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A. Lemonsu, S. Bélair, J. Mailhot, and S. Leroyer

Abstract

Using the Montreal Urban Snow Experiment (MUSE) 2005 database, surface radiation and energy exchanges are simulated in offline mode with the Town Energy Balance (TEB) and the Interactions between Soil, Biosphere, and Atmosphere (ISBA) parameterizations over a heavily populated residential area of Montreal, Quebec, Canada, during the winter–spring transition period (from March to April 2005). The comparison of simulations with flux measurements indicates that the system performs well when roads and alleys are snow covered. In contrast, the storage heat flux is largely underestimated in favor of the sensible heat flux at the end of the period when snow is melted. An evaluation and an improvement of TEB’s snow parameterization have also been conducted by using snow property measurements taken during intensive observational periods. Snow density, depth, and albedo are correctly simulated by TEB for alleys where snow cover is relatively homogeneous. Results are not as good for the evolution of snow on roads, which is more challenging because of spatial and temporal variability related to human activity. An analysis of the residual term of the energy budget—including contributions of snowmelt, heat storage, and anthropogenic heat—is performed by using modeling results and observations. It is found that snowmelt and anthropogenic heat fluxes are reasonably well represented by TEB–ISBA, whereas storage heat flux is underestimated.

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J. A. Milbrandt, M. K. Yau, J. Mailhot, and S. Bélair

Abstract

This paper reports the first evaluation of the Milbrandt–Yau multimoment bulk microphysics scheme against in situ microphysical measurements. The full triple-moment version of the scheme was used to simulate a case of orographically enhanced precipitation with a 3D mesoscale model at high resolution (4- and 1-km grid spacings). The simulations described in this paper also serve as the control runs for the sensitivity experiments that will be examined in Part II of this series. The 13–14 December 2001 case of heavy orographically enhanced precipitation, which occurred over the Oregon Cascades, was selected since it was well observed during the second Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-2) observational campaign. The simulated fields were compared with observed radar reflectivity, vertical velocity, precipitation quantities from rain gauges, and microphysical quantities measured in situ by two instrumented aircraft. The simulated reflectivity structure and values compared favorably to radar observations during the various precipitation stages of the event. The vertical motion field in the simulations corresponded reasonably well to the mountain-wave pattern obtained from in situ and dual-Doppler radar inferred measurements, indicating that biases in the simulations can be attributed in part to the microphysics scheme. The patterns of 18-h accumulated precipitation showed that the model correctly simulated the bulk of the precipitation to accumulate along the coastal mountains and along the windward slope of the Cascades, with reduced precipitation on the lee side of the crest. However, both the 4- and 1-km simulations exhibited a general overprediction of precipitation quantities. The model also exhibited a distinct bias toward overprediction of the snow mass concentration aloft and underprediction of the mass and vertical extent of the pockets of cloud liquid water on the windward side of the Cascades. Nevertheless, the overall spatial distribution of the hydrometeor fields was simulated realistically, including the mean-mass particle diameters for each category and the observed trend of larger snow sizes to be located at lower altitudes.

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G. Balsamo, J-F. Mahfouf, S. Bélair, and G. Deblonde

Abstract

A Canadian Land Data Assimilation System (CaLDAS) for the analysis of land surface prognostic variables is designed and implemented at the Meteorological Service of Canada for the initialization of numerical weather prediction and climate models. The assimilation of different data sources for the production of daily soil moisture and temperature analyses is investigated in a set of observing system simulation experiments over North America. A simplified variational technique is adapted to accommodate different observation types at their appropriate time in a 24-h time window. The screen-level observations of temperature and relative humidity, from conventional synoptic surface observations (SYNOP)/aviation routine weather report (METAR)/surface aviation observation (SA) reports, are considered together with presently available satellite observations provided by the Aqua satellite (microwave C-band), Geostationary Operational Environmental Satellite (GOES) [infrared (IR)], and observations available in the future by the Soil Moisture and Ocean Salinity (SMOS) satellite mission (microwave L-band). The aim of these experiments is to assess the information content brought by each observation type in the land surface analysis. The observation systems are simulated according to their spatial coverage, temporal availability, and nominal or expected errors. The results show that the observable with the largest dynamical response to perturbations of the control variable carries the greatest information content into the analysis. The observational error and the observation frequency counterbalance this feature in the analysis.

If one considers a single observation both for soil moisture and soil temperature analysis, then satellite measurements (L-band, C-band, and IR in decreasing order of importance) are the primary source of information. When observation availability is considered and the highest temporal frequency of screen-level observations is used (1 h), a large amount of information is extracted from SYNOP-like reports. The screen-level observations are shown to provide valuable soil moisture information mainly during the daytime, while during nighttime these observations (and particularly screen-level temperature) are mostly useful for the soil temperature analysis. The results are presented with perspectives for future operational developments and preliminary assimilation experiments are performed with hourly screen-level observations.

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G. Balsamo, J-F. Mahfouf, S. Bélair, and G. Deblonde

Abstract

The aim of this study is to test a land data assimilation prototype for the production of a global daily root-zone soil moisture analysis. This system can assimilate microwave L-band satellite observations such as those from the future Hydros NASA mission. The experiments are considered in the framework of the Interaction Soil Biosphere Atmosphere (ISBA) land surface scheme used operationally at the Meteorological Service of Canada for regional and global weather forecasting. A land surface reference state is obtained after a 1-yr global land surface simulation, forced by near-surface atmospheric fields provided by the Global Soil Wetness Project, second initiative (GSWP-2). A radiative transfer model is applied to simulate the microwave L-band passive emission from the surface. The generated brightness temperature observations are distributed in space and time according to the satellite trajectory specified by the Hydros mission. The impact of uncertainties related to the satellite observations, the land surface, and microwave emission models is investigated. A global daily root-zone soil moisture analysis is produced with a simplified variational scheme. The applicability and performance of the system are evaluated in a data assimilation cycle in which the L-band simulated observations, generated from a land surface reference state, are assimilated to correct a prescribed initial root-zone soil moisture error. The analysis convergence is satisfactory in both summer and winter cases. In summer, when considering a 3-K observation error, 90% of land surface converges toward the reference state with a soil moisture accuracy better than 0.04 m3 m−3 after a 4-week assimilation cycle. A 5-K observation error introduces 1-week delay in the convergence. A study of the analysis error statistics is performed for understanding the properties of the system. Special features associated with the interactions between soil water and soil ice, and the presence of soil moisture vertical gradients, are examined.

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J. A. Milbrandt, M. K. Yau, J. Mailhot, S. Bélair, and R. McTaggart-Cowan

Abstract

This is the second in a series of papers examining the behavior of the Milbrandt–Yau multimoment bulk microphysics scheme for the simulation of the 13–14 December 2001 case of orographically enhanced precipitation observed during the second Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-2) experiment. The sensitivity to the number of predicted moments of the hydrometeor size spectra in the bulk scheme was investigated. The triple-moment control simulations presented in were rerun using double- and single-moment configurations of the multimoment scheme as well the single-moment Kong–Yau scheme. Comparisons of total precipitation and in-cloud hydrometeor mass contents were made between the simulations and observations, with the focus on a 2-h quasi-steady period of heavy stratiform precipitation. The double- and triple-moment simulations were similar; both had realistic precipitation fields, though generally overpredicted in quantity, and had overprediction of snow mass and an underprediction of cloud water aloft. Switching from the triple- to single-moment configuration resulted in a simulation with a precipitation pattern shifted upwind and with a larger positive bias, but with hydrometeor mass fields that corresponded more closely to the observations. Changing the particular single-moment scheme used had a greater impact than changing the number of moments predicted in the same scheme, with the Kong–Yau simulations greatly overpredicting the total precipitation in the lee side of the mountain crest and producing too much snow aloft. Further sensitivity tests indicated that the leeside overprediction in the Kong–Yau runs was most likely due to the combination of the absence of the latent heat effect term in the diffusional growth rate for snow combined with the assumption of instantaneous snow melting in the scheme.

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A. Zadra, R. McTaggart-Cowan, P. A. Vaillancourt, M. Roch, S. Bélair, and A.-M. Leduc

Abstract

Deep convection is one of various complex processes driving the evolution of tropical cyclones (TCs). The scales associated with deep convection are too small to be resolved by global NWP models. In the deep convection parameterization used by the Canadian Global Deterministic Prediction System (GDPS), the trigger function depends on various criteria, one of which is the adjustable “trigger velocity” parameter, a vertical velocity threshold used in the parcel stability test of the scheme. In this study, the sensitivity of the GDPS TC activity and precipitation distribution to convective triggering parameters is investigated by varying this threshold. Multiple basins are considered for three TC seasons, and the impacts of trigger velocity variations on TC statistics (forecast hits, bias, false alarms, and track and intensity errors) and on the model’s genesis potential index (GPI) are measured. It is shown that a reduction of the trigger velocity, from 0.05 to 0.01 m s−1, over the tropical oceans leads to increased convective stabilization of atmospheric columns, as well as an increase in convective precipitation amounts but a reduction in total (subgrid plus grid scale) precipitation accumulations. The trigger adjustment also yields a significant reduction of TC false alarm ratios, with no impact on forecast mean errors for true cyclones other than an expected deterioration of the intensity bias, and a systematic reduction of the average GPI over various basins at all lead times. A conceptual model is proposed to explain the relation between trigger adjustments and TC development.

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A. Lemonsu, S. Bélair, J. Mailhot, M. Benjamin, G. Morneau, B. Harvey, F. Chagnon, M. Jean, and J. Voogt

Abstract

Within the framework of a large urban meteorology program recently launched in Canada, the Montreal Urban Snow Experiment (MUSE) campaign has been conducted in order to document the thermoradiative exchanges in a densely built-up area of Montreal in late winter and spring conditions. The targeted period is of particular scientific interest because it covers the transition period from a mainly snow-covered urban environment to a mainly snow-free environment. The campaign is based on four weeks of observations from 17 March to 14 April 2005. It couples automatic and continuous measurements of radiation and turbulent fluxes, radiative surface temperatures, and air temperature and humidity with manual observations performed during intensive observation periods to supplement the surface temperature observations and to characterize the snow properties. The footprints of radiation and turbulent flux measurements are computed using the surface–sensor–sun urban model and the flux-source area model, respectively. The analysis of the radiometer footprint underscores the difficulty of correctly locating this type of instrument in urban environments, so that the sensor sees a representative combination of the urban and nonurban surfaces. Here, the alley contribution to the upward radiation tends to be overestimated to the detriment of the road contribution. The turbulent footprints cover homogeneous zones in terms of surface characteristics, whatever the wind direction. The initial analysis of the energy balance displays the predominance of the residual term (Q Res = Q* − QHQE) in comparison with the turbulent sensible (QH) and latent (QE) heat fluxes, since its daytime contribution exceeds 50% of the net radiation (Q*). The investigation of energy balances observed at the beginning and at the end of the experiment (i.e., with and without snow) also indicates that the snow plays a significant role in the flux partitioning and the daily pattern of fluxes. Without snow, the energy balance is characteristic of energy balances that have been already observed in densely built-up areas, notably because of the hysteresis observed for Q Res and QH in relation to Q* and because of the high contribution of Q Res, which includes the effect of heat storage inside the urban structures. With snow, the flux partitioning is modified by the snowmelt process leading to contributions of the residual term and latent heat flux, which are larger than in the case without snow to the detriment of the sensible heat flux.

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J. Mailhot, J. W. Strapp, J. I. MacPherson, R. Benoit, S. Bélair, N. R. Donaldson, F. Froude, M. Benjamin, I. Zawadzki, and R. R. Rogers

The MERMOZ (Montreal-96 Experiment on Regional Mixing and Ozone) field experiment was conducted in the greater Montreal area in June 1996. The measurement program was designed to examine several aspects of boundary layer dynamics and chemical transport. The project featured high-resolution real-time simulations with a mesoscale meteorological model driving several air quality models; the deployment of a research aircraft, fully instrumented for turbulent flux measurements; and a number of other supporting meteorological measurements such as two boundary layer wind profilers, a Doppler weather radar, and a special network of surface stations, upper-air soundings, tethersondes, and ozonesondes. An overview of the MERMOZ field program is presented with some preliminary results on various aspects of the experiment.

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J. Mailhot, S. Bélair, M. Charron, C. Doyle, P. Joe, M. Abrahamowicz, N. B. Bernier, B. Denis, A. Erfani, R. Frenette, A. Giguére, G. A. Isaac, N. McLennan, R. McTaggart-Cowan, J. Milbrandt, and L. Tong

The 2010 Winter Olympic and Paralympic Games took place in Vancouver, British Columbia, Canada, on 12–28 February and 12–21 March 2010, respectively. Weather forecasting presents specific challenges at the various Olympic venues, which are located in complex coastal terrain and are often characterized by tricky weather conditions, such as high winds, low visibility, and rapidly varying precipitation types and intensity.

In addition to its current operational products, and in order to provide the best possible guidance and support to the Olympic Forecast Team, Environment Canada has developed several experimental numerical weather prediction systems for the games. These include 1) a regional ensemble prediction system (REPS), 2) high-resolution numerical modeling (down to 1-km horizontal grid spacing), and 3) surface modeling at the microscales (100-m grid spacing). The REPS is based on the limited-area version of the Global Environmental Multiscale model (GEM-LAM), with 20 members at 33-km horizontal grid spacing. The high-resolution models include 2.5- and 1-km grid-spacing configurations of the GEM-LAM, with improved cloud microphysics, geophysical fields, and radiation and cloud–radiation interactions. Finally, two innovative approaches are used to adapt and refine forecasts locally and to better predict surface characteristics, such as snow conditions and near-surface air temperatures. A microscale 2D surface system covers the Olympic venues with forcing from the operational regional and global models. Based on a similar strategy, a single-point model uses surface observations as forcing. The configurations of these experimental numerical weather prediction systems are described, together with some examples and verification results from the winters of 2008 and 2009 using the enhanced mesoscale observing network recently set up for the Olympics.

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P. Joe, S. Belair, N.B. Bernier, V. Bouchet, J. R. Brook, D. Brunet, W. Burrows, J.-P. Charland, A. Dehghan, N. Driedger, C. Duhaime, G. Evans, A.-B. Filion, R. Frenette, J. de Grandpré, I. Gultepe, D. Henderson, A. Herdt, N. Hilker, L. Huang, E. Hung, G. Isaac, C.-H. Jeong, D. Johnston, J. Klaassen, S. Leroyer, H. Lin, M. MacDonald, J. MacPhee, Z. Mariani, T. Munoz, J. Reid, A. Robichaud, Y. Rochon, K. Shairsingh, D. Sills, L. Spacek, C. Stroud, Y. Su, N. Taylor, J. Vanos, J. Voogt, J. M. Wang, T. Wiechers, S. Wren, H. Yang, and T. Yip

Abstract

The Pan and Parapan American Games (PA15) are the third largest sporting event in the world and were held in Toronto in the summer of 2015 (10–26 July and 7–15 August). This was used as an opportunity to coordinate and showcase existing innovative research and development activities related to weather, air quality (AQ), and health at Environment and Climate Change Canada. New observational technologies included weather stations based on compact sensors that were augmented with black globe thermometers, two Doppler lidars, two wave buoys, a 3D lightning mapping array, two new AQ stations, and low-cost AQ and ultraviolet sensors. These were supplemented by observations from other agencies, four mobile vehicles, two mobile AQ laboratories, and two supersites with enhanced vertical profiling. High-resolution modeling for weather (250 m and 1 km), AQ (2.5 km), lake circulation (2 km), and wave models (250-m, 1-km, and 2.5-km ensembles) were run. The focus of the science, which guided the design of the observation network, was to characterize and investigate the lake breeze, which affects thunderstorm initiation, air pollutant transport, and heat stress. Experimental forecasts and nowcasts were provided by research support desks. Web portals provided access to the experimental products for other government departments, public health authorities, and PA15 decision-makers. The data have been released through the government of Canada’s Open Data Portal and as a World Meteorological Organization’s Global Atmospheric Watch Urban Research Meteorology and Environment dataset.

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