Abstract

A series of numerical experiments are carried out solving a coupled set of equations for advection and condensation with a semi-Lagrangian (SL) transport scheme. Canonical validation tests in one dimension show that SL suffers less numerical aberrations than many Eulerian transport schemes. Some monotonic transport constraints are experimented with. These tests confirm, for an SL transport, the statement first enunciated by Grabowski and Smoladdewicz within the context of Eulerian transport that monotonic transport constraints are not sufficient to prevent the development of false ripples when integrating coupled field equations. A constraint is developed and successfully applied to the simplified advection–condensation problem. A two dimensional dynamical model based on the fully elastic equations solved with a semi-implicit semi-Lagrangian scheme is used to simulate the classical moist bubble convection problem; these SL solutions are compared to published results obtained with Eulerian models.

These experiments show that 1) the original (unconstrained) SL transport scheme does produce some small ripples, 2) these ripples are smaller than with most Eulerian schemes, but 3) they may be amplified through the interaction with the condensation process when abrupt concentration changes occur; however, 4) in most applications, these ripples do not seem to contaminate unduly the results of SL simulations.

Abstract

This paper attempts to document the developmental research and early mesoscale results of the new fully nonhydrostatic atmospheric model called MC2 (mesoscale compressible community). Its numerical scheme is the semi-implicit semi-Lagrangian approach conceived and demonstrated by Tanguay, Robert, and Laprise. The dominant effort required to become a full-fledged mesoscale model was to connect it properly to a full-scale and evolving physics package; the enlarged scope of a package previously dedicated to hydrostatic pressure coordinate-type models posed some new questions. The one-way nesting is reviewed and particularly the self-nesting or cascade mode; the potential implication of this mode is explored with a stand-alone forecast experiment and related to the other existing approach employing hemispheric or global variable meshes. One of the strong assets of MC2 is its growing community of users and developers. To demonstrate the wideband characteristic of MC2, that is, its applicability to a large range of atmospheric flows, two very different cases are studied: an Atlantic winter East Coast cyclogenesis (meso-α scale, mostly hydrostatic) and a local (meso-γ scale, partly nonhydrostatic) downslope windstorm occuring over unexpectedly modest topography (Cape Breton Highlands of Nova Scotia, Canada).

Abstract

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.

Abstract

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.

Abstract

This study presents a numerical analysis of the impact of the horizontal resolution on the forecast capability of the Canadian offline land surface prediction system (SPS; formerly known as GEM-Surf) forced by the 15-km Global Environmental Multiscale (GEM) atmospheric model. This system is used to quantify on a statistical basis the subgrid-scale variability of (near-)surface variables for 25-km grid spacing based on the 2.5- or 10-km SPS run at regional scale over the 2012 summer season. The model bias and the distributions characterizing the subgrid-scale variability drastically depend on the geographic areas as well as on the diurnal cycle. These results show the benefits of high-resolution land surface simulations to account for length scales that are more consistent with the scales at which the actual land surface balance is affected by the heterogeneous geophysical fields (i.e., roughness length, land–water mask, glacier mask, and soil texture). The model bias results highlight the potential of an SPS–GEM two-way coupling strategy for refining predictions near the surface through the upscaling of high-resolution surface heat fluxes to the coarser atmospheric grid spacing, with these fluxes being significantly different from those explicitly resolved at 25 km and featuring nonlinear behavior with respect to the horizontal resolution. Since the computational power of meteorological operational centers progressively increases, making it possible to run high-resolution limited-area models, solving the surface at high resolution in a surface–atmosphere fully coupled system becomes a key aspect for improving numerical weather and environmental forecast performance.

Abstract

The purpose of this study is to present the possibilities offered by coupled atmospheric and hydrologic models as a new tool to validate and interpret results produced by atmospheric models. The advantages offered by streamflow observations are different from those offered by conventional precipitation observations. The dependence between basins and subbasins can be very useful, and the integrating effect of the large basins facilitates the evaluation of state-of-the-art atmospheric models by filtering out some of the spatial and temporal variability that complicate the point-by-point verifications that are more commonly used. Streamflow permits a better estimate of the amount of water that has fallen over a region. A comparison of the streamflow predicted by the coupled atmospheric–hydrologic model versus the measured streamflow is sufficiently sensitive to clearly assess atmospheric model improvements resulting from increasing horizontal resolution and altering the treatment of precipitation processes in the model.

A case study using the WATFLOOD hydrologic model developed at the University of Waterloo is presented for several southern Ontario river basins. WATFLOOD is one-way coupled to a nonhydrostatic mesoscale atmospheric model that is integrated at horizontal resolutions of 35, 10, and 3 km. This hydrologic model is also driven by radar-derived precipitation amounts from King City radar observations. Rain gauge observations and measured streamflows are also available for this case, permitting multiple validation comparisons. These experiments show some uncertainties associated with each tool independently, and also the interesting complementary nature of these tools when they are used together. The predicted precipitation patterns are also compared directly with rain gauge observations and with radar data. It is demonstrated that the hydrologic model is sufficiently sensitive and accurate to diagnose model and radar errors. This tool brings an additional degree of verification that will be very important in the improvement of technologies associated with atmospheric models, radar observations, and water resource management.

Abstract

The purpose of this study is to present the impacts of a fully interactive coupling between an atmospheric and a sea ice model over the Gulf of St. Lawrence, Canada. The impacts are assessed in terms of the atmospheric and sea ice forecasts produced by the coupled numerical system. The ocean-ice model has been developed at the Maurice Lamontagne Institute, where it runs operationally at a horizontal resolution of 5 km and is driven (one-way coupling) by atmospheric model forecasts provided by the Meteorological Service of Canada (MSC). In this paper the importance of two-way coupling is assessed by comparing the one-way coupled version with a two-way coupled version in which the atmospheric model interacts with the sea ice model during the simulation. The impacts are examined for a case in which the sea ice conditions are changing rapidly. Two atmospheric model configurations have been studied. The first one has a horizontal grid spacing of 24 km, which is the operational configuration used at the Canadian Meteorological Centre. The second one is a high-resolution configuration with a 4-km horizontal grid spacing. A 48-h forecast has been validated using satellite images for the ice and the clouds, and also using the air temperature and precipitation observations. It is shown that the two-way coupled system improves the atmospheric forecast and has a direct impact on the sea ice forecast. It is also found that forecasts are improved with a fine resolution that better resolves the physical events, fluxes, and forcing. The coupling technique is also briefly described and discussed.

Abstract

The importance of coupling between the atmosphere and the ocean for forecasting on time scales of hours to weeks has been demonstrated for a range of physical processes. Here, the authors evaluate the impact of an interactive air–sea coupling between an operational global deterministic medium-range weather forecasting system and an ice–ocean forecasting system. This system was developed in the context of an experimental forecasting system that is now running operationally at the Canadian Centre for Meteorological and Environmental Prediction. The authors show that the most significant impact is found to be associated with a decreased cyclone intensification, with a reduction in the tropical cyclone false alarm ratio. This results in a 15% decrease in standard deviation errors in geopotential height fields for 120-h forecasts in areas of active cyclone development, with commensurate benefits for wind, temperature, and humidity fields. Whereas impacts on surface fields are found locally in the vicinity of cyclone activity, large-scale improvements in the mid-to-upper troposphere are found with positive global implications for forecast skill. Moreover, coupling is found to produce fairly constant reductions in standard deviation error growth for forecast days 1–7 of about 5% over the northern extratropics in July and August and 15% over the tropics in January and February. To the authors’ knowledge, this is the first time a statistically significant positive impact of coupling has been shown in an operational global medium-range deterministic numerical weather prediction framework.

Abstract

A global deterministic wave prediction system (GDWPS) is used to improve regional forecasts of waves off the Canadian coastline and help support the practice of safe marine activities in Canadian waters. The wave model has a grid spacing of ¼° with spectral resolution of 36 frequency bins and 36 directional bins. The wave model is driven with hourly 10-m winds generated by the operational global atmospheric prediction system. Ice conditions are updated every three hours using the ice concentration forecasts generated by the Global Ice–Ocean Prediction System. Wave forecasts are evaluated over two periods from 15 August to 31 October 2014 and from 15 December 2014 to 28 February 2015, as well as over select cases during the fall of 2012. The global system is shown to improve wave forecast skill over regions where forecasts were previously produced using limited-area models only. The usefulness of a global expansion is demonstrated for large swell events affecting the northeast Pacific. The first validation of a Canadian operational wave forecast system in the Arctic is presented. Improvements in the representation of forecast wave fields associated with tropical cyclones are also demonstrated. Finally, the GDWPS is shown to result in gains of at least 12 h of lead time.