Land surface temperature(LST)is the key variable in land-atmosphere interaction,having an important impact on weather and climate forecasting.However,achieving consistent analysis of LST and the atmosphere in assimila...Land surface temperature(LST)is the key variable in land-atmosphere interaction,having an important impact on weather and climate forecasting.However,achieving consistent analysis of LST and the atmosphere in assimilation is quite challenging.This is because there is limited knowledge about the cross-component background error covariance(BEC)between LST and atmospheric state variables.This study aims to clarify whether there is a relationship between the error of LST and atmospheric variables,and whether this relationship varies spatially and temporally.To this end,the BEC coupled with atmospheric variables and LST was constructed(LST-BEC),and its characteristics were analyzed based on the 2023 mei-yu season.The general characteristics of LST-BEC show that the LST is mainly correlated with the atmospheric temperature and the correlation decreases gradually with a rise in atmospheric height,and the error standard deviation of the LST is noticeably larger than that of the low-level atmospheric temperature.The spatiotemporal characteristics of LST-BEC on the heavy-rain day and light-rain day show that the error correlation and error standard deviation of LST and low-level atmospheric temperature and humidity are closely related to the weather background,and also have obvious diurnal variations.These results provide valuable information for strongly coupled land-atmosphere assimilation.展开更多
Background error covariance plays an important role in any variational data assimilation system, because it determines how information from observations is spread in model space and between different model variables. ...Background error covariance plays an important role in any variational data assimilation system, because it determines how information from observations is spread in model space and between different model variables. In this paper, the use of orthogonal wavelets in representation of background error covariance over a limited area is studied. Based on the WRF model and its 3D-VAR system, an algorithm using orthogonal wavelets to model background error covariance is developed. Because each wavelet function contains information on both position and scale, using a diagonal correlation matrix in wavelet space gives the possibility to represent some anisotropic and inhomogeneous characteristics of background error covariance. The experiments show that local correlation functions are better modeled than spectral methods. The formulation of wavelet background error covariance is tested with the typhoon Kaemi (2006). The results of experiments indicate that the subsequent forecasts of typhoon Kaemi’s track and intensity are significantly improved by the new method.展开更多
The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is propose...The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is proposed to estimate background error covariance for use in Optimum Interpolation. At every model level, anisotropic correlation scales are obtained that give a more detailed description of the spatial correlation structure. Furthermore, the impact of the background field itself is included in the background error covariance. The methodology of the estimation is presented and the structure of the covariance is examined. The results of 20-year assimilation experiments are compared with observations from TOGA-TAO (The Tropical Ocean-Global Atmosphere-Tropical Atmosphere Ocean) array and other analysis data.展开更多
The quality of background error statistics is one of the key components for successful assimilation of observations in a numerical model.The background error covariance(BEC) of ocean waves is generally estimated under...The quality of background error statistics is one of the key components for successful assimilation of observations in a numerical model.The background error covariance(BEC) of ocean waves is generally estimated under an assumption that it is stationary over a period of time and uniform over a domain.However,error statistics are in fact functions of the physical processes governing the meteorological situation and vary with the wave condition.In this paper,we simulated the BEC of the significant wave height(SWH) employing Monte Carlo methods.An interesting result is that the BEC varies consistently with the mean wave direction(MWD).In the model domain,the BEC of the SWH decreases significantly when the MWD changes abruptly.A new BEC model of the SWH based on the correlation between the BEC and MWD was then developed.A case study of regional data assimilation was performed,where the SWH observations of buoy 22001 were used to assess the SWH hindcast.The results show that the new BEC model benefits wave prediction and allows reasonable approximations of anisotropy and inhomogeneous errors.展开更多
Use of data assimilation to initialize hydrometeors plays a vital role in numerical weather prediction(NWP).To directly analyze hydrometeors in data assimilation systems from cloud-sensitive observations,hydrometeor c...Use of data assimilation to initialize hydrometeors plays a vital role in numerical weather prediction(NWP).To directly analyze hydrometeors in data assimilation systems from cloud-sensitive observations,hydrometeor control variables are necessary.Common data assimilation systems theoretically require that the probability density functions(PDFs)of analysis,background,and observation errors should satisfy the Gaussian unbiased assumptions.In this study,a Gaussian transform method is proposed to transform hydrometeors to more Gaussian variables,which is modified from the Softmax function and renamed as Quasi-Softmax transform.The Quasi-Softmax transform method then is compared to the original hydrometeor mixing ratios and their logarithmic transform and Softmax transform.The spatial distribution,the non-Gaussian nature of the background errors,and the characteristics of the background errors of hydrometeors in each method are studied.Compared to the logarithmic and Softmax transform,the Quasi-Softmax method keeps the vertical distribution of the original hydrometeor mixing ratios to the greatest extent.The results of the D′Agostino test show that the hydrometeors transformed by the Quasi-Softmax method are more Gaussian when compared to the other methods.The Gaussian transform has been added to the control variable transform to estimate the background error covariances.Results show that the characteristics of the hydrometeor background errors are reasonable for the Quasi-Softmax method.The transformed hydrometeors using the Quasi-Softmax transform meet the Gaussian unbiased assumptions of the data assimilation system,and are promising control variables for data assimilation systems.展开更多
Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper di...Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC （National Meteorological Center） method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated.展开更多
Background error covariance(BEC)plays an essential role in variational data assimilation.Most variational data assimilation systems still use static BEC.Actually,the characteristics of BEC vary with season,day,and eve...Background error covariance(BEC)plays an essential role in variational data assimilation.Most variational data assimilation systems still use static BEC.Actually,the characteristics of BEC vary with season,day,and even hour of the background.National Meteorological Center-based diurnally varying BECs had been proposed,but the diurnal variation characteristics were gained by climatic samples.Ensemble methods can obtain the background error characteristics that suit the samples in the current moment.Therefore,to gain more reasonable diurnally varying BECs,in this study,ensemble-based diurnally varying BECs are generated and the diurnal variation characteristics are discussed.Their impacts are then evaluated by cycling data assimilation and forecasting experiments for a week based on the operational China Meteorological Administration-Beijing system.Clear diurnal variation in the standard deviation of ensemble forecasts and ensemble-based BECs can be identified,consistent with the diurnal variation characteristics of the atmosphere.The results of one-week cycling data assimilation and forecasting show that the application of diurnally varying BECs reduces the RMSEs in the analysis and 6-h forecast.Detailed analysis of a convective rainfall case shows that the distribution of the accumulated precipitation forecast using the diurnally varying BECs is closer to the observation than using the static BEC.Besides,the cycle-averaged precipitation scores in all magnitudes are improved,especially for the heavy precipitation,indicating the potential of using diurnally varying BEC in operational applications.展开更多
The large-scale and small-scale errors could affect background error covariances for a regional numerical model with the specified grid resolution.Based on the different background error covariances influenced by diff...The large-scale and small-scale errors could affect background error covariances for a regional numerical model with the specified grid resolution.Based on the different background error covariances influenced by different scale errors,this study tries to construct a so-called"optimal background error covariances"to consider the interactions among different scale errors.For this purpose,a linear combination of the forecast differences influenced by information of errors at different scales is used to construct the new forecast differences for estimating optimal background error covariances.By adjusting the relative weight of the forecast differences influenced by information of smaller-scale errors,the relative influence of different scale errors on optimal background error covariances can be changed.For a heavy rainfall case,the corresponding optimal background error covariances can be estimated through choosing proper weighting factor for forecast differences influenced by information of smaller-scale errors.The data assimilation and forecast with these optimal covariances show that,the corresponding analyses and forecasts can lead to superior quality,compared with those using covariances that just introduce influences of larger-or smallerscale errors.Due to the interactions among different scale errors included in optimal background error covariances,relevant analysis increments can properly describe weather systems(processes)at different scales,such as dynamic lifting,thermodynamic instability and advection of moisture at large scale,high-level and low-level jet at synoptic scale,and convective systems at mesoscale and small scale,as well as their interactions.As a result,the corresponding forecasts can be improved.展开更多
In the previous study, the influences of introducing larger- and smaller-scale errors on the background error covariances estimated at the given scales were investigated, respectively. This study used the eovariances ...In the previous study, the influences of introducing larger- and smaller-scale errors on the background error covariances estimated at the given scales were investigated, respectively. This study used the eovariances obtained in the previous study in the data assimilation and model forecast system based on three-dimensional variational method and the Weather Research and Forecasting model. In this study, analyses and forecasts from this system with different covariances for a period of one month were compared, and the causes for differing results were presented. The varia- tions of analysis increments with different-scale errors are consistent with those of variances and correlations of back- ground errors that were reported in the previous paper. In particular, the introduction of smaller-scale errors leads to greater amplitudes in analysis increments for medium-scale wind at the heights of both high- and low-level jets. Tem- perature and humidity analysis increments are greater at the corresponding scales at the middle- and upper-levels. These analysis increments could improve the intensity of the jet-convection system that includes jets at different levels and the coupling between them that is associated with latent heat release. These changes in analyses will contribute to more ac- curate wind and temperature forecasts in the corresponding areas. When smaller-scale errors are included, humidity analysis increments are significantly enhanced at large scales and lower levels, to moisten southern analyses. Thus, dry bias can be corrected, which will improve humidity forecasts. Moreover, the inclusion of larger- （smaller-） scale errors will be beneficial for the accuracy of forecasts of heavy （light） precipitation at large （small） scales because of the ampli- fication （diminution） of the intensity and area in precipitation forecasts.展开更多
基金sponsored by the National Natural Science Foundation of China[grant number U2442218]。
文摘Land surface temperature(LST)is the key variable in land-atmosphere interaction,having an important impact on weather and climate forecasting.However,achieving consistent analysis of LST and the atmosphere in assimilation is quite challenging.This is because there is limited knowledge about the cross-component background error covariance(BEC)between LST and atmospheric state variables.This study aims to clarify whether there is a relationship between the error of LST and atmospheric variables,and whether this relationship varies spatially and temporally.To this end,the BEC coupled with atmospheric variables and LST was constructed(LST-BEC),and its characteristics were analyzed based on the 2023 mei-yu season.The general characteristics of LST-BEC show that the LST is mainly correlated with the atmospheric temperature and the correlation decreases gradually with a rise in atmospheric height,and the error standard deviation of the LST is noticeably larger than that of the low-level atmospheric temperature.The spatiotemporal characteristics of LST-BEC on the heavy-rain day and light-rain day show that the error correlation and error standard deviation of LST and low-level atmospheric temperature and humidity are closely related to the weather background,and also have obvious diurnal variations.These results provide valuable information for strongly coupled land-atmosphere assimilation.
基金National Natural Science Foundation of China (40775064)
文摘Background error covariance plays an important role in any variational data assimilation system, because it determines how information from observations is spread in model space and between different model variables. In this paper, the use of orthogonal wavelets in representation of background error covariance over a limited area is studied. Based on the WRF model and its 3D-VAR system, an algorithm using orthogonal wavelets to model background error covariance is developed. Because each wavelet function contains information on both position and scale, using a diagonal correlation matrix in wavelet space gives the possibility to represent some anisotropic and inhomogeneous characteristics of background error covariance. The experiments show that local correlation functions are better modeled than spectral methods. The formulation of wavelet background error covariance is tested with the typhoon Kaemi (2006). The results of experiments indicate that the subsequent forecasts of typhoon Kaemi’s track and intensity are significantly improved by the new method.
基金supported by the National Key Program for Developing Basic Sciences(G1999032801)the National Natural Science Foundation of China(Grant No.40005007,40233033,and 40221503)
文摘The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is proposed to estimate background error covariance for use in Optimum Interpolation. At every model level, anisotropic correlation scales are obtained that give a more detailed description of the spatial correlation structure. Furthermore, the impact of the background field itself is included in the background error covariance. The methodology of the estimation is presented and the structure of the covariance is examined. The results of 20-year assimilation experiments are compared with observations from TOGA-TAO (The Tropical Ocean-Global Atmosphere-Tropical Atmosphere Ocean) array and other analysis data.
基金Supported by the National Natural Science Foundation of China (Nos.40806011,U1133001)the Open Fund of the Key Laboratory of Ocean Circulation and Waves,Chinese Academy of Sciences(No. KLOCAW0806)
文摘The quality of background error statistics is one of the key components for successful assimilation of observations in a numerical model.The background error covariance(BEC) of ocean waves is generally estimated under an assumption that it is stationary over a period of time and uniform over a domain.However,error statistics are in fact functions of the physical processes governing the meteorological situation and vary with the wave condition.In this paper,we simulated the BEC of the significant wave height(SWH) employing Monte Carlo methods.An interesting result is that the BEC varies consistently with the mean wave direction(MWD).In the model domain,the BEC of the SWH decreases significantly when the MWD changes abruptly.A new BEC model of the SWH based on the correlation between the BEC and MWD was then developed.A case study of regional data assimilation was performed,where the SWH observations of buoy 22001 were used to assess the SWH hindcast.The results show that the new BEC model benefits wave prediction and allows reasonable approximations of anisotropy and inhomogeneous errors.
基金National Key Research and Development Program of China(Grant No.2017YFC1502102)National Natural Science Foundation of China(Grant No.42075148)+1 种基金Graduate Research and Innovation Projects of Jiangsu Province(Grant No.KYCX20_0910)the High-Performance Computing Center of Nanjing University of Information Science and Technology(NUIST).
文摘Use of data assimilation to initialize hydrometeors plays a vital role in numerical weather prediction(NWP).To directly analyze hydrometeors in data assimilation systems from cloud-sensitive observations,hydrometeor control variables are necessary.Common data assimilation systems theoretically require that the probability density functions(PDFs)of analysis,background,and observation errors should satisfy the Gaussian unbiased assumptions.In this study,a Gaussian transform method is proposed to transform hydrometeors to more Gaussian variables,which is modified from the Softmax function and renamed as Quasi-Softmax transform.The Quasi-Softmax transform method then is compared to the original hydrometeor mixing ratios and their logarithmic transform and Softmax transform.The spatial distribution,the non-Gaussian nature of the background errors,and the characteristics of the background errors of hydrometeors in each method are studied.Compared to the logarithmic and Softmax transform,the Quasi-Softmax method keeps the vertical distribution of the original hydrometeor mixing ratios to the greatest extent.The results of the D′Agostino test show that the hydrometeors transformed by the Quasi-Softmax method are more Gaussian when compared to the other methods.The Gaussian transform has been added to the control variable transform to estimate the background error covariances.Results show that the characteristics of the hydrometeor background errors are reasonable for the Quasi-Softmax method.The transformed hydrometeors using the Quasi-Softmax transform meet the Gaussian unbiased assumptions of the data assimilation system,and are promising control variables for data assimilation systems.
基金funded by the Korea Meteorological Administration Research and Development Program under Grant RACS 2010-2016supported by the Brain Korea 21 project of the Ministry of Education and Human Resources Development of the Korean government
文摘Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC （National Meteorological Center） method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated.
基金This work was jointly sponsored by the National Natural Science Foundation of China[grant number 42075148]the Outreach Projects of the State Key Laboratory of Severe Weather[grant number 2021LASWA08]+1 种基金the Outreach Projects of the Key Laboratory of Meteorological Disaster[grant number KLME202209]supported by the High-Performance Computing Center of Nanjing University of Information Science and Technology(NUIST).
文摘Background error covariance(BEC)plays an essential role in variational data assimilation.Most variational data assimilation systems still use static BEC.Actually,the characteristics of BEC vary with season,day,and even hour of the background.National Meteorological Center-based diurnally varying BECs had been proposed,but the diurnal variation characteristics were gained by climatic samples.Ensemble methods can obtain the background error characteristics that suit the samples in the current moment.Therefore,to gain more reasonable diurnally varying BECs,in this study,ensemble-based diurnally varying BECs are generated and the diurnal variation characteristics are discussed.Their impacts are then evaluated by cycling data assimilation and forecasting experiments for a week based on the operational China Meteorological Administration-Beijing system.Clear diurnal variation in the standard deviation of ensemble forecasts and ensemble-based BECs can be identified,consistent with the diurnal variation characteristics of the atmosphere.The results of one-week cycling data assimilation and forecasting show that the application of diurnally varying BECs reduces the RMSEs in the analysis and 6-h forecast.Detailed analysis of a convective rainfall case shows that the distribution of the accumulated precipitation forecast using the diurnally varying BECs is closer to the observation than using the static BEC.Besides,the cycle-averaged precipitation scores in all magnitudes are improved,especially for the heavy precipitation,indicating the potential of using diurnally varying BEC in operational applications.
基金National Natural Science Foundation of China(41130964)National Special Funding Project for Meteorology(GYHY-201006004)
文摘The large-scale and small-scale errors could affect background error covariances for a regional numerical model with the specified grid resolution.Based on the different background error covariances influenced by different scale errors,this study tries to construct a so-called"optimal background error covariances"to consider the interactions among different scale errors.For this purpose,a linear combination of the forecast differences influenced by information of errors at different scales is used to construct the new forecast differences for estimating optimal background error covariances.By adjusting the relative weight of the forecast differences influenced by information of smaller-scale errors,the relative influence of different scale errors on optimal background error covariances can be changed.For a heavy rainfall case,the corresponding optimal background error covariances can be estimated through choosing proper weighting factor for forecast differences influenced by information of smaller-scale errors.The data assimilation and forecast with these optimal covariances show that,the corresponding analyses and forecasts can lead to superior quality,compared with those using covariances that just introduce influences of larger-or smallerscale errors.Due to the interactions among different scale errors included in optimal background error covariances,relevant analysis increments can properly describe weather systems(processes)at different scales,such as dynamic lifting,thermodynamic instability and advection of moisture at large scale,high-level and low-level jet at synoptic scale,and convective systems at mesoscale and small scale,as well as their interactions.As a result,the corresponding forecasts can be improved.
基金National Natural Science Foundation of China through Grants(41461164008,41130964)National Key Project for Basic Research(973 Project)(2015452803)+1 种基金Science and Technology Planning Project for Guangdong Province(2012A061400012)China Meteorological Administration(GYHY201406009)
文摘In the previous study, the influences of introducing larger- and smaller-scale errors on the background error covariances estimated at the given scales were investigated, respectively. This study used the eovariances obtained in the previous study in the data assimilation and model forecast system based on three-dimensional variational method and the Weather Research and Forecasting model. In this study, analyses and forecasts from this system with different covariances for a period of one month were compared, and the causes for differing results were presented. The varia- tions of analysis increments with different-scale errors are consistent with those of variances and correlations of back- ground errors that were reported in the previous paper. In particular, the introduction of smaller-scale errors leads to greater amplitudes in analysis increments for medium-scale wind at the heights of both high- and low-level jets. Tem- perature and humidity analysis increments are greater at the corresponding scales at the middle- and upper-levels. These analysis increments could improve the intensity of the jet-convection system that includes jets at different levels and the coupling between them that is associated with latent heat release. These changes in analyses will contribute to more ac- curate wind and temperature forecasts in the corresponding areas. When smaller-scale errors are included, humidity analysis increments are significantly enhanced at large scales and lower levels, to moisten southern analyses. Thus, dry bias can be corrected, which will improve humidity forecasts. Moreover, the inclusion of larger- （smaller-） scale errors will be beneficial for the accuracy of forecasts of heavy （light） precipitation at large （small） scales because of the ampli- fication （diminution） of the intensity and area in precipitation forecasts.
