(41275102 and 41330527)
文摘The relationship between the radar reflectivity factor （Z） and the rainfall rate （R） is recalculated based on radar ob- servations from 10 Doppler radars and hourly rainfall measurements at 6529 automatic weather stations over the Yangtze-Huaihe River basin. The data were collected by the National 973 Project from June to July 2013 for severe convective weather events. The Z-R relationship is combined with an empirical qr-R relationship to obtain a new Z-qr relationship, which is then used to correct the observational operator for radar reflectivity in the three-dimensional variational （3DVar） data assimilation system of the Weather Research and Forecasting （WRF） model to im-prove the analysis and prediction of severe convective weather over the Yangtze--Huaihe River basin. The perform- ance of the corrected reflectivity operator used in the WRF 3DVar data assimilation system is tested with a heavy rain event that occurred over Jiangsu and Anhui provinces and the surrounding regions on 23 June 2013. It is noted that the observations for this event are not included in the calculation of the Z-R relationship. Three experiments are conducted with the WRF model and its 3DVar system, including a control run without the assimilation of reflectivity data and two assimilation experiments with the original and corrected refleetivity operators. The experimental results show that the assimilation of radar reflectivity data has a positive impact on the rainfall forecast within a few hours with either the original or corrected reflectivity operators, but the corrected reflectivity operator achieves a better per-forrnance on the rainfall forecast than the original operator. The corrected reflectivity operator extends the effective time of radar data assimilation for the prediction of strong reflectivity. The physical variables analyzed with the corrected reflectivity operator present more reasonable mesoscale structures than those obtained with the original re-flectivity operator. This suggests that the new sta
文摘针对2003年7月5日江淮流域一次暴雨过程，以NCEP／NCAR1°×1°再分析资料为背景场，采用WRF（weather research and forecasting）模式及其三维变分同化系统，对雷达径向风和E-VAP（extended velocity azimuth processing）反演的水平风场进行了直接同化和反演同化试验，结果表明：直接同化雷达径向风资料后，增加了初始风场的中小尺度信息，改善了垂直速度条件，且在风场作用下水汽分布得到改善；反演同化资料虽然能增加初始场的中小尺度信息，但效果不如直接同化明显；无论直接同化还是反演同化都能改善降水预报，但直接同化好于反演同化，且间隔5～6min比30min的直接同化模拟效果好。
基金National Key Basic Research and Development(973)Program of China(2014CB441406)National Natural Science Foundation of China(91537209 and 41675001)Basic Research Fund of Chinese Academy of Meteorological Sciences(2016Z002and 2016Y008)
文摘A large number of observational analyses have shown that lightning data can be used to indicate areas of deep convection. It is important to assimilate observed lightning data into numerical models, so that more small-scale information can be incorporated to improve the quality of the initial condition and the subsequent forecasts. In this study, the empirical relationship between flash rate, water vapor mixing ratio, and graupel mixing ratio was used to adjust the model relative humidity, which was then assimilated by using the three-dimensional variational data assimilation system of the Weather Research and Forecasting model in cycling mode at 10-min intervals. To find the appropriate assimilation time-window length that yielded significant improvement in both the initial conditions and subsequent forecasts, four experiments with different assimilation time-window lengths were conducted for a squall line case that occurred on 10 July 2007 in North China. It was found that 60 min was the appropriate assimilation time-window length for this case, and longer assimilation window length was unnecessary since no further improvement was present. Forecasts of 1-h accumulated precipitation during the assimilation period and the subsequent 3-h accumulated precipitation were significantly improved compared with the control experiment without lightning data assimilation. The simulated reflectivity was optimal after 30 min of the forecast, it remained optimal during the following 42 min, and the positive effect from lightning data assimilation began to diminish after 72 min of the forecast. Overall,the improvement from lightning data assimilation can be maintained for about 3 h.
基金Supported by the National Key Research and Development Program of China(2019YFC1510103)Basic Research Fund of the Chinese Academy of Meteorological Sciences(2019Y003)。
文摘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.
基金Supported by the National Key Research and Development Program of China(2018YFF0300103)。
文摘Assimilation of surface observations including 2-m temperature(T_(2m))in numerical weather prediction(NWP)models remains a challenging problem owing to differences between the elevation of model terrain and that of actual observation stations.NWP results can be improved only if surface observations are assimilated appropriately.In this study,a T_(2m)data assimilation scheme that carefully considers misrepresentation of model and station terrain was established by using the three-dimensional variational data assimilation(3DVAR)system of the China Meteorological Administration mesoscale model(CMA-MESO).The corresponding forward observation operator,tangent linear operator,and adjoint operator for the T_(2m)observations under three terrain mismatch treatments were developed.The T_(2m)data were assimilated in the same method as that adopted for temperature sounding data with additional representative errors,when station terrain was 100 m higher than model terrain;otherwise,the T_(2m)data were assimilated by using the surface similarity theory assimilation operator.Furthermore,if station terrain was lower than model terrain,additional representative errors were stipulated and corrected.Test of a rainfall case showed that the observation innovation and analysis residuals both exhibited Gaussian distribution and that the analysis increment was reasonable.Moreover,it was found that on completion of the data assimilation cycle,T_(2m)data assimilation obviously influenced the temperature,wind,and relative humidity fields throughout the troposphere,with the greatest impact evident in the lower layers,and that both the area and the intensity of rainfall were better forecasted,especially for the first 12hours.Long-term continuous experiments for 2–28 February and 5–20 July 2020,further verified that T_(2m)data assimilation reduced deviations not only in T_(2m)but also in 10-m wind forecasts.More importantly,the precipitation equitable threat scores were improved over the two experimental periods.In summary,this study conf
