Stéphane Laroche

Implementation of a 3D variational data assimilation system at the Canadian Meteorological Centre. Part I: The global analysis

Abstract In this paper, the operational 3D variational data assimilation system (3D‐var) of the Canadian Meteorological Centre (CMC) is described and its performance is compared to that of the previously operational statistical interpolation analysis. Deliberately configuring the 3D‐var to be as close as possible to the statistical interpolation system permits an evaluation of the impact of data selection on both the analysis and the resulting forecasts. The current implementation of the 3D‐var is incremental in the horizontal and the vertical since the analysis increments are constructed at a lower horizontal resolution on prescribed pressure levels. They are subsequently interpolated vertically to the σ levels of the model. The results show that although there could be significant differences in the single analysis increments, the impact on the resulting forecasts is neutral. The 3D‐var implements a multivariate covariance model implicitly through changes of variables. It is shown that the implicit cova...

Extension of 3DVAR to 4DVAR: Implementation of 4DVAR at the Meteorological Service of Canada

On 15 March 2005, the Meteorological Service of Canada (MSC) proceeded to the implementation of a four-dimensional variational data assimilation (4DVAR) system, which led to significant improvements in the quality of global forecasts. This paper describes the different elements of MSC’s 4DVAR assimilation system, discusses some issues encountered during the development, and reports on the overall results from the 4DVAR implementation tests. The 4DVAR system adopted an incremental approach with two outer iterations. The simplified model used in the minimization has a horizontal resolution of 170 km and its simplified physics includes vertical diffusion, surface drag, orographic blocking, stratiform condensation, and convection. One important element of the design is its modularity, which has permitted continued progress on the three-dimensional variational data assimilation (3DVAR) component (e.g., addition of new observation types) and the model (e.g., computational and numerical changes). This paper discusses some numerical problems that occur in the vicinity of the Poles where the semi-Lagrangian scheme becomes unstable when there is a simultaneous occurrence of converging meridians and strong wind gradients. These could be removed by filtering the winds in the zonal direction before they are used to estimate the upstream position in the semi-Lagrangian scheme. The results show improvements in all aspects of the forecasts over all regions. The impact is particularly significant in the Southern Hemisphere where 4DVAR is able to extract more information from satellite data. In the Northern Hemisphere, 4DVAR accepts more asynoptic data, in particular coming from profilers and aircrafts. The impact noted is also positive and the short-term forecasts are particularly improved over the west coast of North America. Finally, the dynamical consistency of the 4DVAR global analyses leads to a significant impact on regional forecasts. Experimentation has shown that regional forecasts initiated directly from a 4DVAR global analysis are improved with respect to the regional forecasts resulting from the regional 3DVAR analysis.

The subgrid‐scale orographic blocking parametrization of the GEM Model

Abstract The impact of the physical parametrization called subgrid‐scale orographic blocking, recently introduced in the physics of the Canadian Global Environmental Mutiscale (GEM) model is described. It is based on a formulation by Lott and Miller (1997) and represents the unresolved component of the drag on low‐level winds that are blocked at the flanks of mountains. The blocking term plus the gravity‐wave drag are now part of a unified parametrization of the subgrid orographic drag that became operational in the global GEM model on 11 December 2001. Results from tests made with various configurations of the model are shown, illustrating how the blocking term impacts the large‐scale flow and improves both the short‐ and the medium‐range forecasts, especially in winter. It is shown that at day 5 of the model integrations, the influence of the blocking force applied near the surface is felt by the entire tropospheric and the lower‐stratospheric circulation. A mechanism based on perturbations of the Eliassen‐Palm flux caused by the low‐level forcing is proposed to explain the vertical propagation of the signal generated by the blocking term.

Medium-Range Quantitative Precipitation Forecasts from Canada's New 33-km Deterministic Global Operational System

Abstract The Meteorological Service of Canada (MSC) recently implemented a 33-km version of the Global Environmental Multiscale (GEM) model, with improved physics, for medium-range weather forecasts. Quantitative precipitation forecasts (QPFs) from this new system were compared with those from the previous global operational system (100-km grid size) and with those from MSC’s short-range (48 h) regional system (15-km grid size). The evaluation is based on performance measures that evaluate bias, accuracy, and the value of the QPFs. Results presented in this article consistently show, for these three aspects of the evaluation, that the new global forecast system (GLBNEW) agrees more closely with observations, relative to the performance of the previous global system (GLBOLD). The biases are noticeably smaller with GLBNEW compared with GLBOLD, which severely overpredicts (underpredicts) the frequencies and total amounts associated with weak (strong) precipitation intensities. The accuracy and value scores r...

Impact of the Different Components of 4DVAR on the Global Forecast System of the Meteorological Service of Canada

A four-dimensional variational data assimilation (4DVAR) scheme has recently been implemented in the medium-range weather forecast system of the Meteorological Service of Canada (MSC). The new scheme is now composed of several additional and improved features as compared with the three-dimensional variational data assimilation (3DVAR): the first guess at the appropriate time from the full-resolution model trajectory is used to calculate the misfit to the observations; the tangent linear of the forecast model and its adjoint are employed to propagate the analysis increment and the gradient of the cost function over the 6-h assimilation window; a comprehensive set of simplified physical parameterizations is used during the final minimization process; and the number of frequently reported data, in particular satellite data, has substantially increased. The impact of these 4DVAR components on the forecast skill is reported in this article. This is achieved by comparing data assimilation configurations that range in complexity from the former 3DVAR with the implemented 4DVAR over a 1-month period. It is shown that the implementation of the tangent-linear model and its adjoint as well as the increased number of observations are the two features of the new 4DVAR that contribute the most to the forecast improvement. All the other components provide marginal though positive impact. 4DVAR does not improve the medium-range forecast of tropical storms in general and tends to amplify the existing, too early extratropical transition often observed in the MSC global forecast system with 3DVAR. It is shown that this recurrent problem is, however, more sensitive to the forecast model than the data assimilation scheme employed in this system. Finally, the impact of using a shorter cutoff time for the reception of observations, as the one used in the operational context for the 0000 and 1200 UTC forecasts, is more detrimental with 4DVAR. This result indicates that 4DVAR is more sensitive to observations at the end of the assimilation window than 3DVAR.

Linearization of a Simplified Planetary Boundary Layer Parameterization

Abstract This study examines the linearization properties of a simplified planetary boundary layer parameterization based on the vertical diffusion equations, in which the exchange coefficients are a function of the local Richardson number and wind shear. Spurious noise, associated with this parameterization, develops near the surface in the tangent linear integrations. The origin of this problem is investigated by examining the accuracy of the linearization and the numerical stability of the scheme used to discretize the vertical diffusion equations. The noise is primarily due to the linearization of the exchange coefficients when the atmospheric state is near neutral static stability and when a long time step is employed. A regularization procedure based on the linearization error and a criterion for the numerical stability is proposed and tested. This regularization is compared with those recently adopted by Mahfouf, who neglects the perturbations of the exchange coefficients, and by Janiskova et al., ...

Evaluation of the Impact of Observations on Analyses in 3D- and 4D-Var Based on Information Content

Abstract The degrees of freedom for signal (DFS) is used in data assimilation applications to measure the self-sensitivity of analysis to different observation types. This paper describes a practical method to estimate the DFS of observations from a posteriori statistics. The method does not require the consistency of the error statistics in the analysis system and it is shown that the observational impact on analyses can be estimated from observation departures with respect to analysis or the forecast. This method is first introduced to investigate the impact of a complete set, or subsets, of observations on the analysis for idealized one-dimensional variational data assimilation (1D-Var) analysis experiments and then applied in the framework of the three dimensional (3D)- and four-dimensional (4D)-Var schemes developed at Environment Canada.

Impact of Radiosonde Balloon Drift on Numerical Weather Prediction and Verification

AbstractRadiosonde observations employed in real-time numerical weather prediction (NWP) applications are disseminated through the Global Telecommunication System (GTS) using alphanumeric codes. These codes do not include information about the position and elapsed ascent time of the balloon. Consequently, the horizontal balloon drift has generally been either ignored or estimated in data assimilation systems for NWP. With the increasing resolution of atmospheric models, it is now important to consider the positions and times of radiosonde data in both data assimilation and forecast verification systems. This information is now available in the Binary Universal Form for the Representation of Meteorological Data (BUFR) code for radiosonde data. This latter code will progressively replace the alphanumeric codes for all radiosonde data transmitted on the GTS. As a result, a strategy should be adopted by NWP centers to deal with the various codes for radiosonde data during this transition. In this work, a meth...

The Characteristics of Key Analysis Errors. Part I: Dynamical Balance and Comparison with Observations

Abstract The characteristics of the initial corrections obtained from the Canadian Meteorological Centre (CMC) energy-norm-based key analysis error algorithm that minimizes short-range (24 h) forecast errors were investigated for four specific CMC operational analyses. The results show that both the rotational and the divergent components of the initial corrections are strongly out of balance. Some dispersive modes are also present in the mass component of the initial corrections. The results from one experiment where the initial state errors were known suggest that the current algorithm always selects a set of unbalanced initial corrections with more mass correction than wind correction, regardless of the characteristics of the real initial condition errors. Comparison with observational data showed that the corrected analysis is systematically farther away from the observations than the control analysis even in large forecast error events where most of the forecast errors are believed to have originated...

Use of adjoint sensitivity analysis to diagnose the CMC global analysis performance: A case study

Abstract The sensitivity of forecast errors to initial conditions obtained from the adjoint of a numerical weather prediction model provides new insights into the analysis errors responsible for poor short‐range to mediumrange forecasts. In recent years, we have developed a sensitivity analysis system based on the tangent linear and adjoint of the Global Environmental Multiscale model, in which an iterative procedure minimizing the shortrange forecast errors leads to the so‐called key analysis errors. These errors are dominated by a small number of atmospheric structures, those growing the most rapidly. The algorithm has proven very useful in understanding improvements to the three‐dimensional variational data assimilation (3D‐Var) system implemented in the Canadian Meteorological Centre operational suite in December 2001. The main difference between the old and the new 3D‐Var systems is the assimilation of temperature and surface pressure from surface and upper air stations as opposed to geopotential heights, additional Tiros Operational Vertical Sounder channels, new sources of observations such as temperature observations from aircraft, and wind and temperature from dropsondes. In this paper, we examine key analysis errors of the old 3D‐Var analysis, which led to a very poor 3‐day forecast of a severe winter storm that struck eastern Canada on 10 February 2001. In this case, the same 3‐day forecast from the new 3D‐Var analysis is much better. We compare the difference between the two 3D‐Var analyses and the key analysis errors. We find that the main key analysis errors, in terms of potential vorticity, is located along the west shore of southern California and is characterized by a strong baroclinic structure that has its maximum amplitude in the upper part of the troposphere. The difference between the two analyses is three times more energetic than the key analysis errors and its structure is much more barotropic in the troposphere. However, we show that the large improvement in the new 3D‐Var analysis stems mainly from the reduction of the analysis errors that project onto the key analysis structures.