The sea ice concentration observation from satellite remote sensing includes the spatial multi-scale information.However,traditional data assimilation methods cannot better extract the valuable information due to the ...The sea ice concentration observation from satellite remote sensing includes the spatial multi-scale information.However,traditional data assimilation methods cannot better extract the valuable information due to the complicated variability of the sea ice concentration in the marginal ice zone.A successive corrections analysis using variational optimization method,called spatial multi-scale recursive filter(SMRF),has been designed in this paper to extract multi-scale information resolved by sea ice observations.It is a combination of successive correction methods(SCM)and minimization algorithms,in which various observational scales,from longer to shorter wavelengths,can be extracted successively.As a variational objective analysis scheme,it gains the advantage over the conventional approaches that analyze all scales resolved by observations at one time,and also,the specification of parameters is more convenient.Results of single-observation experiment demonstrate that the SMRF scheme possesses a good ability in propagating observational signals.Further,it shows a superior performance in extracting multi-scale information in a two-dimensional sea ice concentration(SIC)experiment with the real observations from Special Sensor Microwave/Imager SIC(SSMI).展开更多
Parameter estimation is defined as the process to adjust or optimize the model parameter using observations.A long-term problem in ensemble-based parameter estimation methods is that the parameters are assumed to be c...Parameter estimation is defined as the process to adjust or optimize the model parameter using observations.A long-term problem in ensemble-based parameter estimation methods is that the parameters are assumed to be constant during model integration.This assumption will cause underestimation of parameter ensemble spread,such that the parameter ensemble tends to collapse before an optimal solution is found.In this work,a two-stage inflation method is developed for parameter estimation,which can address the collapse of parameter ensemble due to the constant evolution of parameters.In the first stage,adaptive inflation is applied to the augmented states,in which the global scalar parameter is transformed to fields with spatial dependence.In the second stage,extra multiplicative inflation is used to inflate the scalar parameter ensemble to compensate for constant parameter evolution,where the inflation factor is determined according to the spread growth ratio of model states.The observation system simulation experiment with Community Earth System Model(CESM)shows that the second stage of the inflation scheme plays a crucial role in successful parameter estimation.With proper multiplicative inflation factors,the parameter estimation can effectively reduce the parameter biases,providing more accurate analyses.展开更多
基金The National Key Research and Development Program of China under contract Nos 2017YFC1404103 and 2016YFC1401701the National Programme on Global Change and Air-Sea Interaction of China under contract GASI-IPOVAI-04the National Natural Science Foundation of China under contract Nos 41876014 and 41606039.
文摘The sea ice concentration observation from satellite remote sensing includes the spatial multi-scale information.However,traditional data assimilation methods cannot better extract the valuable information due to the complicated variability of the sea ice concentration in the marginal ice zone.A successive corrections analysis using variational optimization method,called spatial multi-scale recursive filter(SMRF),has been designed in this paper to extract multi-scale information resolved by sea ice observations.It is a combination of successive correction methods(SCM)and minimization algorithms,in which various observational scales,from longer to shorter wavelengths,can be extracted successively.As a variational objective analysis scheme,it gains the advantage over the conventional approaches that analyze all scales resolved by observations at one time,and also,the specification of parameters is more convenient.Results of single-observation experiment demonstrate that the SMRF scheme possesses a good ability in propagating observational signals.Further,it shows a superior performance in extracting multi-scale information in a two-dimensional sea ice concentration(SIC)experiment with the real observations from Special Sensor Microwave/Imager SIC(SSMI).
基金The National Key Research and Development Program under contract No.2017YFA0604202the Fundamental Research Funds for the Central Universities under contract No.B210201022the National Natural Science Foundation of China under contract Nos 42176003,41690124,41806032 and 41806038.
文摘Parameter estimation is defined as the process to adjust or optimize the model parameter using observations.A long-term problem in ensemble-based parameter estimation methods is that the parameters are assumed to be constant during model integration.This assumption will cause underestimation of parameter ensemble spread,such that the parameter ensemble tends to collapse before an optimal solution is found.In this work,a two-stage inflation method is developed for parameter estimation,which can address the collapse of parameter ensemble due to the constant evolution of parameters.In the first stage,adaptive inflation is applied to the augmented states,in which the global scalar parameter is transformed to fields with spatial dependence.In the second stage,extra multiplicative inflation is used to inflate the scalar parameter ensemble to compensate for constant parameter evolution,where the inflation factor is determined according to the spread growth ratio of model states.The observation system simulation experiment with Community Earth System Model(CESM)shows that the second stage of the inflation scheme plays a crucial role in successful parameter estimation.With proper multiplicative inflation factors,the parameter estimation can effectively reduce the parameter biases,providing more accurate analyses.