Variational Quality Control of Non-Gaussian Innovations in the GRAPES m3DVAR System: Mass Field Evaluation of Assimilation Experiments

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.

Simulated Sensitivity of the Tropical Cyclone Eyewall Replacement Cycle to the Ambient Temperature Profile

In this study, the impacts of the environmental temperature profile on the tropical cyclone eyewall replacement cycle are examined using idealized numerical simulations. It is found that the environmental thermal condition can greatly affect the formation and structure of a secondary eyewall and the intensity change during the eyewall replacement cycle. Simulation with a warmer thermal profile produces a larger moat and a prolonged eyewall replacement cycle. It is revealed that the enhanced static stability greatly suppresses convection, and thus causes slow secondary eyewall formation. The possible processes influencing the decay of inner eyewall convection are investigated. It is revealed that the demise of the inner eyewall is related to a choking effect associated with outer eyewall convection, the radial distribution of moist entropy fluxes within the moat region, the enhanced static stability in the inner-core region, and the interaction between the inner and outer eyewalls due to the barotropic instability. This study motivates further research into how environmental conditions influence tropical cyclone dynamics and thermodynamics.

Variational quality control of non-Gaussian innovations and its parametric optimizations for the GRAPES m3DVAR system

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.