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- Author or Editor: Cécilien Charette x
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Abstract
The first part of this paper presents the results of a study of the structure of the observed residuals, or differences, between radiosonde data and the short-range forecasts that are used as trial fields in an operational hemispheric data assimilation scheme. The study is based on fitting appropriate functional representations to horizontal correlations of observed height and wind residuals. Rather than represent the height residuals by the sum of a degenerate second-order autoregressive function and an additive constant to account for long-wave error, as in a previous study, we use a representation consisting of a sum of two degenerate third-order autoregressive functions of the form (1 + cr + c2r2 /3) exp(−cr), where r represents radial distance. For the wind residuals, we use the functional form that follows by geostrophy. In addition to examining the structure of the horizontal and vertical correlations, we also present other statistics relating to the performance of the data assimilation procedure, such as vertical profiles of the magnitude of the observed wind and height residuals for various regions.
In the second part of the paper, the results of the study are used as a basis for specifying interpolation statistics for the objective analysis. To evaluate the impact of the new interpolation statistics, various objective measures of analysis performance are examined and parallel 48-h forecasts are performed. It is found that significant improvements result when the new interpolation statistics are used in the data assimilation procedure.
Abstract
The first part of this paper presents the results of a study of the structure of the observed residuals, or differences, between radiosonde data and the short-range forecasts that are used as trial fields in an operational hemispheric data assimilation scheme. The study is based on fitting appropriate functional representations to horizontal correlations of observed height and wind residuals. Rather than represent the height residuals by the sum of a degenerate second-order autoregressive function and an additive constant to account for long-wave error, as in a previous study, we use a representation consisting of a sum of two degenerate third-order autoregressive functions of the form (1 + cr + c2r2 /3) exp(−cr), where r represents radial distance. For the wind residuals, we use the functional form that follows by geostrophy. In addition to examining the structure of the horizontal and vertical correlations, we also present other statistics relating to the performance of the data assimilation procedure, such as vertical profiles of the magnitude of the observed wind and height residuals for various regions.
In the second part of the paper, the results of the study are used as a basis for specifying interpolation statistics for the objective analysis. To evaluate the impact of the new interpolation statistics, various objective measures of analysis performance are examined and parallel 48-h forecasts are performed. It is found that significant improvements result when the new interpolation statistics are used in the data assimilation procedure.
Abstract
An intercomparison of the Environment Canada variational and ensemble Kalman filter (EnKF) data assimilation systems is presented in the context of producing global deterministic numerical weather forecasts. Five different variational data assimilation approaches are considered including four-dimensional variational data assimilation (4D-Var) and three-dimensional variational data assimilation (3D-Var) with first guess at the appropriate time (3D-FGAT). Also included among these is a new approach, called Ensemble-4D-Var (En-4D-Var), that uses 4D ensemble background-error covariances from the EnKF. A description of the experimental configurations and results from single-observation experiments are presented in the first part of this two-part paper. The present paper focuses on results from medium-range deterministic forecasts initialized with analyses from the EnKF and the five variational data assimilation approaches for the period of February 2007. All experiments assimilate exactly the same full set of meteorological observations and use the same configuration of the forecast model to produce global deterministic medium-range forecasts.
The quality of forecasts in the short (medium) range obtained by using the EnKF ensemble mean analysis is slightly degraded (improved) in the extratropics relative to using the 4D-Var analysis with background-error covariances similar to those used operationally. The use of the EnKF flow-dependent error covariances in the variational system (4D-Var or 3D-FGAT) leads to large (modest) forecast improvements in the southern extratropics (tropics) as compared with using covariances similar to the operational system (a gain of up to 9 h at day 5). The En-4D-Var approach leads to (i) either improved or similar forecast quality when compared with the 4D-Var experiment similar to the currently operational system, (ii) slightly worse forecast quality when compared with the 4D-Var experiment with EnKF error covariances, and (iii) generally similar forecast quality when compared with the EnKF experiment.
Abstract
An intercomparison of the Environment Canada variational and ensemble Kalman filter (EnKF) data assimilation systems is presented in the context of producing global deterministic numerical weather forecasts. Five different variational data assimilation approaches are considered including four-dimensional variational data assimilation (4D-Var) and three-dimensional variational data assimilation (3D-Var) with first guess at the appropriate time (3D-FGAT). Also included among these is a new approach, called Ensemble-4D-Var (En-4D-Var), that uses 4D ensemble background-error covariances from the EnKF. A description of the experimental configurations and results from single-observation experiments are presented in the first part of this two-part paper. The present paper focuses on results from medium-range deterministic forecasts initialized with analyses from the EnKF and the five variational data assimilation approaches for the period of February 2007. All experiments assimilate exactly the same full set of meteorological observations and use the same configuration of the forecast model to produce global deterministic medium-range forecasts.
The quality of forecasts in the short (medium) range obtained by using the EnKF ensemble mean analysis is slightly degraded (improved) in the extratropics relative to using the 4D-Var analysis with background-error covariances similar to those used operationally. The use of the EnKF flow-dependent error covariances in the variational system (4D-Var or 3D-FGAT) leads to large (modest) forecast improvements in the southern extratropics (tropics) as compared with using covariances similar to the operational system (a gain of up to 9 h at day 5). The En-4D-Var approach leads to (i) either improved or similar forecast quality when compared with the 4D-Var experiment similar to the currently operational system, (ii) slightly worse forecast quality when compared with the 4D-Var experiment with EnKF error covariances, and (iii) generally similar forecast quality when compared with the EnKF experiment.
Abstract
An intercomparison of the Environment Canada variational and ensemble Kalman filter (EnKF) data assimilation systems is presented in the context of global deterministic NWP. In an EnKF experiment having the same spatial resolution as the inner loop in the four-dimensional variational data assimilation system (4D-Var), the mean of each analysis ensemble is used to initialize the higher-resolution deterministic forecasts. Five different variational data assimilation experiments are also conducted. These include both 4D-Var and 3D-Var (with first guess at appropriate time) experiments using either (i) prescribed background-error covariances similar to those used operationally, which are static in time and include horizontally homogeneous and isotropic correlations; or (ii) flow-dependent covariances computed from the EnKF background ensembles with spatial covariance localization applied. The fifth variational data assimilation experiment is a new approach called the Ensemble-4D-Var (En-4D-Var). This approach uses 4D flow-dependent background-error covariances estimated from EnKF ensembles to produce a 4D analysis without the need for tangent-linear or adjoint versions of the forecast model. In this first part of a two-part paper, results from a series of idealized assimilation experiments are presented. In these experiments, only a single observation or vertical profile of observations is assimilated to explore the impact of various fundamental differences among the EnKF and the various variational data assimilation approaches considered. In particular, differences in the application of covariance localization in the EnKF and variational approaches are shown to have a significant impact on the assimilation of satellite radiance observations. The results also demonstrate that 4D-Var and the EnKF can both produce similar 4D background-error covariances within a 6-h assimilation window. In the second part, results from medium-range deterministic forecasts for the study period of February 2007 are presented for the EnKF and the five variational data assimilation approaches considered.
Abstract
An intercomparison of the Environment Canada variational and ensemble Kalman filter (EnKF) data assimilation systems is presented in the context of global deterministic NWP. In an EnKF experiment having the same spatial resolution as the inner loop in the four-dimensional variational data assimilation system (4D-Var), the mean of each analysis ensemble is used to initialize the higher-resolution deterministic forecasts. Five different variational data assimilation experiments are also conducted. These include both 4D-Var and 3D-Var (with first guess at appropriate time) experiments using either (i) prescribed background-error covariances similar to those used operationally, which are static in time and include horizontally homogeneous and isotropic correlations; or (ii) flow-dependent covariances computed from the EnKF background ensembles with spatial covariance localization applied. The fifth variational data assimilation experiment is a new approach called the Ensemble-4D-Var (En-4D-Var). This approach uses 4D flow-dependent background-error covariances estimated from EnKF ensembles to produce a 4D analysis without the need for tangent-linear or adjoint versions of the forecast model. In this first part of a two-part paper, results from a series of idealized assimilation experiments are presented. In these experiments, only a single observation or vertical profile of observations is assimilated to explore the impact of various fundamental differences among the EnKF and the various variational data assimilation approaches considered. In particular, differences in the application of covariance localization in the EnKF and variational approaches are shown to have a significant impact on the assimilation of satellite radiance observations. The results also demonstrate that 4D-Var and the EnKF can both produce similar 4D background-error covariances within a 6-h assimilation window. In the second part, results from medium-range deterministic forecasts for the study period of February 2007 are presented for the EnKF and the five variational data assimilation approaches considered.
Abstract
The improvement of analysis and data assimilation techniques can have a large impact, as shown here in the context of the Canadian global and regional data assimilation systems. Both of these systems utilize the same analysis component that was recently changed as follows: (a) a completely 3D algorithm replaced the previous split 3D scheme, which involved separate vertical and horizontal steps; (b) the assimilation of SATEM data was revised and is now done in terms of thicknesses over relatively thick layers; (c) an additional analysis level (at 925 hPa) was added and a derived temperature analysis replaced the former temperature analysis; (d) observation and forecast error statistics were revised; and (e) a correction procedure was introduced for certain types of radiosondes to offset the negative impact of solar and longwave radiation.
While many of these changes are interrelated, preventing a systematic evaluation of each in isolation, it is shown that the revised 3D algorithm eliminates a problem that sometimes occurred in areas of dense surface data, SATEM data have a large positive impact in the Southern Hemisphere, and the radiosonde bias-correction scheme very significantly reduces the geopotential height bias observed previously in the upper atmosphere over certain regions, such as western North America.
The overall evaluation of the analysis changes shows that in general the new analysis results in more accurate 6-h forecasts, with the largest improvements in the Tropics and especially in the Southern Hemisphere. In conjunction with these forecast gains, the evaluation of the general circulation statistics for August also show significant changes: the new analyses are more energetic, exhibiting a substantially stronger Hadley circulation and stronger zonal winds about Antarctica. The global forecasts from the revised analysis system consistently exhibit a significantly more rapid spinup of global precipitation as compared to the previous system.
Abstract
The improvement of analysis and data assimilation techniques can have a large impact, as shown here in the context of the Canadian global and regional data assimilation systems. Both of these systems utilize the same analysis component that was recently changed as follows: (a) a completely 3D algorithm replaced the previous split 3D scheme, which involved separate vertical and horizontal steps; (b) the assimilation of SATEM data was revised and is now done in terms of thicknesses over relatively thick layers; (c) an additional analysis level (at 925 hPa) was added and a derived temperature analysis replaced the former temperature analysis; (d) observation and forecast error statistics were revised; and (e) a correction procedure was introduced for certain types of radiosondes to offset the negative impact of solar and longwave radiation.
While many of these changes are interrelated, preventing a systematic evaluation of each in isolation, it is shown that the revised 3D algorithm eliminates a problem that sometimes occurred in areas of dense surface data, SATEM data have a large positive impact in the Southern Hemisphere, and the radiosonde bias-correction scheme very significantly reduces the geopotential height bias observed previously in the upper atmosphere over certain regions, such as western North America.
The overall evaluation of the analysis changes shows that in general the new analysis results in more accurate 6-h forecasts, with the largest improvements in the Tropics and especially in the Southern Hemisphere. In conjunction with these forecast gains, the evaluation of the general circulation statistics for August also show significant changes: the new analyses are more energetic, exhibiting a substantially stronger Hadley circulation and stronger zonal winds about Antarctica. The global forecasts from the revised analysis system consistently exhibit a significantly more rapid spinup of global precipitation as compared to the previous system.
Abstract
A global data assimilation system has been in operation at the Canadian Meteorological Centre (CMC) since March 1991 when it replaced the previous hemispheric system. This paper describes the system and presents an evaluation of its performance from several points of view, including the fit of the analyses and short-range forecasts to observations, the relative roles of various components of the system, the functioning of some specific subcomponents in a particular case, and the ability of the system to represent important as aspects of the mean monthly general circulation. This latter part of the evaluation includes comparisons with the corresponding statistics derived from the analyses of the National Meteorological Center.
The global data assimilation system is found to be functioning well, especially in extratropical regions with reasonable data coverage. Problems and weaknesses of the system are discussed.
Abstract
A global data assimilation system has been in operation at the Canadian Meteorological Centre (CMC) since March 1991 when it replaced the previous hemispheric system. This paper describes the system and presents an evaluation of its performance from several points of view, including the fit of the analyses and short-range forecasts to observations, the relative roles of various components of the system, the functioning of some specific subcomponents in a particular case, and the ability of the system to represent important as aspects of the mean monthly general circulation. This latter part of the evaluation includes comparisons with the corresponding statistics derived from the analyses of the National Meteorological Center.
The global data assimilation system is found to be functioning well, especially in extratropical regions with reasonable data coverage. Problems and weaknesses of the system are discussed.
Abstract
A major set of changes was made to the Environment Canada global deterministic prediction system during the fall of 2014, including the replacement of four-dimensional variational data assimilation (4DVar) by four-dimensional ensemble–variational data assimilation (4DEnVar). The new system provides improved forecast accuracy relative to the previous system, based on results from two sets of two-month data assimilation and forecast experiments. The improvements are largest at shorter lead times, but significant improvements are maintained in the 120-h forecasts for most regions and vertical levels. The improvements result from the combined impact of numerous changes, in addition to the use of 4DEnVar. These include an improved treatment of radiosonde and aircraft observations, an improved radiance bias correction procedure, the assimilation of ground-based GPS data, a doubling of the number of assimilated channels from hyperspectral infrared sounders, and an improved approach for initializing model forecasts. Because of the replacement of 4DVar with 4DEnVar, the new system is also more computationally efficient and easier to parallelize, facilitating a doubling of the analysis increment horizontal resolution. Replacement of a full-field digital filter with the 4D incremental analysis update approach, and the recycling of several key variables that are not directly analyzed significantly reduced the model spinup during both the data assimilation cycle and in medium-range forecasts.
Abstract
A major set of changes was made to the Environment Canada global deterministic prediction system during the fall of 2014, including the replacement of four-dimensional variational data assimilation (4DVar) by four-dimensional ensemble–variational data assimilation (4DEnVar). The new system provides improved forecast accuracy relative to the previous system, based on results from two sets of two-month data assimilation and forecast experiments. The improvements are largest at shorter lead times, but significant improvements are maintained in the 120-h forecasts for most regions and vertical levels. The improvements result from the combined impact of numerous changes, in addition to the use of 4DEnVar. These include an improved treatment of radiosonde and aircraft observations, an improved radiance bias correction procedure, the assimilation of ground-based GPS data, a doubling of the number of assimilated channels from hyperspectral infrared sounders, and an improved approach for initializing model forecasts. Because of the replacement of 4DVar with 4DEnVar, the new system is also more computationally efficient and easier to parallelize, facilitating a doubling of the analysis increment horizontal resolution. Replacement of a full-field digital filter with the 4D incremental analysis update approach, and the recycling of several key variables that are not directly analyzed significantly reduced the model spinup during both the data assimilation cycle and in medium-range forecasts.
Abstract
A new system that resolves the stratosphere was implemented for operational medium-range weather forecasts at the Canadian Meteorological Centre. The model lid was raised from 10 to 0.1 hPa, parameterization schemes for nonorographic gravity wave tendencies and methane oxidation were introduced, and a new radiation scheme was implemented. Because of the higher lid height of 0.1 hPa, new measurements between 10 and 0.1 hPa were also added. This new high-top system resulted not only in dramatically improved forecasts of the stratosphere, but also in large improvements in medium-range tropospheric forecast skill. Pairs of assimilation experiments reveal that most of the stratospheric and tropospheric forecast improvement is obtained without the extra observations in the upper stratosphere. However, these observations further improve forecasts in the winter hemisphere but not in the summer hemisphere. Pairs of forecast experiments were run in which initial conditions were the same for each experiment but the forecast model differed. The large improvements in stratospheric forecast skill are found to be due to the higher lid height of the new model. The new radiation scheme helps to improve tropospheric forecasts. However, the degree of improvement seen in tropospheric forecast skill could not be entirely explained with these purely forecast experiments. It is hypothesized that the cycling of a better model and assimilation provide improved initial conditions, which result in improved forecasts.
Abstract
A new system that resolves the stratosphere was implemented for operational medium-range weather forecasts at the Canadian Meteorological Centre. The model lid was raised from 10 to 0.1 hPa, parameterization schemes for nonorographic gravity wave tendencies and methane oxidation were introduced, and a new radiation scheme was implemented. Because of the higher lid height of 0.1 hPa, new measurements between 10 and 0.1 hPa were also added. This new high-top system resulted not only in dramatically improved forecasts of the stratosphere, but also in large improvements in medium-range tropospheric forecast skill. Pairs of assimilation experiments reveal that most of the stratospheric and tropospheric forecast improvement is obtained without the extra observations in the upper stratosphere. However, these observations further improve forecasts in the winter hemisphere but not in the summer hemisphere. Pairs of forecast experiments were run in which initial conditions were the same for each experiment but the forecast model differed. The large improvements in stratospheric forecast skill are found to be due to the higher lid height of the new model. The new radiation scheme helps to improve tropospheric forecasts. However, the degree of improvement seen in tropospheric forecast skill could not be entirely explained with these purely forecast experiments. It is hypothesized that the cycling of a better model and assimilation provide improved initial conditions, which result in improved forecasts.
