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This study introduces an experimental regional assimilation configuration for a 4D ensemble–variational (4D-EnVar) deterministic weather prediction system. A total of 16 assimilation experiments covering July 2014 are presented to assess both experimental regional climatological background error covariances and updates in the treatment of flow-dependent error covariances. The regional climatological background error covariances are estimated using statistical correlations between variables instead of using balance operators. These error covariance estimates allow the analyses to fit more closely with the assimilated observations than when using the lower-resolution global background error covariances (due to shorter correlation scales), and the ensuing forecasts are significantly improved. The use of ensemble-based background error covariances is also improved by reducing vertical and horizontal localization length scales for the flow-dependent background error covariance component. Also, reducing the number of ensemble members employed in the deterministic analysis (from 256 to 128) reduced computational costs by half without degrading the accuracy of analyses and forecasts. The impact of the relative contributions of the climatological and flow-dependent background error covariance components is also examined. Results show that the experimental regional system benefits from giving a lower (higher) weight to climatological (flow-dependent) error covariances. When compared with the operational assimilation configuration of the continental prediction system, the proposed modifications to the background error covariances improve both surface and upper-air RMSE scores by nearly 1%. Still, the use of a higher-resolution ensemble to estimate flow-dependent background error covariances does not yet provide added value, although it is expected to allow for a better use of dense observations in the future.

This study reports on the progress toward operational weather radar data assimilation in Canada. As a first step, the latent heat nudging (LHN) technique has been tested for a period of 1 month. It is the first time that LHN is used across the North American continent, a domain significantly larger than that of other LHN studies. Other novel aspects of this study include the use of a quality index associated with individual reflectivity measurements and a discussion on matching the effective resolution of the modeled precipitation for a reduction of the representation errors. Various verification scores indicate that LHN has a positive influence on instantaneous precipitation rates for lead times up to 3 h. In comparison, the nowcasting of precipitation rates by a simple Lagrangian extrapolation method yields improvements that last up to approximately 4 h. Verifications against aircraft measurements indicate small but statistically significant improvements throughout the troposphere for lead times up to 24 h.

High-resolution flow-dependent background error covariances can allow for a better usage of dense observation networks in applications of data assimilation for numerical weather prediction. The generation of high-resolution ensembles, however, can be computationally cost prohibitive. In this study, practical and low-cost ensemble generation methods are presented and compared against both global and regional ensemble Kalman filters (G-EnKF and R-EnKF, respectively). The goal is to provide limited-area deterministic assimilation schemes with higher-resolution flow-dependent background error covariances that perform at least as well as those from the G-EnKF when assimilating the same observations. The low-cost methods are based on short-range regional ensemble forecasts initialized from 1) deterministic analysis plus balanced perturbations (filter free approach) and 2) a simplified ensemble square root filter (S-EnSRF), centered on deterministic analyses. The resulting ensembles from the different approaches are used within a 4D ensemble–variational (4D-EnVar) assimilation system covering most of Canada and the northern United States. Diagnostic results show that the mean is an important component of the ensembles. Results also show that the persistence of the homogeneous characteristics of the perturbations in the filter free approach makes this method unsuited for short assimilation time windows since some error structures take longer to develop. The S-EnSRF approach overcomes this limitation by recycling part of the prior perturbations. Results from 1-month assimilation experiments show that the S-EnSRF and R-EnKF experiments provide forecasts of similar quality to those from G-EnKF. Furthermore, results from precipitation verification indicate that the R-EnKF experiment provides the best precipitation accumulation predictions over 24-h periods.

An hourly-cycling ensemble Kalman filter (EnKF) working at 2.5 km horizontal grid spacing is implemented over southern Ontario (Canada) to assimilate Meteorological Terminal Aviation Routine Weathe...

Environment and Climate Change Canada (ECCC) has recently developed an experimental high-resolution EnKF (HREnKF) regional ensemble prediction system, which it tested over the Pacific Northwest of North America for the first half of February 2011. The HREnKF has 2.5-km horizontal grid spacing and assimilates surface and upper-air observations every hour. To determine the benefits of the HREnKF over less expensive alternatives, its 24-h quantitative precipitation forecasts are compared with those from a lower-resolution (15 km) regional ensemble Kalman filter (REnKF) system and to ensembles directly downscaled from the REnKF using the same grid as the HREnKF but with no additional data assimilation (DS). The forecasts are verified against rain gauge observations and gridded precipitation analyses, the latter of which are characterized by uncertainties of comparable magnitude to the model forecast errors. Nonetheless, both deterministic and probabilistic verification indicates robust improvements in forecast skill owing to the finer grids of the HREnKF and DS. The HREnKF exhibits a further improvement in performance over the DS in the first few forecast hours, suggesting a modest positive impact of data assimilation. However, this improvement is not statistically significant and may be attributable to other factors.

This study discusses the construction of a high-resolution ensemble Kalman filter system (the HREnKF) developed for the Marine Environmental Observation Prediction and Response (MEOPAR) network. The HREnKF runs at a horizontal resolution of 2.5 km and is intended to provide forecasts at lead times up to 12 h. This system was adapted from the global EnKF system in operation at Environment and Climate Change Canada. As a first development step, only the most necessary modifications have been implemented. The changes include an hourly cycling frequency, smaller localization radii, and the explicit representation of microphysical processes. To assess its performance and orient future developments, the HREnKF was continuously cycled for a period of 12 days. Verification against surface observations reveals that the skill of the forecasts initialized from the HREnKF is comparable to that of control forecasts also integrated at a resolution of 2.5 km. A key component of this study is the comparison of correlation estimated from ensembles at resolutions of 2.5, 15, and 50 km. At 2.5 km, correlation lengths are smaller than those found at 15 and 50 km. These short correlation lengths demand a high observational density, which is not available over the west coast domain where the HREnKF was tested. The spatial and temporal variability of the correlations is also assessed for the HREnKF system. It is found that correlation patterns are complex and do not generally decrease monotonically away from the reference point around which they are estimated. This result is important as it indicates that separation distance may not be the ideal parameter to use as a basis for localization strategies.

De l’art aux sciences humaines, comment amener des élèves à tirer profit des œuvres en contexte muséal au moyen du numérique mobile ? Nous présentons la mise en place d’un projet de recherche-développement avec le Musée des beaux-arts de Montréal et des écoles primaires montréalaises visant la conception et la mise à l’essai d’un dispositif muséo-techno-didactique. Ce dispositif recourt à un prototype d’application pour les technologies mobiles que nous avons conçu pour une expérience muséale au croisement des arts visuels et des sciences humaines. Après avoir exposé la démarche de conception et la mise à l’essai du dispositif auprès de 3 classes de 3e cycle, nous explorons son potentiel pour le raisonnement en sciences humaines au contact des œuvres d’art, plus spécifiquement sous l’angle de l’interprétation d’œuvres d’art figuratives pour leur valeur documentaire. L’analyse des vidéos réalisées montre l’intérêt du dispositif pour stimuler et recueillir le raisonnement des élèves, mais démontre aussi certaines faiblesses dans la contextualisation des œuvres, possiblement liées à des connaissances limitées concernant les réalités passées étudiées., From art to the humanities, how to get students to take advantage of artworks in museum context using mobile, digital devices? We present the implementation of a research and development project between the Montreal Museum of Fine Arts and Montreal primary schools for the design and testing of a museo-techno-didactic device. This device uses a prototype application for mobile technologies we have designed and affords a museum experience at the intersection of the visual arts and the humanities. After outlining the design process and testing the device with 3 different third-grade classes, we explore its potential for social science reasoning in contact with works of art, specifically in terms of the interpretation of works of figurative art for their documentary value. Analysis of videos made shows the value of the device to stimulate and collect students' thinking but also demonstrates some weaknesses in the contextualization of the works, possibly related to limited knowledge with respect to past contexts that have been studied.

The modifications to the data assimilation component of the Regional Deterministic Prediction System (RDPS) implemented at Environment Canada operations during the fall of 2014 are described. The main change is the replacement of the limited-area four-dimensional variational data assimilation (4DVar) algorithm for the limited-area analysis and the associated three-dimensional variational data assimilation (3DVar) scheme for the synchronous global driver analysis by the four-dimensional ensemble–variational data assimilation (4DEnVar) scheme presented in the first part of this study. It is shown that a 4DEnVar scheme using global background-error covariances can provide RDPS forecasts that are slightly improved compared to the previous operational approach, particularly during the first 24 h of the forecasts and in the summertime convective regime. Further forecast improvements were also made possible by upgrades in the assimilated observational data and by introducing the improved global analysis presented in the first part of this study in the RDPS intermittent cycling strategy. The computational savings brought by the 4DEnVar approach are also discussed.

An 80-member high-resolution ensemble Kalman filter (HREnKF) is implemented for assimilating radar observations with the Canadian Meteorological Center’s (CMC’s) Global Environmental Multiscale Limited-Area Model (GEM-LAM). This system covers the Montréal, Canada, region and assimilates radar data from the McGill Radar Observatory with 4-km data thinning. The GEM-LAM operates in fully nonhydrostatic mode with 58 hybrid vertical levels and 1-km horizontal grid spacing. As a first step toward full radar data assimilation, only radial velocities are directly assimilated in this study. The HREnKF is applied on three 2011 summer cases having different precipitation structures: squall-line structure, isolated small-scale structures, and widespread stratiform precipitation. The short-term ( In HREnKF, the ensemble spread is sufficient to cover the estimated error from innovations and lead to filter convergence. It results in part from a realistic initiation of HREnKF data assimilation cycle by using a Canadian regional EnKF system (itself coupled to a global EnKF) working at meso- and synoptic scales. The filter convergence is confirmed by the HREnKF background fields gradually approaching to radar observations as the assimilation cycling proceeds. At each analysis step, it is clearly shown that unobserved variables are significantly modified through HREnKF cross correlation of errors from the ensemble. Radar reflectivity observations are used to verify the improvements in analyses and short-term forecasts achievable by assimilating only radial velocities. Further developments of the analysis system are discussed.

A high-resolution ensemble Kalman filter (HREnKF) system at the convective scale has been developed based on the Canadian Meteorological Center's operational global ensemble Kalman filter (EnKF) system. This study focuses on the very early stage of transition from purely homogeneous isotropic background error correlations to situation-dependent correlations. It has been found that forecast error structures can develop situation-dependent features in as little as 15 min. Furthermore, the dynamic and thermodynamic variables require different periods of time to build up their own forecast error structures. Differences in these structures between regions with and without precipitation are also investigated. An examination of temperature tendencies revealed that physical processes are as important as dynamical forcing in determining the structure of convective-scale errors structures, and that once physical processes become active, these structures change rapidly before the onset of precipitation. This study is intended to be the basis for a systematic exploration in the near future of the usefulness of the HREnKF system in assimilating high-density observations such as radar data.

As a second step in the development of the Canadian Regional Data Assimilation System following Fillion et al., this study extends the approach to the four-dimensional variational data assimilation (4D-Var) context. Emphasis is first put on illustrating the importance of controlling lateral boundary conditions (LBCs). The use in the minimization of a horizontal grid over a domain exceeding the horizontal grid of the high-resolution nonlinear model is then proposed. The authors examine the performance of this 4D-Var formulation as an upcoming upgrade to the currently operational regional three-dimensional variational data assimilation (3D-Var) system. Forecast verifications against radiosonde data for 118 winter cases and 118 summer cases were performed. Results indicate a slight positive impact up to 48 h against North American radiosondes, but with a significant positive impact (especially for winds) at mid- and high latitudes during the summer. Accumulated precipitation scores over 24 h, whether during the first or second day of the forecasts, are slightly improved. The regional 4D-Var analysis system described in this study can run within current real-time “regional run” allocation for operations at the Canadian Meteorological Center (CMC). Future improvements of this system are briefly mentioned especially regarding the upcoming computer upgrade at CMC.

Rainfall prediction remains one of the most challenging problems in weather forecasting. In order to improve high-resolution quantitative precipitation forecasts (QPF), a new procedure for assimilating rainfall rate derived from radar composite reflectivity has been proposed and tested in a numerical simulation of the Chicago floods of 17–18 July 1996. The methodology is based on the one-dimensional variation scheme (1DVAR) assimilation approach introduced by Fillion and Errico but applied here using the Kain-Fritsch convective parameterization scheme (KF CPS). The novel feature of this work is the continuous assimilation of radar estimated rain rate over a three hour period, rather than a single assimilation at the initial (analysis) time. Most of the characteristics of this precipitation event, including the propagation, regeneration of mesoscale convective systems, the frontal boundary across the Midwest and the evolution of the low-level jet are better captured in the simulation as the radar-estimated precipitation rate is assimilated. The results indicate that precipitation assimilation during the early stage can improve the simulated mesoscale feature of the convection system and shorten the spin-up time significantly. Comparison of precipitation forecasts between the experiments with and without the 1DVAR indicates that the 1DVAR scheme has a positive impact on the QPF up to 36 hours in terms of the bias and bias equalized threat scores.

The differences in the balance characteristics between dry and precipitation areas in estimated short-term forecast error fields are investigated. The motivation is to see if dry and precipitation areas need to be treated differently in atmospheric data assimilation systems. Using an ensemble of lagged forecast differences, it is shown that perturbations are, on average, farther away from geostrophic balance over precipitation areas than over dry areas and that the deviation from geostrophic balance is proportional to the intensity of precipitation. Following these results, the authors investigate whether some improvements in the coupling between mass and rotational wind increments over precipitation areas can be achieved by using only the precipitation points within an ensemble of estimated forecast errors to construct a so-called diabatic balance operator by linear regression. Comparisons with a traditional approach to construct balance operators by linear regression show that the new approach leads to a gradually significant improvement (related to the intensity of the diabatic processes) of the accuracy of the coupling over precipitation areas as judged from an ensemble of lagged forecast differences. Results from a series of simplified data assimilation experiments show that the new balance operators can produce analysis increments that are substantially different from those associated with the traditional balance operator, particularly for observations located in the lower atmosphere. Issues concerning the implementation of this new approach in a full-fledged analysis system are briefly discussed but their investigations are left for a following study.

The McGill University radar data assimilation system is used to initialize a convective storm at high resolution (1 km) from single–Doppler radar observations. In this study, the background term in the assimilation system is improved. Specifically, by assuming the correlation of the errors of the control variables to be isotropic and homogeneous, the background error covariance matrix is modeled by a recursive filter. In addition, a 3-h-prior high-resolution model forecast is used as the background field. The analysis fields from the assimilation system successfully trigger the convective storms in the radar-observed regions from a single assimilation window. Without data assimilation, the modeled storms did not occur at the right time and place. To account for the rapid evolution of the convective storms and to correct the forecast errors with time, a cycling process is applied for a very short-term forecast. It is found that the first assimilation window can maintain the prediction of the storms for less than 1 h. The cycling process helps to maintain the intensity of the storm cells for a longer period of time. However, a comparison of radar observations with the 90-min simulation indicates an error in the position of the convective cells. The error of the radial component of the wind field between the observation and the simulation is larger at the upper levels. A wavelet analysis between the observation and simulated reflectivities indicates that the forecast is able to adequately predict the convective scale (∼10–20 km) during the first 20 min, whereas the simulation has more predictability at the longer scale (>30 km) beyond 20 min.

Balance omega equations have recently been used to try to improve the characterization of balance in variational data assimilation schemes for numerical weather prediction (NWP). Results from Fisher and Fillion et al. indicate that a quasigeostrophic omega equation can be used adequately in the definition of the control variable to represent synoptic-scale balanced vertical motion. For high-resolution limited-area data assimilation and forecasting (1–10-km horizontal resolution), such a diagnostic equation for vertical motion needs to be revisited. Using a state-of-the-art NWP forecast model at 2.5-km horizontal resolution, these issues are examined. Starting from a complete diagnostic partial differential equation for omega, the rhs forcing terms were computed from model-generated fields. These include the streamfunction, temperature, and physical time tendencies of temperature in gridpoint space. To accurately compute one term of second-order importance (i.e., the ageostrophic vorticity tendency forcing term), a special procedure was used. With this procedure it is shown that Charney’s balance equation brings significant information in order to deduce the geostrophic time tendency term. Under these conditions, results show that for phenomena of length scales of 15–100 km over convective regions, a diagnostic equation can capture the major part of the model-generated vertical motion. The limitations of the digital filter initialization approach when used as in Fillion et al. with a cutoff period reduced to 1 h are also illustrated. The potential usefulness of this study for mesoscale atmospheric data assimilation is briefly discussed.

A one-dimensional variational (1DVAR) technique is applied to assimilate rain gauge precipitation data to extend the predictability of the Saguenay flood cyclone associated with a trough-merger event on 19–21 July 1996 in the Saguenay-Lac-Saint-Jean region of Quebec, Canada. Two 60-h simulations initialized at 0000 UTC 19 July were performed with the Canadian Mesoscale Compressible Community (MC2) model. The control (CTL) and NCEP simulations were initialized with the enhanced temperature and moisture profiles obtained from the 1DVAR scheme and the NCEP reanalysis data, respectively. Compared to observations, the CTL simulation reasonably reproduced the observed mass and wind fields and showed a marked improvement in the threat scores for heavy precipitation. The CTL run captured the observed spatial and temporal distribution of precipitation but overpredicted the area of precipitation. Sensitivity experiments showed that the threat (bias) scores are less (somewhat) sensitive to the specification of the observation error of the precipitation data. Of the four precipitation systems present at model initial time, the systems in the vicinity of the southern trough had the biggest impact on the threat score. Potential vorticity diagnostics of the CTL simulation suggested that the initial temperature and moisture field near the southern trough decreased the condensational heating relative to NCEP. This resulted in a stronger zonal wind component in the upper levels associated with the southern trough in CTL that retarded the eastward propagation of the northern trough, resulting in a correct placement of the surface precipitation and an improvement in the threat scores relative to NCEP.

A detailed examination of the Jacobian matrix of sensitivities of the prognostic cloud scheme operational at the European Centre for Medium-Range Weather Forecasts (ECMWF) is presented. These Jacobians exhibit sensitivities of output variables (e.g., cloud condensate) to small input perturbations on temperature and moisture. The coupling of the cloud scheme with the ECMWF convective mass-flux scheme is considered. The sensitivity of the cloud scheme is split into all its contributing parts in order to extract the dominant terms. A selection of contrasted convective cases normally present in a regular operational forecast is considered. Some comparisons are made with a simpler diagnostic cloud scheme. It is shown that the main contributing terms to the sensitivity in cloud condensate l are the following for cases of deep convection: detrainment from moist convection, evaporation processes, and conversion of cloud water into rain. The structure of the Jacobians in terms of temperature and moisture ...

An adjoint model that includes precipitation physics is applied to four synoptic cases in order tocompare the sensitivities of both convective and non-convective precipitation rates with respect to initialperturbations of temperature, water vapor mixing ratio, vorticity or divergence. These are contrastedwith sensitivities of barotropic vorticity. Forecast periods between 1 and 24 h are investigated. Rootmean-square values of the sensitivities as a function of vertical coordinate and field are presented aswell as time series of impacts of optimal perturbations weighted by initial variances of uncertaintiesin the fields. For all the forecast aspects and cases considered, the greatest sensitivity and impacts arewith regard to the temperature field. Precipitation is equally sensitive to vorticity and divergence, butwhen their relative uncertainties are considered, impacts of the vorticity dominate those of divergence.Precipitation is sensitive to initial specific humidity, but so is barotropic vorticity within a cyclone. Thesensitivities of precipitation were not found to increase with forecast periods as much as the sensitivitiesto vorticity, suggesting that even within the same synoptic feature, different processes are responsiblefor the development of these distinct characteristics. Although comparison with corresponding impactsin the nonlinear model suggests that a 24 h period is too long for the adjoint-estimated precipitationimpacts to be good approximations to the nonlinear results in some of the cases, 6-h adjoint-estimatedresults appear useful. DOI: 10.1034/j.1600-0870.2003.201394.x

The degree of imbalance forced by deep convection in a three-dimensional variational analysis scheme (3DVAR) is examined. Simulated surface precipitation rates are used with various degree of errors together with different background atmospheric fields. Dynamical imbalances are defined only for the fastest timescales associated with gravity waves and diagnosed according to the implicit normal mode framework. Local measures of ageostrophic perturbations are also considered over rainy areas. Slow timescale perturbations on internal gravitational modes introduced by convection during 3DVAR are also monitored using temporal evidence of their presence. The diagnostic quantities used here are more appropriate for deep vertical scales where Machenhauer's balance scheme can be justified but must be used with care when discussing possible imbalance for shallow vertical gravity modes (especially for mesoscale data assimilation). These diagnostic measures are not actually used as an explicit constraint in t...

A four-dimensional variational (4DVAR) data assimilation problem may be constrained so that the solution closely fits the observations but is balanced. In this way, the processes of data analysis and initialization are combined. The method of initialization considered here, digital filtering, is widely used in weather forecasting centers. The digital filter was found to control high-frequency noise when implemented as a strong or as a weak constraint in the context of a global shallow water model. Implementation of a strong constraint did not result in a recovery of small scales although some recovery of intermediate scales did occur. Implementation of a weak constraint as a penalty method with a single fixed value of the penalty parameter resulted in analyses that were smooth, but depended upon the choice of the parameter. With a parameter value that was too large, the divergent kinetic energy spectrum of the analysis was excessively damped in the large scales. The rotational kinetic energy spectrum was also affected by the choice of penalty parameter. Both types of constraint were found to adequately control gravity wave noise although caution is advised in choosing the penalty parameter for the simple penalty term method.

Bayes's theorem is applied to the problem of analysing temperature and moisture in a volume of air given a single observation of precipitation amount, utilizing a model of non-convective precipitation and prior estimates of the fields. Results using different statistics and shapes of probability distributions are examined. These include normal, truncated normal, and log normal distributions with special treatment of the value zero. The uncertainly of the model's formulation is considered in addition to uncertainty of observations. The posterior distribution is multi-modal due to the model's formulation using a conditional expression. The dominant mode may be predicted as a non-precipitating slate by the model, although the observation indicates precipitation is present. Means and modes of posterior distributions depend sensitively both on the assumed statistics and the shapes of the underlying distributions. The results suggest that the usual minimization of a cost-function should not be used cavalierly to assimilate precipitation observations.

Some problems posed by the coupling of moist-convective and stratiform precipitation processes for variational assimilation of precipitation-rate data are examined in a 1D-Var framework. Background-error statistics and vertical resolution are chosen to be representative of current operational practice. Three advanced parameterization schemes for moist-convection are studied: the relaxed Arakawa–Schubert (RAS) scheme, Tiedtke’s mass-flux scheme (operational at the European Centre for Medium-Range Weather Forecasts), and the Betts–Miller scheme. Both fractional-stepping and process-splitting approaches for combining physical processes are examined. The behavior of the variational adjustment for background profiles of temperature and specific humidity in the neighborhood of saturation is of particular interest. In the 1D-Var context examined here, it is demonstrated that the introduction of the stratiform precipitation process can have a negative impact on the minimization in the sense that, even wh...

Some basic aspects related to the problem of incorporating moist convective processes in a variational data assimilation framework are considered. The methodology is based on inverse problem theory and is formulated in its simplest context where the adjustment of temperature and humidity fields take place only in the vertical. In contrast to previous studies on the subject, the impact of error statistics from prior information and data sources of information is clarified. The accuracy of linearization of convection operators and the resulting impact in a minimization procedure are examined. The former was investigated using Monte Carlo methods. Versions of two schemes are examined: the Kuo–Anthes scheme and the relaxed Arakawa–Schubert scheme (RAS). It is found, in general, that for nonpathological convective points (i.e., points where convection always remains active during the minimization process), a significant adjustment of convection (and precipitation rate) is realizable within the range o...

To meet the needs of short- and medium-range operational forecasting, the authors propose a unified strategy based on the use of a global variable-resolution model, run in two different configurations. These are as follows: (i) a variable-resolution “regional” configuration (with resolution focused over an area of interest) for detailed forecasts to 2 days, and (ii) a uniform-resolution “medium-range” one, for forecasts to 7 days or longer. This otters significant economy in an operational environment, since there is only one model—instead of the usual two—to maintain, develop, and optimize. It also provides an efficient and conceptually simple solution to the nesting problem for regional forecasting: the planetary waves are adequately resolved around a high-resolution subdomain (which resolves mesoscale disturbances), there are no artificial lateral boundaries with their attendant problems, and there is no abrupt change of resolution across an internal boundary since the resolution varies smooth...

It is shown that implicit normal mode initialization can be applied to a global finite‐element shallow‐water model where a semi‐Lagrangian treatment of the advection terms is used. The local time tendencies required by the initialization scheme are thus furnished by the semi‐Lagrangian model itself. Since the initialization scheme is formulated in physical space, it allows the non‐linear adjustment of the initial mass and wind fields at essentially the same accuracy as standard explicit normal mode initialization without having to rely on the explicit determination of the normal modes of the model. This property is especially useful in this study since we allow the spatial orientation of the model's grid to vary. For such a case, the usual separability in the longitudinal and latitudinal directions of the linearized dynamics is not possible if the variation of the Coriolis parameter is included in the analysis (as it should on the sphere). The use of a rotated coordinate system represents a new a...

Recent studies have demonstrated that variational nonlinear normal-mode initialization can be efficiently implemented in the context of shallow-water models, provided one uses a physical space formulation. The implicit nonlinear normal-mode initialization (INMI) technique provides essentially the same balancing benefit as standard ”explicit“ nonlinear NMI but does not require the explicit computation of the linear free modes of the model. This allows variational initialization with arbitrary horizontal variation of the weights that specify the changes to the analyzed fields during initialization. As a consequence, the variational extension of INMI allows more flexibility to control the relative adjustment of mass and wind fields over data-rich and data-poor regions. The purpose of this paper is to demonstrate the feasibility of variational implicit normal-mode initialization (VINMI) for multilevel models. This new scheme is illustrated on the presently operational Canadian baroclinic regional fin...

Due to its prohibitive computational cost, variational nonlinear normal mode initialization has received little interest during the last 10 years. Recently, soon after the introduction of the framework now called implicit nonlinear normal mode initialization, an efficient reformulation of variational nonlinear normal mode initialization using the implicit technique was demonstrated in the context of a barotropic finite-element regional model. This scheme allowed full variation of the variational weights at a low computational cost. To complement this previous work, the same variational approach for a global spectral shallow-water model is presented here. Similar results regarding the controlling and balancing aspects of the scheme are illustrated. The special form taken by the variational scheme in the context of height-constrained initialization is reconsidered after establishing the relationship between the implicit schemes and quasi-geostrophic theory on an f plane. Possible extensions of the ...

The ability of a digital filter technique to control high-frequency gravity wave noise in numerical weather forecasts based on the primitive equations is examined in the context of a global data assimilation system. The method uses a 12-h forward integration of the complete model to generate a time series that is filtered to give a balanced model state valid 6 h into the integration. This state is free of high-frequency noise and serves as a background field for the next analysis. The technique is referred to as digital filter finalization. The technique is first applied to a long model run in order to identify the impact of the chosen cutoff period in the design of the filter on a properly balanced model state. The robustness of the technique to typical imbalances between the mass and wind fields produced by an operational statistical interpolation procedure is also examined. Results of data assimilation experiments performed with the digital filter finalization and with the currently-operational adiabatic nonlinear normal mode initialization scheme are compared. The digital filter finalization technique examined here is shown to be an accurate, consistent and very simple way to remove the undesirable high-frequency noise from a global model forecast. DOI: 10.1034/j.1600-0870.1995.t01-2-00002.x

It is shown that implicit normal mode initialization can be combined with a variational technique, in order to control the relative magnitudes of the changes to the analyzed mass and wind fields. Since the initialization procedure is expressed entirely in physical space, the use of locally varying weights in the variational integral becomes more straightforward than in previous efforts to combine variational methods with normal mode initialization. We present details of the application to a finite-element model of the shallow water equations on a stereographic projection. It is demonstrated that the use of variational initialization can change the slowly evolving component of the subsequent forecast, as well as eliminate the unrealistic fast component.

Nous traitons de la conception et de l’étude du potentiel d’un dispositif muséo-techno-didactique, parties prenantes d’une recherche-développement réalisée en collaboration avec le Musée McCord et des enseignants d’écoles primaires montréalaises. Le dispositif mis à l’essai tire profit de l’exposition Montréal — Points de vue, des ressources numériques de l’institution et d’une récente application pour technologie mobile. À la suite d’entretiens semi-dirigés réalisés auprès de dyades d’élèves de quatre classes de 5e année, nous étudions le potentiel de ce dispositif sous l’angle du raisonnement verbal en sciences humaines. Si une bonne compréhension de l’interdépendance de l’organisation sociale et du territoire se manifeste dans des segments explicatifs, les résultats obtenus doivent être replacés dans le contexte d’une implication des enseignants allant bien au-delà du temps scolaire habituellement consenti à ce domaine disciplinaire., We present the design and study of the potential of a museological-techno-didactic device researched and developed in collaboration with the McCord Museum and Montreal elementary school teachers. The tested device benefits from the Montreal — Points of View exhibition, the institution’s digital resources, and a recent application for mobile technology. With data collected through semi-structured interviews with dyads of students in four grade 5 classes, we study the potential of this device in terms of verbal reasoning in social sciences. If an understanding of the interdependence of the social organization and the territory is evident in explanatory segments, the results must be seen in the context of teacher involvement that goes well beyond the school time generally allocated to this disciplinary field.