The Canadian Meteorological Centre’s (CMC) three Dimensional Variational (3D-Var) is an incremental analysis system that is currently used by both our global and regional models with very little modifications. During the last few years, it has undergone a series of upgrades from isobaric to a terrain-following coordinate, and most importantly to the direct assimilation of satellite radiances. The quality control (QC) of observations was also upgraded to a variational quality control whereby the data rejection/acceptance decisions are taken consistently during the minimization problem. In terms of radiance data, the system currently uses so-called raw level-1b AMSU-A that are quality-controlled (QC), and bias controlled by the data user and not the producer. The QC and thinning algorithms of the radiance data are more complex and system dependent. Because of QC and bias correction algorithms, the impact of satellite data on CMC’s analyses and forecasts are now very large and comparable to that of radiosonde data in the SH. The resolution of NWP forecast/analysis systems is forever increasing and so is the volume of data from various instruments. The volume of satellite data has become quite a challenge even at the level of preparation and QC prior to the analysis step. One aspect of NWP systems which definitely can benefit from this additional data is the moisture analysis. In that context we have started to use the water sensitive radiances from the AMSU-B instruments onboard NOAA-15, NOAA-16, and NOAA-17. As will be shown, the quality of both the temperature and moisture analyses are significantly improved when using these additional radiance data. Preliminary evaluations indicate marked improvements in 5-10 day temperature forecasts and significant improvements in Quantitative Precipitation Forecast (QPF) skill scores in the first 5 days of 10day forecasts.

The Canadian Meteorological Centre's (CMC) three Dimensional Variational (3D-var) is an incremental analysis system that is currently used by both our global and regional models with very little modifications. During the last few years, it has undergone a series of upgrades from isobaric to a terrain-following coordinate, and most importantly the direct assimilation of satellite radiances. The quality control (QC) of observations was also upgraded to a variational quality control whereby the data rejection/acceptance decisions are taken consistently during the minimization problem.In terms of radiance data, the system currently uses so-called raw level-1b AMSU-A that are QC, and bias controlled by the data user and not the producer. The QC and thinning algorithms of the radiance data are more complex and system dependent, but their impact on analyses and forecasts are very large and now comparable to the radiosonde data in the SH.The resolution of NWP forecast/analysis systems is forever increasing and so is the volume of data from various instruments. The volume of satellite data has become quite a challenge even at the level of ingest and QC. One aspect of NWP systems which definitely can benefit from this additional data is the moisture analysis. In that context we have started to experiment with the ingest of water sensitive radiances from the HIRS and AMSU-13 instruments onboard NOAA-15 and NOAA-16. As will be shown, the quality of both the temperature and moisture analyses are significantly improved when using these additional satellite data. Preliminary evaluations from 10-day forecasts indicate marked improvements in Quantitative Precipitation Forecast (QPF).

All assimilation methods rely on data collected from many sources. There are the conventional data sources like surface observations, radiosonde data, aircraft and ship data to which is now added an increasing amount of satellite data (see also the chapters Observing the atmosphere by R. Swinbank and Assimilation of remote sensing observations in Numerical Weather Prediction, by J.N. Thépaut). Each instrument is prone to error that could be systematic due to an incorrect calibration or random, reflecting the accuracy and representativeness of the measurement. The assimilation methods presented during this course assume that the data used in the assimilation have unbiased errors and are devoid of any serious error due to a malfunction of the instrument. Such errors are referred to as gross errors.

A continuous assimilation of high-density global satellite observations is required in order to improve numerical weather prediction analyses used to start forecasts. Until now, it was assumed that efficiency requirements imposed the use of regression-based models of atmospheric transmittance (typically on fixed pressure layers with coefficients varying for each layer) and prohibited the use of physically based models. Here, it is demonstrated that an explicit calculation of infrared transmittances for each absorbing gas (H2O, CO2, O-3, CH4, N2O, and O-2) can be done efficiently, provided that a monochromatic approach is followed as in a regression model such as Radiative Transfer for TOVS (the TIROS Operational Vertical Sounder) (operational in most weather centers). The classical Goody random model is chosen as a physical formulation for spectral line absorption along with established water vapor and oxygen continua parameterizations. Line-by-line transmittance calculations for 189 atmospheric profiles are used as reference in the evaluation. By adjusting the individual gas optical depths by a constant multiplicative factor (typically near unity), it is shown that an accuracy better than 0.3 K in brightness temperature can be obtained for most satellite infrared sounding channels. Jacobians defining the adjoint of the model are readily obtained by analytical differentiation of the radiance with respect to level temperature and humidity.The new model was introduced into the Canadian Meteorological Center 3D variational data assimilation system, and a comparison was carried out between the regression and physical models for a 2-week period for the first 12 sounding channels of the NOAA-12 satellite. The numerous advantages of the physical model over the regression model are emphasized. Biases introduced by the use of fixed or outdated mixing ratio estimates for CO2, O-3, and CH4 are largely reduced using current and location dependent concentrations. Global statistics and maps of observed minus calculated radiances reveal the general superiority of the physical model. The proposed model is efficient and is well suited for the massive assimilation of satellite radiances and other remote sensing applications.

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.

On 3 November 1993 a new higher-resolution version of the regional forecast system was implemented into operations at the Canadian Meteorological Centre. The changes include modifications to the regional data assimilation system and to the regional finite-element (RFE) forecast model. The main features of the new version of the RFE model include an increase in resolution from 100 to 50 km and to 25 sigma levels in the vertical. The fields that describe the surface characteristics are generated directly on the 50-km grid of the model from high-resolution global geophysical datasets, yielding more derails and a much better definition of the orography and coastlines. The new RFE model also includes an improved package of physical parametrizations, notably for condensation and radiation processes. The major changes to the data assimilation are a higher-resolution analysis, and the assimilation of humidity profiles retrieved from satellite imagery.The new system is evaluated using performance statistics, and case studies are presented to highlight some of the benefits. These include more accurate analyses with a better fit to the data, and more detailed and precise forecasts, particularly for frontal zone structures, jet streams, moisture distribution and precipitation. The new physics package reduces the spinup of the model and the systematic errors in precipitation amounts, and gives better thermal and hydrologic balances.

An algorithm is developed to derive hydrostatically balanced geopotentials at significant levels from radiosonde reports of significant-level temperatures and mandatory-level geopotentials and temperatures. It minimizes the square of the nonhydrostatic differences in a layer where at least one significant-level datum is reported and can be viewed as being a 1D analysis step that returns an estimate of the departures from hydrostatic balance within the layer. The piecewise-polynomial interpolation of the minimization procedure is used to produce an expanded geopotential profile in any layer where significant-level data are reported, and the integrated minimization error can be used as a quality-control measure. The algorithm's performance has been evaluated using the global radiosonde dataset for a given synoptic time, and it is found that it produces equivalent layer-mean temperature errors that are generally smaller than radiosonde observational errors.

The Canadian regional data assimilation system is described. It is a spinup cycle designed to provide the regional finite-element forecast model with more detailed analyses in a dynamically consistent manner. Its operational performance is evaluated using performance statistics, and a case study is presented to highlight some of the benefits. These include analyses that better fit the data and more detailed and accurate forecasts, particularly for precipitation.The system also benefits research applications. To illustrate this the authors describe the preparation of the first set of analyses for the international COMPARE (Comparison of Mesoscale Prediction and Research Experiments) Project. The scientific interest of this explosive marine cyclogenetic case is discussed, together with a useful methodology for determining the minimum domain size required by a regional model to avoid forecast contamination from lateral boundaries.

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 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.

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.

Mesoscale numerical forecasts of cases of explosive cyclogenesis during the Canadian Atlantic Storms Program are presented in order to examine the evolution and structure of the simulated storms, and to assess the model's skill in forecasting significant weather elements. The investigation focuses on a series of sensitivity experiments with various horizontal resolutions, sea surface temperatures (SST), surface energy fluxes and condensation schemes in order to understand the physical mechanisms responsible for explosive deepening. The results confirm the conclusions of several other investigators that realistic simulations of explosive storms and many of their subsynoptic and mesoscale features can be obtained using high resolution models with a complete set of physical processes, even when starting with synoptic-scale analyses only. In particular, the formation and maintenance of an intense southerly low-level jet (LLJ) ahead of the surface cold front appears to be instrumental throughout the rapid deepening phase. Variations in physical parameterization schemes or SST analyses have an impact mostly in the lower levels. However, a critical factor for simulating marine explosive cyclogenesis is identified as evaporation from the ocean. The results indicate that a large fraction of the moisture available for condensation processes and further deepening originates from the air–sea interactions. The coupling provided by the LLJ then becomes a very efficient mechanism by which heat and moisture are carried from the source region into the developing storm. The overall skill of the model in forecasting quantitative precipitation is found to be quite good. Horizontal distributions of cloud and precipitation compare favorably with satellite imagery and the simulated splitting of precipitation into different types agrees quite well with surface reports.

Aspari of the Canadian Atlantic Storms Program (CASP), a meso-a scale version of the regional jinite-element mode1 was set up for the short-term forecasting of East Coast storms during the CASP$eldphase. The main fearures of the mesoscale modelcompared with the operational continental version include a IOO-km resolution over a reduced domain, a modijîedphysics package, improved surface and geophysicaljeld analyses, and the reJnement of initial moisture analyses using satellite imagery. Field evaluarions of the mesoscale and continental models by CASP meteorologists indicated that both models generally underpredicted storm speed and deepening and that the CASP mode1 yielded more accurate storm locations. Mesoscaleforecasts ofprecipitation types and boundary-layer winds were quite realistic when compared with actual station reports. Objective verification scores for II storm cases conjïrm the better meteorological performance of the mesoscale model. The most striking improvements are found at 300 mbfor the geopotential height and near the surface for the temperature. The structure of thejorecast errors suggests rhat high horizonru resolution and a berter convection scheme are important for the simulation of rhe Upper-level trough, while a more realistic sea surface temperature analysis improves the storm evolution ut lower levels. Enhancement of initial moisture analysis using satellite imagery generally increases precipitation amounts in the early hours of the forecast but has Little impact later on.

A simple gravity wave drag parametrization over mountainous terrain is tested for its ability to reduce the systematic errors of medium-range weather forecasts. Following Boer et al. (1984), this parametrization is a function of the low-level wind speed and stability, the local Froude number, and the variance of the subgrid-scale orographie features.A comparison study of ten 7-day forecasts obtained with envelope orography, wave drag or standard orography, shows that wave drag is as effective as envelope orography in reducing the systematic errors. A further comparison where the combined effects of the wave drag and that of a complementary enhanced orography (that is one that includes only the subgrid-scale elements not treated separately by wave drag) are taken into account shows this latter approach to be the most promising in reducing orographically-related systematic errors.