Soil moisture has been shown in many studies to be an important variable in numerical weather prediction (NWP) systems [1, 2, 3]. In many regions of the world, soil moisture exerts an important control on land surface evaporation, and is thus a determining factor in the partitioning of downwelling radiative energy incident at the surface. This energy partitioning into sensible and latent heat surface fluxes plays a major role in the evolution and structure of the atmospheric boundary layer, with associated impacts on clouds, precipitation, and large (synoptic) scale weather systems.

Variational data assimilation methods and related applications depend on the validity of the tangent-linear approximation, which is truly challenging when applied to deep moist convection. A simple strategy for linearizing complex moist convective schemes in numerical weather prediction models is proposed. This strategy represents a trade-off between code development and maintenance on the one hand and expected benefit on the other hand. The generic linearized scheme described hereafter can be used in conjunction with any nonlinear moist convective scheme, thus eliminating the need to linearize complex codes. The universality of the scheme steins from the fact that conditional triggers and Cloud vertical extent are supplied by the trajectory of the nonlinear scheme. In active columns, the convective tendencies cancel the large-scale dynamical tendencies. Potential uses of the scheme include variational data assimilation and diagnostic studies such as tropical singular vectors and key analysis errors with precipitation. The relevance of the methodology is examined with the Global Environmental Multiscale model using the Kain-Fritsch mass-flux scheme that is operational at the Canadian Meteorological Centre. It is shown that the tangent-linear approximation is improved when compared to a linearized convection scheme having its own trigger functions. The improvement is particularly noticeable for the partition between stratiform and convective components of surface precipitation. Finally the examination of adjoint sensitivities of surface precipitation with the simplified linearized scheme triggered by the Kain-Fritsch scheme reveals a more pronounced sensitivity to midlatitude baroclinic instability and less predictability for tropical systems when compared to a Kuo-type scheme. Copyright (C) 2009 Royal Meteorological Society and Crown in the Right of Canada

Currently, satellite radiances in the Canadian Meteorological Centre operational data assimilation system are only assimilated in clear skies. A two-step method, developed at the European Centre for Medium-Range Weather Forecasts, is considered to assimilate Special Sensor Microwave Imager (SSM/I) observations in rainy atmospheres. The first step consists of a one-dimensional variational data assimilation (1DVAR) method. Model temperature and humidity profiles are adjusted by assimilating either SSM/I brightness temperatures or retrieved surface rain rates (derived from SSM/I brightness temperatures). In the second step, 1DVAR column-integrated water vapor analyses are assimilated in four-dimensional variational data assimilation (4DVAR). At the Meteorological Service of Canada, such a 1DVAR assimilation system has been developed. Model profiles are obtained from a research version of the Global Environmental Multi-Scale model. Several issues raised while developing the 1DVAR system are addressed. The impact of the size of the observation error is studied when brightness temperatures are assimilated. For two case studies, analyses are derived when either surface rain rate or brightness temperatures are assimilated. Differences in the analyzed fields between these configurations are discussed and shortcomings of each approach are identified. Results of sensitivity studies are also provided. First the impact of observation error correlation between channels is investigated. Second, the size of the background temperature error is varied to assess its impact on the analyzed column-integrated water vapor. Third, the importance of each moist physical scheme is investigated. Finally, the portability of moist physical schemes specifically developed for data assimilation is discussed.

The performance of a modified version of the snow scheme included in the Interactions between Surface Biosphere-Atmosphere (ISBA) land surface scheme, which was operationally implemented into the regional weather forecast system at the Canadian Meteorological Centre, is examined in this study. Stand-alone verification tests conducted prior to the operational implementation showed that ISBA's new snow package was able to realistically reproduce the main characteristics of a snow cover, such as snow water equivalent and density, for five winter datasets taken at Col de Porte, France, and at Goose Bay, Newfoundland, Canada. A number of modifications to ISBA's snow model (i.e., new liquid water reservoir in the snowpack, new formulation of snow density, and melting effect of incident rainfall on the snowpack) were found to improve the numerical representation of snow characteristics.Objective scores for the fully interactive preimplementation tests carried out with the Canadian regional weather forecast model indicated that ISBA's improved snow scheme only had a minor impact on the model's ability to predict atmospheric circulation. The objective scores revealed that only a thin atmospheric layer above snow-covered surfaces was influenced by the change of land surface scheme, and that over these regions the essential behavior of the atmospheric model was not significantly altered by improvements to the treatment of snow cover. It was shown that this lack of response was most likely related to the treatment of the snow cover fraction in each atmospheric model grid tile. The estimation of snow cover fraction relied on simple formulations that were dependent on poorly known parameters, such as the fractional coverage of vegetation. Results showed that uncertainties of only 15% in vegetation fractional coverage could be responsible for uncertainties of as much as 1-1.5 K in screen-level air temperature. This indicates that some care must be exercised in the specification of vegetation and snow cover fractional coverage.

A new, higher-resolution version of the regional forecast system was implemented into operations at the Canadian Meteorological Centre during 1995. The new version of the regional finite-element forecast model is run at 35-km resolution in the horizontal and 28 sigma levels in the vertical (instead of 50-km and 25 levels in the previous operational version), with a more advanced physics package. The improved physical parametrizations feature the following: 1) modifications to the treatment of surface surface processes; 2) changes to the surface layer formulation; 3) an explicit cloud scheme following Sundqvist for stratiform precipitation; 4) Fritsch-Chappell scheme for deep convection. The new regional forecast system also includes a pseudo-analysis of initial soil moisture content based on model error feed-back.Both objective and subjective evaluations on case studies and in the parallel runs showed improved performance with the new 35-km model. The main points from these verifications indicated a significant reduction of the moist bias near the surface, improvements in the predicted surface temperatures and the diurnal cycle, better forecasts of convective precipitation, and more realistic surface wind forecasts, especially over complex orography due to the increased resolution. The new predicted cloud parameters permit a better representation of the cloud-radiation interactions that are important for the atmospheric energy balance, especially at the surface.

A mesoscale (15 km) version of the Canadian regional finite-element model is used to study a polar low that developed in the Labrador Sea on 11 January 1989, in the wake of an intense cold air outbreak associated with a major synoptic-scale system located to the east of Greenland. The rapid evolution of the polar low is well revealed from satellite imagery showing a complex structure with strong surface winds near the vortex and deep convection nearby during the mature stage. The simulated structure of the polar low agrees quite well with observed features. Based on the detailed mesoscale model outputs, the evolution of the Labrador Sea polar low is discussed at the initiation and mature stages. The polar low developed under a combination of baroclinic and convective processes. At an early stage, baroclinic development takes place in conditions of reversed shear flow, marked by low-level baroclinicity near the ice edge and a mobile upper-level short wave. Rapid modification of the Arctic boundary layer by strong surface heat fluxes is similar to that observed in other areas. Sensitivity experiments indicate that the mutual interaction between the upper-level potential vorticity anomaly and the low-level baroclinicity at the Arctic front, favored by the deep convective boundary layer, appears to trigger the polar low. At the onset of the mature stage, the approach of a cold air dome favors the outbreak of deep convection, in agreement with satellite imagery. As shown by sensitivity experiments, latent heat release from organized convection contributes to the major part of the rapid deepening of the polar low in its mature stage, and sea surface evaporation is the primary feeding mechanism for condensation processes. The structure of the polar low is characterized by a warm core due to the combined effects of warm air seclusion and diabatic heating. Comparisons with previously studied polar lows developing in similar conditions of reversed shear at other locations are discussed.

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.