The existence of outliers can seriously influence the analysis of variational data assimilation.Quality control allows us to effectively eliminate or absorb these outliers to produce better analysis fields.In particul...The existence of outliers can seriously influence the analysis of variational data assimilation.Quality control allows us to effectively eliminate or absorb these outliers to produce better analysis fields.In particular,variational quality control(VarQC) can process gray zone outliers and is thus broadly used in variational data assimilation systems.In this study,governing equations are derived for two VarQC algorithms that utilize different contaminated Gaussian distributions(CGDs): Gaussian plus flat distribution and Huber norm distribution.As such,these VarQC algorithms can handle outliers that have non-Gaussian innovations.Then,these VarQC algorithms are implemented in the Global/Regional Assimilation and PrEdiction System(GRAPES) model-level three-dimensional variational data assimilation(m3 DVAR) system.Tests using artificial observations indicate that the VarQC method using the Huber distribution has stronger robustness for including outliers to improve posterior analysis than the VarQC method using the Gaussian plus flat distribution.Furthermore,real observation experiments show that the distribution of observation analysis weights conform well with theory,indicating that the application of VarQC is effective in the GRAPES m3 DVAR system.Subsequent case study and longperiod data assimilation experiments show that the spatial distribution and amplitude of the observation analysis weights are related to the analysis increments of the mass field(geopotential height and temperature).Compared to the control experiment,VarQC experiments have noticeably better posterior mass fields.Finally,the VarQC method using the Huber distribution is superior to the VarQC method using the Gaussian plus flat distribution,especially at the middle and lower levels.展开更多
The magnitude and distribution of observation innovations,which have an important impact on the analyzed accuracy,are critical variables in data assimilation.Variational quality control(VarQC)based on the contaminated...The magnitude and distribution of observation innovations,which have an important impact on the analyzed accuracy,are critical variables in data assimilation.Variational quality control(VarQC)based on the contaminated Gaussian distribution(CGD)of observation innovations is now widely used in data assimilation,owing to the more reasonable representation of the probability density function of innovations that can sufficiently absorb observations by assigning different weights iteratively.However,the inaccurate parameters prevent VarQC from showing the advantages it should have in the GRAPES(Global/Regional Assimilation and PrEdiction System)m3DVAR system.Consequently,the parameter optimization methods are considerable critical studies to improve VarQC.In this paper,we describe two probable CGDs to include the non-Gaussian distribution of actual observation errors,Gaussian plus flat distribution and Huber norm distribution.The potential optimization methods of the parameters are introduced in detail for different VarQCs.With different parameter configurations,the optimization analysis shows that the Gaussian plus flat distribution and the Huber norm distribution are more consistent with the long-tail distribution of actual innovations compared to the Gaussian distribution.The VarQC’s cost and gradient functions with Huber norm distribution are more reasonable,while the VarQC’s cost function with Gaussian plus flat distribution may converge on different minimums due to its nonconcave properties.The weight functions of two VarQCs gradually decrease with the increase of innovation but show different shapes,and the VarQC with Huber norm distribution shows more elasticity to assimilate the observations with a high contamination rate.Moreover,we reveal a general derivation relationship between the CGDs and VarQCs.A novel schematic interpretation that classifies the assimilated data into three categories in VarQC is presented.They are conducive to the development of a new VarQC method in the future.展开更多
Statistical tests and error analysis of cloud drift winds（CDWs） from the FY-2C satellite were made by using radiosonde observations.According to the error characteristics of the CDW,a bias correction using the therm...Statistical tests and error analysis of cloud drift winds（CDWs） from the FY-2C satellite were made by using radiosonde observations.According to the error characteristics of the CDW,a bias correction using the thermal wind theory was applied in the data quality control.The CDW data were then assimilated into the GRAPES-meso model via the GRAPES-3DVar.A torrential rain event that occurred in northwestern China during 1-2 July 2005 was simulated.The results indicate that the CDW data were mainly distributed above 500 hPa and the largest amount of data were at 250 hPa.The CDW data below 500 hPa had errors in both the wind direction and wind speed,and the distribution of the errors was irregular,so these data were discarded.The CDW data above 500 hPa had smaller errors,which presented a Gaussian distribution,so these data were adopted.With the assimilation of the CDW data,the southwest airflow near the torrential rain area became stronger in the initial wind field,which intensified the moisture transport and water vapor flux convergence,and finally improved the accuracy of the 24-h forecast of the torrential rain in both rain intensity and rain areal coverage.展开更多
基金supported by the China Scholarship Councilprimarily sponsored by the National Key R&D Program of China (Grant No.2018YFC1506702 and Grant No.2017YFC1502000)。
文摘The existence of outliers can seriously influence the analysis of variational data assimilation.Quality control allows us to effectively eliminate or absorb these outliers to produce better analysis fields.In particular,variational quality control(VarQC) can process gray zone outliers and is thus broadly used in variational data assimilation systems.In this study,governing equations are derived for two VarQC algorithms that utilize different contaminated Gaussian distributions(CGDs): Gaussian plus flat distribution and Huber norm distribution.As such,these VarQC algorithms can handle outliers that have non-Gaussian innovations.Then,these VarQC algorithms are implemented in the Global/Regional Assimilation and PrEdiction System(GRAPES) model-level three-dimensional variational data assimilation(m3 DVAR) system.Tests using artificial observations indicate that the VarQC method using the Huber distribution has stronger robustness for including outliers to improve posterior analysis than the VarQC method using the Gaussian plus flat distribution.Furthermore,real observation experiments show that the distribution of observation analysis weights conform well with theory,indicating that the application of VarQC is effective in the GRAPES m3 DVAR system.Subsequent case study and longperiod data assimilation experiments show that the spatial distribution and amplitude of the observation analysis weights are related to the analysis increments of the mass field(geopotential height and temperature).Compared to the control experiment,VarQC experiments have noticeably better posterior mass fields.Finally,the VarQC method using the Huber distribution is superior to the VarQC method using the Gaussian plus flat distribution,especially at the middle and lower levels.
基金sponsored by the National Key R&D Program of China(Nos.2018YFC1506702 and 2017YFC1502000).
文摘The magnitude and distribution of observation innovations,which have an important impact on the analyzed accuracy,are critical variables in data assimilation.Variational quality control(VarQC)based on the contaminated Gaussian distribution(CGD)of observation innovations is now widely used in data assimilation,owing to the more reasonable representation of the probability density function of innovations that can sufficiently absorb observations by assigning different weights iteratively.However,the inaccurate parameters prevent VarQC from showing the advantages it should have in the GRAPES(Global/Regional Assimilation and PrEdiction System)m3DVAR system.Consequently,the parameter optimization methods are considerable critical studies to improve VarQC.In this paper,we describe two probable CGDs to include the non-Gaussian distribution of actual observation errors,Gaussian plus flat distribution and Huber norm distribution.The potential optimization methods of the parameters are introduced in detail for different VarQCs.With different parameter configurations,the optimization analysis shows that the Gaussian plus flat distribution and the Huber norm distribution are more consistent with the long-tail distribution of actual innovations compared to the Gaussian distribution.The VarQC’s cost and gradient functions with Huber norm distribution are more reasonable,while the VarQC’s cost function with Gaussian plus flat distribution may converge on different minimums due to its nonconcave properties.The weight functions of two VarQCs gradually decrease with the increase of innovation but show different shapes,and the VarQC with Huber norm distribution shows more elasticity to assimilate the observations with a high contamination rate.Moreover,we reveal a general derivation relationship between the CGDs and VarQCs.A novel schematic interpretation that classifies the assimilated data into three categories in VarQC is presented.They are conducive to the development of a new VarQC method in the future.
基金Supported by the Natural Science Foundation of Yunnan Province(U0933603)the Yunnan Province Science and Technology Program(2009CA023)the Yunnan Province Key Science and Technology and High-tech Project(2006SG25)
文摘Statistical tests and error analysis of cloud drift winds（CDWs） from the FY-2C satellite were made by using radiosonde observations.According to the error characteristics of the CDW,a bias correction using the thermal wind theory was applied in the data quality control.The CDW data were then assimilated into the GRAPES-meso model via the GRAPES-3DVar.A torrential rain event that occurred in northwestern China during 1-2 July 2005 was simulated.The results indicate that the CDW data were mainly distributed above 500 hPa and the largest amount of data were at 250 hPa.The CDW data below 500 hPa had errors in both the wind direction and wind speed,and the distribution of the errors was irregular,so these data were discarded.The CDW data above 500 hPa had smaller errors,which presented a Gaussian distribution,so these data were adopted.With the assimilation of the CDW data,the southwest airflow near the torrential rain area became stronger in the initial wind field,which intensified the moisture transport and water vapor flux convergence,and finally improved the accuracy of the 24-h forecast of the torrential rain in both rain intensity and rain areal coverage.
